Merge pull request #1252 from freqtrade/release-0.17.2

Release 0.17.2

.coveragerc

@@ -2,4 +2,5 @@
 omit =
     scripts/*
     freqtrade/tests/*
-    freqtrade/vendor/*
+    freqtrade/vendor/*
+    freqtrade/__main__.py

.dockerignore

@@ -4,3 +4,12 @@ Dockerfile
 .dockerignore
 config.json*
 *.sqlite
+.coveragerc
+.eggs
+.github
+.pylintrc
+.travis.yml
+CONTRIBUTING.md
+MANIFEST.in
+README.md
+freqtrade.service

.github/ISSUE_TEMPLATE.md

@@ -1,15 +1,17 @@
 ## Step 1: Have you search for this issue before posting it?
 
-If you have discovered a bug in the bot, please [search our issue tracker](https://github.com/gcarq/freqtrade/issues?q=is%3Aissue). 
+If you have discovered a bug in the bot, please [search our issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue). 
 If it hasn't been reported, please create a new issue.
 
 ## Step 2: Describe your environment
 
   * Python Version: _____ (`python -V`)
+  * CCXT version: _____ (`pip freeze | grep ccxt`)
   * Branch: Master | Develop
   * Last Commit ID: _____ (`git log --format="%H" -n 1`)
- 
+  
 ## Step 3: Describe the problem:
+
 *Explain the problem you have encountered*
 
 ### Steps to reproduce:

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,5 +1,5 @@
 Thank you for sending your pull request. But first, have you included
-unit tests, and is your code PEP8 conformant? [More details](https://github.com/gcarq/freqtrade/blob/develop/CONTRIBUTING.md)
+unit tests, and is your code PEP8 conformant? [More details](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)
 
 ## Summary
 Explain in one sentence the goal of this PR

.gitignore

@@ -1,10 +1,14 @@
 # Freqtrade rules
 freqtrade/tests/testdata/*.json
 hyperopt_conf.py
-config.json
+config*.json
 *.sqlite
 .hyperopt
 logfile.txt
+hyperopt_trials.pickle
+user_data/
+freqtrade-plot.html
+freqtrade-profit-plot.html
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
@@ -86,4 +90,5 @@ target/
 .idea
 .vscode
 
-hyperopt_trials.pickle
+.pytest_cache/
+.mypy_cache/

.pyup.yml

@@ -0,0 +1,4 @@
+# autogenerated pyup.io config file 
+# see https://pyup.io/docs/configuration/ for all available options
+
+schedule: every day

.travis.yml

@@ -13,25 +13,26 @@ addons:
 install:
 - ./install_ta-lib.sh
 - export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
-- pip install --upgrade flake8 coveralls
+- pip install --upgrade flake8 coveralls pytest-random-order pytest-asyncio mypy
 - pip install -r requirements.txt
 - pip install -e .
 jobs:
   include:
-    - script: pytest --cov=freqtrade --cov-config=.coveragerc freqtrade/tests/
+    - script:
+      - pytest --cov=freqtrade --cov-config=.coveragerc freqtrade/tests/
+      - coveralls
     - script:
       - cp config.json.example config.json
-      - python freqtrade/main.py backtesting
+      - python freqtrade/main.py --datadir freqtrade/tests/testdata backtesting
     - script:
       - cp config.json.example config.json
-      - python freqtrade/main.py hyperopt -e 5
+      - python freqtrade/main.py --datadir freqtrade/tests/testdata hyperopt -e 5
     - script: flake8 freqtrade
-after_success:
-- coveralls
+    - script: mypy freqtrade
 notifications:
   slack:
     secure: 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
 cache:
   directories:
     - $HOME/.cache/pip
-    - ta-lib
+    - ta-lib

CONTRIBUTING.md

@@ -7,7 +7,7 @@ Feel like our bot is missing a feature? We welcome your pull requests! Few point
 conformant (max-line-length = 100).
 
 If you are unsure, discuss the feature on our [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtMjQ5NTM0OTYzMzY3LWMxYzE3M2MxNDdjMGM3ZTYwNzFjMGIwZGRjNTc3ZGU3MGE3NzdmZGMwNmU3NDM5ZTNmM2Y3NjRiNzk4NmM4OGE)
-or in a [issue](https://github.com/gcarq/freqtrade/issues) before a PR.
+or in a [issue](https://github.com/freqtrade/freqtrade/issues) before a PR.
 
 
 **Before sending the PR:**
@@ -42,4 +42,21 @@ pip3.6 install flake8 coveralls
 flake8 freqtrade
 ```
 
+We receive a lot of code that fails the `flake8` checks.
+To help with that, we encourage you to install the git pre-commit 
+hook that will warn you when you try to commit code that fails these checks. 
+Guide for installing them is [here](http://flake8.pycqa.org/en/latest/user/using-hooks.html).
 
+## 3. Test if all type-hints are correct
+
+**Install packages** (If not already installed)
+
+``` bash
+pip3.6 install mypy
+```
+
+**Run mypy**
+
+``` bash
+mypy freqtrade
+```

Dockerfile

@@ -1,10 +1,11 @@
-FROM python:3.6.2
+FROM python:3.7.0-slim-stretch
 
 # Install TA-lib
-RUN apt-get update && apt-get -y install build-essential && apt-get clean
+RUN apt-get update && apt-get -y install curl build-essential && apt-get clean
 RUN curl -L http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz | \
   tar xzvf - && \
   cd ta-lib && \
+  sed -i "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h && \
   ./configure && make && make install && \
   cd .. && rm -rf ta-lib
 ENV LD_LIBRARY_PATH /usr/local/lib
@@ -15,9 +16,10 @@ WORKDIR /freqtrade
 
 # Install dependencies
 COPY requirements.txt /freqtrade/
-RUN pip install -r requirements.txt
+RUN pip install numpy --no-cache-dir \
+  && pip install -r requirements.txt --no-cache-dir
 
 # Install and execute
 COPY . /freqtrade/
-RUN pip install -e .
+RUN pip install -e . --no-cache-dir
 ENTRYPOINT ["freqtrade"]

README.md

@@ -1,15 +1,15 @@
 # freqtrade
 
-[![Build Status](https://travis-ci.org/gcarq/freqtrade.svg?branch=develop)](https://travis-ci.org/gcarq/freqtrade)
-[![Coverage Status](https://coveralls.io/repos/github/gcarq/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/gcarq/freqtrade?branch=develop)
+[![Build Status](https://travis-ci.org/freqtrade/freqtrade.svg?branch=develop)](https://travis-ci.org/freqtrade/freqtrade)
+[![Coverage Status](https://coveralls.io/repos/github/freqtrade/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/freqtrade/freqtrade?branch=develop)
+[![Maintainability](https://api.codeclimate.com/v1/badges/5737e6d668200b7518ff/maintainability)](https://codeclimate.com/github/freqtrade/freqtrade/maintainability)
 
+Simple High frequency trading bot for crypto currencies designed to support multi exchanges and be controlled via Telegram.
 
-Simple High frequency trading bot for crypto currencies designed to 
-support multi exchanges and be controlled via Telegram.
-
-![freqtrade](https://raw.githubusercontent.com/gcarq/freqtrade/develop/docs/assets/freqtrade-screenshot.png)
+![freqtrade](https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docs/assets/freqtrade-screenshot.png)
 
 ## Disclaimer
+
 This software is for educational purposes only. Do not risk money which 
 you are afraid to lose. USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS 
 AND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR TRADING RESULTS. 
@@ -21,122 +21,84 @@ expect.
 We strongly recommend you to have coding and Python knowledge. Do not 
 hesitate to read the source code and understand the mechanism of this bot.
 
-## Table of Contents
-- [Features](#features)
-- [Quick start](#quick-start)
-- [Documentations](https://github.com/gcarq/freqtrade/blob/develop/docs/index.md)
-   - [Installation](https://github.com/gcarq/freqtrade/blob/develop/docs/installation.md)
-   - [Configuration](https://github.com/gcarq/freqtrade/blob/develop/docs/configuration.md)
-   - [Strategy Optimization](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-optimization.md)
-   - [Backtesting](https://github.com/gcarq/freqtrade/blob/develop/docs/backtesting.md)
-   - [Hyperopt](https://github.com/gcarq/freqtrade/blob/develop/docs/hyperopt.md)
-- [Support](#support)
-   - [Help](#help--slack)
-   - [Bugs](#bugs--issues)
-   - [Feature Requests](#feature-requests)
-   - [Pull Requests](#pull-requests)
-- [Basic Usage](#basic-usage)
-  - [Bot commands](#bot-commands)
-  - [Telegram RPC commands](#telegram-rpc-commands)
-- [Requirements](#requirements)
-    - [Min hardware required](#min-hardware-required)
-    - [Software requirements](#software-requirements)
+## Exchange marketplaces supported
 
-## Branches
-The project is currently setup in two main branches:
-- `develop` - This branch has often new features, but might also cause 
-breaking changes.
-- `master` - This branch contains the latest stable release. The bot 
-'should' be stable on this branch, and is generally well tested. 
+- [X] [Bittrex](https://bittrex.com/)
+- [X] [Binance](https://www.binance.com/) ([*Note for binance users](#a-note-on-binance))
+- [ ] [113 others to tests](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
 
 ## Features
-- [x] **Based on Python 3.6+**: For botting on any operating system - 
-Windows, macOS and Linux
+
+- [x] **Based on Python 3.6+**: For botting on any operating system - Windows, macOS and Linux
 - [x] **Persistence**: Persistence is achieved through sqlite
 - [x] **Dry-run**: Run the bot without playing money.
 - [x] **Backtesting**: Run a simulation of your buy/sell strategy.
-- [x] **Strategy Optimization**: Optimize your buy/sell strategy 
-parameters with Hyperopts.
-- [x] **Whitelist crypto-currencies**: Select which crypto-currency you
-want to trade.
-- [x] **Blacklist crypto-currencies**: Select which crypto-currency you
-want to avoid.
+- [x] **Strategy Optimization by machine learning**: Use machine learning to optimize your buy/sell strategy parameters with real exchange data.
+- [x] **Whitelist crypto-currencies**: Select which crypto-currency you want to trade.
+- [x] **Blacklist crypto-currencies**: Select which crypto-currency you want to avoid.
 - [x] **Manageable via Telegram**: Manage the bot with Telegram
-- [x] **Display profit/loss in fiat**: Display your profit/loss in
-33 fiat.
-- [x] **Daily summary of profit/loss**: Provide a daily summary
- of your profit/loss.
-- [x] **Performance status report**: Provide a performance status of 
-your current trades.
+- [x] **Display profit/loss in fiat**: Display your profit/loss in 33 fiat.
+- [x] **Daily summary of profit/loss**: Provide a daily summary of your profit/loss.
+- [x] **Performance status report**: Provide a performance status of your current trades.
 
-### Exchange supported
-- [x] Bittrex
-- [ ] Binance
-- [ ] Others
+## Table of Contents
+
+- [Quick start](#quick-start)
+- [Documentations](https://github.com/freqtrade/freqtrade/blob/develop/docs/index.md)
+  - [Installation](https://github.com/freqtrade/freqtrade/blob/develop/docs/installation.md)
+  - [Configuration](https://github.com/freqtrade/freqtrade/blob/develop/docs/configuration.md)
+  - [Strategy Optimization](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md)
+  - [Backtesting](https://github.com/freqtrade/freqtrade/blob/develop/docs/backtesting.md)
+  - [Hyperopt](https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md)
+  - [Sandbox Testing](https://github.com/freqtrade/freqtrade/blob/develop/docs/sandbox-testing.md)
+- [Basic Usage](#basic-usage)
+  - [Bot commands](#bot-commands)
+  - [Telegram RPC commands](#telegram-rpc-commands)
+- [Support](#support)
+  - [Help](#help--slack)
+  - [Bugs](#bugs--issues)
+  - [Feature Requests](#feature-requests)
+  - [Pull Requests](#pull-requests)
+- [Requirements](#requirements)
+  - [Min hardware required](#min-hardware-required)
+  - [Software requirements](#software-requirements)
+ 
 
 ## Quick start
-This quick start section is a very short explanation on how to test the 
-bot in dry-run. We invite you to read the 
-[bot documentation](https://github.com/gcarq/freqtrade/blob/develop/docs/index.md) 
-to ensure you understand how the bot is working.
 
-The following steps are made for Linux/MacOS environment
+Freqtrade provides a Linux/macOS script to install all dependencies and help you to configure the bot.
 
-**1. Clone the repo**
 ```bash
-git clone git@github.com:gcarq/freqtrade.git
-git checkout develop
+git clone git@github.com:freqtrade/freqtrade.git
 cd freqtrade
-```
-**2. Create the config file**  
-Switch `"dry_run": true,`
-```bash
-cp config.json.example config.json
-vi config.json
-```
-**3. Build your docker image and run it**
-```bash
-docker build -t freqtrade .
-docker run --rm -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
+git checkout develop
+./setup.sh --install
 ```
 
+_Windows installation is explained in [Installation doc](https://github.com/freqtrade/freqtrade/blob/develop/docs/installation.md)_
 
-### Help / Slack
-For any questions not covered by the documentation or for further
-information about the bot, we encourage you to join our slack channel.
-- [Click here to join Slack channel](https://join.slack.com/t/highfrequencybot/shared_invite/enQtMjQ5NTM0OTYzMzY3LWMxYzE3M2MxNDdjMGM3ZTYwNzFjMGIwZGRjNTc3ZGU3MGE3NzdmZGMwNmU3NDM5ZTNmM2Y3NjRiNzk4NmM4OGE).
+## Documentation
 
-### [Bugs / Issues](https://github.com/gcarq/freqtrade/issues?q=is%3Aissue)
-If you discover a bug in the bot, please 
-[search our issue tracker](https://github.com/gcarq/freqtrade/issues?q=is%3Aissue) 
-first. If it hasn't been reported, please 
-[create a new issue](https://github.com/gcarq/freqtrade/issues/new) and 
-ensure you follow the template guide so that our team can assist you as 
-quickly as possible.
+We invite you to read the bot documentation to ensure you understand how the bot is working.
 
-### [Feature Requests](https://github.com/gcarq/freqtrade/labels/enhancement)
-Have you a great idea to improve the bot you want to share? Please,
-first search if this feature was not [already discussed](https://github.com/gcarq/freqtrade/labels/enhancement).
-If it hasn't been requested, please 
-[create a new request](https://github.com/gcarq/freqtrade/issues/new) 
-and ensure you follow the template guide so that it does not get lost 
-in the bug reports.
-
-### [Pull Requests](https://github.com/gcarq/freqtrade/pulls)
-Feel like our bot is missing a feature? We welcome your pull requests! 
-Please read our 
-[Contributing document](https://github.com/gcarq/freqtrade/blob/develop/CONTRIBUTING.md)
-to understand the requirements before sending your pull-requests. 
-
-**Important:** Always create your PR against the `develop` branch, not 
-`master`.
+- [Index](https://github.com/freqtrade/freqtrade/blob/develop/docs/index.md)
+- [Installation](https://github.com/freqtrade/freqtrade/blob/develop/docs/installation.md)
+- [Configuration](https://github.com/freqtrade/freqtrade/blob/develop/docs/configuration.md)
+- [Bot usage](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-usage.md)
+  - [How to run the bot](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-usage.md#bot-commands)
+  - [How to use Backtesting](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-usage.md#backtesting-commands)
+  - [How to use Hyperopt](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-usage.md#hyperopt-commands)
+- [Strategy Optimization](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md)
+- [Backtesting](https://github.com/freqtrade/freqtrade/blob/develop/docs/backtesting.md)
+- [Hyperopt](https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md)
 
 ## Basic Usage
 
 ### Bot commands
 
 ```bash
-usage: main.py [-h] [-c PATH] [-v] [--version] [--dynamic-whitelist [INT]]
+usage: main.py [-h] [-v] [--version] [-c PATH] [-d PATH] [-s NAME]
+               [--strategy-path PATH] [--dynamic-whitelist [INT]]
                [--dry-run-db]
                {backtesting,hyperopt} ...
 
@@ -149,10 +111,16 @@ positional arguments:
 
 optional arguments:
   -h, --help            show this help message and exit
-  -c PATH, --config PATH
-                        specify configuration file (default: config.json)
   -v, --verbose         be verbose
   --version             show program's version number and exit
+  -c PATH, --config PATH
+                        specify configuration file (default: config.json)
+  -d PATH, --datadir PATH
+                        path to backtest data (default:
+                        freqtrade/tests/testdata
+  -s NAME, --strategy NAME
+                        specify strategy class name (default: DefaultStrategy)
+  --strategy-path PATH  specify additional strategy lookup path
   --dynamic-whitelist [INT]
                         dynamically generate and update whitelist based on 24h
                         BaseVolume (Default 20 currencies)
@@ -160,36 +128,90 @@ optional arguments:
                         "tradesv3.dry_run.sqlite" instead of memory DB. Work
                         only if dry_run is enabled.
 ```
-More details on:
-- [How to run the bot](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-usage.md#bot-commands)
-- [How to use Backtesting](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-usage.md#backtesting-commands)
-- [How to use Hyperopt](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-usage.md#hyperopt-commands)
-   
+
 ### Telegram RPC commands
-Telegram is not mandatory. However, this is a great way to control your
-bot. More details on our 
-[documentation](https://github.com/gcarq/freqtrade/blob/develop/docs/index.md)
+
+Telegram is not mandatory. However, this is a great way to control your bot. More details on our [documentation](https://github.com/freqtrade/freqtrade/blob/develop/docs/index.md)
 
 - `/start`: Starts the trader
 - `/stop`: Stops the trader
 - `/status [table]`: Lists all open trades
 - `/count`: Displays number of open trades
 - `/profit`: Lists cumulative profit from all finished trades
-- `/forcesell <trade_id>|all`: Instantly sells the given trade 
-(Ignoring `minimum_roi`).
+- `/forcesell <trade_id>|all`: Instantly sells the given trade (Ignoring `minimum_roi`).
 - `/performance`: Show performance of each finished trade grouped by pair
 - `/balance`: Show account balance per currency
 - `/daily <n>`: Shows profit or loss per day, over the last n days
 - `/help`: Show help message
 - `/version`: Show version
 
+
+## Development branches
+
+The project is currently setup in two main branches:
+
+- `develop` - This branch has often new features, but might also cause breaking changes.
+- `master` - This branch contains the latest stable release. The bot 'should' be stable on this branch, and is generally well tested.
+- `feat/*` - These are feature branches, which are being worked on heavily. Please don't use these unless you want to test a specific feature.
+
+
+## A note on Binance
+
+For Binance, please add `"BNB/<STAKE>"` to your blacklist to avoid issues.
+Accounts having BNB accounts use this to pay for fees - if your first trade happens to be on `BNB`, further trades will consume this position and make the initial BNB order unsellable as the expected amount is not there anymore.
+
+## Support
+
+### Help / Slack
+
+For any questions not covered by the documentation or for further
+information about the bot, we encourage you to join our slack channel.
+
+- [Click here to join Slack channel](https://join.slack.com/t/highfrequencybot/shared_invite/enQtMjQ5NTM0OTYzMzY3LWMxYzE3M2MxNDdjMGM3ZTYwNzFjMGIwZGRjNTc3ZGU3MGE3NzdmZGMwNmU3NDM5ZTNmM2Y3NjRiNzk4NmM4OGE).
+
+### [Bugs / Issues](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)
+
+If you discover a bug in the bot, please 
+[search our issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue) 
+first. If it hasn't been reported, please 
+[create a new issue](https://github.com/freqtrade/freqtrade/issues/new) and 
+ensure you follow the template guide so that our team can assist you as 
+quickly as possible.
+
+### [Feature Requests](https://github.com/freqtrade/freqtrade/labels/enhancement)
+
+Have you a great idea to improve the bot you want to share? Please,
+first search if this feature was not [already discussed](https://github.com/freqtrade/freqtrade/labels/enhancement).
+If it hasn't been requested, please 
+[create a new request](https://github.com/freqtrade/freqtrade/issues/new) 
+and ensure you follow the template guide so that it does not get lost 
+in the bug reports.
+
+### [Pull Requests](https://github.com/freqtrade/freqtrade/pulls)
+
+Feel like our bot is missing a feature? We welcome your pull requests! 
+Please read our 
+[Contributing document](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)
+to understand the requirements before sending your pull-requests. 
+
+**Note** before starting any major new feature work, *please open an issue describing what you are planning to do* or talk to us on [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtMjQ5NTM0OTYzMzY3LWMxYzE3M2MxNDdjMGM3ZTYwNzFjMGIwZGRjNTc3ZGU3MGE3NzdmZGMwNmU3NDM5ZTNmM2Y3NjRiNzk4NmM4OGE). This will ensure that interested parties can give valuable feedback on the feature, and let others know that you are working on it.
+
+**Important:** Always create your PR against the `develop` branch, not `master`.
+
 ## Requirements
 
+### Uptodate clock
+
+The clock must be accurate, syncronized to a NTP server very frequently to avoid problems with communication to the exchanges.
+
 ### Min hardware required
+
 To run this bot we recommend you a cloud instance with a minimum of:
-* Minimal (advised) system requirements: 2GB RAM, 1GB disk space, 2vCPU
+
+- Minimal (advised) system requirements: 2GB RAM, 1GB disk space, 2vCPU
 
 ### Software requirements
+
 - [Python 3.6.x](http://docs.python-guide.org/en/latest/starting/installation/) 
 - [pip](https://pip.pypa.io/en/stable/installing/)
 - [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)

bin/freqtrade

@@ -1,4 +1,7 @@
 #!/usr/bin/env python3
 
-from freqtrade.main import main
-main()
+import sys
+
+from freqtrade.main import main, set_loggers
+set_loggers()
+main(sys.argv[1:])

config.json.example

@@ -3,46 +3,57 @@
     "stake_currency": "BTC",
     "stake_amount": 0.05,
     "fiat_display_currency": "USD",
+    "ticker_interval" : "5m",
     "dry_run": false,
-    "minimal_roi": {
-        "40":  0.0,
-        "30":  0.01,
-        "20":  0.02,
-        "0":  0.04
+    "trailing_stop": false,
+    "unfilledtimeout": {
+        "buy": 10,
+        "sell": 30
     },
-    "stoploss": -0.10,
-    "unfilledtimeout": 600,
     "bid_strategy": {
-        "ask_last_balance": 0.0
+        "ask_last_balance": 0.0,
+        "use_order_book": false,
+        "order_book_top": 1,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "ask_strategy":{
+        "use_order_book": false,
+        "order_book_min": 1,
+        "order_book_max": 9
     },
     "exchange": {
         "name": "bittrex",
-        "key": "key",
-        "secret": "secret",
+        "key": "your_exchange_key",
+        "secret": "your_exchange_secret",
+        "ccxt_rate_limit": true,
         "pair_whitelist": [
-            "BTC_ETH",
-            "BTC_LTC",
-            "BTC_ETC",
-            "BTC_DASH",
-            "BTC_ZEC",
-            "BTC_XLM",
-            "BTC_NXT",
-            "BTC_POWR",
-            "BTC_ADA",
-            "BTC_XMR"
+            "ETH/BTC",
+            "LTC/BTC",
+            "ETC/BTC",
+            "DASH/BTC",
+            "ZEC/BTC",
+            "XLM/BTC",
+            "NXT/BTC",
+            "POWR/BTC",
+            "ADA/BTC",
+            "XMR/BTC"
         ],
         "pair_blacklist": [
-            "BTC_DOGE"
+            "DOGE/BTC"
         ]
     },
     "experimental": {
         "use_sell_signal": false,
-        "sell_profit_only": false
+        "sell_profit_only": false,
+        "ignore_roi_if_buy_signal": false
     },
     "telegram": {
         "enabled": true,
-        "token": "token",
-        "chat_id": "chat_id"
+        "token": "your_telegram_token",
+        "chat_id": "your_telegram_chat_id"
     },
     "initial_state": "running",
     "internals": {

config_full.json.example

@@ -0,0 +1,75 @@
+{
+    "max_open_trades": 3,
+    "stake_currency": "BTC",
+    "stake_amount": 0.05,
+    "fiat_display_currency": "USD",
+    "dry_run": false,
+    "ticker_interval": "5m",
+    "trailing_stop": false,
+    "trailing_stop_positive": 0.005,
+    "trailing_stop_positive_offset": 0.0051,
+    "minimal_roi": {
+        "40":  0.0,
+        "30":  0.01,
+        "20":  0.02,
+        "0":  0.04
+    },
+    "stoploss": -0.10,
+    "unfilledtimeout": {
+        "buy": 10,
+        "sell": 30
+    },
+    "bid_strategy": {
+        "ask_last_balance": 0.0,
+        "use_order_book": false,
+        "order_book_top": 1,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "ask_strategy":{
+        "use_order_book": false,
+        "order_book_min": 1,
+        "order_book_max": 9
+    },
+    "exchange": {
+        "name": "bittrex",
+        "key": "your_exchange_key",
+        "secret": "your_exchange_secret",
+        "ccxt_rate_limit": true,
+        "pair_whitelist": [
+            "ETH/BTC",
+            "LTC/BTC",
+            "ETC/BTC",
+            "DASH/BTC",
+            "ZEC/BTC",
+            "XLM/BTC",
+            "NXT/BTC",
+            "POWR/BTC",
+            "ADA/BTC",
+            "XMR/BTC"
+        ],
+        "pair_blacklist": [
+            "DOGE/BTC"
+        ],
+        "outdated_offset": 5
+    },
+    "experimental": {
+        "use_sell_signal": false,
+        "sell_profit_only": false,
+        "ignore_roi_if_buy_signal": false
+    },
+    "telegram": {
+        "enabled": true,
+        "token": "your_telegram_token",
+        "chat_id": "your_telegram_chat_id"
+    },
+    "db_url": "sqlite:///tradesv3.sqlite",
+    "initial_state": "running",
+    "internals": {
+        "process_throttle_secs": 5
+    },
+    "strategy": "DefaultStrategy",
+    "strategy_path": "/some/folder/"
+}

docs/backtesting.md

@@ -1,82 +1,216 @@
 # Backtesting
+
 This page explains how to validate your strategy performance by using 
 Backtesting.
 
 ## Table of Contents
+
 - [Test your strategy with Backtesting](#test-your-strategy-with-backtesting)
 - [Understand the backtesting result](#understand-the-backtesting-result)
 
 ## Test your strategy with Backtesting
+
 Now you have good Buy and Sell strategies, you want to test it against
 real data. This is what we call 
 [backtesting](https://en.wikipedia.org/wiki/Backtesting).
 
-
 Backtesting will use the crypto-currencies (pair) from your config file
 and load static tickers located in 
-[/freqtrade/tests/testdata](https://github.com/gcarq/freqtrade/tree/develop/freqtrade/tests/testdata).
+[/freqtrade/tests/testdata](https://github.com/freqtrade/freqtrade/tree/develop/freqtrade/tests/testdata).  
 If the 5 min and 1 min ticker for the crypto-currencies to test is not 
 already in the `testdata` folder, backtesting will download them 
 automatically. Testdata files will not be updated until you specify it.
 
-The result of backtesting will confirm you if your bot as more chance to
-make a profit than a loss.
-
+The result of backtesting will confirm you if your bot has better odds of making a profit than a loss.
 
 The backtesting is very easy with freqtrade.
 
 ### Run a backtesting against the currencies listed in your config file
-**With 5 min tickers (Per default)**
+#### With 5 min tickers (Per default)
+
 ```bash
-python3 ./freqtrade/main.py backtesting --realistic-simulation
+python3 ./freqtrade/main.py backtesting
 ```
 
-**With 1 min tickers**
+#### With 1 min tickers
+
 ```bash
-python3 ./freqtrade/main.py backtesting --realistic-simulation --ticker-interval 1
+python3 ./freqtrade/main.py backtesting --ticker-interval 1m
 ```
 
-**Reload your testdata files**
+#### Update cached pairs with the latest data
+
 ```bash
-python3 ./freqtrade/main.py backtesting --realistic-simulation --refresh-pairs-cached
+python3 ./freqtrade/main.py backtesting --refresh-pairs-cached
 ```
 
-**With live data (do not alter your testdata files)**
+#### With live data (do not alter your testdata files)
+
 ```bash
-python3 ./freqtrade/main.py backtesting --realistic-simulation --live
+python3 ./freqtrade/main.py backtesting --live
 ```
 
-**Using a different on-disk ticker-data source**
+#### Using a different on-disk ticker-data source
+
 ```bash
 python3 ./freqtrade/main.py backtesting --datadir freqtrade/tests/testdata-20180101
 ```
 
-For help about backtesting usage, please refer to 
-[Backtesting commands](#backtesting-commands).
+#### With a (custom) strategy file
+
+```bash
+python3 ./freqtrade/main.py -s TestStrategy backtesting
+```
+
+Where `-s TestStrategy` refers to the class name within the strategy file `test_strategy.py` found in the `freqtrade/user_data/strategies` directory
+
+#### Exporting trades to file
+
+```bash
+python3 ./freqtrade/main.py backtesting --export trades
+```
+
+The exported trades can be read using the following code for manual analysis, or can be used by the plotting script `plot_dataframe.py` in the scripts folder.
+
+``` python
+import json
+from pathlib import Path
+import pandas as pd
+
+filename=Path('user_data/backtest_data/backtest-result.json')
+
+with filename.open() as file:
+        data = json.load(file)
+
+columns = ["pair", "profit", "opents", "closets", "index", "duration",
+           "open_rate", "close_rate", "open_at_end", "sell_reason"]
+df = pd.DataFrame(data, columns=columns)
+
+df['opents'] = pd.to_datetime(df['opents'],
+                              unit='s',
+                              utc=True,
+                              infer_datetime_format=True
+                             )
+df['closets'] = pd.to_datetime(df['closets'],
+                               unit='s',
+                               utc=True,
+                               infer_datetime_format=True
+                              )
+```
+
+If you have some ideas for interesting / helpful backtest data analysis, feel free to submit a PR so the community can benefit from it.
+
+#### Exporting trades to file specifying a custom filename
+
+```bash
+python3 ./freqtrade/main.py backtesting --export trades --export-filename=backtest_teststrategy.json
+```
+
+#### Running backtest with smaller testset
+
+Use the `--timerange` argument to change how much of the testset
+you want to use. The last N ticks/timeframes will be used.
+
+Example:
+
+```bash
+python3 ./freqtrade/main.py backtesting --timerange=-200
+```
+
+#### Advanced use of timerange
+
+Doing `--timerange=-200` will get the last 200 timeframes
+from your inputdata. You can also specify specific dates,
+or a range span indexed by start and stop.
+
+The full timerange specification:
+
+- Use last 123 tickframes of data: `--timerange=-123`
+- Use first 123 tickframes of data: `--timerange=123-`
+- Use tickframes from line 123 through 456: `--timerange=123-456`
+- Use tickframes till 2018/01/31: `--timerange=-20180131`
+- Use tickframes since 2018/01/31: `--timerange=20180131-`
+- Use tickframes since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
+- Use tickframes between POSIX timestamps 1527595200 1527618600:
+                                                `--timerange=1527595200-1527618600`
+
+#### Downloading new set of ticker data
+
+To download new set of backtesting ticker data, you can use a download script.
+
+If you are using Binance for example:
+
+- create a folder `user_data/data/binance` and copy `pairs.json` in that folder.
+- update the `pairs.json` to contain the currency pairs you are interested in.
+
+```bash
+mkdir -p user_data/data/binance
+cp freqtrade/tests/testdata/pairs.json user_data/data/binance
+```
+
+Then run:
+
+```bash
+python scripts/download_backtest_data.py --exchange binance
+```
+
+This will download ticker data for all the currency pairs you defined in `pairs.json`.
+
+- To use a different folder than the exchange specific default, use `--export user_data/data/some_directory`.
+- To change the exchange used to download the tickers, use `--exchange`. Default is `bittrex`.
+- To use `pairs.json` from some other folder, use `--pairs-file some_other_dir/pairs.json`.
+- To download ticker data for only 10 days, use `--days 10`.
+- Use `--timeframes` to specify which tickers to download. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute tickers.
+
+For help about backtesting usage, please refer to [Backtesting commands](#backtesting-commands).
 
 ## Understand the backtesting result
+
 The most important in the backtesting is to understand the result.
 
 A backtesting result will look like that:
+
 ```
-====================== BACKTESTING REPORT ================================
-pair        buy count    avg profit %    total profit BTC    avg duration
---------  -----------  --------------  ------------------  --------------
-BTC_ETH            56           -0.67         -0.00075455            62.3
-BTC_LTC            38           -0.48         -0.00036315            57.9
-BTC_ETC            42           -1.15         -0.00096469            67.0
-BTC_DASH           72           -0.62         -0.00089368            39.9
-BTC_ZEC            45           -0.46         -0.00041387            63.2
-BTC_XLM            24           -0.88         -0.00041846            47.7
-BTC_NXT            24            0.68          0.00031833            40.2
-BTC_POWR           35            0.98          0.00064887            45.3
-BTC_ADA            43           -0.39         -0.00032292            55.0
-BTC_XMR            40           -0.40         -0.00032181            47.4
-TOTAL             419           -0.41         -0.00348593            52.9
+======================================== BACKTESTING REPORT =========================================
+| pair     |   buy count |   avg profit % |   total profit BTC |   avg duration |   profit |   loss |
+|:---------|------------:|---------------:|-------------------:|---------------:|---------:|-------:|
+| ETH/BTC  |          44 |           0.18 |         0.00159118 |           50.9 |       44 |      0 |
+| LTC/BTC  |          27 |           0.10 |         0.00051931 |          103.1 |       26 |      1 |
+| ETC/BTC  |          24 |           0.05 |         0.00022434 |          166.0 |       22 |      2 |
+| DASH/BTC |          29 |           0.18 |         0.00103223 |          192.2 |       29 |      0 |
+| ZEC/BTC  |          65 |          -0.02 |        -0.00020621 |          202.7 |       62 |      3 |
+| XLM/BTC  |          35 |           0.02 |         0.00012877 |          242.4 |       32 |      3 |
+| BCH/BTC  |          12 |           0.62 |         0.00149284 |           50.0 |       12 |      0 |
+| POWR/BTC |          21 |           0.26 |         0.00108215 |          134.8 |       21 |      0 |
+| ADA/BTC  |          54 |          -0.19 |        -0.00205202 |          191.3 |       47 |      7 |
+| XMR/BTC  |          24 |          -0.43 |        -0.00206013 |          120.6 |       20 |      4 |
+| TOTAL    |         335 |           0.03 |         0.00175246 |          157.9 |      315 |     20 |
+2018-06-13 06:57:27,347 - freqtrade.optimize.backtesting - INFO -
+====================================== LEFT OPEN TRADES REPORT ======================================
+| pair     |   buy count |   avg profit % |   total profit BTC |   avg duration |   profit |   loss |
+|:---------|------------:|---------------:|-------------------:|---------------:|---------:|-------:|
+| ETH/BTC  |           3 |           0.16 |         0.00009619 |           25.0 |        3 |      0 |
+| LTC/BTC  |           1 |          -1.00 |        -0.00020118 |         1085.0 |        0 |      1 |
+| ETC/BTC  |           2 |          -1.80 |        -0.00071933 |         1092.5 |        0 |      2 |
+| DASH/BTC |           0 |         nan    |         0.00000000 |          nan   |        0 |      0 |
+| ZEC/BTC  |           3 |          -4.27 |        -0.00256826 |         1301.7 |        0 |      3 |
+| XLM/BTC  |           3 |          -1.11 |        -0.00066744 |          965.0 |        0 |      3 |
+| BCH/BTC  |           0 |         nan    |         0.00000000 |          nan   |        0 |      0 |
+| POWR/BTC |           0 |         nan    |         0.00000000 |          nan   |        0 |      0 |
+| ADA/BTC  |           7 |          -3.58 |        -0.00503604 |          850.0 |        0 |      7 |
+| XMR/BTC  |           4 |          -3.79 |        -0.00303456 |          291.2 |        0 |      4 |
+| TOTAL    |          23 |          -2.63 |        -0.01213062 |          750.4 |        3 |     20 |
+
 ```
 
+The 1st table will contain all trades the bot made.
+
+The 2nd table will contain all trades the bot had to `forcesell` at the end of the backtest period to prsent a full picture.
+These trades are also included in the first table, but are extracted separately for clarity.
+
 The last line will give you the overall performance of your strategy,
 here:
+
 ```
 TOTAL             419           -0.41         -0.00348593            52.9
 ```
@@ -92,6 +226,7 @@ strategy, your sell strategy, and also by the `minimal_roi` and
 As for an example if your minimal_roi is only `"0":  0.01`. You cannot
 expect the bot to make more profit than 1% (because it will sell every 
 time a trade will reach 1%).
+
 ```json
 "minimal_roi": {
     "0":  0.01
@@ -103,7 +238,33 @@ On the other hand, if you set a too high `minimal_roi` like `"0":  0.55`
 profit. Hence, keep in mind that your performance is a mix of your 
 strategies, your configuration, and the crypto-currency you have set up.
 
+## Backtesting multiple strategies
+
+To backtest multiple strategies, a list of Strategies can be provided.
+
+This is limited to 1 ticker-interval per run, however, data is only loaded once from disk so if you have multiple 
+strategies you'd like to compare, this should give a nice runtime boost.
+
+All listed Strategies need to be in the same folder.
+
+``` bash
+freqtrade backtesting --timerange 20180401-20180410 --ticker-interval 5m --strategy-list Strategy001 Strategy002 --export trades 
+```
+
+This will save the results to `user_data/backtest_data/backtest-result-<strategy>.json`, injecting the strategy-name into the target filename.
+There will be an additional table comparing win/losses of the different strategies (identical to the "Total" row in the first table).
+Detailed output for all strategies one after the other will be available, so make sure to scroll up.
+
+```
+=================================================== Strategy Summary ====================================================
+| Strategy   |   buy count |   avg profit % |   cum profit % |   total profit ETH | avg duration    |   profit |   loss |
+|:-----------|------------:|---------------:|---------------:|-------------------:|:----------------|---------:|-------:|
+| Strategy1  |          19 |          -0.76 |         -14.39 |        -0.01440287 | 15:48:00        |       15 |      4 |
+| Strategy2  |           6 |          -2.73 |         -16.40 |        -0.01641299 | 1 day, 14:12:00 |        3 |      3 |
+```
+
 ## Next step
+
 Great, your strategy is profitable. What if the bot can give your the
 optimal parameters to use for your strategy?  
-Your next step is to learn [how to find optimal parameters with Hyperopt](https://github.com/gcarq/freqtrade/blob/develop/docs/hyperopt.md)
+Your next step is to learn [how to find optimal parameters with Hyperopt](https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md)

docs/bot-optimization.md

@@ -1,38 +1,82 @@
 # Bot Optimization
-This page explains where to customize your strategies, and add new 
-indicators. 
+
+This page explains where to customize your strategies, and add new
+indicators.
 
 ## Table of Contents
-- [Change your strategy](#change-your-strategy)
+
+- [Install a custom strategy file](#install-a-custom-strategy-file)
+- [Customize your strategy](#change-your-strategy)
 - [Add more Indicator](#add-more-indicator)
+- [Where is the default strategy](#where-is-the-default-strategy)
+
+Since the version `0.16.0` the bot allows using custom strategy file.
+
+## Install a custom strategy file
+
+This is very simple. Copy paste your strategy file into the folder
+`user_data/strategies`.
+
+Let assume you have a class called `AwesomeStrategy` in the file `awesome-strategy.py`:
+
+1. Move your file into `user_data/strategies` (you should have `user_data/strategies/awesome-strategy.py`
+2. Start the bot with the param `--strategy AwesomeStrategy` (the parameter is the class name)
+
+```bash
+python3 ./freqtrade/main.py --strategy AwesomeStrategy
+```
 
 ## Change your strategy
-The bot is using buy and sell strategies to buy and sell your trades. 
-Both are customizable.
+
+The bot includes a default strategy file. However, we recommend you to
+use your own file to not have to lose your parameters every time the default
+strategy file will be updated on Github. Put your custom strategy file
+into the folder `user_data/strategies`.
+
+A strategy file contains all the information needed to build a good strategy:
+
+- Buy strategy rules
+- Sell strategy rules
+- Minimal ROI recommended
+- Stoploss recommended
+
+The bot also include a sample strategy called `TestStrategy` you can update: `user_data/strategies/test_strategy.py`.
+You can test it with the parameter: `--strategy TestStrategy`
+
+``` bash
+python3 ./freqtrade/main.py --strategy AwesomeStrategy
+```
+
+### Specify custom strategy location
+
+If you want to use a strategy from a different folder you can pass `--strategy-path`
+
+```bash
+python3 ./freqtrade/main.py --strategy AwesomeStrategy --strategy-path /some/folder
+```
+
+**For the following section we will use the [user_data/strategies/test_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/test_strategy.py)
+file as reference.**
 
 ### Buy strategy
-The default buy strategy is located in the file 
-[freqtrade/analyze.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/analyze.py#L73-L92). 
-Edit the function `populate_buy_trend()` to update your buy strategy.
 
-Sample:
+Edit the method `populate_buy_trend()` into your strategy file to update your buy strategy.
+
+Sample from `user_data/strategies/test_strategy.py`:
+
 ```python
-def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
+def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
     """
     Based on TA indicators, populates the buy signal for the given dataframe
-    :param dataframe: DataFrame
+    :param dataframe: DataFrame populated with indicators
+    :param metadata: Additional information, like the currently traded pair
     :return: DataFrame with buy column
     """
     dataframe.loc[
         (
-            (dataframe['rsi'] < 35) &
-            (dataframe['fastd'] < 35) &
             (dataframe['adx'] > 30) &
-            (dataframe['plus_di'] > 0.5)
-        ) |
-        (
-            (dataframe['adx'] > 65) &
-            (dataframe['plus_di'] > 0.5)
+            (dataframe['tema'] <= dataframe['bb_middleband']) &
+            (dataframe['tema'] > dataframe['tema'].shift(1))
         ),
         'buy'] = 1
 
@@ -40,47 +84,49 @@ def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
 ```
 
 ### Sell strategy
-The default buy strategy is located in the file 
-[freqtrade/analyze.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/analyze.py#L95-L115)
-Edit the function `populate_sell_trend()` to update your buy strategy.
 
-Sample:
+Edit the method `populate_sell_trend()` into your strategy file to update your sell strategy.
+Please note that the sell-signal is only used if `use_sell_signal` is set to true in the configuration.
+
+Sample from `user_data/strategies/test_strategy.py`:
+
 ```python
-def populate_sell_trend(dataframe: DataFrame) -> DataFrame:
+def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
     """
     Based on TA indicators, populates the sell signal for the given dataframe
-    :param dataframe: DataFrame
+    :param dataframe: DataFrame populated with indicators
+    :param metadata: Additional information, like the currently traded pair
     :return: DataFrame with buy column
     """
     dataframe.loc[
-        (
-            (
-                (crossed_above(dataframe['rsi'], 70)) |
-                (crossed_above(dataframe['fastd'], 70))
-            ) &
-            (dataframe['adx'] > 10) &
-            (dataframe['minus_di'] > 0)
-        ) |
         (
             (dataframe['adx'] > 70) &
-            (dataframe['minus_di'] > 0.5)
+            (dataframe['tema'] > dataframe['bb_middleband']) &
+            (dataframe['tema'] < dataframe['tema'].shift(1))
         ),
         'sell'] = 1
     return dataframe
 ```
 
-## Add more Indicator
-As you have seen, buy and sell strategies need indicators. You can see 
-the indicators in the file 
-[freqtrade/analyze.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/analyze.py#L95-L115).
-Of course you can add more indicators by extending the list contained in
-the function `populate_indicators()`.
+## Add more Indicators
+
+As you have seen, buy and sell strategies need indicators. You can add more indicators by extending the list contained in the method `populate_indicators()` from your strategy file.
+
+You should only add the indicators used in either `populate_buy_trend()`, `populate_sell_trend()`, or to populate another indicator, otherwise performance may suffer.
 
 Sample:
+
 ```python
-def populate_indicators(dataframe: DataFrame) -> DataFrame:
+def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
     """
     Adds several different TA indicators to the given DataFrame
+
+    Performance Note: For the best performance be frugal on the number of indicators
+    you are using. Let uncomment only the indicator you are using in your strategies
+    or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
+    :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
+    :param metadata: Additional information, like the currently traded pair
+    :return: a Dataframe with all mandatory indicators for the strategies
     """
     dataframe['sar'] = ta.SAR(dataframe)
     dataframe['adx'] = ta.ADX(dataframe)
@@ -111,7 +157,30 @@ def populate_indicators(dataframe: DataFrame) -> DataFrame:
     return dataframe
 ```
 
+### Metadata dict
+
+The metadata-dict (available for `populate_buy_trend`, `populate_sell_trend`, `populate_indicators`) contains additional information.
+Currently this is `pair`, which can be accessed using `metadata['pair']` - and will return a pair in the format `XRP/BTC`.
+
+### Want more indicator examples
+
+Look into the [user_data/strategies/test_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/test_strategy.py).
+Then uncomment indicators you need.
+
+### Where is the default strategy?
+
+The default buy strategy is located in the file
+[freqtrade/default_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/strategy/default_strategy.py).
+
+### Further strategy ideas
+
+To get additional Ideas for strategies, head over to our [strategy repository](https://github.com/freqtrade/freqtrade-strategies). Feel free to use them as they are - but results will depend on the current market situation, pairs used etc. - therefore please backtest the strategy for your exchange/desired pairs first, evaluate carefully, use at your own risk.
+Feel free to use any of them as inspiration for your own strategies.
+We're happy to accept Pull Requests containing new Strategies to that repo.
+
+We also got a *strategy-sharing* channel in our [Slack community](https://join.slack.com/t/highfrequencybot/shared_invite/enQtMjQ5NTM0OTYzMzY3LWMxYzE3M2MxNDdjMGM3ZTYwNzFjMGIwZGRjNTc3ZGU3MGE3NzdmZGMwNmU3NDM5ZTNmM2Y3NjRiNzk4NmM4OGE) which is a great place to get and/or share ideas.
 
 ## Next step
-Now you have a perfect strategy you probably want to backtesting it. 
-Your next step is to learn [How to use the Backtesting](https://github.com/gcarq/freqtrade/blob/develop/docs/backtesting.md).
+
+Now you have a perfect strategy you probably want to backtest it.
+Your next step is to learn [How to use the Backtesting](https://github.com/freqtrade/freqtrade/blob/develop/docs/backtesting.md).

docs/bot-usage.md

@@ -1,17 +1,20 @@
 # Bot usage
-This page explains the difference parameters of the bot and how to run 
-it.
+
+This page explains the difference parameters of the bot and how to run it.
 
 ## Table of Contents
+
 - [Bot commands](#bot-commands)
 - [Backtesting commands](#backtesting-commands)
 - [Hyperopt commands](#hyperopt-commands)
 
 ## Bot commands
+
 ```
-usage: main.py [-h] [-c PATH] [-v] [--version] [--dynamic-whitelist [INT]]
-               [--dry-run-db]
-               {backtesting,hyperopt} ...
+usage: freqtrade [-h] [-v] [--version] [-c PATH] [-d PATH] [-s NAME]
+                 [--strategy-path PATH] [--dynamic-whitelist [INT]]
+                 [--db-url PATH]
+                 {backtesting,hyperopt} ...
 
 Simple High Frequency Trading Bot for crypto currencies
 
@@ -22,22 +25,25 @@ positional arguments:
 
 optional arguments:
   -h, --help            show this help message and exit
-  -c PATH, --config PATH
-                        specify configuration file (default: config.json)
   -v, --verbose         be verbose
   --version             show program's version number and exit
-  -dd PATH, --datadir PATH
-                        Path is from where backtesting and hyperopt will load the
-                        ticker data files (default freqdata/tests/testdata).
+  -c PATH, --config PATH
+                        specify configuration file (default: config.json)
+  -d PATH, --datadir PATH
+                        path to backtest data
+  -s NAME, --strategy NAME
+                        specify strategy class name (default: DefaultStrategy)
+  --strategy-path PATH  specify additional strategy lookup path
   --dynamic-whitelist [INT]
                         dynamically generate and update whitelist based on 24h
-                        BaseVolume (Default 20 currencies)
-  --dry-run-db          Force dry run to use a local DB
-                        "tradesv3.dry_run.sqlite" instead of memory DB. Work
-                        only if dry_run is enabled.
+                        BaseVolume (default: 20)
+  --db-url PATH         Override trades database URL, this is useful if
+                        dry_run is enabled or in custom deployments (default:
+                        sqlite:///tradesv3.sqlite)
 ```
 
 ### How to use a different config file?
+
 The bot allows you to select which config file you want to use. Per 
 default, the bot will load the file `./config.json`
 
@@ -45,18 +51,57 @@ default, the bot will load the file `./config.json`
 python3 ./freqtrade/main.py -c path/far/far/away/config.json 
 ```
 
+### How to use --strategy?
+
+This parameter will allow you to load your custom strategy class.
+Per default without `--strategy` or `-s` the bot will load the
+`DefaultStrategy` included with the bot (`freqtrade/strategy/default_strategy.py`).
+
+The bot will search your strategy file within `user_data/strategies` and `freqtrade/strategy`.
+
+To load a strategy, simply pass the class name (e.g.: `CustomStrategy`) in this parameter.
+
+**Example:**  
+In `user_data/strategies` you have a file `my_awesome_strategy.py` which has
+a strategy class called `AwesomeStrategy` to load it:
+
+```bash
+python3 ./freqtrade/main.py --strategy AwesomeStrategy
+```
+
+If the bot does not find your strategy file, it will display in an error 
+message the reason (File not found, or errors in your code).
+
+Learn more about strategy file in [optimize your bot](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md).
+
+### How to use --strategy-path?
+
+This parameter allows you to add an additional strategy lookup path, which gets
+checked before the default locations (The passed path must be a folder!):
+```bash
+python3 ./freqtrade/main.py --strategy AwesomeStrategy --strategy-path /some/folder
+```
+
+#### How to install a strategy?
+
+This is very simple. Copy paste your strategy file into the folder 
+`user_data/strategies` or use `--strategy-path`. And voila, the bot is ready to use it.
+
 ### How to use --dynamic-whitelist?
+
 Per default `--dynamic-whitelist` will retrieve the 20 currencies based 
 on BaseVolume. This value can be changed when you run the script.
 
 **By Default**  
 Get the 20 currencies based on BaseVolume.  
+
 ```bash
 python3 ./freqtrade/main.py --dynamic-whitelist
 ```
 
 **Customize the number of currencies to retrieve**  
 Get the 30 currencies based on BaseVolume.  
+
 ```bash
 python3 ./freqtrade/main.py --dynamic-whitelist 30
 ```
@@ -66,40 +111,65 @@ python3 ./freqtrade/main.py --dynamic-whitelist 30
 negative value (e.g -2), `--dynamic-whitelist` will use the default
 value (20).
 
-### How to use --dry-run-db?
+### How to use --db-url?
+
 When you run the bot in Dry-run mode, per default no transactions are 
 stored in a database. If you want to store your bot actions in a DB 
-using `--dry-run-db`. This command will use a separate database file 
-`tradesv3.dry_run.sqlite`
+using `--db-url`. This can also be used to specify a custom database
+in production mode. Example command:
 
 ```bash
-python3 ./freqtrade/main.py -c config.json --dry-run-db
+python3 ./freqtrade/main.py -c config.json --db-url sqlite:///tradesv3.dry_run.sqlite
 ```
 
-
 ## Backtesting commands
 
 Backtesting also uses the config specified via `-c/--config`.
 
 ```
-usage: freqtrade backtesting [-h] [-l] [-i INT] [--realistic-simulation]
-                             [-r]
+usage: freqtrade backtesting [-h] [-i TICKER_INTERVAL] [--eps] [--dmmp]
+                             [--timerange TIMERANGE] [-l] [-r]
+                             [--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
+                             [--export EXPORT] [--export-filename PATH]
 
 optional arguments:
   -h, --help            show this help message and exit
+  -i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
+                        specify ticker interval (1m, 5m, 30m, 1h, 1d)
+  --eps, --enable-position-stacking
+                        Allow buying the same pair multiple times (position
+                        stacking)
+  --dmmp, --disable-max-market-positions
+                        Disable applying `max_open_trades` during backtest
+                        (same as setting `max_open_trades` to a very high
+                        number)
+  --timerange TIMERANGE
+                        specify what timerange of data to use.
   -l, --live            using live data
-  -i INT, --ticker-interval INT
-                        specify ticker interval in minutes (default: 5)
-  --realistic-simulation
-                        uses max_open_trades from config to simulate real
-                        world limitations
   -r, --refresh-pairs-cached
-                        refresh the pairs files in tests/testdata with 
-                        the latest data from Bittrex. Use it if you want
-                        to run your backtesting with up-to-date data.
+                        refresh the pairs files in tests/testdata with the
+                        latest data from the exchange. Use it if you want to
+                        run your backtesting with up-to-date data.
+  --strategy-list STRATEGY_LIST [STRATEGY_LIST ...]
+                        Provide a commaseparated list of strategies to
+                        backtest Please note that ticker-interval needs to be
+                        set either in config or via command line. When using
+                        this together with --export trades, the strategy-name
+                        is injected into the filename (so backtest-data.json
+                        becomes backtest-data-DefaultStrategy.json
+  --export EXPORT       export backtest results, argument are: trades Example
+                        --export=trades
+  --export-filename PATH
+                        Save backtest results to this filename requires
+                        --export to be set as well Example --export-
+                        filename=user_data/backtest_data/backtest_today.json
+                        (default: user_data/backtest_data/backtest-
+                        result.json)
+
 ```
 
 ### How to use --refresh-pairs-cached parameter?
+
 The first time your run Backtesting, it will take the pairs you have 
 set in your config file and download data from Bittrex. 
 
@@ -111,29 +181,42 @@ to come back to the previous version.**
 To test your strategy with latest data, we recommend continuing using 
 the parameter `-l` or `--live`.
 
-
 ## Hyperopt commands
 
-It is possible to use hyperopt for trading strategy optimization.
-Hyperopt uses an internal json config return by `hyperopt_optimize_conf()` 
-located in `freqtrade/optimize/hyperopt_conf.py`.
+To optimize your strategy, you can use hyperopt parameter hyperoptimization
+to find optimal parameter values for your stategy.
 
 ```
-usage: freqtrade hyperopt [-h] [-e INT] [--use-mongodb]
+usage: freqtrade hyperopt [-h] [-i TICKER_INTERVAL] [--eps] [--dmmp]
+                          [--timerange TIMERANGE] [-e INT]
+                          [-s {all,buy,roi,stoploss} [{all,buy,roi,stoploss} ...]]
 
 optional arguments:
   -h, --help            show this help message and exit
+  -i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
+                        specify ticker interval (1m, 5m, 30m, 1h, 1d)
+  --eps, --enable-position-stacking
+                        Allow buying the same pair multiple times (position
+                        stacking)
+  --dmmp, --disable-max-market-positions
+                        Disable applying `max_open_trades` during backtest
+                        (same as setting `max_open_trades` to a very high
+                        number)
+  --timerange TIMERANGE
+                        specify what timerange of data to use.
   -e INT, --epochs INT  specify number of epochs (default: 100)
-  --use-mongodb         parallelize evaluations with mongodb (requires mongod
-                        in PATH)
+  -s {all,buy,roi,stoploss} [{all,buy,roi,stoploss} ...], --spaces {all,buy,roi,stoploss} [{all,buy,roi,stoploss} ...]
+                        Specify which parameters to hyperopt. Space separate
+                        list. Default: all
 
 ```
 
 ## A parameter missing in the configuration?
+
 All parameters for `main.py`, `backtesting`, `hyperopt` are referenced
-in [misc.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/misc.py#L84)
+in [misc.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/misc.py#L84)
 
 ## Next step
-The optimal strategy of the bot will change with time depending of the
-market trends. The next step is to 
-[optimize your bot](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-optimization.md).
+
+The optimal strategy of the bot will change with time depending of the market trends. The next step is to 
+[optimize your bot](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md).

docs/configuration.md

@@ -1,12 +1,15 @@
 # Configure the bot
+
 This page explains how to configure your `config.json` file.
 
 ## Table of Contents
+
 - [Bot commands](#bot-commands)
 - [Backtesting commands](#backtesting-commands)
 - [Hyperopt commands](#hyperopt-commands)
 
 ## Setup config.json
+
 We recommend to copy and use the `config.json.example` as a template
 for your bot configuration.
 
@@ -16,32 +19,65 @@ The table below will list all configuration parameters.
 |----------|---------|----------|-------------|
 | `max_open_trades` | 3 | Yes | Number of trades open your bot will have.
 | `stake_currency` | BTC | Yes | Crypto-currency used for trading.
-| `stake_amount` | 0.05 | Yes | Amount of crypto-currency your bot will use for each trade. Per default, the bot will use (0.05 BTC x 3) = 0.15 BTC in total will be always engaged.
+| `stake_amount` | 0.05 | Yes | Amount of crypto-currency your bot will use for each trade. Per default, the bot will use (0.05 BTC x 3) = 0.15 BTC in total will be always engaged. Set it to 'unlimited' to allow the bot to use all avaliable balance.
+| `ticker_interval` | [1m, 5m, 30m, 1h, 1d] | No | The ticker interval to use (1min, 5 min, 30 min, 1 hour or 1 day). Default is 5 minutes
 | `fiat_display_currency` | USD | Yes | Fiat currency used to show your profits. More information below. 
-| `dry_run` | true | Yes | Define if the bot must be in Dry-run or production mode. 
-| `minimal_roi` | See below | Yes | Set the threshold in percent the bot will use to sell a trade. More information below. 
-| `stoploss` | -0.10 | No | Value of the stoploss in percent used by the bot. More information below. 
-| `unfilledtimeout` | 0 | No | How long (in minutes) the bot will wait for an unfilled order to complete, after which the order will be cancelled.
+| `dry_run` | true | Yes | Define if the bot must be in Dry-run or production mode.
+| `process_only_new_candles` | false | No | If set to true indicators are processed only once a new candle arrives. If false each loop populates the indicators, this will mean the same candle is processed many times creating system load but can be useful of your strategy depends on tick data not only candle. Can be set either in Configuration or in the strategy. 
+| `minimal_roi` | See below | No | Set the threshold in percent the bot will use to sell a trade. More information below. If set, this parameter will override `minimal_roi` from your strategy file. 
+| `stoploss` | -0.10 | No | Value of the stoploss in percent used by the bot. More information below. If set, this parameter will override `stoploss` from your strategy file. 
+| `trailing_stop` | false | No | Enables trailing stop-loss (based on `stoploss` in either configuration or strategy file).
+| `trailing_stop_positve` | 0 | No | Changes stop-loss once profit has been reached.
+| `trailing_stop_positve_offset` | 0 | No | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive.
+| `unfilledtimeout.buy` | 10 | Yes | How long (in minutes) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled.
+| `unfilledtimeout.sell` | 10 | Yes | How long (in minutes) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled.
 | `bid_strategy.ask_last_balance` | 0.0 | Yes | Set the bidding price. More information below.
-| `exchange.name` | bittrex | Yes | Name of the exchange class to use.
+| `bid_strategy.use_order_book` | false | No | Allows buying of pair using the rates in Order Book Bids.
+| `bid_strategy.order_book_top` | 0 | No | Bot will use the top N rate in Order Book Bids. Ie. a value of 2 will allow the bot to pick the 2nd bid rate in Order Book Bids.
+| `bid_strategy.check_depth_of_market.enabled` | false | No | Does not buy if the % difference of buy orders and sell orders is met in Order Book.
+| `bid_strategy.check_depth_of_market.bids_to_ask_delta` | 0 | No | The % difference of buy orders and sell orders found in Order Book. A value lesser than 1 means sell orders is greater, while value greater than 1 means buy orders is higher.
+| `ask_strategy.use_order_book` | false | No | Allows selling of open traded pair using the rates in Order Book Asks.
+| `ask_strategy.order_book_min` | 0 | No | Bot will scan from the top min to max Order Book Asks searching for a profitable rate.
+| `ask_strategy.order_book_max` | 0 | No | Bot will scan from the top min to max Order Book Asks searching for a profitable rate.
+| `exchange.name` | bittrex | Yes | Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename).
 | `exchange.key` | key | No | API key to use for the exchange. Only required when you are in production mode.
 | `exchange.secret` | secret | No | API secret to use for the exchange. Only required when you are in production mode.
 | `exchange.pair_whitelist` | [] | No | List of currency to use by the bot. Can be overrided with `--dynamic-whitelist` param.
 | `exchange.pair_blacklist` | [] | No | List of currency the bot must avoid. Useful when using `--dynamic-whitelist` param.
+| `exchange.ccxt_rate_limit` | True | No | Have CCXT handle Exchange rate limits. Depending on the exchange, having this to false can lead to temporary bans from the exchange.
 | `experimental.use_sell_signal` | false | No | Use your sell strategy in addition of the `minimal_roi`.
+| `experimental.sell_profit_only` | false | No | waits until you have made a positive profit before taking a sell decision.
+| `experimental.ignore_roi_if_buy_signal` | false | No | Does not sell if the buy-signal is still active. Takes preference over `minimal_roi` and `use_sell_signal`
 | `telegram.enabled` | true | Yes | Enable or not the usage of Telegram.
-| `telegram.token` | token | No | Your Telegram bot token. Only required is `enable` is `true`.
-| `telegram.chat_id` | chat_id | No | Your personal Telegram account id. Only required is `enable` is `true`.
+| `telegram.token` | token | No | Your Telegram bot token. Only required if `telegram.enabled` is `true`.
+| `telegram.chat_id` | chat_id | No | Your personal Telegram account id. Only required if `telegram.enabled` is `true`.
+| `webhook.enabled` | false | No | Enable useage of Webhook notifications
+| `webhook.url` | false | No | URL for the webhook. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details.
+| `webhook.webhookbuy` | false | No | Payload to send on buy. Only required if `webhook.enabled` is `true`. See the [webhook documentationV](webhook-config.md) for more details.
+| `webhook.webhooksell` | false | No | Payload to send on sell. Only required if `webhook.enabled` is `true`. See the [webhook documentationV](webhook-config.md) for more details.
+| `webhook.webhookstatus` | false | No | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentationV](webhook-config.md) for more details.
+| `db_url` | `sqlite:///tradesv3.sqlite` | No | Declares database URL to use. NOTE: This defaults to `sqlite://` if `dry_run` is `True`.
 | `initial_state` | running | No | Defines the initial application state. More information below.
+| `strategy` | DefaultStrategy | No | Defines Strategy class to use.
+| `strategy_path` | null | No | Adds an additional strategy lookup path (must be a folder).
 | `internals.process_throttle_secs` | 5 | Yes | Set the process throttle. Value in second.
 
-The definition of each config parameters is in 
-[misc.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/misc.py#L205).
+The definition of each config parameters is in [misc.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/misc.py#L205).
+
+### Understand stake_amount
+
+`stake_amount` is an amount of crypto-currency your bot will use for each trade.
+The minimal value is 0.0005. If there is not enough crypto-currency in 
+the account an exception is generated.
+To allow the bot to trade all the avaliable `stake_currency` in your account set `stake_amount` = `unlimited`.
+In this case a trade amount is calclulated as `currency_balanse / (max_open_trades - current_open_trades)`.
 
 ### Understand minimal_roi
+
 `minimal_roi` is a JSON object where the key is a duration
 in minutes and the value is the minimum ROI in percent.
 See the example below:
+
 ```
 "minimal_roi": {
     "40": 0.0,    # Sell after 40 minutes if the profit is not negative
@@ -51,78 +87,152 @@ See the example below:
 },
 ```
 
+Most of the strategy files already include the optimal `minimal_roi`
+value. This parameter is optional. If you use it, it will take over the
+`minimal_roi` value from the strategy file.
+
 ### Understand stoploss
+
 `stoploss` is loss in percentage that should trigger a sale.
 For example value `-0.10` will cause immediate sell if the
 profit dips below -10% for a given trade. This parameter is optional.
 
+Most of the strategy files already include the optimal `stoploss`
+value. This parameter is optional. If you use it, it will take over the
+`stoploss` value from the strategy file.
+
+### Understand trailing stoploss
+
+Go to the [trailing stoploss Documentation](stoploss.md) for details on trailing stoploss.
+
 ### Understand initial_state
+
 `initial_state` is an optional field that defines the initial application state.
 Possible values are `running` or `stopped`. (default=`running`)
 If the value is `stopped` the bot has to be started with `/start` first.
 
+### Understand process_throttle_secs
+
+`process_throttle_secs` is an optional field that defines in seconds how long the bot should wait
+before asking the strategy if we should buy or a sell an asset. After each wait period, the strategy is asked again for
+every opened trade wether or not we should sell, and for all the remaining pairs (either the dynamic list of pairs or
+the static list of pairs) if we should buy.
+
 ### Understand ask_last_balance
+
 `ask_last_balance` sets the bidding price. Value `0.0` will use `ask` price, `1.0` will
 use the `last` price and values between those interpolate between ask and last
 price. Using `ask` price will guarantee quick success in bid, but bot will also
 end up paying more then would probably have been necessary.
 
+### What values for exchange.name?
+
+Freqtrade is based on [CCXT library](https://github.com/ccxt/ccxt) that supports 115 cryptocurrency
+exchange markets and trading APIs. The complete up-to-date list can be found in the
+[CCXT repo homepage](https://github.com/ccxt/ccxt/tree/master/python). However, the bot was tested
+with only Bittrex and Binance.
+
+The bot was tested with the following exchanges:
+
+- [Bittrex](https://bittrex.com/): "bittrex"
+- [Binance](https://www.binance.com/): "binance"
+
+Feel free to test other exchanges and submit your PR to improve the bot.
+
 ### What values for fiat_display_currency?
-`fiat_display_currency` set the fiat to use for the conversion form coin to fiat in Telegram. 
-The valid value are: "AUD", "BRL", "CAD", "CHF", "CLP", "CNY", "CZK", "DKK", "EUR", "GBP", "HKD", "HUF", "IDR", "ILS", "INR", "JPY", "KRW", "MXN", "MYR", "NOK", "NZD", "PHP", "PKR", "PLN", "RUB", "SEK", "SGD", "THB", "TRY", "TWD", "ZAR", "USD".
+
+`fiat_display_currency` set the base currency to use for the conversion from coin to fiat in Telegram.
+The valid values are: "AUD", "BRL", "CAD", "CHF", "CLP", "CNY", "CZK", "DKK", "EUR", "GBP", "HKD", "HUF", "IDR", "ILS", "INR", "JPY", "KRW", "MXN", "MYR", "NOK", "NZD", "PHP", "PKR", "PLN", "RUB", "SEK", "SGD", "THB", "TRY", "TWD", "ZAR", "USD".
+In addition to central bank currencies, a range of cryto currencies are supported.
+The valid values are: "BTC", "ETH", "XRP", "LTC", "BCH", "USDT".
 
 ## Switch to dry-run mode
+
 We recommend starting the bot in dry-run mode to see how your bot will
 behave and how is the performance of your strategy. In Dry-run mode the
 bot does not engage your money. It only runs a live simulation without
 creating trades.
 
 ### To switch your bot in Dry-run mode:
+
 1. Edit your `config.json`  file
-2. Switch dry-run to true
+2. Switch dry-run to true and specify db_url for a persistent db
+
 ```json
 "dry_run": true,
+"db_url": "sqlite///tradesv3.dryrun.sqlite",
 ```
 
-3. Remove your Bittrex API key (change them by fake api credentials)
+3. Remove your Exchange API key (change them by fake api credentials)
+
 ```json
 "exchange": {
         "name": "bittrex",
         "key": "key",
         "secret": "secret",
         ...
-}   
+}
 ```
 
 Once you will be happy with your bot performance, you can switch it to 
 production mode.
 
 ## Switch to production mode
+
 In production mode, the bot will engage your money. Be careful a wrong 
 strategy can lose all your money. Be aware of what you are doing when 
 you run it in production mode.
 
 ### To switch your bot in production mode:
+
 1. Edit your `config.json`  file
 
-2. Switch dry-run to false
+2. Switch dry-run to false and don't forget to adapt your database URL if set
+
 ```json
 "dry_run": false,
 ```
 
-3. Insert your Bittrex API key (change them by fake api keys)
+3. Insert your Exchange API key (change them by fake api keys)
+
 ```json
 "exchange": {
         "name": "bittrex",
         "key": "af8ddd35195e9dc500b9a6f799f6f5c93d89193b",
         "secret": "08a9dc6db3d7b53e1acebd9275677f4b0a04f1a5",
         ...
-}       
-```
-If you have not your Bittrex API key yet, 
-[see our tutorial](https://github.com/gcarq/freqtrade/blob/develop/docs/pre-requisite.md).
+}
 
+```
+If you have not your Bittrex API key yet, [see our tutorial](https://github.com/freqtrade/freqtrade/blob/develop/docs/pre-requisite.md).
+
+
+### Embedding Strategies
+
+FreqTrade provides you with with an easy way to embed the strategy into your configuration file. 
+This is done by utilizing BASE64 encoding and providing this string at the strategy configuration field,
+in your chosen config file.
+
+##### Encoding a string as BASE64
+
+This is a quick example, how to generate the BASE64 string in python
+
+```python
+from base64 import urlsafe_b64encode
+
+with open(file, 'r') as f:
+    content = f.read()
+content = urlsafe_b64encode(content.encode('utf-8'))
+```
+
+The variable 'content', will contain the strategy file in a BASE64 encoded form. Which can now be set in your configurations file as following
+
+```json
+"strategy": "NameOfStrategy:BASE64String"
+```
+
+Please ensure that 'NameOfStrategy' is identical to the strategy name!
 
 ## Next step
-Now you have configured your config.json, the next step is to 
-[start your bot](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-usage.md).
+
+Now you have configured your config.json, the next step is to [start your bot](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-usage.md).

docs/faq.md

@@ -2,20 +2,70 @@
 
 #### I have waited 5 minutes, why hasn't the bot made any trades yet?!
 
-Depending on the buy strategy, the amount of whitelisted coins, the situation of the market etc, it can take up to hours to find good entry position for a trade. Be patient!
+Depending on the buy strategy, the amount of whitelisted coins, the 
+situation of the market etc, it can take up to hours to find good entry 
+position for a trade. Be patient!
 
 #### I have made 12 trades already, why is my total profit negative?!
 
-I understand your disappointment but unfortunately 12 trades is just not enough to say anything. If you run backtesting, you can see that our current algorithm does leave you on the plus side, but that is after thousands of trades and even there, you will be left with losses on specific coins that you have traded tens if not hundreds of times. We of course constantly aim to improve the bot but it will _always_ be a gamble, which should leave you with modest wins on monthly basis but you can't say much from few trades.
+I understand your disappointment but unfortunately 12 trades is just 
+not enough to say anything. If you run backtesting, you can see that our 
+current algorithm does leave you on the plus side, but that is after 
+thousands of trades and even there, you will be left with losses on 
+specific coins that you have traded tens if not hundreds of times. We 
+of course constantly aim to improve the bot but it will _always_ be a 
+gamble, which should leave you with modest wins on monthly basis but 
+you can't say much from few trades.
 
-#### I’d like to change the stake amount. Can I just stop the bot with /stop and then change the config.json and run it again?
+#### I’d like to change the stake amount. Can I just stop the bot with 
+/stop and then change the config.json and run it again?
 
-Not quite. Trades are persisted to a database but the configuration is currently only read when the bot is killed and restarted. `/stop` more like pauses. You can stop your bot, adjust settings and start it again.
+Not quite. Trades are persisted to a database but the configuration is 
+currently only read when the bot is killed and restarted. `/stop` more 
+like pauses. You can stop your bot, adjust settings and start it again.
 
 #### I want to improve the bot with a new strategy
 
-That's great. We have a nice backtesting and hyperoptimizing setup. See the tutorial [[here|Testing-new-strategies-with-Hyperopt]].
+That's great. We have a nice backtesting and hyperoptimizing setup. See 
+the tutorial [here|Testing-new-strategies-with-Hyperopt](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-usage.md#hyperopt-commands).
 
-#### Is there a setting to only SELL the coins being held and not perform anymore BUYS?
+#### Is there a setting to only SELL the coins being held and not 
+perform anymore BUYS?
+
+You can use the `/forcesell all` command from Telegram. 
+
+### How many epoch do I need to get a good Hyperopt result?
+Per default Hyperopts without `-e` or `--epochs` parameter will only 
+run 100 epochs, means 100 evals of your triggers, guards, .... Too few 
+to find a great result (unless if you are very lucky), so you probably 
+have to run it for 10.000 or more. But it will take an eternity to 
+compute.
+
+We recommend you to run it at least 10.000 epochs:
+```bash
+python3 ./freqtrade/main.py hyperopt -e 10000
+```
+
+or if you want intermediate result to see
+```bash
+for i in {1..100}; do python3 ./freqtrade/main.py hyperopt -e 100; done
+```
+
+#### Why it is so long to run hyperopt?
+Finding a great Hyperopt results takes time. 
+
+If you wonder why it takes a while to find great hyperopt results
+
+This answer was written during the under the release 0.15.1, when we had
+:
+- 8 triggers
+- 9 guards: let's say we evaluate even 10 values from each
+- 1 stoploss calculation: let's say we want 10 values from that too to 
+be evaluated
+
+The following calculation is still very rough and not very precise
+but it will give the idea. With only these triggers and guards there is 
+already 8*10^9*10 evaluations. A roughly total of 80 billion evals. 
+Did you run 100 000 evals? Congrats, you've done roughly 1 / 100 000 th 
+of the search space.
 
-You can use the `/forcesell all` command from Telegram.

docs/hyperopt.md

@@ -1,157 +1,116 @@
 # Hyperopt
-This page explains how to tune your strategy by finding the optimal
-parameters with Hyperopt.
+This page explains how to tune your strategy by finding the optimal 
+parameters, a process called hyperparameter optimization. The bot uses several 
+algorithms included in the `scikit-optimize` package to accomplish this. The
+search will burn all your CPU cores, make your laptop sound like a fighter jet
+and still take a long time.
+
+*Note:* Hyperopt will crash when used with only 1 CPU Core as found out in [Issue #1133](https://github.com/freqtrade/freqtrade/issues/1133)
 
 ## Table of Contents
 - [Prepare your Hyperopt](#prepare-hyperopt)
-    - [1. Configure your Guards and Triggers](#1-configure-your-guards-and-triggers)
-    - [2. Update the hyperopt config file](#2-update-the-hyperopt-config-file)
-- [Advanced Hyperopt notions](#advanced-notions)
-    - [Understand the Guards and Triggers](#understand-the-guards-and-triggers)
+- [Configure your Guards and Triggers](#configure-your-guards-and-triggers)
+- [Solving a Mystery](#solving-a-mystery)
+- [Adding New Indicators](#adding-new-indicators)
 - [Execute Hyperopt](#execute-hyperopt)
-    - [Hyperopt with MongoDB](#hyperopt-with-mongoDB)
 - [Understand the hyperopts result](#understand-the-backtesting-result)
 
-## Prepare Hyperopt
-Before we start digging in Hyperopt, we recommend you to take a look at 
-out Hyperopt file 
-[freqtrade/optimize/hyperopt.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py)
+## Prepare Hyperopting
+We recommend you start by taking a look at `hyperopt.py` file located in [freqtrade/optimize](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py)
  
-### 1. Configure your Guards and Triggers
-There are two places you need to change to add a new buy strategy for 
-testing:
-- Inside the [populate_buy_trend()](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L167-L207).
-- Inside the [SPACE dict](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L47-L94).
+### Configure your Guards and Triggers
+There are two places you need to change to add a new buy strategy for testing:
+- Inside [populate_buy_trend()](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L278-L294).
+- Inside [hyperopt_space()](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L218-L229) 
+and the associated methods `indicator_space`, `roi_space`, `stoploss_space`.
 
-There you have two different type of indicators: 1. `guards` and 2. 
-`triggers`.
-1. Guards are conditions like "never buy if ADX < 10", or never buy if 
-current price is over EMA10.
+There you have two different type of indicators: 1. `guards` and 2. `triggers`.
+1. Guards are conditions like "never buy if ADX < 10", or "never buy if 
+current price is over EMA10".
 2. Triggers are ones that actually trigger buy in specific moment, like 
-"buy when EMA5 crosses over EMA10" or buy when close price touches lower 
-bollinger band.
+"buy when EMA5 crosses over EMA10" or "buy when close price touches lower 
+bollinger band".
 
-HyperOpt will, for each eval round, pick just ONE trigger, and possibly 
-multiple guards. So that the constructed strategy will be something like 
+Hyperoptimization will, for each eval round, pick one trigger and possibly 
+multiple guards. The constructed strategy will be something like 
 "*buy exactly when close price touches lower bollinger band, BUT only if 
 ADX > 10*".
 
+If you have updated the buy strategy, ie. changed the contents of
+`populate_buy_trend()` method you have to update the `guards` and 
+`triggers` hyperopts must use.
 
-If you have updated the buy strategy, means change the content of
-`populate_buy_trend()` function you have to update the `guards` and 
-`triggers` hyperopts must used.
+## Solving a Mystery
 
-As for an example if your `populate_buy_trend()` function is:
-```python
-def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
-    dataframe.loc[
-        (dataframe['rsi'] < 35) &
-        (dataframe['adx'] > 65),
-        'buy'] = 1
+Let's say you are curious: should you use MACD crossings or lower Bollinger 
+Bands to trigger your buys. And you also wonder should you use RSI or ADX to 
+help with those buy decisions. If you decide to use RSI or ADX, which values 
+should I use for them? So let's use hyperparameter optimization to solve this 
+mystery.
 
-    return dataframe
+We will start by defining a search space:
+
+```
+    def indicator_space() -> List[Dimension]:
+        """
+        Define your Hyperopt space for searching strategy parameters
+        """
+        return [
+            Integer(20, 40, name='adx-value'),
+            Integer(20, 40, name='rsi-value'),
+            Categorical([True, False], name='adx-enabled'),
+            Categorical([True, False], name='rsi-enabled'),
+            Categorical(['bb_lower', 'macd_cross_signal'], name='trigger')
+        ]
 ```
 
-Your hyperopt file must contains `guards` to find the right value for 
-`(dataframe['adx'] > 65)` & and `(dataframe['plus_di'] > 0.5)`. That 
-means you will need to enable/disable triggers.
- 
-In our case the `SPACE` and `populate_buy_trend` in hyperopt.py file 
-will be look like:
-```python
-SPACE = {
-    'rsi': hp.choice('rsi', [
-        {'enabled': False},
-        {'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
-    ]),
-    'adx': hp.choice('adx', [
-        {'enabled': False},
-        {'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)}
-    ]),
-    'trigger': hp.choice('trigger', [
-        {'type': 'lower_bb'},
-        {'type': 'faststoch10'},
-        {'type': 'ao_cross_zero'},
-        {'type': 'ema5_cross_ema10'},
-        {'type': 'macd_cross_signal'},
-        {'type': 'sar_reversal'},
-        {'type': 'stochf_cross'},
-        {'type': 'ht_sine'},
-    ]),
-}
+Above definition says: I have five parameters I want you to randomly combine 
+to find the best combination. Two of them are integer values (`adx-value` 
+and `rsi-value`) and I want you test in the range of values 20 to 40.
+Then we have three category variables. First two are either `True` or `False`.
+We use these to either enable or disable the ADX and RSI guards. The last
+one we call `trigger` and use it to decide which buy trigger we want to use.
 
-... 
+So let's write the buy strategy using these values:
 
-def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
-        conditions = []
-        # GUARDS AND TRENDS
-        if params['adx']['enabled']:
-            conditions.append(dataframe['adx'] > params['adx']['value'])
-        if params['rsi']['enabled']:
-            conditions.append(dataframe['rsi'] < params['rsi']['value'])
+```
+        def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
+            conditions = []
+            # GUARDS AND TRENDS
+            if 'adx-enabled' in params and params['adx-enabled']:
+                conditions.append(dataframe['adx'] > params['adx-value'])
+            if 'rsi-enabled' in params and params['rsi-enabled']:
+                conditions.append(dataframe['rsi'] < params['rsi-value'])
 
-        # TRIGGERS
-        triggers = {
-            'lower_bb': dataframe['tema'] <= dataframe['blower'],
-            'faststoch10': (crossed_above(dataframe['fastd'], 10.0)),
-            'ao_cross_zero': (crossed_above(dataframe['ao'], 0.0)),
-            'ema5_cross_ema10': (crossed_above(dataframe['ema5'], dataframe['ema10'])),
-            'macd_cross_signal': (crossed_above(dataframe['macd'], dataframe['macdsignal'])),
-            'sar_reversal': (crossed_above(dataframe['close'], dataframe['sar'])),
-            'stochf_cross': (crossed_above(dataframe['fastk'], dataframe['fastd'])),
-            'ht_sine': (crossed_above(dataframe['htleadsine'], dataframe['htsine'])),
-        }
-        ...
+            # TRIGGERS
+            if params['trigger'] == 'bb_lower':
+                conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
+            if params['trigger'] == 'macd_cross_signal':
+                conditions.append(qtpylib.crossed_above(
+                    dataframe['macd'], dataframe['macdsignal']
+                ))
+
+            dataframe.loc[
+                reduce(lambda x, y: x & y, conditions),
+                'buy'] = 1
+
+            return dataframe
+
+        return populate_buy_trend
 ```
 
+Hyperopting will now call this `populate_buy_trend` as many times you ask it (`epochs`)
+with different value combinations. It will then use the given historical data and make
+buys based on the buy signals generated with the above function and based on the results
+it will end with telling you which paramter combination produced the best profits.
 
-### 2. Update the hyperopt config file
-Hyperopt is using a dedicated config file. At this moment hyperopt 
-cannot use your config file. It is also made on purpose to allow you
-testing your strategy with different configurations.
+The search for best parameters starts with a few random combinations and then uses a 
+regressor algorithm (currently ExtraTreesRegressor) to quickly find a parameter combination
+that minimizes the value of the objective function `calculate_loss` in `hyperopt.py`.
 
-The Hyperopt configuration is located in 
-[freqtrade/optimize/hyperopt_conf.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/optimize/hyperopt_conf.py).
-
-
-## Advanced notions
-### Understand the Guards and Triggers
-When you need to add the new guards and triggers to be hyperopt 
-parameters, you do this by adding them into the [SPACE dict](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L47-L94).
-
-If it's a trigger, you add one line to the 'trigger' choice group and that's it.
-
-If it's a guard, you will add a line like this:
-```
-'rsi': hp.choice('rsi', [
-    {'enabled': False},
-    {'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
-]),
-```
-This says, "*one of guards is RSI, it can have two values, enabled or 
-disabled. If it is enabled, try different values for it between 20 and 40*".
-
-So, the part of the strategy builder using the above setting looks like 
-this:
-```
-if params['rsi']['enabled']:
-    conditions.append(dataframe['rsi'] < params['rsi']['value'])
-```
-It checks if Hyperopt wants the RSI guard to be enabled for this 
-round `params['rsi']['enabled']` and if it is, then it will add a 
-condition that says RSI must be < than the value hyperopt picked 
-for this evaluation, that is given in the `params['rsi']['value']`.
-
-That's it. Now you can add new parts of strategies to Hyperopt and it 
-will try all the combinations with all different values in the search 
-for best working algo.
-
-
-### Add a new Indicators
-If you want to test an indicator that isn't used by the bot currently, 
-you need to add it to 
-[freqtrade/analyze.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/analyze.py#L40-L70)
-inside the `populate_indicators` function.
+The above setup expects to find ADX, RSI and Bollinger Bands in the populated indicators.
+When you want to test an indicator that isn't used by the bot currently, remember to 
+add it to the `populate_indicators()` method in `hyperopt.py`.
 
 ## Execute Hyperopt
 Once you have updated your hyperopt configuration you can run it. 
@@ -160,135 +119,79 @@ it will take time you will have the result (more than 30 mins).
 
 We strongly recommend to use `screen` to prevent any connection loss.
 ```bash
-python3 ./freqtrade/main.py -c config.json hyperopt
+python3 ./freqtrade/main.py -c config.json hyperopt -e 5000
 ```
 
-### Execute hyperopt with different ticker-data source
-If you would like to learn parameters using an alternate ticke-data that
-you have on-disk, use the --datadir PATH option. Default hyperopt will
-use data from directory freqtrade/tests/testdata.
+The `-e` flag will set how many evaluations hyperopt will do. We recommend
+running at least several thousand evaluations.
 
-### Hyperopt with MongoDB
-Hyperopt with MongoDB, is like Hyperopt under steroids. As you saw by
-executing the previous command is the execution takes a long time. 
-To accelerate it you can use hyperopt with MongoDB.
+### Execute Hyperopt with Different Ticker-Data Source
+If you would like to hyperopt parameters using an alternate ticker data that
+you have on-disk, use the `--datadir PATH` option. Default hyperopt will
+use data from directory `user_data/data`.
 
-To run hyperopt with MongoDb you will need 3 terminals.
+### Running Hyperopt with Smaller Testset
+Use the `--timeperiod` argument to change how much of the testset
+you want to use. The last N ticks/timeframes will be used.
+Example:
 
-**Terminal 1: Start MongoDB**
 ```bash
-cd <freqtrade> 
-source .env/bin/activate
-python3 scripts/start-mongodb.py
+python3 ./freqtrade/main.py hyperopt --timeperiod -200
 ```
 
-**Terminal 2: Start Hyperopt worker**
-```bash
-cd <freqtrade> 
-source .env/bin/activate
-python3 scripts/start-hyperopt-worker.py
-```
+### Running Hyperopt with Smaller Search Space
+Use the `--spaces` argument to limit the search space used by hyperopt.
+Letting Hyperopt optimize everything is a huuuuge search space. Often it 
+might make more sense to start by just searching for initial buy algorithm. 
+Or maybe you just want to optimize your stoploss or roi table for that awesome 
+new buy strategy you have.
 
-**Terminal 3: Start Hyperopt with MongoDB**
-```bash
-cd <freqtrade> 
-source .env/bin/activate
-python3 ./freqtrade/main.py -c config.json hyperopt --use-mongodb
-```
+Legal values are:
 
-**Re-run an Hyperopt**
-To re-run Hyperopt you have to delete the existing MongoDB table.
-```bash
-cd <freqtrade> 
-rm -rf .hyperopt/mongodb/
-```
+- `all`: optimize everything
+- `buy`: just search for a new buy strategy
+- `roi`: just optimize the minimal profit table for your strategy
+- `stoploss`: search for the best stoploss value
+- space-separated list of any of the above values for example `--spaces roi stoploss`
 
-## Understand the hyperopts result 
-Once Hyperopt is completed you can use the result to adding new buy 
-signal. Given following result from hyperopt:
-```
-Best parameters:
-{
-    "adx": {
-        "enabled": true,
-        "value": 15.0
-    },
-    "fastd": {
-        "enabled": true,
-        "value": 40.0
-    },
-    "green_candle": {
-        "enabled": true
-    },
-    "mfi": {
-        "enabled": false
-    },
-    "over_sar": {
-        "enabled": false
-    },
-    "rsi": {
-        "enabled": true,
-        "value": 37.0
-    },
-    "trigger": {
-        "type": "lower_bb"
-    },
-    "uptrend_long_ema": {
-        "enabled": true
-    },
-    "uptrend_short_ema": {
-        "enabled": false
-    },
-    "uptrend_sma": {
-        "enabled": false
-    }
-}
+## Understand the Hyperopts Result 
+Once Hyperopt is completed you can use the result to create a new strategy.
+Given the following result from hyperopt:
 
-Best Result:
-  2197 trades. Avg profit  1.84%. Total profit  0.79367541 BTC. Avg duration 241.0 mins.
+```
+Best result:
+   135 trades. Avg profit  0.57%. Total profit  0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins.
+with values:
+{'adx-value': 44, 'rsi-value': 29, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'bb_lower'}
 ```
 
 You should understand this result like:
-- You should **consider** the guard "adx" (`"adx"` is `"enabled": true`) 
-and the best value is `15.0` (`"value": 15.0,`)
-- You should **consider** the guard "fastd" (`"fastd"` is `"enabled": 
-true`) and the best value is `40.0` (`"value": 40.0,`)
-- You should **consider** to enable the guard "green_candle" 
-(`"green_candle"` is `"enabled": true`) but this guards as no 
-customizable value.
-- You should **ignore** the guard "mfi" (`"mfi"` is `"enabled": false`)
-- and so on...
+- The buy trigger that worked best was `bb_lower`.
+- You should not use ADX because `adx-enabled: False`) 
+- You should **consider** using the RSI indicator (`rsi-enabled: True` and the best value is `29.0` (`rsi-value: 29.0`)
 
-
-You have to look from 
-[freqtrade/optimize/hyperopt.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L170-L200) 
-what those values match to.   
+You have to look inside your strategy file into `buy_strategy_generator()` 
+method, what those values match to.   
   
-So for example you had `adx:` with the `value: 15.0` so we would look 
-at `adx`-block from 
-[freqtrade/optimize/hyperopt.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L178-L179). 
-That translates to the following code block to 
-[analyze.populate_buy_trend()](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/analyze.py#L73)
+So for example you had `rsi-value: 29.0` so we would look 
+at `rsi`-block, that translates to the following code block:
 ```
-(dataframe['adx'] > 15.0)
+(dataframe['rsi'] < 29.0)
 ```
   
-So translating your whole hyperopt result to as the new buy-signal 
-would be the following:
+Translating your whole hyperopt result as the new buy-signal 
+would then look like:
 ```
-def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
+def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
     dataframe.loc[
         (
-            (dataframe['adx'] > 15.0) & # adx-value
-            (dataframe['fastd'] < 40.0) & # fastd-value
-            (dataframe['close'] > dataframe['open']) & # green_candle
-            (dataframe['rsi'] < 37.0) & # rsi-value
-            (dataframe['ema50'] > dataframe['ema100']) # uptrend_long_ema
+            (dataframe['rsi'] < 29.0) &  # rsi-value
+            dataframe['close'] < dataframe['bb_lowerband']  # trigger
         ),
         'buy'] = 1
     return dataframe
 ```
 
-## Next step
+## Next Step
 Now you have a perfect bot and want to control it from Telegram. Your
-next step is to learn the [Telegram usage](https://github.com/gcarq/freqtrade/blob/develop/docs/telegram-usage.md).
+next step is to learn the [Telegram usage](https://github.com/freqtrade/freqtrade/blob/develop/docs/telegram-usage.md).

docs/index.md

@@ -1,4 +1,5 @@
 # freqtrade documentation
+
 Welcome to freqtrade documentation. Please feel free to contribute to
 this documentation if you see it became outdated by sending us a 
 Pull-request. Do not hesitate to reach us on 
@@ -6,27 +7,30 @@ Pull-request. Do not hesitate to reach us on
  if you do not find the answer to your questions.
 
 ## Table of Contents
-- [Pre-requisite](https://github.com/gcarq/freqtrade/blob/develop/docs/pre-requisite.md)
-	- [Setup your Bittrex account](https://github.com/gcarq/freqtrade/blob/develop/docs/pre-requisite.md#setup-your-bittrex-account)
-	- [Setup your Telegram bot](https://github.com/gcarq/freqtrade/blob/develop/docs/pre-requisite.md#setup-your-telegram-bot)
-- [Bot Installation](https://github.com/gcarq/freqtrade/blob/develop/docs/installation.md)
-    - [Install with Docker (all platforms)](https://github.com/gcarq/freqtrade/blob/develop/docs/installation.md#docker)
-    - [Install on Linux Ubuntu](https://github.com/gcarq/freqtrade/blob/develop/docs/installation.md#21-linux---ubuntu-1604)
-    - [Install on MacOS](https://github.com/gcarq/freqtrade/blob/develop/docs/installation.md#23-macos-installation)
-    - [Install on Windows](https://github.com/gcarq/freqtrade/blob/develop/docs/installation.md#windows)
-- [Bot Configuration](https://github.com/gcarq/freqtrade/blob/develop/docs/configuration.md)
-- [Bot usage (Start your bot)](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-usage.md)
-    - [Bot commands](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-usage.md#bot-commands)
-    - [Backtesting commands](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-usage.md#backtesting-commands)
-    - [Hyperopt commands](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-usage.md#hyperopt-commands)
-- [Bot Optimization](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-optimization.md)
-	- [Change your strategy](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-optimization.md#change-your-strategy)
-    - [Add more Indicator](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-optimization.md#add-more-indicator)
-    - [Test your strategy with Backtesting](https://github.com/gcarq/freqtrade/blob/develop/docs/backtesting.md)
-    - [Find optimal parameters with Hyperopt](https://github.com/gcarq/freqtrade/blob/develop/docs/hyperopt.md)
-- [Control the bot with telegram](https://github.com/gcarq/freqtrade/blob/develop/docs/telegram-usage.md)
-- [Contribute to the project](https://github.com/gcarq/freqtrade/blob/develop/CONTRIBUTING.md)
-	- [How to contribute](https://github.com/gcarq/freqtrade/blob/develop/CONTRIBUTING.md)
-	- [Run tests & Check PEP8 compliance](https://github.com/gcarq/freqtrade/blob/develop/CONTRIBUTING.md)
-- [FAQ](https://github.com/gcarq/freqtrade/blob/develop/docs/faq.md)
-    - [SQL cheatsheet](https://github.com/gcarq/freqtrade/blob/develop/docs/sql_cheatsheet.md)
+
+- [Pre-requisite](https://github.com/freqtrade/freqtrade/blob/develop/docs/pre-requisite.md)
+	- [Setup your Bittrex account](https://github.com/freqtrade/freqtrade/blob/develop/docs/pre-requisite.md#setup-your-bittrex-account)
+	- [Setup your Telegram bot](https://github.com/freqtrade/freqtrade/blob/develop/docs/pre-requisite.md#setup-your-telegram-bot)
+- [Bot Installation](https://github.com/freqtrade/freqtrade/blob/develop/docs/installation.md)
+    - [Install with Docker (all platforms)](https://github.com/freqtrade/freqtrade/blob/develop/docs/installation.md#docker)
+    - [Install on Linux Ubuntu](https://github.com/freqtrade/freqtrade/blob/develop/docs/installation.md#21-linux---ubuntu-1604)
+    - [Install on MacOS](https://github.com/freqtrade/freqtrade/blob/develop/docs/installation.md#23-macos-installation)
+    - [Install on Windows](https://github.com/freqtrade/freqtrade/blob/develop/docs/installation.md#windows)
+- [Bot Configuration](https://github.com/freqtrade/freqtrade/blob/develop/docs/configuration.md)
+- [Bot usage (Start your bot)](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-usage.md)
+    - [Bot commands](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-usage.md#bot-commands)
+    - [Backtesting commands](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-usage.md#backtesting-commands)
+    - [Hyperopt commands](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-usage.md#hyperopt-commands)
+- [Bot Optimization](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md)
+	- [Change your strategy](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md#change-your-strategy)
+    - [Add more Indicator](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md#add-more-indicator)
+    - [Test your strategy with Backtesting](https://github.com/freqtrade/freqtrade/blob/develop/docs/backtesting.md)
+    - [Find optimal parameters with Hyperopt](https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md)
+- [Control the bot with telegram](https://github.com/freqtrade/freqtrade/blob/develop/docs/telegram-usage.md)
+- [Receive notifications via webhook](https://github.com/freqtrade/freqtrade/blob/develop/docs/webhook-config.md)
+- [Contribute to the project](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)
+	- [How to contribute](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)
+	- [Run tests & Check PEP8 compliance](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)
+- [FAQ](https://github.com/freqtrade/freqtrade/blob/develop/docs/faq.md)
+    - [SQL cheatsheet](https://github.com/freqtrade/freqtrade/blob/develop/docs/sql_cheatsheet.md)
+- [Sandbox Testing](https://github.com/freqtrade/freqtrade/blob/develop/docs/sandbox-testing.md))

docs/installation.md

@@ -1,111 +1,177 @@
-# Install the bot
+# Installation
+
 This page explains how to prepare your environment for running the bot.
-To understand how to set up the bot please read the Bot
-[Bot configuration](https://github.com/gcarq/freqtrade/blob/develop/docs/configuration.md)
-page.
+
+To understand how to set up the bot please read the [Bot Configuration](https://github.com/freqtrade/freqtrade/blob/develop/docs/configuration.md) page.
 
 ## Table of Contents
-- [Docker Automatic Installation](#docker)
-- [Linux or Mac manual Installation](#linux--mac)
-    - [Linux - Ubuntu 16.04](#21-linux---ubuntu-1604)
-    - [Linux - Other distro](#22-linux---other-distro)
-    - [MacOS installation](#23-macos-installation)
-    - [Advanced Linux ](#advanced-linux)
-- [Windows manual Installation](#windows)
 
-# Docker
+* [Table of Contents](#table-of-contents)
+* [Easy Installation - Linux Script](#easy-installation---linux-script)
+* [Automatic Installation - Docker](#automatic-installation---docker)
+* [Custom Linux MacOS Installation](#custom-installation)
+	- [Requirements](#requirements)
+	- [Linux - Ubuntu 16.04](#linux---ubuntu-1604)
+	- [MacOS](#macos)
+	- [Setup Config and virtual env](#setup-config-and-virtual-env)
+* [Windows](#windows)
 
-## Easy installation
-Start by downloading Docker for your platform:
-- [Mac](https://www.docker.com/products/docker#/mac)
-- [Windows](https://www.docker.com/products/docker#/windows)
-- [Linux](https://www.docker.com/products/docker#/linux)
+<!-- /TOC -->
 
-Once you have Docker installed, simply create the config file
-(e.g. `config.json`) and then create a Docker image for `freqtrade`
-using the Dockerfile in this repo.
+------
+
+## Easy Installation - Linux Script
+
+If you are on Debian, Ubuntu or MacOS a freqtrade provides a script to Install, Update, Configure, and Reset your bot.
 
-### 1. Prepare the bot
-1. Clone the git
 ```bash
-git clone https://github.com/gcarq/freqtrade.git
+$ ./setup.sh
+usage:
+	-i,--install    Install freqtrade from scratch
+	-u,--update     Command git pull to update.
+	-r,--reset      Hard reset your develop/master branch.
+	-c,--config     Easy config generator (Will override your existing file).
 ```
-2. (Optional) Checkout the develop branch
+
+### --install
+
+This script will install everything you need to run the bot:
+
+* Mandatory software as: `Python3`, `ta-lib`, `wget`
+* Setup your virtualenv
+* Configure your `config.json` file
+
+This script is a combination of `install script` `--reset`, `--config`
+
+### --update
+
+Update parameter will pull the last version of your current branch and update your virtualenv.
+
+### --reset
+
+Reset parameter will hard reset your branch (only if you are on `master` or `develop`) and recreate your virtualenv.
+
+### --config
+
+Config parameter is a `config.json` configurator. This script will ask you questions to setup your bot and create your `config.json`.
+
+------
+
+## Automatic Installation - Docker
+
+Start by downloading Docker for your platform:
+
+* [Mac](https://www.docker.com/products/docker#/mac)
+* [Windows](https://www.docker.com/products/docker#/windows)
+* [Linux](https://www.docker.com/products/docker#/linux)
+
+Once you have Docker installed, simply create the config file (e.g. `config.json`) and then create a Docker image for `freqtrade` using the Dockerfile in this repo.
+
+### 1. Prepare the Bot
+
+#### 1.1. Clone the git repository
+
+```bash
+git clone https://github.com/freqtrade/freqtrade.git
+```
+
+#### 1.2. (Optional) Checkout the develop branch
+
 ```bash
 git checkout develop
 ```
-3. Go into the new directory
+
+#### 1.3. Go into the new directory
+
 ```bash
 cd freqtrade
 ```
-4. Copy `config.sample` to `config.json`
-```bash
-cp config.json.example config.json
-```
-To edit the config please refer to the [Bot Configuration](https://github.com/gcarq/freqtrade/blob/develop/docs/configuration.md) page
-5. Create your DB file (Optional, the bot will create it if it is missing)
-```bash
-# For Production
-touch tradesv3.sqlite
 
-# For Dry-run
+#### 1.4. Copy `config.json.example` to `config.json`
+
+```bash
+cp -n config.json.example config.json
+```
+
+> To edit the config please refer to the [Bot Configuration](https://github.com/freqtrade/freqtrade/blob/develop/docs/configuration.md) page.
+
+#### 1.5. Create your database file *(optional - the bot will create it if it is missing)*
+
+Production
+
+```bash
+touch tradesv3.sqlite
+````
+
+Dry-Run
+
+```bash
 touch tradesv3.dryrun.sqlite
 ```
 
-### 2. Build the docker image
+### 2. Build the Docker image
+
 ```bash
 cd freqtrade
 docker build -t freqtrade .
 ```
 
-For security reasons, your configuration file will not be included in the
-image, you will need to bind mount it. It is also advised to bind mount
-a sqlite database file (see the "5. Run a restartable docker image"
-section) to keep it between  updates.
+For security reasons, your configuration file will not be included in the image, you will need to bind mount it. It is also advised to bind mount an SQLite database file (see the "5. Run a restartable docker image" section) to keep it between  updates.
 
-### 3. Verify the docker image
-After build process you can verify that the image was created with:
-```
+### 3. Verify the Docker image
+
+After the build process you can verify that the image was created with:
+
+```bash
 docker images
 ```
 
-### 4. Run the docker image
-You can run a one-off container that is immediately deleted upon exiting with
-the following command (config.json must be in the current working directory):
+### 4. Run the Docker image
 
-```
-docker run --rm -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
+You can run a one-off container that is immediately deleted upon exiting with the following command (`config.json` must be in the current working directory):
+
+```bash
+docker run --rm -v /etc/localtime:/etc/localtime:ro -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
 ```
 
-In this example, the database will be created inside the docker instance
-and will be lost when you will refresh your image.
+There is known issue in OSX Docker versions after 17.09.1, whereby /etc/localtime cannot be shared causing Docker to not start. A work-around for this is to start with the following cmd.
+
+```bash
+docker run --rm -e TZ=`ls -la /etc/localtime | cut -d/ -f8-9` -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
+```
+
+More information on this docker issue and work-around can be read [here](https://github.com/docker/for-mac/issues/2396)
+
+In this example, the database will be created inside the docker instance and will be lost when you will refresh your image.
 
 ### 5. Run a restartable docker image
-To run a restartable instance in the background (feel free to place your
-configuration and database files wherever it feels comfortable on your
-filesystem).
 
-**5.1. Move your config file and database**
+To run a restartable instance in the background (feel free to place your configuration and database files wherever it feels comfortable on your filesystem).
+
+#### 5.1. Move your config file and database
+
 ```bash
 mkdir ~/.freqtrade
 mv config.json ~/.freqtrade
 mv tradesv3.sqlite ~/.freqtrade
 ```
 
-**5.2. Run the docker image**
+#### 5.2. Run the docker image
+
 ```bash
 docker run -d \
   --name freqtrade \
+  -v /etc/localtime:/etc/localtime:ro \
   -v ~/.freqtrade/config.json:/freqtrade/config.json \
   -v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
-  freqtrade
+  freqtrade --db-url sqlite:///tradesv3.sqlite
 ```
-If you are using `dry_run=True` it's not necessary to mount
-`tradesv3.sqlite`, but you can mount `tradesv3.dryrun.sqlite` if you
-plan to use the dry run mode with the param `--dry-run-db`.
 
+*Note*: db-url defaults to `sqlite:///tradesv3.sqlite` but it defaults to `sqlite://` if `dry_run=True` is being used.
+To override this behaviour use a custom db-url value: i.e.: `--db-url sqlite:///tradesv3.dryrun.sqlite`
 
 ### 6. Monitor your Docker instance
+
 You can then use the following commands to monitor and manage your container:
 
 ```bash
@@ -116,38 +182,92 @@ docker stop freqtrade
 docker start freqtrade
 ```
 
-You do not need to rebuild the image for configuration changes, it will
-suffice to edit `config.json` and restart the container.
+For more information on how to operate Docker, please refer to the [official Docker documentation](https://docs.docker.com/).
 
+*Note*: You do not need to rebuild the image for configuration changes, it will suffice to edit `config.json` and restart the container.
+
+### 7. Backtest with docker
+
+The following assumes that the above steps (1-4) have been completed successfully.
+Also, backtest-data should be available at `~/.freqtrade/user_data/`.
+
+``` bash
+docker run -d \
+  --name freqtrade \
+  -v /etc/localtime:/etc/localtime:ro \
+  -v ~/.freqtrade/config.json:/freqtrade/config.json \
+  -v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
+  -v ~/.freqtrade/user_data/:/freqtrade/user_data/ \
+  freqtrade --strategy AwsomelyProfitableStrategy backtesting
+```
+
+Head over to the [Backtesting Documentation](https://github.com/freqtrade/freqtrade/blob/develop/docs/backtesting.md) for more details.
+
+*Note*: Additional parameters can be appended after the image name (`freqtrade` in the above example).
+
+------
+
+## Custom Installation
+
+We've included/collected install instructions for Ubuntu 16.04, MacOS, and Windows. These are guidelines and your success may vary with other distros.
+OS Specific steps are listed first, the [common](#common) section below is necessary for all systems.
+
+### Requirements
 
-# Linux / MacOS
-## 1. Requirements
 Click each one for install guide:
-- [Python 3.6.x](http://docs.python-guide.org/en/latest/starting/installation/),
-note the bot was not tested on Python >= 3.7.x
-- [pip](https://pip.pypa.io/en/stable/installing/)
-- [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
-- [virtualenv](https://virtualenv.pypa.io/en/stable/installation/) (Recommended)
-- [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html)
 
-## 2. First install required packages
-This bot require Python 3.6 and TA-LIB
+* [Python >= 3.6.x](http://docs.python-guide.org/en/latest/starting/installation/)
+* [pip](https://pip.pypa.io/en/stable/installing/)
+* [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
+* [virtualenv](https://virtualenv.pypa.io/en/stable/installation/) (Recommended)
+* [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html)
 
-### 2.1 Linux - Ubuntu 16.04
+### Linux - Ubuntu 16.04
+
+#### Install Python 3.6, Git, and wget
 
-**2.1.1. Install Python 3.6, Git, and wget**
 ```bash
 sudo add-apt-repository ppa:jonathonf/python-3.6
 sudo apt-get update
 sudo apt-get install python3.6 python3.6-venv python3.6-dev build-essential autoconf libtool pkg-config make wget git
 ```
 
-**2.1.2. Install TA-LIB**
-Official webpage: https://mrjbq7.github.io/ta-lib/install.html
+#### Raspberry Pi / Raspbian
+
+Before installing FreqTrade on a Raspberry Pi running the official Raspbian Image, make sure you have at least Python 3.6 installed. The default image only provides Python 3.5. Probably the easiest way to get a recent version of python is [miniconda](https://repo.continuum.io/miniconda/).
+
+The following assumes that miniconda3 is installed and available in your environment, and is installed.
+It's recommended to use (mini)conda for this as installation/compilation of `scipy` and `pandas` takes a long time.
+
+``` bash
+conda config --add channels rpi
+conda install python=3.6
+conda create -n freqtrade python=3.6
+conda install scipy pandas
+
+pip install -r requirements.txt
+pip install -e .
 ```
+
+### MacOS
+
+#### Install Python 3.6, git, wget and ta-lib
+
+```bash
+brew install python3 git wget
+```
+
+### common
+
+#### 1. Install TA-Lib
+
+Official webpage: https://mrjbq7.github.io/ta-lib/install.html
+
+```bash
 wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
 tar xvzf ta-lib-0.4.0-src.tar.gz
 cd ta-lib
+sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h
 ./configure --prefix=/usr
 make
 make install
@@ -155,92 +275,126 @@ cd ..
 rm -rf ./ta-lib*
 ```
 
-**2.1.3. [Optional] Install MongoDB**
-Install MongoDB if you plan to optimize your strategy with Hyperopt.
+*Note*: An already downloaded version of ta-lib is included in the repository, as the sourceforge.net source seems to have problems frequently.
+
+#### 2. Setup your Python virtual environment (virtualenv)
+
+*Note*: This step is optional but strongly recommended to keep your system organized
 
 ```bash
-sudo apt-get install mongodb-org
+python3 -m venv .env
+source .env/bin/activate
 ```
-Complete tutorial on [Digital Ocean: How to Install MongoDB on Ubuntu 16.04](https://www.digitalocean.com/community/tutorials/how-to-install-mongodb-on-ubuntu-16-04)
 
-### 2.2. Linux - Other distro
-If you are on a different Linux OS you maybe have to adapt things like:
+#### 3. Install FreqTrade
 
-- package manager (for example yum instead of apt-get)
-- package names
+Clone the git repository:
 
-### 2.3. MacOS installation
-
-**2.3.1. Install Python 3.6, git and wget**
 ```bash
-brew install python3 git wget
+git clone https://github.com/freqtrade/freqtrade.git
+
 ```
 
-**2.3.2. [Optional] Install MongoDB**
-Install MongoDB if you plan to optimize your strategy with Hyperopt.
+Optionally checkout the stable/master branch:
+
 ```bash
-curl -O https://fastdl.mongodb.org/osx/mongodb-osx-ssl-x86_64-3.4.10.tgz
-tar -zxvf mongodb-osx-ssl-x86_64-3.4.10.tgz
-mkdir -p <path_freqtrade>/env/mongodb
-cp -R -n mongodb-osx-x86_64-3.4.10/ <path_freqtrade>/env/mongodb
-export PATH=<path_freqtrade>/env/mongodb/bin:$PATH
+git checkout master
 ```
 
-## 3. Clone the repo
-The following steps are made for Linux/mac environment
-1. Clone the git `git clone https://github.com/gcarq/freqtrade.git`
-2. (Optional) Checkout the develop branch `git checkout develop`
+#### 4. Initialize the configuration
 
-## 4. Prepare the bot
 ```bash
 cd freqtrade
 cp config.json.example config.json
 ```
-To edit the config please refer to [Bot Configuration](https://github.com/gcarq/freqtrade/blob/develop/docs/configuration.md)
 
-## 5. Setup your virtual env
-```bash
-python3.6 -m venv .env
-source .env/bin/activate
-pip3.6 install -r requirements.txt
-pip3.6 install -e .
+> *To edit the config please refer to [Bot Configuration](https://github.com/freqtrade/freqtrade/blob/develop/docs/configuration.md).*
+
+#### 5. Install python dependencies
+
+``` bash
+pip3 install --upgrade pip
+pip3 install -r requirements.txt
+pip3 install -e .
 ```
 
-## 6. Run the bot
-If this is the first time you run the bot, ensure you are running it
-in Dry-run `"dry_run": true,` otherwise it will start to buy and sell coins.
+#### 6. Run the Bot
+
+If this is the first time you run the bot, ensure you are running it in Dry-run `"dry_run": true,` otherwise it will start to buy and sell coins.
 
 ```bash
 python3.6 ./freqtrade/main.py -c config.json
 ```
 
-### Advanced Linux
-**systemd service file**
-Copy `./freqtrade.service` to your systemd user directory (usually `~/.config/systemd/user`)
-and update `WorkingDirectory` and `ExecStart` to match your setup.
+*Note*: If you run the bot on a server, you should consider using [Docker](#automatic-installation---docker) a terminal multiplexer like `screen` or [`tmux`](https://en.wikipedia.org/wiki/Tmux) to avoid that the bot is stopped on logout.
+
+#### 7. [Optional] Configure `freqtrade` as a `systemd` service
+
+From the freqtrade repo... copy `freqtrade.service` to your systemd user directory (usually `~/.config/systemd/user`) and update `WorkingDirectory` and `ExecStart` to match your setup.
+
 After that you can start the daemon with:
+
 ```bash
 systemctl --user start freqtrade
 ```
 
-# Windows
-We do recommend Windows users to use [Docker](#docker) this will work
-much easier and smoother (also safer).
+For this to be persistent (run when user is logged out) you'll need to enable `linger` for your freqtrade user.
+
+```bash
+sudo loginctl enable-linger "$USER"
+```
+
+------
+
+## Windows
+
+We recommend that Windows users use [Docker](#docker) as this will work much easier and smoother (also more secure).
+
+If that is not possible, try using the Windows Linux subsystem (WSL) - for which the Ubuntu instructions should work.
+If that is not available on your system, feel free to try the instructions below, which led to success for some.
+
+### Install freqtrade manually
+
+#### Clone the git repository
+
+```bash
+git clone https://github.com/freqtrade/freqtrade.git
+```
+
+copy paste `config.json` to ``\path\freqtrade-develop\freqtrade`
+
+#### Install ta-lib
+
+Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7/ta-lib#windows).
+
+As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of inofficial precompiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which needs to be downloaded and installed using `pip install TA_Lib‑0.4.17‑cp36‑cp36m‑win32.whl` (make sure to use the version matching your python version)
 
 ```cmd
-#copy paste config.json to \path\freqtrade-develop\freqtrade
 >cd \path\freqtrade-develop
 >python -m venv .env
 >cd .env\Scripts
 >activate.bat
 >cd \path\freqtrade-develop
+REM optionally install ta-lib from wheel
+REM >pip install TA_Lib‑0.4.17‑cp36‑cp36m‑win32.whl
 >pip install -r requirements.txt
 >pip install -e .
->cd freqtrade
->python main.py
+>python freqtrade\main.py
 ```
-*Thanks [Owdr](https://github.com/Owdr) for the commands. Source: [Issue #222](https://github.com/gcarq/freqtrade/issues/222)*
 
-## Next step
-Now you have an environment ready, the next step is to
-[configure your bot](https://github.com/gcarq/freqtrade/blob/develop/docs/configuration.md).
+> Thanks [Owdr](https://github.com/Owdr) for the commands. Source: [Issue #222](https://github.com/freqtrade/freqtrade/issues/222)
+
+#### Error during installation under Windows
+
+``` bash
+error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual C++ Build Tools": http://landinghub.visualstudio.com/visual-cpp-build-tools
+```
+
+Unfortunately, many packages requiring compilation don't provide a pre-build wheel. It is therefore mandatory to have a C/C++ compiler installed and available for your python environment to use.
+
+The easiest way is to download install Microsoft Visual Studio Community [here](https://visualstudio.microsoft.com/downloads/) and make sure to install "Common Tools for Visual C++" to enable building c code on Windows. Unfortunately, this is a heavy download / dependency (~4Gb) so you might want to consider WSL or docker first.
+
+---
+
+Now you have an environment ready, the next step is
+[Bot Configuration](https://github.com/freqtrade/freqtrade/blob/develop/docs/configuration.md)...

docs/plotting.md

@@ -0,0 +1,87 @@
+# Plotting
+This page explains how to plot prices, indicator, profits.
+
+## Table of Contents
+- [Plot price and indicators](#plot-price-and-indicators)
+- [Plot profit](#plot-profit)
+
+## Installation
+
+Plotting scripts use Plotly library. Install/upgrade it with:
+
+```
+pip install --upgrade plotly
+```
+
+At least version 2.3.0 is required.
+
+## Plot price and indicators
+Usage for the price plotter:
+
+```
+script/plot_dataframe.py [-h] [-p pair] [--live]
+```
+
+Example
+```
+python scripts/plot_dataframe.py -p BTC/ETH
+```
+
+The `-p` pair argument, can be used to specify what
+pair you would like to plot.
+
+**Advanced use**
+
+To plot the current live price use the `--live` flag:
+```
+python scripts/plot_dataframe.py -p BTC/ETH --live
+```
+
+To plot a timerange (to zoom in):
+```
+python scripts/plot_dataframe.py -p BTC/ETH --timerange=100-200
+```
+Timerange doesn't work with live data.
+
+To plot trades stored in a database use `--db-url` argument:
+```
+python scripts/plot_dataframe.py --db-url sqlite:///tradesv3.dry_run.sqlite -p BTC/ETH
+```
+
+To plot a test strategy the strategy should have first be backtested. 
+The results may then be plotted with the -s argument:
+```
+python scripts/plot_dataframe.py -s Strategy_Name -p BTC/ETH --datadir user_data/data/<exchange_name>/
+```
+
+## Plot profit
+
+The profit plotter show a picture with three plots:
+1) Average closing price for all pairs
+2) The summarized profit made by backtesting.
+   Note that this is not the real-world profit, but
+   more of an estimate.
+3) Each pair individually profit
+
+The first graph is good to get a grip of how the overall market
+progresses.
+
+The second graph will show how you algorithm works or doesnt.
+Perhaps you want an algorithm that steadily makes small profits,
+or one that acts less seldom, but makes big swings.
+
+The third graph can be useful to spot outliers, events in pairs
+that makes profit spikes.
+
+Usage for the profit plotter:
+
+```
+script/plot_profit.py [-h] [-p pair] [--datadir directory] [--ticker_interval num]
+```
+
+The `-p` pair argument, can be used to plot a single pair
+
+Example
+```
+python3 scripts/plot_profit.py --datadir ../freqtrade/freqtrade/tests/testdata-20171221/ -p BTC_LTC
+```

docs/pre-requisite.md

@@ -15,7 +15,7 @@ The only things you need is a working Telegram bot and its API token.
 Below we explain how to create your Telegram Bot, and how to get your
 Telegram user id.
 
-### 1. Create your instagram bot
+### 1. Create your Telegram bot
 **1.1. Start a chat with https://telegram.me/BotFather**  
 **1.2. Send the message** `/newbot`  
 *BotFather response:*
@@ -39,8 +39,10 @@ Use this token to access the HTTP API:
 
 For a description of the Bot API, see this page: https://core.telegram.org/bots/api
 ```
+**1.6. Don't forget to start the conversation with your bot, by clicking /START button**  
 
 ### 2. Get your user id
 **2.1. Talk to https://telegram.me/userinfobot**  
 **2.2. Get your "Id", you will use it for the config parameter 
-`chat_id`.**
+`chat_id`.**
+

docs/sandbox-testing.md

@@ -0,0 +1,141 @@
+# Sandbox API testing
+
+Where an exchange provides a sandbox for risk-free integration, or end-to-end, testing CCXT provides access to these.
+
+This document is a *light overview of configuring Freqtrade and GDAX sandbox.
+This can be useful to developers and trader alike as Freqtrade is quite customisable.
+
+When testing your API connectivity, make sure to use the following URLs.
+***Website**
+https://public.sandbox.gdax.com
+***REST API**
+https://api-public.sandbox.gdax.com
+
+---
+
+# Configure a Sandbox account on Gdax
+
+Aim of this document section
+
+- An sanbox account
+- create 2FA (needed to create an API)
+- Add test 50BTC to account 
+- Create :
+- - API-KEY
+- - API-Secret
+- - API Password
+
+## Acccount
+
+This link will redirect to the sandbox main page to login / create account dialogues:
+https://public.sandbox.pro.coinbase.com/orders/
+
+After registration and Email confimation you wil be redirected into your sanbox account.  It is easy to verify you're in sandbox by checking the URL bar.
+> https://public.sandbox.pro.coinbase.com/
+
+## Enable 2Fa (a prerequisite to creating sandbox API Keys)
+
+From within sand box site select your profile, top right.
+>Or as a direct link: https://public.sandbox.pro.coinbase.com/profile
+
+From the menu panel to the left of the screen select
+
+> Security: "*View or Update*"
+
+In the new site select "enable authenticator" as typical google Authenticator.
+
+- open Google Authenticator on your phone
+- scan barcode
+- enter your generated 2fa
+
+## Enable API Access
+
+From within sandbox select profile>api>create api-keys
+>or as a direct link: https://public.sandbox.pro.coinbase.com/profile/api
+
+Click on "create one" and ensure  **view** and **trade**  are "checked" and sumbit your 2FA
+
+- **Copy and paste the Passphase** into a notepade this will be needed later
+- **Copy and paste the API Secret** popup into a notepad this will needed later
+- **Copy and paste the API Key** into a notepad this will needed later
+
+## Add 50 BTC test funds
+
+To add funds, use the web interface deposit and withdraw buttons.
+
+To begin select 'Wallets' from the top menu.
+> Or as a direct link: https://public.sandbox.pro.coinbase.com/wallets
+
+- Deposits (bottom left of screen)
+- - Deposit Funds Bitcoin
+- - - Coinbase BTC Wallet
+- - - - Max (50 BTC)
+- - - - - Deposit
+
+*This process may be repeated for other currencies, ETH as example*
+
+---
+
+# Configure Freqtrade to use Gax Sandbox 
+
+The aim of this document section
+
+- Enable sandbox URLs in Freqtrade
+- Configure API
+- - secret
+- - key
+- - passphrase
+ 
+## Sandbox URLs
+
+Freqtrade makes use of CCXT which in turn provides a list of URLs to Freqtrade.
+These include `['test']` and `['api']`.
+
+- `[Test]` if available will point to an Exchanges sandbox. 
+- `[Api]` normally used, and resolves to live API target on the exchange 
+
+To make use of sandbox / test add "sandbox": true, to your config.json
+
+```json
+  "exchange": {
+        "name": "gdax",
+        "sandbox": true,
+        "key": "5wowfxemogxeowo;heiohgmd",
+        "secret": "/ZMH1P62rCVmwefewrgcewX8nh4gob+lywxfwfxwwfxwfNsH1ySgvWCUR/w==",
+        "password": "1bkjfkhfhfu6sr",
+        "outdated_offset": 5
+        "pair_whitelist": [
+            "BTC/USD"
+```
+
+Also insert your 
+
+- api-key  (noted earlier)
+- api-secret (noted earlier)
+- password (the passphrase - noted earlier)
+
+---
+
+## You should now be ready to test your sandbox
+
+Ensure Freqtrade logs show the sandbox URL, and trades made are shown in sandbox.
+** Typically the  BTC/USD has the most activity in sandbox to test against.
+
+## GDAX - Old Candles problem
+
+It is my experience that GDAX sandbox candles may be 20+- minutes out of date. This can cause trades to fail as one of Freqtrades safety checks.
+
+To disable this check, add / change the `"outdated_offset"` parameter in the exchange section of your configuration to adjust for this delay.
+Example based on the above configuration:
+
+```json
+  "exchange": {
+        "name": "gdax",
+        "sandbox": true,
+        "key": "5wowfxemogxeowo;heiohgmd",
+        "secret": "/ZMH1P62rCVmwefewrgcewX8nh4gob+lywxfwfxwwfxwfNsH1ySgvWCUR/w==",
+        "password": "1bkjfkhfhfu6sr",
+        "outdated_offset": 30
+        "pair_whitelist": [
+            "BTC/USD"
+```

docs/sql_cheatsheet.md

@@ -32,9 +32,12 @@ CREATE TABLE trades (
 	exchange VARCHAR NOT NULL,
 	pair VARCHAR NOT NULL,
 	is_open BOOLEAN NOT NULL,
-	fee FLOAT NOT NULL,
+	fee_open FLOAT NOT NULL,
+	fee_close FLOAT NOT NULL,
 	open_rate FLOAT,
+	open_rate_requested FLOAT,
 	close_rate FLOAT,
+	close_rate_requested FLOAT,
 	close_profit FLOAT,
 	stake_amount FLOAT NOT NULL,
 	amount FLOAT,
@@ -56,7 +59,7 @@ SELECT * FROM trades;
 
 ```sql
 UPDATE trades
-SET is_open=0, close_date=<close_date>, close_rate=<close_rate>, close_profit=close_rate/open_rate  
+SET is_open=0, close_date=<close_date>, close_rate=<close_rate>, close_profit=close_rate/open_rate-1  
 WHERE id=<trade_ID_to_update>;
 ```
 
@@ -67,12 +70,24 @@ SET is_open=0, close_date='2017-12-20 03:08:45.103418', close_rate=0.19638016, c
 WHERE id=31;
 ```
 
+## Insert manually a new trade
+
+```sql
+INSERT 
+INTO trades (exchange, pair, is_open, fee_open, fee_close, open_rate, stake_amount, amount, open_date) 
+VALUES ('BITTREX', 'BTC_<COIN>', 1, 0.0025, 0.0025, <open_rate>, <stake_amount>, <amount>, '<datetime>')
+```
+
+**Example:**
+```sql
+INSERT INTO trades (exchange, pair, is_open, fee_open, fee_close, open_rate, stake_amount, amount, open_date) VALUES ('BITTREX', 'BTC_ETC', 1, 0.0025, 0.0025, 0.00258580, 0.002, 0.7715262081, '2017-11-28 12:44:24.000000')
+```
 
 ## Fix wrong fees in the table
 If your DB was created before 
-[PR#200](https://github.com/gcarq/freqtrade/pull/200) was merged
+[PR#200](https://github.com/freqtrade/freqtrade/pull/200) was merged
 (before 12/23/17).
 
 ```sql
 UPDATE trades SET fee=0.0025 WHERE fee=0.005;
-```
+```

docs/stoploss.md

@@ -0,0 +1,51 @@
+# Stop Loss support
+
+At this stage the bot contains the following stoploss support modes:
+
+1. static stop loss, defined in either the strategy or configuration
+2. trailing stop loss, defined in the configuration
+3. trailing stop loss, custom positive loss, defined in configuration
+
+## Static Stop Loss
+
+This is very simple, basically you define a stop loss of x in your strategy file or alternative in the configuration, which
+will overwrite the strategy definition. This will basically try to sell your asset, the second the loss exceeds the defined loss.
+
+## Trail Stop Loss
+
+The initial value for this stop loss, is defined in your strategy or configuration. Just as you would define your Stop Loss normally.
+To enable this Feauture all you have to do is to define the configuration element:
+
+``` json
+"trailing_stop" : True
+```
+
+This will now activate an algorithm, which automatically moves your stop loss up every time the price of your asset increases.
+
+For example, simplified math,
+
+* you buy an asset at a price of 100$
+* your stop loss is defined at 2%
+* which means your stop loss, gets triggered once your asset dropped below 98$
+* assuming your asset now increases to 102$
+* your stop loss, will now be 2% of 102$ or 99.96$
+* now your asset drops in value to 101$, your stop loss, will still be 99.96$
+
+basically what this means is that your stop loss will be adjusted to be always be 2% of the highest observed price
+
+### Custom positive loss
+
+Due to demand, it is possible to have a default stop loss, when you are in the red with your buy, but once your profit surpasses a certain percentage,
+the system will utilize a new stop loss, which can be a different value. For example your default stop loss is 5%, but once you have 1.1% profit,
+it will be changed to be only a 1% stop loss, which trails the green candles until it goes below them.
+
+Both values can be configured in the main configuration file and requires `"trailing_stop": true` to be set to true.
+
+``` json
+    "trailing_stop_positive":  0.01,
+    "trailing_stop_positive_offset":  0.011,
+```
+
+The 0.01 would translate to a 1% stop loss, once you hit 1.1% profit.
+
+You should also make sure to have this value higher than your minimal ROI, otherwise minimal ROI will apply first and sell your trade.

docs/telegram-usage.md

@@ -4,7 +4,7 @@ This page explains how to command your bot with Telegram.
 
 ## Pre-requisite
 To control your bot with Telegram, you need first to 
-[set up a Telegram bot](https://github.com/gcarq/freqtrade/blob/develop/docs/pre-requisite.md) 
+[set up a Telegram bot](https://github.com/freqtrade/freqtrade/blob/develop/docs/pre-requisite.md) 
 and add your Telegram API keys into your config file.
 
 ## Telegram commands
@@ -15,7 +15,8 @@ official commands. You can ask at any moment for help with `/help`.
 |  Command | Default | Description |
 |----------|---------|-------------|
 | `/start` | | Starts the trader
-| `/stop` | | Starts the trader
+| `/stop` | | Stops the trader
+| `/reload_conf` | | Reloads the configuration file
 | `/status` | | Lists all open trades
 | `/status table` | | List all open trades in a table format
 | `/count` | | Displays number of trades used and available
@@ -42,7 +43,7 @@ Below, example of Telegram message you will receive for each command.
 For each open trade, the bot will send you the following message.
 
 > **Trade ID:** `123`  
-> **Current Pair:** BTC_CVC  
+> **Current Pair:** CVC/BTC
 > **Open Since:** `1 days ago`  
 > **Amount:** `26.64180098`  
 > **Open Rate:** `0.00007489`  
@@ -57,8 +58,8 @@ Return the status of all open trades in a table format.
 ```
    ID  Pair      Since    Profit  
 ----  --------  -------  --------  
-  67  BTC_SC    1 d      13.33%  
- 123  BTC_CVC   1 h      12.95%  
+  67  SC/BTC    1 d      13.33%
+ 123  CVC/BTC   1 h      12.95%
 ```
 
 ## /count
@@ -83,7 +84,7 @@ Return a summary of your profit/loss and performance.
 > **First Trade opened:** `3 days ago`  
 > **Latest Trade opened:** `2 minutes ago`  
 > **Avg. Duration:** `2:33:45`  
-> **Best Performing:** `BTC_PAY: 50.23%`
+> **Best Performing:** `PAY/BTC: 50.23%`
 
 ## /forcesell <trade_id>
 
@@ -92,11 +93,11 @@ Return a summary of your profit/loss and performance.
 ## /performance
 Return the performance of each crypto-currency the bot has sold.
 > Performance:  
-> 1. `BTC_RCN 57.77%`  
-> 2. `BTC_PAY 56.91%`  
-> 3. `BTC_VIB 47.07%`  
-> 4. `BTC_SALT 30.24%`  
-> 5. `BTC_STORJ 27.24%`  
+> 1. `RCN/BTC 57.77%`
+> 2. `PAY/BTC 56.91%`
+> 3. `VIB/BTC 47.07%`
+> 4. `SALT/BTC 30.24%`
+> 5. `STORJ/BTC 27.24%`
 > ...
 
 ## /balance
@@ -126,4 +127,11 @@ Day         Profit BTC      Profit USD
 ```
 
 ## /version
-> **Version:** `0.14.3` 
+> **Version:** `0.14.3` 
+
+### using proxy with telegram
+```
+$ export HTTP_PROXY="http://addr:port"
+$ export HTTPS_PROXY="http://addr:port"
+$ freqtrade
+```

docs/webhook-config.md

@@ -0,0 +1,74 @@
+# Webhook usage
+
+This page explains how to configure your bot to talk to webhooks.
+
+## Configuration
+
+Enable webhooks by adding a webhook-section to your configuration file, and setting `webhook.enabled` to `true`.
+
+Sample configuration (tested using IFTTT).
+
+```json
+  "webhook": {
+        "enabled": true,
+        "url": "https://maker.ifttt.com/trigger/<YOUREVENT>/with/key/<YOURKEY>/",
+        "webhookbuy": {
+            "value1": "Buying {pair}",
+            "value2": "limit {limit:8f}",
+            "value3": "{stake_amount:8f} {stake_currency}"
+        },
+        "webhooksell": {
+            "value1": "Selling {pair}",
+            "value2": "limit {limit:8f}",
+            "value3": "profit: {profit_amount:8f} {stake_currency}"
+        },
+        "webhookstatus": {
+            "value1": "Status: {status}",
+            "value2": "",
+            "value3": ""
+        }
+    },
+```
+
+The url in `webhook.url` should point to the correct url for your webhook. If you're using [IFTTT](https://ifttt.com) (as shown in the sample above) please insert our event and key to the url.
+
+Different payloads can be configured for different events. Not all fields are necessary, but you should configure at least one of the dicts, otherwise the webhook will never be called.
+
+### Webhookbuy
+
+The fields in `webhook.webhookbuy` are filled when the bot executes a buy. Parameters are filled using string.format.
+Possible parameters are:
+
+* exchange
+* pair
+* market_url
+* limit
+* stake_amount
+* stake_amount_fiat
+* stake_currency
+* fiat_currency
+
+### Webhooksell
+
+The fields in `webhook.webhooksell` are filled when the bot sells a trade. Parameters are filled using string.format.
+Possible parameters are:
+
+* exchange
+* pair
+* gain
+* market_url
+* limit
+* amount
+* open_rate
+* current_rate
+* profit_amount
+* profit_percent
+* profit_fiat
+* stake_currency
+* fiat_currency
+
+### Webhookstatus
+
+The fields in `webhook.webhookstatus` are used for regular status messages (Started / Stopped / ...). Parameters are filled using string.format.
+
+The only possible value here is `{status}`.

freqtrade/__init__.py

@@ -1,5 +1,5 @@
 """ FreqTrade bot """
-__version__ = '0.15.1'
+__version__ = '0.17.1'
 
 
 class DependencyException(BaseException):
@@ -12,5 +12,14 @@ class DependencyException(BaseException):
 class OperationalException(BaseException):
     """
     Requires manual intervention.
-    This happens when an exchange returns an unexpected error during runtime.
+    This happens when an exchange returns an unexpected error during runtime
+    or given configuration is invalid.
+    """
+
+
+class TemporaryError(BaseException):
+    """
+    Temporary network or exchange related error.
+    This could happen when an exchange is congested, unavailable, or the user
+    has networking problems. Usually resolves itself after a time.
     """

freqtrade/__main__.py

@@ -0,0 +1,15 @@
+#!/usr/bin/env python3
+"""
+__main__.py for Freqtrade
+To launch Freqtrade as a module
+
+> python -m freqtrade (with Python >= 3.6)
+"""
+
+import sys
+
+from freqtrade import main
+
+if __name__ == '__main__':
+    main.set_loggers()
+    main.main(sys.argv[1:])

freqtrade/analyze.py

@@ -1,315 +0,0 @@
-"""
-Functions to analyze ticker data with indicators and produce buy and sell signals
-"""
-import logging
-from datetime import timedelta
-from enum import Enum
-from typing import Dict, List
-
-import arrow
-import talib.abstract as ta
-from pandas import DataFrame, to_datetime
-
-import freqtrade.vendor.qtpylib.indicators as qtpylib
-from freqtrade.exchange import get_ticker_history
-
-logger = logging.getLogger(__name__)
-
-
-class SignalType(Enum):
-    """ Enum to distinguish between buy and sell signals """
-    BUY = "buy"
-    SELL = "sell"
-
-
-def parse_ticker_dataframe(ticker: list) -> DataFrame:
-    """
-    Analyses the trend for the given ticker history
-    :param ticker: See exchange.get_ticker_history
-    :return: DataFrame
-    """
-    columns = {'C': 'close', 'V': 'volume', 'O': 'open', 'H': 'high', 'L': 'low', 'T': 'date'}
-    frame = DataFrame(ticker) \
-        .drop('BV', 1) \
-        .rename(columns=columns)
-    frame['date'] = to_datetime(frame['date'], utc=True, infer_datetime_format=True)
-    frame.sort_values('date', inplace=True)
-    return frame
-
-
-def populate_indicators(dataframe: DataFrame) -> DataFrame:
-    """
-    Adds several different TA indicators to the given DataFrame
-
-    Performance Note: For the best performance be frugal on the number of indicators
-    you are using. Let uncomment only the indicator you are using in your strategies
-    or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
-    """
-
-    # Momentum Indicator
-    # ------------------------------------
-
-    # ADX
-    dataframe['adx'] = ta.ADX(dataframe)
-
-    # Awesome oscillator
-    dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
-    """
-    # Commodity Channel Index: values Oversold:<-100, Overbought:>100
-    dataframe['cci'] = ta.CCI(dataframe)
-    """
-    # MACD
-    macd = ta.MACD(dataframe)
-    dataframe['macd'] = macd['macd']
-    dataframe['macdsignal'] = macd['macdsignal']
-    dataframe['macdhist'] = macd['macdhist']
-
-    # MFI
-    dataframe['mfi'] = ta.MFI(dataframe)
-
-    # Minus Directional Indicator / Movement
-    dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
-    dataframe['minus_di'] = ta.MINUS_DI(dataframe)
-
-    # Plus Directional Indicator / Movement
-    dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
-    dataframe['plus_di'] = ta.PLUS_DI(dataframe)
-    """
-    # ROC
-    dataframe['roc'] = ta.ROC(dataframe)
-    """
-    # RSI
-    dataframe['rsi'] = ta.RSI(dataframe)
-    """
-    # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
-    rsi = 0.1 * (dataframe['rsi'] - 50)
-    dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
-
-    # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
-    dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
-
-    # Stoch
-    stoch = ta.STOCH(dataframe)
-    dataframe['slowd'] = stoch['slowd']
-    dataframe['slowk'] = stoch['slowk']
-    """
-    # Stoch fast
-    stoch_fast = ta.STOCHF(dataframe)
-    dataframe['fastd'] = stoch_fast['fastd']
-    dataframe['fastk'] = stoch_fast['fastk']
-    """
-    # Stoch RSI
-    stoch_rsi = ta.STOCHRSI(dataframe)
-    dataframe['fastd_rsi'] = stoch_rsi['fastd']
-    dataframe['fastk_rsi'] = stoch_rsi['fastk']
-    """
-
-    # Overlap Studies
-    # ------------------------------------
-
-    # Previous Bollinger bands
-    # Because ta.BBANDS implementation is broken with small numbers, it actually
-    # returns middle band for all the three bands. Switch to qtpylib.bollinger_bands
-    # and use middle band instead.
-    dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
-    """
-    # Bollinger bands
-    bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
-    dataframe['bb_lowerband'] = bollinger['lower']
-    dataframe['bb_middleband'] = bollinger['mid']
-    dataframe['bb_upperband'] = bollinger['upper']
-    """
-
-    # EMA - Exponential Moving Average
-    dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
-    dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
-    dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
-    dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
-
-    # SAR Parabol
-    dataframe['sar'] = ta.SAR(dataframe)
-
-    # SMA - Simple Moving Average
-    dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
-
-    # TEMA - Triple Exponential Moving Average
-    dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
-
-    # Cycle Indicator
-    # ------------------------------------
-    # Hilbert Transform Indicator - SineWave
-    hilbert = ta.HT_SINE(dataframe)
-    dataframe['htsine'] = hilbert['sine']
-    dataframe['htleadsine'] = hilbert['leadsine']
-
-    # Pattern Recognition - Bullish candlestick patterns
-    # ------------------------------------
-    """
-    # Hammer: values [0, 100]
-    dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
-
-    # Inverted Hammer: values [0, 100]
-    dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
-
-    # Dragonfly Doji: values [0, 100]
-    dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
-
-    # Piercing Line: values [0, 100]
-    dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
-
-    # Morningstar: values [0, 100]
-    dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
-
-    # Three White Soldiers: values [0, 100]
-    dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
-    """
-
-    # Pattern Recognition - Bearish candlestick patterns
-    # ------------------------------------
-    """
-    # Hanging Man: values [0, 100]
-    dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
-
-    # Shooting Star: values [0, 100]
-    dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
-
-    # Gravestone Doji: values [0, 100]
-    dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
-
-    # Dark Cloud Cover: values [0, 100]
-    dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
-
-    # Evening Doji Star: values [0, 100]
-    dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
-
-    # Evening Star: values [0, 100]
-    dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
-    """
-
-    # Pattern Recognition - Bullish/Bearish candlestick patterns
-    # ------------------------------------
-    """
-    # Three Line Strike: values [0, -100, 100]
-    dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
-
-    # Spinning Top: values [0, -100, 100]
-    dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
-
-    # Engulfing: values [0, -100, 100]
-    dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
-
-    # Harami: values [0, -100, 100]
-    dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
-
-    # Three Outside Up/Down: values [0, -100, 100]
-    dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
-
-    # Three Inside Up/Down: values [0, -100, 100]
-    dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
-    """
-
-    # Chart type
-    # ------------------------------------
-    """
-    # Heikinashi stategy
-    heikinashi = qtpylib.heikinashi(dataframe)
-    dataframe['ha_open'] = heikinashi['open']
-    dataframe['ha_close'] = heikinashi['close']
-    dataframe['ha_high'] = heikinashi['high']
-    dataframe['ha_low'] = heikinashi['low']
-    """
-
-    return dataframe
-
-
-def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
-    """
-    Based on TA indicators, populates the buy signal for the given dataframe
-    :param dataframe: DataFrame
-    :return: DataFrame with buy column
-    """
-    dataframe.loc[
-        (
-            (dataframe['rsi'] < 35) &
-            (dataframe['fastd'] < 35) &
-            (dataframe['adx'] > 30) &
-            (dataframe['plus_di'] > 0.5)
-        ) |
-        (
-            (dataframe['adx'] > 65) &
-            (dataframe['plus_di'] > 0.5)
-        ),
-        'buy'] = 1
-
-    return dataframe
-
-
-def populate_sell_trend(dataframe: DataFrame) -> DataFrame:
-    """
-    Based on TA indicators, populates the sell signal for the given dataframe
-    :param dataframe: DataFrame
-    :return: DataFrame with buy column
-    """
-    dataframe.loc[
-        (
-            (
-                (qtpylib.crossed_above(dataframe['rsi'], 70)) |
-                (qtpylib.crossed_above(dataframe['fastd'], 70))
-            ) &
-            (dataframe['adx'] > 10) &
-            (dataframe['minus_di'] > 0)
-        ) |
-        (
-            (dataframe['adx'] > 70) &
-            (dataframe['minus_di'] > 0.5)
-        ),
-        'sell'] = 1
-    return dataframe
-
-
-def analyze_ticker(ticker_history: List[Dict]) -> DataFrame:
-    """
-    Parses the given ticker history and returns a populated DataFrame
-    add several TA indicators and buy signal to it
-    :return DataFrame with ticker data and indicator data
-    """
-    dataframe = parse_ticker_dataframe(ticker_history)
-    dataframe = populate_indicators(dataframe)
-    dataframe = populate_buy_trend(dataframe)
-    dataframe = populate_sell_trend(dataframe)
-    return dataframe
-
-
-def get_signal(pair: str, signal: SignalType) -> bool:
-    """
-    Calculates current signal based several technical analysis indicators
-    :param pair: pair in format BTC_ANT or BTC-ANT
-    :return: True if pair is good for buying, False otherwise
-    """
-    ticker_hist = get_ticker_history(pair)
-    if not ticker_hist:
-        logger.warning('Empty ticker history for pair %s', pair)
-        return False
-
-    try:
-        dataframe = analyze_ticker(ticker_hist)
-    except ValueError as ex:
-        logger.warning('Unable to analyze ticker for pair %s: %s', pair, str(ex))
-        return False
-    except Exception as ex:
-        logger.exception('Unexpected error when analyzing ticker for pair %s: %s', pair, str(ex))
-        return False
-
-    if dataframe.empty:
-        return False
-
-    latest = dataframe.iloc[-1]
-
-    # Check if dataframe is out of date
-    signal_date = arrow.get(latest['date'])
-    if signal_date < arrow.now() - timedelta(minutes=10):
-        return False
-
-    result = latest[signal.value] == 1
-    logger.debug('%s_trigger: %s (pair=%s, signal=%s)', signal.value, latest['date'], pair, result)
-    return result

freqtrade/arguments.py

@@ -0,0 +1,362 @@
+"""
+This module contains the argument manager class
+"""
+
+import argparse
+import os
+import re
+from typing import List, NamedTuple, Optional
+
+import arrow
+
+from freqtrade import __version__, constants
+
+
+class TimeRange(NamedTuple):
+    """
+    NamedTuple Defining timerange inputs.
+    [start/stop]type defines if [start/stop]ts shall be used.
+    if *type is none, don't use corresponding startvalue.
+    """
+    starttype: Optional[str] = None
+    stoptype: Optional[str] = None
+    startts: int = 0
+    stopts: int = 0
+
+
+class Arguments(object):
+    """
+    Arguments Class. Manage the arguments received by the cli
+    """
+
+    def __init__(self, args: List[str], description: str) -> None:
+        self.args = args
+        self.parsed_arg: Optional[argparse.Namespace] = None
+        self.parser = argparse.ArgumentParser(description=description)
+
+    def _load_args(self) -> None:
+        self.common_args_parser()
+        self._build_subcommands()
+
+    def get_parsed_arg(self) -> argparse.Namespace:
+        """
+        Return the list of arguments
+        :return: List[str] List of arguments
+        """
+        if self.parsed_arg is None:
+            self._load_args()
+            self.parsed_arg = self.parse_args()
+
+        return self.parsed_arg
+
+    def parse_args(self) -> argparse.Namespace:
+        """
+        Parses given arguments and returns an argparse Namespace instance.
+        """
+        parsed_arg = self.parser.parse_args(self.args)
+
+        return parsed_arg
+
+    def common_args_parser(self) -> None:
+        """
+        Parses given common arguments and returns them as a parsed object.
+        """
+        self.parser.add_argument(
+            '-v', '--verbose',
+            help='verbose mode (-vv for more, -vvv to get all messages)',
+            action='count',
+            dest='loglevel',
+            default=0,
+        )
+        self.parser.add_argument(
+            '--version',
+            action='version',
+            version=f'%(prog)s {__version__}'
+        )
+        self.parser.add_argument(
+            '-c', '--config',
+            help='specify configuration file (default: %(default)s)',
+            dest='config',
+            default='config.json',
+            type=str,
+            metavar='PATH',
+        )
+        self.parser.add_argument(
+            '-d', '--datadir',
+            help='path to backtest data',
+            dest='datadir',
+            default=None,
+            type=str,
+            metavar='PATH',
+        )
+        self.parser.add_argument(
+            '-s', '--strategy',
+            help='specify strategy class name (default: %(default)s)',
+            dest='strategy',
+            default='DefaultStrategy',
+            type=str,
+            metavar='NAME',
+        )
+        self.parser.add_argument(
+            '--strategy-path',
+            help='specify additional strategy lookup path',
+            dest='strategy_path',
+            type=str,
+            metavar='PATH',
+        )
+        self.parser.add_argument(
+            '--dynamic-whitelist',
+            help='dynamically generate and update whitelist'
+                 ' based on 24h BaseVolume (default: %(const)s)',
+            dest='dynamic_whitelist',
+            const=constants.DYNAMIC_WHITELIST,
+            type=int,
+            metavar='INT',
+            nargs='?',
+        )
+        self.parser.add_argument(
+            '--db-url',
+            help='Override trades database URL, this is useful if dry_run is enabled'
+                 ' or in custom deployments (default: %(default)s)',
+            dest='db_url',
+            type=str,
+            metavar='PATH',
+        )
+
+    @staticmethod
+    def backtesting_options(parser: argparse.ArgumentParser) -> None:
+        """
+        Parses given arguments for Backtesting scripts.
+        """
+        parser.add_argument(
+            '-l', '--live',
+            help='using live data',
+            action='store_true',
+            dest='live',
+        )
+        parser.add_argument(
+            '-r', '--refresh-pairs-cached',
+            help='refresh the pairs files in tests/testdata with the latest data from the '
+                 'exchange. Use it if you want to run your backtesting with up-to-date data.',
+            action='store_true',
+            dest='refresh_pairs',
+        )
+        parser.add_argument(
+            '--strategy-list',
+            help='Provide a commaseparated list of strategies to backtest '
+                 'Please note that ticker-interval needs to be set either in config '
+                 'or via command line. When using this together with --export trades, '
+                 'the strategy-name is injected into the filename '
+                 '(so backtest-data.json becomes backtest-data-DefaultStrategy.json',
+            nargs='+',
+            dest='strategy_list',
+        )
+        parser.add_argument(
+            '--export',
+            help='export backtest results, argument are: trades\
+                  Example --export=trades',
+            type=str,
+            default=None,
+            dest='export',
+        )
+        parser.add_argument(
+            '--export-filename',
+            help='Save backtest results to this filename \
+                  requires --export to be set as well\
+                  Example --export-filename=user_data/backtest_data/backtest_today.json\
+                  (default: %(default)s)',
+            type=str,
+            default=os.path.join('user_data', 'backtest_data', 'backtest-result.json'),
+            dest='exportfilename',
+            metavar='PATH',
+        )
+
+    @staticmethod
+    def optimizer_shared_options(parser: argparse.ArgumentParser) -> None:
+        """
+        Parses given common arguments for Backtesting and Hyperopt scripts.
+        :param parser:
+        :return:
+        """
+        parser.add_argument(
+            '-i', '--ticker-interval',
+            help='specify ticker interval (1m, 5m, 30m, 1h, 1d)',
+            dest='ticker_interval',
+            type=str,
+        )
+        parser.add_argument(
+            '--eps', '--enable-position-stacking',
+            help='Allow buying the same pair multiple times (position stacking)',
+            action='store_true',
+            dest='position_stacking',
+            default=False
+        )
+
+        parser.add_argument(
+            '--dmmp', '--disable-max-market-positions',
+            help='Disable applying `max_open_trades` during backtest '
+                 '(same as setting `max_open_trades` to a very high number)',
+            action='store_false',
+            dest='use_max_market_positions',
+            default=True
+        )
+
+        parser.add_argument(
+            '--timerange',
+            help='specify what timerange of data to use.',
+            default=None,
+            type=str,
+            dest='timerange',
+        )
+
+    @staticmethod
+    def hyperopt_options(parser: argparse.ArgumentParser) -> None:
+        """
+        Parses given arguments for Hyperopt scripts.
+        """
+        parser.add_argument(
+            '-e', '--epochs',
+            help='specify number of epochs (default: %(default)d)',
+            dest='epochs',
+            default=constants.HYPEROPT_EPOCH,
+            type=int,
+            metavar='INT',
+        )
+        parser.add_argument(
+            '-s', '--spaces',
+            help='Specify which parameters to hyperopt. Space separate list. \
+                  Default: %(default)s',
+            choices=['all', 'buy', 'roi', 'stoploss'],
+            default='all',
+            nargs='+',
+            dest='spaces',
+        )
+
+    def _build_subcommands(self) -> None:
+        """
+        Builds and attaches all subcommands
+        :return: None
+        """
+        from freqtrade.optimize import backtesting, hyperopt
+
+        subparsers = self.parser.add_subparsers(dest='subparser')
+
+        # Add backtesting subcommand
+        backtesting_cmd = subparsers.add_parser('backtesting', help='backtesting module')
+        backtesting_cmd.set_defaults(func=backtesting.start)
+        self.optimizer_shared_options(backtesting_cmd)
+        self.backtesting_options(backtesting_cmd)
+
+        # Add hyperopt subcommand
+        hyperopt_cmd = subparsers.add_parser('hyperopt', help='hyperopt module')
+        hyperopt_cmd.set_defaults(func=hyperopt.start)
+        self.optimizer_shared_options(hyperopt_cmd)
+        self.hyperopt_options(hyperopt_cmd)
+
+    @staticmethod
+    def parse_timerange(text: Optional[str]) -> TimeRange:
+        """
+        Parse the value of the argument --timerange to determine what is the range desired
+        :param text: value from --timerange
+        :return: Start and End range period
+        """
+        if text is None:
+            return TimeRange(None, None, 0, 0)
+        syntax = [(r'^-(\d{8})$', (None, 'date')),
+                  (r'^(\d{8})-$', ('date', None)),
+                  (r'^(\d{8})-(\d{8})$', ('date', 'date')),
+                  (r'^-(\d{10})$', (None, 'date')),
+                  (r'^(\d{10})-$', ('date', None)),
+                  (r'^(\d{10})-(\d{10})$', ('date', 'date')),
+                  (r'^(-\d+)$', (None, 'line')),
+                  (r'^(\d+)-$', ('line', None)),
+                  (r'^(\d+)-(\d+)$', ('index', 'index'))]
+        for rex, stype in syntax:
+            # Apply the regular expression to text
+            match = re.match(rex, text)
+            if match:  # Regex has matched
+                rvals = match.groups()
+                index = 0
+                start: int = 0
+                stop: int = 0
+                if stype[0]:
+                    starts = rvals[index]
+                    if stype[0] == 'date' and len(starts) == 8:
+                        start = arrow.get(starts, 'YYYYMMDD').timestamp
+                    else:
+                        start = int(starts)
+                    index += 1
+                if stype[1]:
+                    stops = rvals[index]
+                    if stype[1] == 'date' and len(stops) == 8:
+                        stop = arrow.get(stops, 'YYYYMMDD').timestamp
+                    else:
+                        stop = int(stops)
+                return TimeRange(stype[0], stype[1], start, stop)
+        raise Exception('Incorrect syntax for timerange "%s"' % text)
+
+    def scripts_options(self) -> None:
+        """
+        Parses given arguments for scripts.
+        """
+        self.parser.add_argument(
+            '-p', '--pair',
+            help='Show profits for only this pairs. Pairs are comma-separated.',
+            dest='pair',
+            default=None
+        )
+
+    def testdata_dl_options(self) -> None:
+        """
+        Parses given arguments for testdata download
+        """
+        self.parser.add_argument(
+            '--pairs-file',
+            help='File containing a list of pairs to download',
+            dest='pairs_file',
+            default=None,
+            metavar='PATH',
+        )
+
+        self.parser.add_argument(
+            '--export',
+            help='Export files to given dir',
+            dest='export',
+            default=None,
+            metavar='PATH',
+        )
+
+        self.parser.add_argument(
+            '--days',
+            help='Download data for number of days',
+            dest='days',
+            type=int,
+            metavar='INT',
+            default=None
+        )
+
+        self.parser.add_argument(
+            '--exchange',
+            help='Exchange name (default: %(default)s)',
+            dest='exchange',
+            type=str,
+            default='bittrex'
+        )
+
+        self.parser.add_argument(
+            '-t', '--timeframes',
+            help='Specify which tickers to download. Space separated list. \
+                  Default: %(default)s',
+            choices=['1m', '3m', '5m', '15m', '30m', '1h', '2h', '4h',
+                     '6h', '8h', '12h', '1d', '3d', '1w'],
+            default=['1m', '5m'],
+            nargs='+',
+            dest='timeframes',
+        )
+
+        self.parser.add_argument(
+            '--erase',
+            help='Clean all existing data for the selected exchange/pairs/timeframes',
+            dest='erase',
+            action='store_true'
+        )

freqtrade/configuration.py

@@ -0,0 +1,276 @@
+"""
+This module contains the configuration class
+"""
+import json
+import logging
+import os
+from argparse import Namespace
+from typing import Any, Dict, Optional
+
+import ccxt
+from jsonschema import Draft4Validator, validate
+from jsonschema.exceptions import ValidationError, best_match
+
+from freqtrade import OperationalException, constants
+logger = logging.getLogger(__name__)
+
+
+def set_loggers(log_level: int = 0) -> None:
+    """
+    Set the logger level for Third party libs
+    :return: None
+    """
+
+    logging.getLogger('requests').setLevel(logging.INFO if log_level <= 1 else logging.DEBUG)
+    logging.getLogger("urllib3").setLevel(logging.INFO if log_level <= 1 else logging.DEBUG)
+    logging.getLogger('ccxt.base.exchange').setLevel(
+        logging.INFO if log_level <= 2 else logging.DEBUG)
+    logging.getLogger('telegram').setLevel(logging.INFO)
+
+
+class Configuration(object):
+    """
+    Class to read and init the bot configuration
+    Reuse this class for the bot, backtesting, hyperopt and every script that required configuration
+    """
+    def __init__(self, args: Namespace) -> None:
+        self.args = args
+        self.config: Optional[Dict[str, Any]] = None
+
+    def load_config(self) -> Dict[str, Any]:
+        """
+        Extract information for sys.argv and load the bot configuration
+        :return: Configuration dictionary
+        """
+        logger.info('Using config: %s ...', self.args.config)
+        config = self._load_config_file(self.args.config)
+
+        # Set strategy if not specified in config and or if it's non default
+        if self.args.strategy != constants.DEFAULT_STRATEGY or not config.get('strategy'):
+            config.update({'strategy': self.args.strategy})
+
+        if self.args.strategy_path:
+            config.update({'strategy_path': self.args.strategy_path})
+
+        # Load Common configuration
+        config = self._load_common_config(config)
+
+        # Load Backtesting
+        config = self._load_backtesting_config(config)
+
+        # Load Hyperopt
+        config = self._load_hyperopt_config(config)
+
+        return config
+
+    def _load_config_file(self, path: str) -> Dict[str, Any]:
+        """
+        Loads a config file from the given path
+        :param path: path as str
+        :return: configuration as dictionary
+        """
+        try:
+            with open(path) as file:
+                conf = json.load(file)
+        except FileNotFoundError:
+            raise OperationalException(
+                f'Config file "{path}" not found!'
+                ' Please create a config file or check whether it exists.')
+
+        if 'internals' not in conf:
+            conf['internals'] = {}
+        logger.info('Validating configuration ...')
+
+        return self._validate_config(conf)
+
+    def _load_common_config(self, config: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Extract information for sys.argv and load common configuration
+        :return: configuration as dictionary
+        """
+
+        # Log level
+        if 'loglevel' in self.args and self.args.loglevel:
+            config.update({'verbosity': self.args.loglevel})
+        else:
+            config.update({'verbosity': 0})
+        logging.basicConfig(
+            level=logging.INFO if config['verbosity'] < 1 else logging.DEBUG,
+            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+        )
+        set_loggers(config['verbosity'])
+        logger.info('Verbosity set to %s', config['verbosity'])
+
+        # Add dynamic_whitelist if found
+        if 'dynamic_whitelist' in self.args and self.args.dynamic_whitelist:
+            config.update({'dynamic_whitelist': self.args.dynamic_whitelist})
+            logger.info(
+                'Parameter --dynamic-whitelist detected. '
+                'Using dynamically generated whitelist. '
+                '(not applicable with Backtesting and Hyperopt)'
+            )
+
+        if self.args.db_url and self.args.db_url != constants.DEFAULT_DB_PROD_URL:
+            config.update({'db_url': self.args.db_url})
+            logger.info('Parameter --db-url detected ...')
+        else:
+            # Set default here
+            config.update({'db_url': constants.DEFAULT_DB_PROD_URL})
+
+        if config.get('dry_run', False):
+            logger.info('Dry run is enabled')
+            if config.get('db_url') in [None, constants.DEFAULT_DB_PROD_URL]:
+                # Default to in-memory db for dry_run if not specified
+                config['db_url'] = constants.DEFAULT_DB_DRYRUN_URL
+        else:
+            if not config.get('db_url', None):
+                config['db_url'] = constants.DEFAULT_DB_PROD_URL
+            logger.info('Dry run is disabled')
+
+        logger.info(f'Using DB: "{config["db_url"]}"')
+
+        # Check if the exchange set by the user is supported
+        self.check_exchange(config)
+
+        return config
+
+    def _create_default_datadir(self, config: Dict[str, Any]) -> str:
+        exchange_name = config.get('exchange', {}).get('name').lower()
+        default_path = os.path.join('user_data', 'data', exchange_name)
+        if not os.path.isdir(default_path):
+            os.makedirs(default_path)
+            logger.info(f'Created data directory: {default_path}')
+        return default_path
+
+    def _load_backtesting_config(self, config: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Extract information for sys.argv and load Backtesting configuration
+        :return: configuration as dictionary
+        """
+
+        # If -i/--ticker-interval is used we override the configuration parameter
+        # (that will override the strategy configuration)
+        if 'ticker_interval' in self.args and self.args.ticker_interval:
+            config.update({'ticker_interval': self.args.ticker_interval})
+            logger.info('Parameter -i/--ticker-interval detected ...')
+            logger.info('Using ticker_interval: %s ...', config.get('ticker_interval'))
+
+        # If -l/--live is used we add it to the configuration
+        if 'live' in self.args and self.args.live:
+            config.update({'live': True})
+            logger.info('Parameter -l/--live detected ...')
+
+        # If --enable-position-stacking is used we add it to the configuration
+        if 'position_stacking' in self.args and self.args.position_stacking:
+            config.update({'position_stacking': True})
+            logger.info('Parameter --enable-position-stacking detected ...')
+
+        # If --disable-max-market-positions is used we add it to the configuration
+        if 'use_max_market_positions' in self.args and not self.args.use_max_market_positions:
+            config.update({'use_max_market_positions': False})
+            logger.info('Parameter --disable-max-market-positions detected ...')
+            logger.info('max_open_trades set to unlimited ...')
+        else:
+            logger.info('Using max_open_trades: %s ...', config.get('max_open_trades'))
+
+        # If --timerange is used we add it to the configuration
+        if 'timerange' in self.args and self.args.timerange:
+            config.update({'timerange': self.args.timerange})
+            logger.info('Parameter --timerange detected: %s ...', self.args.timerange)
+
+        # If --datadir is used we add it to the configuration
+        if 'datadir' in self.args and self.args.datadir:
+            config.update({'datadir': self.args.datadir})
+        else:
+            config.update({'datadir': self._create_default_datadir(config)})
+        logger.info('Using data folder: %s ...', config.get('datadir'))
+
+        # If -r/--refresh-pairs-cached is used we add it to the configuration
+        if 'refresh_pairs' in self.args and self.args.refresh_pairs:
+            config.update({'refresh_pairs': True})
+            logger.info('Parameter -r/--refresh-pairs-cached detected ...')
+
+        if 'strategy_list' in self.args and self.args.strategy_list:
+            config.update({'strategy_list': self.args.strategy_list})
+            logger.info('Using strategy list of %s Strategies', len(self.args.strategy_list))
+
+        if 'ticker_interval' in self.args and self.args.ticker_interval:
+            config.update({'ticker_interval': self.args.ticker_interval})
+            logger.info('Overriding ticker interval with Command line argument')
+
+        # If --export is used we add it to the configuration
+        if 'export' in self.args and self.args.export:
+            config.update({'export': self.args.export})
+            logger.info('Parameter --export detected: %s ...', self.args.export)
+
+        # If --export-filename is used we add it to the configuration
+        if 'export' in config and 'exportfilename' in self.args and self.args.exportfilename:
+            config.update({'exportfilename': self.args.exportfilename})
+            logger.info('Storing backtest results to %s ...', self.args.exportfilename)
+
+        return config
+
+    def _load_hyperopt_config(self, config: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Extract information for sys.argv and load Hyperopt configuration
+        :return: configuration as dictionary
+        """
+        # If --epochs is used we add it to the configuration
+        if 'epochs' in self.args and self.args.epochs:
+            config.update({'epochs': self.args.epochs})
+            logger.info('Parameter --epochs detected ...')
+            logger.info('Will run Hyperopt with for %s epochs ...', config.get('epochs'))
+
+        # If --spaces is used we add it to the configuration
+        if 'spaces' in self.args and self.args.spaces:
+            config.update({'spaces': self.args.spaces})
+            logger.info('Parameter -s/--spaces detected: %s', config.get('spaces'))
+
+        return config
+
+    def _validate_config(self, conf: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Validate the configuration follow the Config Schema
+        :param conf: Config in JSON format
+        :return: Returns the config if valid, otherwise throw an exception
+        """
+        try:
+            validate(conf, constants.CONF_SCHEMA)
+            return conf
+        except ValidationError as exception:
+            logger.critical(
+                'Invalid configuration. See config.json.example. Reason: %s',
+                exception
+            )
+            raise ValidationError(
+                best_match(Draft4Validator(constants.CONF_SCHEMA).iter_errors(conf)).message
+            )
+
+    def get_config(self) -> Dict[str, Any]:
+        """
+        Return the config. Use this method to get the bot config
+        :return: Dict: Bot config
+        """
+        if self.config is None:
+            self.config = self.load_config()
+
+        return self.config
+
+    def check_exchange(self, config: Dict[str, Any]) -> bool:
+        """
+        Check if the exchange name in the config file is supported by Freqtrade
+        :return: True or raised an exception if the exchange if not supported
+        """
+        exchange = config.get('exchange', {}).get('name').lower()
+        if exchange not in ccxt.exchanges:
+
+            exception_msg = f'Exchange "{exchange}" not supported.\n' \
+                            f'The following exchanges are supported: {", ".join(ccxt.exchanges)}'
+
+            logger.critical(exception_msg)
+            raise OperationalException(
+                exception_msg
+            )
+
+        logger.debug('Exchange "%s" supported', exchange)
+        return True

freqtrade/constants.py

@@ -0,0 +1,183 @@
+# pragma pylint: disable=too-few-public-methods
+
+"""
+bot constants
+"""
+DYNAMIC_WHITELIST = 20  # pairs
+PROCESS_THROTTLE_SECS = 5  # sec
+TICKER_INTERVAL = 5  # min
+HYPEROPT_EPOCH = 100  # epochs
+RETRY_TIMEOUT = 30  # sec
+DEFAULT_STRATEGY = 'DefaultStrategy'
+DEFAULT_DB_PROD_URL = 'sqlite:///tradesv3.sqlite'
+DEFAULT_DB_DRYRUN_URL = 'sqlite://'
+UNLIMITED_STAKE_AMOUNT = 'unlimited'
+
+
+TICKER_INTERVAL_MINUTES = {
+    '1m': 1,
+    '3m': 3,
+    '5m': 5,
+    '15m': 15,
+    '30m': 30,
+    '1h': 60,
+    '2h': 120,
+    '4h': 240,
+    '6h': 360,
+    '8h': 480,
+    '12h': 720,
+    '1d': 1440,
+    '3d': 4320,
+    '1w': 10080,
+}
+
+SUPPORTED_FIAT = [
+    "AUD", "BRL", "CAD", "CHF", "CLP", "CNY", "CZK", "DKK",
+    "EUR", "GBP", "HKD", "HUF", "IDR", "ILS", "INR", "JPY",
+    "KRW", "MXN", "MYR", "NOK", "NZD", "PHP", "PKR", "PLN",
+    "RUB", "SEK", "SGD", "THB", "TRY", "TWD", "ZAR", "USD",
+    "BTC", "XBT", "ETH", "XRP", "LTC", "BCH", "USDT"
+    ]
+
+# Required json-schema for user specified config
+CONF_SCHEMA = {
+    'type': 'object',
+    'properties': {
+        'max_open_trades': {'type': 'integer', 'minimum': 0},
+        'ticker_interval': {'type': 'string', 'enum': list(TICKER_INTERVAL_MINUTES.keys())},
+        'stake_currency': {'type': 'string', 'enum': ['BTC', 'XBT', 'ETH', 'USDT', 'EUR', 'USD']},
+        'stake_amount': {
+            "type": ["number", "string"],
+            "minimum": 0.0005,
+            "pattern": UNLIMITED_STAKE_AMOUNT
+        },
+        'fiat_display_currency': {'type': 'string', 'enum': SUPPORTED_FIAT},
+        'dry_run': {'type': 'boolean'},
+        'process_only_new_candles': {'type': 'boolean'},
+        'minimal_roi': {
+            'type': 'object',
+            'patternProperties': {
+                '^[0-9.]+$': {'type': 'number'}
+            },
+            'minProperties': 1
+        },
+        'stoploss': {'type': 'number', 'maximum': 0, 'exclusiveMaximum': True},
+        'trailing_stop': {'type': 'boolean'},
+        'trailing_stop_positive': {'type': 'number', 'minimum': 0, 'maximum': 1},
+        'trailing_stop_positive_offset': {'type': 'number', 'minimum': 0, 'maximum': 1},
+        'unfilledtimeout': {
+            'type': 'object',
+            'properties': {
+                'buy': {'type': 'number', 'minimum': 3},
+                'sell': {'type': 'number', 'minimum': 10}
+            }
+        },
+        'bid_strategy': {
+            'type': 'object',
+            'properties': {
+                'ask_last_balance': {
+                    'type': 'number',
+                    'minimum': 0,
+                    'maximum': 1,
+                    'exclusiveMaximum': False,
+                    'use_order_book': {'type': 'boolean'},
+                    'order_book_top': {'type': 'number', 'maximum': 20, 'minimum': 1},
+                    'check_depth_of_market': {
+                        'type': 'object',
+                        'properties': {
+                            'enabled': {'type': 'boolean'},
+                            'bids_to_ask_delta': {'type': 'number', 'minimum': 0},
+                        }
+                    },
+                },
+            },
+            'required': ['ask_last_balance']
+        },
+        'ask_strategy': {
+            'type': 'object',
+            'properties': {
+                'use_order_book': {'type': 'boolean'},
+                'order_book_min': {'type': 'number', 'minimum': 1},
+                'order_book_max': {'type': 'number', 'minimum': 1, 'maximum': 50}
+            }
+        },
+        'exchange': {'$ref': '#/definitions/exchange'},
+        'experimental': {
+            'type': 'object',
+            'properties': {
+                'use_sell_signal': {'type': 'boolean'},
+                'sell_profit_only': {'type': 'boolean'},
+                'ignore_roi_if_buy_signal_true': {'type': 'boolean'}
+            }
+        },
+        'telegram': {
+            'type': 'object',
+            'properties': {
+                'enabled': {'type': 'boolean'},
+                'token': {'type': 'string'},
+                'chat_id': {'type': 'string'},
+            },
+            'required': ['enabled', 'token', 'chat_id']
+        },
+        'webhook': {
+            'type': 'object',
+            'properties': {
+                'enabled': {'type': 'boolean'},
+                'webhookbuy': {'type': 'object'},
+                'webhooksell': {'type': 'object'},
+                'webhookstatus': {'type': 'object'},
+            },
+        },
+        'db_url': {'type': 'string'},
+        'initial_state': {'type': 'string', 'enum': ['running', 'stopped']},
+        'internals': {
+            'type': 'object',
+            'properties': {
+                'process_throttle_secs': {'type': 'number'},
+                'interval': {'type': 'integer'}
+            }
+        }
+    },
+    'definitions': {
+        'exchange': {
+            'type': 'object',
+            'properties': {
+                'name': {'type': 'string'},
+                'sandbox': {'type': 'boolean'},
+                'key': {'type': 'string'},
+                'secret': {'type': 'string'},
+                'password': {'type': 'string'},
+                'uid': {'type': 'string'},
+                'pair_whitelist': {
+                    'type': 'array',
+                    'items': {
+                        'type': 'string',
+                        'pattern': '^[0-9A-Z]+/[0-9A-Z]+$'
+                    },
+                    'uniqueItems': True
+                },
+                'pair_blacklist': {
+                    'type': 'array',
+                    'items': {
+                        'type': 'string',
+                        'pattern': '^[0-9A-Z]+/[0-9A-Z]+$'
+                    },
+                    'uniqueItems': True
+                },
+                'outdated_offset': {'type': 'integer', 'minimum': 1}
+            },
+            'required': ['name', 'key', 'secret', 'pair_whitelist']
+        }
+    },
+    'anyOf': [
+        {'required': ['exchange']}
+    ],
+    'required': [
+        'max_open_trades',
+        'stake_currency',
+        'stake_amount',
+        'dry_run',
+        'bid_strategy',
+        'telegram'
+    ]
+}

freqtrade/exchange/__init__.py

@@ -1,185 +1,648 @@
 # pragma pylint: disable=W0603
 """ Cryptocurrency Exchanges support """
-import enum
 import logging
+import inspect
 from random import randint
-from typing import List, Dict, Any, Optional
+from typing import List, Dict, Tuple, Any, Optional
+from datetime import datetime
+from math import floor, ceil
 
+import asyncio
+import ccxt
+import ccxt.async_support as ccxt_async
 import arrow
-import requests
-from cachetools import cached, TTLCache
 
-from freqtrade import OperationalException
-from freqtrade.exchange.bittrex import Bittrex
-from freqtrade.exchange.interface import Exchange
+from freqtrade import constants, OperationalException, DependencyException, TemporaryError
 
 logger = logging.getLogger(__name__)
 
-# Current selected exchange
-_API: Exchange = None
-_CONF: dict = {}
-
-# Holds all open sell orders for dry_run
-_DRY_RUN_OPEN_ORDERS: Dict[str, Any] = {}
+API_RETRY_COUNT = 4
 
 
-class Exchanges(enum.Enum):
-    """
-    Maps supported exchange names to correspondent classes.
-    """
-    BITTREX = Bittrex
+# Urls to exchange markets, insert quote and base with .format()
+_EXCHANGE_URLS = {
+    ccxt.bittrex.__name__: '/Market/Index?MarketName={quote}-{base}',
+    ccxt.binance.__name__: '/tradeDetail.html?symbol={base}_{quote}'
+}
 
 
-def init(config: dict) -> None:
-    """
-    Initializes this module with the given config,
-    it does basic validation whether the specified
-    exchange and pairs are valid.
-    :param config: config to use
-    :return: None
-    """
-    global _CONF, _API
-
-    _CONF.update(config)
-
-    if config['dry_run']:
-        logger.info('Instance is running with dry_run enabled')
-
-    exchange_config = config['exchange']
-
-    # Find matching class for the given exchange name
-    name = exchange_config['name']
-    try:
-        exchange_class = Exchanges[name.upper()].value
-    except KeyError:
-        raise OperationalException('Exchange {} is not supported'.format(name))
-
-    _API = exchange_class(exchange_config)
-
-    # Check if all pairs are available
-    validate_pairs(config['exchange']['pair_whitelist'])
+def retrier_async(f):
+    async def wrapper(*args, **kwargs):
+        count = kwargs.pop('count', API_RETRY_COUNT)
+        try:
+            return await f(*args, **kwargs)
+        except (TemporaryError, DependencyException) as ex:
+            logger.warning('%s() returned exception: "%s"', f.__name__, ex)
+            if count > 0:
+                count -= 1
+                kwargs.update({'count': count})
+                logger.warning('retrying %s() still for %s times', f.__name__, count)
+                return await wrapper(*args, **kwargs)
+            else:
+                logger.warning('Giving up retrying: %s()', f.__name__)
+                raise ex
+    return wrapper
 
 
-def validate_pairs(pairs: List[str]) -> None:
-    """
-    Checks if all given pairs are tradable on the current exchange.
-    Raises OperationalException if one pair is not available.
-    :param pairs: list of pairs
-    :return: None
-    """
-    try:
-        markets = _API.get_markets()
-    except requests.exceptions.RequestException as e:
-        logger.warning('Unable to validate pairs (assuming they are correct). Reason: %s', e)
-        return
+def retrier(f):
+    def wrapper(*args, **kwargs):
+        count = kwargs.pop('count', API_RETRY_COUNT)
+        try:
+            return f(*args, **kwargs)
+        except (TemporaryError, DependencyException) as ex:
+            logger.warning('%s() returned exception: "%s"', f.__name__, ex)
+            if count > 0:
+                count -= 1
+                kwargs.update({'count': count})
+                logger.warning('retrying %s() still for %s times', f.__name__, count)
+                return wrapper(*args, **kwargs)
+            else:
+                logger.warning('Giving up retrying: %s()', f.__name__)
+                raise ex
+    return wrapper
 
-    stake_cur = _CONF['stake_currency']
-    for pair in pairs:
-        if not pair.startswith(stake_cur):
+
+class Exchange(object):
+
+    # Current selected exchange
+    _api: ccxt.Exchange = None
+    _api_async: ccxt_async.Exchange = None
+    _conf: Dict = {}
+
+    # Holds all open sell orders for dry_run
+    _dry_run_open_orders: Dict[str, Any] = {}
+
+    def __init__(self, config: dict) -> None:
+        """
+        Initializes this module with the given config,
+        it does basic validation whether the specified
+        exchange and pairs are valid.
+        :return: None
+        """
+        self._conf.update(config)
+
+        self._cached_ticker: Dict[str, Any] = {}
+
+        # Holds last candle refreshed time of each pair
+        self._pairs_last_refresh_time: Dict[str, int] = {}
+
+        # Holds candles
+        self.klines: Dict[str, Any] = {}
+
+        if config['dry_run']:
+            logger.info('Instance is running with dry_run enabled')
+
+        exchange_config = config['exchange']
+        self._api = self._init_ccxt(exchange_config)
+        self._api_async = self._init_ccxt(exchange_config, ccxt_async)
+
+        logger.info('Using Exchange "%s"', self.name)
+
+        self.markets = self._load_markets()
+        # Check if all pairs are available
+        self.validate_pairs(config['exchange']['pair_whitelist'])
+
+        if config.get('ticker_interval'):
+            # Check if timeframe is available
+            self.validate_timeframes(config['ticker_interval'])
+
+    def __del__(self):
+        """
+        Destructor - clean up async stuff
+        """
+        logger.debug("Exchange object destroyed, closing async loop")
+        if self._api_async and inspect.iscoroutinefunction(self._api_async.close):
+            asyncio.get_event_loop().run_until_complete(self._api_async.close())
+
+    def _init_ccxt(self, exchange_config: dict, ccxt_module=ccxt) -> ccxt.Exchange:
+        """
+        Initialize ccxt with given config and return valid
+        ccxt instance.
+        """
+        # Find matching class for the given exchange name
+        name = exchange_config['name']
+
+        if name not in ccxt_module.exchanges:
+            raise OperationalException(f'Exchange {name} is not supported')
+        try:
+            api = getattr(ccxt_module, name.lower())({
+                'apiKey': exchange_config.get('key'),
+                'secret': exchange_config.get('secret'),
+                'password': exchange_config.get('password'),
+                'uid': exchange_config.get('uid', ''),
+                'enableRateLimit': exchange_config.get('ccxt_rate_limit', True)
+            })
+        except (KeyError, AttributeError):
+            raise OperationalException(f'Exchange {name} is not supported')
+
+        self.set_sandbox(api, exchange_config, name)
+
+        return api
+
+    @property
+    def name(self) -> str:
+        """exchange Name (from ccxt)"""
+        return self._api.name
+
+    @property
+    def id(self) -> str:
+        """exchange ccxt id"""
+        return self._api.id
+
+    def set_sandbox(self, api, exchange_config: dict, name: str):
+        if exchange_config.get('sandbox'):
+            if api.urls.get('test'):
+                api.urls['api'] = api.urls['test']
+                logger.info("Enabled Sandbox API on %s", name)
+            else:
+                logger.warning(name, "No Sandbox URL in CCXT, exiting. "
+                                     "Please check your config.json")
+                raise OperationalException(f'Exchange {name} does not provide a sandbox api')
+
+    def _load_async_markets(self) -> None:
+        try:
+            if self._api_async:
+                asyncio.get_event_loop().run_until_complete(self._api_async.load_markets())
+
+        except ccxt.BaseError as e:
+            logger.warning('Could not load async markets. Reason: %s', e)
+            return
+
+    def _load_markets(self) -> Dict[str, Any]:
+        """ Initialize markets both sync and async """
+        try:
+            markets = self._api.load_markets()
+            self._load_async_markets()
+            return markets
+        except ccxt.BaseError as e:
+            logger.warning('Unable to initialize markets. Reason: %s', e)
+        return {}
+
+    def validate_pairs(self, pairs: List[str]) -> None:
+        """
+        Checks if all given pairs are tradable on the current exchange.
+        Raises OperationalException if one pair is not available.
+        :param pairs: list of pairs
+        :return: None
+        """
+
+        if not self.markets:
+            logger.warning('Unable to validate pairs (assuming they are correct).')
+        #     return
+
+        stake_cur = self._conf['stake_currency']
+        for pair in pairs:
+            # Note: ccxt has BaseCurrency/QuoteCurrency format for pairs
+            # TODO: add a support for having coins in BTC/USDT format
+            if not pair.endswith(stake_cur):
+                raise OperationalException(
+                    f'Pair {pair} not compatible with stake_currency: {stake_cur}')
+            if self.markets and pair not in self.markets:
+                raise OperationalException(
+                    f'Pair {pair} is not available at {self.name}')
+
+    def validate_timeframes(self, timeframe: List[str]) -> None:
+        """
+        Checks if ticker interval from config is a supported timeframe on the exchange
+        """
+        timeframes = self._api.timeframes
+        if timeframe not in timeframes:
             raise OperationalException(
-                'Pair {} not compatible with stake_currency: {}'.format(pair, stake_cur)
-            )
-        if pair not in markets:
+                f'Invalid ticker {timeframe}, this Exchange supports {timeframes}')
+
+    def exchange_has(self, endpoint: str) -> bool:
+        """
+        Checks if exchange implements a specific API endpoint.
+        Wrapper around ccxt 'has' attribute
+        :param endpoint: Name of endpoint (e.g. 'fetchOHLCV', 'fetchTickers')
+        :return: bool
+        """
+        return endpoint in self._api.has and self._api.has[endpoint]
+
+    def symbol_amount_prec(self, pair, amount: float):
+        '''
+        Returns the amount to buy or sell to a precision the Exchange accepts
+        Rounded down
+        '''
+        if self._api.markets[pair]['precision']['amount']:
+            symbol_prec = self._api.markets[pair]['precision']['amount']
+            big_amount = amount * pow(10, symbol_prec)
+            amount = floor(big_amount) / pow(10, symbol_prec)
+        return amount
+
+    def symbol_price_prec(self, pair, price: float):
+        '''
+        Returns the price buying or selling with to the precision the Exchange accepts
+        Rounds up
+        '''
+        if self._api.markets[pair]['precision']['price']:
+            symbol_prec = self._api.markets[pair]['precision']['price']
+            big_price = price * pow(10, symbol_prec)
+            price = ceil(big_price) / pow(10, symbol_prec)
+        return price
+
+    def buy(self, pair: str, rate: float, amount: float) -> Dict:
+        if self._conf['dry_run']:
+            order_id = f'dry_run_buy_{randint(0, 10**6)}'
+            self._dry_run_open_orders[order_id] = {
+                'pair': pair,
+                'price': rate,
+                'amount': amount,
+                'type': 'limit',
+                'side': 'buy',
+                'remaining': 0.0,
+                'datetime': arrow.utcnow().isoformat(),
+                'status': 'closed',
+                'fee': None
+            }
+            return {'id': order_id}
+
+        try:
+            # Set the precision for amount and price(rate) as accepted by the exchange
+            amount = self.symbol_amount_prec(pair, amount)
+            rate = self.symbol_price_prec(pair, rate)
+
+            return self._api.create_limit_buy_order(pair, amount, rate)
+        except ccxt.InsufficientFunds as e:
+            raise DependencyException(
+                f'Insufficient funds to create limit buy order on market {pair}.'
+                f'Tried to buy amount {amount} at rate {rate} (total {rate*amount}).'
+                f'Message: {e}')
+        except ccxt.InvalidOrder as e:
+            raise DependencyException(
+                f'Could not create limit buy order on market {pair}.'
+                f'Tried to buy amount {amount} at rate {rate} (total {rate*amount}).'
+                f'Message: {e}')
+        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f'Could not place buy order due to {e.__class__.__name__}. Message: {e}')
+        except ccxt.BaseError as e:
+            raise OperationalException(e)
+
+    def sell(self, pair: str, rate: float, amount: float) -> Dict:
+        if self._conf['dry_run']:
+            order_id = f'dry_run_sell_{randint(0, 10**6)}'
+            self._dry_run_open_orders[order_id] = {
+                'pair': pair,
+                'price': rate,
+                'amount': amount,
+                'type': 'limit',
+                'side': 'sell',
+                'remaining': 0.0,
+                'datetime': arrow.utcnow().isoformat(),
+                'status': 'closed'
+            }
+            return {'id': order_id}
+
+        try:
+            # Set the precision for amount and price(rate) as accepted by the exchange
+            amount = self.symbol_amount_prec(pair, amount)
+            rate = self.symbol_price_prec(pair, rate)
+
+            return self._api.create_limit_sell_order(pair, amount, rate)
+        except ccxt.InsufficientFunds as e:
+            raise DependencyException(
+                f'Insufficient funds to create limit sell order on market {pair}.'
+                f'Tried to sell amount {amount} at rate {rate} (total {rate*amount}).'
+                f'Message: {e}')
+        except ccxt.InvalidOrder as e:
+            raise DependencyException(
+                f'Could not create limit sell order on market {pair}.'
+                f'Tried to sell amount {amount} at rate {rate} (total {rate*amount}).'
+                f'Message: {e}')
+        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f'Could not place sell order due to {e.__class__.__name__}. Message: {e}')
+        except ccxt.BaseError as e:
+            raise OperationalException(e)
+
+    @retrier
+    def get_balance(self, currency: str) -> float:
+        if self._conf['dry_run']:
+            return 999.9
+
+        # ccxt exception is already handled by get_balances
+        balances = self.get_balances()
+        balance = balances.get(currency)
+        if balance is None:
+            raise TemporaryError(
+                f'Could not get {currency} balance due to malformed exchange response: {balances}')
+        return balance['free']
+
+    @retrier
+    def get_balances(self) -> dict:
+        if self._conf['dry_run']:
+            return {}
+
+        try:
+            balances = self._api.fetch_balance()
+            # Remove additional info from ccxt results
+            balances.pop("info", None)
+            balances.pop("free", None)
+            balances.pop("total", None)
+            balances.pop("used", None)
+
+            return balances
+        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f'Could not get balance due to {e.__class__.__name__}. Message: {e}')
+        except ccxt.BaseError as e:
+            raise OperationalException(e)
+
+    @retrier
+    def get_tickers(self) -> Dict:
+        try:
+            return self._api.fetch_tickers()
+        except ccxt.NotSupported as e:
             raise OperationalException(
-                'Pair {} is not available at {}'.format(pair, _API.name.lower()))
+                f'Exchange {self._api.name} does not support fetching tickers in batch.'
+                f'Message: {e}')
+        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f'Could not load tickers due to {e.__class__.__name__}. Message: {e}')
+        except ccxt.BaseError as e:
+            raise OperationalException(e)
 
+    @retrier
+    def get_ticker(self, pair: str, refresh: Optional[bool] = True) -> dict:
+        if refresh or pair not in self._cached_ticker.keys():
+            try:
+                data = self._api.fetch_ticker(pair)
+                try:
+                    self._cached_ticker[pair] = {
+                        'bid': float(data['bid']),
+                        'ask': float(data['ask']),
+                    }
+                except KeyError:
+                    logger.debug("Could not cache ticker data for %s", pair)
+                return data
+            except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+                raise TemporaryError(
+                    f'Could not load ticker due to {e.__class__.__name__}. Message: {e}')
+            except ccxt.BaseError as e:
+                raise OperationalException(e)
+        else:
+            logger.info("returning cached ticker-data for %s", pair)
+            return self._cached_ticker[pair]
 
-def buy(pair: str, rate: float, amount: float) -> str:
-    if _CONF['dry_run']:
-        global _DRY_RUN_OPEN_ORDERS
-        order_id = 'dry_run_buy_{}'.format(randint(0, 10**6))
-        _DRY_RUN_OPEN_ORDERS[order_id] = {
-            'pair': pair,
-            'rate': rate,
-            'amount': amount,
-            'type': 'LIMIT_BUY',
-            'remaining': 0.0,
-            'opened': arrow.utcnow().datetime,
-            'closed': arrow.utcnow().datetime,
-        }
-        return order_id
+    def get_history(self, pair: str, tick_interval: str,
+                    since_ms: int) -> List:
+        """
+        Gets candle history using asyncio and returns the list of candles.
+        Handles all async doing.
+        """
+        return asyncio.get_event_loop().run_until_complete(
+            self._async_get_history(pair=pair, tick_interval=tick_interval,
+                                    since_ms=since_ms))
 
-    return _API.buy(pair, rate, amount)
+    async def _async_get_history(self, pair: str,
+                                 tick_interval: str,
+                                 since_ms: int) -> List:
+        # Assume exchange returns 500 candles
+        _LIMIT = 500
 
+        one_call = constants.TICKER_INTERVAL_MINUTES[tick_interval] * 60 * _LIMIT * 1000
+        logger.debug("one_call: %s", one_call)
+        input_coroutines = [self._async_get_candle_history(
+            pair, tick_interval, since) for since in
+            range(since_ms, arrow.utcnow().timestamp * 1000, one_call)]
+        tickers = await asyncio.gather(*input_coroutines, return_exceptions=True)
 
-def sell(pair: str, rate: float, amount: float) -> str:
-    if _CONF['dry_run']:
-        global _DRY_RUN_OPEN_ORDERS
-        order_id = 'dry_run_sell_{}'.format(randint(0, 10**6))
-        _DRY_RUN_OPEN_ORDERS[order_id] = {
-            'pair': pair,
-            'rate': rate,
-            'amount': amount,
-            'type': 'LIMIT_SELL',
-            'remaining': 0.0,
-            'opened': arrow.utcnow().datetime,
-            'closed': arrow.utcnow().datetime,
-        }
-        return order_id
+        # Combine tickers
+        data: List = []
+        for tick in tickers:
+            if tick[0] == pair:
+                data.extend(tick[1])
+        # Sort data again after extending the result - above calls return in "async order" order
+        data = sorted(data, key=lambda x: x[0])
+        logger.info("downloaded %s with length %s.", pair, len(data))
+        return data
 
-    return _API.sell(pair, rate, amount)
+    def refresh_tickers(self, pair_list: List[str], ticker_interval: str) -> None:
+        """
+        Refresh tickers asyncronously and return the result.
+        """
+        logger.debug("Refreshing klines for %d pairs", len(pair_list))
+        asyncio.get_event_loop().run_until_complete(
+            self.async_get_candles_history(pair_list, ticker_interval))
 
+    async def async_get_candles_history(self, pairs: List[str],
+                                        tick_interval: str) -> List[Tuple[str, List]]:
+        """Download ohlcv history for pair-list asyncronously """
+        input_coroutines = [self._async_get_candle_history(
+            symbol, tick_interval) for symbol in pairs]
+        tickers = await asyncio.gather(*input_coroutines, return_exceptions=True)
+        return tickers
 
-def get_balance(currency: str) -> float:
-    if _CONF['dry_run']:
-        return 999.9
+    @retrier_async
+    async def _async_get_candle_history(self, pair: str, tick_interval: str,
+                                        since_ms: Optional[int] = None) -> Tuple[str, List]:
+        try:
+            # fetch ohlcv asynchronously
+            logger.debug("fetching %s since %s ...", pair, since_ms)
 
-    return _API.get_balance(currency)
+            # Calculating ticker interval in second
+            interval_in_sec = constants.TICKER_INTERVAL_MINUTES[tick_interval] * 60
 
+            # If (last update time) + (interval in second) is greater or equal than now
+            # that means we don't have to hit the API as there is no new candle
+            # so we fetch it from local cache
+            if (not since_ms and
+                    self._pairs_last_refresh_time.get(pair, 0) + interval_in_sec >=
+                    arrow.utcnow().timestamp):
+                data = self.klines[pair]
+                logger.debug("Using cached klines data for %s ...", pair)
+            else:
+                data = await self._api_async.fetch_ohlcv(pair, timeframe=tick_interval,
+                                                         since=since_ms)
 
-def get_balances():
-    if _CONF['dry_run']:
-        return []
+            # Because some exchange sort Tickers ASC and other DESC.
+            # Ex: Bittrex returns a list of tickers ASC (oldest first, newest last)
+            # when GDAX returns a list of tickers DESC (newest first, oldest last)
+            data = sorted(data, key=lambda x: x[0])
 
-    return _API.get_balances()
+            # keeping last candle time as last refreshed time of the pair
+            if data:
+                self._pairs_last_refresh_time[pair] = data[-1][0] // 1000
 
+            # keeping candles in cache
+            self.klines[pair] = data
 
-def get_ticker(pair: str, refresh: Optional[bool] = True) -> dict:
-    return _API.get_ticker(pair, refresh)
+            logger.debug("done fetching %s ...", pair)
+            return pair, data
 
+        except ccxt.NotSupported as e:
+            raise OperationalException(
+                f'Exchange {self._api.name} does not support fetching historical candlestick data.'
+                f'Message: {e}')
+        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f'Could not load ticker history due to {e.__class__.__name__}. Message: {e}')
+        except ccxt.BaseError as e:
+            raise OperationalException(f'Could not fetch ticker data. Msg: {e}')
 
-@cached(TTLCache(maxsize=100, ttl=30))
-def get_ticker_history(pair: str, tick_interval: Optional[int] = 5) -> List[Dict]:
-    return _API.get_ticker_history(pair, tick_interval)
+    @retrier
+    def get_candle_history(self, pair: str, tick_interval: str,
+                           since_ms: Optional[int] = None) -> List[Dict]:
+        try:
+            # last item should be in the time interval [now - tick_interval, now]
+            till_time_ms = arrow.utcnow().shift(
+                            minutes=-constants.TICKER_INTERVAL_MINUTES[tick_interval]
+                        ).timestamp * 1000
+            # it looks as if some exchanges return cached data
+            # and they update it one in several minute, so 10 mins interval
+            # is necessary to skeep downloading of an empty array when all
+            # chached data was already downloaded
+            till_time_ms = min(till_time_ms, arrow.utcnow().shift(minutes=-10).timestamp * 1000)
 
+            data: List[Dict[Any, Any]] = []
+            while not since_ms or since_ms < till_time_ms:
+                data_part = self._api.fetch_ohlcv(pair, timeframe=tick_interval, since=since_ms)
 
-def cancel_order(order_id: str) -> None:
-    if _CONF['dry_run']:
-        return
+                # Because some exchange sort Tickers ASC and other DESC.
+                # Ex: Bittrex returns a list of tickers ASC (oldest first, newest last)
+                # when GDAX returns a list of tickers DESC (newest first, oldest last)
+                data_part = sorted(data_part, key=lambda x: x[0])
 
-    return _API.cancel_order(order_id)
+                if not data_part:
+                    break
 
+                logger.debug('Downloaded data for %s time range [%s, %s]',
+                             pair,
+                             arrow.get(data_part[0][0] / 1000).format(),
+                             arrow.get(data_part[-1][0] / 1000).format())
 
-def get_order(order_id: str) -> Dict:
-    if _CONF['dry_run']:
-        order = _DRY_RUN_OPEN_ORDERS[order_id]
-        order.update({
-            'id': order_id
-        })
-        return order
+                data.extend(data_part)
+                since_ms = data[-1][0] + 1
 
-    return _API.get_order(order_id)
+            return data
+        except ccxt.NotSupported as e:
+            raise OperationalException(
+                f'Exchange {self._api.name} does not support fetching historical candlestick data.'
+                f'Message: {e}')
+        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f'Could not load ticker history due to {e.__class__.__name__}. Message: {e}')
+        except ccxt.BaseError as e:
+            raise OperationalException(f'Could not fetch ticker data. Msg: {e}')
 
+    @retrier
+    def cancel_order(self, order_id: str, pair: str) -> None:
+        if self._conf['dry_run']:
+            return
 
-def get_pair_detail_url(pair: str) -> str:
-    return _API.get_pair_detail_url(pair)
+        try:
+            return self._api.cancel_order(order_id, pair)
+        except ccxt.InvalidOrder as e:
+            raise DependencyException(
+                f'Could not cancel order. Message: {e}')
+        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f'Could not cancel order due to {e.__class__.__name__}. Message: {e}')
+        except ccxt.BaseError as e:
+            raise OperationalException(e)
 
+    @retrier
+    def get_order(self, order_id: str, pair: str) -> Dict:
+        if self._conf['dry_run']:
+            order = self._dry_run_open_orders[order_id]
+            order.update({
+                'id': order_id
+            })
+            return order
+        try:
+            return self._api.fetch_order(order_id, pair)
+        except ccxt.InvalidOrder as e:
+            raise DependencyException(
+                f'Could not get order. Message: {e}')
+        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f'Could not get order due to {e.__class__.__name__}. Message: {e}')
+        except ccxt.BaseError as e:
+            raise OperationalException(e)
 
-def get_markets() -> List[str]:
-    return _API.get_markets()
+    @retrier
+    def get_order_book(self, pair: str, limit: int = 100) -> dict:
+        """
+        get order book level 2 from exchange
 
+        Notes:
+        20180619: bittrex doesnt support limits -.-
+        20180619: binance support limits but only on specific range
+        """
+        try:
+            if self._api.name == 'Binance':
+                limit_range = [5, 10, 20, 50, 100, 500, 1000]
+                # get next-higher step in the limit_range list
+                limit = min(list(filter(lambda x: limit <= x, limit_range)))
+                # above script works like loop below (but with slightly better performance):
+                #   for limitx in limit_range:
+                #        if limit <= limitx:
+                #           limit = limitx
+                #           break
 
-def get_market_summaries() -> List[Dict]:
-    return _API.get_market_summaries()
+            return self._api.fetch_l2_order_book(pair, limit)
+        except ccxt.NotSupported as e:
+            raise OperationalException(
+                f'Exchange {self._api.name} does not support fetching order book.'
+                f'Message: {e}')
+        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f'Could not get order book due to {e.__class__.__name__}. Message: {e}')
+        except ccxt.BaseError as e:
+            raise OperationalException(e)
 
+    @retrier
+    def get_trades_for_order(self, order_id: str, pair: str, since: datetime) -> List:
+        if self._conf['dry_run']:
+            return []
+        if not self.exchange_has('fetchMyTrades'):
+            return []
+        try:
+            # Allow 5s offset to catch slight time offsets (discovered in #1185)
+            my_trades = self._api.fetch_my_trades(pair, since.timestamp() - 5)
+            matched_trades = [trade for trade in my_trades if trade['order'] == order_id]
 
-def get_name() -> str:
-    return _API.name
+            return matched_trades
 
+        except ccxt.NetworkError as e:
+            raise TemporaryError(
+                f'Could not get trades due to networking error. Message: {e}')
+        except ccxt.BaseError as e:
+            raise OperationalException(e)
 
-def get_fee() -> float:
-    return _API.fee
+    def get_pair_detail_url(self, pair: str) -> str:
+        try:
+            url_base = self._api.urls.get('www')
+            base, quote = pair.split('/')
 
+            return url_base + _EXCHANGE_URLS[self._api.id].format(base=base, quote=quote)
+        except KeyError:
+            logger.warning('Could not get exchange url for %s', self.name)
+            return ""
 
-def get_wallet_health() -> List[Dict]:
-    return _API.get_wallet_health()
+    @retrier
+    def get_markets(self) -> List[dict]:
+        try:
+            return self._api.fetch_markets()
+        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f'Could not load markets due to {e.__class__.__name__}. Message: {e}')
+        except ccxt.BaseError as e:
+            raise OperationalException(e)
+
+    @retrier
+    def get_fee(self, symbol='ETH/BTC', type='', side='', amount=1,
+                price=1, taker_or_maker='maker') -> float:
+        try:
+            # validate that markets are loaded before trying to get fee
+            if self._api.markets is None or len(self._api.markets) == 0:
+                self._api.load_markets()
+
+            return self._api.calculate_fee(symbol=symbol, type=type, side=side, amount=amount,
+                                           price=price, takerOrMaker=taker_or_maker)['rate']
+        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f'Could not get fee info due to {e.__class__.__name__}. Message: {e}')
+        except ccxt.BaseError as e:
+            raise OperationalException(e)

freqtrade/exchange/bittrex.py

@@ -1,226 +0,0 @@
-import logging
-import requests
-from typing import Dict, List, Optional
-
-from bittrex.bittrex import Bittrex as _Bittrex
-from bittrex.bittrex import API_V1_1, API_V2_0
-from requests.exceptions import ContentDecodingError
-
-from freqtrade import OperationalException
-from freqtrade.exchange.interface import Exchange
-
-logger = logging.getLogger(__name__)
-
-_API: _Bittrex = None
-_API_V2: _Bittrex = None
-_EXCHANGE_CONF: dict = {}
-
-# API socket timeout
-API_TIMEOUT = 60
-
-
-def custom_requests(request_url, apisign):
-    """
-    Set timeout for requests
-    """
-    return requests.get(
-        request_url,
-        headers={"apisign": apisign},
-        timeout=API_TIMEOUT
-    ).json()
-
-
-class Bittrex(Exchange):
-    """
-    Bittrex API wrapper.
-    """
-    # Base URL and API endpoints
-    BASE_URL: str = 'https://www.bittrex.com'
-    PAIR_DETAIL_METHOD: str = BASE_URL + '/Market/Index'
-
-    def __init__(self, config: dict) -> None:
-        global _API, _API_V2, _EXCHANGE_CONF
-
-        _EXCHANGE_CONF.update(config)
-        _API = _Bittrex(
-            api_key=_EXCHANGE_CONF['key'],
-            api_secret=_EXCHANGE_CONF['secret'],
-            calls_per_second=1,
-            api_version=API_V1_1,
-            dispatch=custom_requests
-        )
-        _API_V2 = _Bittrex(
-            api_key=_EXCHANGE_CONF['key'],
-            api_secret=_EXCHANGE_CONF['secret'],
-            calls_per_second=1,
-            api_version=API_V2_0,
-            dispatch=custom_requests
-        )
-        self.cached_ticker = {}
-
-    @staticmethod
-    def _validate_response(response) -> None:
-        """
-        Validates the given bittrex response
-        and raises a ContentDecodingError if a non-fatal issue happened.
-        """
-        temp_error_messages = [
-            'NO_API_RESPONSE',
-            'MIN_TRADE_REQUIREMENT_NOT_MET',
-        ]
-        if response['message'] in temp_error_messages:
-            raise ContentDecodingError('Got {}'.format(response['message']))
-
-    @property
-    def fee(self) -> float:
-        # 0.25 %: See https://bittrex.com/fees
-        return 0.0025
-
-    def buy(self, pair: str, rate: float, amount: float) -> str:
-        data = _API.buy_limit(pair.replace('_', '-'), amount, rate)
-        if not data['success']:
-            Bittrex._validate_response(data)
-            raise OperationalException('{message} params=({pair}, {rate}, {amount})'.format(
-                message=data['message'],
-                pair=pair,
-                rate=rate,
-                amount=amount))
-        return data['result']['uuid']
-
-    def sell(self, pair: str, rate: float, amount: float) -> str:
-        data = _API.sell_limit(pair.replace('_', '-'), amount, rate)
-        if not data['success']:
-            Bittrex._validate_response(data)
-            raise OperationalException('{message} params=({pair}, {rate}, {amount})'.format(
-                message=data['message'],
-                pair=pair,
-                rate=rate,
-                amount=amount))
-        return data['result']['uuid']
-
-    def get_balance(self, currency: str) -> float:
-        data = _API.get_balance(currency)
-        if not data['success']:
-            Bittrex._validate_response(data)
-            raise OperationalException('{message} params=({currency})'.format(
-                message=data['message'],
-                currency=currency))
-        return float(data['result']['Balance'] or 0.0)
-
-    def get_balances(self):
-        data = _API.get_balances()
-        if not data['success']:
-            Bittrex._validate_response(data)
-            raise OperationalException('{message}'.format(message=data['message']))
-        return data['result']
-
-    def get_ticker(self, pair: str, refresh: Optional[bool] = True) -> dict:
-        if refresh or pair not in self.cached_ticker.keys():
-            data = _API.get_ticker(pair.replace('_', '-'))
-            if not data['success']:
-                Bittrex._validate_response(data)
-                raise OperationalException('{message} params=({pair})'.format(
-                    message=data['message'],
-                    pair=pair))
-
-            if not data.get('result') \
-                    or not data['result'].get('Bid') \
-                    or not data['result'].get('Ask') \
-                    or not data['result'].get('Last'):
-                raise ContentDecodingError('{message} params=({pair})'.format(
-                    message='Got invalid response from bittrex',
-                    pair=pair))
-            # Update the pair
-            self.cached_ticker[pair] = {
-                'bid': float(data['result']['Bid']),
-                'ask': float(data['result']['Ask']),
-                'last': float(data['result']['Last']),
-            }
-        return self.cached_ticker[pair]
-
-    def get_ticker_history(self, pair: str, tick_interval: int) -> List[Dict]:
-        if tick_interval == 1:
-            interval = 'oneMin'
-        elif tick_interval == 5:
-            interval = 'fiveMin'
-        else:
-            raise ValueError('Cannot parse tick_interval: {}'.format(tick_interval))
-
-        data = _API_V2.get_candles(pair.replace('_', '-'), interval)
-
-        # These sanity check are necessary because bittrex cannot keep their API stable.
-        if not data.get('result'):
-            raise ContentDecodingError('{message} params=({pair})'.format(
-                message='Got invalid response from bittrex',
-                pair=pair))
-
-        for prop in ['C', 'V', 'O', 'H', 'L', 'T']:
-            for tick in data['result']:
-                if prop not in tick.keys():
-                    raise ContentDecodingError('{message} params=({pair})'.format(
-                        message='Required property {} not present in response'.format(prop),
-                        pair=pair))
-
-        if not data['success']:
-            Bittrex._validate_response(data)
-            raise OperationalException('{message} params=({pair})'.format(
-                message=data['message'],
-                pair=pair))
-
-        return data['result']
-
-    def get_order(self, order_id: str) -> Dict:
-        data = _API.get_order(order_id)
-        if not data['success']:
-            Bittrex._validate_response(data)
-            raise OperationalException('{message} params=({order_id})'.format(
-                message=data['message'],
-                order_id=order_id))
-        data = data['result']
-        return {
-            'id': data['OrderUuid'],
-            'type': data['Type'],
-            'pair': data['Exchange'].replace('-', '_'),
-            'opened': data['Opened'],
-            'rate': data['PricePerUnit'],
-            'amount': data['Quantity'],
-            'remaining': data['QuantityRemaining'],
-            'closed': data['Closed'],
-        }
-
-    def cancel_order(self, order_id: str) -> None:
-        data = _API.cancel(order_id)
-        if not data['success']:
-            Bittrex._validate_response(data)
-            raise OperationalException('{message} params=({order_id})'.format(
-                message=data['message'],
-                order_id=order_id))
-
-    def get_pair_detail_url(self, pair: str) -> str:
-        return self.PAIR_DETAIL_METHOD + '?MarketName={}'.format(pair.replace('_', '-'))
-
-    def get_markets(self) -> List[str]:
-        data = _API.get_markets()
-        if not data['success']:
-            Bittrex._validate_response(data)
-            raise OperationalException('{message}'.format(message=data['message']))
-        return [m['MarketName'].replace('-', '_') for m in data['result']]
-
-    def get_market_summaries(self) -> List[Dict]:
-        data = _API.get_market_summaries()
-        if not data['success']:
-            Bittrex._validate_response(data)
-            raise OperationalException('{message}'.format(message=data['message']))
-        return data['result']
-
-    def get_wallet_health(self) -> List[Dict]:
-        data = _API_V2.get_wallet_health()
-        if not data['success']:
-            Bittrex._validate_response(data)
-            raise OperationalException('{message}'.format(message=data['message']))
-        return [{
-            'Currency': entry['Health']['Currency'],
-            'IsActive': entry['Health']['IsActive'],
-            'LastChecked': entry['Health']['LastChecked'],
-            'Notice': entry['Currency'].get('Notice'),
-        } for entry in data['result']]

freqtrade/exchange/exchange_helpers.py

@@ -0,0 +1,58 @@
+"""
+Functions to analyze ticker data with indicators and produce buy and sell signals
+"""
+import logging
+import pandas as pd
+from pandas import DataFrame, to_datetime
+
+logger = logging.getLogger(__name__)
+
+
+def parse_ticker_dataframe(ticker: list) -> DataFrame:
+    """
+    Analyses the trend for the given ticker history
+    :param ticker: See exchange.get_candle_history
+    :return: DataFrame
+    """
+    cols = ['date', 'open', 'high', 'low', 'close', 'volume']
+    frame = DataFrame(ticker, columns=cols)
+
+    frame['date'] = to_datetime(frame['date'],
+                                unit='ms',
+                                utc=True,
+                                infer_datetime_format=True)
+
+    # group by index and aggregate results to eliminate duplicate ticks
+    frame = frame.groupby(by='date', as_index=False, sort=True).agg({
+        'open': 'first',
+        'high': 'max',
+        'low': 'min',
+        'close': 'last',
+        'volume': 'max',
+    })
+    frame.drop(frame.tail(1).index, inplace=True)     # eliminate partial candle
+    return frame
+
+
+def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
+    """
+    Gets order book list, returns dataframe with below format per suggested by creslin
+    -------------------------------------------------------------------
+     b_sum       b_size       bids       asks       a_size       a_sum
+    -------------------------------------------------------------------
+    """
+    cols = ['bids', 'b_size']
+
+    bids_frame = DataFrame(bids, columns=cols)
+    # add cumulative sum column
+    bids_frame['b_sum'] = bids_frame['b_size'].cumsum()
+    cols2 = ['asks', 'a_size']
+    asks_frame = DataFrame(asks, columns=cols2)
+    # add cumulative sum column
+    asks_frame['a_sum'] = asks_frame['a_size'].cumsum()
+
+    frame = pd.concat([bids_frame['b_sum'], bids_frame['b_size'], bids_frame['bids'],
+                       asks_frame['asks'], asks_frame['a_size'], asks_frame['a_sum']], axis=1,
+                      keys=['b_sum', 'b_size', 'bids', 'asks', 'a_size', 'a_sum'])
+    # logger.info('order book %s', frame )
+    return frame

freqtrade/exchange/interface.py

@@ -1,172 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Dict, List, Optional
-
-
-class Exchange(ABC):
-    @property
-    def name(self) -> str:
-        """
-        Name of the exchange.
-        :return: str representation of the class name
-        """
-        return self.__class__.__name__
-
-    @property
-    def fee(self) -> float:
-        """
-        Fee for placing an order
-        :return: percentage in float
-        """
-
-    @abstractmethod
-    def buy(self, pair: str, rate: float, amount: float) -> str:
-        """
-        Places a limit buy order.
-        :param pair: Pair as str, format: BTC_ETH
-        :param rate: Rate limit for order
-        :param amount: The amount to purchase
-        :return: order_id of the placed buy order
-        """
-
-    @abstractmethod
-    def sell(self, pair: str, rate: float, amount: float) -> str:
-        """
-        Places a limit sell order.
-        :param pair: Pair as str, format: BTC_ETH
-        :param rate: Rate limit for order
-        :param amount: The amount to sell
-        :return: order_id of the placed sell order
-        """
-
-    @abstractmethod
-    def get_balance(self, currency: str) -> float:
-        """
-        Gets account balance.
-        :param currency: Currency as str, format: BTC
-        :return: float
-        """
-
-    @abstractmethod
-    def get_balances(self) -> List[dict]:
-        """
-        Gets account balances across currencies
-        :return: List of dicts, format: [
-          {
-            'Currency': str,
-            'Balance': float,
-            'Available': float,
-            'Pending': float,
-          }
-          ...
-        ]
-        """
-
-    @abstractmethod
-    def get_ticker(self, pair: str, refresh: Optional[bool] = True) -> dict:
-        """
-        Gets ticker for given pair.
-        :param pair: Pair as str, format: BTC_ETC
-        :param refresh: Shall we query a new value or a cached value is enough
-        :return: dict, format: {
-            'bid': float,
-            'ask': float,
-            'last': float
-        }
-        """
-
-    @abstractmethod
-    def get_ticker_history(self, pair: str, tick_interval: int) -> List[Dict]:
-        """
-        Gets ticker history for given pair.
-        :param pair: Pair as str, format: BTC_ETC
-        :param tick_interval: ticker interval in minutes
-        :return: list, format: [
-            {
-                'O': float,       (Open)
-                'H': float,       (High)
-                'L': float,       (Low)
-                'C': float,       (Close)
-                'V': float,       (Volume)
-                'T': datetime,    (Time)
-                'BV': float,      (Base Volume)
-            },
-            ...
-        ]
-        """
-
-    def get_order(self, order_id: str) -> Dict:
-        """
-        Get order details for the given order_id.
-        :param order_id: ID as str
-        :return: dict, format: {
-            'id': str,
-            'type': str,
-            'pair': str,
-            'opened': str ISO 8601 datetime,
-            'closed': str ISO 8601 datetime,
-            'rate': float,
-            'amount': float,
-            'remaining': int
-        }
-        """
-
-    @abstractmethod
-    def cancel_order(self, order_id: str) -> None:
-        """
-        Cancels order for given order_id.
-        :param order_id: ID as str
-        :return: None
-        """
-
-    @abstractmethod
-    def get_pair_detail_url(self, pair: str) -> str:
-        """
-        Returns the market detail url for the given pair.
-        :param pair: Pair as str, format: BTC_ETC
-        :return: URL as str
-        """
-
-    @abstractmethod
-    def get_markets(self) -> List[str]:
-        """
-        Returns all available markets.
-        :return: List of all available pairs
-        """
-
-    @abstractmethod
-    def get_market_summaries(self) -> List[Dict]:
-        """
-        Returns a 24h market summary for all available markets
-        :return: list, format: [
-            {
-                'MarketName': str,
-                'High': float,
-                'Low': float,
-                'Volume': float,
-                'Last': float,
-                'TimeStamp': datetime,
-                'BaseVolume': float,
-                'Bid': float,
-                'Ask': float,
-                'OpenBuyOrders': int,
-                'OpenSellOrders': int,
-                'PrevDay': float,
-                'Created': datetime
-            },
-            ...
-        ]
-        """
-
-    @abstractmethod
-    def get_wallet_health(self) -> List[Dict]:
-        """
-        Returns a list of all wallet health information
-        :return: list, format: [
-            {
-                'Currency': str,
-                'IsActive': bool,
-                'LastChecked': str,
-                'Notice': str
-            },
-            ...
-        """

freqtrade/fiat_convert.py

@@ -1,12 +1,24 @@
+"""
+Module that define classes to convert Crypto-currency to FIAT
+e.g BTC to USD
+"""
+
 import logging
 import time
+from typing import Dict, List
+
+from coinmarketcap import Market
+
+from freqtrade.constants import SUPPORTED_FIAT
 
-from pymarketcap import Pymarketcap
 
 logger = logging.getLogger(__name__)
 
 
-class CryptoFiat():
+class CryptoFiat(object):
+    """
+    Object to describe what is the price of Crypto-currency in a FIAT
+    """
     # Constants
     CACHE_DURATION = 6 * 60 * 60  # 6 hours
 
@@ -24,7 +36,7 @@ class CryptoFiat():
         self.price = 0.0
 
         # Private attributes
-        self._expiration = 0
+        self._expiration = 0.0
 
         self.crypto_symbol = crypto_symbol.upper()
         self.fiat_symbol = fiat_symbol.upper()
@@ -48,22 +60,40 @@ class CryptoFiat():
         return self._expiration - time.time() <= 0
 
 
-class CryptoToFiatConverter():
-    # Constants
-    SUPPORTED_FIAT = [
-        "AUD", "BRL", "CAD", "CHF", "CLP", "CNY", "CZK", "DKK",
-        "EUR", "GBP", "HKD", "HUF", "IDR", "ILS", "INR", "JPY",
-        "KRW", "MXN", "MYR", "NOK", "NZD", "PHP", "PKR", "PLN",
-        "RUB", "SEK", "SGD", "THB", "TRY", "TWD", "ZAR", "USD"
-    ]
+class CryptoToFiatConverter(object):
+    """
+    Main class to initiate Crypto to FIAT.
+    This object contains a list of pair Crypto, FIAT
+    This object is also a Singleton
+    """
+    __instance = None
+    _coinmarketcap: Market = None
+
+    _cryptomap: Dict = {}
+
+    def __new__(cls):
+        if CryptoToFiatConverter.__instance is None:
+            CryptoToFiatConverter.__instance = object.__new__(cls)
+            try:
+                CryptoToFiatConverter._coinmarketcap = Market()
+            except BaseException:
+                CryptoToFiatConverter._coinmarketcap = None
+        return CryptoToFiatConverter.__instance
 
     def __init__(self) -> None:
-        try:
-            self._coinmarketcap = Pymarketcap()
-        except BaseException:
-            self._coinmarketcap = None
+        self._pairs: List[CryptoFiat] = []
+        self._load_cryptomap()
 
-        self._pairs = []
+    def _load_cryptomap(self) -> None:
+        try:
+            coinlistings = self._coinmarketcap.listings()
+            self._cryptomap = dict(map(lambda coin: (coin["symbol"], str(coin["id"])),
+                                       coinlistings["data"]))
+        except (BaseException) as exception:
+            logger.error(
+                "Could not load FIAT Cryptocurrency map for the following problem: %s",
+                type(exception).__name__
+            )
 
     def convert_amount(self, crypto_amount: float, crypto_symbol: str, fiat_symbol: str) -> float:
         """
@@ -73,6 +103,8 @@ class CryptoToFiatConverter():
         :param fiat_symbol: fiat to convert to
         :return: float, value in fiat of the crypto-currency amount
         """
+        if crypto_symbol == fiat_symbol:
+            return crypto_amount
         price = self.get_price(crypto_symbol=crypto_symbol, fiat_symbol=fiat_symbol)
         return float(crypto_amount) * float(price)
 
@@ -88,7 +120,7 @@ class CryptoToFiatConverter():
 
         # Check if the fiat convertion you want is supported
         if not self._is_supported_fiat(fiat=fiat_symbol):
-            raise ValueError('The fiat {} is not supported.'.format(fiat_symbol))
+            raise ValueError(f'The fiat {fiat_symbol} is not supported.')
 
         # Get the pair that interest us and return the price in fiat
         for pair in self._pairs:
@@ -140,7 +172,7 @@ class CryptoToFiatConverter():
 
         fiat = fiat.upper()
 
-        return fiat in self.SUPPORTED_FIAT
+        return fiat in SUPPORTED_FIAT
 
     def _find_price(self, crypto_symbol: str, fiat_symbol: str) -> float:
         """
@@ -151,13 +183,24 @@ class CryptoToFiatConverter():
         """
         # Check if the fiat convertion you want is supported
         if not self._is_supported_fiat(fiat=fiat_symbol):
-            raise ValueError('The fiat {} is not supported.'.format(fiat_symbol))
+            raise ValueError(f'The fiat {fiat_symbol} is not supported.')
+
+        # No need to convert if both crypto and fiat are the same
+        if crypto_symbol == fiat_symbol:
+            return 1.0
+
+        if crypto_symbol not in self._cryptomap:
+            # return 0 for unsupported stake currencies (fiat-convert should not break the bot)
+            logger.warning("unsupported crypto-symbol %s - returning 0.0", crypto_symbol)
+            return 0.0
+
         try:
             return float(
                 self._coinmarketcap.ticker(
-                    currency=crypto_symbol,
+                    currency=self._cryptomap[crypto_symbol],
                     convert=fiat_symbol
-                )['price_' + fiat_symbol.lower()]
+                )['data']['quotes'][fiat_symbol.upper()]['price']
             )
-        except BaseException:
+        except BaseException as exception:
+            logger.error("Error in _find_price: %s", exception)
             return 0.0

freqtrade/freqtradebot.py

@@ -0,0 +1,770 @@
+"""
+Freqtrade is the main module of this bot. It contains the class Freqtrade()
+"""
+
+import copy
+import logging
+import time
+import traceback
+from datetime import datetime
+from typing import Any, Callable, Dict, List, Optional
+
+import arrow
+from requests.exceptions import RequestException
+
+from cachetools import TTLCache, cached
+
+from freqtrade import (DependencyException, OperationalException,
+                       TemporaryError, __version__, constants, persistence)
+from freqtrade.exchange import Exchange
+from freqtrade.persistence import Trade
+from freqtrade.rpc import RPCManager, RPCMessageType
+from freqtrade.state import State
+from freqtrade.strategy.interface import SellType
+from freqtrade.strategy.resolver import IStrategy, StrategyResolver
+from freqtrade.exchange.exchange_helpers import order_book_to_dataframe
+
+logger = logging.getLogger(__name__)
+
+
+class FreqtradeBot(object):
+    """
+    Freqtrade is the main class of the bot.
+    This is from here the bot start its logic.
+    """
+
+    def __init__(self, config: Dict[str, Any])-> None:
+        """
+        Init all variables and object the bot need to work
+        :param config: configuration dict, you can use the Configuration.get_config()
+        method to get the config dict.
+        """
+
+        logger.info(
+            'Starting freqtrade %s',
+            __version__,
+        )
+
+        # Init bot states
+        self.state = State.STOPPED
+
+        # Init objects
+        self.config = config
+        self.strategy: IStrategy = StrategyResolver(self.config).strategy
+        self.rpc: RPCManager = RPCManager(self)
+        self.persistence = None
+        self.exchange = Exchange(self.config)
+        self._init_modules()
+
+    def _init_modules(self) -> None:
+        """
+        Initializes all modules and updates the config
+        :return: None
+        """
+        # Initialize all modules
+
+        persistence.init(self.config)
+
+        # Set initial application state
+        initial_state = self.config.get('initial_state')
+
+        if initial_state:
+            self.state = State[initial_state.upper()]
+        else:
+            self.state = State.STOPPED
+
+    def cleanup(self) -> None:
+        """
+        Cleanup pending resources on an already stopped bot
+        :return: None
+        """
+        logger.info('Cleaning up modules ...')
+        self.rpc.cleanup()
+        persistence.cleanup()
+
+    def worker(self, old_state: State = None) -> State:
+        """
+        Trading routine that must be run at each loop
+        :param old_state: the previous service state from the previous call
+        :return: current service state
+        """
+        # Log state transition
+        state = self.state
+        if state != old_state:
+            self.rpc.send_msg({
+                'type': RPCMessageType.STATUS_NOTIFICATION,
+                'status': f'{state.name.lower()}'
+            })
+            logger.info('Changing state to: %s', state.name)
+            if state == State.RUNNING:
+                self._startup_messages()
+
+        if state == State.STOPPED:
+            time.sleep(1)
+        elif state == State.RUNNING:
+            min_secs = self.config.get('internals', {}).get(
+                'process_throttle_secs',
+                constants.PROCESS_THROTTLE_SECS
+            )
+
+            nb_assets = self.config.get('dynamic_whitelist', None)
+
+            self._throttle(func=self._process,
+                           min_secs=min_secs,
+                           nb_assets=nb_assets)
+        return state
+
+    def _startup_messages(self) -> None:
+        if self.config.get('dry_run', False):
+            self.rpc.send_msg({
+                'type': RPCMessageType.WARNING_NOTIFICATION,
+                'status': 'Dry run is enabled. All trades are simulated.'
+            })
+        stake_currency = self.config['stake_currency']
+        stake_amount = self.config['stake_amount']
+        minimal_roi = self.config['minimal_roi']
+        ticker_interval = self.config['ticker_interval']
+        exchange_name = self.config['exchange']['name']
+        strategy_name = self.config.get('strategy', '')
+        self.rpc.send_msg({
+            'type': RPCMessageType.CUSTOM_NOTIFICATION,
+            'status': f'*Exchange:* `{exchange_name}`\n'
+                      f'*Stake per trade:* `{stake_amount} {stake_currency}`\n'
+                      f'*Minimum ROI:* `{minimal_roi}`\n'
+                      f'*Ticker Interval:* `{ticker_interval}`\n'
+                      f'*Strategy:* `{strategy_name}`'
+        })
+        if self.config.get('dynamic_whitelist', False):
+            top_pairs = 'top ' + str(self.config.get('dynamic_whitelist', 20))
+            specific_pairs = ''
+        else:
+            top_pairs = 'whitelisted'
+            specific_pairs = '\n' + ', '.join(self.config['exchange'].get('pair_whitelist', ''))
+        self.rpc.send_msg({
+            'type': RPCMessageType.STATUS_NOTIFICATION,
+            'status': f'Searching for {top_pairs} {stake_currency} pairs to buy and sell...'
+                      f'{specific_pairs}'
+        })
+
+    def _throttle(self, func: Callable[..., Any], min_secs: float, *args, **kwargs) -> Any:
+        """
+        Throttles the given callable that it
+        takes at least `min_secs` to finish execution.
+        :param func: Any callable
+        :param min_secs: minimum execution time in seconds
+        :return: Any
+        """
+        start = time.time()
+        result = func(*args, **kwargs)
+        end = time.time()
+        duration = max(min_secs - (end - start), 0.0)
+        logger.debug('Throttling %s for %.2f seconds', func.__name__, duration)
+        time.sleep(duration)
+        return result
+
+    def _process(self, nb_assets: Optional[int] = 0) -> bool:
+        """
+        Queries the persistence layer for open trades and handles them,
+        otherwise a new trade is created.
+        :param: nb_assets: the maximum number of pairs to be traded at the same time
+        :return: True if one or more trades has been created or closed, False otherwise
+        """
+        state_changed = False
+        try:
+            # Refresh whitelist based on wallet maintenance
+            sanitized_list = self._refresh_whitelist(
+                self._gen_pair_whitelist(
+                    self.config['stake_currency']
+                ) if nb_assets else self.config['exchange']['pair_whitelist']
+            )
+
+            # Keep only the subsets of pairs wanted (up to nb_assets)
+            final_list = sanitized_list[:nb_assets] if nb_assets else sanitized_list
+            self.config['exchange']['pair_whitelist'] = final_list
+
+            # Refreshing candles
+            self.exchange.refresh_tickers(final_list, self.strategy.ticker_interval)
+
+            # Query trades from persistence layer
+            trades = Trade.query.filter(Trade.is_open.is_(True)).all()
+
+            # First process current opened trades
+            for trade in trades:
+                state_changed |= self.process_maybe_execute_sell(trade)
+
+            # Then looking for buy opportunities
+            if len(trades) < self.config['max_open_trades']:
+                state_changed = self.process_maybe_execute_buy()
+
+            if 'unfilledtimeout' in self.config:
+                # Check and handle any timed out open orders
+                self.check_handle_timedout()
+                Trade.session.flush()
+
+        except TemporaryError as error:
+            logger.warning('%s, retrying in 30 seconds...', error)
+            time.sleep(constants.RETRY_TIMEOUT)
+        except OperationalException:
+            tb = traceback.format_exc()
+            hint = 'Issue `/start` if you think it is safe to restart.'
+            self.rpc.send_msg({
+                'type': RPCMessageType.STATUS_NOTIFICATION,
+                'status': f'OperationalException:\n```\n{tb}```{hint}'
+            })
+            logger.exception('OperationalException. Stopping trader ...')
+            self.state = State.STOPPED
+        return state_changed
+
+    @cached(TTLCache(maxsize=1, ttl=1800))
+    def _gen_pair_whitelist(self, base_currency: str, key: str = 'quoteVolume') -> List[str]:
+        """
+        Updates the whitelist with with a dynamically generated list
+        :param base_currency: base currency as str
+        :param key: sort key (defaults to 'quoteVolume')
+        :return: List of pairs
+        """
+
+        if not self.exchange.exchange_has('fetchTickers'):
+            raise OperationalException(
+                'Exchange does not support dynamic whitelist.'
+                'Please edit your config and restart the bot'
+            )
+
+        tickers = self.exchange.get_tickers()
+        # check length so that we make sure that '/' is actually in the string
+        tickers = [v for k, v in tickers.items()
+                   if len(k.split('/')) == 2 and k.split('/')[1] == base_currency]
+
+        sorted_tickers = sorted(tickers, reverse=True, key=lambda t: t[key])
+        pairs = [s['symbol'] for s in sorted_tickers]
+        return pairs
+
+    def _refresh_whitelist(self, whitelist: List[str]) -> List[str]:
+        """
+        Check available markets and remove pair from whitelist if necessary
+        :param whitelist: the sorted list (based on BaseVolume) of pairs the user might want to
+        trade
+        :return: the list of pairs the user wants to trade without the one unavailable or
+        black_listed
+        """
+        sanitized_whitelist = whitelist
+        markets = self.exchange.get_markets()
+
+        markets = [m for m in markets if m['quote'] == self.config['stake_currency']]
+        known_pairs = set()
+        for market in markets:
+            pair = market['symbol']
+            # pair is not int the generated dynamic market, or in the blacklist ... ignore it
+            if pair not in whitelist or pair in self.config['exchange'].get('pair_blacklist', []):
+                continue
+            # else the pair is valid
+            known_pairs.add(pair)
+            # Market is not active
+            if not market['active']:
+                sanitized_whitelist.remove(pair)
+                logger.info(
+                    'Ignoring %s from whitelist. Market is not active.',
+                    pair
+                )
+
+        # We need to remove pairs that are unknown
+        final_list = [x for x in sanitized_whitelist if x in known_pairs]
+
+        return final_list
+
+    def get_target_bid(self, pair: str, ticker: Dict[str, float]) -> float:
+        """
+        Calculates bid target between current ask price and last price
+        :param ticker: Ticker to use for getting Ask and Last Price
+        :return: float: Price
+        """
+        if ticker['ask'] < ticker['last']:
+            ticker_rate = ticker['ask']
+        else:
+            balance = self.config['bid_strategy']['ask_last_balance']
+            ticker_rate = ticker['ask'] + balance * (ticker['last'] - ticker['ask'])
+
+        used_rate = ticker_rate
+        config_bid_strategy = self.config.get('bid_strategy', {})
+        if 'use_order_book' in config_bid_strategy and\
+                config_bid_strategy.get('use_order_book', False):
+            logger.info('Getting price from order book')
+            order_book_top = config_bid_strategy.get('order_book_top', 1)
+            order_book = self.exchange.get_order_book(pair, order_book_top)
+            logger.debug('order_book %s', order_book)
+            # top 1 = index 0
+            order_book_rate = order_book['bids'][order_book_top - 1][0]
+            # if ticker has lower rate, then use ticker ( usefull if down trending )
+            logger.info('...top %s order book buy rate %0.8f', order_book_top, order_book_rate)
+            if ticker_rate < order_book_rate:
+                logger.info('...using ticker rate instead %0.8f', ticker_rate)
+                used_rate = ticker_rate
+            else:
+                used_rate = order_book_rate
+        else:
+            logger.info('Using Last Ask / Last Price')
+            used_rate = ticker_rate
+
+        return used_rate
+
+    def _get_trade_stake_amount(self) -> Optional[float]:
+        """
+        Check if stake amount can be fulfilled with the available balance
+        for the stake currency
+        :return: float: Stake Amount
+        """
+        stake_amount = self.config['stake_amount']
+        avaliable_amount = self.exchange.get_balance(self.config['stake_currency'])
+
+        if stake_amount == constants.UNLIMITED_STAKE_AMOUNT:
+            open_trades = len(Trade.query.filter(Trade.is_open.is_(True)).all())
+            if open_trades >= self.config['max_open_trades']:
+                logger.warning('Can\'t open a new trade: max number of trades is reached')
+                return None
+            return avaliable_amount / (self.config['max_open_trades'] - open_trades)
+
+        # Check if stake_amount is fulfilled
+        if avaliable_amount < stake_amount:
+            raise DependencyException(
+                'Available balance(%f %s) is lower than stake amount(%f %s)' % (
+                    avaliable_amount, self.config['stake_currency'],
+                    stake_amount, self.config['stake_currency'])
+            )
+
+        return stake_amount
+
+    def _get_min_pair_stake_amount(self, pair: str, price: float) -> Optional[float]:
+        markets = self.exchange.get_markets()
+        markets = [m for m in markets if m['symbol'] == pair]
+        if not markets:
+            raise ValueError(f'Can\'t get market information for symbol {pair}')
+
+        market = markets[0]
+
+        if 'limits' not in market:
+            return None
+
+        min_stake_amounts = []
+        limits = market['limits']
+        if ('cost' in limits and 'min' in limits['cost']
+                and limits['cost']['min'] is not None):
+            min_stake_amounts.append(limits['cost']['min'])
+
+        if ('amount' in limits and 'min' in limits['amount']
+                and limits['amount']['min'] is not None):
+            min_stake_amounts.append(limits['amount']['min'] * price)
+
+        if not min_stake_amounts:
+            return None
+
+        amount_reserve_percent = 1 - 0.05  # reserve 5% + stoploss
+        if self.strategy.stoploss is not None:
+            amount_reserve_percent += self.strategy.stoploss
+        # it should not be more than 50%
+        amount_reserve_percent = max(amount_reserve_percent, 0.5)
+        return min(min_stake_amounts) / amount_reserve_percent
+
+    def create_trade(self) -> bool:
+        """
+        Checks the implemented trading indicator(s) for a randomly picked pair,
+        if one pair triggers the buy_signal a new trade record gets created
+        :return: True if a trade object has been created and persisted, False otherwise
+        """
+        interval = self.strategy.ticker_interval
+        stake_amount = self._get_trade_stake_amount()
+
+        if not stake_amount:
+            return False
+
+        logger.info(
+            'Checking buy signals to create a new trade with stake_amount: %f ...',
+            stake_amount
+        )
+        whitelist = copy.deepcopy(self.config['exchange']['pair_whitelist'])
+
+        # Remove currently opened and latest pairs from whitelist
+        for trade in Trade.query.filter(Trade.is_open.is_(True)).all():
+            if trade.pair in whitelist:
+                whitelist.remove(trade.pair)
+                logger.debug('Ignoring %s in pair whitelist', trade.pair)
+
+        if not whitelist:
+            raise DependencyException('No currency pairs in whitelist')
+
+        # running get_signal on historical data fetched
+        # to find buy signals
+        for _pair in whitelist:
+            (buy, sell) = self.strategy.get_signal(_pair, interval, self.exchange.klines.get(_pair))
+            if buy and not sell:
+                bidstrat_check_depth_of_market = self.config.get('bid_strategy', {}).\
+                    get('check_depth_of_market', {})
+                if (bidstrat_check_depth_of_market.get('enabled', False)) and\
+                        (bidstrat_check_depth_of_market.get('bids_to_ask_delta', 0) > 0):
+                    if self._check_depth_of_market_buy(_pair, bidstrat_check_depth_of_market):
+                        return self.execute_buy(_pair, stake_amount)
+                    else:
+                        return False
+                return self.execute_buy(_pair, stake_amount)
+
+        return False
+
+    def _check_depth_of_market_buy(self, pair: str, conf: Dict) -> bool:
+        """
+        Checks depth of market before executing a buy
+        """
+        conf_bids_to_ask_delta = conf.get('bids_to_ask_delta', 0)
+        logger.info('checking depth of market for %s', pair)
+        order_book = self.exchange.get_order_book(pair, 1000)
+        order_book_data_frame = order_book_to_dataframe(order_book['bids'], order_book['asks'])
+        order_book_bids = order_book_data_frame['b_size'].sum()
+        order_book_asks = order_book_data_frame['a_size'].sum()
+        bids_ask_delta = order_book_bids / order_book_asks
+        logger.info('bids: %s, asks: %s, delta: %s', order_book_bids,
+                    order_book_asks, bids_ask_delta)
+        if bids_ask_delta >= conf_bids_to_ask_delta:
+            return True
+        return False
+
+    def execute_buy(self, pair: str, stake_amount: float) -> bool:
+        """
+        Executes a limit buy for the given pair
+        :param pair: pair for which we want to create a LIMIT_BUY
+        :return: None
+        """
+        pair_s = pair.replace('_', '/')
+        pair_url = self.exchange.get_pair_detail_url(pair)
+        stake_currency = self.config['stake_currency']
+        fiat_currency = self.config.get('fiat_display_currency', None)
+
+        # Calculate amount
+        buy_limit = self.get_target_bid(pair, self.exchange.get_ticker(pair))
+
+        min_stake_amount = self._get_min_pair_stake_amount(pair_s, buy_limit)
+        if min_stake_amount is not None and min_stake_amount > stake_amount:
+            logger.warning(
+                f'Can\'t open a new trade for {pair_s}: stake amount'
+                f' is too small ({stake_amount} < {min_stake_amount})'
+            )
+            return False
+
+        amount = stake_amount / buy_limit
+
+        order_id = self.exchange.buy(pair, buy_limit, amount)['id']
+
+        self.rpc.send_msg({
+            'type': RPCMessageType.BUY_NOTIFICATION,
+            'exchange': self.exchange.name.capitalize(),
+            'pair': pair_s,
+            'market_url': pair_url,
+            'limit': buy_limit,
+            'stake_amount': stake_amount,
+            'stake_currency': stake_currency,
+            'fiat_currency': fiat_currency
+        })
+        # Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
+        fee = self.exchange.get_fee(symbol=pair, taker_or_maker='maker')
+        trade = Trade(
+            pair=pair,
+            stake_amount=stake_amount,
+            amount=amount,
+            fee_open=fee,
+            fee_close=fee,
+            open_rate=buy_limit,
+            open_rate_requested=buy_limit,
+            open_date=datetime.utcnow(),
+            exchange=self.exchange.id,
+            open_order_id=order_id,
+            strategy=self.strategy.get_strategy_name(),
+            ticker_interval=constants.TICKER_INTERVAL_MINUTES[self.config['ticker_interval']]
+        )
+        Trade.session.add(trade)
+        Trade.session.flush()
+        return True
+
+    def process_maybe_execute_buy(self) -> bool:
+        """
+        Tries to execute a buy trade in a safe way
+        :return: True if executed
+        """
+        try:
+            # Create entity and execute trade
+            if self.create_trade():
+                return True
+
+            logger.info('Found no buy signals for whitelisted currencies. Trying again..')
+            return False
+        except DependencyException as exception:
+            logger.warning('Unable to create trade: %s', exception)
+            return False
+
+    def process_maybe_execute_sell(self, trade: Trade) -> bool:
+        """
+        Tries to execute a sell trade
+        :return: True if executed
+        """
+        try:
+            # Get order details for actual price per unit
+            if trade.open_order_id:
+                # Update trade with order values
+                logger.info('Found open order for %s', trade)
+                order = self.exchange.get_order(trade.open_order_id, trade.pair)
+                # Try update amount (binance-fix)
+                try:
+                    new_amount = self.get_real_amount(trade, order)
+                    if order['amount'] != new_amount:
+                        order['amount'] = new_amount
+                        # Fee was applied, so set to 0
+                        trade.fee_open = 0
+
+                except OperationalException as exception:
+                    logger.warning("could not update trade amount: %s", exception)
+
+                trade.update(order)
+
+            if trade.is_open and trade.open_order_id is None:
+                # Check if we can sell our current pair
+                return self.handle_trade(trade)
+        except DependencyException as exception:
+            logger.warning('Unable to sell trade: %s', exception)
+        return False
+
+    def get_real_amount(self, trade: Trade, order: Dict) -> float:
+        """
+        Get real amount for the trade
+        Necessary for self.exchanges which charge fees in base currency (e.g. binance)
+        """
+        order_amount = order['amount']
+        # Only run for closed orders
+        if trade.fee_open == 0 or order['status'] == 'open':
+            return order_amount
+
+        # use fee from order-dict if possible
+        if 'fee' in order and order['fee'] and (order['fee'].keys() >= {'currency', 'cost'}):
+            if trade.pair.startswith(order['fee']['currency']):
+                new_amount = order_amount - order['fee']['cost']
+                logger.info("Applying fee on amount for %s (from %s to %s) from Order",
+                            trade, order['amount'], new_amount)
+                return new_amount
+
+        # Fallback to Trades
+        trades = self.exchange.get_trades_for_order(trade.open_order_id, trade.pair,
+                                                    trade.open_date)
+
+        if len(trades) == 0:
+            logger.info("Applying fee on amount for %s failed: myTrade-Dict empty found", trade)
+            return order_amount
+        amount = 0
+        fee_abs = 0
+        for exectrade in trades:
+            amount += exectrade['amount']
+            if "fee" in exectrade and (exectrade['fee'].keys() >= {'currency', 'cost'}):
+                # only applies if fee is in quote currency!
+                if trade.pair.startswith(exectrade['fee']['currency']):
+                    fee_abs += exectrade['fee']['cost']
+
+        if amount != order_amount:
+            logger.warning(f"amount {amount} does not match amount {trade.amount}")
+            raise OperationalException("Half bought? Amounts don't match")
+        real_amount = amount - fee_abs
+        if fee_abs != 0:
+            logger.info(f"""Applying fee on amount for {trade} \
+(from {order_amount} to {real_amount}) from Trades""")
+        return real_amount
+
+    def handle_trade(self, trade: Trade) -> bool:
+        """
+        Sells the current pair if the threshold is reached and updates the trade record.
+        :return: True if trade has been sold, False otherwise
+        """
+        if not trade.is_open:
+            raise ValueError(f'attempt to handle closed trade: {trade}')
+
+        logger.debug('Handling %s ...', trade)
+        sell_rate = self.exchange.get_ticker(trade.pair)['bid']
+
+        (buy, sell) = (False, False)
+        experimental = self.config.get('experimental', {})
+        if experimental.get('use_sell_signal') or experimental.get('ignore_roi_if_buy_signal'):
+            ticker = self.exchange.klines.get(trade.pair)
+            (buy, sell) = self.strategy.get_signal(trade.pair, self.strategy.ticker_interval,
+                                                   ticker)
+
+        config_ask_strategy = self.config.get('ask_strategy', {})
+        if config_ask_strategy.get('use_order_book', False):
+            logger.info('Using order book for selling...')
+            # logger.debug('Order book %s',orderBook)
+            order_book_min = config_ask_strategy.get('order_book_min', 1)
+            order_book_max = config_ask_strategy.get('order_book_max', 1)
+
+            order_book = self.exchange.get_order_book(trade.pair, order_book_max)
+
+            for i in range(order_book_min, order_book_max + 1):
+                order_book_rate = order_book['asks'][i - 1][0]
+
+                # if orderbook has higher rate (high profit),
+                # use orderbook, otherwise just use bids rate
+                logger.info('  order book asks top %s: %0.8f', i, order_book_rate)
+                if sell_rate < order_book_rate:
+                    sell_rate = order_book_rate
+
+                if self.check_sell(trade, sell_rate, buy, sell):
+                    return True
+                    break
+        else:
+            logger.info('checking sell')
+            if self.check_sell(trade, sell_rate, buy, sell):
+                return True
+
+        logger.info('Found no sell signals for whitelisted currencies. Trying again..')
+        return False
+
+    def check_sell(self, trade: Trade, sell_rate: float, buy: bool, sell: bool) -> bool:
+        should_sell = self.strategy.should_sell(trade, sell_rate, datetime.utcnow(), buy, sell)
+        if should_sell.sell_flag:
+            self.execute_sell(trade, sell_rate, should_sell.sell_type)
+            logger.info('excuted sell')
+            return True
+        return False
+
+    def check_handle_timedout(self) -> None:
+        """
+        Check if any orders are timed out and cancel if neccessary
+        :param timeoutvalue: Number of minutes until order is considered timed out
+        :return: None
+        """
+        buy_timeout = self.config['unfilledtimeout']['buy']
+        sell_timeout = self.config['unfilledtimeout']['sell']
+        buy_timeoutthreashold = arrow.utcnow().shift(minutes=-buy_timeout).datetime
+        sell_timeoutthreashold = arrow.utcnow().shift(minutes=-sell_timeout).datetime
+
+        for trade in Trade.query.filter(Trade.open_order_id.isnot(None)).all():
+            try:
+                # FIXME: Somehow the query above returns results
+                # where the open_order_id is in fact None.
+                # This is probably because the record got
+                # updated via /forcesell in a different thread.
+                if not trade.open_order_id:
+                    continue
+                order = self.exchange.get_order(trade.open_order_id, trade.pair)
+            except (RequestException, DependencyException):
+                logger.info(
+                    'Cannot query order for %s due to %s',
+                    trade,
+                    traceback.format_exc())
+                continue
+            ordertime = arrow.get(order['datetime']).datetime
+
+            # Check if trade is still actually open
+            if int(order['remaining']) == 0:
+                continue
+
+            # Check if trade is still actually open
+            if order['status'] == 'open':
+                if order['side'] == 'buy' and ordertime < buy_timeoutthreashold:
+                    self.handle_timedout_limit_buy(trade, order)
+                elif order['side'] == 'sell' and ordertime < sell_timeoutthreashold:
+                    self.handle_timedout_limit_sell(trade, order)
+
+    # FIX: 20180110, why is cancel.order unconditionally here, whereas
+    #                it is conditionally called in the
+    #                handle_timedout_limit_sell()?
+    def handle_timedout_limit_buy(self, trade: Trade, order: Dict) -> bool:
+        """Buy timeout - cancel order
+        :return: True if order was fully cancelled
+        """
+        pair_s = trade.pair.replace('_', '/')
+        self.exchange.cancel_order(trade.open_order_id, trade.pair)
+        if order['remaining'] == order['amount']:
+            # if trade is not partially completed, just delete the trade
+            Trade.session.delete(trade)
+            Trade.session.flush()
+            logger.info('Buy order timeout for %s.', trade)
+            self.rpc.send_msg({
+                'type': RPCMessageType.STATUS_NOTIFICATION,
+                'status': f'Unfilled buy order for {pair_s} cancelled due to timeout'
+            })
+            return True
+
+        # if trade is partially complete, edit the stake details for the trade
+        # and close the order
+        trade.amount = order['amount'] - order['remaining']
+        trade.stake_amount = trade.amount * trade.open_rate
+        trade.open_order_id = None
+        logger.info('Partial buy order timeout for %s.', trade)
+        self.rpc.send_msg({
+            'type': RPCMessageType.STATUS_NOTIFICATION,
+            'status': f'Remaining buy order for {pair_s} cancelled due to timeout'
+        })
+        return False
+
+    # FIX: 20180110, should cancel_order() be cond. or unconditionally called?
+    def handle_timedout_limit_sell(self, trade: Trade, order: Dict) -> bool:
+        """
+        Sell timeout - cancel order and update trade
+        :return: True if order was fully cancelled
+        """
+        pair_s = trade.pair.replace('_', '/')
+        if order['remaining'] == order['amount']:
+            # if trade is not partially completed, just cancel the trade
+            self.exchange.cancel_order(trade.open_order_id, trade.pair)
+            trade.close_rate = None
+            trade.close_profit = None
+            trade.close_date = None
+            trade.is_open = True
+            trade.open_order_id = None
+            self.rpc.send_msg({
+                'type': RPCMessageType.STATUS_NOTIFICATION,
+                'status': f'Unfilled sell order for {pair_s} cancelled due to timeout'
+            })
+            logger.info('Sell order timeout for %s.', trade)
+            return True
+
+        # TODO: figure out how to handle partially complete sell orders
+        return False
+
+    def execute_sell(self, trade: Trade, limit: float, sell_reason: SellType) -> None:
+        """
+        Executes a limit sell for the given trade and limit
+        :param trade: Trade instance
+        :param limit: limit rate for the sell order
+        :param sellreason: Reason the sell was triggered
+        :return: None
+        """
+        # Execute sell and update trade record
+        order_id = self.exchange.sell(str(trade.pair), limit, trade.amount)['id']
+        trade.open_order_id = order_id
+        trade.close_rate_requested = limit
+        trade.sell_reason = sell_reason.value
+
+        profit_trade = trade.calc_profit(rate=limit)
+        current_rate = self.exchange.get_ticker(trade.pair)['bid']
+        profit_percent = trade.calc_profit_percent(limit)
+        pair_url = self.exchange.get_pair_detail_url(trade.pair)
+        gain = "profit" if profit_percent > 0 else "loss"
+
+        msg = {
+            'type': RPCMessageType.SELL_NOTIFICATION,
+            'exchange': trade.exchange.capitalize(),
+            'pair': trade.pair,
+            'gain': gain,
+            'market_url': pair_url,
+            'limit': limit,
+            'amount': trade.amount,
+            'open_rate': trade.open_rate,
+            'current_rate': current_rate,
+            'profit_amount': profit_trade,
+            'profit_percent': profit_percent,
+        }
+
+        # For regular case, when the configuration exists
+        if 'stake_currency' in self.config and 'fiat_display_currency' in self.config:
+            stake_currency = self.config['stake_currency']
+            fiat_currency = self.config['fiat_display_currency']
+            msg.update({
+                'stake_currency': stake_currency,
+                'fiat_currency': fiat_currency,
+            })
+
+        # Send the message
+        self.rpc.send_msg(msg)
+        Trade.session.flush()

freqtrade/indicator_helpers.py

@@ -0,0 +1,40 @@
+from math import cos, exp, pi, sqrt
+
+import numpy as np
+import talib as ta
+from pandas import Series
+
+
+def went_up(series: Series) -> bool:
+    return series > series.shift(1)
+
+
+def went_down(series: Series) -> bool:
+    return series < series.shift(1)
+
+
+def ehlers_super_smoother(series: Series, smoothing: float = 6) -> Series:
+    magic = pi * sqrt(2) / smoothing
+    a1 = exp(-magic)
+    coeff2 = 2 * a1 * cos(magic)
+    coeff3 = -a1 * a1
+    coeff1 = (1 - coeff2 - coeff3) / 2
+
+    filtered = series.copy()
+
+    for i in range(2, len(series)):
+        filtered.iloc[i] = coeff1 * (series.iloc[i] + series.iloc[i-1]) + \
+            coeff2 * filtered.iloc[i-1] + coeff3 * filtered.iloc[i-2]
+
+    return filtered
+
+
+def fishers_inverse(series: Series, smoothing: float = 0) -> np.ndarray:
+    """ Does a smoothed fishers inverse transformation.
+        Can be used with any oscillator that goes from 0 to 100 like RSI or MFI """
+    v1 = 0.1 * (series - 50)
+    if smoothing > 0:
+        v2 = ta.WMA(v1.values, timeperiod=smoothing)
+    else:
+        v2 = v1
+    return (np.exp(2 * v2)-1) / (np.exp(2 * v2) + 1)

freqtrade/main.py

@@ -1,472 +1,89 @@
 #!/usr/bin/env python3
-import copy
-import json
+"""
+Main Freqtrade bot script.
+Read the documentation to know what cli arguments you need.
+"""
 import logging
 import sys
-import time
-import traceback
-from datetime import datetime
-from typing import Dict, List, Optional
+from argparse import Namespace
+from typing import List
 
-import arrow
-import requests
-from cachetools import cached, TTLCache
-
-from freqtrade import (DependencyException, OperationalException, __version__,
-                       exchange, persistence, rpc)
-from freqtrade.analyze import SignalType, get_signal
-from freqtrade.fiat_convert import CryptoToFiatConverter
-from freqtrade.misc import (State, get_state, load_config, parse_args,
-                            throttle, update_state)
-from freqtrade.persistence import Trade
+from freqtrade import OperationalException
+from freqtrade.arguments import Arguments
+from freqtrade.configuration import Configuration, set_loggers
+from freqtrade.freqtradebot import FreqtradeBot
+from freqtrade.state import State
+from freqtrade.rpc import RPCMessageType
 
 logger = logging.getLogger('freqtrade')
 
-_CONF = {}
 
-
-def refresh_whitelist(whitelist: List[str]) -> List[str]:
+def main(sysargv: List[str]) -> None:
     """
-    Check wallet health and remove pair from whitelist if necessary
-    :param whitelist: the sorted list (based on BaseVolume) of pairs the user might want to trade
-    :return: the list of pairs the user wants to trade without the one unavailable or black_listed
-    """
-    sanitized_whitelist = whitelist
-    health = exchange.get_wallet_health()
-    known_pairs = set()
-    for status in health:
-        pair = '{}_{}'.format(_CONF['stake_currency'], status['Currency'])
-        # pair is not int the generated dynamic market, or in the blacklist ... ignore it
-        if pair not in whitelist or pair in _CONF['exchange'].get('pair_blacklist', []):
-            continue
-        # else the pair is valid
-        known_pairs.add(pair)
-        # Market is not active
-        if not status['IsActive']:
-            sanitized_whitelist.remove(pair)
-            logger.info(
-                'Ignoring %s from whitelist (reason: %s).',
-                pair, status.get('Notice') or 'wallet is not active'
-            )
-
-    # We need to remove pairs that are unknown
-    final_list = [x for x in sanitized_whitelist if x in known_pairs]
-    return final_list
-
-
-def _process(nb_assets: Optional[int] = 0) -> bool:
-    """
-    Queries the persistence layer for open trades and handles them,
-    otherwise a new trade is created.
-    :param: nb_assets: the maximum number of pairs to be traded at the same time
-    :return: True if a trade has been created or closed, False otherwise
-    """
-    state_changed = False
-    try:
-        # Refresh whitelist based on wallet maintenance
-        sanitized_list = refresh_whitelist(
-            gen_pair_whitelist(
-                _CONF['stake_currency']
-            ) if nb_assets else _CONF['exchange']['pair_whitelist']
-        )
-
-        # Keep only the subsets of pairs wanted (up to nb_assets)
-        final_list = sanitized_list[:nb_assets] if nb_assets else sanitized_list
-        _CONF['exchange']['pair_whitelist'] = final_list
-
-        # Query trades from persistence layer
-        trades = Trade.query.filter(Trade.is_open.is_(True)).all()
-        if len(trades) < _CONF['max_open_trades']:
-            try:
-                # Create entity and execute trade
-                state_changed = create_trade(float(_CONF['stake_amount']))
-                if not state_changed:
-                    logger.info(
-                        'Checked all whitelisted currencies. '
-                        'Found no suitable entry positions for buying. Will keep looking ...'
-                    )
-            except DependencyException as exception:
-                logger.warning('Unable to create trade: %s', exception)
-
-        for trade in trades:
-            # Get order details for actual price per unit
-            if trade.open_order_id:
-                # Update trade with order values
-                logger.info('Got open order for %s', trade)
-                trade.update(exchange.get_order(trade.open_order_id))
-
-            if trade.is_open and trade.open_order_id is None:
-                # Check if we can sell our current pair
-                state_changed = handle_trade(trade) or state_changed
-
-        if 'unfilledtimeout' in _CONF:
-            # Check and handle any timed out open orders
-            check_handle_timedout(_CONF['unfilledtimeout'])
-
-            Trade.session.flush()
-    except (requests.exceptions.RequestException, json.JSONDecodeError) as error:
-        logger.warning(
-            'Got %s in _process(), retrying in 30 seconds...',
-            error
-        )
-        time.sleep(30)
-    except OperationalException:
-        rpc.send_msg('*Status:* Got OperationalException:\n```\n{traceback}```{hint}'.format(
-            traceback=traceback.format_exc(),
-            hint='Issue `/start` if you think it is safe to restart.'
-        ))
-        logger.exception('Got OperationalException. Stopping trader ...')
-        update_state(State.STOPPED)
-    return state_changed
-
-
-def check_handle_timedout(timeoutvalue: int) -> None:
-    """
-    Check if any orders are timed out and cancel if neccessary
-    :param timeoutvalue: Number of minutes until order is considered timed out
+    This function will initiate the bot and start the trading loop.
     :return: None
     """
-    timeoutthreashold = arrow.utcnow().shift(minutes=-timeoutvalue).datetime
-
-    for trade in Trade.query.filter(Trade.open_order_id.isnot(None)).all():
-        order = exchange.get_order(trade.open_order_id)
-        ordertime = arrow.get(order['opened'])
-
-        if order['type'] == "LIMIT_BUY" and ordertime < timeoutthreashold:
-            # Buy timeout - cancel order
-            exchange.cancel_order(trade.open_order_id)
-            if order['remaining'] == order['amount']:
-                # if trade is not partially completed, just delete the trade
-                Trade.session.delete(trade)
-                Trade.session.flush()
-                logger.info('Buy order timeout for %s.', trade)
-            else:
-                # if trade is partially complete, edit the stake details for the trade
-                # and close the order
-                trade.amount = order['amount'] - order['remaining']
-                trade.stake_amount = trade.amount * trade.open_rate
-                trade.open_order_id = None
-                logger.info('Partial buy order timeout for %s.', trade)
-        elif order['type'] == "LIMIT_SELL" and ordertime < timeoutthreashold:
-            # Sell timeout - cancel order and update trade
-            if order['remaining'] == order['amount']:
-                # if trade is not partially completed, just cancel the trade
-                exchange.cancel_order(trade.open_order_id)
-                trade.close_rate = None
-                trade.close_profit = None
-                trade.close_date = None
-                trade.is_open = True
-                trade.open_order_id = None
-                logger.info('Sell order timeout for %s.', trade)
-                return True
-            else:
-                # TODO: figure out how to handle partially complete sell orders
-                pass
-
-
-def execute_sell(trade: Trade, limit: float) -> None:
-    """
-    Executes a limit sell for the given trade and limit
-    :param trade: Trade instance
-    :param limit: limit rate for the sell order
-    :return: None
-    """
-    # Execute sell and update trade record
-    order_id = exchange.sell(str(trade.pair), limit, trade.amount)
-    trade.open_order_id = order_id
-
-    fmt_exp_profit = round(trade.calc_profit_percent(rate=limit) * 100, 2)
-    profit_trade = trade.calc_profit(rate=limit)
-
-    message = '*{exchange}:* Selling [{pair}]({pair_url}) with limit `{limit:.8f}`'.format(
-        exchange=trade.exchange,
-        pair=trade.pair.replace('_', '/'),
-        pair_url=exchange.get_pair_detail_url(trade.pair),
-        limit=limit
+    arguments = Arguments(
+        sysargv,
+        'Simple High Frequency Trading Bot for crypto currencies'
     )
+    args = arguments.get_parsed_arg()
 
-    # For regular case, when the configuration exists
-    if 'stake_currency' in _CONF and 'fiat_display_currency' in _CONF:
-        fiat_converter = CryptoToFiatConverter()
-        profit_fiat = fiat_converter.convert_amount(
-            profit_trade,
-            _CONF['stake_currency'],
-            _CONF['fiat_display_currency']
-        )
-        message += '` ({gain}: {profit_percent:.2f}%, {profit_coin:.8f} {coin}`' \
-                   '` / {profit_fiat:.3f} {fiat})`'.format(
-                       gain="profit" if fmt_exp_profit > 0 else "loss",
-                       profit_percent=fmt_exp_profit,
-                       profit_coin=profit_trade,
-                       coin=_CONF['stake_currency'],
-                       profit_fiat=profit_fiat,
-                       fiat=_CONF['fiat_display_currency'],
-                   )
-    # Because telegram._forcesell does not have the configuration
-    # Ignore the FIAT value and does not show the stake_currency as well
-    else:
-        message += '` ({gain}: {profit_percent:.2f}%, {profit_coin:.8f})`'.format(
-            gain="profit" if fmt_exp_profit > 0 else "loss",
-            profit_percent=fmt_exp_profit,
-            profit_coin=profit_trade
-        )
-
-    # Send the message
-    rpc.send_msg(message)
-    Trade.session.flush()
-
-
-def min_roi_reached(trade: Trade, current_rate: float, current_time: datetime) -> bool:
-    """
-    Based an earlier trade and current price and ROI configuration, decides whether bot should sell
-    :return True if bot should sell at current rate
-    """
-    current_profit = trade.calc_profit_percent(current_rate)
-    if 'stoploss' in _CONF and current_profit < float(_CONF['stoploss']):
-        logger.debug('Stop loss hit.')
-        return True
-
-    # Check if time matches and current rate is above threshold
-    time_diff = (current_time - trade.open_date).total_seconds() / 60
-    for duration, threshold in sorted(_CONF['minimal_roi'].items()):
-        if time_diff > float(duration) and current_profit > threshold:
-            return True
-
-    logger.debug('Threshold not reached. (cur_profit: %1.2f%%)', float(current_profit) * 100.0)
-    return False
-
-
-def handle_trade(trade: Trade) -> bool:
-    """
-    Sells the current pair if the threshold is reached and updates the trade record.
-    :return: True if trade has been sold, False otherwise
-    """
-    if not trade.is_open:
-        raise ValueError('attempt to handle closed trade: {}'.format(trade))
-
-    logger.debug('Handling %s ...', trade)
-    current_rate = exchange.get_ticker(trade.pair)['bid']
-
-    # Check if minimal roi has been reached
-    if min_roi_reached(trade, current_rate, datetime.utcnow()):
-        logger.debug('Executing sell due to ROI ...')
-        execute_sell(trade, current_rate)
-        return True
-
-    # Experimental: Check if sell signal has been enabled and triggered
-    if _CONF.get('experimental', {}).get('use_sell_signal'):
-        # Experimental: Check if the trade is profitable before selling it (avoid selling at loss)
-        if _CONF.get('experimental', {}).get('sell_profit_only'):
-            logger.debug('Checking if trade is profitable ...')
-            if trade.calc_profit(rate=current_rate) <= 0:
-                return False
-        logger.debug('Checking sell_signal ...')
-        if get_signal(trade.pair, SignalType.SELL):
-            logger.debug('Executing sell due to sell signal ...')
-            execute_sell(trade, current_rate)
-            return True
-
-    return False
-
-
-def get_target_bid(ticker: Dict[str, float]) -> float:
-    """ Calculates bid target between current ask price and last price """
-    if ticker['ask'] < ticker['last']:
-        return ticker['ask']
-    balance = _CONF['bid_strategy']['ask_last_balance']
-    return ticker['ask'] + balance * (ticker['last'] - ticker['ask'])
-
-
-def create_trade(stake_amount: float) -> bool:
-    """
-    Checks the implemented trading indicator(s) for a randomly picked pair,
-    if one pair triggers the buy_signal a new trade record gets created
-    :param stake_amount: amount of btc to spend
-    :return: True if a trade object has been created and persisted, False otherwise
-    """
-    logger.info(
-        'Checking buy signals to create a new trade with stake_amount: %f ...',
-        stake_amount
-    )
-    whitelist = copy.deepcopy(_CONF['exchange']['pair_whitelist'])
-    # Check if stake_amount is fulfilled
-    if exchange.get_balance(_CONF['stake_currency']) < stake_amount:
-        raise DependencyException(
-            'stake amount is not fulfilled (currency={})'.format(_CONF['stake_currency'])
-        )
-
-    # Remove currently opened and latest pairs from whitelist
-    for trade in Trade.query.filter(Trade.is_open.is_(True)).all():
-        if trade.pair in whitelist:
-            whitelist.remove(trade.pair)
-            logger.debug('Ignoring %s in pair whitelist', trade.pair)
-    if not whitelist:
-        raise DependencyException('No pair in whitelist')
-
-    # Pick pair based on StochRSI buy signals
-    for _pair in whitelist:
-        if get_signal(_pair, SignalType.BUY):
-            pair = _pair
-            break
-    else:
-        return False
-
-    # Calculate amount
-    buy_limit = get_target_bid(exchange.get_ticker(pair))
-    amount = stake_amount / buy_limit
-
-    order_id = exchange.buy(pair, buy_limit, amount)
-
-    fiat_converter = CryptoToFiatConverter()
-    stake_amount_fiat = fiat_converter.convert_amount(
-        stake_amount,
-        _CONF['stake_currency'],
-        _CONF['fiat_display_currency']
-    )
-
-    # Create trade entity and return
-    rpc.send_msg('*{}:* Buying [{}]({}) with limit `{:.8f} ({:.6f} {}, {:.3f} {})` '.format(
-        exchange.get_name().upper(),
-        pair.replace('_', '/'),
-        exchange.get_pair_detail_url(pair),
-        buy_limit, stake_amount, _CONF['stake_currency'],
-        stake_amount_fiat, _CONF['fiat_display_currency']
-    ))
-    # Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
-    trade = Trade(
-        pair=pair,
-        stake_amount=stake_amount,
-        amount=amount,
-        fee=exchange.get_fee(),
-        open_rate=buy_limit,
-        open_date=datetime.utcnow(),
-        exchange=exchange.get_name().upper(),
-        open_order_id=order_id
-    )
-    Trade.session.add(trade)
-    Trade.session.flush()
-    return True
-
-
-def init(config: dict, db_url: Optional[str] = None) -> None:
-    """
-    Initializes all modules and updates the config
-    :param config: config as dict
-    :param db_url: database connector string for sqlalchemy (Optional)
-    :return: None
-    """
-    # Initialize all modules
-    rpc.init(config)
-    persistence.init(config, db_url)
-    exchange.init(config)
-
-    # Set initial application state
-    initial_state = config.get('initial_state')
-    if initial_state:
-        update_state(State[initial_state.upper()])
-    else:
-        update_state(State.STOPPED)
-
-
-@cached(TTLCache(maxsize=1, ttl=1800))
-def gen_pair_whitelist(base_currency: str, key: str = 'BaseVolume') -> List[str]:
-    """
-    Updates the whitelist with with a dynamically generated list
-    :param base_currency: base currency as str
-    :param key: sort key (defaults to 'BaseVolume')
-    :return: List of pairs
-    """
-    summaries = sorted(
-        (s for s in exchange.get_market_summaries() if s['MarketName'].startswith(base_currency)),
-        key=lambda s: s.get(key) or 0.0,
-        reverse=True
-    )
-
-    return [s['MarketName'].replace('-', '_') for s in summaries]
-
-
-def cleanup() -> None:
-    """
-    Cleanup the application state und finish all pending tasks
-    :return: None
-    """
-    rpc.send_msg('*Status:* `Stopping trader...`')
-    logger.info('Stopping trader and cleaning up modules...')
-    update_state(State.STOPPED)
-    persistence.cleanup()
-    rpc.cleanup()
-    exit(0)
-
-
-def main(sysargv=sys.argv[1:]) -> None:
-    """
-    Loads and validates the config and handles the main loop
-    :return: None
-    """
-    global _CONF
-    args = parse_args(sysargv,
-                      'Simple High Frequency Trading Bot for crypto currencies')
-
-    # A subcommand has been issued
+    # A subcommand has been issued.
+    # Means if Backtesting or Hyperopt have been called we exit the bot
     if hasattr(args, 'func'):
         args.func(args)
-        exit(0)
-
-    # Initialize logger
-    logging.basicConfig(
-        level=args.loglevel,
-        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
-    )
-
-    logger.info(
-        'Starting freqtrade %s (loglevel=%s)',
-        __version__,
-        logging.getLevelName(args.loglevel)
-    )
-
-    # Load and validate configuration
-    _CONF = load_config(args.config)
-
-    # Initialize all modules and start main loop
-    if args.dynamic_whitelist:
-        logger.info('Using dynamically generated whitelist. (--dynamic-whitelist detected)')
-
-    # If the user ask for Dry run with a local DB instead of memory
-    if args.dry_run_db:
-        if _CONF.get('dry_run', False):
-            _CONF.update({'dry_run_db': True})
-            logger.info(
-                'Dry_run will use the DB file: "tradesv3.dry_run.sqlite". (--dry_run_db detected)'
-            )
-        else:
-            logger.info('Dry run is disabled. (--dry_run_db ignored)')
+        return
 
+    freqtrade = None
+    return_code = 1
     try:
-        init(_CONF)
-        old_state = None
-        while True:
-            new_state = get_state()
-            # Log state transition
-            if new_state != old_state:
-                rpc.send_msg('*Status:* `{}`'.format(new_state.name.lower()))
-                logger.info('Changing state to: %s', new_state.name)
+        # Load and validate configuration
+        config = Configuration(args).get_config()
+
+        # Init the bot
+        freqtrade = FreqtradeBot(config)
+
+        state = None
+        while 1:
+            state = freqtrade.worker(old_state=state)
+            if state == State.RELOAD_CONF:
+                freqtrade = reconfigure(freqtrade, args)
 
-            if new_state == State.STOPPED:
-                time.sleep(1)
-            elif new_state == State.RUNNING:
-                throttle(
-                    _process,
-                    min_secs=_CONF['internals'].get('process_throttle_secs', 10),
-                    nb_assets=args.dynamic_whitelist,
-                )
-            old_state = new_state
     except KeyboardInterrupt:
-        logger.info('Got SIGINT, aborting ...')
+        logger.info('SIGINT received, aborting ...')
+        return_code = 0
+    except OperationalException as e:
+        logger.error(str(e))
+        return_code = 2
     except BaseException:
-        logger.exception('Got fatal exception!')
+        logger.exception('Fatal exception!')
     finally:
-        cleanup()
+        if freqtrade:
+            freqtrade.rpc.send_msg({
+                'type': RPCMessageType.STATUS_NOTIFICATION,
+                'status': 'process died'
+            })
+            freqtrade.cleanup()
+        sys.exit(return_code)
+
+
+def reconfigure(freqtrade: FreqtradeBot, args: Namespace) -> FreqtradeBot:
+    """
+    Cleans up current instance, reloads the configuration and returns the new instance
+    """
+    # Clean up current modules
+    freqtrade.cleanup()
+
+    # Create new instance
+    freqtrade = FreqtradeBot(Configuration(args).get_config())
+    freqtrade.rpc.send_msg({
+        'type': RPCMessageType.STATUS_NOTIFICATION,
+        'status': 'config reloaded'
+    })
+    return freqtrade
 
 
 if __name__ == '__main__':
-    main()
+    set_loggers()
+    main(sys.argv[1:])

freqtrade/misc.py

@@ -1,318 +1,91 @@
-import argparse
-import enum
+"""
+Various tool function for Freqtrade and scripts
+"""
+
+import gzip
 import json
 import logging
-import time
-import os
-from typing import Any, Callable, Dict, List
+import re
+from datetime import datetime
+from typing import Dict
 
-from jsonschema import Draft4Validator, validate
-from jsonschema.exceptions import ValidationError, best_match
-from wrapt import synchronized
-
-from freqtrade import __version__
+import numpy as np
+from pandas import DataFrame
 
 logger = logging.getLogger(__name__)
 
 
-class State(enum.Enum):
-    RUNNING = 0
-    STOPPED = 1
-
-
-# Current application state
-_STATE = State.STOPPED
-
-
-@synchronized
-def update_state(state: State) -> None:
+def shorten_date(_date: str) -> str:
     """
-    Updates the application state
-    :param state: new state
-    :return: None
+    Trim the date so it fits on small screens
     """
-    global _STATE
-    _STATE = state
+    new_date = re.sub('seconds?', 'sec', _date)
+    new_date = re.sub('minutes?', 'min', new_date)
+    new_date = re.sub('hours?', 'h', new_date)
+    new_date = re.sub('days?', 'd', new_date)
+    new_date = re.sub('^an?', '1', new_date)
+    return new_date
 
 
-@synchronized
-def get_state() -> State:
+############################################
+# Used by scripts                          #
+# Matplotlib doesn't support ::datetime64, #
+# so we need to convert it into ::datetime #
+############################################
+def datesarray_to_datetimearray(dates: np.ndarray) -> np.ndarray:
     """
-    Gets the current application state
+    Convert an pandas-array of timestamps into
+    An numpy-array of datetimes
+    :return: numpy-array of datetime
+    """
+    times = []
+    dates = dates.astype(datetime)
+    for index in range(0, dates.size):
+        date = dates[index].to_pydatetime()
+        times.append(date)
+    return np.array(times)
+
+
+def common_datearray(dfs: Dict[str, DataFrame]) -> np.ndarray:
+    """
+    Return dates from Dataframe
+    :param dfs: Dict with format pair: pair_data
+    :return: List of dates
+    """
+    alldates = {}
+    for pair, pair_data in dfs.items():
+        dates = datesarray_to_datetimearray(pair_data['date'])
+        for date in dates:
+            alldates[date] = 1
+    lst = []
+    for date, _ in alldates.items():
+        lst.append(date)
+    arr = np.array(lst)
+    return np.sort(arr, axis=0)
+
+
+def file_dump_json(filename, data, is_zip=False) -> None:
+    """
+    Dump JSON data into a file
+    :param filename: file to create
+    :param data: JSON Data to save
     :return:
     """
-    return _STATE
+    print(f'dumping json to "{filename}"')
+
+    if is_zip:
+        if not filename.endswith('.gz'):
+            filename = filename + '.gz'
+        with gzip.open(filename, 'w') as fp:
+            json.dump(data, fp, default=str)
+    else:
+        with open(filename, 'w') as fp:
+            json.dump(data, fp, default=str)
 
 
-def load_config(path: str) -> Dict:
+def format_ms_time(date: int) -> str:
     """
-    Loads a config file from the given path
-    :param path: path as str
-    :return: configuration as dictionary
+    convert MS date to readable format.
+    : epoch-string in ms
     """
-    with open(path) as file:
-        conf = json.load(file)
-    if 'internals' not in conf:
-        conf['internals'] = {}
-    logger.info('Validating configuration ...')
-    try:
-        validate(conf, CONF_SCHEMA)
-        return conf
-    except ValidationError as exception:
-        logger.fatal('Invalid configuration. See config.json.example. Reason: %s', exception)
-        raise ValidationError(
-            best_match(Draft4Validator(CONF_SCHEMA).iter_errors(conf)).message
-        )
-
-
-def throttle(func: Callable[..., Any], min_secs: float, *args, **kwargs) -> Any:
-    """
-    Throttles the given callable that it
-    takes at least `min_secs` to finish execution.
-    :param func: Any callable
-    :param min_secs: minimum execution time in seconds
-    :return: Any
-    """
-    start = time.time()
-    result = func(*args, **kwargs)
-    end = time.time()
-    duration = max(min_secs - (end - start), 0.0)
-    logger.debug('Throttling %s for %.2f seconds', func.__name__, duration)
-    time.sleep(duration)
-    return result
-
-
-def common_args_parser(description: str):
-    """
-    Parses given common arguments and returns them as a parsed object.
-    """
-    parser = argparse.ArgumentParser(
-        description=description
-    )
-    parser.add_argument(
-        '-v', '--verbose',
-        help='be verbose',
-        action='store_const',
-        dest='loglevel',
-        const=logging.DEBUG,
-        default=logging.INFO,
-    )
-    parser.add_argument(
-        '--version',
-        action='version',
-        version='%(prog)s {}'.format(__version__),
-    )
-    parser.add_argument(
-        '-c', '--config',
-        help='specify configuration file (default: config.json)',
-        dest='config',
-        default='config.json',
-        type=str,
-        metavar='PATH',
-    )
-    return parser
-
-
-def parse_args(args: List[str], description: str):
-    """
-    Parses given arguments and returns an argparse Namespace instance.
-    Returns None if a sub command has been selected and executed.
-    """
-    parser = common_args_parser(description)
-    parser.add_argument(
-        '--dry-run-db',
-        help='Force dry run to use a local DB "tradesv3.dry_run.sqlite" \
-             instead of memory DB. Work only if dry_run is enabled.',
-        action='store_true',
-        dest='dry_run_db',
-    )
-    parser.add_argument(
-        '-dd', '--datadir',
-        help='path to backtest data (default freqdata/tests/testdata',
-        dest='datadir',
-        default=os.path.join('freqtrade', 'tests', 'testdata'),
-        type=str,
-        metavar='PATH',
-    )
-    parser.add_argument(
-        '--dynamic-whitelist',
-        help='dynamically generate and update whitelist \
-             based on 24h BaseVolume (Default 20 currencies)',  # noqa
-        dest='dynamic_whitelist',
-        const=20,
-        type=int,
-        metavar='INT',
-        nargs='?',
-    )
-
-    build_subcommands(parser)
-    return parser.parse_args(args)
-
-
-def build_subcommands(parser: argparse.ArgumentParser) -> None:
-    """ Builds and attaches all subcommands """
-    from freqtrade.optimize import backtesting, hyperopt
-
-    subparsers = parser.add_subparsers(dest='subparser')
-
-    # Add backtesting subcommand
-    backtesting_cmd = subparsers.add_parser('backtesting', help='backtesting module')
-    backtesting_cmd.set_defaults(func=backtesting.start)
-    backtesting_cmd.add_argument(
-        '-l', '--live',
-        action='store_true',
-        dest='live',
-        help='using live data',
-    )
-    backtesting_cmd.add_argument(
-        '-i', '--ticker-interval',
-        help='specify ticker interval in minutes (default: 5)',
-        dest='ticker_interval',
-        default=5,
-        type=int,
-        metavar='INT',
-    )
-    backtesting_cmd.add_argument(
-        '--realistic-simulation',
-        help='uses max_open_trades from config to simulate real world limitations',
-        action='store_true',
-        dest='realistic_simulation',
-    )
-    backtesting_cmd.add_argument(
-        '-r', '--refresh-pairs-cached',
-        help='refresh the pairs files in tests/testdata with the latest data from Bittrex. \
-              Use it if you want to run your backtesting with up-to-date data.',
-        action='store_true',
-        dest='refresh_pairs',
-    )
-
-    # Add hyperopt subcommand
-    hyperopt_cmd = subparsers.add_parser('hyperopt', help='hyperopt module')
-    hyperopt_cmd.set_defaults(func=hyperopt.start)
-    hyperopt_cmd.add_argument(
-        '-e', '--epochs',
-        help='specify number of epochs (default: 100)',
-        dest='epochs',
-        default=100,
-        type=int,
-        metavar='INT',
-    )
-    hyperopt_cmd.add_argument(
-        '--use-mongodb',
-        help='parallelize evaluations with mongodb (requires mongod in PATH)',
-        dest='mongodb',
-        action='store_true',
-    )
-    hyperopt_cmd.add_argument(
-        '-i', '--ticker-interval',
-        help='specify ticker interval in minutes (default: 5)',
-        dest='ticker_interval',
-        default=5,
-        type=int,
-        metavar='INT',
-    )
-
-
-# Required json-schema for user specified config
-CONF_SCHEMA = {
-    'type': 'object',
-    'properties': {
-        'max_open_trades': {'type': 'integer', 'minimum': 1},
-        'stake_currency': {'type': 'string', 'enum': ['BTC', 'ETH', 'USDT']},
-        'stake_amount': {'type': 'number', 'minimum': 0.0005},
-        'fiat_display_currency': {'type': 'string', 'enum': ['AUD', 'BRL', 'CAD', 'CHF',
-                                                             'CLP', 'CNY', 'CZK', 'DKK',
-                                                             'EUR', 'GBP', 'HKD', 'HUF',
-                                                             'IDR', 'ILS', 'INR', 'JPY',
-                                                             'KRW', 'MXN', 'MYR', 'NOK',
-                                                             'NZD', 'PHP', 'PKR', 'PLN',
-                                                             'RUB', 'SEK', 'SGD', 'THB',
-                                                             'TRY', 'TWD', 'ZAR', 'USD']},
-        'dry_run': {'type': 'boolean'},
-        'minimal_roi': {
-            'type': 'object',
-            'patternProperties': {
-                '^[0-9.]+$': {'type': 'number'}
-            },
-            'minProperties': 1
-        },
-        'stoploss': {'type': 'number', 'maximum': 0, 'exclusiveMaximum': True},
-        'unfilledtimeout': {'type': 'integer', 'minimum': 0},
-        'bid_strategy': {
-            'type': 'object',
-            'properties': {
-                'ask_last_balance': {
-                    'type': 'number',
-                    'minimum': 0,
-                    'maximum': 1,
-                    'exclusiveMaximum': False
-                },
-            },
-            'required': ['ask_last_balance']
-        },
-        'exchange': {'$ref': '#/definitions/exchange'},
-        'experimental': {
-            'type': 'object',
-            'properties': {
-                'use_sell_signal': {'type': 'boolean'},
-                'sell_profit_only': {'type': 'boolean'}
-            }
-        },
-        'telegram': {
-            'type': 'object',
-            'properties': {
-                'enabled': {'type': 'boolean'},
-                'token': {'type': 'string'},
-                'chat_id': {'type': 'string'},
-            },
-            'required': ['enabled', 'token', 'chat_id']
-        },
-        'initial_state': {'type': 'string', 'enum': ['running', 'stopped']},
-        'internals': {
-            'type': 'object',
-            'properties': {
-                'process_throttle_secs': {'type': 'number'}
-            }
-        }
-    },
-    'definitions': {
-        'exchange': {
-            'type': 'object',
-            'properties': {
-                'name': {'type': 'string'},
-                'key': {'type': 'string'},
-                'secret': {'type': 'string'},
-                'pair_whitelist': {
-                    'type': 'array',
-                    'items': {
-                        'type': 'string',
-                        'pattern': '^[0-9A-Z]+_[0-9A-Z]+$'
-                    },
-                    'uniqueItems': True
-                },
-                'pair_blacklist': {
-                    'type': 'array',
-                    'items': {
-                        'type': 'string',
-                        'pattern': '^[0-9A-Z]+_[0-9A-Z]+$'
-                    },
-                    'uniqueItems': True
-                }
-            },
-            'required': ['name', 'key', 'secret', 'pair_whitelist']
-        }
-    },
-    'anyOf': [
-        {'required': ['exchange']}
-    ],
-    'required': [
-        'max_open_trades',
-        'stake_currency',
-        'stake_amount',
-        'fiat_display_currency',
-        'dry_run',
-        'minimal_roi',
-        'bid_strategy',
-        'telegram'
-    ]
-}
+    return datetime.fromtimestamp(date/1000.0).strftime('%Y-%m-%dT%H:%M:%S')

freqtrade/optimize/__init__.py

@@ -1,133 +1,245 @@
 # pragma pylint: disable=missing-docstring
 
+import gzip
+try:
+    import ujson as json
+    _UJSON = True
+except ImportError:
+    # see mypy/issues/1153
+    import json  # type: ignore
+    _UJSON = False
 import logging
-import json
 import os
-from typing import Optional, List, Dict
-from pandas import DataFrame
-from freqtrade.exchange import get_ticker_history
-from freqtrade.optimize.hyperopt_conf import hyperopt_optimize_conf
-from freqtrade.analyze import populate_indicators, parse_ticker_dataframe
+from typing import Optional, List, Dict, Tuple, Any
+import arrow
+
+from freqtrade import misc, constants, OperationalException
+from freqtrade.exchange import Exchange
+from freqtrade.arguments import TimeRange
 
 logger = logging.getLogger(__name__)
 
 
-def load_tickerdata_file(datadir, pair, ticker_interval):
+def json_load(data):
+    """Try to load data with ujson"""
+    if _UJSON:
+        return json.load(data, precise_float=True)
+    else:
+        return json.load(data)
+
+
+def trim_tickerlist(tickerlist: List[Dict], timerange: TimeRange) -> List[Dict]:
+    if not tickerlist:
+        return tickerlist
+
+    start_index = 0
+    stop_index = len(tickerlist)
+
+    if timerange.starttype == 'line':
+        stop_index = timerange.startts
+    if timerange.starttype == 'index':
+        start_index = timerange.startts
+    elif timerange.starttype == 'date':
+        while (start_index < len(tickerlist) and
+               tickerlist[start_index][0] < timerange.startts * 1000):
+            start_index += 1
+
+    if timerange.stoptype == 'line':
+        start_index = len(tickerlist) + timerange.stopts
+    if timerange.stoptype == 'index':
+        stop_index = timerange.stopts
+    elif timerange.stoptype == 'date':
+        while (stop_index > 0 and
+               tickerlist[stop_index-1][0] > timerange.stopts * 1000):
+            stop_index -= 1
+
+    if start_index > stop_index:
+        raise ValueError(f'The timerange [{timerange.startts},{timerange.stopts}] is incorrect')
+
+    return tickerlist[start_index:stop_index]
+
+
+def load_tickerdata_file(
+        datadir: str, pair: str,
+        ticker_interval: str,
+        timerange: Optional[TimeRange] = None) -> Optional[List[Dict]]:
     """
     Load a pair from file,
     :return dict OR empty if unsuccesful
     """
     path = make_testdata_path(datadir)
-    file = '{abspath}/{pair}-{ticker_interval}.json'.format(
-        abspath=path,
-        pair=pair,
-        ticker_interval=ticker_interval,
-    )
-    # The file does not exist we download it
-    if not os.path.isfile(file):
+    pair_s = pair.replace('/', '_')
+    file = os.path.join(path, f'{pair_s}-{ticker_interval}.json')
+    gzipfile = file + '.gz'
+
+    # If the file does not exist we download it when None is returned.
+    # If file exists, read the file, load the json
+    if os.path.isfile(gzipfile):
+        logger.debug('Loading ticker data from file %s', gzipfile)
+        with gzip.open(gzipfile) as tickerdata:
+            pairdata = json.load(tickerdata)
+    elif os.path.isfile(file):
+        logger.debug('Loading ticker data from file %s', file)
+        with open(file) as tickerdata:
+            pairdata = json.load(tickerdata)
+    else:
         return None
 
-    # Read the file, load the json
-    with open(file) as tickerdata:
-        pairdata = json.load(tickerdata)
+    if timerange:
+        pairdata = trim_tickerlist(pairdata, timerange)
     return pairdata
 
 
-def load_data(datadir: str, ticker_interval: int = 5, pairs: Optional[List[str]] = None,
-              refresh_pairs: Optional[bool] = False) -> Dict[str, List]:
+def load_data(datadir: str,
+              ticker_interval: str,
+              pairs: List[str],
+              refresh_pairs: Optional[bool] = False,
+              exchange: Optional[Exchange] = None,
+              timerange: TimeRange = TimeRange(None, None, 0, 0)) -> Dict[str, List]:
     """
     Loads ticker history data for the given parameters
-    :param ticker_interval: ticker interval in minutes
-    :param pairs: list of pairs
     :return: dict
     """
     result = {}
 
-    _pairs = pairs or hyperopt_optimize_conf()['exchange']['pair_whitelist']
-
     # If the user force the refresh of pairs
     if refresh_pairs:
         logger.info('Download data for all pairs and store them in %s', datadir)
-        download_pairs(datadir, _pairs)
+        if not exchange:
+            raise OperationalException("Exchange needs to be initialized when "
+                                       "calling load_data with refresh_pairs=True")
+        download_pairs(datadir, exchange, pairs, ticker_interval, timerange=timerange)
+
+    for pair in pairs:
+        pairdata = load_tickerdata_file(datadir, pair, ticker_interval, timerange=timerange)
+        if pairdata:
+            result[pair] = pairdata
+        else:
+            logger.warning(
+                'No data for pair: "%s", Interval: %s. '
+                'Use --refresh-pairs-cached to download the data',
+                pair,
+                ticker_interval
+            )
 
-    for pair in _pairs:
-        pairdata = load_tickerdata_file(datadir, pair, ticker_interval)
-        if not pairdata:
-            # download the tickerdata from exchange
-            download_backtesting_testdata(datadir, pair=pair, interval=ticker_interval)
-            # and retry reading the pair
-            pairdata = load_tickerdata_file(datadir, pair, ticker_interval)
-        result[pair] = pairdata
     return result
 
 
-def preprocess(tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
-    """Creates a dataframe and populates indicators for given ticker data"""
-    return {pair: populate_indicators(parse_ticker_dataframe(pair_data))
-            for pair, pair_data in tickerdata.items()}
-
-
 def make_testdata_path(datadir: str) -> str:
     """Return the path where testdata files are stored"""
-    return datadir or os.path.abspath(os.path.join(os.path.dirname(__file__),
-                                                   '..', 'tests', 'testdata'))
+    return datadir or os.path.abspath(
+        os.path.join(
+            os.path.dirname(__file__), '..', 'tests', 'testdata'
+        )
+    )
 
 
-def download_pairs(datadir, pairs: List[str]) -> bool:
-    """For each pairs passed in parameters, download 1 and 5 ticker intervals"""
+def download_pairs(datadir, exchange: Exchange, pairs: List[str],
+                   ticker_interval: str,
+                   timerange: TimeRange = TimeRange(None, None, 0, 0)) -> bool:
+    """For each pairs passed in parameters, download the ticker intervals"""
     for pair in pairs:
         try:
-            for interval in [1, 5]:
-                download_backtesting_testdata(datadir, pair=pair, interval=interval)
+            download_backtesting_testdata(datadir,
+                                          exchange=exchange,
+                                          pair=pair,
+                                          tick_interval=ticker_interval,
+                                          timerange=timerange)
         except BaseException:
-            logger.info('Failed to download the pair: "{pair}", Interval: {interval} min'.format(
-                pair=pair,
-                interval=interval,
-            ))
+            logger.info(
+                'Failed to download the pair: "%s", Interval: %s',
+                pair,
+                ticker_interval
+            )
             return False
     return True
 
 
-def download_backtesting_testdata(datadir: str, pair: str, interval: int = 5) -> bool:
+def load_cached_data_for_updating(filename: str,
+                                  tick_interval: str,
+                                  timerange: Optional[TimeRange]) -> Tuple[
+                                                                                  List[Any],
+                                                                                  Optional[int]]:
     """
-    Download the latest 1 and 5 ticker intervals from Bittrex for the pairs passed in parameters
-    Based on @Rybolov work: https://github.com/rybolov/freqtrade-data
-    :param pairs: list of pairs to download
-    :return: bool
+    Load cached data and choose what part of the data should be updated
     """
 
-    path = make_testdata_path(datadir)
-    logger.info('Download the pair: "{pair}", Interval: {interval} min'.format(
-        pair=pair,
-        interval=interval,
-    ))
+    since_ms = None
 
-    filepair = pair.replace("-", "_")
-    filename = os.path.join(path, '{pair}-{interval}.json'.format(
-        pair=filepair,
-        interval=interval,
-    ))
-    filename = filename.replace('USDT_BTC', 'BTC_FAKEBULL')
+    # user sets timerange, so find the start time
+    if timerange:
+        if timerange.starttype == 'date':
+            since_ms = timerange.startts * 1000
+        elif timerange.stoptype == 'line':
+            num_minutes = timerange.stopts * constants.TICKER_INTERVAL_MINUTES[tick_interval]
+            since_ms = arrow.utcnow().shift(minutes=num_minutes).timestamp * 1000
 
+    # read the cached file
     if os.path.isfile(filename):
-        with open(filename, "rt") as fp:
-            data = json.load(fp)
-        logger.debug("Current Start: {}".format(data[1]['T']))
-        logger.debug("Current End: {}".format(data[-1:][0]['T']))
+        with open(filename, "rt") as file:
+            data = json_load(file)
+            # remove the last item, because we are not sure if it is correct
+            # it could be fetched when the candle was incompleted
+            if data:
+                data.pop()
     else:
         data = []
-        logger.debug("Current Start: None")
-        logger.debug("Current End: None")
 
-    new_data = get_ticker_history(pair=pair, tick_interval=int(interval))
-    for row in new_data:
-        if row not in data:
-            data.append(row)
-    logger.debug("New Start: {}".format(data[1]['T']))
-    logger.debug("New End: {}".format(data[-1:][0]['T']))
-    data = sorted(data, key=lambda data: data['T'])
+    if data:
+        if since_ms and since_ms < data[0][0]:
+            # the data is requested for earlier period than the cache has
+            # so fully redownload all the data
+            data = []
+        else:
+            # a part of the data was already downloaded, so
+            # download unexist data only
+            since_ms = data[-1][0] + 1
 
-    with open(filename, "wt") as fp:
-        json.dump(data, fp)
+    return (data, since_ms)
 
-    return True
+
+def download_backtesting_testdata(datadir: str,
+                                  exchange: Exchange,
+                                  pair: str,
+                                  tick_interval: str = '5m',
+                                  timerange: Optional[TimeRange] = None) -> None:
+
+    """
+    Download the latest ticker intervals from the exchange for the pair passed in parameters
+    The data is downloaded starting from the last correct ticker interval data that
+    exists in a cache. If timerange starts earlier than the data in the cache,
+    the full data will be redownloaded
+
+    Based on @Rybolov work: https://github.com/rybolov/freqtrade-data
+    :param pair: pair to download
+    :param tick_interval: ticker interval
+    :param timerange: range of time to download
+    :return: None
+
+    """
+    path = make_testdata_path(datadir)
+    filepair = pair.replace("/", "_")
+    filename = os.path.join(path, f'{filepair}-{tick_interval}.json')
+
+    logger.info(
+        'Download the pair: "%s", Interval: %s',
+        pair,
+        tick_interval
+    )
+
+    data, since_ms = load_cached_data_for_updating(filename, tick_interval, timerange)
+
+    logger.debug("Current Start: %s", misc.format_ms_time(data[1][0]) if data else 'None')
+    logger.debug("Current End: %s", misc.format_ms_time(data[-1][0]) if data else 'None')
+
+    # Default since_ms to 30 days if nothing is given
+    new_data = exchange.get_history(pair=pair, tick_interval=tick_interval,
+                                    since_ms=since_ms if since_ms
+                                    else
+                                    int(arrow.utcnow().shift(days=-30).float_timestamp) * 1000)
+    data.extend(new_data)
+
+    logger.debug("New Start: %s", misc.format_ms_time(data[0][0]))
+    logger.debug("New End: %s", misc.format_ms_time(data[-1][0]))
+
+    misc.file_dump_json(filename, data)

freqtrade/optimize/backtesting.py

@@ -1,197 +1,450 @@
-# pragma pylint: disable=missing-docstring,W0212
+# pragma pylint: disable=missing-docstring, W0212, too-many-arguments
 
+"""
+This module contains the backtesting logic
+"""
 import logging
-from typing import Dict, Tuple
+import operator
+from argparse import Namespace
+from copy import deepcopy
+from datetime import datetime, timedelta
+from pathlib import Path
+from typing import Any, Dict, List, NamedTuple, Optional, Tuple
 
 import arrow
-from pandas import DataFrame, Series
+from pandas import DataFrame
 from tabulate import tabulate
 
-import freqtrade.misc as misc
 import freqtrade.optimize as optimize
-from freqtrade import exchange
-from freqtrade.analyze import populate_buy_trend, populate_sell_trend
-from freqtrade.exchange import Bittrex
-from freqtrade.main import min_roi_reached
-from freqtrade.optimize import preprocess
+from freqtrade import DependencyException, constants
+from freqtrade.arguments import Arguments
+from freqtrade.configuration import Configuration
+from freqtrade.exchange import Exchange
+from freqtrade.misc import file_dump_json
 from freqtrade.persistence import Trade
+from freqtrade.strategy.interface import SellType
+from freqtrade.strategy.resolver import IStrategy, StrategyResolver
 
 logger = logging.getLogger(__name__)
 
 
-def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
+class BacktestResult(NamedTuple):
     """
-    Get the maximum timeframe for the given backtest data
-    :param data: dictionary with preprocessed backtesting data
-    :return: tuple containing min_date, max_date
+    NamedTuple Defining BacktestResults inputs.
     """
-    all_dates = Series([])
-    for pair, pair_data in data.items():
-        all_dates = all_dates.append(pair_data['date'])
-    all_dates.sort_values(inplace=True)
-    return arrow.get(all_dates.iloc[0]), arrow.get(all_dates.iloc[-1])
+    pair: str
+    profit_percent: float
+    profit_abs: float
+    open_time: datetime
+    close_time: datetime
+    open_index: int
+    close_index: int
+    trade_duration: float
+    open_at_end: bool
+    open_rate: float
+    close_rate: float
+    sell_reason: SellType
 
 
-def generate_text_table(
-        data: Dict[str, Dict], results: DataFrame, stake_currency, ticker_interval) -> str:
+class Backtesting(object):
     """
-    Generates and returns a text table for the given backtest data and the results dataframe
-    :return: pretty printed table with tabulate as str
+    Backtesting class, this class contains all the logic to run a backtest
+
+    To run a backtest:
+    backtesting = Backtesting(config)
+    backtesting.start()
     """
-    floatfmt = ('s', 'd', '.2f', '.8f', '.1f')
-    tabular_data = []
-    headers = ['pair', 'buy count', 'avg profit %',
-               'total profit ' + stake_currency, 'avg duration', 'profit', 'loss']
-    for pair in data:
-        result = results[results.currency == pair]
+
+    def __init__(self, config: Dict[str, Any]) -> None:
+        self.config = config
+
+        # Reset keys for backtesting
+        self.config['exchange']['key'] = ''
+        self.config['exchange']['secret'] = ''
+        self.config['exchange']['password'] = ''
+        self.config['exchange']['uid'] = ''
+        self.config['dry_run'] = True
+        self.strategylist: List[IStrategy] = []
+        if self.config.get('strategy_list', None):
+            # Force one interval
+            self.ticker_interval = str(self.config.get('ticker_interval'))
+            for strat in list(self.config['strategy_list']):
+                stratconf = deepcopy(self.config)
+                stratconf['strategy'] = strat
+                self.strategylist.append(StrategyResolver(stratconf).strategy)
+
+        else:
+            # only one strategy
+            self.strategylist.append(StrategyResolver(self.config).strategy)
+        # Load one strategy
+        self._set_strategy(self.strategylist[0])
+
+        self.exchange = Exchange(self.config)
+        self.fee = self.exchange.get_fee()
+
+    def _set_strategy(self, strategy):
+        """
+        Load strategy into backtesting
+        """
+        self.strategy = strategy
+        self.ticker_interval = self.config.get('ticker_interval')
+        self.tickerdata_to_dataframe = strategy.tickerdata_to_dataframe
+        self.advise_buy = strategy.advise_buy
+        self.advise_sell = strategy.advise_sell
+
+    @staticmethod
+    def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
+        """
+        Get the maximum timeframe for the given backtest data
+        :param data: dictionary with preprocessed backtesting data
+        :return: tuple containing min_date, max_date
+        """
+        timeframe = [
+            (arrow.get(frame['date'].min()), arrow.get(frame['date'].max()))
+            for frame in data.values()
+        ]
+        return min(timeframe, key=operator.itemgetter(0))[0], \
+            max(timeframe, key=operator.itemgetter(1))[1]
+
+    def _generate_text_table(self, data: Dict[str, Dict], results: DataFrame,
+                             skip_nan: bool = False) -> str:
+        """
+        Generates and returns a text table for the given backtest data and the results dataframe
+        :return: pretty printed table with tabulate as str
+        """
+        stake_currency = str(self.config.get('stake_currency'))
+
+        floatfmt = ('s', 'd', '.2f', '.2f', '.8f', 'd', '.1f', '.1f')
+        tabular_data = []
+        headers = ['pair', 'buy count', 'avg profit %', 'cum profit %',
+                   'total profit ' + stake_currency, 'avg duration', 'profit', 'loss']
+        for pair in data:
+            result = results[results.pair == pair]
+            if skip_nan and result.profit_abs.isnull().all():
+                continue
+
+            tabular_data.append([
+                pair,
+                len(result.index),
+                result.profit_percent.mean() * 100.0,
+                result.profit_percent.sum() * 100.0,
+                result.profit_abs.sum(),
+                str(timedelta(
+                    minutes=round(result.trade_duration.mean()))) if not result.empty else '0:00',
+                len(result[result.profit_abs > 0]),
+                len(result[result.profit_abs < 0])
+            ])
+
+        # Append Total
         tabular_data.append([
-            pair,
-            len(result.index),
-            result.profit_percent.mean() * 100.0,
-            result.profit_BTC.sum(),
-            result.duration.mean() * ticker_interval,
-            result.profit.sum(),
-            result.loss.sum()
+            'TOTAL',
+            len(results.index),
+            results.profit_percent.mean() * 100.0,
+            results.profit_percent.sum() * 100.0,
+            results.profit_abs.sum(),
+            str(timedelta(
+                minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
+            len(results[results.profit_abs > 0]),
+            len(results[results.profit_abs < 0])
         ])
+        return tabulate(tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="pipe")
 
-    # Append Total
-    tabular_data.append([
-        'TOTAL',
-        len(results.index),
-        results.profit_percent.mean() * 100.0,
-        results.profit_BTC.sum(),
-        results.duration.mean() * ticker_interval,
-        results.profit.sum(),
-        results.loss.sum()
-    ])
-    return tabulate(tabular_data, headers=headers, floatfmt=floatfmt)
+    def _generate_text_table_sell_reason(self, data: Dict[str, Dict], results: DataFrame) -> str:
+        """
+        Generate small table outlining Backtest results
+        """
+        tabular_data = []
+        headers = ['Sell Reason', 'Count']
+        for reason, count in results['sell_reason'].value_counts().iteritems():
+            tabular_data.append([reason.value,  count])
+        return tabulate(tabular_data, headers=headers, tablefmt="pipe")
 
+    def _generate_text_table_strategy(self, all_results: dict) -> str:
+        """
+        Generate summary table per strategy
+        """
+        stake_currency = str(self.config.get('stake_currency'))
 
-def backtest(stake_amount: float, processed: Dict[str, DataFrame],
-             max_open_trades: int = 0, realistic: bool = True, sell_profit_only: bool = False,
-             stoploss: int = -1.00, use_sell_signal: bool = False) -> DataFrame:
-    """
-    Implements backtesting functionality
-    :param stake_amount: btc amount to use for each trade
-    :param processed: a processed dictionary with format {pair, data}
-    :param max_open_trades: maximum number of concurrent trades (default: 0, disabled)
-    :param realistic: do we try to simulate realistic trades? (default: True)
-    :return: DataFrame
-    """
-    trades = []
-    trade_count_lock: dict = {}
-    exchange._API = Bittrex({'key': '', 'secret': ''})
-    for pair, pair_data in processed.items():
-        pair_data['buy'], pair_data['sell'] = 0, 0
-        ticker = populate_sell_trend(populate_buy_trend(pair_data))
-        # for each buy point
-        lock_pair_until = None
-        buy_subset = ticker[ticker.buy == 1][['buy', 'open', 'close', 'date', 'sell']]
-        for row in buy_subset.itertuples(index=True):
-            if realistic:
-                if lock_pair_until is not None and row.Index <= lock_pair_until:
-                    continue
+        floatfmt = ('s', 'd', '.2f', '.2f', '.8f', 'd', '.1f', '.1f')
+        tabular_data = []
+        headers = ['Strategy', 'buy count', 'avg profit %', 'cum profit %',
+                   'total profit ' + stake_currency, 'avg duration', 'profit', 'loss']
+        for strategy, results in all_results.items():
+            tabular_data.append([
+                strategy,
+                len(results.index),
+                results.profit_percent.mean() * 100.0,
+                results.profit_percent.sum() * 100.0,
+                results.profit_abs.sum(),
+                str(timedelta(
+                    minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
+                len(results[results.profit_abs > 0]),
+                len(results[results.profit_abs < 0])
+            ])
+        return tabulate(tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="pipe")
+
+    def _store_backtest_result(self, recordfilename: str, results: DataFrame,
+                               strategyname: Optional[str] = None) -> None:
+
+        records = [(t.pair, t.profit_percent, t.open_time.timestamp(),
+                    t.close_time.timestamp(), t.open_index - 1, t.trade_duration,
+                    t.open_rate, t.close_rate, t.open_at_end, t.sell_reason.value)
+                   for index, t in results.iterrows()]
+
+        if records:
+            if strategyname:
+                # Inject strategyname to filename
+                recname = Path(recordfilename)
+                recordfilename = str(Path.joinpath(
+                    recname.parent, f'{recname.stem}-{strategyname}').with_suffix(recname.suffix))
+            logger.info('Dumping backtest results to %s', recordfilename)
+            file_dump_json(recordfilename, records)
+
+    def _get_sell_trade_entry(
+            self, pair: str, buy_row: DataFrame,
+            partial_ticker: List, trade_count_lock: Dict, args: Dict) -> Optional[BacktestResult]:
+
+        stake_amount = args['stake_amount']
+        max_open_trades = args.get('max_open_trades', 0)
+        trade = Trade(
+            open_rate=buy_row.open,
+            open_date=buy_row.date,
+            stake_amount=stake_amount,
+            amount=stake_amount / buy_row.open,
+            fee_open=self.fee,
+            fee_close=self.fee
+        )
+
+        # calculate win/lose forwards from buy point
+        for sell_row in partial_ticker:
             if max_open_trades > 0:
-                # Check if max_open_trades has already been reached for the given date
-                if not trade_count_lock.get(row.date, 0) < max_open_trades:
-                    continue
+                # Increase trade_count_lock for every iteration
+                trade_count_lock[sell_row.date] = trade_count_lock.get(sell_row.date, 0) + 1
 
-            if max_open_trades > 0:
-                # Increase lock
-                trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
+            buy_signal = sell_row.buy
+            sell = self.strategy.should_sell(trade, sell_row.open, sell_row.date, buy_signal,
+                                             sell_row.sell)
+            if sell.sell_flag:
 
-            trade = Trade(
-                open_rate=row.close,
-                open_date=row.date,
-                stake_amount=stake_amount,
-                amount=stake_amount / row.open,
-                fee=exchange.get_fee()
+                return BacktestResult(pair=pair,
+                                      profit_percent=trade.calc_profit_percent(rate=sell_row.open),
+                                      profit_abs=trade.calc_profit(rate=sell_row.open),
+                                      open_time=buy_row.date,
+                                      close_time=sell_row.date,
+                                      trade_duration=int((
+                                          sell_row.date - buy_row.date).total_seconds() // 60),
+                                      open_index=buy_row.Index,
+                                      close_index=sell_row.Index,
+                                      open_at_end=False,
+                                      open_rate=buy_row.open,
+                                      close_rate=sell_row.open,
+                                      sell_reason=sell.sell_type
+                                      )
+        if partial_ticker:
+            # no sell condition found - trade stil open at end of backtest period
+            sell_row = partial_ticker[-1]
+            btr = BacktestResult(pair=pair,
+                                 profit_percent=trade.calc_profit_percent(rate=sell_row.open),
+                                 profit_abs=trade.calc_profit(rate=sell_row.open),
+                                 open_time=buy_row.date,
+                                 close_time=sell_row.date,
+                                 trade_duration=int((
+                                     sell_row.date - buy_row.date).total_seconds() // 60),
+                                 open_index=buy_row.Index,
+                                 close_index=sell_row.Index,
+                                 open_at_end=True,
+                                 open_rate=buy_row.open,
+                                 close_rate=sell_row.open,
+                                 sell_reason=SellType.FORCE_SELL
+                                 )
+            logger.debug('Force_selling still open trade %s with %s perc - %s', btr.pair,
+                         btr.profit_percent, btr.profit_abs)
+            return btr
+        return None
+
+    def backtest(self, args: Dict) -> DataFrame:
+        """
+        Implements backtesting functionality
+
+        NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
+        Of course try to not have ugly code. By some accessor are sometime slower than functions.
+        Avoid, logging on this method
+
+        :param args: a dict containing:
+            stake_amount: btc amount to use for each trade
+            processed: a processed dictionary with format {pair, data}
+            max_open_trades: maximum number of concurrent trades (default: 0, disabled)
+            position_stacking: do we allow position stacking? (default: False)
+        :return: DataFrame
+        """
+        headers = ['date', 'buy', 'open', 'close', 'sell']
+        processed = args['processed']
+        max_open_trades = args.get('max_open_trades', 0)
+        position_stacking = args.get('position_stacking', False)
+        trades = []
+        trade_count_lock: Dict = {}
+        for pair, pair_data in processed.items():
+            pair_data['buy'], pair_data['sell'] = 0, 0  # cleanup from previous run
+
+            ticker_data = self.advise_sell(
+                self.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
+
+            # to avoid using data from future, we buy/sell with signal from previous candle
+            ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
+            ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
+
+            ticker_data.drop(ticker_data.head(1).index, inplace=True)
+
+            # Convert from Pandas to list for performance reasons
+            # (Looping Pandas is slow.)
+            ticker = [x for x in ticker_data.itertuples()]
+
+            lock_pair_until = None
+            for index, row in enumerate(ticker):
+                if row.buy == 0 or row.sell == 1:
+                    continue  # skip rows where no buy signal or that would immediately sell off
+
+                if not position_stacking:
+                    if lock_pair_until is not None and row.date <= lock_pair_until:
+                        continue
+                if max_open_trades > 0:
+                    # Check if max_open_trades has already been reached for the given date
+                    if not trade_count_lock.get(row.date, 0) < max_open_trades:
+                        continue
+
+                    trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
+
+                trade_entry = self._get_sell_trade_entry(pair, row, ticker[index + 1:],
+                                                         trade_count_lock, args)
+
+                if trade_entry:
+                    lock_pair_until = trade_entry.close_time
+                    trades.append(trade_entry)
+                else:
+                    # Set lock_pair_until to end of testing period if trade could not be closed
+                    # This happens only if the buy-signal was with the last candle
+                    lock_pair_until = ticker_data.iloc[-1].date
+
+        return DataFrame.from_records(trades, columns=BacktestResult._fields)
+
+    def start(self) -> None:
+        """
+        Run a backtesting end-to-end
+        :return: None
+        """
+        data: Dict[str, Any] = {}
+        pairs = self.config['exchange']['pair_whitelist']
+        logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
+        logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
+
+        if self.config.get('live'):
+            logger.info('Downloading data for all pairs in whitelist ...')
+            self.exchange.refresh_tickers(pairs, self.ticker_interval)
+            data = self.exchange.klines
+        else:
+            logger.info('Using local backtesting data (using whitelist in given config) ...')
+
+            timerange = Arguments.parse_timerange(None if self.config.get(
+                'timerange') is None else str(self.config.get('timerange')))
+            data = optimize.load_data(
+                self.config['datadir'],
+                pairs=pairs,
+                ticker_interval=self.ticker_interval,
+                refresh_pairs=self.config.get('refresh_pairs', False),
+                exchange=self.exchange,
+                timerange=timerange
             )
 
-            # calculate win/lose forwards from buy point
-            sell_subset = ticker[row.Index + 1:][['close', 'date', 'sell']]
-            for row2 in sell_subset.itertuples(index=True):
-                if max_open_trades > 0:
-                    # Increase trade_count_lock for every iteration
-                    trade_count_lock[row2.date] = trade_count_lock.get(row2.date, 0) + 1
+        if not data:
+            logger.critical("No data found. Terminating.")
+            return
+        # Use max_open_trades in backtesting, except --disable-max-market-positions is set
+        if self.config.get('use_max_market_positions', True):
+            max_open_trades = self.config['max_open_trades']
+        else:
+            logger.info('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
+            max_open_trades = 0
+        all_results = {}
 
-                current_profit_percent = trade.calc_profit_percent(rate=row2.close)
-                if (sell_profit_only and current_profit_percent < 0):
-                    continue
-                if min_roi_reached(trade, row2.close, row2.date) or \
-                    (row2.sell == 1 and use_sell_signal) or \
-                        current_profit_percent <= stoploss:
-                    current_profit_btc = trade.calc_profit(rate=row2.close)
-                    lock_pair_until = row2.Index
+        for strat in self.strategylist:
+            logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
+            self._set_strategy(strat)
 
-                    trades.append(
-                        (
-                            pair,
-                            current_profit_percent,
-                            current_profit_btc,
-                            row2.Index - row.Index,
-                            current_profit_btc > 0,
-                            current_profit_btc < 0
-                        )
-                    )
-                    break
-    labels = ['currency', 'profit_percent', 'profit_BTC', 'duration', 'profit', 'loss']
-    return DataFrame.from_records(trades, columns=labels)
+            # need to reprocess data every time to populate signals
+            preprocessed = self.tickerdata_to_dataframe(data)
+
+            # Print timeframe
+            min_date, max_date = self.get_timeframe(preprocessed)
+            logger.info(
+                'Measuring data from %s up to %s (%s days)..',
+                min_date.isoformat(),
+                max_date.isoformat(),
+                (max_date - min_date).days
+            )
+
+            # Execute backtest and print results
+            all_results[self.strategy.get_strategy_name()] = self.backtest(
+                {
+                    'stake_amount': self.config.get('stake_amount'),
+                    'processed': preprocessed,
+                    'max_open_trades': max_open_trades,
+                    'position_stacking': self.config.get('position_stacking', False),
+                }
+            )
+
+        for strategy, results in all_results.items():
+
+            if self.config.get('export', False):
+                self._store_backtest_result(self.config['exportfilename'], results,
+                                            strategy if len(self.strategylist) > 1 else None)
+
+            print(f"Result for strategy {strategy}")
+            print(' BACKTESTING REPORT '.center(119, '='))
+            print(self._generate_text_table(data, results))
+
+            print(' SELL REASON STATS '.center(119, '='))
+            print(self._generate_text_table_sell_reason(data, results))
+
+            print(' LEFT OPEN TRADES REPORT '.center(119, '='))
+            print(self._generate_text_table(data, results.loc[results.open_at_end], True))
+            print()
+        if len(all_results) > 1:
+            # Print Strategy summary table
+            print(' Strategy Summary '.center(119, '='))
+            print(self._generate_text_table_strategy(all_results))
+            print('\nFor more details, please look at the detail tables above')
 
 
-def start(args):
-    # Initialize logger
-    logging.basicConfig(
-        level=args.loglevel,
-        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
-    )
+def setup_configuration(args: Namespace) -> Dict[str, Any]:
+    """
+    Prepare the configuration for the backtesting
+    :param args: Cli args from Arguments()
+    :return: Configuration
+    """
+    configuration = Configuration(args)
+    config = configuration.get_config()
 
-    exchange._API = Bittrex({'key': '', 'secret': ''})
+    # Ensure we do not use Exchange credentials
+    config['exchange']['key'] = ''
+    config['exchange']['secret'] = ''
 
-    logger.info('Using config: %s ...', args.config)
-    config = misc.load_config(args.config)
+    if config['stake_amount'] == constants.UNLIMITED_STAKE_AMOUNT:
+        raise DependencyException('stake amount could not be "%s" for backtesting' %
+                                  constants.UNLIMITED_STAKE_AMOUNT)
 
-    logger.info('Using ticker_interval: %s ...', args.ticker_interval)
+    return config
 
-    data = {}
-    pairs = config['exchange']['pair_whitelist']
-    if args.live:
-        logger.info('Downloading data for all pairs in whitelist ...')
-        for pair in pairs:
-            data[pair] = exchange.get_ticker_history(pair, args.ticker_interval)
-    else:
-        logger.info('Using local backtesting data (using whitelist in given config) ...')
-        data = optimize.load_data(args.datadir, pairs=pairs, ticker_interval=args.ticker_interval,
-                                  refresh_pairs=args.refresh_pairs)
 
-        logger.info('Using stake_currency: %s ...', config['stake_currency'])
-        logger.info('Using stake_amount: %s ...', config['stake_amount'])
+def start(args: Namespace) -> None:
+    """
+    Start Backtesting script
+    :param args: Cli args from Arguments()
+    :return: None
+    """
+    # Initialize configuration
+    config = setup_configuration(args)
+    logger.info('Starting freqtrade in Backtesting mode')
 
-    max_open_trades = 0
-    if args.realistic_simulation:
-        logger.info('Using max_open_trades: %s ...', config['max_open_trades'])
-        max_open_trades = config['max_open_trades']
-
-    # Monkey patch config
-    from freqtrade import main
-    main._CONF = config
-
-    preprocessed = preprocess(data)
-    # Print timeframe
-    min_date, max_date = get_timeframe(preprocessed)
-    logger.info('Measuring data from %s up to %s ...', min_date.isoformat(), max_date.isoformat())
-
-    # Execute backtest and print results
-    results = backtest(
-        stake_amount=config['stake_amount'],
-        processed=preprocessed,
-        max_open_trades=max_open_trades,
-        realistic=args.realistic_simulation,
-        sell_profit_only=config.get('experimental', {}).get('sell_profit_only', False),
-        stoploss=config.get('stoploss'),
-        use_sell_signal=config.get('experimental', {}).get('use_sell_signal', False)
-    )
-    logger.info(
-        '\n==================================== BACKTESTING REPORT ====================================\n%s',  # noqa
-        generate_text_table(data, results, config['stake_currency'], args.ticker_interval)
-    )
+    # Initialize backtesting object
+    backtesting = Backtesting(config)
+    backtesting.start()

freqtrade/optimize/hyperopt.py

@@ -1,318 +1,414 @@
-# pragma pylint: disable=missing-docstring,W0212,W0603
+# pragma pylint: disable=too-many-instance-attributes, pointless-string-statement
 
+"""
+This module contains the hyperopt logic
+"""
 
-import json
 import logging
-import sys
-import pickle
-import signal
+import multiprocessing
 import os
+import sys
+from argparse import Namespace
 from functools import reduce
 from math import exp
 from operator import itemgetter
+from typing import Any, Callable, Dict, List
 
-from hyperopt import STATUS_FAIL, STATUS_OK, Trials, fmin, hp, space_eval, tpe
-from hyperopt.mongoexp import MongoTrials
+import talib.abstract as ta
 from pandas import DataFrame
+from sklearn.externals.joblib import Parallel, delayed, dump, load
+from skopt import Optimizer
+from skopt.space import Categorical, Dimension, Integer, Real
 
-from freqtrade import main  # noqa
-from freqtrade import exchange, optimize
-from freqtrade.exchange import Bittrex
-from freqtrade.misc import load_config
-from freqtrade.optimize.backtesting import backtest
-from freqtrade.optimize.hyperopt_conf import hyperopt_optimize_conf
-from freqtrade.vendor.qtpylib.indicators import crossed_above
-
-# Remove noisy log messages
-logging.getLogger('hyperopt.mongoexp').setLevel(logging.WARNING)
-logging.getLogger('hyperopt.tpe').setLevel(logging.WARNING)
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+from freqtrade.arguments import Arguments
+from freqtrade.configuration import Configuration
+from freqtrade.optimize import load_data
+from freqtrade.optimize.backtesting import Backtesting
 
 logger = logging.getLogger(__name__)
 
-# set TARGET_TRADES to suit your number concurrent trades so its realistic to 20days of data
-TARGET_TRADES = 1100
-TOTAL_TRIES = 0
-_CURRENT_TRIES = 0
-CURRENT_BEST_LOSS = 100
-
-# max average trade duration in minutes
-# if eval ends with higher value, we consider it a failed eval
-MAX_ACCEPTED_TRADE_DURATION = 240
-
-# this is expexted avg profit * expected trade count
-# for example 3.5%, 1100 trades, EXPECTED_MAX_PROFIT = 3.85
-EXPECTED_MAX_PROFIT = 3.85
-
-# Configuration and data used by hyperopt
-PROCESSED = None  # optimize.preprocess(optimize.load_data())
-OPTIMIZE_CONFIG = hyperopt_optimize_conf()
-
-# Hyperopt Trials
-TRIALS_FILE = os.path.join('freqtrade', 'optimize', 'hyperopt_trials.pickle')
-TRIALS = Trials()
-
-# Monkey patch config
-from freqtrade import main  # noqa
-main._CONF = OPTIMIZE_CONFIG
+MAX_LOSS = 100000  # just a big enough number to be bad result in loss optimization
+TICKERDATA_PICKLE = os.path.join('user_data', 'hyperopt_tickerdata.pkl')
 
 
-SPACE = {
-    'mfi': hp.choice('mfi', [
-        {'enabled': False},
-        {'enabled': True, 'value': hp.quniform('mfi-value', 5, 25, 1)}
-    ]),
-    'fastd': hp.choice('fastd', [
-        {'enabled': False},
-        {'enabled': True, 'value': hp.quniform('fastd-value', 10, 50, 1)}
-    ]),
-    'adx': hp.choice('adx', [
-        {'enabled': False},
-        {'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)}
-    ]),
-    'rsi': hp.choice('rsi', [
-        {'enabled': False},
-        {'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
-    ]),
-    'uptrend_long_ema': hp.choice('uptrend_long_ema', [
-        {'enabled': False},
-        {'enabled': True}
-    ]),
-    'uptrend_short_ema': hp.choice('uptrend_short_ema', [
-        {'enabled': False},
-        {'enabled': True}
-    ]),
-    'over_sar': hp.choice('over_sar', [
-        {'enabled': False},
-        {'enabled': True}
-    ]),
-    'green_candle': hp.choice('green_candle', [
-        {'enabled': False},
-        {'enabled': True}
-    ]),
-    'uptrend_sma': hp.choice('uptrend_sma', [
-        {'enabled': False},
-        {'enabled': True}
-    ]),
-    'trigger': hp.choice('trigger', [
-        {'type': 'lower_bb'},
-        {'type': 'faststoch10'},
-        {'type': 'ao_cross_zero'},
-        {'type': 'ema5_cross_ema10'},
-        {'type': 'macd_cross_signal'},
-        {'type': 'sar_reversal'},
-        {'type': 'stochf_cross'},
-        {'type': 'ht_sine'},
-    ]),
-    'stoploss': hp.uniform('stoploss', -0.5, -0.02),
-}
+class Hyperopt(Backtesting):
+    """
+    Hyperopt class, this class contains all the logic to run a hyperopt simulation
 
+    To run a backtest:
+    hyperopt = Hyperopt(config)
+    hyperopt.start()
+    """
+    def __init__(self, config: Dict[str, Any]) -> None:
+        super().__init__(config)
+        # set TARGET_TRADES to suit your number concurrent trades so its realistic
+        # to the number of days
+        self.target_trades = 600
+        self.total_tries = config.get('epochs', 0)
+        self.current_best_loss = 100
 
-def save_trials(trials, trials_path=TRIALS_FILE):
-    """Save hyperopt trials to file"""
-    logger.info('Saving Trials to \'{}\''.format(trials_path))
-    pickle.dump(trials, open(trials_path, 'wb'))
+        # max average trade duration in minutes
+        # if eval ends with higher value, we consider it a failed eval
+        self.max_accepted_trade_duration = 300
 
+        # this is expexted avg profit * expected trade count
+        # for example 3.5%, 1100 trades, self.expected_max_profit = 3.85
+        # check that the reported Σ% values do not exceed this!
+        self.expected_max_profit = 3.0
 
-def read_trials(trials_path=TRIALS_FILE):
-    """Read hyperopt trials file"""
-    logger.info('Reading Trials from \'{}\''.format(trials_path))
-    trials = pickle.load(open(trials_path, 'rb'))
-    os.remove(trials_path)
-    return trials
+        # Previous evaluations
+        self.trials_file = os.path.join('user_data', 'hyperopt_results.pickle')
+        self.trials: List = []
 
+    def get_args(self, params):
+        dimensions = self.hyperopt_space()
+        # Ensure the number of dimensions match
+        # the number of parameters in the list x.
+        if len(params) != len(dimensions):
+            raise ValueError('Mismatch in number of search-space dimensions. '
+                             f'len(dimensions)=={len(dimensions)} and len(x)=={len(params)}')
 
-def log_trials_result(trials):
-    vals = json.dumps(trials.best_trial['misc']['vals'], indent=4)
-    results = trials.best_trial['result']['result']
-    logger.info('Best result:\n%s\nwith values:\n%s', results, vals)
+        # Create a dict where the keys are the names of the dimensions
+        # and the values are taken from the list of parameters x.
+        arg_dict = {dim.name: value for dim, value in zip(dimensions, params)}
+        return arg_dict
 
-
-def log_results(results):
-    """ log results if it is better than any previous evaluation """
-    global CURRENT_BEST_LOSS
-
-    if results['loss'] < CURRENT_BEST_LOSS:
-        CURRENT_BEST_LOSS = results['loss']
-        logger.info('{:5d}/{}: {}'.format(
-            results['current_tries'],
-            results['total_tries'],
-            results['result']))
-    else:
-        print('.', end='')
-        sys.stdout.flush()
-
-
-def calculate_loss(total_profit: float, trade_count: int, trade_duration: float):
-    """ objective function, returns smaller number for more optimal results """
-    trade_loss = 1 - 0.35 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.2)
-    profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
-    duration_loss = min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
-    return trade_loss + profit_loss + duration_loss
-
-
-def optimizer(params):
-    global _CURRENT_TRIES
-
-    from freqtrade.optimize import backtesting
-    backtesting.populate_buy_trend = buy_strategy_generator(params)
-
-    results = backtest(OPTIMIZE_CONFIG['stake_amount'], PROCESSED, stoploss=params['stoploss'])
-    result_explanation = format_results(results)
-
-    total_profit = results.profit_percent.sum()
-    trade_count = len(results.index)
-    trade_duration = results.duration.mean() * 5
-
-    if trade_count == 0 or trade_duration > MAX_ACCEPTED_TRADE_DURATION:
-        print('.', end='')
-        return {
-            'status': STATUS_FAIL,
-            'loss': float('inf')
-        }
-
-    loss = calculate_loss(total_profit, trade_count, trade_duration)
-
-    _CURRENT_TRIES += 1
-
-    log_results({
-        'loss': loss,
-        'current_tries': _CURRENT_TRIES,
-        'total_tries': TOTAL_TRIES,
-        'result': result_explanation,
-    })
-
-    return {
-        'loss': loss,
-        'status': STATUS_OK,
-        'result': result_explanation,
-    }
-
-
-def format_results(results: DataFrame):
-    return ('{:6d} trades. Avg profit {: 5.2f}%. '
-            'Total profit {: 11.8f} BTC. Avg duration {:5.1f} mins.').format(
-                len(results.index),
-                results.profit_percent.mean() * 100.0,
-                results.profit_BTC.sum(),
-                results.duration.mean() * 5,
-    )
-
-
-def buy_strategy_generator(params):
-    def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
-        conditions = []
-        # GUARDS AND TRENDS
-        if params['uptrend_long_ema']['enabled']:
-            conditions.append(dataframe['ema50'] > dataframe['ema100'])
-        if params['uptrend_short_ema']['enabled']:
-            conditions.append(dataframe['ema5'] > dataframe['ema10'])
-        if params['mfi']['enabled']:
-            conditions.append(dataframe['mfi'] < params['mfi']['value'])
-        if params['fastd']['enabled']:
-            conditions.append(dataframe['fastd'] < params['fastd']['value'])
-        if params['adx']['enabled']:
-            conditions.append(dataframe['adx'] > params['adx']['value'])
-        if params['rsi']['enabled']:
-            conditions.append(dataframe['rsi'] < params['rsi']['value'])
-        if params['over_sar']['enabled']:
-            conditions.append(dataframe['close'] > dataframe['sar'])
-        if params['green_candle']['enabled']:
-            conditions.append(dataframe['close'] > dataframe['open'])
-        if params['uptrend_sma']['enabled']:
-            prevsma = dataframe['sma'].shift(1)
-            conditions.append(dataframe['sma'] > prevsma)
-
-        # TRIGGERS
-        triggers = {
-            'lower_bb': dataframe['tema'] <= dataframe['blower'],
-            'faststoch10': (crossed_above(dataframe['fastd'], 10.0)),
-            'ao_cross_zero': (crossed_above(dataframe['ao'], 0.0)),
-            'ema5_cross_ema10': (crossed_above(dataframe['ema5'], dataframe['ema10'])),
-            'macd_cross_signal': (crossed_above(dataframe['macd'], dataframe['macdsignal'])),
-            'sar_reversal': (crossed_above(dataframe['close'], dataframe['sar'])),
-            'stochf_cross': (crossed_above(dataframe['fastk'], dataframe['fastd'])),
-            'ht_sine': (crossed_above(dataframe['htleadsine'], dataframe['htsine'])),
-        }
-        conditions.append(triggers.get(params['trigger']['type']))
-
-        dataframe.loc[
-            reduce(lambda x, y: x & y, conditions),
-            'buy'] = 1
+    @staticmethod
+    def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe['adx'] = ta.ADX(dataframe)
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        dataframe['mfi'] = ta.MFI(dataframe)
+        dataframe['rsi'] = ta.RSI(dataframe)
+        stoch_fast = ta.STOCHF(dataframe)
+        dataframe['fastd'] = stoch_fast['fastd']
+        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+        # Bollinger bands
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        dataframe['bb_lowerband'] = bollinger['lower']
+        dataframe['sar'] = ta.SAR(dataframe)
 
         return dataframe
-    return populate_buy_trend
 
+    def save_trials(self) -> None:
+        """
+        Save hyperopt trials to file
+        """
+        if self.trials:
+            logger.info('Saving %d evaluations to \'%s\'', len(self.trials), self.trials_file)
+            dump(self.trials, self.trials_file)
 
-def start(args):
-    global TOTAL_TRIES, PROCESSED, SPACE, TRIALS, _CURRENT_TRIES
+    def read_trials(self) -> List:
+        """
+        Read hyperopt trials file
+        """
+        logger.info('Reading Trials from \'%s\'', self.trials_file)
+        trials = load(self.trials_file)
+        os.remove(self.trials_file)
+        return trials
 
-    TOTAL_TRIES = args.epochs
+    def log_trials_result(self) -> None:
+        """
+        Display Best hyperopt result
+        """
+        results = sorted(self.trials, key=itemgetter('loss'))
+        best_result = results[0]
+        logger.info(
+            'Best result:\n%s\nwith values:\n%s',
+            best_result['result'],
+            best_result['params']
+        )
+        if 'roi_t1' in best_result['params']:
+            logger.info('ROI table:\n%s', self.generate_roi_table(best_result['params']))
 
-    exchange._API = Bittrex({'key': '', 'secret': ''})
+    def log_results(self, results) -> None:
+        """
+        Log results if it is better than any previous evaluation
+        """
+        if results['loss'] < self.current_best_loss:
+            current = results['current_tries']
+            total = results['total_tries']
+            res = results['result']
+            loss = results['loss']
+            self.current_best_loss = results['loss']
+            log_msg = f'\n{current:5d}/{total}: {res}. Loss {loss:.5f}'
+            print(log_msg)
+        else:
+            print('.', end='')
+            sys.stdout.flush()
 
-    # Initialize logger
-    logging.basicConfig(
-        level=args.loglevel,
-        format='\n%(message)s',
-    )
+    def calculate_loss(self, total_profit: float, trade_count: int, trade_duration: float) -> float:
+        """
+        Objective function, returns smaller number for more optimal results
+        """
+        trade_loss = 1 - 0.25 * exp(-(trade_count - self.target_trades) ** 2 / 10 ** 5.8)
+        profit_loss = max(0, 1 - total_profit / self.expected_max_profit)
+        duration_loss = 0.4 * min(trade_duration / self.max_accepted_trade_duration, 1)
+        result = trade_loss + profit_loss + duration_loss
+        return result
 
-    logger.info('Using config: %s ...', args.config)
-    config = load_config(args.config)
-    pairs = config['exchange']['pair_whitelist']
-    PROCESSED = optimize.preprocess(optimize.load_data(
-        args.datadir, pairs=pairs, ticker_interval=args.ticker_interval))
+    @staticmethod
+    def generate_roi_table(params: Dict) -> Dict[int, float]:
+        """
+        Generate the ROI table that will be used by Hyperopt
+        """
+        roi_table = {}
+        roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
+        roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
+        roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
+        roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
 
-    if args.mongodb:
-        logger.info('Using mongodb ...')
-        logger.info('Start scripts/start-mongodb.sh and start-hyperopt-worker.sh manually!')
+        return roi_table
 
-        db_name = 'freqtrade_hyperopt'
-        TRIALS = MongoTrials('mongo://127.0.0.1:1234/{}/jobs'.format(db_name), exp_key='exp1')
-    else:
-        logger.info('Preparing Trials..')
-        signal.signal(signal.SIGINT, signal_handler)
-        # read trials file if we have one
-        if os.path.exists(TRIALS_FILE):
-            TRIALS = read_trials()
+    @staticmethod
+    def roi_space() -> List[Dimension]:
+        """
+        Values to search for each ROI steps
+        """
+        return [
+            Integer(10, 120, name='roi_t1'),
+            Integer(10, 60, name='roi_t2'),
+            Integer(10, 40, name='roi_t3'),
+            Real(0.01, 0.04, name='roi_p1'),
+            Real(0.01, 0.07, name='roi_p2'),
+            Real(0.01, 0.20, name='roi_p3'),
+        ]
 
-            _CURRENT_TRIES = len(TRIALS.results)
-            TOTAL_TRIES = TOTAL_TRIES + _CURRENT_TRIES
-            logger.info(
-                'Continuing with trials. Current: {}, Total: {}'
-                .format(_CURRENT_TRIES, TOTAL_TRIES))
+    @staticmethod
+    def stoploss_space() -> List[Dimension]:
+        """
+        Stoploss search space
+        """
+        return [
+            Real(-0.5, -0.02, name='stoploss'),
+        ]
 
-    try:
-        best_parameters = fmin(
-            fn=optimizer,
-            space=SPACE,
-            algo=tpe.suggest,
-            max_evals=TOTAL_TRIES,
-            trials=TRIALS
+    @staticmethod
+    def indicator_space() -> List[Dimension]:
+        """
+        Define your Hyperopt space for searching strategy parameters
+        """
+        return [
+            Integer(10, 25, name='mfi-value'),
+            Integer(15, 45, name='fastd-value'),
+            Integer(20, 50, name='adx-value'),
+            Integer(20, 40, name='rsi-value'),
+            Categorical([True, False], name='mfi-enabled'),
+            Categorical([True, False], name='fastd-enabled'),
+            Categorical([True, False], name='adx-enabled'),
+            Categorical([True, False], name='rsi-enabled'),
+            Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
+        ]
+
+    def has_space(self, space: str) -> bool:
+        """
+        Tell if a space value is contained in the configuration
+        """
+        if space in self.config['spaces'] or 'all' in self.config['spaces']:
+            return True
+        return False
+
+    def hyperopt_space(self) -> List[Dimension]:
+        """
+        Return the space to use during Hyperopt
+        """
+        spaces: List[Dimension] = []
+        if self.has_space('buy'):
+            spaces += Hyperopt.indicator_space()
+        if self.has_space('roi'):
+            spaces += Hyperopt.roi_space()
+        if self.has_space('stoploss'):
+            spaces += Hyperopt.stoploss_space()
+        return spaces
+
+    @staticmethod
+    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the buy strategy parameters to be used by hyperopt
+        """
+        def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+            """
+            Buy strategy Hyperopt will build and use
+            """
+            conditions = []
+            # GUARDS AND TRENDS
+            if 'mfi-enabled' in params and params['mfi-enabled']:
+                conditions.append(dataframe['mfi'] < params['mfi-value'])
+            if 'fastd-enabled' in params and params['fastd-enabled']:
+                conditions.append(dataframe['fastd'] < params['fastd-value'])
+            if 'adx-enabled' in params and params['adx-enabled']:
+                conditions.append(dataframe['adx'] > params['adx-value'])
+            if 'rsi-enabled' in params and params['rsi-enabled']:
+                conditions.append(dataframe['rsi'] < params['rsi-value'])
+
+            # TRIGGERS
+            if params['trigger'] == 'bb_lower':
+                conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
+            if params['trigger'] == 'macd_cross_signal':
+                conditions.append(qtpylib.crossed_above(
+                    dataframe['macd'], dataframe['macdsignal']
+                ))
+            if params['trigger'] == 'sar_reversal':
+                conditions.append(qtpylib.crossed_above(
+                    dataframe['close'], dataframe['sar']
+                ))
+
+            dataframe.loc[
+                reduce(lambda x, y: x & y, conditions),
+                'buy'] = 1
+
+            return dataframe
+
+        return populate_buy_trend
+
+    def generate_optimizer(self, _params) -> Dict:
+        params = self.get_args(_params)
+
+        if self.has_space('roi'):
+            self.strategy.minimal_roi = self.generate_roi_table(params)
+
+        if self.has_space('buy'):
+            self.advise_buy = self.buy_strategy_generator(params)
+
+        if self.has_space('stoploss'):
+            self.strategy.stoploss = params['stoploss']
+
+        processed = load(TICKERDATA_PICKLE)
+        results = self.backtest(
+            {
+                'stake_amount': self.config['stake_amount'],
+                'processed': processed,
+                'position_stacking': self.config.get('position_stacking', True),
+            }
+        )
+        result_explanation = self.format_results(results)
+
+        total_profit = results.profit_percent.sum()
+        trade_count = len(results.index)
+        trade_duration = results.trade_duration.mean()
+
+        if trade_count == 0:
+            return {
+                'loss': MAX_LOSS,
+                'params': params,
+                'result': result_explanation,
+            }
+
+        loss = self.calculate_loss(total_profit, trade_count, trade_duration)
+
+        return {
+            'loss': loss,
+            'params': params,
+            'result': result_explanation,
+        }
+
+    def format_results(self, results: DataFrame) -> str:
+        """
+        Return the format result in a string
+        """
+        trades = len(results.index)
+        avg_profit = results.profit_percent.mean() * 100.0
+        total_profit = results.profit_abs.sum()
+        stake_cur = self.config['stake_currency']
+        profit = results.profit_percent.sum()
+        duration = results.trade_duration.mean()
+
+        return (f'{trades:6d} trades. Avg profit {avg_profit: 5.2f}%. '
+                f'Total profit {total_profit: 11.8f} {stake_cur} '
+                f'({profit:.4f}Σ%). Avg duration {duration:5.1f} mins.')
+
+    def get_optimizer(self, cpu_count) -> Optimizer:
+        return Optimizer(
+            self.hyperopt_space(),
+            base_estimator="ET",
+            acq_optimizer="auto",
+            n_initial_points=30,
+            acq_optimizer_kwargs={'n_jobs': cpu_count}
         )
 
-        results = sorted(TRIALS.results, key=itemgetter('loss'))
-        best_result = results[0]['result']
+    def run_optimizer_parallel(self, parallel, asked) -> List:
+        return parallel(delayed(self.generate_optimizer)(v) for v in asked)
 
-    except ValueError:
-        best_parameters = {}
-        best_result = 'Sorry, Hyperopt was not able to find good parameters. Please ' \
-                      'try with more epochs (param: -e).'
+    def load_previous_results(self):
+        """ read trials file if we have one """
+        if os.path.exists(self.trials_file) and os.path.getsize(self.trials_file) > 0:
+            self.trials = self.read_trials()
+            logger.info(
+                'Loaded %d previous evaluations from disk.',
+                len(self.trials)
+            )
 
-    # Improve best parameter logging display
-    if best_parameters:
-        best_parameters = space_eval(SPACE, best_parameters)
+    def start(self) -> None:
+        timerange = Arguments.parse_timerange(None if self.config.get(
+            'timerange') is None else str(self.config.get('timerange')))
+        data = load_data(
+            datadir=str(self.config.get('datadir')),
+            pairs=self.config['exchange']['pair_whitelist'],
+            ticker_interval=self.ticker_interval,
+            timerange=timerange
+        )
 
-    logger.info('Best parameters:\n%s', json.dumps(best_parameters, indent=4))
-    logger.info('Best Result:\n%s', best_result)
+        if self.has_space('buy'):
+            self.strategy.advise_indicators = Hyperopt.populate_indicators  # type: ignore
+        dump(self.tickerdata_to_dataframe(data), TICKERDATA_PICKLE)
+        self.exchange = None  # type: ignore
+        self.load_previous_results()
 
-    # Store trials result to file to resume next time
-    save_trials(TRIALS)
+        cpus = multiprocessing.cpu_count()
+        logger.info(f'Found {cpus} CPU cores. Let\'s make them scream!')
+
+        opt = self.get_optimizer(cpus)
+        EVALS = max(self.total_tries // cpus, 1)
+        try:
+            with Parallel(n_jobs=cpus) as parallel:
+                for i in range(EVALS):
+                    asked = opt.ask(n_points=cpus)
+                    f_val = self.run_optimizer_parallel(parallel, asked)
+                    opt.tell(asked, [i['loss'] for i in f_val])
+
+                    self.trials += f_val
+                    for j in range(cpus):
+                        self.log_results({
+                            'loss': f_val[j]['loss'],
+                            'current_tries': i * cpus + j,
+                            'total_tries': self.total_tries,
+                            'result': f_val[j]['result'],
+                        })
+        except KeyboardInterrupt:
+            print('User interrupted..')
+
+        self.save_trials()
+        self.log_trials_result()
 
 
-def signal_handler(sig, frame):
-    """Hyperopt SIGINT handler"""
-    logger.info('Hyperopt received {}'.format(signal.Signals(sig).name))
+def start(args: Namespace) -> None:
+    """
+    Start Backtesting script
+    :param args: Cli args from Arguments()
+    :return: None
+    """
 
-    save_trials(TRIALS)
-    log_trials_result(TRIALS)
-    sys.exit(0)
+    # Remove noisy log messages
+    logging.getLogger('hyperopt.tpe').setLevel(logging.WARNING)
+
+    # Initialize configuration
+    # Monkey patch the configuration with hyperopt_conf.py
+    configuration = Configuration(args)
+    logger.info('Starting freqtrade in Hyperopt mode')
+    config = configuration.load_config()
+
+    config['exchange']['key'] = ''
+    config['exchange']['secret'] = ''
+
+    if config.get('strategy') and config.get('strategy') != 'DefaultStrategy':
+        logger.error("Please don't use --strategy for hyperopt.")
+        logger.error(
+            "Read the documentation at "
+            "https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md "
+            "to understand how to configure hyperopt.")
+        raise ValueError("--strategy configured but not supported for hyperopt")
+    # Initialize backtesting object
+    hyperopt = Hyperopt(config)
+    hyperopt.start()

freqtrade/optimize/hyperopt_conf.py

@@ -1,41 +0,0 @@
-"""
-File that contains the configuration for Hyperopt
-"""
-
-
-def hyperopt_optimize_conf() -> dict:
-    """
-    This function is used to define which parameters Hyperopt must used.
-    The "pair_whitelist" is only used is your are using Hyperopt with MongoDB,
-    without MongoDB, Hyperopt will use the pair your have set in your config file.
-    :return:
-    """
-    return {
-        'max_open_trades': 3,
-        'stake_currency': 'BTC',
-        'stake_amount': 0.01,
-        "minimal_roi": {
-            '40': 0.0,
-            '30': 0.01,
-            '20': 0.02,
-            '0': 0.04,
-        },
-        'stoploss': -0.10,
-        "bid_strategy": {
-            "ask_last_balance": 0.0
-        },
-        "exchange": {
-            "pair_whitelist": [
-                "BTC_ETH",
-                "BTC_LTC",
-                "BTC_ETC",
-                "BTC_DASH",
-                "BTC_ZEC",
-                "BTC_XLM",
-                "BTC_NXT",
-                "BTC_POWR",
-                "BTC_ADA",
-                "BTC_XMR"
-            ]
-        }
-    }

freqtrade/persistence.py

@@ -1,51 +1,135 @@
+"""
+This module contains the class to persist trades into SQLite
+"""
+
 import logging
 from datetime import datetime
 from decimal import Decimal, getcontext
-from typing import Dict, Optional
+from typing import Any, Dict, Optional
 
 import arrow
 from sqlalchemy import (Boolean, Column, DateTime, Float, Integer, String,
-                        create_engine)
-from sqlalchemy.engine import Engine
+                        create_engine, inspect)
+from sqlalchemy.exc import NoSuchModuleError
 from sqlalchemy.ext.declarative import declarative_base
 from sqlalchemy.orm.scoping import scoped_session
 from sqlalchemy.orm.session import sessionmaker
 from sqlalchemy.pool import StaticPool
 
+from freqtrade import OperationalException
+
 logger = logging.getLogger(__name__)
 
-_CONF = {}
-_DECL_BASE = declarative_base()
+_DECL_BASE: Any = declarative_base()
+_SQL_DOCS_URL = 'http://docs.sqlalchemy.org/en/latest/core/engines.html#database-urls'
 
 
-def init(config: dict, engine: Optional[Engine] = None) -> None:
+def init(config: Dict) -> None:
     """
     Initializes this module with the given config,
     registers all known command handlers
     and starts polling for message updates
     :param config: config to use
-    :param engine: database engine for sqlalchemy (Optional)
     :return: None
     """
-    _CONF.update(config)
-    if not engine:
-        if _CONF.get('dry_run', False):
-            # the user wants dry run to use a DB
-            if _CONF.get('dry_run_db', False):
-                engine = create_engine('sqlite:///tradesv3.dry_run.sqlite')
-            # Otherwise dry run will store in memory
-            else:
-                engine = create_engine('sqlite://',
-                                       connect_args={'check_same_thread': False},
-                                       poolclass=StaticPool,
-                                       echo=False)
-        else:
-            engine = create_engine('sqlite:///tradesv3.sqlite')
+    db_url = config.get('db_url', None)
+    kwargs = {}
+
+    # Take care of thread ownership if in-memory db
+    if db_url == 'sqlite://':
+        kwargs.update({
+            'connect_args': {'check_same_thread': False},
+            'poolclass': StaticPool,
+            'echo': False,
+        })
+
+    try:
+        engine = create_engine(db_url, **kwargs)
+    except NoSuchModuleError:
+        raise OperationalException(f'Given value for db_url: \'{db_url}\' '
+                                   f'is no valid database URL! (See {_SQL_DOCS_URL})')
 
     session = scoped_session(sessionmaker(bind=engine, autoflush=True, autocommit=True))
     Trade.session = session()
     Trade.query = session.query_property()
     _DECL_BASE.metadata.create_all(engine)
+    check_migrate(engine)
+
+    # Clean dry_run DB if the db is not in-memory
+    if config.get('dry_run', False) and db_url != 'sqlite://':
+        clean_dry_run_db()
+
+
+def has_column(columns, searchname: str) -> bool:
+    return len(list(filter(lambda x: x["name"] == searchname, columns))) == 1
+
+
+def get_column_def(columns, column: str, default: str) -> str:
+    return default if not has_column(columns, column) else column
+
+
+def check_migrate(engine) -> None:
+    """
+    Checks if migration is necessary and migrates if necessary
+    """
+    inspector = inspect(engine)
+
+    cols = inspector.get_columns('trades')
+    tabs = inspector.get_table_names()
+    table_back_name = 'trades_bak'
+    for i, table_back_name in enumerate(tabs):
+        table_back_name = f'trades_bak{i}'
+        logger.debug(f'trying {table_back_name}')
+
+    # Check for latest column
+    if not has_column(cols, 'ticker_interval'):
+        logger.info(f'Running database migration - backup available as {table_back_name}')
+
+        fee_open = get_column_def(cols, 'fee_open', 'fee')
+        fee_close = get_column_def(cols, 'fee_close', 'fee')
+        open_rate_requested = get_column_def(cols, 'open_rate_requested', 'null')
+        close_rate_requested = get_column_def(cols, 'close_rate_requested', 'null')
+        stop_loss = get_column_def(cols, 'stop_loss', '0.0')
+        initial_stop_loss = get_column_def(cols, 'initial_stop_loss', '0.0')
+        max_rate = get_column_def(cols, 'max_rate', '0.0')
+        sell_reason = get_column_def(cols, 'sell_reason', 'null')
+        strategy = get_column_def(cols, 'strategy', 'null')
+        ticker_interval = get_column_def(cols, 'ticker_interval', 'null')
+
+        # Schema migration necessary
+        engine.execute(f"alter table trades rename to {table_back_name}")
+        # let SQLAlchemy create the schema as required
+        _DECL_BASE.metadata.create_all(engine)
+
+        # Copy data back - following the correct schema
+        engine.execute(f"""insert into trades
+                (id, exchange, pair, is_open, fee_open, fee_close, open_rate,
+                open_rate_requested, close_rate, close_rate_requested, close_profit,
+                stake_amount, amount, open_date, close_date, open_order_id,
+                stop_loss, initial_stop_loss, max_rate, sell_reason, strategy,
+                ticker_interval
+                )
+            select id, lower(exchange),
+                case
+                    when instr(pair, '_') != 0 then
+                    substr(pair,    instr(pair, '_') + 1) || '/' ||
+                    substr(pair, 1, instr(pair, '_') - 1)
+                    else pair
+                    end
+                pair,
+                is_open, {fee_open} fee_open, {fee_close} fee_close,
+                open_rate, {open_rate_requested} open_rate_requested, close_rate,
+                {close_rate_requested} close_rate_requested, close_profit,
+                stake_amount, amount, open_date, close_date, open_order_id,
+                {stop_loss} stop_loss, {initial_stop_loss} initial_stop_loss,
+                {max_rate} max_rate, {sell_reason} sell_reason, {strategy} strategy,
+                {ticker_interval} ticker_interval
+                from {table_back_name}
+             """)
+
+        # Reread columns - the above recreated the table!
+        inspector = inspect(engine)
+        cols = inspector.get_columns('trades')
 
 
 def cleanup() -> None:
@@ -56,31 +140,93 @@ def cleanup() -> None:
     Trade.session.flush()
 
 
+def clean_dry_run_db() -> None:
+    """
+    Remove open_order_id from a Dry_run DB
+    :return: None
+    """
+    for trade in Trade.query.filter(Trade.open_order_id.isnot(None)).all():
+        # Check we are updating only a dry_run order not a prod one
+        if 'dry_run' in trade.open_order_id:
+            trade.open_order_id = None
+
+
 class Trade(_DECL_BASE):
+    """
+    Class used to define a trade structure
+    """
     __tablename__ = 'trades'
 
     id = Column(Integer, primary_key=True)
     exchange = Column(String, nullable=False)
-    pair = Column(String, nullable=False)
-    is_open = Column(Boolean, nullable=False, default=True)
-    fee = Column(Float, nullable=False, default=0.0)
+    pair = Column(String, nullable=False, index=True)
+    is_open = Column(Boolean, nullable=False, default=True, index=True)
+    fee_open = Column(Float, nullable=False, default=0.0)
+    fee_close = Column(Float, nullable=False, default=0.0)
     open_rate = Column(Float)
+    open_rate_requested = Column(Float)
     close_rate = Column(Float)
+    close_rate_requested = Column(Float)
     close_profit = Column(Float)
     stake_amount = Column(Float, nullable=False)
     amount = Column(Float)
     open_date = Column(DateTime, nullable=False, default=datetime.utcnow)
     close_date = Column(DateTime)
     open_order_id = Column(String)
+    # absolute value of the stop loss
+    stop_loss = Column(Float, nullable=True, default=0.0)
+    # absolute value of the initial stop loss
+    initial_stop_loss = Column(Float, nullable=True, default=0.0)
+    # absolute value of the highest reached price
+    max_rate = Column(Float, nullable=True, default=0.0)
+    sell_reason = Column(String, nullable=True)
+    strategy = Column(String, nullable=True)
+    ticker_interval = Column(Integer, nullable=True)
 
     def __repr__(self):
-        return 'Trade(id={}, pair={}, amount={:.8f}, open_rate={:.8f}, open_since={})'.format(
-            self.id,
-            self.pair,
-            self.amount,
-            self.open_rate,
-            arrow.get(self.open_date).humanize() if self.is_open else 'closed'
-        )
+        open_since = arrow.get(self.open_date).humanize() if self.is_open else 'closed'
+
+        return (f'Trade(id={self.id}, pair={self.pair}, amount={self.amount:.8f}, '
+                f'open_rate={self.open_rate:.8f}, open_since={open_since})')
+
+    def adjust_stop_loss(self, current_price: float, stoploss: float, initial: bool = False):
+        """this adjusts the stop loss to it's most recently observed setting"""
+
+        if initial and not (self.stop_loss is None or self.stop_loss == 0):
+            # Don't modify if called with initial and nothing to do
+            return
+
+        new_loss = float(current_price * (1 - abs(stoploss)))
+
+        # keeping track of the highest observed rate for this trade
+        if self.max_rate is None:
+            self.max_rate = current_price
+        else:
+            if current_price > self.max_rate:
+                self.max_rate = current_price
+
+        # no stop loss assigned yet
+        if not self.stop_loss:
+            logger.debug("assigning new stop loss")
+            self.stop_loss = new_loss
+            self.initial_stop_loss = new_loss
+
+        # evaluate if the stop loss needs to be updated
+        else:
+            if new_loss > self.stop_loss:  # stop losses only walk up, never down!
+                self.stop_loss = new_loss
+                logger.debug("adjusted stop loss")
+            else:
+                logger.debug("keeping current stop loss")
+
+        logger.debug(
+            f"{self.pair} - current price {current_price:.8f}, "
+            f"bought at {self.open_rate:.8f} and calculated "
+            f"stop loss is at: {self.initial_stop_loss:.8f} initial "
+            f"stop at {self.stop_loss:.8f}. "
+            f"trailing stop loss saved us: "
+            f"{float(self.stop_loss) - float(self.initial_stop_loss):.8f} "
+            f"and max observed rate was {self.max_rate:.8f}")
 
     def update(self, order: Dict) -> None:
         """
@@ -88,23 +234,24 @@ class Trade(_DECL_BASE):
         :param order: order retrieved by exchange.get_order()
         :return: None
         """
+        order_type = order['type']
         # Ignore open and cancelled orders
-        if not order['closed'] or order['rate'] is None:
+        if order['status'] == 'open' or order['price'] is None:
             return
 
         logger.info('Updating trade (id=%d) ...', self.id)
 
         getcontext().prec = 8  # Bittrex do not go above 8 decimal
-        if order['type'] == 'LIMIT_BUY':
+        if order_type == 'limit' and order['side'] == 'buy':
             # Update open rate and actual amount
-            self.open_rate = Decimal(order['rate'])
+            self.open_rate = Decimal(order['price'])
             self.amount = Decimal(order['amount'])
             logger.info('LIMIT_BUY has been fulfilled for %s.', self)
             self.open_order_id = None
-        elif order['type'] == 'LIMIT_SELL':
-            self.close(order['rate'])
+        elif order_type == 'limit' and order['side'] == 'sell':
+            self.close(order['price'])
         else:
-            raise ValueError('Unknown order type: {}'.format(order['type']))
+            raise ValueError(f'Unknown order type: {order_type}')
         cleanup()
 
     def close(self, rate: float) -> None:
@@ -134,7 +281,7 @@ class Trade(_DECL_BASE):
         getcontext().prec = 8
 
         buy_trade = (Decimal(self.amount) * Decimal(self.open_rate))
-        fees = buy_trade * Decimal(fee or self.fee)
+        fees = buy_trade * Decimal(fee or self.fee_open)
         return float(buy_trade + fees)
 
     def calc_close_trade_price(
@@ -155,7 +302,7 @@ class Trade(_DECL_BASE):
             return 0.0
 
         sell_trade = (Decimal(self.amount) * Decimal(rate or self.close_rate))
-        fees = sell_trade * Decimal(fee or self.fee)
+        fees = sell_trade * Decimal(fee or self.fee_close)
         return float(sell_trade - fees)
 
     def calc_profit(
@@ -172,10 +319,11 @@ class Trade(_DECL_BASE):
         """
         open_trade_price = self.calc_open_trade_price()
         close_trade_price = self.calc_close_trade_price(
-            rate=Decimal(rate or self.close_rate),
-            fee=Decimal(fee or self.fee)
+            rate=(rate or self.close_rate),
+            fee=(fee or self.fee_close)
         )
-        return float("{0:.8f}".format(close_trade_price - open_trade_price))
+        profit = close_trade_price - open_trade_price
+        return float(f"{profit:.8f}")
 
     def calc_profit_percent(
             self,
@@ -185,14 +333,15 @@ class Trade(_DECL_BASE):
         Calculates the profit in percentage (including fee).
         :param rate: rate to compare with (optional).
         If rate is not set self.close_rate will be used
+        :param fee: fee to use on the close rate (optional).
         :return: profit in percentage as float
         """
         getcontext().prec = 8
 
         open_trade_price = self.calc_open_trade_price()
         close_trade_price = self.calc_close_trade_price(
-            rate=Decimal(rate or self.close_rate),
-            fee=Decimal(fee or self.fee)
+            rate=(rate or self.close_rate),
+            fee=(fee or self.fee_close)
         )
-
-        return float("{0:.8f}".format((close_trade_price / open_trade_price) - 1))
+        profit_percent = (close_trade_price / open_trade_price) - 1
+        return float(f"{profit_percent:.8f}")

freqtrade/rpc/__init__.py

@@ -1,42 +1,2 @@
-import logging
-
-from . import telegram
-
-logger = logging.getLogger(__name__)
-
-
-REGISTERED_MODULES = []
-
-
-def init(config: dict) -> None:
-    """
-    Initializes all enabled rpc modules
-    :param config: config to use
-    :return: None
-    """
-
-    if config['telegram'].get('enabled', False):
-        logger.info('Enabling rpc.telegram ...')
-        REGISTERED_MODULES.append('telegram')
-        telegram.init(config)
-
-
-def cleanup() -> None:
-    """
-    Stops all enabled rpc modules
-    :return: None
-    """
-    if 'telegram' in REGISTERED_MODULES:
-        logger.debug('Cleaning up rpc.telegram ...')
-        telegram.cleanup()
-
-
-def send_msg(msg: str) -> None:
-    """
-    Send given markdown message to all registered rpc modules
-    :param msg: message
-    :return: None
-    """
-    logger.info(msg)
-    if 'telegram' in REGISTERED_MODULES:
-        telegram.send_msg(msg)
+from .rpc import RPC, RPCMessageType, RPCException  # noqa
+from .rpc_manager import RPCManager  # noqa

freqtrade/rpc/rpc.py

@@ -0,0 +1,407 @@
+"""
+This module contains class to define a RPC communications
+"""
+import logging
+from abc import abstractmethod
+from datetime import timedelta, datetime, date
+from decimal import Decimal
+from enum import Enum
+from typing import Dict, Any, List, Optional
+
+import arrow
+import sqlalchemy as sql
+from numpy import mean, nan_to_num
+from pandas import DataFrame
+
+from freqtrade import TemporaryError
+from freqtrade.fiat_convert import CryptoToFiatConverter
+from freqtrade.misc import shorten_date
+from freqtrade.persistence import Trade
+from freqtrade.state import State
+from freqtrade.strategy.interface import SellType
+
+logger = logging.getLogger(__name__)
+
+
+class RPCMessageType(Enum):
+    STATUS_NOTIFICATION = 'status'
+    WARNING_NOTIFICATION = 'warning'
+    CUSTOM_NOTIFICATION = 'custom'
+    BUY_NOTIFICATION = 'buy'
+    SELL_NOTIFICATION = 'sell'
+
+    def __repr__(self):
+        return self.value
+
+
+class RPCException(Exception):
+    """
+    Should be raised with a rpc-formatted message in an _rpc_* method
+    if the required state is wrong, i.e.:
+
+    raise RPCException('*Status:* `no active trade`')
+    """
+    def __init__(self, message: str) -> None:
+        super().__init__(self)
+        self.message = message
+
+    def __str__(self):
+        return self.message
+
+
+class RPC(object):
+    """
+    RPC class can be used to have extra feature, like bot data, and access to DB data
+    """
+    # Bind _fiat_converter if needed in each RPC handler
+    _fiat_converter: Optional[CryptoToFiatConverter] = None
+
+    def __init__(self, freqtrade) -> None:
+        """
+        Initializes all enabled rpc modules
+        :param freqtrade: Instance of a freqtrade bot
+        :return: None
+        """
+        self._freqtrade = freqtrade
+
+    @property
+    def name(self) -> str:
+        """ Returns the lowercase name of the implementation """
+        return self.__class__.__name__.lower()
+
+    @abstractmethod
+    def cleanup(self) -> None:
+        """ Cleanup pending module resources """
+
+    @abstractmethod
+    def send_msg(self, msg: Dict[str, str]) -> None:
+        """ Sends a message to all registered rpc modules """
+
+    def _rpc_trade_status(self) -> List[Dict[str, Any]]:
+        """
+        Below follows the RPC backend it is prefixed with rpc_ to raise awareness that it is
+        a remotely exposed function
+        """
+        # Fetch open trade
+        trades = Trade.query.filter(Trade.is_open.is_(True)).all()
+        if self._freqtrade.state != State.RUNNING:
+            raise RPCException('trader is not running')
+        elif not trades:
+            raise RPCException('no active trade')
+        else:
+            results = []
+            for trade in trades:
+                order = None
+                if trade.open_order_id:
+                    order = self._freqtrade.exchange.get_order(trade.open_order_id, trade.pair)
+                # calculate profit and send message to user
+                current_rate = self._freqtrade.exchange.get_ticker(trade.pair, False)['bid']
+                current_profit = trade.calc_profit_percent(current_rate)
+                fmt_close_profit = (f'{round(trade.close_profit * 100, 2):.2f}%'
+                                    if trade.close_profit else None)
+                results.append(dict(
+                    trade_id=trade.id,
+                    pair=trade.pair,
+                    market_url=self._freqtrade.exchange.get_pair_detail_url(trade.pair),
+                    date=arrow.get(trade.open_date),
+                    open_rate=trade.open_rate,
+                    close_rate=trade.close_rate,
+                    current_rate=current_rate,
+                    amount=round(trade.amount, 8),
+                    close_profit=fmt_close_profit,
+                    current_profit=round(current_profit * 100, 2),
+                    open_order='({} {} rem={:.8f})'.format(
+                      order['type'], order['side'], order['remaining']
+                    ) if order else None,
+                ))
+            return results
+
+    def _rpc_status_table(self) -> DataFrame:
+        trades = Trade.query.filter(Trade.is_open.is_(True)).all()
+        if self._freqtrade.state != State.RUNNING:
+            raise RPCException('trader is not running')
+        elif not trades:
+            raise RPCException('no active order')
+        else:
+            trades_list = []
+            for trade in trades:
+                # calculate profit and send message to user
+                current_rate = self._freqtrade.exchange.get_ticker(trade.pair, False)['bid']
+                trade_perc = (100 * trade.calc_profit_percent(current_rate))
+                trades_list.append([
+                    trade.id,
+                    trade.pair,
+                    shorten_date(arrow.get(trade.open_date).humanize(only_distance=True)),
+                    f'{trade_perc:.2f}%'
+                ])
+
+            columns = ['ID', 'Pair', 'Since', 'Profit']
+            df_statuses = DataFrame.from_records(trades_list, columns=columns)
+            df_statuses = df_statuses.set_index(columns[0])
+            return df_statuses
+
+    def _rpc_daily_profit(
+            self, timescale: int,
+            stake_currency: str, fiat_display_currency: str) -> List[List[Any]]:
+        today = datetime.utcnow().date()
+        profit_days: Dict[date, Dict] = {}
+
+        if not (isinstance(timescale, int) and timescale > 0):
+            raise RPCException('timescale must be an integer greater than 0')
+
+        for day in range(0, timescale):
+            profitday = today - timedelta(days=day)
+            trades = Trade.query \
+                .filter(Trade.is_open.is_(False)) \
+                .filter(Trade.close_date >= profitday)\
+                .filter(Trade.close_date < (profitday + timedelta(days=1)))\
+                .order_by(Trade.close_date)\
+                .all()
+            curdayprofit = sum(trade.calc_profit() for trade in trades)
+            profit_days[profitday] = {
+                'amount': f'{curdayprofit:.8f}',
+                'trades': len(trades)
+            }
+
+        return [
+            [
+                key,
+                '{value:.8f} {symbol}'.format(
+                    value=float(value['amount']),
+                    symbol=stake_currency
+                ),
+                '{value:.3f} {symbol}'.format(
+                    value=self._fiat_converter.convert_amount(
+                        value['amount'],
+                        stake_currency,
+                        fiat_display_currency
+                    ) if self._fiat_converter else 0,
+                    symbol=fiat_display_currency
+                ),
+                '{value} trade{s}'.format(
+                    value=value['trades'],
+                    s='' if value['trades'] < 2 else 's'
+                ),
+            ]
+            for key, value in profit_days.items()
+        ]
+
+    def _rpc_trade_statistics(
+            self, stake_currency: str, fiat_display_currency: str) -> Dict[str, Any]:
+        """ Returns cumulative profit statistics """
+        trades = Trade.query.order_by(Trade.id).all()
+
+        profit_all_coin = []
+        profit_all_percent = []
+        profit_closed_coin = []
+        profit_closed_percent = []
+        durations = []
+
+        for trade in trades:
+            current_rate: float = 0.0
+
+            if not trade.open_rate:
+                continue
+            if trade.close_date:
+                durations.append((trade.close_date - trade.open_date).total_seconds())
+
+            if not trade.is_open:
+                profit_percent = trade.calc_profit_percent()
+                profit_closed_coin.append(trade.calc_profit())
+                profit_closed_percent.append(profit_percent)
+            else:
+                # Get current rate
+                current_rate = self._freqtrade.exchange.get_ticker(trade.pair, False)['bid']
+                profit_percent = trade.calc_profit_percent(rate=current_rate)
+
+            profit_all_coin.append(
+                trade.calc_profit(rate=Decimal(trade.close_rate or current_rate))
+            )
+            profit_all_percent.append(profit_percent)
+
+        best_pair = Trade.session.query(
+            Trade.pair, sql.func.sum(Trade.close_profit).label('profit_sum')
+        ).filter(Trade.is_open.is_(False)) \
+            .group_by(Trade.pair) \
+            .order_by(sql.text('profit_sum DESC')).first()
+
+        if not best_pair:
+            raise RPCException('no closed trade')
+
+        bp_pair, bp_rate = best_pair
+
+        # Prepare data to display
+        profit_closed_coin_sum = round(sum(profit_closed_coin), 8)
+        profit_closed_percent = round(nan_to_num(mean(profit_closed_percent)) * 100, 2)
+        profit_closed_fiat = self._fiat_converter.convert_amount(
+            profit_closed_coin_sum,
+            stake_currency,
+            fiat_display_currency
+        ) if self._fiat_converter else 0
+
+        profit_all_coin_sum = round(sum(profit_all_coin), 8)
+        profit_all_percent = round(nan_to_num(mean(profit_all_percent)) * 100, 2)
+        profit_all_fiat = self._fiat_converter.convert_amount(
+            profit_all_coin_sum,
+            stake_currency,
+            fiat_display_currency
+        ) if self._fiat_converter else 0
+
+        num = float(len(durations) or 1)
+        return {
+            'profit_closed_coin': profit_closed_coin_sum,
+            'profit_closed_percent': profit_closed_percent,
+            'profit_closed_fiat': profit_closed_fiat,
+            'profit_all_coin': profit_all_coin_sum,
+            'profit_all_percent': profit_all_percent,
+            'profit_all_fiat': profit_all_fiat,
+            'trade_count': len(trades),
+            'first_trade_date': arrow.get(trades[0].open_date).humanize(),
+            'latest_trade_date': arrow.get(trades[-1].open_date).humanize(),
+            'avg_duration': str(timedelta(seconds=sum(durations) / num)).split('.')[0],
+            'best_pair': bp_pair,
+            'best_rate': round(bp_rate * 100, 2),
+        }
+
+    def _rpc_balance(self, fiat_display_currency: str) -> Dict:
+        """ Returns current account balance per crypto """
+        output = []
+        total = 0.0
+        for coin, balance in self._freqtrade.exchange.get_balances().items():
+            if not balance['total']:
+                continue
+
+            if coin == 'BTC':
+                rate = 1.0
+            else:
+                try:
+                    if coin == 'USDT':
+                        rate = 1.0 / self._freqtrade.exchange.get_ticker('BTC/USDT', False)['bid']
+                    else:
+                        rate = self._freqtrade.exchange.get_ticker(coin + '/BTC', False)['bid']
+                except TemporaryError:
+                    continue
+            est_btc: float = rate * balance['total']
+            total = total + est_btc
+            output.append({
+                'currency': coin,
+                'available': balance['free'],
+                'balance': balance['total'],
+                'pending': balance['used'],
+                'est_btc': est_btc,
+            })
+        if total == 0.0:
+            raise RPCException('all balances are zero')
+
+        symbol = fiat_display_currency
+        value = self._fiat_converter.convert_amount(total, 'BTC',
+                                                    symbol) if self._fiat_converter else 0
+        return {
+            'currencies': output,
+            'total': total,
+            'symbol': symbol,
+            'value': value,
+        }
+
+    def _rpc_start(self) -> Dict[str, str]:
+        """ Handler for start """
+        if self._freqtrade.state == State.RUNNING:
+            return {'status': 'already running'}
+
+        self._freqtrade.state = State.RUNNING
+        return {'status': 'starting trader ...'}
+
+    def _rpc_stop(self) -> Dict[str, str]:
+        """ Handler for stop """
+        if self._freqtrade.state == State.RUNNING:
+            self._freqtrade.state = State.STOPPED
+            return {'status': 'stopping trader ...'}
+
+        return {'status': 'already stopped'}
+
+    def _rpc_reload_conf(self) -> Dict[str, str]:
+        """ Handler for reload_conf. """
+        self._freqtrade.state = State.RELOAD_CONF
+        return {'status': 'reloading config ...'}
+
+    def _rpc_forcesell(self, trade_id) -> None:
+        """
+        Handler for forcesell <id>.
+        Sells the given trade at current price
+        """
+        def _exec_forcesell(trade: Trade) -> None:
+            # Check if there is there is an open order
+            if trade.open_order_id:
+                order = self._freqtrade.exchange.get_order(trade.open_order_id, trade.pair)
+
+                # Cancel open LIMIT_BUY orders and close trade
+                if order and order['status'] == 'open' \
+                        and order['type'] == 'limit' \
+                        and order['side'] == 'buy':
+                    self._freqtrade.exchange.cancel_order(trade.open_order_id, trade.pair)
+                    trade.close(order.get('price') or trade.open_rate)
+                    # Do the best effort, if we don't know 'filled' amount, don't try selling
+                    if order['filled'] is None:
+                        return
+                    trade.amount = order['filled']
+
+                # Ignore trades with an attached LIMIT_SELL order
+                if order and order['status'] == 'open' \
+                        and order['type'] == 'limit' \
+                        and order['side'] == 'sell':
+                    return
+
+            # Get current rate and execute sell
+            current_rate = self._freqtrade.exchange.get_ticker(trade.pair, False)['bid']
+            self._freqtrade.execute_sell(trade, current_rate, SellType.FORCE_SELL)
+        # ---- EOF def _exec_forcesell ----
+
+        if self._freqtrade.state != State.RUNNING:
+            raise RPCException('trader is not running')
+
+        if trade_id == 'all':
+            # Execute sell for all open orders
+            for trade in Trade.query.filter(Trade.is_open.is_(True)).all():
+                _exec_forcesell(trade)
+            return
+
+        # Query for trade
+        trade = Trade.query.filter(
+            sql.and_(
+                Trade.id == trade_id,
+                Trade.is_open.is_(True)
+            )
+        ).first()
+        if not trade:
+            logger.warning('forcesell: Invalid argument received')
+            raise RPCException('invalid argument')
+
+        _exec_forcesell(trade)
+        Trade.session.flush()
+
+    def _rpc_performance(self) -> List[Dict]:
+        """
+        Handler for performance.
+        Shows a performance statistic from finished trades
+        """
+        if self._freqtrade.state != State.RUNNING:
+            raise RPCException('trader is not running')
+
+        pair_rates = Trade.session.query(Trade.pair,
+                                         sql.func.sum(Trade.close_profit).label('profit_sum'),
+                                         sql.func.count(Trade.pair).label('count')) \
+            .filter(Trade.is_open.is_(False)) \
+            .group_by(Trade.pair) \
+            .order_by(sql.text('profit_sum DESC')) \
+            .all()
+        return [
+            {'pair': pair, 'profit': round(rate * 100, 2), 'count': count}
+            for pair, rate, count in pair_rates
+        ]
+
+    def _rpc_count(self) -> List[Trade]:
+        """ Returns the number of trades running """
+        if self._freqtrade.state != State.RUNNING:
+            raise RPCException('trader is not running')
+
+        return Trade.query.filter(Trade.is_open.is_(True)).all()

freqtrade/rpc/rpc_manager.py

@@ -0,0 +1,53 @@
+"""
+This module contains class to manage RPC communications (Telegram, Slack, ...)
+"""
+import logging
+from typing import List, Dict, Any
+
+from freqtrade.rpc import RPC
+
+logger = logging.getLogger(__name__)
+
+
+class RPCManager(object):
+    """
+    Class to manage RPC objects (Telegram, Slack, ...)
+    """
+    def __init__(self, freqtrade) -> None:
+        """ Initializes all enabled rpc modules """
+        self.registered_modules: List[RPC] = []
+
+        # Enable telegram
+        if freqtrade.config['telegram'].get('enabled', False):
+            logger.info('Enabling rpc.telegram ...')
+            from freqtrade.rpc.telegram import Telegram
+            self.registered_modules.append(Telegram(freqtrade))
+
+        # Enable Webhook
+        if freqtrade.config.get('webhook', {}).get('enabled', False):
+            logger.info('Enabling rpc.webhook ...')
+            from freqtrade.rpc.webhook import Webhook
+            self.registered_modules.append(Webhook(freqtrade))
+
+    def cleanup(self) -> None:
+        """ Stops all enabled rpc modules """
+        logger.info('Cleaning up rpc modules ...')
+        while self.registered_modules:
+            mod = self.registered_modules.pop()
+            logger.debug('Cleaning up rpc.%s ...', mod.name)
+            mod.cleanup()
+            del mod
+
+    def send_msg(self, msg: Dict[str, Any]) -> None:
+        """
+        Send given message to all registered rpc modules.
+        A message consists of one or more key value pairs of strings.
+        e.g.:
+        {
+            'status': 'stopping bot'
+        }
+        """
+        logger.info('Sending rpc message: %s', msg)
+        for mod in self.registered_modules:
+            logger.debug('Forwarding message to rpc.%s', mod.name)
+            mod.send_msg(msg)

freqtrade/rpc/webhook.py

@@ -0,0 +1,66 @@
+"""
+This module manages webhook communication
+"""
+import logging
+from typing import Any,  Dict
+
+from requests import post, RequestException
+
+from freqtrade.rpc import RPC, RPCMessageType
+
+
+logger = logging.getLogger(__name__)
+
+logger.debug('Included module rpc.webhook ...')
+
+
+class Webhook(RPC):
+    """  This class handles all webhook communication """
+
+    def __init__(self, freqtrade) -> None:
+        """
+        Init the Webhook class, and init the super class RPC
+        :param freqtrade: Instance of a freqtrade bot
+        :return: None
+        """
+        super().__init__(freqtrade)
+
+        self._config = freqtrade.config
+        self._url = self._config['webhook']['url']
+
+    def cleanup(self) -> None:
+        """
+        Cleanup pending module resources.
+        This will do nothing for webhooks, they will simply not be called anymore
+        """
+        pass
+
+    def send_msg(self, msg: Dict[str, Any]) -> None:
+        """ Send a message to telegram channel """
+        try:
+
+            if msg['type'] == RPCMessageType.BUY_NOTIFICATION:
+                valuedict = self._config['webhook'].get('webhookbuy', None)
+            elif msg['type'] == RPCMessageType.SELL_NOTIFICATION:
+                valuedict = self._config['webhook'].get('webhooksell', None)
+            elif msg['type'] == RPCMessageType.STATUS_NOTIFICATION:
+                valuedict = self._config['webhook'].get('webhookstatus', None)
+            else:
+                raise NotImplementedError('Unknown message type: {}'.format(msg['type']))
+            if not valuedict:
+                logger.info("Message type %s not configured for webhooks", msg['type'])
+                return
+
+            payload = {key: value.format(**msg) for (key, value) in valuedict.items()}
+            self._send_msg(payload)
+        except KeyError as exc:
+            logger.exception("Problem calling Webhook. Please check your webhook configuration. "
+                             "Exception: %s", exc)
+
+    def _send_msg(self, payload: dict) -> None:
+        """do the actual call to the webhook"""
+
+        try:
+            post(self._url, data=payload)
+        except RequestException as exc:
+            logger.warning("Could not call webhook url. Exception: %s", exc)

freqtrade/state.py

@@ -0,0 +1,15 @@
+# pragma pylint: disable=too-few-public-methods
+
+"""
+Bot state constant
+"""
+import enum
+
+
+class State(enum.Enum):
+    """
+    Bot application states
+    """
+    RUNNING = 0
+    STOPPED = 1
+    RELOAD_CONF = 2

freqtrade/strategy/__init__.py

@@ -0,0 +1,44 @@
+import logging
+import sys
+from copy import deepcopy
+
+from freqtrade.strategy.interface import IStrategy
+# Import Default-Strategy to have hyperopt correctly resolve
+from freqtrade.strategy.default_strategy import DefaultStrategy  # noqa: F401
+
+logger = logging.getLogger(__name__)
+
+
+def import_strategy(strategy: IStrategy, config: dict) -> IStrategy:
+    """
+    Imports given Strategy instance to global scope
+    of freqtrade.strategy and returns an instance of it
+    """
+
+    # Copy all attributes from base class and class
+
+    comb = {**strategy.__class__.__dict__, **strategy.__dict__}
+
+    # Delete '_abc_impl' from dict as deepcopy fails on 3.7 with
+    # `TypeError: can't pickle _abc_data objects``
+    # This will only apply to python 3.7
+    if sys.version_info.major == 3 and sys.version_info.minor == 7 and '_abc_impl' in comb:
+        del comb['_abc_impl']
+
+    attr = deepcopy(comb)
+    # Adjust module name
+    attr['__module__'] = 'freqtrade.strategy'
+
+    name = strategy.__class__.__name__
+    clazz = type(name, (IStrategy,), attr)
+
+    logger.debug(
+        'Imported strategy %s.%s as %s.%s',
+        strategy.__module__, strategy.__class__.__name__,
+        clazz.__module__, strategy.__class__.__name__,
+    )
+
+    # Modify global scope to declare class
+    globals()[name] = clazz
+
+    return clazz(config)

freqtrade/strategy/default_strategy.py

@@ -0,0 +1,245 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+
+import talib.abstract as ta
+from pandas import DataFrame
+
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+from freqtrade.indicator_helpers import fishers_inverse
+from freqtrade.strategy.interface import IStrategy
+
+
+class DefaultStrategy(IStrategy):
+    """
+    Default Strategy provided by freqtrade bot.
+    You can override it with your own strategy
+    """
+
+    # Minimal ROI designed for the strategy
+    minimal_roi = {
+        "40":  0.0,
+        "30":  0.01,
+        "20":  0.02,
+        "0":  0.04
+    }
+
+    # Optimal stoploss designed for the strategy
+    stoploss = -0.10
+
+    # Optimal ticker interval for the strategy
+    ticker_interval = '5m'
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Adds several different TA indicators to the given DataFrame
+
+        Performance Note: For the best performance be frugal on the number of indicators
+        you are using. Let uncomment only the indicator you are using in your strategies
+        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
+        :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
+        :param metadata: Additional information, like the currently traded pair
+        :return: a Dataframe with all mandatory indicators for the strategies
+        """
+
+        # Momentum Indicator
+        # ------------------------------------
+
+        # ADX
+        dataframe['adx'] = ta.ADX(dataframe)
+
+        # Awesome oscillator
+        dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
+        """
+        # Commodity Channel Index: values Oversold:<-100, Overbought:>100
+        dataframe['cci'] = ta.CCI(dataframe)
+        """
+        # MACD
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        dataframe['macdhist'] = macd['macdhist']
+
+        # MFI
+        dataframe['mfi'] = ta.MFI(dataframe)
+
+        # Minus Directional Indicator / Movement
+        dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
+        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
+        # Plus Directional Indicator / Movement
+        dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
+        dataframe['plus_di'] = ta.PLUS_DI(dataframe)
+        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
+        """
+        # ROC
+        dataframe['roc'] = ta.ROC(dataframe)
+        """
+        # RSI
+        dataframe['rsi'] = ta.RSI(dataframe)
+
+        # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
+        dataframe['fisher_rsi'] = fishers_inverse(dataframe['rsi'])
+
+        # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
+        dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
+
+        # Stoch
+        stoch = ta.STOCH(dataframe)
+        dataframe['slowd'] = stoch['slowd']
+        dataframe['slowk'] = stoch['slowk']
+
+        # Stoch fast
+        stoch_fast = ta.STOCHF(dataframe)
+        dataframe['fastd'] = stoch_fast['fastd']
+        dataframe['fastk'] = stoch_fast['fastk']
+        """
+        # Stoch RSI
+        stoch_rsi = ta.STOCHRSI(dataframe)
+        dataframe['fastd_rsi'] = stoch_rsi['fastd']
+        dataframe['fastk_rsi'] = stoch_rsi['fastk']
+        """
+
+        # Overlap Studies
+        # ------------------------------------
+
+        # Previous Bollinger bands
+        # Because ta.BBANDS implementation is broken with small numbers, it actually
+        # returns middle band for all the three bands. Switch to qtpylib.bollinger_bands
+        # and use middle band instead.
+        dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
+
+        # Bollinger bands
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        dataframe['bb_lowerband'] = bollinger['lower']
+        dataframe['bb_middleband'] = bollinger['mid']
+        dataframe['bb_upperband'] = bollinger['upper']
+
+        # EMA - Exponential Moving Average
+        dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
+        dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
+        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
+        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
+        dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
+
+        # SAR Parabol
+        dataframe['sar'] = ta.SAR(dataframe)
+
+        # SMA - Simple Moving Average
+        dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
+
+        # TEMA - Triple Exponential Moving Average
+        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
+
+        # Cycle Indicator
+        # ------------------------------------
+        # Hilbert Transform Indicator - SineWave
+        hilbert = ta.HT_SINE(dataframe)
+        dataframe['htsine'] = hilbert['sine']
+        dataframe['htleadsine'] = hilbert['leadsine']
+
+        # Pattern Recognition - Bullish candlestick patterns
+        # ------------------------------------
+        """
+        # Hammer: values [0, 100]
+        dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
+        # Inverted Hammer: values [0, 100]
+        dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
+        # Dragonfly Doji: values [0, 100]
+        dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
+        # Piercing Line: values [0, 100]
+        dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
+        # Morningstar: values [0, 100]
+        dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
+        # Three White Soldiers: values [0, 100]
+        dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
+        """
+
+        # Pattern Recognition - Bearish candlestick patterns
+        # ------------------------------------
+        """
+        # Hanging Man: values [0, 100]
+        dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
+        # Shooting Star: values [0, 100]
+        dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
+        # Gravestone Doji: values [0, 100]
+        dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
+        # Dark Cloud Cover: values [0, 100]
+        dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
+        # Evening Doji Star: values [0, 100]
+        dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
+        # Evening Star: values [0, 100]
+        dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
+        """
+
+        # Pattern Recognition - Bullish/Bearish candlestick patterns
+        # ------------------------------------
+        """
+        # Three Line Strike: values [0, -100, 100]
+        dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
+        # Spinning Top: values [0, -100, 100]
+        dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
+        # Engulfing: values [0, -100, 100]
+        dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
+        # Harami: values [0, -100, 100]
+        dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
+        # Three Outside Up/Down: values [0, -100, 100]
+        dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
+        # Three Inside Up/Down: values [0, -100, 100]
+        dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
+        """
+
+        # Chart type
+        # ------------------------------------
+        # Heikinashi stategy
+        heikinashi = qtpylib.heikinashi(dataframe)
+        dataframe['ha_open'] = heikinashi['open']
+        dataframe['ha_close'] = heikinashi['close']
+        dataframe['ha_high'] = heikinashi['high']
+        dataframe['ha_low'] = heikinashi['low']
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the buy signal for the given dataframe
+        :param dataframe: DataFrame
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+                (dataframe['rsi'] < 35) &
+                (dataframe['fastd'] < 35) &
+                (dataframe['adx'] > 30) &
+                (dataframe['plus_di'] > 0.5)
+            ) |
+            (
+                (dataframe['adx'] > 65) &
+                (dataframe['plus_di'] > 0.5)
+            ),
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the sell signal for the given dataframe
+        :param dataframe: DataFrame
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+                (
+                    (qtpylib.crossed_above(dataframe['rsi'], 70)) |
+                    (qtpylib.crossed_above(dataframe['fastd'], 70))
+                ) &
+                (dataframe['adx'] > 10) &
+                (dataframe['minus_di'] > 0)
+            ) |
+            (
+                (dataframe['adx'] > 70) &
+                (dataframe['minus_di'] > 0.5)
+            ),
+            'sell'] = 1
+        return dataframe

freqtrade/strategy/interface.py

@@ -0,0 +1,353 @@
+"""
+IStrategy interface
+This module defines the interface to apply for strategies
+"""
+import logging
+from abc import ABC, abstractmethod
+from datetime import datetime
+from enum import Enum
+from typing import Dict, List, NamedTuple, Optional, Tuple
+import warnings
+
+import arrow
+from pandas import DataFrame
+
+from freqtrade import constants
+from freqtrade.exchange.exchange_helpers import parse_ticker_dataframe
+from freqtrade.persistence import Trade
+
+logger = logging.getLogger(__name__)
+
+
+class SignalType(Enum):
+    """
+    Enum to distinguish between buy and sell signals
+    """
+    BUY = "buy"
+    SELL = "sell"
+
+
+class SellType(Enum):
+    """
+    Enum to distinguish between sell reasons
+    """
+    ROI = "roi"
+    STOP_LOSS = "stop_loss"
+    TRAILING_STOP_LOSS = "trailing_stop_loss"
+    SELL_SIGNAL = "sell_signal"
+    FORCE_SELL = "force_sell"
+    NONE = ""
+
+
+class SellCheckTuple(NamedTuple):
+    """
+    NamedTuple for Sell type + reason
+    """
+    sell_flag: bool
+    sell_type: SellType
+
+
+class IStrategy(ABC):
+    """
+    Interface for freqtrade strategies
+    Defines the mandatory structure must follow any custom strategies
+
+    Attributes you can use:
+        minimal_roi -> Dict: Minimal ROI designed for the strategy
+        stoploss -> float: optimal stoploss designed for the strategy
+        ticker_interval -> str: value of the ticker interval to use for the strategy
+    """
+
+    _populate_fun_len: int = 0
+    _buy_fun_len: int = 0
+    _sell_fun_len: int = 0
+    # associated minimal roi
+    minimal_roi: Dict
+
+    # associated stoploss
+    stoploss: float
+
+    # associated ticker interval
+    ticker_interval: str
+
+    # run "populate_indicators" only for new candle
+    process_only_new_candles: bool = False
+
+    # Dict to determine if analysis is necessary
+    _last_candle_seen_per_pair: Dict[str, datetime] = {}
+
+    def __init__(self, config: dict) -> None:
+        self.config = config
+        self._last_candle_seen_per_pair = {}
+
+    @abstractmethod
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Populate indicators that will be used in the Buy and Sell strategy
+        :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
+        :param metadata: Additional information, like the currently traded pair
+        :return: a Dataframe with all mandatory indicators for the strategies
+        """
+
+    @abstractmethod
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the buy signal for the given dataframe
+        :param dataframe: DataFrame
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with buy column
+        """
+
+    @abstractmethod
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the sell signal for the given dataframe
+        :param dataframe: DataFrame
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with sell column
+        """
+
+    def get_strategy_name(self) -> str:
+        """
+        Returns strategy class name
+        """
+        return self.__class__.__name__
+
+    def analyze_ticker(self, ticker_history: List[Dict], metadata: dict) -> DataFrame:
+        """
+        Parses the given ticker history and returns a populated DataFrame
+        add several TA indicators and buy signal to it
+        :return DataFrame with ticker data and indicator data
+        """
+
+        dataframe = parse_ticker_dataframe(ticker_history)
+
+        pair = str(metadata.get('pair'))
+
+        # Test if seen this pair and last candle before.
+        # always run if process_only_new_candles is set to true
+        if (not self.process_only_new_candles or
+                self._last_candle_seen_per_pair.get(pair, None) != dataframe.iloc[-1]['date']):
+            # Defs that only make change on new candle data.
+            logging.debug("TA Analysis Launched")
+            dataframe = self.advise_indicators(dataframe, metadata)
+            dataframe = self.advise_buy(dataframe, metadata)
+            dataframe = self.advise_sell(dataframe, metadata)
+            self._last_candle_seen_per_pair[pair] = dataframe.iloc[-1]['date']
+        else:
+            logging.debug("Skippinig TA Analysis for already analyzed candle")
+            dataframe['buy'] = 0
+            dataframe['sell'] = 0
+
+        # Other Defs in strategy that want to be called every loop here
+        # twitter_sell = self.watch_twitter_feed(dataframe, metadata)
+        logging.debug("Loop Analysis Launched")
+
+        return dataframe
+
+    def get_signal(self, pair: str, interval: str,
+                   ticker_hist: Optional[List[Dict]]) -> Tuple[bool, bool]:
+        """
+        Calculates current signal based several technical analysis indicators
+        :param pair: pair in format ANT/BTC
+        :param interval: Interval to use (in min)
+        :return: (Buy, Sell) A bool-tuple indicating buy/sell signal
+        """
+        if not ticker_hist:
+            logger.warning('Empty ticker history for pair %s', pair)
+            return False, False
+
+        try:
+            dataframe = self.analyze_ticker(ticker_hist, {'pair': pair})
+        except ValueError as error:
+            logger.warning(
+                'Unable to analyze ticker for pair %s: %s',
+                pair,
+                str(error)
+            )
+            return False, False
+        except Exception as error:
+            logger.exception(
+                'Unexpected error when analyzing ticker for pair %s: %s',
+                pair,
+                str(error)
+            )
+            return False, False
+
+        if dataframe.empty:
+            logger.warning('Empty dataframe for pair %s', pair)
+            return False, False
+
+        latest = dataframe.iloc[-1]
+
+        # Check if dataframe is out of date
+        signal_date = arrow.get(latest['date'])
+        interval_minutes = constants.TICKER_INTERVAL_MINUTES[interval]
+        offset = self.config.get('exchange', {}).get('outdated_offset', 5)
+        if signal_date < (arrow.utcnow().shift(minutes=-(interval_minutes * 2 + offset))):
+            logger.warning(
+                'Outdated history for pair %s. Last tick is %s minutes old',
+                pair,
+                (arrow.utcnow() - signal_date).seconds // 60
+            )
+            return False, False
+
+        (buy, sell) = latest[SignalType.BUY.value] == 1, latest[SignalType.SELL.value] == 1
+        logger.debug(
+            'trigger: %s (pair=%s) buy=%s sell=%s',
+            latest['date'],
+            pair,
+            str(buy),
+            str(sell)
+        )
+        return buy, sell
+
+    def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool,
+                    sell: bool) -> SellCheckTuple:
+        """
+        This function evaluate if on the condition required to trigger a sell has been reached
+        if the threshold is reached and updates the trade record.
+        :return: True if trade should be sold, False otherwise
+        """
+        current_profit = trade.calc_profit_percent(rate)
+        stoplossflag = self.stop_loss_reached(current_rate=rate, trade=trade, current_time=date,
+                                              current_profit=current_profit)
+        if stoplossflag.sell_flag:
+            return stoplossflag
+
+        experimental = self.config.get('experimental', {})
+
+        if buy and experimental.get('ignore_roi_if_buy_signal', False):
+            logger.debug('Buy signal still active - not selling.')
+            return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
+
+        # Check if minimal roi has been reached and no longer in buy conditions (avoiding a fee)
+        if self.min_roi_reached(trade=trade, current_profit=current_profit, current_time=date):
+            logger.debug('Required profit reached. Selling..')
+            return SellCheckTuple(sell_flag=True, sell_type=SellType.ROI)
+
+        if experimental.get('sell_profit_only', False):
+            logger.debug('Checking if trade is profitable..')
+            if trade.calc_profit(rate=rate) <= 0:
+                return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
+        if sell and not buy and experimental.get('use_sell_signal', False):
+            logger.debug('Sell signal received. Selling..')
+            return SellCheckTuple(sell_flag=True, sell_type=SellType.SELL_SIGNAL)
+
+        return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
+
+    def stop_loss_reached(self, current_rate: float, trade: Trade, current_time: datetime,
+                          current_profit: float) -> SellCheckTuple:
+        """
+        Based on current profit of the trade and configured (trailing) stoploss,
+        decides to sell or not
+        :param current_profit: current profit in percent
+        """
+
+        trailing_stop = self.config.get('trailing_stop', False)
+
+        trade.adjust_stop_loss(trade.open_rate, self.stoploss, initial=True)
+
+        # evaluate if the stoploss was hit
+        if self.stoploss is not None and trade.stop_loss >= current_rate:
+            selltype = SellType.STOP_LOSS
+            if trailing_stop:
+                selltype = SellType.TRAILING_STOP_LOSS
+                logger.debug(
+                    f"HIT STOP: current price at {current_rate:.6f}, "
+                    f"stop loss is {trade.stop_loss:.6f}, "
+                    f"initial stop loss was at {trade.initial_stop_loss:.6f}, "
+                    f"trade opened at {trade.open_rate:.6f}")
+                logger.debug(f"trailing stop saved {trade.stop_loss - trade.initial_stop_loss:.6f}")
+
+            logger.debug('Stop loss hit.')
+            return SellCheckTuple(sell_flag=True, sell_type=selltype)
+
+        # update the stop loss afterwards, after all by definition it's supposed to be hanging
+        if trailing_stop:
+
+            # check if we have a special stop loss for positive condition
+            # and if profit is positive
+            stop_loss_value = self.stoploss
+            sl_offset = self.config.get('trailing_stop_positive_offset', 0.0)
+
+            if 'trailing_stop_positive' in self.config and current_profit > sl_offset:
+
+                # Ignore mypy error check in configuration that this is a float
+                stop_loss_value = self.config.get('trailing_stop_positive')  # type: ignore
+                logger.debug(f"using positive stop loss mode: {stop_loss_value} "
+                             f"with offset {sl_offset:.4g} "
+                             f"since we have profit {current_profit:.4f}%")
+
+            trade.adjust_stop_loss(current_rate, stop_loss_value)
+
+        return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
+
+    def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool:
+        """
+        Based an earlier trade and current price and ROI configuration, decides whether bot should
+        sell
+        :return True if bot should sell at current rate
+        """
+
+        # Check if time matches and current rate is above threshold
+        time_diff = (current_time.timestamp() - trade.open_date.timestamp()) / 60
+        for duration, threshold in self.minimal_roi.items():
+            if time_diff <= duration:
+                return False
+            if current_profit > threshold:
+                return True
+
+        return False
+
+    def tickerdata_to_dataframe(self, tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
+        """
+        Creates a dataframe and populates indicators for given ticker data
+        """
+        return {pair: self.advise_indicators(parse_ticker_dataframe(pair_data), {'pair': pair})
+                for pair, pair_data in tickerdata.items()}
+
+    def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Populate indicators that will be used in the Buy and Sell strategy
+        This method should not be overridden.
+        :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
+        :param metadata: Additional information, like the currently traded pair
+        :return: a Dataframe with all mandatory indicators for the strategies
+        """
+        if self._populate_fun_len == 2:
+            warnings.warn("deprecated - check out the Sample strategy to see "
+                          "the current function headers!", DeprecationWarning)
+            return self.populate_indicators(dataframe)  # type: ignore
+        else:
+            return self.populate_indicators(dataframe, metadata)
+
+    def advise_buy(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the buy signal for the given dataframe
+        This method should not be overridden.
+        :param dataframe: DataFrame
+        :param pair: Additional information, like the currently traded pair
+        :return: DataFrame with buy column
+        """
+        if self._buy_fun_len == 2:
+            warnings.warn("deprecated - check out the Sample strategy to see "
+                          "the current function headers!", DeprecationWarning)
+            return self.populate_buy_trend(dataframe)  # type: ignore
+        else:
+            return self.populate_buy_trend(dataframe, metadata)
+
+    def advise_sell(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the sell signal for the given dataframe
+        This method should not be overridden.
+        :param dataframe: DataFrame
+        :param pair: Additional information, like the currently traded pair
+        :return: DataFrame with sell column
+        """
+        if self._sell_fun_len == 2:
+            warnings.warn("deprecated - check out the Sample strategy to see "
+                          "the current function headers!", DeprecationWarning)
+            return self.populate_sell_trend(dataframe)  # type: ignore
+        else:
+            return self.populate_sell_trend(dataframe, metadata)

freqtrade/strategy/resolver.py

@@ -0,0 +1,178 @@
+# pragma pylint: disable=attribute-defined-outside-init
+
+"""
+This module load custom strategies
+"""
+import importlib.util
+import inspect
+import logging
+import os
+import tempfile
+from base64 import urlsafe_b64decode
+from collections import OrderedDict
+from pathlib import Path
+from typing import Dict, Optional, Type
+
+from freqtrade import constants
+from freqtrade.strategy import import_strategy
+from freqtrade.strategy.interface import IStrategy
+
+logger = logging.getLogger(__name__)
+
+
+class StrategyResolver(object):
+    """
+    This class contains all the logic to load custom strategy class
+    """
+
+    __slots__ = ['strategy']
+
+    def __init__(self, config: Optional[Dict] = None) -> None:
+        """
+        Load the custom class from config parameter
+        :param config: configuration dictionary or None
+        """
+        config = config or {}
+
+        # Verify the strategy is in the configuration, otherwise fallback to the default strategy
+        strategy_name = config.get('strategy') or constants.DEFAULT_STRATEGY
+        self.strategy: IStrategy = self._load_strategy(strategy_name,
+                                                       config=config,
+                                                       extra_dir=config.get('strategy_path'))
+
+        # Set attributes
+        # Check if we need to override configuration
+        if 'minimal_roi' in config:
+            self.strategy.minimal_roi = config['minimal_roi']
+            logger.info("Override strategy 'minimal_roi' with value in config file: %s.",
+                        config['minimal_roi'])
+        else:
+            config['minimal_roi'] = self.strategy.minimal_roi
+
+        if 'stoploss' in config:
+            self.strategy.stoploss = config['stoploss']
+            logger.info(
+                "Override strategy 'stoploss' with value in config file: %s.", config['stoploss']
+            )
+        else:
+            config['stoploss'] = self.strategy.stoploss
+
+        if 'ticker_interval' in config:
+            self.strategy.ticker_interval = config['ticker_interval']
+            logger.info(
+                "Override strategy 'ticker_interval' with value in config file: %s.",
+                config['ticker_interval']
+            )
+        else:
+            config['ticker_interval'] = self.strategy.ticker_interval
+
+        if 'process_only_new_candles' in config:
+            self.strategy.process_only_new_candles = config['process_only_new_candles']
+            logger.info(
+                "Override process_only_new_candles 'process_only_new_candles' "
+                "with value in config file: %s.", config['process_only_new_candles']
+            )
+        else:
+            config['process_only_new_candles'] = self.strategy.process_only_new_candles
+
+        # Sort and apply type conversions
+        self.strategy.minimal_roi = OrderedDict(sorted(
+            {int(key): value for (key, value) in self.strategy.minimal_roi.items()}.items(),
+            key=lambda t: t[0]))
+        self.strategy.stoploss = float(self.strategy.stoploss)
+
+    def _load_strategy(
+            self, strategy_name: str, config: dict, extra_dir: Optional[str] = None) -> IStrategy:
+        """
+        Search and loads the specified strategy.
+        :param strategy_name: name of the module to import
+        :param config: configuration for the strategy
+        :param extra_dir: additional directory to search for the given strategy
+        :return: Strategy instance or None
+        """
+        current_path = os.path.dirname(os.path.realpath(__file__))
+        abs_paths = [
+            os.path.join(os.getcwd(), 'user_data', 'strategies'),
+            current_path,
+        ]
+
+        if extra_dir:
+            # Add extra strategy directory on top of search paths
+            abs_paths.insert(0, extra_dir)
+
+        if ":" in strategy_name:
+            logger.info("loading base64 endocded strategy")
+            strat = strategy_name.split(":")
+
+            if len(strat) == 2:
+                temp = Path(tempfile.mkdtemp("freq", "strategy"))
+                name = strat[0] + ".py"
+
+                temp.joinpath(name).write_text(urlsafe_b64decode(strat[1]).decode('utf-8'))
+                temp.joinpath("__init__.py").touch()
+
+                strategy_name = os.path.splitext(name)[0]
+
+                # register temp path with the bot
+                abs_paths.insert(0, str(temp.resolve()))
+
+        for path in abs_paths:
+            try:
+                strategy = self._search_strategy(path, strategy_name=strategy_name, config=config)
+                if strategy:
+                    logger.info('Using resolved strategy %s from \'%s\'', strategy_name, path)
+                    strategy._populate_fun_len = len(
+                        inspect.getfullargspec(strategy.populate_indicators).args)
+                    strategy._buy_fun_len = len(
+                        inspect.getfullargspec(strategy.populate_buy_trend).args)
+                    strategy._sell_fun_len = len(
+                        inspect.getfullargspec(strategy.populate_sell_trend).args)
+
+                    return import_strategy(strategy, config=config)
+            except FileNotFoundError:
+                logger.warning('Path "%s" does not exist', path)
+
+        raise ImportError(
+            "Impossible to load Strategy '{}'. This class does not exist"
+            " or contains Python code errors".format(strategy_name)
+        )
+
+    @staticmethod
+    def _get_valid_strategies(module_path: str, strategy_name: str) -> Optional[Type[IStrategy]]:
+        """
+        Returns a list of all possible strategies for the given module_path
+        :param module_path: absolute path to the module
+        :param strategy_name: Class name of the strategy
+        :return: Tuple with (name, class) or None
+        """
+
+        # Generate spec based on absolute path
+        spec = importlib.util.spec_from_file_location('unknown', module_path)
+        module = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(module)  # type: ignore # importlib does not use typehints
+
+        valid_strategies_gen = (
+            obj for name, obj in inspect.getmembers(module, inspect.isclass)
+            if strategy_name == name and IStrategy in obj.__bases__
+        )
+        return next(valid_strategies_gen, None)
+
+    @staticmethod
+    def _search_strategy(directory: str, strategy_name: str, config: dict) -> Optional[IStrategy]:
+        """
+        Search for the strategy_name in the given directory
+        :param directory: relative or absolute directory path
+        :return: name of the strategy class
+        """
+        logger.debug('Searching for strategy %s in \'%s\'', strategy_name, directory)
+        for entry in os.listdir(directory):
+            # Only consider python files
+            if not entry.endswith('.py'):
+                logger.debug('Ignoring %s', entry)
+                continue
+            strategy = StrategyResolver._get_valid_strategies(
+                os.path.abspath(os.path.join(directory, entry)), strategy_name
+            )
+            if strategy:
+                return strategy(config)
+        return None

freqtrade/tests/conftest.py

@@ -1,16 +1,88 @@
 # pragma pylint: disable=missing-docstring
+import json
+import logging
 from datetime import datetime
-from unittest.mock import MagicMock
+from functools import reduce
+from typing import Dict, Optional
+from unittest.mock import MagicMock, PropertyMock
 
 import arrow
 import pytest
-from jsonschema import validate
 from telegram import Chat, Message, Update
 
-from freqtrade.misc import CONF_SCHEMA
+from freqtrade.exchange.exchange_helpers import parse_ticker_dataframe
+from freqtrade.exchange import Exchange
+from freqtrade.freqtradebot import FreqtradeBot
+
+logging.getLogger('').setLevel(logging.INFO)
 
 
-@pytest.fixture(scope="module")
+def log_has(line, logs):
+    # caplog mocker returns log as a tuple: ('freqtrade.something', logging.WARNING, 'foobar')
+    # and we want to match line against foobar in the tuple
+    return reduce(lambda a, b: a or b,
+                  filter(lambda x: x[2] == line, logs),
+                  False)
+
+
+def patch_exchange(mocker, api_mock=None) -> None:
+    mocker.patch('freqtrade.exchange.Exchange._load_markets', MagicMock(return_value={}))
+    mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
+    mocker.patch('freqtrade.exchange.Exchange.name', PropertyMock(return_value="Bittrex"))
+    mocker.patch('freqtrade.exchange.Exchange.id', PropertyMock(return_value="bittrex"))
+    if api_mock:
+        mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
+    else:
+        mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock())
+
+
+def get_patched_exchange(mocker, config, api_mock=None) -> Exchange:
+    patch_exchange(mocker, api_mock)
+    exchange = Exchange(config)
+    return exchange
+
+
+# Functions for recurrent object patching
+def get_patched_freqtradebot(mocker, config) -> FreqtradeBot:
+    """
+    This function patch _init_modules() to not call dependencies
+    :param mocker: a Mocker object to apply patches
+    :param config: Config to pass to the bot
+    :return: None
+    """
+    # mocker.patch('freqtrade.fiat_convert.Market', {'price_usd': 12345.0})
+    patch_coinmarketcap(mocker, {'price_usd': 12345.0})
+    mocker.patch('freqtrade.freqtradebot.RPCManager', MagicMock())
+    mocker.patch('freqtrade.freqtradebot.persistence.init', MagicMock())
+    patch_exchange(mocker, None)
+    mocker.patch('freqtrade.freqtradebot.RPCManager._init', MagicMock())
+    mocker.patch('freqtrade.freqtradebot.RPCManager.send_msg', MagicMock())
+
+    return FreqtradeBot(config)
+
+
+def patch_coinmarketcap(mocker, value: Optional[Dict[str, float]] = None) -> None:
+    """
+    Mocker to coinmarketcap to speed up tests
+    :param mocker: mocker to patch coinmarketcap class
+    :return: None
+    """
+
+    tickermock = MagicMock(return_value={'price_usd': 12345.0})
+    listmock = MagicMock(return_value={'data': [{'id': 1, 'name': 'Bitcoin', 'symbol': 'BTC',
+                                                 'website_slug': 'bitcoin'},
+                                                {'id': 1027, 'name': 'Ethereum', 'symbol': 'ETH',
+                                                 'website_slug': 'ethereum'}
+                                                ]})
+    mocker.patch.multiple(
+        'freqtrade.fiat_convert.Market',
+        ticker=tickermock,
+        listings=listmock,
+
+    )
+
+
+@pytest.fixture(scope="function")
 def default_conf():
     """ Returns validated configuration suitable for most tests """
     configuration = {
@@ -18,6 +90,7 @@ def default_conf():
         "stake_currency": "BTC",
         "stake_amount": 0.001,
         "fiat_display_currency": "USD",
+        "ticker_interval": '5m',
         "dry_run": True,
         "minimal_roi": {
             "40": 0.0,
@@ -26,9 +99,23 @@ def default_conf():
             "0": 0.04
         },
         "stoploss": -0.10,
-        "unfilledtimeout": 600,
+        "unfilledtimeout": {
+            "buy": 10,
+            "sell": 30
+        },
         "bid_strategy": {
-            "ask_last_balance": 0.0
+            "ask_last_balance": 0.0,
+            "use_order_book": False,
+            "order_book_top": 1,
+            "check_depth_of_market": {
+                "enabled": False,
+                "bids_to_ask_delta": 1
+            }
+        },
+        "ask_strategy": {
+            "use_order_book": False,
+            "order_book_min": 1,
+            "order_book_max": 1
         },
         "exchange": {
             "name": "bittrex",
@@ -36,11 +123,10 @@ def default_conf():
             "key": "key",
             "secret": "secret",
             "pair_whitelist": [
-                "BTC_ETH",
-                "BTC_TKN",
-                "BTC_TRST",
-                "BTC_SWT",
-                "BTC_BCC"
+                "ETH/BTC",
+                "LTC/BTC",
+                "XRP/BTC",
+                "NEO/BTC"
             ]
         },
         "telegram": {
@@ -48,9 +134,10 @@ def default_conf():
             "token": "token",
             "chat_id": "0"
         },
-        "initial_state": "running"
+        "initial_state": "running",
+        "db_url": "sqlite://",
+        "loglevel": logging.DEBUG,
     }
-    validate(configuration, CONF_SCHEMA)
     return configuration
 
 
@@ -61,6 +148,11 @@ def update():
     return _update
 
 
+@pytest.fixture
+def fee():
+    return MagicMock(return_value=0.0025)
+
+
 @pytest.fixture
 def ticker():
     return MagicMock(return_value={
@@ -89,46 +181,178 @@ def ticker_sell_down():
 
 
 @pytest.fixture
-def health():
-    return MagicMock(return_value=[{
-        'Currency': 'BTC',
-        'IsActive': True,
-        'LastChecked': '2017-11-13T20:15:00.00',
-        'Notice': None
-    }, {
-        'Currency': 'ETH',
-        'IsActive': True,
-        'LastChecked': '2017-11-13T20:15:00.00',
-        'Notice': None
-    }, {
-        'Currency': 'TRST',
-        'IsActive': True,
-        'LastChecked': '2017-11-13T20:15:00.00',
-        'Notice': None
-    }, {
-        'Currency': 'SWT',
-        'IsActive': True,
-        'LastChecked': '2017-11-13T20:15:00.00',
-        'Notice': None
-    }, {
-        'Currency': 'BCC',
-        'IsActive': False,
-        'LastChecked': '2017-11-13T20:15:00.00',
-        'Notice': None
-    }])
+def markets():
+    return MagicMock(return_value=[
+        {
+            'id': 'ethbtc',
+            'symbol': 'ETH/BTC',
+            'base': 'ETH',
+            'quote': 'BTC',
+            'active': True,
+            'precision': {
+                'price': 8,
+                'amount': 8,
+                'cost': 8,
+            },
+            'lot': 0.00000001,
+            'limits': {
+                'amount': {
+                    'min': 0.01,
+                    'max': 1000,
+                },
+                'price': 500000,
+                'cost': {
+                    'min': 1,
+                    'max': 500000,
+                },
+            },
+            'info': '',
+        },
+        {
+            'id': 'tknbtc',
+            'symbol': 'TKN/BTC',
+            'base': 'TKN',
+            'quote': 'BTC',
+            'active': True,
+            'precision': {
+                'price': 8,
+                'amount': 8,
+                'cost': 8,
+            },
+            'lot': 0.00000001,
+            'limits': {
+                'amount': {
+                    'min': 0.01,
+                    'max': 1000,
+                },
+                'price': 500000,
+                'cost': {
+                    'min': 1,
+                    'max': 500000,
+                },
+            },
+            'info': '',
+        },
+        {
+            'id': 'blkbtc',
+            'symbol': 'BLK/BTC',
+            'base': 'BLK',
+            'quote': 'BTC',
+            'active': True,
+            'precision': {
+                'price': 8,
+                'amount': 8,
+                'cost': 8,
+            },
+            'lot': 0.00000001,
+            'limits': {
+                'amount': {
+                    'min': 0.01,
+                    'max': 1000,
+                },
+                'price': 500000,
+                'cost': {
+                    'min': 1,
+                    'max': 500000,
+                },
+            },
+            'info': '',
+        },
+        {
+            'id': 'ltcbtc',
+            'symbol': 'LTC/BTC',
+            'base': 'LTC',
+            'quote': 'BTC',
+            'active': False,
+            'precision': {
+                'price': 8,
+                'amount': 8,
+                'cost': 8,
+            },
+            'lot': 0.00000001,
+            'limits': {
+                'amount': {
+                    'min': 0.01,
+                    'max': 1000,
+                },
+                'price': 500000,
+                'cost': {
+                    'min': 1,
+                    'max': 500000,
+                },
+            },
+            'info': '',
+        },
+        {
+            'id': 'xrpbtc',
+            'symbol': 'XRP/BTC',
+            'base': 'XRP',
+            'quote': 'BTC',
+            'active': False,
+            'precision': {
+                'price': 8,
+                'amount': 8,
+                'cost': 8,
+            },
+            'lot': 0.00000001,
+            'limits': {
+                'amount': {
+                    'min': 0.01,
+                    'max': 1000,
+                },
+                'price': 500000,
+                'cost': {
+                    'min': 1,
+                    'max': 500000,
+                },
+            },
+            'info': '',
+        },
+        {
+            'id': 'neobtc',
+            'symbol': 'NEO/BTC',
+            'base': 'NEO',
+            'quote': 'BTC',
+            'active': False,
+            'precision': {
+                'price': 8,
+                'amount': 8,
+                'cost': 8,
+            },
+            'lot': 0.00000001,
+            'limits': {
+                'amount': {
+                    'min': 0.01,
+                    'max': 1000,
+                },
+                'price': 500000,
+                'cost': {
+                    'min': 1,
+                    'max': 500000,
+                },
+            },
+            'info': '',
+        }
+    ])
 
 
 @pytest.fixture
+def markets_empty():
+    return MagicMock(return_value=[])
+
+
+@pytest.fixture(scope='function')
 def limit_buy_order():
     return {
         'id': 'mocked_limit_buy',
-        'type': 'LIMIT_BUY',
+        'type': 'limit',
+        'side': 'buy',
         'pair': 'mocked',
-        'opened': str(arrow.utcnow().datetime),
-        'rate': 0.00001099,
+        'datetime': arrow.utcnow().isoformat(),
+        'price': 0.00001099,
         'amount': 90.99181073,
         'remaining': 0.0,
-        'closed': str(arrow.utcnow().datetime),
+        'status': 'closed'
     }
 
 
@@ -136,12 +360,14 @@ def limit_buy_order():
 def limit_buy_order_old():
     return {
         'id': 'mocked_limit_buy_old',
-        'type': 'LIMIT_BUY',
-        'pair': 'BTC_ETH',
-        'opened': str(arrow.utcnow().shift(minutes=-601).datetime),
-        'rate': 0.00001099,
+        'type': 'limit',
+        'side': 'buy',
+        'pair': 'mocked',
+        'datetime': str(arrow.utcnow().shift(minutes=-601).datetime),
+        'price': 0.00001099,
         'amount': 90.99181073,
         'remaining': 90.99181073,
+        'status': 'open'
     }
 
 
@@ -149,12 +375,14 @@ def limit_buy_order_old():
 def limit_sell_order_old():
     return {
         'id': 'mocked_limit_sell_old',
-        'type': 'LIMIT_SELL',
-        'pair': 'BTC_ETH',
-        'opened': str(arrow.utcnow().shift(minutes=-601).datetime),
-        'rate': 0.00001099,
+        'type': 'limit',
+        'side': 'sell',
+        'pair': 'ETH/BTC',
+        'datetime': arrow.utcnow().shift(minutes=-601).isoformat(),
+        'price': 0.00001099,
         'amount': 90.99181073,
         'remaining': 90.99181073,
+        'status': 'open'
     }
 
 
@@ -162,12 +390,14 @@ def limit_sell_order_old():
 def limit_buy_order_old_partial():
     return {
         'id': 'mocked_limit_buy_old_partial',
-        'type': 'LIMIT_BUY',
-        'pair': 'BTC_ETH',
-        'opened': str(arrow.utcnow().shift(minutes=-601).datetime),
-        'rate': 0.00001099,
+        'type': 'limit',
+        'side': 'buy',
+        'pair': 'ETH/BTC',
+        'datetime': arrow.utcnow().shift(minutes=-601).isoformat(),
+        'price': 0.00001099,
         'amount': 90.99181073,
         'remaining': 67.99181073,
+        'status': 'open'
     }
 
 
@@ -175,44 +405,350 @@ def limit_buy_order_old_partial():
 def limit_sell_order():
     return {
         'id': 'mocked_limit_sell',
-        'type': 'LIMIT_SELL',
+        'type': 'limit',
+        'side': 'sell',
         'pair': 'mocked',
-        'opened': str(arrow.utcnow().datetime),
-        'rate': 0.00001173,
+        'datetime': arrow.utcnow().isoformat(),
+        'price': 0.00001173,
         'amount': 90.99181073,
         'remaining': 0.0,
-        'closed': str(arrow.utcnow().datetime),
+        'status': 'closed'
     }
 
 
+@pytest.fixture
+def order_book_l2():
+    return MagicMock(return_value={
+        'bids': [
+            [0.043936, 10.442],
+            [0.043935, 31.865],
+            [0.043933, 11.212],
+            [0.043928, 0.088],
+            [0.043925, 10.0],
+            [0.043921, 10.0],
+            [0.04392, 37.64],
+            [0.043899, 0.066],
+            [0.043885, 0.676],
+            [0.04387, 22.758]
+        ],
+        'asks': [
+            [0.043949, 0.346],
+            [0.04395, 0.608],
+            [0.043951, 3.948],
+            [0.043954, 0.288],
+            [0.043958, 9.277],
+            [0.043995, 1.566],
+            [0.044, 0.588],
+            [0.044002, 0.992],
+            [0.044003, 0.095],
+            [0.04402, 37.64]
+        ],
+        'timestamp': None,
+        'datetime': None,
+        'nonce': 288004540
+    })
+
+
 @pytest.fixture
 def ticker_history():
     return [
-        {
-            "O": 8.794e-05,
-            "H": 8.948e-05,
-            "L": 8.794e-05,
-            "C": 8.88e-05,
-            "V": 991.09056638,
-            "T": "2017-11-26T08:50:00",
-            "BV": 0.0877869
-        },
-        {
-            "O": 8.88e-05,
-            "H": 8.942e-05,
-            "L": 8.88e-05,
-            "C": 8.893e-05,
-            "V": 658.77935965,
-            "T": "2017-11-26T08:55:00",
-            "BV": 0.05874751
-        },
-        {
-            "O": 8.891e-05,
-            "H": 8.893e-05,
-            "L": 8.875e-05,
-            "C": 8.877e-05,
-            "V": 7920.73570705,
-            "T": "2017-11-26T09:00:00",
-            "BV": 0.7039405
-        }
+        [
+            1511686200000,  # unix timestamp ms
+            8.794e-05,  # open
+            8.948e-05,  # high
+            8.794e-05,  # low
+            8.88e-05,  # close
+            0.0877869,  # volume (in quote currency)
+        ],
+        [
+            1511686500000,
+            8.88e-05,
+            8.942e-05,
+            8.88e-05,
+            8.893e-05,
+            0.05874751,
+        ],
+        [
+            1511686800000,
+            8.891e-05,
+            8.893e-05,
+            8.875e-05,
+            8.877e-05,
+            0.7039405
+        ]
     ]
+
+
+@pytest.fixture
+def tickers():
+    return MagicMock(return_value={
+        'ETH/BTC': {
+            'symbol': 'ETH/BTC',
+            'timestamp': 1522014806207,
+            'datetime': '2018-03-25T21:53:26.207Z',
+            'high': 0.061697,
+            'low': 0.060531,
+            'bid': 0.061588,
+            'bidVolume': 3.321,
+            'ask': 0.061655,
+            'askVolume': 0.212,
+            'vwap': 0.06105296,
+            'open': 0.060809,
+            'close': 0.060761,
+            'first': None,
+            'last': 0.061588,
+            'change': 1.281,
+            'percentage': None,
+            'average': None,
+            'baseVolume': 111649.001,
+            'quoteVolume': 6816.50176926,
+            'info': {}
+        },
+        'TKN/BTC': {
+            'symbol': 'TKN/BTC',
+            'timestamp': 1522014806169,
+            'datetime': '2018-03-25T21:53:26.169Z',
+            'high': 0.01885,
+            'low': 0.018497,
+            'bid': 0.018799,
+            'bidVolume': 8.38,
+            'ask': 0.018802,
+            'askVolume': 15.0,
+            'vwap': 0.01869197,
+            'open': 0.018585,
+            'close': 0.018573,
+            'baseVolume': 81058.66,
+            'quoteVolume': 2247.48374509,
+        },
+        'BLK/BTC': {
+            'symbol': 'BLK/BTC',
+            'timestamp': 1522014806072,
+            'datetime': '2018-03-25T21:53:26.720Z',
+            'high': 0.007745,
+            'low': 0.007512,
+            'bid': 0.007729,
+            'bidVolume': 0.01,
+            'ask': 0.007743,
+            'askVolume': 21.37,
+            'vwap': 0.00761466,
+            'open': 0.007653,
+            'close': 0.007652,
+            'first': None,
+            'last': 0.007743,
+            'change': 1.176,
+            'percentage': None,
+            'average': None,
+            'baseVolume': 295152.26,
+            'quoteVolume': 1515.14631229,
+            'info': {}
+        },
+        'LTC/BTC': {
+            'symbol': 'LTC/BTC',
+            'timestamp': 1523787258992,
+            'datetime': '2018-04-15T10:14:19.992Z',
+            'high': 0.015978,
+            'low': 0.0157,
+            'bid': 0.015954,
+            'bidVolume': 12.83,
+            'ask': 0.015957,
+            'askVolume': 0.49,
+            'vwap': 0.01581636,
+            'open': 0.015823,
+            'close': 0.01582,
+            'first': None,
+            'last': 0.015951,
+            'change': 0.809,
+            'percentage': None,
+            'average': None,
+            'baseVolume': 88620.68,
+            'quoteVolume': 1401.65697943,
+            'info': {}
+        },
+        'ETH/USDT': {
+            'symbol': 'ETH/USDT',
+            'timestamp': 1522014804118,
+            'datetime': '2018-03-25T21:53:24.118Z',
+            'high': 530.88,
+            'low': 512.0,
+            'bid': 529.73,
+            'bidVolume': 0.2,
+            'ask': 530.21,
+            'askVolume': 0.2464,
+            'vwap': 521.02438405,
+            'open': 527.27,
+            'close': 528.42,
+            'first': None,
+            'last': 530.21,
+            'change': 0.558,
+            'percentage': None,
+            'average': None,
+            'baseVolume': 72300.0659,
+            'quoteVolume': 37670097.3022171,
+            'info': {}
+        },
+        'TKN/USDT': {
+            'symbol': 'TKN/USDT',
+            'timestamp': 1522014806198,
+            'datetime': '2018-03-25T21:53:26.198Z',
+            'high': 8718.0,
+            'low': 8365.77,
+            'bid': 8603.64,
+            'bidVolume': 0.15846,
+            'ask': 8603.67,
+            'askVolume': 0.069147,
+            'vwap': 8536.35621697,
+            'open': 8680.0,
+            'close': 8680.0,
+            'first': None,
+            'last': 8603.67,
+            'change': -0.879,
+            'percentage': None,
+            'average': None,
+            'baseVolume': 30414.604298,
+            'quoteVolume': 259629896.48584127,
+            'info': {}
+        },
+        'BLK/USDT': {
+            'symbol': 'BLK/USDT',
+            'timestamp': 1522014806145,
+            'datetime': '2018-03-25T21:53:26.145Z',
+            'high': 66.95,
+            'low': 63.38,
+            'bid': 66.473,
+            'bidVolume': 4.968,
+            'ask': 66.54,
+            'askVolume': 2.704,
+            'vwap': 65.0526901,
+            'open': 66.43,
+            'close': 66.383,
+            'first': None,
+            'last': 66.5,
+            'change': 0.105,
+            'percentage': None,
+            'average': None,
+            'baseVolume': 294106.204,
+            'quoteVolume': 19132399.743954,
+            'info': {}
+        },
+        'LTC/USDT': {
+            'symbol': 'LTC/USDT',
+            'timestamp': 1523787257812,
+            'datetime': '2018-04-15T10:14:18.812Z',
+            'high': 129.94,
+            'low': 124.0,
+            'bid': 129.28,
+            'bidVolume': 0.03201,
+            'ask': 129.52,
+            'askVolume': 0.14529,
+            'vwap': 126.92838682,
+            'open': 127.0,
+            'close': 127.1,
+            'first': None,
+            'last': 129.28,
+            'change': 1.795,
+            'percentage': None,
+            'average': None,
+            'baseVolume': 59698.79897,
+            'quoteVolume': 29132399.743954,
+            'info': {}
+        }
+    })
+
+
+@pytest.fixture
+def result():
+    with open('freqtrade/tests/testdata/UNITTEST_BTC-1m.json') as data_file:
+        return parse_ticker_dataframe(json.load(data_file))
+
+# FIX:
+# Create an fixture/function
+# that inserts a trade of some type and open-status
+# return the open-order-id
+# See tests in rpc/main that could use this
+
+
+@pytest.fixture(scope="function")
+def trades_for_order():
+    return [{'info': {'id': 34567,
+                      'orderId': 123456,
+                      'price': '0.24544100',
+                      'qty': '8.00000000',
+                      'commission': '0.00800000',
+                      'commissionAsset': 'LTC',
+                      'time': 1521663363189,
+                      'isBuyer': True,
+                      'isMaker': False,
+                      'isBestMatch': True},
+             'timestamp': 1521663363189,
+             'datetime': '2018-03-21T20:16:03.189Z',
+             'symbol': 'LTC/ETH',
+             'id': '34567',
+             'order': '123456',
+             'type': None,
+             'side': 'buy',
+             'price': 0.245441,
+             'cost': 1.963528,
+             'amount': 8.0,
+             'fee': {'cost': 0.008, 'currency': 'LTC'}}]
+
+
+@pytest.fixture(scope="function")
+def trades_for_order2():
+    return [{'info': {'id': 34567,
+                      'orderId': 123456,
+                      'price': '0.24544100',
+                      'qty': '8.00000000',
+                      'commission': '0.00800000',
+                      'commissionAsset': 'LTC',
+                      'time': 1521663363189,
+                      'isBuyer': True,
+                      'isMaker': False,
+                      'isBestMatch': True},
+             'timestamp': 1521663363189,
+             'datetime': '2018-03-21T20:16:03.189Z',
+             'symbol': 'LTC/ETH',
+             'id': '34567',
+             'order': '123456',
+             'type': None,
+             'side': 'buy',
+             'price': 0.245441,
+             'cost': 1.963528,
+             'amount': 4.0,
+             'fee': {'cost': 0.004, 'currency': 'LTC'}},
+            {'info': {'id': 34567,
+                      'orderId': 123456,
+                      'price': '0.24544100',
+                      'qty': '8.00000000',
+                      'commission': '0.00800000',
+                      'commissionAsset': 'LTC',
+                      'time': 1521663363189,
+                      'isBuyer': True,
+                      'isMaker': False,
+                      'isBestMatch': True},
+             'timestamp': 1521663363189,
+             'datetime': '2018-03-21T20:16:03.189Z',
+             'symbol': 'LTC/ETH',
+             'id': '34567',
+             'order': '123456',
+             'type': None,
+             'side': 'buy',
+             'price': 0.245441,
+             'cost': 1.963528,
+             'amount': 4.0,
+             'fee': {'cost': 0.004, 'currency': 'LTC'}}]
+
+
+@pytest.fixture
+def buy_order_fee():
+    return {
+        'id': 'mocked_limit_buy_old',
+        'type': 'limit',
+        'side': 'buy',
+        'pair': 'mocked',
+        'datetime': str(arrow.utcnow().shift(minutes=-601).datetime),
+        'price': 0.245441,
+        'amount': 8.0,
+        'remaining': 90.99181073,
+        'status': 'closed',
+        'fee': None
+    }

freqtrade/tests/exchange/test_exchange_bittrex.py

@@ -1,337 +0,0 @@
-# pragma pylint: disable=missing-docstring,C0103
-
-import pytest
-from unittest.mock import MagicMock
-from requests.exceptions import ContentDecodingError
-
-from freqtrade.exchange.bittrex import Bittrex
-import freqtrade.exchange.bittrex as btx
-
-
-# Eat this flake8
-# +------------------+
-# |  bittrex.Bittrex |
-# +------------------+
-#         |
-#       (mock Fake_bittrex)
-#         |
-# +-----------------------------+
-# |  freqtrade.exchange.Bittrex |
-# +-----------------------------+
-# Call into Bittrex will flow up to the
-# external package bittrex.Bittrex.
-# By inserting a mock, we redirect those
-# calls.
-# The faked bittrex API is called just 'fb'
-# The freqtrade.exchange.Bittrex is a
-# wrapper, and is called 'wb'
-
-
-def _stub_config():
-    return {'key': '',
-            'secret': ''}
-
-
-class FakeBittrex():
-    def __init__(self, success=True):
-        self.success = True  # Believe in yourself
-        self.result = None
-        self.get_ticker_call_count = 0
-        # This is really ugly, doing side-effect during instance creation
-        # But we're allowed to in testing-code
-        btx._API = MagicMock()
-        btx._API.buy_limit = self.fake_buysell_limit
-        btx._API.sell_limit = self.fake_buysell_limit
-        btx._API.get_balance = self.fake_get_balance
-        btx._API.get_balances = self.fake_get_balances
-        btx._API.get_ticker = self.fake_get_ticker
-        btx._API.get_order = self.fake_get_order
-        btx._API.cancel = self.fake_cancel_order
-        btx._API.get_markets = self.fake_get_markets
-        btx._API.get_market_summaries = self.fake_get_market_summaries
-        btx._API_V2 = MagicMock()
-        btx._API_V2.get_candles = self.fake_get_candles
-        btx._API_V2.get_wallet_health = self.fake_get_wallet_health
-
-    def fake_buysell_limit(self, pair, amount, limit):
-        return {'success': self.success,
-                'result': {'uuid': '1234'},
-                'message': 'barter'}
-
-    def fake_get_balance(self, cur):
-        return {'success': self.success,
-                'result': {'Balance': 1234},
-                'message': 'unbalanced'}
-
-    def fake_get_balances(self):
-        return {'success': self.success,
-                'result': [{'BTC_ETH': 1234}],
-                'message': 'no balances'}
-
-    def fake_get_ticker(self, pair):
-        self.get_ticker_call_count += 1
-        return self.result or {'success': self.success,
-                               'result': {'Bid': 1, 'Ask': 1, 'Last': 1},
-                               'message': 'NO_API_RESPONSE'}
-
-    def fake_get_candles(self, pair, interval):
-        return self.result or {'success': self.success,
-                               'result': [{'C': 0, 'V': 0, 'O': 0, 'H': 0, 'L': 0, 'T': 0}],
-                               'message': 'candles lit'}
-
-    def fake_get_order(self, uuid):
-        return {'success': self.success,
-                'result': {'OrderUuid': 'ABC123',
-                           'Type': 'Type',
-                           'Exchange': 'BTC_ETH',
-                           'Opened': True,
-                           'PricePerUnit': 1,
-                           'Quantity': 1,
-                           'QuantityRemaining': 1,
-                           'Closed': True
-                           },
-                'message': 'lost'}
-
-    def fake_cancel_order(self, uuid):
-        return self.result or {'success': self.success,
-                               'message': 'no such order'}
-
-    def fake_get_markets(self):
-        return self.result or {'success': self.success,
-                               'message': 'market gone',
-                               'result': [{'MarketName': '-_'}]}
-
-    def fake_get_market_summaries(self):
-        return self.result or {'success': self.success,
-                               'message': 'no summary',
-                               'result': ['sum']}
-
-    def fake_get_wallet_health(self):
-        return self.result or {'success': self.success,
-                               'message': 'bad health',
-                               'result': [{'Health': {'Currency': 'BTC_ETH',
-                                                      'IsActive': True,
-                                                      'LastChecked': 0},
-                                           'Currency': {'Notice': True}}]}
-
-
-# The freqtrade.exchange.bittrex is called wrap_bittrex
-# to not confuse naming with bittrex.bittrex
-def make_wrap_bittrex():
-    conf = _stub_config()
-    wb = btx.Bittrex(conf)
-    return wb
-
-
-def test_exchange_bittrex_class():
-    conf = _stub_config()
-    b = Bittrex(conf)
-    assert isinstance(b, Bittrex)
-    slots = dir(b)
-    for name in ['fee', 'buy', 'sell', 'get_balance', 'get_balances',
-                 'get_ticker', 'get_ticker_history', 'get_order',
-                 'cancel_order', 'get_pair_detail_url', 'get_markets',
-                 'get_market_summaries', 'get_wallet_health']:
-        assert name in slots
-        # FIX: ensure that the slot is also a method in the class
-        # getattr(b, name) => bound method Bittrex.buy
-        # type(getattr(b, name)) => class 'method'
-
-
-def test_exchange_bittrex_fee():
-    fee = Bittrex.fee.__get__(Bittrex)
-    assert fee >= 0 and fee < 0.1  # Fee is 0-10 %
-
-
-def test_exchange_bittrex_buy_good(mocker):
-    wb = make_wrap_bittrex()
-    fb = FakeBittrex()
-    uuid = wb.buy('BTC_ETH', 1, 1)
-    assert uuid == fb.fake_buysell_limit(1, 2, 3)['result']['uuid']
-
-    fb.success = False
-    with pytest.raises(btx.OperationalException, match=r'barter.*'):
-        wb.buy('BAD', 1, 1)
-
-
-def test_exchange_bittrex_sell_good(mocker):
-    wb = make_wrap_bittrex()
-    fb = FakeBittrex()
-    uuid = wb.sell('BTC_ETH', 1, 1)
-    assert uuid == fb.fake_buysell_limit(1, 2, 3)['result']['uuid']
-
-    fb.success = False
-    with pytest.raises(btx.OperationalException, match=r'barter.*'):
-        uuid = wb.sell('BAD', 1, 1)
-
-
-def test_exchange_bittrex_get_balance(mocker):
-    wb = make_wrap_bittrex()
-    fb = FakeBittrex()
-    bal = wb.get_balance('BTC_ETH')
-    assert bal == fb.fake_get_balance(1)['result']['Balance']
-
-    fb.success = False
-    with pytest.raises(btx.OperationalException, match=r'unbalanced'):
-        wb.get_balance('BTC_ETH')
-
-
-def test_exchange_bittrex_get_balances():
-    wb = make_wrap_bittrex()
-    fb = FakeBittrex()
-    bals = wb.get_balances()
-    assert bals == fb.fake_get_balances()['result']
-
-    fb.success = False
-    with pytest.raises(btx.OperationalException, match=r'no balances'):
-        wb.get_balances()
-
-
-def test_exchange_bittrex_get_ticker():
-    wb = make_wrap_bittrex()
-    fb = FakeBittrex()
-
-    # Poll ticker, which updates the cache
-    tick = wb.get_ticker('BTC_ETH')
-    for x in ['bid', 'ask', 'last']:
-        assert x in tick
-    # Ensure the side-effect was made (update the ticker cache)
-    assert 'BTC_ETH' in wb.cached_ticker.keys()
-
-    # taint the cache, so we can recognize the cache wall utilized
-    wb.cached_ticker['BTC_ETH']['bid'] = 1234
-    # Poll again, getting the cached result
-    fb.get_ticker_call_count = 0
-    tick = wb.get_ticker('BTC_ETH', False)
-    # Ensure the result was from the cache, and that we didn't call exchange
-    assert wb.cached_ticker['BTC_ETH']['bid'] == 1234
-    assert fb.get_ticker_call_count == 0
-
-
-def test_exchange_bittrex_get_ticker_bad():
-    wb = make_wrap_bittrex()
-    fb = FakeBittrex()
-    fb.result = {'success': True,
-                 'result': {'Bid': 1}}  # incomplete result
-    with pytest.raises(ContentDecodingError, match=r'.*Got invalid response from bittrex params.*'):
-        wb.get_ticker('BTC_ETH')
-    fb.result = {'success': False,
-                 'message': 'gone bad'
-                 }
-    with pytest.raises(btx.OperationalException, match=r'.*gone bad.*'):
-        wb.get_ticker('BTC_ETH')
-
-
-def test_exchange_bittrex_get_ticker_history_one():
-    wb = make_wrap_bittrex()
-    FakeBittrex()
-    assert wb.get_ticker_history('BTC_ETH', 1)
-
-
-def test_exchange_bittrex_get_ticker_history():
-    wb = make_wrap_bittrex()
-    fb = FakeBittrex()
-    assert wb.get_ticker_history('BTC_ETH', 5)
-    with pytest.raises(ValueError, match=r'.*Cannot parse tick_interval.*'):
-        wb.get_ticker_history('BTC_ETH', 2)
-
-    fb.success = False
-    with pytest.raises(btx.OperationalException, match=r'candles lit.*'):
-        wb.get_ticker_history('BTC_ETH', 5)
-
-    fb.success = True
-    with pytest.raises(ContentDecodingError, match=r'.*Got invalid response from bittrex.*'):
-        fb.result = {'bad': 0}
-        wb.get_ticker_history('BTC_ETH', 5)
-
-    with pytest.raises(ContentDecodingError, match=r'.*Required property C not present.*'):
-        fb.result = {'success': True,
-                     'result': [{'V': 0, 'O': 0, 'H': 0, 'L': 0, 'T': 0}],  # close is missing
-                     'message': 'candles lit'}
-        wb.get_ticker_history('BTC_ETH', 5)
-
-
-def test_exchange_bittrex_get_order():
-    wb = make_wrap_bittrex()
-    fb = FakeBittrex()
-    order = wb.get_order('someUUID')
-    assert order['id'] == 'ABC123'
-    fb.success = False
-    with pytest.raises(btx.OperationalException, match=r'lost'):
-        wb.get_order('someUUID')
-
-
-def test_exchange_bittrex_cancel_order():
-    wb = make_wrap_bittrex()
-    fb = FakeBittrex()
-    wb.cancel_order('someUUID')
-    with pytest.raises(btx.OperationalException, match=r'no such order'):
-        fb.success = False
-        wb.cancel_order('someUUID')
-    # Note: this can be a bug in exchange.bittrex._validate_response
-    with pytest.raises(KeyError):
-        fb.result = {'success': False}  # message is missing!
-        wb.cancel_order('someUUID')
-    with pytest.raises(btx.OperationalException, match=r'foo'):
-        fb.result = {'success': False, 'message': 'foo'}
-        wb.cancel_order('someUUID')
-
-
-def test_exchange_get_pair_detail_url():
-    wb = make_wrap_bittrex()
-    assert wb.get_pair_detail_url('BTC_ETH')
-
-
-def test_exchange_get_markets():
-    wb = make_wrap_bittrex()
-    fb = FakeBittrex()
-    x = wb.get_markets()
-    assert x == ['__']
-    with pytest.raises(btx.OperationalException, match=r'market gone'):
-        fb.success = False
-        wb.get_markets()
-
-
-def test_exchange_get_market_summaries():
-    wb = make_wrap_bittrex()
-    fb = FakeBittrex()
-    assert ['sum'] == wb.get_market_summaries()
-    with pytest.raises(btx.OperationalException, match=r'no summary'):
-        fb.success = False
-        wb.get_market_summaries()
-
-
-def test_exchange_get_wallet_health():
-    wb = make_wrap_bittrex()
-    fb = FakeBittrex()
-    x = wb.get_wallet_health()
-    assert x[0]['Currency'] == 'BTC_ETH'
-    with pytest.raises(btx.OperationalException, match=r'bad health'):
-        fb.success = False
-        wb.get_wallet_health()
-
-
-def test_validate_response_success():
-    response = {
-        'message': '',
-        'result': [],
-    }
-    Bittrex._validate_response(response)
-
-
-def test_validate_response_no_api_response():
-    response = {
-        'message': 'NO_API_RESPONSE',
-        'result': None,
-    }
-    with pytest.raises(ContentDecodingError, match=r'.*NO_API_RESPONSE.*'):
-        Bittrex._validate_response(response)
-
-
-def test_validate_response_min_trade_requirement_not_met():
-    response = {
-        'message': 'MIN_TRADE_REQUIREMENT_NOT_MET',
-        'result': None,
-    }
-    with pytest.raises(ContentDecodingError, match=r'.*MIN_TRADE_REQUIREMENT_NOT_MET.*'):
-        Bittrex._validate_response(response)

freqtrade/tests/exchange/test_exchange_helpers.py

@@ -0,0 +1,21 @@
+# pragma pylint: disable=missing-docstring, C0103
+
+from freqtrade.exchange.exchange_helpers import parse_ticker_dataframe
+
+
+def test_dataframe_correct_length(result):
+    dataframe = parse_ticker_dataframe(result)
+    assert len(result.index) - 1 == len(dataframe.index)    # last partial candle removed
+
+
+def test_dataframe_correct_columns(result):
+    assert result.columns.tolist() == \
+        ['date', 'open', 'high', 'low', 'close', 'volume']
+
+
+def test_parse_ticker_dataframe(ticker_history):
+    columns = ['date', 'open', 'high', 'low', 'close', 'volume']
+
+    # Test file with BV data
+    dataframe = parse_ticker_dataframe(ticker_history)
+    assert dataframe.columns.tolist() == columns

freqtrade/tests/optimize/test_backtesting.py

@@ -1,140 +1,592 @@
-# pragma pylint: disable=missing-docstring,W0212
+# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
 
-import logging
+import json
 import math
-import pandas as pd
+import random
+from typing import List
 from unittest.mock import MagicMock
-from freqtrade import exchange, optimize
-from freqtrade.exchange import Bittrex
-from freqtrade.optimize import preprocess
-from freqtrade.optimize.backtesting import backtest, generate_text_table, get_timeframe
-import freqtrade.optimize.backtesting as backtesting
+
+import numpy as np
+import pandas as pd
+import pytest
+from arrow import Arrow
+
+from freqtrade import DependencyException, constants, optimize
+from freqtrade.arguments import Arguments, TimeRange
+from freqtrade.optimize.backtesting import (Backtesting, setup_configuration,
+                                            start)
+from freqtrade.tests.conftest import log_has, patch_exchange
+from freqtrade.strategy.interface import SellType
+from freqtrade.strategy.default_strategy import DefaultStrategy
 
 
-def test_generate_text_table():
-    results = pd.DataFrame(
-        {
-            'currency': ['BTC_ETH', 'BTC_ETH'],
-            'profit_percent': [0.1, 0.2],
-            'profit_BTC': [0.2, 0.4],
-            'duration': [10, 30],
-            'profit': [2, 0],
-            'loss': [0, 0]
-        }
-    )
-    print(generate_text_table({'BTC_ETH': {}}, results, 'BTC', 5))
-    assert generate_text_table({'BTC_ETH': {}}, results, 'BTC', 5) == (
-        'pair       buy count    avg profit %    total profit BTC    avg duration    profit    loss\n'  # noqa
-        '-------  -----------  --------------  ------------------  --------------  --------  ------\n'  # noqa
-        'BTC_ETH            2           15.00          0.60000000           100.0         2       0\n'  # noqa
-        'TOTAL              2           15.00          0.60000000           100.0         2       0')  # noqa
+def get_args(args) -> List[str]:
+    return Arguments(args, '').get_parsed_arg()
 
 
-def test_get_timeframe():
-    data = preprocess(optimize.load_data(
-        None, ticker_interval=1, pairs=['BTC_UNITEST']))
-    min_date, max_date = get_timeframe(data)
-    assert min_date.isoformat() == '2017-11-04T23:02:00+00:00'
-    assert max_date.isoformat() == '2017-11-14T22:59:00+00:00'
-
-
-def test_backtest(default_conf, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    exchange._API = Bittrex({'key': '', 'secret': ''})
-
-    data = optimize.load_data(None, ticker_interval=5, pairs=['BTC_ETH'])
-    results = backtest(default_conf['stake_amount'],
-                       optimize.preprocess(data), 10, True)
-    assert not results.empty
-
-
-def test_backtest_1min_ticker_interval(default_conf, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    exchange._API = Bittrex({'key': '', 'secret': ''})
-
-    # Run a backtesting for an exiting 5min ticker_interval
-    data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'])
-    results = backtest(default_conf['stake_amount'],
-                       optimize.preprocess(data), 1, True)
-    assert not results.empty
-
-
-def trim_dictlist(dl, num):
+def trim_dictlist(dict_list, num):
     new = {}
-    for pair, pair_data in dl.items():
+    for pair, pair_data in dict_list.items():
         new[pair] = pair_data[num:]
     return new
 
 
 def load_data_test(what):
-    data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'])
-    data = trim_dictlist(data, -100)
-    pair = data['BTC_UNITEST']
+    timerange = TimeRange(None, 'line', 0, -101)
+    data = optimize.load_data(None, ticker_interval='1m',
+                              pairs=['UNITTEST/BTC'], timerange=timerange)
+    pair = data['UNITTEST/BTC']
     datalen = len(pair)
     # Depending on the what parameter we now adjust the
     # loaded data looks:
-    # pair :: [{'O': 0.123, 'H': 0.123, 'L': 0.123,
-    #           'C': 0.123, 'V': 123.123,
-    #           'T': '2017-11-04T23:02:00', 'BV': 0.123}]
+    # pair :: [[    1509836520000,   unix timestamp in ms
+    #               0.00162008,      open
+    #               0.00162008,      high
+    #               0.00162008,      low
+    #               0.00162008,      close
+    #               108.14853839     base volume
+    #           ]]
     base = 0.001
     if what == 'raise':
-        return {'BTC_UNITEST':
-                [{'T': pair[x]['T'],  # Keep old dates
-                  'V': pair[x]['V'],  # Keep old volume
-                  'BV': pair[x]['BV'],  # keep too
-                  'O': x * base,        # But replace O,H,L,C
-                  'H': x * base + 0.0001,
-                  'L': x * base - 0.0001,
-                  'C': x * base} for x in range(0, datalen)]}
+        return {'UNITTEST/BTC': [
+            [
+                pair[x][0],  # Keep old dates
+                x * base,  # But replace O,H,L,C
+                x * base + 0.0001,
+                x * base - 0.0001,
+                x * base,
+                pair[x][5],  # Keep old volume
+            ] for x in range(0, datalen)
+        ]}
     if what == 'lower':
-        return {'BTC_UNITEST':
-                [{'T': pair[x]['T'],  # Keep old dates
-                  'V': pair[x]['V'],  # Keep old volume
-                  'BV': pair[x]['BV'],  # keep too
-                  'O': 1 - x * base,        # But replace O,H,L,C
-                  'H': 1 - x * base + 0.0001,
-                  'L': 1 - x * base - 0.0001,
-                  'C': 1 - x * base} for x in range(0, datalen)]}
+        return {'UNITTEST/BTC': [
+            [
+                pair[x][0],  # Keep old dates
+                1 - x * base,  # But replace O,H,L,C
+                1 - x * base + 0.0001,
+                1 - x * base - 0.0001,
+                1 - x * base,
+                pair[x][5]  # Keep old volume
+            ] for x in range(0, datalen)
+        ]}
     if what == 'sine':
         hz = 0.1  # frequency
-        return {'BTC_UNITEST':
-                [{'T': pair[x]['T'],  # Keep old dates
-                  'V': pair[x]['V'],  # Keep old volume
-                  'BV': pair[x]['BV'],  # keep too
-                  # But replace O,H,L,C
-                  'O': math.sin(x * hz) / 1000 + base,
-                  'H': math.sin(x * hz) / 1000 + base + 0.0001,
-                  'L': math.sin(x * hz) / 1000 + base - 0.0001,
-                  'C': math.sin(x * hz) / 1000 + base} for x in range(0, datalen)]}
+        return {'UNITTEST/BTC': [
+            [
+                pair[x][0],  # Keep old dates
+                math.sin(x * hz) / 1000 + base,  # But replace O,H,L,C
+                math.sin(x * hz) / 1000 + base + 0.0001,
+                math.sin(x * hz) / 1000 + base - 0.0001,
+                math.sin(x * hz) / 1000 + base,
+                pair[x][5]  # Keep old volume
+            ] for x in range(0, datalen)
+        ]}
     return data
 
 
-def simple_backtest(config, contour, num_results):
+def simple_backtest(config, contour, num_results, mocker) -> None:
+    patch_exchange(mocker)
+    backtesting = Backtesting(config)
+
     data = load_data_test(contour)
-    processed = optimize.preprocess(data)
+    processed = backtesting.tickerdata_to_dataframe(data)
     assert isinstance(processed, dict)
-    results = backtest(config['stake_amount'], processed, 1, True)
+    results = backtesting.backtest(
+        {
+            'stake_amount': config['stake_amount'],
+            'processed': processed,
+            'max_open_trades': 1,
+            'position_stacking': False
+        }
+    )
     # results :: <class 'pandas.core.frame.DataFrame'>
     assert len(results) == num_results
 
 
-# Test backtest on offline data
-# loaded by freqdata/optimize/__init__.py::load_data()
+def mocked_load_data(datadir, pairs=[], ticker_interval='0m', refresh_pairs=False,
+                     timerange=None, exchange=None):
+    tickerdata = optimize.load_tickerdata_file(datadir, 'UNITTEST/BTC', '1m', timerange=timerange)
+    pairdata = {'UNITTEST/BTC': tickerdata}
+    return pairdata
 
 
-def test_backtest2(default_conf, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    data = optimize.load_data(None, ticker_interval=5, pairs=['BTC_ETH'])
-    results = backtest(default_conf['stake_amount'],
-                       optimize.preprocess(data), 10, True)
+# use for mock ccxt.fetch_ohlvc'
+def _load_pair_as_ticks(pair, tickfreq):
+    ticks = optimize.load_data(None, ticker_interval=tickfreq, pairs=[pair])
+    ticks = trim_dictlist(ticks, -201)
+    return ticks[pair]
+
+
+# FIX: fixturize this?
+def _make_backtest_conf(mocker, conf=None, pair='UNITTEST/BTC', record=None):
+    data = optimize.load_data(None, ticker_interval='8m', pairs=[pair])
+    data = trim_dictlist(data, -201)
+    patch_exchange(mocker)
+    backtesting = Backtesting(conf)
+    return {
+        'stake_amount': conf['stake_amount'],
+        'processed': backtesting.tickerdata_to_dataframe(data),
+        'max_open_trades': 10,
+        'position_stacking': False,
+        'record': record
+    }
+
+
+def _trend(signals, buy_value, sell_value):
+    n = len(signals['low'])
+    buy = np.zeros(n)
+    sell = np.zeros(n)
+    for i in range(0, len(signals['buy'])):
+        if random.random() > 0.5:  # Both buy and sell signals at same timeframe
+            buy[i] = buy_value
+            sell[i] = sell_value
+    signals['buy'] = buy
+    signals['sell'] = sell
+    return signals
+
+
+def _trend_alternate(dataframe=None, metadata=None):
+    signals = dataframe
+    low = signals['low']
+    n = len(low)
+    buy = np.zeros(n)
+    sell = np.zeros(n)
+    for i in range(0, len(buy)):
+        if i % 2 == 0:
+            buy[i] = 1
+        else:
+            sell[i] = 1
+    signals['buy'] = buy
+    signals['sell'] = sell
+    return dataframe
+
+
+# Unit tests
+def test_setup_configuration_without_arguments(mocker, default_conf, caplog) -> None:
+    mocker.patch('freqtrade.configuration.open', mocker.mock_open(
+        read_data=json.dumps(default_conf)
+    ))
+
+    args = [
+        '--config', 'config.json',
+        '--strategy', 'DefaultStrategy',
+        'backtesting'
+    ]
+
+    config = setup_configuration(get_args(args))
+    assert 'max_open_trades' in config
+    assert 'stake_currency' in config
+    assert 'stake_amount' in config
+    assert 'exchange' in config
+    assert 'pair_whitelist' in config['exchange']
+    assert 'datadir' in config
+    assert log_has(
+        'Using data folder: {} ...'.format(config['datadir']),
+        caplog.record_tuples
+    )
+    assert 'ticker_interval' in config
+    assert not log_has('Parameter -i/--ticker-interval detected ...', caplog.record_tuples)
+
+    assert 'live' not in config
+    assert not log_has('Parameter -l/--live detected ...', caplog.record_tuples)
+
+    assert 'position_stacking' not in config
+    assert not log_has('Parameter --enable-position-stacking detected ...', caplog.record_tuples)
+
+    assert 'refresh_pairs' not in config
+    assert not log_has('Parameter -r/--refresh-pairs-cached detected ...', caplog.record_tuples)
+
+    assert 'timerange' not in config
+    assert 'export' not in config
+
+
+def test_setup_configuration_with_arguments(mocker, default_conf, caplog) -> None:
+    mocker.patch('freqtrade.configuration.open', mocker.mock_open(
+        read_data=json.dumps(default_conf)
+    ))
+
+    args = [
+        '--config', 'config.json',
+        '--strategy', 'DefaultStrategy',
+        '--datadir', '/foo/bar',
+        'backtesting',
+        '--ticker-interval', '1m',
+        '--live',
+        '--enable-position-stacking',
+        '--disable-max-market-positions',
+        '--refresh-pairs-cached',
+        '--timerange', ':100',
+        '--export', '/bar/foo',
+        '--export-filename', 'foo_bar.json'
+    ]
+
+    config = setup_configuration(get_args(args))
+    assert 'max_open_trades' in config
+    assert 'stake_currency' in config
+    assert 'stake_amount' in config
+    assert 'exchange' in config
+    assert 'pair_whitelist' in config['exchange']
+    assert 'datadir' in config
+    assert log_has(
+        'Using data folder: {} ...'.format(config['datadir']),
+        caplog.record_tuples
+    )
+    assert 'ticker_interval' in config
+    assert log_has('Parameter -i/--ticker-interval detected ...', caplog.record_tuples)
+    assert log_has(
+        'Using ticker_interval: 1m ...',
+        caplog.record_tuples
+    )
+
+    assert 'live' in config
+    assert log_has('Parameter -l/--live detected ...', caplog.record_tuples)
+
+    assert 'position_stacking' in config
+    assert log_has('Parameter --enable-position-stacking detected ...', caplog.record_tuples)
+
+    assert 'use_max_market_positions' in config
+    assert log_has('Parameter --disable-max-market-positions detected ...', caplog.record_tuples)
+    assert log_has('max_open_trades set to unlimited ...', caplog.record_tuples)
+
+    assert 'refresh_pairs' in config
+    assert log_has('Parameter -r/--refresh-pairs-cached detected ...', caplog.record_tuples)
+    assert 'timerange' in config
+    assert log_has(
+        'Parameter --timerange detected: {} ...'.format(config['timerange']),
+        caplog.record_tuples
+    )
+
+    assert 'export' in config
+    assert log_has(
+        'Parameter --export detected: {} ...'.format(config['export']),
+        caplog.record_tuples
+    )
+    assert 'exportfilename' in config
+    assert log_has(
+        'Storing backtest results to {} ...'.format(config['exportfilename']),
+        caplog.record_tuples
+    )
+
+
+def test_setup_configuration_unlimited_stake_amount(mocker, default_conf, caplog) -> None:
+    default_conf['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
+
+    mocker.patch('freqtrade.configuration.open', mocker.mock_open(
+        read_data=json.dumps(default_conf)
+    ))
+
+    args = [
+        '--config', 'config.json',
+        '--strategy', 'DefaultStrategy',
+        'backtesting'
+    ]
+
+    with pytest.raises(DependencyException, match=r'.*stake amount.*'):
+        setup_configuration(get_args(args))
+
+
+def test_start(mocker, fee, default_conf, caplog) -> None:
+    start_mock = MagicMock()
+    mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
+    patch_exchange(mocker)
+    mocker.patch('freqtrade.optimize.backtesting.Backtesting.start', start_mock)
+    mocker.patch('freqtrade.configuration.open', mocker.mock_open(
+        read_data=json.dumps(default_conf)
+    ))
+    args = [
+        '--config', 'config.json',
+        '--strategy', 'DefaultStrategy',
+        'backtesting'
+    ]
+    args = get_args(args)
+    start(args)
+    assert log_has(
+        'Starting freqtrade in Backtesting mode',
+        caplog.record_tuples
+    )
+    assert start_mock.call_count == 1
+
+
+def test_backtesting_init(mocker, default_conf) -> None:
+    patch_exchange(mocker)
+    get_fee = mocker.patch('freqtrade.exchange.Exchange.get_fee', MagicMock(return_value=0.5))
+    backtesting = Backtesting(default_conf)
+    assert backtesting.config == default_conf
+    assert backtesting.ticker_interval == '5m'
+    assert callable(backtesting.tickerdata_to_dataframe)
+    assert callable(backtesting.advise_buy)
+    assert callable(backtesting.advise_sell)
+    get_fee.assert_called()
+    assert backtesting.fee == 0.5
+
+
+def test_tickerdata_to_dataframe(default_conf, mocker) -> None:
+    patch_exchange(mocker)
+    timerange = TimeRange(None, 'line', 0, -100)
+    tick = optimize.load_tickerdata_file(None, 'UNITTEST/BTC', '1m', timerange=timerange)
+    tickerlist = {'UNITTEST/BTC': tick}
+
+    backtesting = Backtesting(default_conf)
+    data = backtesting.tickerdata_to_dataframe(tickerlist)
+    assert len(data['UNITTEST/BTC']) == 99
+
+    # Load strategy to compare the result between Backtesting function and strategy are the same
+    strategy = DefaultStrategy(default_conf)
+    data2 = strategy.tickerdata_to_dataframe(tickerlist)
+    assert data['UNITTEST/BTC'].equals(data2['UNITTEST/BTC'])
+
+
+def test_get_timeframe(default_conf, mocker) -> None:
+    patch_exchange(mocker)
+    backtesting = Backtesting(default_conf)
+
+    data = backtesting.tickerdata_to_dataframe(
+        optimize.load_data(
+            None,
+            ticker_interval='1m',
+            pairs=['UNITTEST/BTC']
+        )
+    )
+    min_date, max_date = backtesting.get_timeframe(data)
+    assert min_date.isoformat() == '2017-11-04T23:02:00+00:00'
+    assert max_date.isoformat() == '2017-11-14T22:58:00+00:00'
+
+
+def test_generate_text_table(default_conf, mocker):
+    patch_exchange(mocker)
+    backtesting = Backtesting(default_conf)
+
+    results = pd.DataFrame(
+        {
+            'pair': ['ETH/BTC', 'ETH/BTC'],
+            'profit_percent': [0.1, 0.2],
+            'profit_abs': [0.2, 0.4],
+            'trade_duration': [10, 30],
+            'profit': [2, 0],
+            'loss': [0, 0]
+        }
+    )
+
+    result_str = (
+        '| pair    |   buy count |   avg profit % |   cum profit % |   '
+        'total profit BTC | avg duration   |   profit |   loss |\n'
+        '|:--------|------------:|---------------:|---------------:|'
+        '-------------------:|:---------------|---------:|-------:|\n'
+        '| ETH/BTC |           2 |          15.00 |          30.00 |         '
+        '0.60000000 | 0:20:00        |        2 |      0 |\n'
+        '| TOTAL   |           2 |          15.00 |          30.00 |         '
+        '0.60000000 | 0:20:00        |        2 |      0 |'
+    )
+    assert backtesting._generate_text_table(data={'ETH/BTC': {}}, results=results) == result_str
+
+
+def test_generate_text_table_sell_reason(default_conf, mocker):
+    patch_exchange(mocker)
+    backtesting = Backtesting(default_conf)
+
+    results = pd.DataFrame(
+        {
+            'pair': ['ETH/BTC', 'ETH/BTC', 'ETH/BTC'],
+            'profit_percent': [0.1, 0.2, 0.3],
+            'profit_abs': [0.2, 0.4, 0.5],
+            'trade_duration': [10, 30, 10],
+            'profit': [2, 0, 0],
+            'loss': [0, 0, 1],
+            'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
+        }
+    )
+
+    result_str = (
+        '| Sell Reason   |   Count |\n'
+        '|:--------------|--------:|\n'
+        '| roi           |       2 |\n'
+        '| stop_loss     |       1 |'
+    )
+    assert backtesting._generate_text_table_sell_reason(
+        data={'ETH/BTC': {}}, results=results) == result_str
+
+
+def test_generate_text_table_strategyn(default_conf, mocker):
+    """
+    Test Backtesting.generate_text_table_sell_reason() method
+    """
+    patch_exchange(mocker)
+    backtesting = Backtesting(default_conf)
+    results = {}
+    results['ETH/BTC'] = pd.DataFrame(
+        {
+            'pair': ['ETH/BTC', 'ETH/BTC', 'ETH/BTC'],
+            'profit_percent': [0.1, 0.2, 0.3],
+            'profit_abs': [0.2, 0.4, 0.5],
+            'trade_duration': [10, 30, 10],
+            'profit': [2, 0, 0],
+            'loss': [0, 0, 1],
+            'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
+        }
+    )
+    results['LTC/BTC'] = pd.DataFrame(
+        {
+            'pair': ['LTC/BTC', 'LTC/BTC', 'LTC/BTC'],
+            'profit_percent': [0.4, 0.2, 0.3],
+            'profit_abs': [0.4, 0.4, 0.5],
+            'trade_duration': [15, 30, 15],
+            'profit': [4, 1, 0],
+            'loss': [0, 0, 1],
+            'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
+        }
+    )
+
+    result_str = (
+        '| Strategy   |   buy count |   avg profit % |   cum profit % '
+        '|   total profit BTC | avg duration   |   profit |   loss |\n'
+        '|:-----------|------------:|---------------:|---------------:'
+        '|-------------------:|:---------------|---------:|-------:|\n'
+        '| ETH/BTC    |           3 |          20.00 |          60.00 '
+        '|         1.10000000 | 0:17:00        |        3 |      0 |\n'
+        '| LTC/BTC    |           3 |          30.00 |          90.00 '
+        '|         1.30000000 | 0:20:00        |        3 |      0 |'
+    )
+    print(backtesting._generate_text_table_strategy(all_results=results))
+    assert backtesting._generate_text_table_strategy(all_results=results) == result_str
+
+
+def test_backtesting_start(default_conf, mocker, caplog) -> None:
+    def get_timeframe(input1, input2):
+        return Arrow(2017, 11, 14, 21, 17), Arrow(2017, 11, 14, 22, 59)
+
+    mocker.patch('freqtrade.optimize.load_data', mocked_load_data)
+    mocker.patch('freqtrade.exchange.Exchange.refresh_tickers', MagicMock())
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        'freqtrade.optimize.backtesting.Backtesting',
+        backtest=MagicMock(),
+        _generate_text_table=MagicMock(return_value='1'),
+        get_timeframe=get_timeframe,
+    )
+
+    default_conf['exchange']['pair_whitelist'] = ['UNITTEST/BTC']
+    default_conf['ticker_interval'] = 1
+    default_conf['live'] = False
+    default_conf['datadir'] = None
+    default_conf['export'] = None
+    default_conf['timerange'] = '-100'
+
+    backtesting = Backtesting(default_conf)
+    backtesting.start()
+    # check the logs, that will contain the backtest result
+    exists = [
+        'Using local backtesting data (using whitelist in given config) ...',
+        'Using stake_currency: BTC ...',
+        'Using stake_amount: 0.001 ...',
+        'Measuring data from 2017-11-14T21:17:00+00:00 '
+        'up to 2017-11-14T22:59:00+00:00 (0 days)..'
+    ]
+    for line in exists:
+        assert log_has(line, caplog.record_tuples)
+
+
+def test_backtesting_start_no_data(default_conf, mocker, caplog) -> None:
+    def get_timeframe(input1, input2):
+        return Arrow(2017, 11, 14, 21, 17), Arrow(2017, 11, 14, 22, 59)
+
+    mocker.patch('freqtrade.optimize.load_data', MagicMock(return_value={}))
+    mocker.patch('freqtrade.exchange.Exchange.refresh_tickers', MagicMock())
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        'freqtrade.optimize.backtesting.Backtesting',
+        backtest=MagicMock(),
+        _generate_text_table=MagicMock(return_value='1'),
+        get_timeframe=get_timeframe,
+    )
+
+    default_conf['exchange']['pair_whitelist'] = ['UNITTEST/BTC']
+    default_conf['ticker_interval'] = "1m"
+    default_conf['live'] = False
+    default_conf['datadir'] = None
+    default_conf['export'] = None
+    default_conf['timerange'] = '20180101-20180102'
+
+    backtesting = Backtesting(default_conf)
+    backtesting.start()
+    # check the logs, that will contain the backtest result
+
+    assert log_has('No data found. Terminating.', caplog.record_tuples)
+
+
+def test_backtest(default_conf, fee, mocker) -> None:
+    mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
+    patch_exchange(mocker)
+    backtesting = Backtesting(default_conf)
+    pair = 'UNITTEST/BTC'
+    data = optimize.load_data(None, ticker_interval='5m', pairs=['UNITTEST/BTC'])
+    data = trim_dictlist(data, -200)
+    data_processed = backtesting.tickerdata_to_dataframe(data)
+    results = backtesting.backtest(
+        {
+            'stake_amount': default_conf['stake_amount'],
+            'processed': data_processed,
+            'max_open_trades': 10,
+            'position_stacking': False
+        }
+    )
     assert not results.empty
+    assert len(results) == 2
+
+    expected = pd.DataFrame(
+        {'pair': [pair, pair],
+         'profit_percent': [0.00029975, 0.00056708],
+         'profit_abs': [1.49e-06, 7.6e-07],
+         'open_time': [Arrow(2018, 1, 29, 18, 40, 0).datetime,
+                       Arrow(2018, 1, 30, 3, 30, 0).datetime],
+         'close_time': [Arrow(2018, 1, 29, 22, 40, 0).datetime,
+                        Arrow(2018, 1, 30, 4, 20, 0).datetime],
+         'open_index': [77, 183],
+         'close_index': [125, 193],
+         'trade_duration': [240, 50],
+         'open_at_end': [False, False],
+         'open_rate': [0.104445, 0.10302485],
+         'close_rate': [0.105, 0.10359999],
+         'sell_reason': [SellType.ROI, SellType.ROI]
+         })
+    pd.testing.assert_frame_equal(results, expected)
+    data_pair = data_processed[pair]
+    for _, t in results.iterrows():
+        ln = data_pair.loc[data_pair["date"] == t["open_time"]]
+        # Check open trade rate alignes to open rate
+        assert ln is not None
+        assert round(ln.iloc[0]["open"], 6) == round(t["open_rate"], 6)
+        # check close trade rate alignes to close rate
+        ln = data_pair.loc[data_pair["date"] == t["close_time"]]
+        assert round(ln.iloc[0]["open"], 6) == round(t["close_rate"], 6)
 
 
-def test_processed(default_conf, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
+def test_backtest_1min_ticker_interval(default_conf, fee, mocker) -> None:
+    mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
+    patch_exchange(mocker)
+    backtesting = Backtesting(default_conf)
+
+    # Run a backtesting for an exiting 5min ticker_interval
+    data = optimize.load_data(None, ticker_interval='1m', pairs=['UNITTEST/BTC'])
+    data = trim_dictlist(data, -200)
+    results = backtesting.backtest(
+        {
+            'stake_amount': default_conf['stake_amount'],
+            'processed': backtesting.tickerdata_to_dataframe(data),
+            'max_open_trades': 1,
+            'position_stacking': False
+        }
+    )
+    assert not results.empty
+    assert len(results) == 1
+
+
+def test_processed(default_conf, mocker) -> None:
+    patch_exchange(mocker)
+    backtesting = Backtesting(default_conf)
+
     dict_of_tickerrows = load_data_test('raise')
-    dataframes = optimize.preprocess(dict_of_tickerrows)
-    dataframe = dataframes['BTC_UNITEST']
+    dataframes = backtesting.tickerdata_to_dataframe(dict_of_tickerrows)
+    dataframe = dataframes['UNITTEST/BTC']
     cols = dataframe.columns
     # assert the dataframe got some of the indicator columns
     for col in ['close', 'high', 'low', 'open', 'date',
@@ -142,36 +594,247 @@ def test_processed(default_conf, mocker):
         assert col in cols
 
 
-def test_backtest_pricecontours(default_conf, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    tests = [['raise', 17], ['lower', 0], ['sine', 17]]
+def test_backtest_pricecontours(default_conf, fee, mocker) -> None:
+    mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
+    tests = [['raise', 18], ['lower', 0], ['sine', 16]]
     for [contour, numres] in tests:
-        simple_backtest(default_conf, contour, numres)
+        simple_backtest(default_conf, contour, numres, mocker)
 
 
-def mocked_load_data(datadir, pairs=[], ticker_interval=0, refresh_pairs=False):
-    tickerdata = optimize.load_tickerdata_file(datadir, 'BTC_UNITEST', 1)
-    pairdata = {'BTC_UNITEST': tickerdata}
-    return trim_dictlist(pairdata, -100)
+# Test backtest using offline data (testdata directory)
+def test_backtest_ticks(default_conf, fee, mocker):
+    mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
+    patch_exchange(mocker)
+    ticks = [1, 5]
+    fun = Backtesting(default_conf).advise_buy
+    for _ in ticks:
+        backtest_conf = _make_backtest_conf(mocker, conf=default_conf)
+        backtesting = Backtesting(default_conf)
+        backtesting.advise_buy = fun  # Override
+        backtesting.advise_sell = fun  # Override
+        results = backtesting.backtest(backtest_conf)
+        assert not results.empty
 
 
-def test_backtest_start(default_conf, mocker, caplog):
-    default_conf['exchange']['pair_whitelist'] = ['BTC_UNITEST']
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.misc.load_config', new=lambda s: default_conf)
-    mocker.patch.multiple('freqtrade.optimize',
-                          load_data=mocked_load_data)
-    args = MagicMock()
-    args.ticker_interval = 1
-    args.level = 10
-    args.live = False
-    args.datadir = None
-    backtesting.start(args)
+def test_backtest_clash_buy_sell(mocker, default_conf):
+    # Override the default buy trend function in our default_strategy
+    def fun(dataframe=None, pair=None):
+        buy_value = 1
+        sell_value = 1
+        return _trend(dataframe, buy_value, sell_value)
+
+    backtest_conf = _make_backtest_conf(mocker, conf=default_conf)
+    backtesting = Backtesting(default_conf)
+    backtesting.advise_buy = fun  # Override
+    backtesting.advise_sell = fun  # Override
+    results = backtesting.backtest(backtest_conf)
+    assert results.empty
+
+
+def test_backtest_only_sell(mocker, default_conf):
+    # Override the default buy trend function in our default_strategy
+    def fun(dataframe=None, pair=None):
+        buy_value = 0
+        sell_value = 1
+        return _trend(dataframe, buy_value, sell_value)
+
+    backtest_conf = _make_backtest_conf(mocker, conf=default_conf)
+    backtesting = Backtesting(default_conf)
+    backtesting.advise_buy = fun  # Override
+    backtesting.advise_sell = fun  # Override
+    results = backtesting.backtest(backtest_conf)
+    assert results.empty
+
+
+def test_backtest_alternate_buy_sell(default_conf, fee, mocker):
+    mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
+    backtest_conf = _make_backtest_conf(mocker, conf=default_conf, pair='UNITTEST/BTC')
+    backtesting = Backtesting(default_conf)
+    backtesting.advise_buy = _trend_alternate  # Override
+    backtesting.advise_sell = _trend_alternate  # Override
+    results = backtesting.backtest(backtest_conf)
+    backtesting._store_backtest_result("test_.json", results)
+    assert len(results) == 4
+    # One trade was force-closed at the end
+    assert len(results.loc[results.open_at_end]) == 1
+
+
+def test_backtest_record(default_conf, fee, mocker):
+    names = []
+    records = []
+    patch_exchange(mocker)
+    mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
+    mocker.patch(
+        'freqtrade.optimize.backtesting.file_dump_json',
+        new=lambda n, r: (names.append(n), records.append(r))
+    )
+
+    backtesting = Backtesting(default_conf)
+    results = pd.DataFrame({"pair": ["UNITTEST/BTC", "UNITTEST/BTC",
+                                     "UNITTEST/BTC", "UNITTEST/BTC"],
+                            "profit_percent": [0.003312, 0.010801, 0.013803, 0.002780],
+                            "profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
+                            "open_time": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
+                                          Arrow(2017, 11, 14, 21, 36, 00).datetime,
+                                          Arrow(2017, 11, 14, 22, 12, 00).datetime,
+                                          Arrow(2017, 11, 14, 22, 44, 00).datetime],
+                            "close_time": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
+                                           Arrow(2017, 11, 14, 22, 10, 00).datetime,
+                                           Arrow(2017, 11, 14, 22, 43, 00).datetime,
+                                           Arrow(2017, 11, 14, 22, 58, 00).datetime],
+                            "open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
+                            "close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
+                            "open_index": [1, 119, 153, 185],
+                            "close_index": [118, 151, 184, 199],
+                            "trade_duration": [123, 34, 31, 14],
+                            "open_at_end": [False, False, False, True],
+                            "sell_reason": [SellType.ROI, SellType.STOP_LOSS,
+                                            SellType.ROI, SellType.FORCE_SELL]
+                            })
+    backtesting._store_backtest_result("backtest-result.json", results)
+    assert len(results) == 4
+    # Assert file_dump_json was only called once
+    assert names == ['backtest-result.json']
+    records = records[0]
+    # Ensure records are of correct type
+    assert len(records) == 4
+
+    # reset test to test with strategy name
+    names = []
+    records = []
+    backtesting._store_backtest_result("backtest-result.json", results, "DefStrat")
+    assert len(results) == 4
+    # Assert file_dump_json was only called once
+    assert names == ['backtest-result-DefStrat.json']
+    records = records[0]
+    # Ensure records are of correct type
+    assert len(records) == 4
+
+    # ('UNITTEST/BTC', 0.00331158, '1510684320', '1510691700', 0, 117)
+    # Below follows just a typecheck of the schema/type of trade-records
+    oix = None
+    for (pair, profit, date_buy, date_sell, buy_index, dur,
+         openr, closer, open_at_end, sell_reason) in records:
+        assert pair == 'UNITTEST/BTC'
+        assert isinstance(profit, float)
+        # FIX: buy/sell should be converted to ints
+        assert isinstance(date_buy, float)
+        assert isinstance(date_sell, float)
+        assert isinstance(openr, float)
+        assert isinstance(closer, float)
+        assert isinstance(open_at_end, bool)
+        assert isinstance(sell_reason, str)
+        isinstance(buy_index, pd._libs.tslib.Timestamp)
+        if oix:
+            assert buy_index > oix
+        oix = buy_index
+        assert dur > 0
+
+
+def test_backtest_start_live(default_conf, mocker, caplog):
+    default_conf['exchange']['pair_whitelist'] = ['UNITTEST/BTC']
+
+    async def load_pairs(pair, timeframe, since):
+        return _load_pair_as_ticks(pair, timeframe)
+
+    api_mock = MagicMock()
+    api_mock.fetch_ohlcv = load_pairs
+
+    patch_exchange(mocker, api_mock)
+    mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', MagicMock())
+    mocker.patch('freqtrade.optimize.backtesting.Backtesting._generate_text_table', MagicMock())
+    mocker.patch('freqtrade.configuration.open', mocker.mock_open(
+        read_data=json.dumps(default_conf)
+    ))
+
+    args = [
+        '--config', 'config.json',
+        '--strategy', 'DefaultStrategy',
+        '--datadir', 'freqtrade/tests/testdata',
+        'backtesting',
+        '--ticker-interval', '1m',
+        '--live',
+        '--timerange', '-100',
+        '--enable-position-stacking',
+        '--disable-max-market-positions'
+    ]
+    args = get_args(args)
+    start(args)
     # check the logs, that will contain the backtest result
-    exists = ['Using max_open_trades: 1 ...',
-              'Using stake_amount: 0.001 ...',
-              'Measuring data from 2017-11-14T21:17:00+00:00 up to 2017-11-14T22:59:00+00:00 ...']
+    exists = [
+        'Parameter -i/--ticker-interval detected ...',
+        'Using ticker_interval: 1m ...',
+        'Parameter -l/--live detected ...',
+        'Ignoring max_open_trades (--disable-max-market-positions was used) ...',
+        'Parameter --timerange detected: -100 ...',
+        'Using data folder: freqtrade/tests/testdata ...',
+        'Using stake_currency: BTC ...',
+        'Using stake_amount: 0.001 ...',
+        'Downloading data for all pairs in whitelist ...',
+        'Measuring data from 2017-11-14T19:31:00+00:00 up to 2017-11-14T22:58:00+00:00 (0 days)..',
+        'Parameter --enable-position-stacking detected ...'
+    ]
+
     for line in exists:
-        assert ('freqtrade.optimize.backtesting',
-                logging.INFO,
-                line) in caplog.record_tuples
+        assert log_has(line, caplog.record_tuples)
+
+
+def test_backtest_start_multi_strat(default_conf, mocker, caplog):
+    default_conf['exchange']['pair_whitelist'] = ['UNITTEST/BTC']
+
+    async def load_pairs(pair, timeframe, since):
+        return _load_pair_as_ticks(pair, timeframe)
+    api_mock = MagicMock()
+    api_mock.fetch_ohlcv = load_pairs
+
+    patch_exchange(mocker, api_mock)
+    backtestmock = MagicMock()
+    mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', backtestmock)
+    gen_table_mock = MagicMock()
+    mocker.patch('freqtrade.optimize.backtesting.Backtesting._generate_text_table', gen_table_mock)
+    gen_strattable_mock = MagicMock()
+    mocker.patch('freqtrade.optimize.backtesting.Backtesting._generate_text_table_strategy',
+                 gen_strattable_mock)
+    mocker.patch('freqtrade.configuration.open', mocker.mock_open(
+        read_data=json.dumps(default_conf)
+    ))
+
+    args = [
+        '--config', 'config.json',
+        '--datadir', 'freqtrade/tests/testdata',
+        'backtesting',
+        '--ticker-interval', '1m',
+        '--live',
+        '--timerange', '-100',
+        '--enable-position-stacking',
+        '--disable-max-market-positions',
+        '--strategy-list',
+        'DefaultStrategy',
+        'TestStrategy',
+    ]
+    args = get_args(args)
+    start(args)
+    # 2 backtests, 4 tables
+    assert backtestmock.call_count == 2
+    assert gen_table_mock.call_count == 4
+    assert gen_strattable_mock.call_count == 1
+
+    # check the logs, that will contain the backtest result
+    exists = [
+        'Parameter -i/--ticker-interval detected ...',
+        'Using ticker_interval: 1m ...',
+        'Parameter -l/--live detected ...',
+        'Ignoring max_open_trades (--disable-max-market-positions was used) ...',
+        'Parameter --timerange detected: -100 ...',
+        'Using data folder: freqtrade/tests/testdata ...',
+        'Using stake_currency: BTC ...',
+        'Using stake_amount: 0.001 ...',
+        'Downloading data for all pairs in whitelist ...',
+        'Measuring data from 2017-11-14T19:31:00+00:00 up to 2017-11-14T22:58:00+00:00 (0 days)..',
+        'Parameter --enable-position-stacking detected ...',
+        'Running backtesting for Strategy DefaultStrategy',
+        'Running backtesting for Strategy TestStrategy',
+    ]
+
+    for line in exists:
+        assert log_has(line, caplog.record_tuples)

freqtrade/tests/optimize/test_hyperopt.py

@@ -1,223 +1,324 @@
 # pragma pylint: disable=missing-docstring,W0212,C0103
-from freqtrade.optimize.hyperopt import calculate_loss, TARGET_TRADES, EXPECTED_MAX_PROFIT, start, \
-    log_results, save_trials, read_trials
+import os
+from unittest.mock import MagicMock
+
+import pandas as pd
+import pytest
+
+from freqtrade.optimize.__init__ import load_tickerdata_file
+from freqtrade.optimize.hyperopt import Hyperopt, start
+from freqtrade.strategy.resolver import StrategyResolver
+from freqtrade.tests.conftest import log_has, patch_exchange
+from freqtrade.tests.optimize.test_backtesting import get_args
 
 
-def test_loss_calculation_prefer_correct_trade_count():
-    correct = calculate_loss(1, TARGET_TRADES, 20)
-    over = calculate_loss(1, TARGET_TRADES + 100, 20)
-    under = calculate_loss(1, TARGET_TRADES - 100, 20)
-    assert over > correct
-    assert under > correct
+@pytest.fixture(scope='function')
+def hyperopt(default_conf, mocker):
+    patch_exchange(mocker)
+    return Hyperopt(default_conf)
 
 
-def test_loss_calculation_prefer_shorter_trades():
-    shorter = calculate_loss(1, 100, 20)
-    longer = calculate_loss(1, 100, 30)
-    assert shorter < longer
-
-
-def test_loss_calculation_has_limited_profit():
-    correct = calculate_loss(EXPECTED_MAX_PROFIT, TARGET_TRADES, 20)
-    over = calculate_loss(EXPECTED_MAX_PROFIT * 2, TARGET_TRADES, 20)
-    under = calculate_loss(EXPECTED_MAX_PROFIT / 2, TARGET_TRADES, 20)
-    assert over == correct
-    assert under > correct
-
-
-def create_trials(mocker):
+# Functions for recurrent object patching
+def create_trials(mocker, hyperopt) -> None:
     """
     When creating trials, mock the hyperopt Trials so that *by default*
       - we don't create any pickle'd files in the filesystem
       - we might have a pickle'd file so make sure that we return
         false when looking for it
     """
-    mocker.patch('freqtrade.optimize.hyperopt.TRIALS_FILE',
-                 return_value='freqtrade/tests/optimize/ut_trials.pickle')
-    mocker.patch('freqtrade.optimize.hyperopt.os.path.exists',
-                 return_value=False)
-    mocker.patch('freqtrade.optimize.hyperopt.save_trials',
-                 return_value=None)
-    mocker.patch('freqtrade.optimize.hyperopt.read_trials',
-                 return_value=None)
-    mocker.patch('freqtrade.optimize.hyperopt.os.remove',
-                 return_value=True)
-    return mocker.Mock(
-        results=[{
-            'loss': 1,
-            'result': 'foo',
-            'status': 'ok'
-        }],
-        best_trial={'misc': {'vals': {'adx': 999}}}
+    hyperopt.trials_file = os.path.join('freqtrade', 'tests', 'optimize', 'ut_trials.pickle')
+
+    mocker.patch('freqtrade.optimize.hyperopt.os.path.exists', return_value=False)
+    mocker.patch('freqtrade.optimize.hyperopt.os.path.getsize', return_value=1)
+    mocker.patch('freqtrade.optimize.hyperopt.os.remove', return_value=True)
+    mocker.patch('freqtrade.optimize.hyperopt.dump', return_value=None)
+
+    return [{'loss': 1, 'result': 'foo', 'params': {}}]
+
+
+def test_start(mocker, default_conf, caplog) -> None:
+    start_mock = MagicMock()
+    mocker.patch(
+        'freqtrade.configuration.Configuration._load_config_file',
+        lambda *args, **kwargs: default_conf
+    )
+    mocker.patch('freqtrade.optimize.hyperopt.Hyperopt.start', start_mock)
+    patch_exchange(mocker)
+
+    args = [
+        '--config', 'config.json',
+        '--strategy', 'DefaultStrategy',
+        'hyperopt',
+        '--epochs', '5'
+    ]
+    args = get_args(args)
+    StrategyResolver({'strategy': 'DefaultStrategy'})
+    start(args)
+
+    import pprint
+    pprint.pprint(caplog.record_tuples)
+
+    assert log_has(
+        'Starting freqtrade in Hyperopt mode',
+        caplog.record_tuples
+    )
+    assert start_mock.call_count == 1
+
+
+def test_start_failure(mocker, default_conf, caplog) -> None:
+    start_mock = MagicMock()
+    mocker.patch(
+        'freqtrade.configuration.Configuration._load_config_file',
+        lambda *args, **kwargs: default_conf
+    )
+    mocker.patch('freqtrade.optimize.hyperopt.Hyperopt.start', start_mock)
+    patch_exchange(mocker)
+
+    args = [
+        '--config', 'config.json',
+        '--strategy', 'TestStrategy',
+        'hyperopt',
+        '--epochs', '5'
+    ]
+    args = get_args(args)
+    StrategyResolver({'strategy': 'DefaultStrategy'})
+    with pytest.raises(ValueError):
+        start(args)
+    assert log_has(
+        "Please don't use --strategy for hyperopt.",
+        caplog.record_tuples
     )
 
 
-def test_start_calls_fmin(mocker):
-    trials = create_trials(mocker)
-    mocker.patch('freqtrade.optimize.hyperopt.TRIALS', return_value=trials)
-    mocker.patch('freqtrade.optimize.hyperopt.sorted',
-                 return_value=trials.results)
-    mocker.patch('freqtrade.optimize.preprocess')
-    mocker.patch('freqtrade.optimize.load_data')
-    mock_fmin = mocker.patch('freqtrade.optimize.hyperopt.fmin', return_value={})
+def test_loss_calculation_prefer_correct_trade_count(hyperopt) -> None:
+    StrategyResolver({'strategy': 'DefaultStrategy'})
 
-    args = mocker.Mock(epochs=1, config='config.json.example', mongodb=False)
-    start(args)
-
-    mock_fmin.assert_called_once()
+    correct = hyperopt.calculate_loss(1, hyperopt.target_trades, 20)
+    over = hyperopt.calculate_loss(1, hyperopt.target_trades + 100, 20)
+    under = hyperopt.calculate_loss(1, hyperopt.target_trades - 100, 20)
+    assert over > correct
+    assert under > correct
 
 
-def test_start_uses_mongotrials(mocker):
-    mock_mongotrials = mocker.patch('freqtrade.optimize.hyperopt.MongoTrials',
-                                    return_value=create_trials(mocker))
-    mocker.patch('freqtrade.optimize.preprocess')
-    mocker.patch('freqtrade.optimize.load_data')
-    mocker.patch('freqtrade.optimize.hyperopt.fmin', return_value={})
-
-    args = mocker.Mock(epochs=1, config='config.json.example', mongodb=True)
-    start(args)
-
-    mock_mongotrials.assert_called_once()
+def test_loss_calculation_prefer_shorter_trades(hyperopt) -> None:
+    shorter = hyperopt.calculate_loss(1, 100, 20)
+    longer = hyperopt.calculate_loss(1, 100, 30)
+    assert shorter < longer
 
 
-def test_log_results_if_loss_improves(mocker):
-    logger = mocker.patch('freqtrade.optimize.hyperopt.logger.info')
-    global CURRENT_BEST_LOSS
-    CURRENT_BEST_LOSS = 2
-    log_results({
-        'loss': 1,
-        'current_tries': 1,
-        'total_tries': 2,
-        'result': 'foo'
-    })
-
-    logger.assert_called_once()
+def test_loss_calculation_has_limited_profit(hyperopt) -> None:
+    correct = hyperopt.calculate_loss(hyperopt.expected_max_profit, hyperopt.target_trades, 20)
+    over = hyperopt.calculate_loss(hyperopt.expected_max_profit * 2, hyperopt.target_trades, 20)
+    under = hyperopt.calculate_loss(hyperopt.expected_max_profit / 2, hyperopt.target_trades, 20)
+    assert over == correct
+    assert under > correct
 
 
-def test_no_log_if_loss_does_not_improve(mocker):
-    logger = mocker.patch('freqtrade.optimize.hyperopt.logger.info')
-    global CURRENT_BEST_LOSS
-    CURRENT_BEST_LOSS = 2
-    log_results({
-        'loss': 3,
-    })
-
-    assert not logger.called
+def test_log_results_if_loss_improves(hyperopt, capsys) -> None:
+    hyperopt.current_best_loss = 2
+    hyperopt.log_results(
+        {
+            'loss': 1,
+            'current_tries': 1,
+            'total_tries': 2,
+            'result': 'foo'
+        }
+    )
+    out, err = capsys.readouterr()
+    assert '    1/2: foo. Loss 1.00000' in out
 
 
-def test_fmin_best_results(mocker, caplog):
-    fmin_result = {
-        "adx": 1,
-        "adx-value": 15.0,
-        "fastd": 1,
-        "fastd-value": 40.0,
-        "green_candle": 1,
-        "mfi": 0,
-        "over_sar": 0,
-        "rsi": 1,
-        "rsi-value": 37.0,
-        "trigger": 2,
-        "uptrend_long_ema": 1,
-        "uptrend_short_ema": 0,
-        "uptrend_sma": 0,
-        "stoploss": -0.1,
+def test_no_log_if_loss_does_not_improve(hyperopt, caplog) -> None:
+    hyperopt.current_best_loss = 2
+    hyperopt.log_results(
+        {
+            'loss': 3,
+        }
+    )
+    assert caplog.record_tuples == []
+
+
+def test_save_trials_saves_trials(mocker, hyperopt, caplog) -> None:
+    trials = create_trials(mocker, hyperopt)
+    mock_dump = mocker.patch('freqtrade.optimize.hyperopt.dump', return_value=None)
+    hyperopt.trials = trials
+    hyperopt.save_trials()
+
+    trials_file = os.path.join('freqtrade', 'tests', 'optimize', 'ut_trials.pickle')
+    assert log_has(
+        'Saving 1 evaluations to \'{}\''.format(trials_file),
+        caplog.record_tuples
+    )
+    mock_dump.assert_called_once()
+
+
+def test_read_trials_returns_trials_file(mocker, hyperopt, caplog) -> None:
+    trials = create_trials(mocker, hyperopt)
+    mock_load = mocker.patch('freqtrade.optimize.hyperopt.load', return_value=trials)
+    hyperopt_trial = hyperopt.read_trials()
+    trials_file = os.path.join('freqtrade', 'tests', 'optimize', 'ut_trials.pickle')
+    assert log_has(
+        'Reading Trials from \'{}\''.format(trials_file),
+        caplog.record_tuples
+    )
+    assert hyperopt_trial == trials
+    mock_load.assert_called_once()
+
+
+def test_roi_table_generation(hyperopt) -> None:
+    params = {
+        'roi_t1': 5,
+        'roi_t2': 10,
+        'roi_t3': 15,
+        'roi_p1': 1,
+        'roi_p2': 2,
+        'roi_p3': 3,
     }
 
-    mocker.patch('freqtrade.optimize.hyperopt.MongoTrials', return_value=create_trials(mocker))
-    mocker.patch('freqtrade.optimize.preprocess')
-    mocker.patch('freqtrade.optimize.load_data')
-    mocker.patch('freqtrade.optimize.hyperopt.fmin', return_value=fmin_result)
+    assert hyperopt.generate_roi_table(params) == {0: 6, 15: 3, 25: 1, 30: 0}
 
-    args = mocker.Mock(epochs=1, config='config.json.example')
-    start(args)
 
-    exists = [
-        'Best parameters',
-        '"adx": {\n        "enabled": true,\n        "value": 15.0\n    },',
-        '"green_candle": {\n        "enabled": true\n    },',
-        '"mfi": {\n        "enabled": false\n    },',
-        '"trigger": {\n        "type": "ao_cross_zero"\n    },',
-        '"stoploss": -0.1',
+def test_start_calls_optimizer(mocker, default_conf, caplog) -> None:
+    dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
+    mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock())
+    mocker.patch('freqtrade.optimize.hyperopt.multiprocessing.cpu_count', MagicMock(return_value=1))
+    parallel = mocker.patch(
+        'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
+        MagicMock(return_value=[{'loss': 1, 'result': 'foo result', 'params': {}}])
+    )
+    patch_exchange(mocker)
+
+    default_conf.update({'config': 'config.json.example'})
+    default_conf.update({'epochs': 1})
+    default_conf.update({'timerange': None})
+    default_conf.update({'spaces': 'all'})
+
+    hyperopt = Hyperopt(default_conf)
+    hyperopt.tickerdata_to_dataframe = MagicMock()
+
+    hyperopt.start()
+    parallel.assert_called_once()
+
+    assert 'Best result:\nfoo result\nwith values:\n{}' in caplog.text
+    assert dumper.called
+
+
+def test_format_results(hyperopt):
+    # Test with BTC as stake_currency
+    trades = [
+        ('ETH/BTC', 2, 2, 123),
+        ('LTC/BTC', 1, 1, 123),
+        ('XPR/BTC', -1, -2, -246)
     ]
+    labels = ['currency', 'profit_percent', 'profit_abs', 'trade_duration']
+    df = pd.DataFrame.from_records(trades, columns=labels)
 
-    for line in exists:
-        assert line in caplog.text
+    result = hyperopt.format_results(df)
+    assert result.find(' 66.67%')
+    assert result.find('Total profit 1.00000000 BTC')
+    assert result.find('2.0000Σ %')
 
-
-def test_fmin_throw_value_error(mocker, caplog):
-    mocker.patch('freqtrade.optimize.hyperopt.MongoTrials', return_value=create_trials(mocker))
-    mocker.patch('freqtrade.optimize.preprocess')
-    mocker.patch('freqtrade.optimize.load_data')
-    mocker.patch('freqtrade.optimize.hyperopt.fmin', side_effect=ValueError())
-
-    args = mocker.Mock(epochs=1, config='config.json.example')
-    start(args)
-
-    exists = [
-        'Best Result:',
-        'Sorry, Hyperopt was not able to find good parameters. Please try with more epochs '
-        '(param: -e).',
+    # Test with EUR as stake_currency
+    trades = [
+        ('ETH/EUR', 2, 2, 123),
+        ('LTC/EUR', 1, 1, 123),
+        ('XPR/EUR', -1, -2, -246)
     ]
-
-    for line in exists:
-        assert line in caplog.text
+    df = pd.DataFrame.from_records(trades, columns=labels)
+    result = hyperopt.format_results(df)
+    assert result.find('Total profit 1.00000000 EUR')
 
 
-def test_resuming_previous_hyperopt_results_succeeds(mocker):
-    import freqtrade.optimize.hyperopt as hyperopt
-    trials = create_trials(mocker)
-    mocker.patch('freqtrade.optimize.hyperopt.TRIALS',
-                 return_value=trials)
-    mocker.patch('freqtrade.optimize.hyperopt.os.path.exists',
-                 return_value=True)
-    mocker.patch('freqtrade.optimize.hyperopt.len',
-                 return_value=len(trials.results))
-    mock_read = mocker.patch('freqtrade.optimize.hyperopt.read_trials',
-                             return_value=trials)
-    mock_save = mocker.patch('freqtrade.optimize.hyperopt.save_trials',
-                             return_value=None)
-    mocker.patch('freqtrade.optimize.hyperopt.sorted',
-                 return_value=trials.results)
-    mocker.patch('freqtrade.optimize.preprocess')
-    mocker.patch('freqtrade.optimize.load_data')
-    mocker.patch('freqtrade.optimize.hyperopt.fmin',
-                 return_value={})
-    args = mocker.Mock(epochs=1,
-                       config='config.json.example',
-                       mongodb=False)
+def test_has_space(hyperopt):
+    hyperopt.config.update({'spaces': ['buy', 'roi']})
+    assert hyperopt.has_space('roi')
+    assert hyperopt.has_space('buy')
+    assert not hyperopt.has_space('stoploss')
 
-    start(args)
-
-    mock_read.assert_called_once()
-    mock_save.assert_called_once()
-
-    current_tries = hyperopt._CURRENT_TRIES
-    total_tries = hyperopt.TOTAL_TRIES
-
-    assert current_tries == len(trials.results)
-    assert total_tries == (current_tries + len(trials.results))
+    hyperopt.config.update({'spaces': ['all']})
+    assert hyperopt.has_space('buy')
 
 
-def test_save_trials_saves_trials(mocker):
-    trials = create_trials(mocker)
-    mock_dump = mocker.patch('freqtrade.optimize.hyperopt.pickle.dump',
-                             return_value=None)
-    trials_path = mocker.patch('freqtrade.optimize.hyperopt.TRIALS_FILE',
-                               return_value='ut_trials.pickle')
-    mocker.patch('freqtrade.optimize.hyperopt.open',
-                 return_value=trials_path)
-    save_trials(trials, trials_path)
+def test_populate_indicators(hyperopt) -> None:
+    tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
+    tickerlist = {'UNITTEST/BTC': tick}
+    dataframes = hyperopt.tickerdata_to_dataframe(tickerlist)
+    dataframe = hyperopt.populate_indicators(dataframes['UNITTEST/BTC'], {'pair': 'UNITTEST/BTC'})
 
-    mock_dump.assert_called_once_with(trials, trials_path)
+    # Check if some indicators are generated. We will not test all of them
+    assert 'adx' in dataframe
+    assert 'mfi' in dataframe
+    assert 'rsi' in dataframe
 
 
-def test_read_trials_returns_trials_file(mocker):
-    trials = create_trials(mocker)
-    mock_load = mocker.patch('freqtrade.optimize.hyperopt.pickle.load',
-                             return_value=trials)
-    mock_open = mocker.patch('freqtrade.optimize.hyperopt.open',
-                             return_value=mock_load)
+def test_buy_strategy_generator(hyperopt) -> None:
+    tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
+    tickerlist = {'UNITTEST/BTC': tick}
+    dataframes = hyperopt.tickerdata_to_dataframe(tickerlist)
+    dataframe = hyperopt.populate_indicators(dataframes['UNITTEST/BTC'], {'pair': 'UNITTEST/BTC'})
 
-    assert read_trials() == trials
-    mock_open.assert_called_once()
-    mock_load.assert_called_once()
+    populate_buy_trend = hyperopt.buy_strategy_generator(
+        {
+            'adx-value': 20,
+            'fastd-value': 20,
+            'mfi-value': 20,
+            'rsi-value': 20,
+            'adx-enabled': True,
+            'fastd-enabled': True,
+            'mfi-enabled': True,
+            'rsi-enabled': True,
+            'trigger': 'bb_lower'
+        }
+    )
+    result = populate_buy_trend(dataframe, {'pair': 'UNITTEST/BTC'})
+    # Check if some indicators are generated. We will not test all of them
+    assert 'buy' in result
+    assert 1 in result['buy']
+
+
+def test_generate_optimizer(mocker, default_conf) -> None:
+    default_conf.update({'config': 'config.json.example'})
+    default_conf.update({'timerange': None})
+    default_conf.update({'spaces': 'all'})
+
+    trades = [
+        ('POWR/BTC', 0.023117, 0.000233, 100)
+    ]
+    labels = ['currency', 'profit_percent', 'profit_abs', 'trade_duration']
+    backtest_result = pd.DataFrame.from_records(trades, columns=labels)
+
+    mocker.patch(
+        'freqtrade.optimize.hyperopt.Hyperopt.backtest',
+        MagicMock(return_value=backtest_result)
+    )
+    patch_exchange(mocker)
+    mocker.patch('freqtrade.optimize.hyperopt.load', MagicMock())
+
+    optimizer_param = {
+        'adx-value': 0,
+        'fastd-value': 35,
+        'mfi-value': 0,
+        'rsi-value': 0,
+        'adx-enabled': False,
+        'fastd-enabled': True,
+        'mfi-enabled': False,
+        'rsi-enabled': False,
+        'trigger': 'macd_cross_signal',
+        'roi_t1': 60.0,
+        'roi_t2': 30.0,
+        'roi_t3': 20.0,
+        'roi_p1': 0.01,
+        'roi_p2': 0.01,
+        'roi_p3': 0.1,
+        'stoploss': -0.4,
+    }
+    response_expected = {
+        'loss': 1.9840569076926293,
+        'result': '     1 trades. Avg profit  2.31%. Total profit  0.00023300 BTC '
+                  '(0.0231Σ%). Avg duration 100.0 mins.',
+        'params': optimizer_param
+    }
+
+    hyperopt = Hyperopt(default_conf)
+    generate_optimizer_value = hyperopt.generate_optimizer(list(optimizer_param.values()))
+    assert generate_optimizer_value == response_expected

freqtrade/tests/optimize/test_hyperopt_config.py

@@ -1,16 +0,0 @@
-# pragma pylint: disable=missing-docstring,W0212
-
-from freqtrade.optimize.hyperopt_conf import hyperopt_optimize_conf
-
-
-def test_hyperopt_optimize_conf():
-    hyperopt_conf = hyperopt_optimize_conf()
-
-    assert "max_open_trades" in hyperopt_conf
-    assert "stake_currency" in hyperopt_conf
-    assert "stake_amount" in hyperopt_conf
-    assert "minimal_roi" in hyperopt_conf
-    assert "stoploss" in hyperopt_conf
-    assert "bid_strategy" in hyperopt_conf
-    assert "exchange" in hyperopt_conf
-    assert "pair_whitelist" in hyperopt_conf['exchange']

freqtrade/tests/optimize/test_optimize.py

@@ -1,15 +1,24 @@
-# pragma pylint: disable=missing-docstring,W0212
+# pragma pylint: disable=missing-docstring, protected-access, C0103
 
+import json
 import os
-import logging
+import uuid
 from shutil import copyfile
-from freqtrade import exchange, optimize
-from freqtrade.exchange import Bittrex
-from freqtrade.optimize.__init__ import make_testdata_path, download_pairs,\
-    download_backtesting_testdata, load_tickerdata_file
 
-# Change this if modifying BTC_UNITEST testdatafile
-_btc_unittest_length = 13681
+import arrow
+
+from freqtrade import optimize
+from freqtrade.arguments import TimeRange
+from freqtrade.misc import file_dump_json
+from freqtrade.optimize.__init__ import (download_backtesting_testdata,
+                                         download_pairs,
+                                         load_cached_data_for_updating,
+                                         load_tickerdata_file,
+                                         make_testdata_path, trim_tickerlist)
+from freqtrade.tests.conftest import get_patched_exchange, log_has
+
+# Change this if modifying UNITTEST/BTC testdatafile
+_BTC_UNITTEST_LENGTH = 13681
 
 
 def _backup_file(file: str, copy_file: bool = False) -> None:
@@ -43,134 +52,384 @@ def _clean_test_file(file: str) -> None:
         os.rename(file_swp, file)
 
 
-def test_load_data_5min_ticker(default_conf, ticker_history, mocker, caplog):
-    mocker.patch('freqtrade.optimize.get_ticker_history', return_value=ticker_history)
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-
-    exchange._API = Bittrex({'key': '', 'secret': ''})
-
-    file = 'freqtrade/tests/testdata/BTC_ETH-5.json'
+def test_load_data_30min_ticker(ticker_history, mocker, caplog, default_conf) -> None:
+    mocker.patch('freqtrade.exchange.Exchange.get_history', return_value=ticker_history)
+    file = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'UNITTEST_BTC-30m.json')
     _backup_file(file, copy_file=True)
-    optimize.load_data(None, pairs=['BTC_ETH'])
+    optimize.load_data(None, pairs=['UNITTEST/BTC'], ticker_interval='30m')
     assert os.path.isfile(file) is True
-    assert ('freqtrade.optimize',
-            logging.INFO,
-            'Download the pair: "BTC_ETH", Interval: 5 min'
-            ) not in caplog.record_tuples
+    assert not log_has('Download the pair: "UNITTEST/BTC", Interval: 30m', caplog.record_tuples)
     _clean_test_file(file)
 
 
-def test_load_data_1min_ticker(default_conf, ticker_history, mocker, caplog):
-    mocker.patch('freqtrade.optimize.get_ticker_history', return_value=ticker_history)
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
+def test_load_data_5min_ticker(ticker_history, mocker, caplog, default_conf) -> None:
+    mocker.patch('freqtrade.exchange.Exchange.get_history', return_value=ticker_history)
 
-    exchange._API = Bittrex({'key': '', 'secret': ''})
-
-    file = 'freqtrade/tests/testdata/BTC_ETH-1.json'
+    file = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'UNITTEST_BTC-5m.json')
     _backup_file(file, copy_file=True)
-    optimize.load_data(None, ticker_interval=1, pairs=['BTC_ETH'])
+    optimize.load_data(None, pairs=['UNITTEST/BTC'], ticker_interval='5m')
     assert os.path.isfile(file) is True
-    assert ('freqtrade.optimize',
-            logging.INFO,
-            'Download the pair: "BTC_ETH", Interval: 1 min'
-            ) not in caplog.record_tuples
+    assert not log_has('Download the pair: "UNITTEST/BTC", Interval: 5m', caplog.record_tuples)
     _clean_test_file(file)
 
 
-def test_load_data_with_new_pair_1min(default_conf, ticker_history, mocker, caplog):
-    mocker.patch('freqtrade.optimize.get_ticker_history', return_value=ticker_history)
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
+def test_load_data_1min_ticker(ticker_history, mocker, caplog) -> None:
+    mocker.patch('freqtrade.exchange.Exchange.get_history', return_value=ticker_history)
+    file = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'UNITTEST_BTC-1m.json')
+    _backup_file(file, copy_file=True)
+    optimize.load_data(None, ticker_interval='1m', pairs=['UNITTEST/BTC'])
+    assert os.path.isfile(file) is True
+    assert not log_has('Download the pair: "UNITTEST/BTC", Interval: 1m', caplog.record_tuples)
+    _clean_test_file(file)
 
-    exchange._API = Bittrex({'key': '', 'secret': ''})
 
-    file = 'freqtrade/tests/testdata/BTC_MEME-1.json'
+def test_load_data_with_new_pair_1min(ticker_history, mocker, caplog, default_conf) -> None:
+    """
+    Test load_data() with 1 min ticker
+    """
+    mocker.patch('freqtrade.exchange.Exchange.get_history', return_value=ticker_history)
+    exchange = get_patched_exchange(mocker, default_conf)
+    file = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'MEME_BTC-1m.json')
+
     _backup_file(file)
-    optimize.load_data(None, ticker_interval=1, pairs=['BTC_MEME'])
+    # do not download a new pair if refresh_pairs isn't set
+    optimize.load_data(None,
+                       ticker_interval='1m',
+                       refresh_pairs=False,
+                       pairs=['MEME/BTC'])
+    assert os.path.isfile(file) is False
+    assert log_has('No data for pair: "MEME/BTC", Interval: 1m. '
+                   'Use --refresh-pairs-cached to download the data',
+                   caplog.record_tuples)
+
+    # download a new pair if refresh_pairs is set
+    optimize.load_data(None,
+                       ticker_interval='1m',
+                       refresh_pairs=True,
+                       exchange=exchange,
+                       pairs=['MEME/BTC'])
     assert os.path.isfile(file) is True
-    assert ('freqtrade.optimize',
-            logging.INFO,
-            'Download the pair: "BTC_MEME", Interval: 1 min'
-            ) in caplog.record_tuples
+    assert log_has('Download the pair: "MEME/BTC", Interval: 1m', caplog.record_tuples)
     _clean_test_file(file)
 
 
-def test_testdata_path():
+def test_testdata_path() -> None:
     assert os.path.join('freqtrade', 'tests', 'testdata') in make_testdata_path(None)
 
 
-def test_download_pairs(default_conf, ticker_history, mocker):
-    mocker.patch('freqtrade.optimize.__init__.get_ticker_history', return_value=ticker_history)
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    exchange._API = Bittrex({'key': '', 'secret': ''})
-
-    file1_1 = 'freqtrade/tests/testdata/BTC_MEME-1.json'
-    file1_5 = 'freqtrade/tests/testdata/BTC_MEME-5.json'
-    file2_1 = 'freqtrade/tests/testdata/BTC_CFI-1.json'
-    file2_5 = 'freqtrade/tests/testdata/BTC_CFI-5.json'
+def test_download_pairs(ticker_history, mocker, default_conf) -> None:
+    mocker.patch('freqtrade.exchange.Exchange.get_history', return_value=ticker_history)
+    exchange = get_patched_exchange(mocker, default_conf)
+    file1_1 = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'MEME_BTC-1m.json')
+    file1_5 = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'MEME_BTC-5m.json')
+    file2_1 = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'CFI_BTC-1m.json')
+    file2_5 = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'CFI_BTC-5m.json')
 
     _backup_file(file1_1)
     _backup_file(file1_5)
     _backup_file(file2_1)
     _backup_file(file2_5)
 
-    assert download_pairs(None, pairs=['BTC-MEME', 'BTC-CFI']) is True
+    assert os.path.isfile(file1_1) is False
+    assert os.path.isfile(file2_1) is False
+
+    assert download_pairs(None, exchange,
+                          pairs=['MEME/BTC', 'CFI/BTC'], ticker_interval='1m') is True
 
     assert os.path.isfile(file1_1) is True
-    assert os.path.isfile(file1_5) is True
     assert os.path.isfile(file2_1) is True
+
+    # clean files freshly downloaded
+    _clean_test_file(file1_1)
+    _clean_test_file(file2_1)
+
+    assert os.path.isfile(file1_5) is False
+    assert os.path.isfile(file2_5) is False
+
+    assert download_pairs(None, exchange,
+                          pairs=['MEME/BTC', 'CFI/BTC'], ticker_interval='5m') is True
+
+    assert os.path.isfile(file1_5) is True
     assert os.path.isfile(file2_5) is True
 
     # clean files freshly downloaded
-    _clean_test_file(file1_1)
     _clean_test_file(file1_5)
-    _clean_test_file(file2_1)
     _clean_test_file(file2_5)
 
 
-def test_download_pairs_exception(default_conf, ticker_history, mocker, caplog):
-    mocker.patch('freqtrade.optimize.__init__.get_ticker_history', return_value=ticker_history)
+def test_load_cached_data_for_updating(mocker) -> None:
+    datadir = os.path.join(os.path.dirname(__file__), '..', 'testdata')
+
+    test_data = None
+    test_filename = os.path.join(datadir, 'UNITTEST_BTC-1m.json')
+    with open(test_filename, "rt") as file:
+        test_data = json.load(file)
+
+    # change now time to test 'line' cases
+    # now = last cached item + 1 hour
+    now_ts = test_data[-1][0] / 1000 + 60 * 60
+    mocker.patch('arrow.utcnow', return_value=arrow.get(now_ts))
+
+    # timeframe starts earlier than the cached data
+    # should fully update data
+    timerange = TimeRange('date', None, test_data[0][0] / 1000 - 1, 0)
+    data, start_ts = load_cached_data_for_updating(test_filename,
+                                                   '1m',
+                                                   timerange)
+    assert data == []
+    assert start_ts == test_data[0][0] - 1000
+
+    # same with 'line' timeframe
+    num_lines = (test_data[-1][0] - test_data[1][0]) / 1000 / 60 + 120
+    data, start_ts = load_cached_data_for_updating(test_filename,
+                                                   '1m',
+                                                   TimeRange(None, 'line', 0, -num_lines))
+    assert data == []
+    assert start_ts < test_data[0][0] - 1
+
+    # timeframe starts in the center of the cached data
+    # should return the chached data w/o the last item
+    timerange = TimeRange('date', None, test_data[0][0] / 1000 + 1, 0)
+    data, start_ts = load_cached_data_for_updating(test_filename,
+                                                   '1m',
+                                                   timerange)
+    assert data == test_data[:-1]
+    assert test_data[-2][0] < start_ts < test_data[-1][0]
+
+    # same with 'line' timeframe
+    num_lines = (test_data[-1][0] - test_data[1][0]) / 1000 / 60 + 30
+    timerange = TimeRange(None, 'line', 0, -num_lines)
+    data, start_ts = load_cached_data_for_updating(test_filename,
+                                                   '1m',
+                                                   timerange)
+    assert data == test_data[:-1]
+    assert test_data[-2][0] < start_ts < test_data[-1][0]
+
+    # timeframe starts after the chached data
+    # should return the chached data w/o the last item
+    timerange = TimeRange('date', None, test_data[-1][0] / 1000 + 1, 0)
+    data, start_ts = load_cached_data_for_updating(test_filename,
+                                                   '1m',
+                                                   timerange)
+    assert data == test_data[:-1]
+    assert test_data[-2][0] < start_ts < test_data[-1][0]
+
+    # same with 'line' timeframe
+    num_lines = 30
+    timerange = TimeRange(None, 'line', 0, -num_lines)
+    data, start_ts = load_cached_data_for_updating(test_filename,
+                                                   '1m',
+                                                   timerange)
+    assert data == test_data[:-1]
+    assert test_data[-2][0] < start_ts < test_data[-1][0]
+
+    # no timeframe is set
+    # should return the chached data w/o the last item
+    num_lines = 30
+    timerange = TimeRange(None, 'line', 0, -num_lines)
+    data, start_ts = load_cached_data_for_updating(test_filename,
+                                                   '1m',
+                                                   timerange)
+    assert data == test_data[:-1]
+    assert test_data[-2][0] < start_ts < test_data[-1][0]
+
+    # no datafile exist
+    # should return timestamp start time
+    timerange = TimeRange('date', None, now_ts - 10000, 0)
+    data, start_ts = load_cached_data_for_updating(test_filename + 'unexist',
+                                                   '1m',
+                                                   timerange)
+    assert data == []
+    assert start_ts == (now_ts - 10000) * 1000
+
+    # same with 'line' timeframe
+    num_lines = 30
+    timerange = TimeRange(None, 'line', 0, -num_lines)
+    data, start_ts = load_cached_data_for_updating(test_filename + 'unexist',
+                                                   '1m',
+                                                   timerange)
+    assert data == []
+    assert start_ts == (now_ts - num_lines * 60) * 1000
+
+    # no datafile exist, no timeframe is set
+    # should return an empty array and None
+    data, start_ts = load_cached_data_for_updating(test_filename + 'unexist',
+                                                   '1m',
+                                                   None)
+    assert data == []
+    assert start_ts is None
+
+
+def test_download_pairs_exception(ticker_history, mocker, caplog, default_conf) -> None:
+    mocker.patch('freqtrade.exchange.Exchange.get_history', return_value=ticker_history)
     mocker.patch('freqtrade.optimize.__init__.download_backtesting_testdata',
                  side_effect=BaseException('File Error'))
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    exchange._API = Bittrex({'key': '', 'secret': ''})
+    exchange = get_patched_exchange(mocker, default_conf)
 
-    file1_1 = 'freqtrade/tests/testdata/BTC_MEME-1.json'
-    file1_5 = 'freqtrade/tests/testdata/BTC_MEME-5.json'
+    file1_1 = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'MEME_BTC-1m.json')
+    file1_5 = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'MEME_BTC-5m.json')
     _backup_file(file1_1)
     _backup_file(file1_5)
 
-    download_pairs(None, pairs=['BTC-MEME'])
+    download_pairs(None, exchange, pairs=['MEME/BTC'], ticker_interval='1m')
     # clean files freshly downloaded
     _clean_test_file(file1_1)
     _clean_test_file(file1_5)
-    assert ('freqtrade.optimize.__init__',
-            logging.INFO,
-            'Failed to download the pair: "BTC-MEME", Interval: 1 min'
-            ) in caplog.record_tuples
+    assert log_has('Failed to download the pair: "MEME/BTC", Interval: 1m', caplog.record_tuples)
 
 
-def test_download_backtesting_testdata(default_conf, ticker_history, mocker):
-    mocker.patch('freqtrade.optimize.__init__.get_ticker_history', return_value=ticker_history)
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    exchange._API = Bittrex({'key': '', 'secret': ''})
+def test_download_backtesting_testdata(ticker_history, mocker, default_conf) -> None:
+    mocker.patch('freqtrade.exchange.Exchange.get_history', return_value=ticker_history)
+    exchange = get_patched_exchange(mocker, default_conf)
 
     # Download a 1 min ticker file
-    file1 = 'freqtrade/tests/testdata/BTC_XEL-1.json'
+    file1 = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'XEL_BTC-1m.json')
     _backup_file(file1)
-    download_backtesting_testdata(None, pair="BTC-XEL", interval=1)
+    download_backtesting_testdata(None, exchange, pair="XEL/BTC", tick_interval='1m')
     assert os.path.isfile(file1) is True
     _clean_test_file(file1)
 
     # Download a 5 min ticker file
-    file2 = 'freqtrade/tests/testdata/BTC_STORJ-5.json'
+    file2 = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'STORJ_BTC-5m.json')
     _backup_file(file2)
 
-    download_backtesting_testdata(None, pair="BTC-STORJ", interval=5)
+    download_backtesting_testdata(None, exchange, pair="STORJ/BTC", tick_interval='5m')
     assert os.path.isfile(file2) is True
     _clean_test_file(file2)
 
 
-def test_load_tickerdata_file():
-    assert not load_tickerdata_file(None, 'BTC_UNITEST', 7)
-    tickerdata = load_tickerdata_file(None, 'BTC_UNITEST', 1)
-    assert _btc_unittest_length == len(tickerdata)
+def test_download_backtesting_testdata2(mocker, default_conf) -> None:
+    tick = [
+        [1509836520000, 0.00162008, 0.00162008, 0.00162008, 0.00162008, 108.14853839],
+        [1509836580000, 0.00161, 0.00161, 0.00161, 0.00161, 82.390199]
+    ]
+    json_dump_mock = mocker.patch('freqtrade.misc.file_dump_json', return_value=None)
+    mocker.patch('freqtrade.exchange.Exchange.get_history', return_value=tick)
+    exchange = get_patched_exchange(mocker, default_conf)
+    download_backtesting_testdata(None, exchange, pair="UNITTEST/BTC", tick_interval='1m')
+    download_backtesting_testdata(None, exchange, pair="UNITTEST/BTC", tick_interval='3m')
+    assert json_dump_mock.call_count == 2
+
+
+def test_load_tickerdata_file() -> None:
+    # 7 does not exist in either format.
+    assert not load_tickerdata_file(None, 'UNITTEST/BTC', '7m')
+    # 1 exists only as a .json
+    tickerdata = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
+    assert _BTC_UNITTEST_LENGTH == len(tickerdata)
+    # 8 .json is empty and will fail if it's loaded. .json.gz is a copy of 1.json
+    tickerdata = load_tickerdata_file(None, 'UNITTEST/BTC', '8m')
+    assert _BTC_UNITTEST_LENGTH == len(tickerdata)
+
+
+def test_init(default_conf, mocker) -> None:
+    exchange = get_patched_exchange(mocker, default_conf)
+    assert {} == optimize.load_data(
+        '',
+        exchange=exchange,
+        pairs=[],
+        refresh_pairs=True,
+        ticker_interval=default_conf['ticker_interval']
+    )
+
+
+def test_trim_tickerlist() -> None:
+    file = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'UNITTEST_BTC-1m.json')
+    with open(file) as data_file:
+        ticker_list = json.load(data_file)
+    ticker_list_len = len(ticker_list)
+
+    # Test the pattern ^(-\d+)$
+    # This pattern uses the latest N elements
+    timerange = TimeRange(None, 'line', 0, -5)
+    ticker = trim_tickerlist(ticker_list, timerange)
+    ticker_len = len(ticker)
+
+    assert ticker_len == 5
+    assert ticker_list[0] is not ticker[0]  # The first element should be different
+    assert ticker_list[-1] is ticker[-1]  # The last element must be the same
+
+    # Test the pattern ^(\d+)-$
+    # This pattern keep X element from the end
+    timerange = TimeRange('line', None, 5, 0)
+    ticker = trim_tickerlist(ticker_list, timerange)
+    ticker_len = len(ticker)
+
+    assert ticker_len == 5
+    assert ticker_list[0] is ticker[0]  # The first element must be the same
+    assert ticker_list[-1] is not ticker[-1]  # The last element should be different
+
+    # Test the pattern ^(\d+)-(\d+)$
+    # This pattern extract a window
+    timerange = TimeRange('index', 'index', 5, 10)
+    ticker = trim_tickerlist(ticker_list, timerange)
+    ticker_len = len(ticker)
+
+    assert ticker_len == 5
+    assert ticker_list[0] is not ticker[0]  # The first element should be different
+    assert ticker_list[5] is ticker[0]  # The list starts at the index 5
+    assert ticker_list[9] is ticker[-1]  # The list ends at the index 9 (5 elements)
+
+    # Test the pattern ^(\d{8})-(\d{8})$
+    # This pattern extract a window between the dates
+    timerange = TimeRange('date', 'date', ticker_list[5][0] / 1000, ticker_list[10][0] / 1000 - 1)
+    ticker = trim_tickerlist(ticker_list, timerange)
+    ticker_len = len(ticker)
+
+    assert ticker_len == 5
+    assert ticker_list[0] is not ticker[0]  # The first element should be different
+    assert ticker_list[5] is ticker[0]  # The list starts at the index 5
+    assert ticker_list[9] is ticker[-1]  # The list ends at the index 9 (5 elements)
+
+    # Test the pattern ^-(\d{8})$
+    # This pattern extracts elements from the start to the date
+    timerange = TimeRange(None, 'date', 0, ticker_list[10][0] / 1000 - 1)
+    ticker = trim_tickerlist(ticker_list, timerange)
+    ticker_len = len(ticker)
+
+    assert ticker_len == 10
+    assert ticker_list[0] is ticker[0]  # The start of the list is included
+    assert ticker_list[9] is ticker[-1]  # The element 10 is not included
+
+    # Test the pattern ^(\d{8})-$
+    # This pattern extracts elements from the date to now
+    timerange = TimeRange('date', None, ticker_list[10][0] / 1000 - 1, None)
+    ticker = trim_tickerlist(ticker_list, timerange)
+    ticker_len = len(ticker)
+
+    assert ticker_len == ticker_list_len - 10
+    assert ticker_list[10] is ticker[0]  # The first element is element #10
+    assert ticker_list[-1] is ticker[-1]  # The last element is the same
+
+    # Test a wrong pattern
+    # This pattern must return the list unchanged
+    timerange = TimeRange(None, None, None, 5)
+    ticker = trim_tickerlist(ticker_list, timerange)
+    ticker_len = len(ticker)
+
+    assert ticker_list_len == ticker_len
+
+
+def test_file_dump_json() -> None:
+    file = os.path.join(os.path.dirname(__file__), '..', 'testdata',
+                        'test_{id}.json'.format(id=str(uuid.uuid4())))
+    data = {'bar': 'foo'}
+
+    # check the file we will create does not exist
+    assert os.path.isfile(file) is False
+
+    # Create the Json file
+    file_dump_json(file, data)
+
+    # Check the file was create
+    assert os.path.isfile(file) is True
+
+    # Open the Json file created and test the data is in it
+    with open(file) as data_file:
+        json_from_file = json.load(data_file)
+
+    assert 'bar' in json_from_file
+    assert json_from_file['bar'] == 'foo'
+
+    # Remove the file
+    _clean_test_file(file)

freqtrade/tests/rpc/test_rpc.py

@@ -1,57 +1,534 @@
-# pragma pylint: disable=missing-docstring, too-many-arguments, too-many-ancestors, C0103
-from copy import deepcopy
-from unittest.mock import MagicMock
+# pragma pylint: disable=missing-docstring, C0103
+# pragma pylint: disable=invalid-sequence-index, invalid-name, too-many-arguments
 
-from freqtrade.rpc import init, cleanup, send_msg
+from datetime import datetime
+from unittest.mock import MagicMock, ANY
+
+import pytest
+
+from freqtrade import TemporaryError
+from freqtrade.fiat_convert import CryptoToFiatConverter
+from freqtrade.freqtradebot import FreqtradeBot
+from freqtrade.persistence import Trade
+from freqtrade.rpc import RPC, RPCException
+from freqtrade.state import State
+from freqtrade.tests.test_freqtradebot import patch_get_signal
+from freqtrade.tests.conftest import patch_coinmarketcap, patch_exchange
 
 
-def test_init_telegram_enabled(default_conf, mocker):
-    module_list = []
-    mocker.patch('freqtrade.rpc.REGISTERED_MODULES', module_list)
-    telegram_mock = mocker.patch('freqtrade.rpc.telegram.init', MagicMock())
-
-    init(default_conf)
-
-    assert telegram_mock.call_count == 1
-    assert 'telegram' in module_list
+# Functions for recurrent object patching
+def prec_satoshi(a, b) -> float:
+    """
+    :return: True if A and B differs less than one satoshi.
+    """
+    return abs(a - b) < 0.00000001
 
 
-def test_init_telegram_disabled(default_conf, mocker):
-    module_list = []
-    mocker.patch('freqtrade.rpc.REGISTERED_MODULES', module_list)
-    telegram_mock = mocker.patch('freqtrade.rpc.telegram.init', MagicMock())
+# Unit tests
+def test_rpc_trade_status(default_conf, ticker, fee, markets, mocker) -> None:
+    patch_coinmarketcap(mocker)
+    mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
+    mocker.patch.multiple(
+        'freqtrade.exchange.Exchange',
+        _load_markets=MagicMock(return_value={}),
+        get_ticker=ticker,
+        get_fee=fee,
+        get_markets=markets
+    )
 
-    conf = deepcopy(default_conf)
-    conf['telegram']['enabled'] = False
-    init(conf)
+    freqtradebot = FreqtradeBot(default_conf)
+    patch_get_signal(freqtradebot, (True, False))
+    rpc = RPC(freqtradebot)
 
-    assert telegram_mock.call_count == 0
-    assert 'telegram' not in module_list
+    freqtradebot.state = State.STOPPED
+    with pytest.raises(RPCException, match=r'.*trader is not running*'):
+        rpc._rpc_trade_status()
+
+    freqtradebot.state = State.RUNNING
+    with pytest.raises(RPCException, match=r'.*no active trade*'):
+        rpc._rpc_trade_status()
+
+    freqtradebot.create_trade()
+    results = rpc._rpc_trade_status()
+
+    assert {
+        'trade_id': 1,
+        'pair': 'ETH/BTC',
+        'market_url': 'https://bittrex.com/Market/Index?MarketName=BTC-ETH',
+        'date': ANY,
+        'open_rate': 1.099e-05,
+        'close_rate': None,
+        'current_rate': 1.098e-05,
+        'amount': 90.99181074,
+        'close_profit': None,
+        'current_profit': -0.59,
+        'open_order': '(limit buy rem=0.00000000)'
+    } == results[0]
 
 
-def test_cleanup_telegram_enabled(mocker):
-    mocker.patch('freqtrade.rpc.REGISTERED_MODULES', ['telegram'])
-    telegram_mock = mocker.patch('freqtrade.rpc.telegram.cleanup', MagicMock())
-    cleanup()
-    assert telegram_mock.call_count == 1
+def test_rpc_status_table(default_conf, ticker, fee, markets, mocker) -> None:
+    patch_coinmarketcap(mocker)
+    patch_exchange(mocker)
+    mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
+    mocker.patch.multiple(
+        'freqtrade.exchange.Exchange',
+        get_ticker=ticker,
+        get_fee=fee,
+        get_markets=markets
+    )
+
+    freqtradebot = FreqtradeBot(default_conf)
+    patch_get_signal(freqtradebot, (True, False))
+    rpc = RPC(freqtradebot)
+
+    freqtradebot.state = State.STOPPED
+    with pytest.raises(RPCException, match=r'.*trader is not running*'):
+        rpc._rpc_status_table()
+
+    freqtradebot.state = State.RUNNING
+    with pytest.raises(RPCException, match=r'.*no active order*'):
+        rpc._rpc_status_table()
+
+    freqtradebot.create_trade()
+    result = rpc._rpc_status_table()
+    assert 'just now' in result['Since'].all()
+    assert 'ETH/BTC' in result['Pair'].all()
+    assert '-0.59%' in result['Profit'].all()
 
 
-def test_cleanup_telegram_disabled(mocker):
-    mocker.patch('freqtrade.rpc.REGISTERED_MODULES', [])
-    telegram_mock = mocker.patch('freqtrade.rpc.telegram.cleanup', MagicMock())
-    cleanup()
-    assert telegram_mock.call_count == 0
+def test_rpc_daily_profit(default_conf, update, ticker, fee,
+                          limit_buy_order, limit_sell_order, markets, mocker) -> None:
+    patch_coinmarketcap(mocker, value={'price_usd': 15000.0})
+    patch_exchange(mocker)
+    mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
+    mocker.patch.multiple(
+        'freqtrade.exchange.Exchange',
+        get_ticker=ticker,
+        get_fee=fee,
+        get_markets=markets
+    )
+
+    freqtradebot = FreqtradeBot(default_conf)
+    patch_get_signal(freqtradebot, (True, False))
+    stake_currency = default_conf['stake_currency']
+    fiat_display_currency = default_conf['fiat_display_currency']
+
+    rpc = RPC(freqtradebot)
+    rpc._fiat_converter = CryptoToFiatConverter()
+    # Create some test data
+    freqtradebot.create_trade()
+    trade = Trade.query.first()
+    assert trade
+
+    # Simulate buy & sell
+    trade.update(limit_buy_order)
+    trade.update(limit_sell_order)
+    trade.close_date = datetime.utcnow()
+    trade.is_open = False
+
+    # Try valid data
+    update.message.text = '/daily 2'
+    days = rpc._rpc_daily_profit(7, stake_currency, fiat_display_currency)
+    assert len(days) == 7
+    for day in days:
+        # [datetime.date(2018, 1, 11), '0.00000000 BTC', '0.000 USD']
+        assert (day[1] == '0.00000000 BTC' or
+                day[1] == '0.00006217 BTC')
+
+        assert (day[2] == '0.000 USD' or
+                day[2] == '0.933 USD')
+    # ensure first day is current date
+    assert str(days[0][0]) == str(datetime.utcnow().date())
+
+    # Try invalid data
+    with pytest.raises(RPCException, match=r'.*must be an integer greater than 0*'):
+        rpc._rpc_daily_profit(0, stake_currency, fiat_display_currency)
 
 
-def test_send_msg_telegram_enabled(mocker):
-    mocker.patch('freqtrade.rpc.REGISTERED_MODULES', ['telegram'])
-    telegram_mock = mocker.patch('freqtrade.rpc.telegram.send_msg', MagicMock())
-    send_msg('test')
-    assert telegram_mock.call_count == 1
+def test_rpc_trade_statistics(default_conf, ticker, ticker_sell_up, fee,
+                              limit_buy_order, limit_sell_order, markets, mocker) -> None:
+    mocker.patch.multiple(
+        'freqtrade.fiat_convert.Market',
+        ticker=MagicMock(return_value={'price_usd': 15000.0}),
+    )
+    patch_coinmarketcap(mocker)
+    patch_exchange(mocker)
+    mocker.patch('freqtrade.rpc.rpc.CryptoToFiatConverter._find_price', return_value=15000.0)
+    mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
+    mocker.patch.multiple(
+        'freqtrade.exchange.Exchange',
+        get_ticker=ticker,
+        get_fee=fee,
+        get_markets=markets
+    )
+
+    freqtradebot = FreqtradeBot(default_conf)
+    patch_get_signal(freqtradebot, (True, False))
+    stake_currency = default_conf['stake_currency']
+    fiat_display_currency = default_conf['fiat_display_currency']
+
+    rpc = RPC(freqtradebot)
+    rpc._fiat_converter = CryptoToFiatConverter()
+
+    with pytest.raises(RPCException, match=r'.*no closed trade*'):
+        rpc._rpc_trade_statistics(stake_currency, fiat_display_currency)
+
+    # Create some test data
+    freqtradebot.create_trade()
+    trade = Trade.query.first()
+    # Simulate fulfilled LIMIT_BUY order for trade
+    trade.update(limit_buy_order)
+
+    # Update the ticker with a market going up
+    mocker.patch.multiple(
+        'freqtrade.exchange.Exchange',
+        get_ticker=ticker_sell_up
+    )
+    trade.update(limit_sell_order)
+    trade.close_date = datetime.utcnow()
+    trade.is_open = False
+
+    freqtradebot.create_trade()
+    trade = Trade.query.first()
+    # Simulate fulfilled LIMIT_BUY order for trade
+    trade.update(limit_buy_order)
+
+    # Update the ticker with a market going up
+    mocker.patch.multiple(
+        'freqtrade.exchange.Exchange',
+        get_ticker=ticker_sell_up
+    )
+    trade.update(limit_sell_order)
+    trade.close_date = datetime.utcnow()
+    trade.is_open = False
+
+    stats = rpc._rpc_trade_statistics(stake_currency, fiat_display_currency)
+    assert prec_satoshi(stats['profit_closed_coin'], 6.217e-05)
+    assert prec_satoshi(stats['profit_closed_percent'], 6.2)
+    assert prec_satoshi(stats['profit_closed_fiat'], 0.93255)
+    assert prec_satoshi(stats['profit_all_coin'], 5.632e-05)
+    assert prec_satoshi(stats['profit_all_percent'], 2.81)
+    assert prec_satoshi(stats['profit_all_fiat'], 0.8448)
+    assert stats['trade_count'] == 2
+    assert stats['first_trade_date'] == 'just now'
+    assert stats['latest_trade_date'] == 'just now'
+    assert stats['avg_duration'] == '0:00:00'
+    assert stats['best_pair'] == 'ETH/BTC'
+    assert prec_satoshi(stats['best_rate'], 6.2)
 
 
-def test_send_msg_telegram_disabled(mocker):
-    mocker.patch('freqtrade.rpc.REGISTERED_MODULES', [])
-    telegram_mock = mocker.patch('freqtrade.rpc.telegram.send_msg', MagicMock())
-    send_msg('test')
-    assert telegram_mock.call_count == 0
+# Test that rpc_trade_statistics can handle trades that lacks
+# trade.open_rate (it is set to None)
+def test_rpc_trade_statistics_closed(mocker, default_conf, ticker, fee, markets,
+                                     ticker_sell_up, limit_buy_order, limit_sell_order):
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        'freqtrade.fiat_convert.Market',
+        ticker=MagicMock(return_value={'price_usd': 15000.0}),
+    )
+    mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
+    mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
+    mocker.patch.multiple(
+        'freqtrade.exchange.Exchange',
+        get_ticker=ticker,
+        get_fee=fee,
+        get_markets=markets
+    )
+
+    freqtradebot = FreqtradeBot(default_conf)
+    patch_get_signal(freqtradebot, (True, False))
+    stake_currency = default_conf['stake_currency']
+    fiat_display_currency = default_conf['fiat_display_currency']
+
+    rpc = RPC(freqtradebot)
+
+    # Create some test data
+    freqtradebot.create_trade()
+    trade = Trade.query.first()
+    # Simulate fulfilled LIMIT_BUY order for trade
+    trade.update(limit_buy_order)
+    # Update the ticker with a market going up
+    mocker.patch.multiple(
+        'freqtrade.exchange.Exchange',
+        get_ticker=ticker_sell_up,
+        get_fee=fee
+    )
+    trade.update(limit_sell_order)
+    trade.close_date = datetime.utcnow()
+    trade.is_open = False
+
+    for trade in Trade.query.order_by(Trade.id).all():
+        trade.open_rate = None
+
+    stats = rpc._rpc_trade_statistics(stake_currency, fiat_display_currency)
+    assert prec_satoshi(stats['profit_closed_coin'], 0)
+    assert prec_satoshi(stats['profit_closed_percent'], 0)
+    assert prec_satoshi(stats['profit_closed_fiat'], 0)
+    assert prec_satoshi(stats['profit_all_coin'], 0)
+    assert prec_satoshi(stats['profit_all_percent'], 0)
+    assert prec_satoshi(stats['profit_all_fiat'], 0)
+    assert stats['trade_count'] == 1
+    assert stats['first_trade_date'] == 'just now'
+    assert stats['latest_trade_date'] == 'just now'
+    assert stats['avg_duration'] == '0:00:00'
+    assert stats['best_pair'] == 'ETH/BTC'
+    assert prec_satoshi(stats['best_rate'], 6.2)
+
+
+def test_rpc_balance_handle(default_conf, mocker):
+    mock_balance = {
+        'BTC': {
+            'free': 10.0,
+            'total': 12.0,
+            'used': 2.0,
+        },
+        'ETH': {
+            'free': 1.0,
+            'total': 5.0,
+            'used': 4.0,
+        }
+    }
+    # ETH will be skipped due to mocked Error below
+
+    mocker.patch.multiple(
+        'freqtrade.fiat_convert.Market',
+        ticker=MagicMock(return_value={'price_usd': 15000.0}),
+    )
+    patch_coinmarketcap(mocker)
+    patch_exchange(mocker)
+    mocker.patch('freqtrade.rpc.rpc.CryptoToFiatConverter._find_price', return_value=15000.0)
+    mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
+    mocker.patch.multiple(
+        'freqtrade.exchange.Exchange',
+        get_balances=MagicMock(return_value=mock_balance),
+        get_ticker=MagicMock(side_effect=TemporaryError('Could not load ticker due to xxx'))
+    )
+
+    freqtradebot = FreqtradeBot(default_conf)
+    patch_get_signal(freqtradebot, (True, False))
+    rpc = RPC(freqtradebot)
+    rpc._fiat_converter = CryptoToFiatConverter()
+
+    result = rpc._rpc_balance(default_conf['fiat_display_currency'])
+    assert prec_satoshi(result['total'], 12)
+    assert prec_satoshi(result['value'], 180000)
+    assert 'USD' == result['symbol']
+    assert result['currencies'] == [{
+        'currency': 'BTC',
+        'available': 10.0,
+        'balance': 12.0,
+        'pending': 2.0,
+        'est_btc': 12.0,
+    }]
+    assert result['total'] == 12.0
+
+
+def test_rpc_start(mocker, default_conf) -> None:
+    patch_coinmarketcap(mocker)
+    patch_exchange(mocker)
+    mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
+    mocker.patch.multiple(
+        'freqtrade.exchange.Exchange',
+        get_ticker=MagicMock()
+    )
+
+    freqtradebot = FreqtradeBot(default_conf)
+    patch_get_signal(freqtradebot, (True, False))
+    rpc = RPC(freqtradebot)
+    freqtradebot.state = State.STOPPED
+
+    result = rpc._rpc_start()
+    assert {'status': 'starting trader ...'} == result
+    assert freqtradebot.state == State.RUNNING
+
+    result = rpc._rpc_start()
+    assert {'status': 'already running'} == result
+    assert freqtradebot.state == State.RUNNING
+
+
+def test_rpc_stop(mocker, default_conf) -> None:
+    patch_coinmarketcap(mocker)
+    patch_exchange(mocker)
+    mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
+    mocker.patch.multiple(
+        'freqtrade.exchange.Exchange',
+        get_ticker=MagicMock()
+    )
+
+    freqtradebot = FreqtradeBot(default_conf)
+    patch_get_signal(freqtradebot, (True, False))
+    rpc = RPC(freqtradebot)
+    freqtradebot.state = State.RUNNING
+
+    result = rpc._rpc_stop()
+    assert {'status': 'stopping trader ...'} == result
+    assert freqtradebot.state == State.STOPPED
+
+    result = rpc._rpc_stop()
+
+    assert {'status': 'already stopped'} == result
+    assert freqtradebot.state == State.STOPPED
+
+
+def test_rpc_forcesell(default_conf, ticker, fee, mocker, markets) -> None:
+    patch_coinmarketcap(mocker)
+    patch_exchange(mocker)
+    mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
+
+    cancel_order_mock = MagicMock()
+    mocker.patch.multiple(
+        'freqtrade.exchange.Exchange',
+        get_ticker=ticker,
+        cancel_order=cancel_order_mock,
+        get_order=MagicMock(
+            return_value={
+                'status': 'closed',
+                'type': 'limit',
+                'side': 'buy'
+            }
+        ),
+        get_fee=fee,
+        get_markets=markets
+    )
+
+    freqtradebot = FreqtradeBot(default_conf)
+    patch_get_signal(freqtradebot, (True, False))
+    rpc = RPC(freqtradebot)
+
+    freqtradebot.state = State.STOPPED
+    with pytest.raises(RPCException, match=r'.*trader is not running*'):
+        rpc._rpc_forcesell(None)
+
+    freqtradebot.state = State.RUNNING
+    with pytest.raises(RPCException, match=r'.*invalid argument*'):
+        rpc._rpc_forcesell(None)
+
+    rpc._rpc_forcesell('all')
+
+    freqtradebot.create_trade()
+    rpc._rpc_forcesell('all')
+
+    rpc._rpc_forcesell('1')
+
+    freqtradebot.state = State.STOPPED
+    with pytest.raises(RPCException, match=r'.*trader is not running*'):
+        rpc._rpc_forcesell(None)
+
+    with pytest.raises(RPCException, match=r'.*trader is not running*'):
+        rpc._rpc_forcesell('all')
+
+    freqtradebot.state = State.RUNNING
+    assert cancel_order_mock.call_count == 0
+    # make an limit-buy open trade
+    trade = Trade.query.filter(Trade.id == '1').first()
+    filled_amount = trade.amount / 2
+    mocker.patch(
+        'freqtrade.exchange.Exchange.get_order',
+        return_value={
+            'status': 'open',
+            'type': 'limit',
+            'side': 'buy',
+            'filled': filled_amount
+        }
+    )
+    # check that the trade is called, which is done by ensuring exchange.cancel_order is called
+    # and trade amount is updated
+    rpc._rpc_forcesell('1')
+    assert cancel_order_mock.call_count == 1
+    assert trade.amount == filled_amount
+
+    freqtradebot.create_trade()
+    trade = Trade.query.filter(Trade.id == '2').first()
+    amount = trade.amount
+    # make an limit-buy open trade, if there is no 'filled', don't sell it
+    mocker.patch(
+        'freqtrade.exchange.Exchange.get_order',
+        return_value={
+            'status': 'open',
+            'type': 'limit',
+            'side': 'buy',
+            'filled': None
+        }
+    )
+    # check that the trade is called, which is done by ensuring exchange.cancel_order is called
+    rpc._rpc_forcesell('2')
+    assert cancel_order_mock.call_count == 2
+    assert trade.amount == amount
+
+    freqtradebot.create_trade()
+    # make an limit-sell open trade
+    mocker.patch(
+        'freqtrade.exchange.Exchange.get_order',
+        return_value={
+            'status': 'open',
+            'type': 'limit',
+            'side': 'sell'
+        }
+    )
+    rpc._rpc_forcesell('3')
+    # status quo, no exchange calls
+    assert cancel_order_mock.call_count == 2
+
+
+def test_performance_handle(default_conf, ticker, limit_buy_order, fee,
+                            limit_sell_order, markets, mocker) -> None:
+    patch_coinmarketcap(mocker)
+    patch_exchange(mocker)
+    mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
+    mocker.patch.multiple(
+        'freqtrade.exchange.Exchange',
+        get_balances=MagicMock(return_value=ticker),
+        get_ticker=ticker,
+        get_fee=fee,
+        get_markets=markets
+    )
+
+    freqtradebot = FreqtradeBot(default_conf)
+    patch_get_signal(freqtradebot, (True, False))
+    rpc = RPC(freqtradebot)
+
+    # Create some test data
+    freqtradebot.create_trade()
+    trade = Trade.query.first()
+    assert trade
+
+    # Simulate fulfilled LIMIT_BUY order for trade
+    trade.update(limit_buy_order)
+
+    # Simulate fulfilled LIMIT_SELL order for trade
+    trade.update(limit_sell_order)
+
+    trade.close_date = datetime.utcnow()
+    trade.is_open = False
+    res = rpc._rpc_performance()
+    assert len(res) == 1
+    assert res[0]['pair'] == 'ETH/BTC'
+    assert res[0]['count'] == 1
+    assert prec_satoshi(res[0]['profit'], 6.2)
+
+
+def test_rpc_count(mocker, default_conf, ticker, fee, markets) -> None:
+    patch_coinmarketcap(mocker)
+    patch_exchange(mocker)
+    mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
+    mocker.patch.multiple(
+        'freqtrade.exchange.Exchange',
+        get_balances=MagicMock(return_value=ticker),
+        get_ticker=ticker,
+        get_fee=fee,
+        get_markets=markets
+    )
+
+    freqtradebot = FreqtradeBot(default_conf)
+    patch_get_signal(freqtradebot, (True, False))
+    rpc = RPC(freqtradebot)
+
+    trades = rpc._rpc_count()
+    nb_trades = len(trades)
+    assert nb_trades == 0
+
+    # Create some test data
+    freqtradebot.create_trade()
+    trades = rpc._rpc_count()
+    nb_trades = len(trades)
+    assert nb_trades == 1

freqtrade/tests/rpc/test_rpc_manager.py

@@ -0,0 +1,115 @@
+# pragma pylint: disable=missing-docstring, C0103
+
+import logging
+from unittest.mock import MagicMock
+
+from freqtrade.rpc import RPCMessageType, RPCManager
+from freqtrade.tests.conftest import log_has, get_patched_freqtradebot
+
+
+def test__init__(mocker, default_conf) -> None:
+    default_conf['telegram']['enabled'] = False
+
+    rpc_manager = RPCManager(get_patched_freqtradebot(mocker, default_conf))
+    assert rpc_manager.registered_modules == []
+
+
+def test_init_telegram_disabled(mocker, default_conf, caplog) -> None:
+    caplog.set_level(logging.DEBUG)
+    default_conf['telegram']['enabled'] = False
+    rpc_manager = RPCManager(get_patched_freqtradebot(mocker, default_conf))
+
+    assert not log_has('Enabling rpc.telegram ...', caplog.record_tuples)
+    assert rpc_manager.registered_modules == []
+
+
+def test_init_telegram_enabled(mocker, default_conf, caplog) -> None:
+    caplog.set_level(logging.DEBUG)
+    mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock())
+    rpc_manager = RPCManager(get_patched_freqtradebot(mocker, default_conf))
+
+    assert log_has('Enabling rpc.telegram ...', caplog.record_tuples)
+    len_modules = len(rpc_manager.registered_modules)
+    assert len_modules == 1
+    assert 'telegram' in [mod.name for mod in rpc_manager.registered_modules]
+
+
+def test_cleanup_telegram_disabled(mocker, default_conf, caplog) -> None:
+    caplog.set_level(logging.DEBUG)
+    telegram_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.cleanup', MagicMock())
+    default_conf['telegram']['enabled'] = False
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    rpc_manager = RPCManager(freqtradebot)
+    rpc_manager.cleanup()
+
+    assert not log_has('Cleaning up rpc.telegram ...', caplog.record_tuples)
+    assert telegram_mock.call_count == 0
+
+
+def test_cleanup_telegram_enabled(mocker, default_conf, caplog) -> None:
+    caplog.set_level(logging.DEBUG)
+    mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock())
+    telegram_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.cleanup', MagicMock())
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    rpc_manager = RPCManager(freqtradebot)
+
+    # Check we have Telegram as a registered modules
+    assert 'telegram' in [mod.name for mod in rpc_manager.registered_modules]
+
+    rpc_manager.cleanup()
+    assert log_has('Cleaning up rpc.telegram ...', caplog.record_tuples)
+    assert 'telegram' not in [mod.name for mod in rpc_manager.registered_modules]
+    assert telegram_mock.call_count == 1
+
+
+def test_send_msg_telegram_disabled(mocker, default_conf, caplog) -> None:
+    telegram_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.send_msg', MagicMock())
+    default_conf['telegram']['enabled'] = False
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    rpc_manager = RPCManager(freqtradebot)
+    rpc_manager.send_msg({
+        'type': RPCMessageType.STATUS_NOTIFICATION,
+        'status': 'test'
+    })
+
+    assert log_has("Sending rpc message: {'type': status, 'status': 'test'}", caplog.record_tuples)
+    assert telegram_mock.call_count == 0
+
+
+def test_send_msg_telegram_enabled(mocker, default_conf, caplog) -> None:
+    telegram_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.send_msg', MagicMock())
+    mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock())
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    rpc_manager = RPCManager(freqtradebot)
+    rpc_manager.send_msg({
+        'type': RPCMessageType.STATUS_NOTIFICATION,
+        'status': 'test'
+    })
+
+    assert log_has("Sending rpc message: {'type': status, 'status': 'test'}", caplog.record_tuples)
+    assert telegram_mock.call_count == 1
+
+
+def test_init_webhook_disabled(mocker, default_conf, caplog) -> None:
+    caplog.set_level(logging.DEBUG)
+    default_conf['telegram']['enabled'] = False
+    default_conf['webhook'] = {'enabled': False}
+    rpc_manager = RPCManager(get_patched_freqtradebot(mocker, default_conf))
+
+    assert not log_has('Enabling rpc.webhook ...', caplog.record_tuples)
+    assert rpc_manager.registered_modules == []
+
+
+def test_init_webhook_enabled(mocker, default_conf, caplog) -> None:
+    caplog.set_level(logging.DEBUG)
+    default_conf['telegram']['enabled'] = False
+    default_conf['webhook'] = {'enabled': True, 'url': "https://DEADBEEF.com"}
+    rpc_manager = RPCManager(get_patched_freqtradebot(mocker, default_conf))
+
+    assert log_has('Enabling rpc.webhook ...', caplog.record_tuples)
+    assert len(rpc_manager.registered_modules) == 1
+    assert 'webhook' in [mod.name for mod in rpc_manager.registered_modules]

freqtrade/tests/rpc/test_rpc_webhook.py

@@ -0,0 +1,166 @@
+# pragma pylint: disable=missing-docstring, C0103, protected-access
+
+from unittest.mock import MagicMock
+
+import pytest
+from requests import RequestException
+
+from freqtrade.rpc import RPCMessageType
+from freqtrade.rpc.webhook import Webhook
+from freqtrade.tests.conftest import get_patched_freqtradebot, log_has
+
+
+def get_webhook_dict() -> dict:
+    return {
+          "enabled": True,
+          "url": "https://maker.ifttt.com/trigger/freqtrade_test/with/key/c764udvJ5jfSlswVRukZZ2/",
+          "webhookbuy": {
+              "value1": "Buying {pair}",
+              "value2": "limit {limit:8f}",
+              "value3": "{stake_amount:8f} {stake_currency}"
+          },
+          "webhooksell": {
+              "value1": "Selling {pair}",
+              "value2": "limit {limit:8f}",
+              "value3": "profit: {profit_amount:8f} {stake_currency}"
+          },
+          "webhookstatus": {
+              "value1": "Status: {status}",
+              "value2": "",
+              "value3": ""
+          }
+      }
+
+
+def test__init__(mocker, default_conf):
+    default_conf['webhook'] = {'enabled': True, 'url': "https://DEADBEEF.com"}
+    webhook = Webhook(get_patched_freqtradebot(mocker, default_conf))
+    assert webhook._config == default_conf
+
+
+def test_send_msg(default_conf, mocker):
+    default_conf["webhook"] = get_webhook_dict()
+    msg_mock = MagicMock()
+    mocker.patch("freqtrade.rpc.webhook.Webhook._send_msg", msg_mock)
+    webhook = Webhook(get_patched_freqtradebot(mocker, default_conf))
+    msg = {
+        'type': RPCMessageType.BUY_NOTIFICATION,
+        'exchange': 'Bittrex',
+        'pair': 'ETH/BTC',
+        'market_url': "http://mockedurl/ETH_BTC",
+        'limit': 0.005,
+        'stake_amount': 0.8,
+        'stake_amount_fiat': 500,
+        'stake_currency': 'BTC',
+        'fiat_currency': 'EUR'
+    }
+    msg_mock = MagicMock()
+    mocker.patch("freqtrade.rpc.webhook.Webhook._send_msg", msg_mock)
+    webhook.send_msg(msg=msg)
+    assert msg_mock.call_count == 1
+    assert (msg_mock.call_args[0][0]["value1"] ==
+            default_conf["webhook"]["webhookbuy"]["value1"].format(**msg))
+    assert (msg_mock.call_args[0][0]["value2"] ==
+            default_conf["webhook"]["webhookbuy"]["value2"].format(**msg))
+    assert (msg_mock.call_args[0][0]["value3"] ==
+            default_conf["webhook"]["webhookbuy"]["value3"].format(**msg))
+    # Test sell
+    msg_mock = MagicMock()
+    mocker.patch("freqtrade.rpc.webhook.Webhook._send_msg", msg_mock)
+    msg = {
+        'type': RPCMessageType.SELL_NOTIFICATION,
+        'exchange': 'Bittrex',
+        'pair': 'ETH/BTC',
+        'gain': "profit",
+        'market_url': "http://mockedurl/ETH_BTC",
+        'limit': 0.005,
+        'amount': 0.8,
+        'open_rate': 0.004,
+        'current_rate': 0.005,
+        'profit_amount': 0.001,
+        'profit_percent': 0.20,
+        'stake_currency': 'BTC',
+    }
+    webhook.send_msg(msg=msg)
+    assert msg_mock.call_count == 1
+    assert (msg_mock.call_args[0][0]["value1"] ==
+            default_conf["webhook"]["webhooksell"]["value1"].format(**msg))
+    assert (msg_mock.call_args[0][0]["value2"] ==
+            default_conf["webhook"]["webhooksell"]["value2"].format(**msg))
+    assert (msg_mock.call_args[0][0]["value3"] ==
+            default_conf["webhook"]["webhooksell"]["value3"].format(**msg))
+
+    # Test notification
+    msg = {
+        'type': RPCMessageType.STATUS_NOTIFICATION,
+        'status': 'Unfilled sell order for BTC cancelled due to timeout'
+    }
+    msg_mock = MagicMock()
+    mocker.patch("freqtrade.rpc.webhook.Webhook._send_msg", msg_mock)
+    webhook.send_msg(msg)
+    assert msg_mock.call_count == 1
+    assert (msg_mock.call_args[0][0]["value1"] ==
+            default_conf["webhook"]["webhookstatus"]["value1"].format(**msg))
+    assert (msg_mock.call_args[0][0]["value2"] ==
+            default_conf["webhook"]["webhookstatus"]["value2"].format(**msg))
+    assert (msg_mock.call_args[0][0]["value3"] ==
+            default_conf["webhook"]["webhookstatus"]["value3"].format(**msg))
+
+
+def test_exception_send_msg(default_conf, mocker, caplog):
+    default_conf["webhook"] = get_webhook_dict()
+    default_conf["webhook"]["webhookbuy"] = None
+
+    webhook = Webhook(get_patched_freqtradebot(mocker, default_conf))
+    webhook.send_msg({'type': RPCMessageType.BUY_NOTIFICATION})
+    assert log_has(f"Message type {RPCMessageType.BUY_NOTIFICATION} not configured for webhooks",
+                   caplog.record_tuples)
+
+    default_conf["webhook"] = get_webhook_dict()
+    default_conf["webhook"]["webhookbuy"]["value1"] = "{DEADBEEF:8f}"
+    msg_mock = MagicMock()
+    mocker.patch("freqtrade.rpc.webhook.Webhook._send_msg", msg_mock)
+    webhook = Webhook(get_patched_freqtradebot(mocker, default_conf))
+    msg = {
+        'type': RPCMessageType.BUY_NOTIFICATION,
+        'exchange': 'Bittrex',
+        'pair': 'ETH/BTC',
+        'market_url': "http://mockedurl/ETH_BTC",
+        'limit': 0.005,
+        'stake_amount': 0.8,
+        'stake_amount_fiat': 500,
+        'stake_currency': 'BTC',
+        'fiat_currency': 'EUR'
+    }
+    webhook.send_msg(msg)
+    assert log_has("Problem calling Webhook. Please check your webhook configuration. "
+                   "Exception: 'DEADBEEF'", caplog.record_tuples)
+
+    msg_mock = MagicMock()
+    mocker.patch("freqtrade.rpc.webhook.Webhook._send_msg", msg_mock)
+    msg = {
+        'type': 'DEADBEEF',
+        'status': 'whatever'
+    }
+    with pytest.raises(NotImplementedError):
+        webhook.send_msg(msg)
+
+
+def test__send_msg(default_conf, mocker, caplog):
+    default_conf["webhook"] = get_webhook_dict()
+    webhook = Webhook(get_patched_freqtradebot(mocker, default_conf))
+    msg = {'value1': 'DEADBEEF',
+           'value2': 'ALIVEBEEF',
+           'value3': 'FREQTRADE'}
+    post = MagicMock()
+    mocker.patch("freqtrade.rpc.webhook.post", post)
+    webhook._send_msg(msg)
+
+    assert post.call_count == 1
+    assert post.call_args[1] == {'data': msg}
+    assert post.call_args[0] == (default_conf['webhook']['url'], )
+
+    post = MagicMock(side_effect=RequestException)
+    mocker.patch("freqtrade.rpc.webhook.post", post)
+    webhook._send_msg(msg)
+    assert log_has('Could not call webhook url. Exception: ', caplog.record_tuples)

freqtrade/tests/strategy/legacy_strategy.py

@@ -0,0 +1,235 @@
+
+# --- Do not remove these libs ---
+from freqtrade.strategy.interface import IStrategy
+from pandas import DataFrame
+# --------------------------------
+
+# Add your lib to import here
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+import numpy  # noqa
+
+
+# This class is a sample. Feel free to customize it.
+class TestStrategyLegacy(IStrategy):
+    """
+    This is a test strategy using the legacy function headers, which will be
+    removed in a future update.
+    Please do not use this as a template, but refer to user_data/strategy/TestStrategy.py
+    for a uptodate version of this template.
+
+    """
+
+    # Minimal ROI designed for the strategy.
+    # This attribute will be overridden if the config file contains "minimal_roi"
+    minimal_roi = {
+        "40": 0.0,
+        "30": 0.01,
+        "20": 0.02,
+        "0": 0.04
+    }
+
+    # Optimal stoploss designed for the strategy
+    # This attribute will be overridden if the config file contains "stoploss"
+    stoploss = -0.10
+
+    # Optimal ticker interval for the strategy
+    ticker_interval = '5m'
+
+    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
+        """
+        Adds several different TA indicators to the given DataFrame
+
+        Performance Note: For the best performance be frugal on the number of indicators
+        you are using. Let uncomment only the indicator you are using in your strategies
+        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
+        """
+
+        # Momentum Indicator
+        # ------------------------------------
+
+        # ADX
+        dataframe['adx'] = ta.ADX(dataframe)
+
+        """
+        # Awesome oscillator
+        dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
+
+        # Commodity Channel Index: values Oversold:<-100, Overbought:>100
+        dataframe['cci'] = ta.CCI(dataframe)
+
+        # MACD
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        dataframe['macdhist'] = macd['macdhist']
+
+        # MFI
+        dataframe['mfi'] = ta.MFI(dataframe)
+
+        # Minus Directional Indicator / Movement
+        dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
+        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
+        # Plus Directional Indicator / Movement
+        dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
+        dataframe['plus_di'] = ta.PLUS_DI(dataframe)
+        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
+        # ROC
+        dataframe['roc'] = ta.ROC(dataframe)
+
+        # RSI
+        dataframe['rsi'] = ta.RSI(dataframe)
+
+        # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
+        rsi = 0.1 * (dataframe['rsi'] - 50)
+        dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
+
+        # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
+        dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
+
+        # Stoch
+        stoch = ta.STOCH(dataframe)
+        dataframe['slowd'] = stoch['slowd']
+        dataframe['slowk'] = stoch['slowk']
+
+        # Stoch fast
+        stoch_fast = ta.STOCHF(dataframe)
+        dataframe['fastd'] = stoch_fast['fastd']
+        dataframe['fastk'] = stoch_fast['fastk']
+
+        # Stoch RSI
+        stoch_rsi = ta.STOCHRSI(dataframe)
+        dataframe['fastd_rsi'] = stoch_rsi['fastd']
+        dataframe['fastk_rsi'] = stoch_rsi['fastk']
+        """
+
+        # Overlap Studies
+        # ------------------------------------
+
+        # Bollinger bands
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        dataframe['bb_lowerband'] = bollinger['lower']
+        dataframe['bb_middleband'] = bollinger['mid']
+        dataframe['bb_upperband'] = bollinger['upper']
+
+        """
+        # EMA - Exponential Moving Average
+        dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
+        dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
+        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
+        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
+        dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
+
+        # SAR Parabol
+        dataframe['sar'] = ta.SAR(dataframe)
+
+        # SMA - Simple Moving Average
+        dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
+        """
+
+        # TEMA - Triple Exponential Moving Average
+        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
+
+        # Cycle Indicator
+        # ------------------------------------
+        # Hilbert Transform Indicator - SineWave
+        hilbert = ta.HT_SINE(dataframe)
+        dataframe['htsine'] = hilbert['sine']
+        dataframe['htleadsine'] = hilbert['leadsine']
+
+        # Pattern Recognition - Bullish candlestick patterns
+        # ------------------------------------
+        """
+        # Hammer: values [0, 100]
+        dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
+        # Inverted Hammer: values [0, 100]
+        dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
+        # Dragonfly Doji: values [0, 100]
+        dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
+        # Piercing Line: values [0, 100]
+        dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
+        # Morningstar: values [0, 100]
+        dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
+        # Three White Soldiers: values [0, 100]
+        dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
+        """
+
+        # Pattern Recognition - Bearish candlestick patterns
+        # ------------------------------------
+        """
+        # Hanging Man: values [0, 100]
+        dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
+        # Shooting Star: values [0, 100]
+        dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
+        # Gravestone Doji: values [0, 100]
+        dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
+        # Dark Cloud Cover: values [0, 100]
+        dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
+        # Evening Doji Star: values [0, 100]
+        dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
+        # Evening Star: values [0, 100]
+        dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
+        """
+
+        # Pattern Recognition - Bullish/Bearish candlestick patterns
+        # ------------------------------------
+        """
+        # Three Line Strike: values [0, -100, 100]
+        dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
+        # Spinning Top: values [0, -100, 100]
+        dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
+        # Engulfing: values [0, -100, 100]
+        dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
+        # Harami: values [0, -100, 100]
+        dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
+        # Three Outside Up/Down: values [0, -100, 100]
+        dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
+        # Three Inside Up/Down: values [0, -100, 100]
+        dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
+        """
+
+        # Chart type
+        # ------------------------------------
+        """
+        # Heikinashi stategy
+        heikinashi = qtpylib.heikinashi(dataframe)
+        dataframe['ha_open'] = heikinashi['open']
+        dataframe['ha_close'] = heikinashi['close']
+        dataframe['ha_high'] = heikinashi['high']
+        dataframe['ha_low'] = heikinashi['low']
+        """
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
+        """
+        Based on TA indicators, populates the buy signal for the given dataframe
+        :param dataframe: DataFrame
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+                (dataframe['adx'] > 30) &
+                (dataframe['tema'] <= dataframe['bb_middleband']) &
+                (dataframe['tema'] > dataframe['tema'].shift(1))
+            ),
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
+        """
+        Based on TA indicators, populates the sell signal for the given dataframe
+        :param dataframe: DataFrame
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+                (dataframe['adx'] > 70) &
+                (dataframe['tema'] > dataframe['bb_middleband']) &
+                (dataframe['tema'] < dataframe['tema'].shift(1))
+            ),
+            'sell'] = 1
+        return dataframe

freqtrade/tests/strategy/test_default_strategy.py

@@ -0,0 +1,35 @@
+import json
+
+import pytest
+from pandas import DataFrame
+
+from freqtrade.exchange.exchange_helpers import parse_ticker_dataframe
+from freqtrade.strategy.default_strategy import DefaultStrategy
+
+
+@pytest.fixture
+def result():
+    with open('freqtrade/tests/testdata/ETH_BTC-1m.json') as data_file:
+        return parse_ticker_dataframe(json.load(data_file))
+
+
+def test_default_strategy_structure():
+    assert hasattr(DefaultStrategy, 'minimal_roi')
+    assert hasattr(DefaultStrategy, 'stoploss')
+    assert hasattr(DefaultStrategy, 'ticker_interval')
+    assert hasattr(DefaultStrategy, 'populate_indicators')
+    assert hasattr(DefaultStrategy, 'populate_buy_trend')
+    assert hasattr(DefaultStrategy, 'populate_sell_trend')
+
+
+def test_default_strategy(result):
+    strategy = DefaultStrategy({})
+
+    metadata = {'pair': 'ETH/BTC'}
+    assert type(strategy.minimal_roi) is dict
+    assert type(strategy.stoploss) is float
+    assert type(strategy.ticker_interval) is str
+    indicators = strategy.populate_indicators(result, metadata)
+    assert type(indicators) is DataFrame
+    assert type(strategy.populate_buy_trend(indicators, metadata)) is DataFrame
+    assert type(strategy.populate_sell_trend(indicators, metadata)) is DataFrame

freqtrade/tests/strategy/test_interface.py

@@ -0,0 +1,202 @@
+# pragma pylint: disable=missing-docstring, C0103
+
+import logging
+from unittest.mock import MagicMock
+
+import arrow
+from pandas import DataFrame
+
+from freqtrade.arguments import TimeRange
+from freqtrade.optimize.__init__ import load_tickerdata_file
+from freqtrade.persistence import Trade
+from freqtrade.tests.conftest import get_patched_exchange, log_has
+from freqtrade.strategy.default_strategy import DefaultStrategy
+
+# Avoid to reinit the same object again and again
+_STRATEGY = DefaultStrategy(config={})
+
+
+def test_returns_latest_buy_signal(mocker, default_conf):
+    mocker.patch.object(
+        _STRATEGY, 'analyze_ticker',
+        return_value=DataFrame([{'buy': 1, 'sell': 0, 'date': arrow.utcnow()}])
+    )
+    assert _STRATEGY.get_signal('ETH/BTC', '5m', MagicMock()) == (True, False)
+
+    mocker.patch.object(
+        _STRATEGY, 'analyze_ticker',
+        return_value=DataFrame([{'buy': 0, 'sell': 1, 'date': arrow.utcnow()}])
+    )
+    assert _STRATEGY.get_signal('ETH/BTC', '5m', MagicMock()) == (False, True)
+
+
+def test_returns_latest_sell_signal(mocker, default_conf):
+    mocker.patch.object(
+        _STRATEGY, 'analyze_ticker',
+        return_value=DataFrame([{'sell': 1, 'buy': 0, 'date': arrow.utcnow()}])
+    )
+
+    assert _STRATEGY.get_signal('ETH/BTC', '5m', MagicMock()) == (False, True)
+
+    mocker.patch.object(
+        _STRATEGY, 'analyze_ticker',
+        return_value=DataFrame([{'sell': 0, 'buy': 1, 'date': arrow.utcnow()}])
+    )
+    assert _STRATEGY.get_signal('ETH/BTC', '5m', MagicMock()) == (True, False)
+
+
+def test_get_signal_empty(default_conf, mocker, caplog):
+    assert (False, False) == _STRATEGY.get_signal('foo', default_conf['ticker_interval'],
+                                                  None)
+    assert log_has('Empty ticker history for pair foo', caplog.record_tuples)
+
+
+def test_get_signal_exception_valueerror(default_conf, mocker, caplog):
+    caplog.set_level(logging.INFO)
+    mocker.patch.object(
+        _STRATEGY, 'analyze_ticker',
+        side_effect=ValueError('xyz')
+    )
+    assert (False, False) == _STRATEGY.get_signal('foo', default_conf['ticker_interval'], 1)
+    assert log_has('Unable to analyze ticker for pair foo: xyz', caplog.record_tuples)
+
+
+def test_get_signal_empty_dataframe(default_conf, mocker, caplog):
+    caplog.set_level(logging.INFO)
+    mocker.patch.object(
+        _STRATEGY, 'analyze_ticker',
+        return_value=DataFrame([])
+    )
+    assert (False, False) == _STRATEGY.get_signal('xyz', default_conf['ticker_interval'], 1)
+    assert log_has('Empty dataframe for pair xyz', caplog.record_tuples)
+
+
+def test_get_signal_old_dataframe(default_conf, mocker, caplog):
+    caplog.set_level(logging.INFO)
+    # default_conf defines a 5m interval. we check interval * 2 + 5m
+    # this is necessary as the last candle is removed (partial candles) by default
+    oldtime = arrow.utcnow().shift(minutes=-16)
+    ticks = DataFrame([{'buy': 1, 'date': oldtime}])
+    mocker.patch.object(
+        _STRATEGY, 'analyze_ticker',
+        return_value=DataFrame(ticks)
+    )
+    assert (False, False) == _STRATEGY.get_signal('xyz', default_conf['ticker_interval'], 1)
+    assert log_has(
+        'Outdated history for pair xyz. Last tick is 16 minutes old',
+        caplog.record_tuples
+    )
+
+
+def test_get_signal_handles_exceptions(mocker, default_conf):
+    exchange = get_patched_exchange(mocker, default_conf)
+    mocker.patch.object(
+        _STRATEGY, 'analyze_ticker',
+        side_effect=Exception('invalid ticker history ')
+    )
+    assert _STRATEGY.get_signal(exchange, 'ETH/BTC', '5m') == (False, False)
+
+
+def test_tickerdata_to_dataframe(default_conf) -> None:
+    strategy = DefaultStrategy(default_conf)
+
+    timerange = TimeRange(None, 'line', 0, -100)
+    tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m', timerange=timerange)
+    tickerlist = {'UNITTEST/BTC': tick}
+    data = strategy.tickerdata_to_dataframe(tickerlist)
+    assert len(data['UNITTEST/BTC']) == 99       # partial candle was removed
+
+
+def test_min_roi_reached(default_conf, fee) -> None:
+    strategy = DefaultStrategy(default_conf)
+    strategy.minimal_roi = {0: 0.1, 20: 0.05, 55: 0.01}
+    trade = Trade(
+        pair='ETH/BTC',
+        stake_amount=0.001,
+        open_date=arrow.utcnow().shift(hours=-1).datetime,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange='bittrex',
+        open_rate=1,
+    )
+
+    assert not strategy.min_roi_reached(trade, 0.01, arrow.utcnow().shift(minutes=-55).datetime)
+    assert strategy.min_roi_reached(trade, 0.12, arrow.utcnow().shift(minutes=-55).datetime)
+
+    assert not strategy.min_roi_reached(trade, 0.04, arrow.utcnow().shift(minutes=-39).datetime)
+    assert strategy.min_roi_reached(trade, 0.06, arrow.utcnow().shift(minutes=-39).datetime)
+
+    assert not strategy.min_roi_reached(trade, -0.01, arrow.utcnow().shift(minutes=-1).datetime)
+    assert strategy.min_roi_reached(trade, 0.02, arrow.utcnow().shift(minutes=-1).datetime)
+
+
+def test_analyze_ticker_default(ticker_history, mocker, caplog) -> None:
+    caplog.set_level(logging.DEBUG)
+    ind_mock = MagicMock(side_effect=lambda x, meta: x)
+    buy_mock = MagicMock(side_effect=lambda x, meta: x)
+    sell_mock = MagicMock(side_effect=lambda x, meta: x)
+    mocker.patch.multiple(
+        'freqtrade.strategy.interface.IStrategy',
+        advise_indicators=ind_mock,
+        advise_buy=buy_mock,
+        advise_sell=sell_mock,
+
+    )
+    strategy = DefaultStrategy({})
+    strategy.analyze_ticker(ticker_history, {'pair': 'ETH/BTC'})
+    assert ind_mock.call_count == 1
+    assert buy_mock.call_count == 1
+    assert buy_mock.call_count == 1
+
+    assert log_has('TA Analysis Launched', caplog.record_tuples)
+    assert not log_has('Skippinig TA Analysis for already analyzed candle',
+                       caplog.record_tuples)
+    caplog.clear()
+
+    strategy.analyze_ticker(ticker_history, {'pair': 'ETH/BTC'})
+    # No analysis happens as process_only_new_candles is true
+    assert ind_mock.call_count == 2
+    assert buy_mock.call_count == 2
+    assert buy_mock.call_count == 2
+    assert log_has('TA Analysis Launched', caplog.record_tuples)
+    assert not log_has('Skippinig TA Analysis for already analyzed candle',
+                       caplog.record_tuples)
+
+
+def test_analyze_ticker_skip_analyze(ticker_history, mocker, caplog) -> None:
+    caplog.set_level(logging.DEBUG)
+    ind_mock = MagicMock(side_effect=lambda x, meta: x)
+    buy_mock = MagicMock(side_effect=lambda x, meta: x)
+    sell_mock = MagicMock(side_effect=lambda x, meta: x)
+    mocker.patch.multiple(
+        'freqtrade.strategy.interface.IStrategy',
+        advise_indicators=ind_mock,
+        advise_buy=buy_mock,
+        advise_sell=sell_mock,
+
+    )
+    strategy = DefaultStrategy({})
+    strategy.process_only_new_candles = True
+
+    ret = strategy.analyze_ticker(ticker_history, {'pair': 'ETH/BTC'})
+    assert ind_mock.call_count == 1
+    assert buy_mock.call_count == 1
+    assert buy_mock.call_count == 1
+    assert log_has('TA Analysis Launched', caplog.record_tuples)
+    assert not log_has('Skippinig TA Analysis for already analyzed candle',
+                       caplog.record_tuples)
+    caplog.clear()
+
+    ret = strategy.analyze_ticker(ticker_history, {'pair': 'ETH/BTC'})
+    # No analysis happens as process_only_new_candles is true
+    assert ind_mock.call_count == 1
+    assert buy_mock.call_count == 1
+    assert buy_mock.call_count == 1
+    # only skipped analyze adds buy and sell columns, otherwise it's all mocked
+    assert 'buy' in ret
+    assert 'sell' in ret
+    assert ret['buy'].sum() == 0
+    assert ret['sell'].sum() == 0
+    assert not log_has('TA Analysis Launched', caplog.record_tuples)
+    assert log_has('Skippinig TA Analysis for already analyzed candle',
+                   caplog.record_tuples)

freqtrade/tests/strategy/test_strategy.py

@@ -0,0 +1,238 @@
+# pragma pylint: disable=missing-docstring, protected-access, C0103
+import logging
+from base64 import urlsafe_b64encode
+from os import path
+import warnings
+
+import pytest
+from pandas import DataFrame
+
+from freqtrade.strategy import import_strategy
+from freqtrade.strategy.default_strategy import DefaultStrategy
+from freqtrade.strategy.interface import IStrategy
+from freqtrade.strategy.resolver import StrategyResolver
+
+
+def test_import_strategy(caplog):
+    caplog.set_level(logging.DEBUG)
+    default_config = {}
+
+    strategy = DefaultStrategy(default_config)
+    strategy.some_method = lambda *args, **kwargs: 42
+
+    assert strategy.__module__ == 'freqtrade.strategy.default_strategy'
+    assert strategy.some_method() == 42
+
+    imported_strategy = import_strategy(strategy, default_config)
+
+    assert dir(strategy) == dir(imported_strategy)
+
+    assert imported_strategy.__module__ == 'freqtrade.strategy'
+    assert imported_strategy.some_method() == 42
+
+    assert (
+        'freqtrade.strategy',
+        logging.DEBUG,
+        'Imported strategy freqtrade.strategy.default_strategy.DefaultStrategy '
+        'as freqtrade.strategy.DefaultStrategy',
+    ) in caplog.record_tuples
+
+
+def test_search_strategy():
+    default_config = {}
+    default_location = path.join(path.dirname(
+        path.realpath(__file__)), '..', '..', 'strategy'
+    )
+    assert isinstance(
+        StrategyResolver._search_strategy(
+            default_location,
+            config=default_config,
+            strategy_name='DefaultStrategy'
+        ),
+        IStrategy
+    )
+    assert StrategyResolver._search_strategy(
+        default_location,
+        config=default_config,
+        strategy_name='NotFoundStrategy'
+    ) is None
+
+
+def test_load_strategy(result):
+    resolver = StrategyResolver({'strategy': 'TestStrategy'})
+    metadata = {'pair': 'ETH/BTC'}
+    assert 'adx' in resolver.strategy.advise_indicators(result, metadata=metadata)
+
+
+def test_load_strategy_byte64(result):
+    with open("freqtrade/tests/strategy/test_strategy.py", "r") as file:
+        encoded_string = urlsafe_b64encode(file.read().encode("utf-8")).decode("utf-8")
+    resolver = StrategyResolver({'strategy': 'TestStrategy:{}'.format(encoded_string)})
+    assert 'adx' in resolver.strategy.advise_indicators(result, 'ETH/BTC')
+
+
+def test_load_strategy_invalid_directory(result, caplog):
+    resolver = StrategyResolver()
+    extra_dir = path.join('some', 'path')
+    resolver._load_strategy('TestStrategy', config={}, extra_dir=extra_dir)
+
+    assert (
+        'freqtrade.strategy.resolver',
+        logging.WARNING,
+        'Path "{}" does not exist'.format(extra_dir),
+    ) in caplog.record_tuples
+
+    assert 'adx' in resolver.strategy.advise_indicators(result, {'pair': 'ETH/BTC'})
+
+
+def test_load_not_found_strategy():
+    strategy = StrategyResolver()
+    with pytest.raises(ImportError,
+                       match=r'Impossible to load Strategy \'NotFoundStrategy\'.'
+                             r' This class does not exist or contains Python code errors'):
+        strategy._load_strategy(strategy_name='NotFoundStrategy', config={})
+
+
+def test_strategy(result):
+    config = {'strategy': 'DefaultStrategy'}
+
+    resolver = StrategyResolver(config)
+    metadata = {'pair': 'ETH/BTC'}
+    assert resolver.strategy.minimal_roi[0] == 0.04
+    assert config["minimal_roi"]['0'] == 0.04
+
+    assert resolver.strategy.stoploss == -0.10
+    assert config['stoploss'] == -0.10
+
+    assert resolver.strategy.ticker_interval == '5m'
+    assert config['ticker_interval'] == '5m'
+
+    df_indicators = resolver.strategy.advise_indicators(result, metadata=metadata)
+    assert 'adx' in df_indicators
+
+    dataframe = resolver.strategy.advise_buy(df_indicators, metadata=metadata)
+    assert 'buy' in dataframe.columns
+
+    dataframe = resolver.strategy.advise_sell(df_indicators, metadata=metadata)
+    assert 'sell' in dataframe.columns
+
+
+def test_strategy_override_minimal_roi(caplog):
+    caplog.set_level(logging.INFO)
+    config = {
+        'strategy': 'DefaultStrategy',
+        'minimal_roi': {
+            "0": 0.5
+        }
+    }
+    resolver = StrategyResolver(config)
+
+    assert resolver.strategy.minimal_roi[0] == 0.5
+    assert ('freqtrade.strategy.resolver',
+            logging.INFO,
+            "Override strategy 'minimal_roi' with value in config file: {'0': 0.5}."
+            ) in caplog.record_tuples
+
+
+def test_strategy_override_stoploss(caplog):
+    caplog.set_level(logging.INFO)
+    config = {
+        'strategy': 'DefaultStrategy',
+        'stoploss': -0.5
+    }
+    resolver = StrategyResolver(config)
+
+    assert resolver.strategy.stoploss == -0.5
+    assert ('freqtrade.strategy.resolver',
+            logging.INFO,
+            "Override strategy 'stoploss' with value in config file: -0.5."
+            ) in caplog.record_tuples
+
+
+def test_strategy_override_ticker_interval(caplog):
+    caplog.set_level(logging.INFO)
+
+    config = {
+        'strategy': 'DefaultStrategy',
+        'ticker_interval': 60
+    }
+    resolver = StrategyResolver(config)
+
+    assert resolver.strategy.ticker_interval == 60
+    assert ('freqtrade.strategy.resolver',
+            logging.INFO,
+            "Override strategy 'ticker_interval' with value in config file: 60."
+            ) in caplog.record_tuples
+
+
+def test_strategy_override_process_only_new_candles(caplog):
+    caplog.set_level(logging.INFO)
+
+    config = {
+        'strategy': 'DefaultStrategy',
+        'process_only_new_candles': True
+    }
+    resolver = StrategyResolver(config)
+
+    assert resolver.strategy.process_only_new_candles
+    assert ('freqtrade.strategy.resolver',
+            logging.INFO,
+            "Override process_only_new_candles 'process_only_new_candles' "
+            "with value in config file: True."
+            ) in caplog.record_tuples
+
+
+def test_deprecate_populate_indicators(result):
+    default_location = path.join(path.dirname(path.realpath(__file__)))
+    resolver = StrategyResolver({'strategy': 'TestStrategyLegacy',
+                                 'strategy_path': default_location})
+    with warnings.catch_warnings(record=True) as w:
+        # Cause all warnings to always be triggered.
+        warnings.simplefilter("always")
+        indicators = resolver.strategy.advise_indicators(result, 'ETH/BTC')
+        assert len(w) == 1
+        assert issubclass(w[-1].category, DeprecationWarning)
+        assert "deprecated - check out the Sample strategy to see the current function headers!" \
+            in str(w[-1].message)
+
+    with warnings.catch_warnings(record=True) as w:
+            # Cause all warnings to always be triggered.
+        warnings.simplefilter("always")
+        resolver.strategy.advise_buy(indicators, 'ETH/BTC')
+        assert len(w) == 1
+        assert issubclass(w[-1].category, DeprecationWarning)
+        assert "deprecated - check out the Sample strategy to see the current function headers!" \
+            in str(w[-1].message)
+
+    with warnings.catch_warnings(record=True) as w:
+        # Cause all warnings to always be triggered.
+        warnings.simplefilter("always")
+        resolver.strategy.advise_sell(indicators, 'ETH_BTC')
+        assert len(w) == 1
+        assert issubclass(w[-1].category, DeprecationWarning)
+        assert "deprecated - check out the Sample strategy to see the current function headers!" \
+            in str(w[-1].message)
+
+
+def test_call_deprecated_function(result, monkeypatch):
+    default_location = path.join(path.dirname(path.realpath(__file__)))
+    resolver = StrategyResolver({'strategy': 'TestStrategyLegacy',
+                                 'strategy_path': default_location})
+    metadata = {'pair': 'ETH/BTC'}
+
+    # Make sure we are using a legacy function
+    assert resolver.strategy._populate_fun_len == 2
+    assert resolver.strategy._buy_fun_len == 2
+    assert resolver.strategy._sell_fun_len == 2
+
+    indicator_df = resolver.strategy.advise_indicators(result, metadata=metadata)
+    assert type(indicator_df) is DataFrame
+    assert 'adx' in indicator_df.columns
+
+    buydf = resolver.strategy.advise_buy(result, metadata=metadata)
+    assert type(buydf) is DataFrame
+    assert 'buy' in buydf.columns
+
+    selldf = resolver.strategy.advise_sell(result, metadata=metadata)
+    assert type(selldf) is DataFrame
+    assert 'sell' in selldf

freqtrade/tests/test_acl_pair.py

@@ -1,141 +1,87 @@
-from freqtrade.main import refresh_whitelist, gen_pair_whitelist
+# pragma pylint: disable=missing-docstring,C0103,protected-access
+
+from unittest.mock import MagicMock
+
+from freqtrade.tests.conftest import get_patched_freqtradebot
+
+import pytest
 
 # whitelist, blacklist, filtering, all of that will
 # eventually become some rules to run on a generic ACL engine
 # perhaps try to anticipate that by using some python package
 
 
-def whitelist_conf():
-    return {
-        'stake_currency': 'BTC',
-        'exchange': {
-            'pair_whitelist': [
-                'BTC_ETH',
-                'BTC_TKN',
-                'BTC_TRST',
-                'BTC_SWT',
-                'BTC_BCC'
-            ],
-            'pair_blacklist': [
-                'BTC_BLK'
-            ],
-        },
-    }
+@pytest.fixture(scope="function")
+def whitelist_conf(default_conf):
+    default_conf['stake_currency'] = 'BTC'
+    default_conf['exchange']['pair_whitelist'] = [
+        'ETH/BTC',
+        'TKN/BTC',
+        'TRST/BTC',
+        'SWT/BTC',
+        'BCC/BTC'
+    ]
+    default_conf['exchange']['pair_blacklist'] = [
+        'BLK/BTC'
+    ]
+
+    return default_conf
 
 
-def get_market_summaries():
-    return [{
-        'MarketName': 'BTC-TKN',
-        'High': 0.00000919,
-        'Low': 0.00000820,
-        'Volume': 74339.61396015,
-        'Last': 0.00000820,
-        'BaseVolume': 1664,
-        'TimeStamp': '2014-07-09T07:19:30.15',
-        'Bid': 0.00000820,
-        'Ask': 0.00000831,
-        'OpenBuyOrders': 15,
-        'OpenSellOrders': 15,
-        'PrevDay': 0.00000821,
-        'Created': '2014-03-20T06:00:00',
-        'DisplayMarketName': ''
-    }, {
-        'MarketName': 'BTC-ETH',
-        'High': 0.00000072,
-        'Low': 0.00000001,
-        'Volume': 166340678.42280999,
-        'Last': 0.00000005,
-        'BaseVolume': 42,
-        'TimeStamp': '2014-07-09T07:21:40.51',
-        'Bid': 0.00000004,
-        'Ask': 0.00000005,
-        'OpenBuyOrders': 18,
-        'OpenSellOrders': 18,
-        'PrevDay': 0.00000002,
-        'Created': '2014-05-30T07:57:49.637',
-        'DisplayMarketName': ''
-    }, {
-        'MarketName': 'BTC-BLK',
-        'High': 0.00000072,
-        'Low': 0.00000001,
-        'Volume': 166340678.42280999,
-        'Last': 0.00000005,
-        'BaseVolume': 3,
-        'TimeStamp': '2014-07-09T07:21:40.51',
-        'Bid': 0.00000004,
-        'Ask': 0.00000005,
-        'OpenBuyOrders': 18,
-        'OpenSellOrders': 18,
-        'PrevDay': 0.00000002,
-        'Created': '2014-05-30T07:57:49.637',
-        'DisplayMarketName': ''
-    }]
+def test_refresh_market_pair_not_in_whitelist(mocker, markets, whitelist_conf):
 
+    freqtradebot = get_patched_freqtradebot(mocker, whitelist_conf)
 
-def get_health():
-    return [{'Currency': 'ETH',
-             'IsActive': True
-             },
-            {'Currency': 'TKN',
-             'IsActive': True
-             },
-            {'Currency': 'BLK',
-             'IsActive': True
-             }
-            ]
-
-
-def get_health_empty():
-    return []
-
-
-def test_refresh_market_pair_not_in_whitelist(mocker):
-    conf = whitelist_conf()
-    mocker.patch.dict('freqtrade.main._CONF', conf)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          get_wallet_health=get_health)
-    refreshedwhitelist = refresh_whitelist(
-        conf['exchange']['pair_whitelist'] + ['BTC_XXX'])
+    mocker.patch('freqtrade.exchange.Exchange.get_markets', markets)
+    refreshedwhitelist = freqtradebot._refresh_whitelist(
+        whitelist_conf['exchange']['pair_whitelist'] + ['XXX/BTC']
+    )
     # List ordered by BaseVolume
-    whitelist = ['BTC_ETH', 'BTC_TKN']
+    whitelist = ['ETH/BTC', 'TKN/BTC']
     # Ensure all except those in whitelist are removed
     assert whitelist == refreshedwhitelist
 
 
-def test_refresh_whitelist(mocker):
-    conf = whitelist_conf()
-    mocker.patch.dict('freqtrade.main._CONF', conf)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          get_wallet_health=get_health)
-    refreshedwhitelist = refresh_whitelist(conf['exchange']['pair_whitelist'])
+def test_refresh_whitelist(mocker, markets, whitelist_conf):
+    freqtradebot = get_patched_freqtradebot(mocker, whitelist_conf)
+
+    mocker.patch('freqtrade.exchange.Exchange.get_markets', markets)
+    refreshedwhitelist = freqtradebot._refresh_whitelist(
+        whitelist_conf['exchange']['pair_whitelist'])
+
     # List ordered by BaseVolume
-    whitelist = ['BTC_ETH', 'BTC_TKN']
+    whitelist = ['ETH/BTC', 'TKN/BTC']
     # Ensure all except those in whitelist are removed
     assert whitelist == refreshedwhitelist
 
 
-def test_refresh_whitelist_dynamic(mocker):
-    conf = whitelist_conf()
-    mocker.patch.dict('freqtrade.main._CONF', conf)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          get_wallet_health=get_health)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          get_market_summaries=get_market_summaries)
+def test_refresh_whitelist_dynamic(mocker, markets, tickers, whitelist_conf):
+    freqtradebot = get_patched_freqtradebot(mocker, whitelist_conf)
+    mocker.patch.multiple(
+        'freqtrade.exchange.Exchange',
+        get_markets=markets,
+        get_tickers=tickers,
+        exchange_has=MagicMock(return_value=True)
+    )
+
     # argument: use the whitelist dynamically by exchange-volume
-    whitelist = ['BTC_TKN', 'BTC_ETH']
-    refreshedwhitelist = refresh_whitelist(
-        gen_pair_whitelist(conf['stake_currency']))
+    whitelist = ['ETH/BTC', 'TKN/BTC']
+
+    refreshedwhitelist = freqtradebot._refresh_whitelist(
+        freqtradebot._gen_pair_whitelist(whitelist_conf['stake_currency'])
+    )
+
     assert whitelist == refreshedwhitelist
 
 
-def test_refresh_whitelist_dynamic_empty(mocker):
-    conf = whitelist_conf()
-    mocker.patch.dict('freqtrade.main._CONF', conf)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          get_wallet_health=get_health_empty)
+def test_refresh_whitelist_dynamic_empty(mocker, markets_empty, whitelist_conf):
+    freqtradebot = get_patched_freqtradebot(mocker, whitelist_conf)
+    mocker.patch('freqtrade.exchange.Exchange.get_markets', markets_empty)
+
     # argument: use the whitelist dynamically by exchange-volume
     whitelist = []
-    conf['exchange']['pair_whitelist'] = []
-    refresh_whitelist(whitelist)
-    pairslist = conf['exchange']['pair_whitelist']
+    whitelist_conf['exchange']['pair_whitelist'] = []
+    freqtradebot._refresh_whitelist(whitelist)
+    pairslist = whitelist_conf['exchange']['pair_whitelist']
+
     assert set(whitelist) == set(pairslist)

freqtrade/tests/test_analyze.py

@@ -1,74 +0,0 @@
-# pragma pylint: disable=missing-docstring,W0621
-import json
-from unittest.mock import MagicMock
-
-import arrow
-import pytest
-from pandas import DataFrame
-
-from freqtrade.analyze import (SignalType, get_signal, parse_ticker_dataframe,
-                               populate_buy_trend, populate_indicators,
-                               populate_sell_trend)
-
-
-@pytest.fixture
-def result():
-    with open('freqtrade/tests/testdata/BTC_ETH-1.json') as data_file:
-        return parse_ticker_dataframe(json.load(data_file))
-
-
-def test_dataframe_correct_columns(result):
-    assert result.columns.tolist() == \
-        ['close', 'high', 'low', 'open', 'date', 'volume']
-
-
-def test_dataframe_correct_length(result):
-    assert len(result.index) == 14395
-
-
-def test_populates_buy_trend(result):
-    dataframe = populate_buy_trend(populate_indicators(result))
-    assert 'buy' in dataframe.columns
-
-
-def test_populates_sell_trend(result):
-    dataframe = populate_sell_trend(populate_indicators(result))
-    assert 'sell' in dataframe.columns
-
-
-def test_returns_latest_buy_signal(mocker):
-    mocker.patch('freqtrade.analyze.get_ticker_history', return_value=MagicMock())
-    mocker.patch(
-        'freqtrade.analyze.analyze_ticker',
-        return_value=DataFrame([{'buy': 1, 'date': arrow.utcnow()}])
-    )
-    assert get_signal('BTC-ETH', SignalType.BUY)
-
-    mocker.patch(
-        'freqtrade.analyze.analyze_ticker',
-        return_value=DataFrame([{'buy': 0, 'date': arrow.utcnow()}])
-    )
-    assert not get_signal('BTC-ETH', SignalType.BUY)
-
-
-def test_returns_latest_sell_signal(mocker):
-    mocker.patch('freqtrade.analyze.get_ticker_history', return_value=MagicMock())
-    mocker.patch(
-        'freqtrade.analyze.analyze_ticker',
-        return_value=DataFrame([{'sell': 1, 'date': arrow.utcnow()}])
-    )
-    assert get_signal('BTC-ETH', SignalType.SELL)
-
-    mocker.patch(
-        'freqtrade.analyze.analyze_ticker',
-        return_value=DataFrame([{'sell': 0, 'date': arrow.utcnow()}])
-    )
-    assert not get_signal('BTC-ETH', SignalType.SELL)
-
-
-def test_get_signal_handles_exceptions(mocker):
-    mocker.patch('freqtrade.analyze.get_ticker_history', return_value=MagicMock())
-    mocker.patch('freqtrade.analyze.analyze_ticker',
-                 side_effect=Exception('invalid ticker history '))
-
-    assert not get_signal('BTC-ETH', SignalType.BUY)

freqtrade/tests/test_arguments.py

@@ -0,0 +1,181 @@
+# pragma pylint: disable=missing-docstring, C0103
+
+import argparse
+
+import pytest
+
+from freqtrade.arguments import Arguments, TimeRange
+
+
+# Parse common command-line-arguments. Used for all tools
+def test_parse_args_none() -> None:
+    arguments = Arguments([], '')
+    assert isinstance(arguments, Arguments)
+    assert isinstance(arguments.parser, argparse.ArgumentParser)
+
+
+def test_parse_args_defaults() -> None:
+    args = Arguments([], '').get_parsed_arg()
+    assert args.config == 'config.json'
+    assert args.dynamic_whitelist is None
+    assert args.loglevel == 0
+
+
+def test_parse_args_config() -> None:
+    args = Arguments(['-c', '/dev/null'], '').get_parsed_arg()
+    assert args.config == '/dev/null'
+
+    args = Arguments(['--config', '/dev/null'], '').get_parsed_arg()
+    assert args.config == '/dev/null'
+
+
+def test_parse_args_db_url() -> None:
+    args = Arguments(['--db-url', 'sqlite:///test.sqlite'], '').get_parsed_arg()
+    assert args.db_url == 'sqlite:///test.sqlite'
+
+
+def test_parse_args_verbose() -> None:
+    args = Arguments(['-v'], '').get_parsed_arg()
+    assert args.loglevel == 1
+
+    args = Arguments(['--verbose'], '').get_parsed_arg()
+    assert args.loglevel == 1
+
+
+def test_scripts_options() -> None:
+    arguments = Arguments(['-p', 'ETH/BTC'], '')
+    arguments.scripts_options()
+    args = arguments.get_parsed_arg()
+    assert args.pair == 'ETH/BTC'
+
+
+def test_parse_args_version() -> None:
+    with pytest.raises(SystemExit, match=r'0'):
+        Arguments(['--version'], '').get_parsed_arg()
+
+
+def test_parse_args_invalid() -> None:
+    with pytest.raises(SystemExit, match=r'2'):
+        Arguments(['-c'], '').get_parsed_arg()
+
+
+def test_parse_args_strategy() -> None:
+    args = Arguments(['--strategy', 'SomeStrategy'], '').get_parsed_arg()
+    assert args.strategy == 'SomeStrategy'
+
+
+def test_parse_args_strategy_invalid() -> None:
+    with pytest.raises(SystemExit, match=r'2'):
+        Arguments(['--strategy'], '').get_parsed_arg()
+
+
+def test_parse_args_strategy_path() -> None:
+    args = Arguments(['--strategy-path', '/some/path'], '').get_parsed_arg()
+    assert args.strategy_path == '/some/path'
+
+
+def test_parse_args_strategy_path_invalid() -> None:
+    with pytest.raises(SystemExit, match=r'2'):
+        Arguments(['--strategy-path'], '').get_parsed_arg()
+
+
+def test_parse_args_dynamic_whitelist() -> None:
+    args = Arguments(['--dynamic-whitelist'], '').get_parsed_arg()
+    assert args.dynamic_whitelist == 20
+
+
+def test_parse_args_dynamic_whitelist_10() -> None:
+    args = Arguments(['--dynamic-whitelist', '10'], '').get_parsed_arg()
+    assert args.dynamic_whitelist == 10
+
+
+def test_parse_args_dynamic_whitelist_invalid_values() -> None:
+    with pytest.raises(SystemExit, match=r'2'):
+        Arguments(['--dynamic-whitelist', 'abc'], '').get_parsed_arg()
+
+
+def test_parse_timerange_incorrect() -> None:
+    assert TimeRange(None, 'line', 0, -200) == Arguments.parse_timerange('-200')
+    assert TimeRange('line', None, 200, 0) == Arguments.parse_timerange('200-')
+    assert TimeRange('index', 'index', 200, 500) == Arguments.parse_timerange('200-500')
+
+    assert TimeRange('date', None, 1274486400, 0) == Arguments.parse_timerange('20100522-')
+    assert TimeRange(None, 'date', 0, 1274486400) == Arguments.parse_timerange('-20100522')
+    timerange = Arguments.parse_timerange('20100522-20150730')
+    assert timerange == TimeRange('date', 'date', 1274486400, 1438214400)
+
+    # Added test for unix timestamp - BTC genesis date
+    assert TimeRange('date', None, 1231006505, 0) == Arguments.parse_timerange('1231006505-')
+    assert TimeRange(None, 'date', 0, 1233360000) == Arguments.parse_timerange('-1233360000')
+    timerange = Arguments.parse_timerange('1231006505-1233360000')
+    assert TimeRange('date', 'date', 1231006505, 1233360000) == timerange
+
+    # TODO: Find solution for the following case (passing timestamp in ms)
+    timerange = Arguments.parse_timerange('1231006505000-1233360000000')
+    assert TimeRange('date', 'date', 1231006505, 1233360000) != timerange
+
+    with pytest.raises(Exception, match=r'Incorrect syntax.*'):
+        Arguments.parse_timerange('-')
+
+
+def test_parse_args_backtesting_invalid() -> None:
+    with pytest.raises(SystemExit, match=r'2'):
+        Arguments(['backtesting --ticker-interval'], '').get_parsed_arg()
+
+    with pytest.raises(SystemExit, match=r'2'):
+        Arguments(['backtesting --ticker-interval', 'abc'], '').get_parsed_arg()
+
+
+def test_parse_args_backtesting_custom() -> None:
+    args = [
+        '-c', 'test_conf.json',
+        'backtesting',
+        '--live',
+        '--ticker-interval', '1m',
+        '--refresh-pairs-cached',
+        '--strategy-list',
+        'DefaultStrategy',
+        'TestStrategy'
+        ]
+    call_args = Arguments(args, '').get_parsed_arg()
+    assert call_args.config == 'test_conf.json'
+    assert call_args.live is True
+    assert call_args.loglevel == 0
+    assert call_args.subparser == 'backtesting'
+    assert call_args.func is not None
+    assert call_args.ticker_interval == '1m'
+    assert call_args.refresh_pairs is True
+    assert type(call_args.strategy_list) is list
+    assert len(call_args.strategy_list) == 2
+
+
+def test_parse_args_hyperopt_custom() -> None:
+    args = [
+        '-c', 'test_conf.json',
+        'hyperopt',
+        '--epochs', '20',
+        '--spaces', 'buy'
+    ]
+    call_args = Arguments(args, '').get_parsed_arg()
+    assert call_args.config == 'test_conf.json'
+    assert call_args.epochs == 20
+    assert call_args.loglevel == 0
+    assert call_args.subparser == 'hyperopt'
+    assert call_args.spaces == ['buy']
+    assert call_args.func is not None
+
+
+def test_testdata_dl_options() -> None:
+    args = [
+        '--pairs-file', 'file_with_pairs',
+        '--export', 'export/folder',
+        '--days', '30',
+        '--exchange', 'binance'
+    ]
+    arguments = Arguments(args, '')
+    arguments.testdata_dl_options()
+    args = arguments.parse_args()
+    assert args.pairs_file == 'file_with_pairs'
+    assert args.export == 'export/folder'
+    assert args.days == 30
+    assert args.exchange == 'binance'

freqtrade/tests/test_configuration.py

@@ -0,0 +1,450 @@
+# pragma pylint: disable=missing-docstring, protected-access, invalid-name
+
+import json
+from argparse import Namespace
+import logging
+from unittest.mock import MagicMock
+
+import pytest
+from jsonschema import validate, ValidationError
+
+from freqtrade import constants
+from freqtrade import OperationalException
+from freqtrade.arguments import Arguments
+from freqtrade.configuration import Configuration, set_loggers
+from freqtrade.constants import DEFAULT_DB_DRYRUN_URL, DEFAULT_DB_PROD_URL
+from freqtrade.tests.conftest import log_has
+
+
+def test_load_config_invalid_pair(default_conf) -> None:
+    default_conf['exchange']['pair_whitelist'].append('ETH-BTC')
+
+    with pytest.raises(ValidationError, match=r'.*does not match.*'):
+        configuration = Configuration(Namespace())
+        configuration._validate_config(default_conf)
+
+
+def test_load_config_missing_attributes(default_conf) -> None:
+    default_conf.pop('exchange')
+
+    with pytest.raises(ValidationError, match=r'.*\'exchange\' is a required property.*'):
+        configuration = Configuration(Namespace())
+        configuration._validate_config(default_conf)
+
+
+def test_load_config_incorrect_stake_amount(default_conf) -> None:
+    default_conf['stake_amount'] = 'fake'
+
+    with pytest.raises(ValidationError, match=r'.*\'fake\' does not match \'unlimited\'.*'):
+        configuration = Configuration(Namespace())
+        configuration._validate_config(default_conf)
+
+
+def test_load_config_file(default_conf, mocker, caplog) -> None:
+    file_mock = mocker.patch('freqtrade.configuration.open', mocker.mock_open(
+        read_data=json.dumps(default_conf)
+    ))
+
+    configuration = Configuration(Namespace())
+    validated_conf = configuration._load_config_file('somefile')
+    assert file_mock.call_count == 1
+    assert validated_conf.items() >= default_conf.items()
+    assert 'internals' in validated_conf
+    assert log_has('Validating configuration ...', caplog.record_tuples)
+
+
+def test_load_config_max_open_trades_zero(default_conf, mocker, caplog) -> None:
+    default_conf['max_open_trades'] = 0
+    file_mock = mocker.patch('freqtrade.configuration.open', mocker.mock_open(
+        read_data=json.dumps(default_conf)
+    ))
+
+    Configuration(Namespace())._load_config_file('somefile')
+    assert file_mock.call_count == 1
+    assert log_has('Validating configuration ...', caplog.record_tuples)
+
+
+def test_load_config_file_exception(mocker) -> None:
+    mocker.patch(
+        'freqtrade.configuration.open',
+        MagicMock(side_effect=FileNotFoundError('File not found'))
+    )
+    configuration = Configuration(Namespace())
+
+    with pytest.raises(OperationalException, match=r'.*Config file "somefile" not found!*'):
+        configuration._load_config_file('somefile')
+
+
+def test_load_config(default_conf, mocker) -> None:
+    mocker.patch('freqtrade.configuration.open', mocker.mock_open(
+        read_data=json.dumps(default_conf)
+    ))
+
+    args = Arguments([], '').get_parsed_arg()
+    configuration = Configuration(args)
+    validated_conf = configuration.load_config()
+
+    assert validated_conf.get('strategy') == 'DefaultStrategy'
+    assert validated_conf.get('strategy_path') is None
+    assert 'dynamic_whitelist' not in validated_conf
+
+
+def test_load_config_with_params(default_conf, mocker) -> None:
+    mocker.patch('freqtrade.configuration.open', mocker.mock_open(
+        read_data=json.dumps(default_conf)
+    ))
+    arglist = [
+        '--dynamic-whitelist', '10',
+        '--strategy', 'TestStrategy',
+        '--strategy-path', '/some/path',
+        '--db-url', 'sqlite:///someurl',
+    ]
+    args = Arguments(arglist, '').get_parsed_arg()
+    configuration = Configuration(args)
+    validated_conf = configuration.load_config()
+
+    assert validated_conf.get('dynamic_whitelist') == 10
+    assert validated_conf.get('strategy') == 'TestStrategy'
+    assert validated_conf.get('strategy_path') == '/some/path'
+    assert validated_conf.get('db_url') == 'sqlite:///someurl'
+
+    conf = default_conf.copy()
+    conf["dry_run"] = False
+    del conf["db_url"]
+    mocker.patch('freqtrade.configuration.open', mocker.mock_open(
+        read_data=json.dumps(conf)
+    ))
+
+    arglist = [
+        '--dynamic-whitelist', '10',
+        '--strategy', 'TestStrategy',
+        '--strategy-path', '/some/path'
+    ]
+    args = Arguments(arglist, '').get_parsed_arg()
+
+    configuration = Configuration(args)
+    validated_conf = configuration.load_config()
+    assert validated_conf.get('db_url') == DEFAULT_DB_PROD_URL
+
+    # Test dry=run with ProdURL
+    conf = default_conf.copy()
+    conf["dry_run"] = True
+    conf["db_url"] = DEFAULT_DB_PROD_URL
+    mocker.patch('freqtrade.configuration.open', mocker.mock_open(
+        read_data=json.dumps(conf)
+    ))
+
+    arglist = [
+        '--dynamic-whitelist', '10',
+        '--strategy', 'TestStrategy',
+        '--strategy-path', '/some/path'
+    ]
+    args = Arguments(arglist, '').get_parsed_arg()
+
+    configuration = Configuration(args)
+    validated_conf = configuration.load_config()
+    assert validated_conf.get('db_url') == DEFAULT_DB_DRYRUN_URL
+
+
+def test_load_custom_strategy(default_conf, mocker) -> None:
+    default_conf.update({
+        'strategy': 'CustomStrategy',
+        'strategy_path': '/tmp/strategies',
+    })
+    mocker.patch('freqtrade.configuration.open', mocker.mock_open(
+        read_data=json.dumps(default_conf)
+    ))
+
+    args = Arguments([], '').get_parsed_arg()
+    configuration = Configuration(args)
+    validated_conf = configuration.load_config()
+
+    assert validated_conf.get('strategy') == 'CustomStrategy'
+    assert validated_conf.get('strategy_path') == '/tmp/strategies'
+
+
+def test_show_info(default_conf, mocker, caplog) -> None:
+    mocker.patch('freqtrade.configuration.open', mocker.mock_open(
+        read_data=json.dumps(default_conf)
+    ))
+    arglist = [
+        '--dynamic-whitelist', '10',
+        '--strategy', 'TestStrategy',
+        '--db-url', 'sqlite:///tmp/testdb',
+    ]
+    args = Arguments(arglist, '').get_parsed_arg()
+
+    configuration = Configuration(args)
+    configuration.get_config()
+
+    assert log_has(
+        'Parameter --dynamic-whitelist detected. '
+        'Using dynamically generated whitelist. '
+        '(not applicable with Backtesting and Hyperopt)',
+        caplog.record_tuples
+    )
+    assert log_has('Using DB: "sqlite:///tmp/testdb"', caplog.record_tuples)
+    assert log_has('Dry run is enabled', caplog.record_tuples)
+
+
+def test_setup_configuration_without_arguments(mocker, default_conf, caplog) -> None:
+    mocker.patch('freqtrade.configuration.open', mocker.mock_open(
+        read_data=json.dumps(default_conf)
+    ))
+    arglist = [
+        '--config', 'config.json',
+        '--strategy', 'DefaultStrategy',
+        'backtesting'
+    ]
+
+    args = Arguments(arglist, '').get_parsed_arg()
+
+    configuration = Configuration(args)
+    config = configuration.get_config()
+    assert 'max_open_trades' in config
+    assert 'stake_currency' in config
+    assert 'stake_amount' in config
+    assert 'exchange' in config
+    assert 'pair_whitelist' in config['exchange']
+    assert 'datadir' in config
+    assert log_has(
+        'Using data folder: {} ...'.format(config['datadir']),
+        caplog.record_tuples
+    )
+    assert 'ticker_interval' in config
+    assert not log_has('Parameter -i/--ticker-interval detected ...', caplog.record_tuples)
+
+    assert 'live' not in config
+    assert not log_has('Parameter -l/--live detected ...', caplog.record_tuples)
+
+    assert 'position_stacking' not in config
+    assert not log_has('Parameter --enable-position-stacking detected ...', caplog.record_tuples)
+
+    assert 'refresh_pairs' not in config
+    assert not log_has('Parameter -r/--refresh-pairs-cached detected ...', caplog.record_tuples)
+
+    assert 'timerange' not in config
+    assert 'export' not in config
+
+
+def test_setup_configuration_with_arguments(mocker, default_conf, caplog) -> None:
+    mocker.patch('freqtrade.configuration.open', mocker.mock_open(
+        read_data=json.dumps(default_conf)
+    ))
+
+    arglist = [
+        '--config', 'config.json',
+        '--strategy', 'DefaultStrategy',
+        '--datadir', '/foo/bar',
+        'backtesting',
+        '--ticker-interval', '1m',
+        '--live',
+        '--enable-position-stacking',
+        '--disable-max-market-positions',
+        '--refresh-pairs-cached',
+        '--timerange', ':100',
+        '--export', '/bar/foo'
+    ]
+
+    args = Arguments(arglist, '').get_parsed_arg()
+
+    configuration = Configuration(args)
+    config = configuration.get_config()
+    assert 'max_open_trades' in config
+    assert 'stake_currency' in config
+    assert 'stake_amount' in config
+    assert 'exchange' in config
+    assert 'pair_whitelist' in config['exchange']
+    assert 'datadir' in config
+    assert log_has(
+        'Using data folder: {} ...'.format(config['datadir']),
+        caplog.record_tuples
+    )
+    assert 'ticker_interval' in config
+    assert log_has('Parameter -i/--ticker-interval detected ...', caplog.record_tuples)
+    assert log_has(
+        'Using ticker_interval: 1m ...',
+        caplog.record_tuples
+    )
+
+    assert 'live' in config
+    assert log_has('Parameter -l/--live detected ...', caplog.record_tuples)
+
+    assert 'position_stacking'in config
+    assert log_has('Parameter --enable-position-stacking detected ...', caplog.record_tuples)
+
+    assert 'use_max_market_positions' in config
+    assert log_has('Parameter --disable-max-market-positions detected ...', caplog.record_tuples)
+    assert log_has('max_open_trades set to unlimited ...', caplog.record_tuples)
+
+    assert 'refresh_pairs'in config
+    assert log_has('Parameter -r/--refresh-pairs-cached detected ...', caplog.record_tuples)
+    assert 'timerange' in config
+    assert log_has(
+        'Parameter --timerange detected: {} ...'.format(config['timerange']),
+        caplog.record_tuples
+    )
+
+    assert 'export' in config
+    assert log_has(
+        'Parameter --export detected: {} ...'.format(config['export']),
+        caplog.record_tuples
+    )
+
+
+def test_setup_configuration_with_stratlist(mocker, default_conf, caplog) -> None:
+    """
+    Test setup_configuration() function
+    """
+    mocker.patch('freqtrade.configuration.open', mocker.mock_open(
+        read_data=json.dumps(default_conf)
+    ))
+
+    arglist = [
+        '--config', 'config.json',
+        'backtesting',
+        '--ticker-interval', '1m',
+        '--export', '/bar/foo',
+        '--strategy-list',
+        'DefaultStrategy',
+        'TestStrategy'
+    ]
+
+    args = Arguments(arglist, '').get_parsed_arg()
+
+    configuration = Configuration(args)
+    config = configuration.get_config()
+    assert 'max_open_trades' in config
+    assert 'stake_currency' in config
+    assert 'stake_amount' in config
+    assert 'exchange' in config
+    assert 'pair_whitelist' in config['exchange']
+    assert 'datadir' in config
+    assert log_has(
+        'Using data folder: {} ...'.format(config['datadir']),
+        caplog.record_tuples
+    )
+    assert 'ticker_interval' in config
+    assert log_has('Parameter -i/--ticker-interval detected ...', caplog.record_tuples)
+    assert log_has(
+        'Using ticker_interval: 1m ...',
+        caplog.record_tuples
+    )
+
+    assert 'strategy_list' in config
+    assert log_has('Using strategy list of 2 Strategies', caplog.record_tuples)
+
+    assert 'position_stacking' not in config
+
+    assert 'use_max_market_positions' not in config
+
+    assert 'timerange' not in config
+
+    assert 'export' in config
+    assert log_has(
+        'Parameter --export detected: {} ...'.format(config['export']),
+        caplog.record_tuples
+    )
+
+
+def test_hyperopt_with_arguments(mocker, default_conf, caplog) -> None:
+    mocker.patch('freqtrade.configuration.open', mocker.mock_open(
+        read_data=json.dumps(default_conf)
+    ))
+    arglist = [
+        'hyperopt',
+        '--epochs', '10',
+        '--spaces', 'all',
+    ]
+    args = Arguments(arglist, '').get_parsed_arg()
+
+    configuration = Configuration(args)
+    config = configuration.get_config()
+
+    assert 'epochs' in config
+    assert int(config['epochs']) == 10
+    assert log_has('Parameter --epochs detected ...', caplog.record_tuples)
+    assert log_has('Will run Hyperopt with for 10 epochs ...', caplog.record_tuples)
+
+    assert 'spaces' in config
+    assert config['spaces'] == ['all']
+    assert log_has('Parameter -s/--spaces detected: [\'all\']', caplog.record_tuples)
+
+
+def test_check_exchange(default_conf) -> None:
+    configuration = Configuration(Namespace())
+
+    # Test a valid exchange
+    default_conf.get('exchange').update({'name': 'BITTREX'})
+    assert configuration.check_exchange(default_conf)
+
+    # Test a valid exchange
+    default_conf.get('exchange').update({'name': 'binance'})
+    assert configuration.check_exchange(default_conf)
+
+    # Test a invalid exchange
+    default_conf.get('exchange').update({'name': 'unknown_exchange'})
+    configuration.config = default_conf
+
+    with pytest.raises(
+        OperationalException,
+        match=r'.*Exchange "unknown_exchange" not supported.*'
+    ):
+        configuration.check_exchange(default_conf)
+
+
+def test_cli_verbose_with_params(default_conf, mocker, caplog) -> None:
+    mocker.patch('freqtrade.configuration.open', mocker.mock_open(
+        read_data=json.dumps(default_conf)))
+    # Prevent setting loggers
+    mocker.patch('freqtrade.configuration.set_loggers', MagicMock)
+    arglist = ['-vvv']
+    args = Arguments(arglist, '').get_parsed_arg()
+
+    configuration = Configuration(args)
+    validated_conf = configuration.load_config()
+
+    assert validated_conf.get('verbosity') == 3
+    assert log_has('Verbosity set to 3', caplog.record_tuples)
+
+
+def test_set_loggers() -> None:
+    # Reset Logging to Debug, otherwise this fails randomly as it's set globally
+    logging.getLogger('requests').setLevel(logging.DEBUG)
+    logging.getLogger("urllib3").setLevel(logging.DEBUG)
+    logging.getLogger('ccxt.base.exchange').setLevel(logging.DEBUG)
+    logging.getLogger('telegram').setLevel(logging.DEBUG)
+
+    previous_value1 = logging.getLogger('requests').level
+    previous_value2 = logging.getLogger('ccxt.base.exchange').level
+    previous_value3 = logging.getLogger('telegram').level
+
+    set_loggers()
+
+    value1 = logging.getLogger('requests').level
+    assert previous_value1 is not value1
+    assert value1 is logging.INFO
+
+    value2 = logging.getLogger('ccxt.base.exchange').level
+    assert previous_value2 is not value2
+    assert value2 is logging.INFO
+
+    value3 = logging.getLogger('telegram').level
+    assert previous_value3 is not value3
+    assert value3 is logging.INFO
+
+    set_loggers(log_level=2)
+
+    assert logging.getLogger('requests').level is logging.DEBUG
+    assert logging.getLogger('ccxt.base.exchange').level is logging.INFO
+    assert logging.getLogger('telegram').level is logging.INFO
+
+    set_loggers(log_level=3)
+
+    assert logging.getLogger('requests').level is logging.DEBUG
+    assert logging.getLogger('ccxt.base.exchange').level is logging.DEBUG
+    assert logging.getLogger('telegram').level is logging.INFO
+
+
+def test_validate_default_conf(default_conf) -> None:
+    validate(default_conf, constants.CONF_SCHEMA)

freqtrade/tests/test_dataframe.py

@@ -1,27 +1,32 @@
+# pragma pylint: disable=missing-docstring, C0103
+
 import pandas
 
-import freqtrade.optimize
-from freqtrade import analyze
+from freqtrade.optimize import load_data
+from freqtrade.strategy.resolver import StrategyResolver
 
-_pairs = ['BTC_ETH']
+_pairs = ['ETH/BTC']
 
 
-def load_dataframe_pair(pairs):
-    ld = freqtrade.optimize.load_data(None, ticker_interval=5, pairs=pairs)
+def load_dataframe_pair(pairs, strategy):
+    ld = load_data(None, ticker_interval='5m', pairs=pairs)
     assert isinstance(ld, dict)
     assert isinstance(pairs[0], str)
     dataframe = ld[pairs[0]]
-    dataframe = analyze.analyze_ticker(dataframe)
+
+    dataframe = strategy.analyze_ticker(dataframe, {'pair': pairs[0]})
     return dataframe
 
 
 def test_dataframe_load():
-    dataframe = load_dataframe_pair(_pairs)
+    strategy = StrategyResolver({'strategy': 'DefaultStrategy'}).strategy
+    dataframe = load_dataframe_pair(_pairs, strategy)
     assert isinstance(dataframe, pandas.core.frame.DataFrame)
 
 
 def test_dataframe_columns_exists():
-    dataframe = load_dataframe_pair(_pairs)
+    strategy = StrategyResolver({'strategy': 'DefaultStrategy'}).strategy
+    dataframe = load_dataframe_pair(_pairs, strategy)
     assert 'high' in dataframe.columns
     assert 'low' in dataframe.columns
     assert 'close' in dataframe.columns

freqtrade/tests/test_fiat_convert.py

@@ -1,11 +1,14 @@
-# pragma pylint: disable=missing-docstring, too-many-arguments, too-many-ancestors, C0103
+# pragma pylint: disable=missing-docstring, too-many-arguments, too-many-ancestors,
+# pragma pylint: disable=protected-access, C0103
 
 import time
 from unittest.mock import MagicMock
 
 import pytest
+from requests.exceptions import RequestException
 
 from freqtrade.fiat_convert import CryptoFiat, CryptoToFiatConverter
+from freqtrade.tests.conftest import log_has, patch_coinmarketcap
 
 
 def test_pair_convertion_object():
@@ -36,7 +39,8 @@ def test_pair_convertion_object():
     assert pair_convertion.price == 30000.123
 
 
-def test_fiat_convert_is_supported():
+def test_fiat_convert_is_supported(mocker):
+    patch_coinmarketcap(mocker)
     fiat_convert = CryptoToFiatConverter()
     assert fiat_convert._is_supported_fiat(fiat='USD') is True
     assert fiat_convert._is_supported_fiat(fiat='usd') is True
@@ -44,35 +48,39 @@ def test_fiat_convert_is_supported():
     assert fiat_convert._is_supported_fiat(fiat='ABC') is False
 
 
-def test_fiat_convert_add_pair():
+def test_fiat_convert_add_pair(mocker):
+    patch_coinmarketcap(mocker)
+
     fiat_convert = CryptoToFiatConverter()
 
-    assert len(fiat_convert._pairs) == 0
+    pair_len = len(fiat_convert._pairs)
+    assert pair_len == 0
 
     fiat_convert._add_pair(crypto_symbol='btc', fiat_symbol='usd', price=12345.0)
-    assert len(fiat_convert._pairs) == 1
+    pair_len = len(fiat_convert._pairs)
+    assert pair_len == 1
     assert fiat_convert._pairs[0].crypto_symbol == 'BTC'
     assert fiat_convert._pairs[0].fiat_symbol == 'USD'
     assert fiat_convert._pairs[0].price == 12345.0
 
     fiat_convert._add_pair(crypto_symbol='btc', fiat_symbol='Eur', price=13000.2)
-    assert len(fiat_convert._pairs) == 2
+    pair_len = len(fiat_convert._pairs)
+    assert pair_len == 2
     assert fiat_convert._pairs[1].crypto_symbol == 'BTC'
     assert fiat_convert._pairs[1].fiat_symbol == 'EUR'
     assert fiat_convert._pairs[1].price == 13000.2
 
 
 def test_fiat_convert_find_price(mocker):
-    api_mock = MagicMock(return_value={
-        'price_usd': 12345.0,
-        'price_eur': 13000.2
-    })
-    mocker.patch('freqtrade.fiat_convert.Pymarketcap.ticker', api_mock)
+    patch_coinmarketcap(mocker)
+
     fiat_convert = CryptoToFiatConverter()
 
     with pytest.raises(ValueError, match=r'The fiat ABC is not supported.'):
         fiat_convert._find_price(crypto_symbol='BTC', fiat_symbol='ABC')
 
+    assert fiat_convert.get_price(crypto_symbol='XRP', fiat_symbol='USD') == 0.0
+
     mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=12345.0)
     assert fiat_convert.get_price(crypto_symbol='BTC', fiat_symbol='USD') == 12345.0
     assert fiat_convert.get_price(crypto_symbol='btc', fiat_symbol='usd') == 12345.0
@@ -81,12 +89,17 @@ def test_fiat_convert_find_price(mocker):
     assert fiat_convert.get_price(crypto_symbol='BTC', fiat_symbol='EUR') == 13000.2
 
 
+def test_fiat_convert_unsupported_crypto(mocker, caplog):
+    mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._cryptomap', return_value=[])
+    patch_coinmarketcap(mocker)
+    fiat_convert = CryptoToFiatConverter()
+    assert fiat_convert._find_price(crypto_symbol='CRYPTO_123', fiat_symbol='EUR') == 0.0
+    assert log_has('unsupported crypto-symbol CRYPTO_123 - returning 0.0', caplog.record_tuples)
+
+
 def test_fiat_convert_get_price(mocker):
-    api_mock = MagicMock(return_value={
-        'price_usd': 28000.0,
-        'price_eur': 15000.0
-    })
-    mocker.patch('freqtrade.fiat_convert.Pymarketcap.ticker', api_mock)
+    patch_coinmarketcap(mocker)
+
     mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=28000.0)
 
     fiat_convert = CryptoToFiatConverter()
@@ -95,7 +108,8 @@ def test_fiat_convert_get_price(mocker):
         fiat_convert.get_price(crypto_symbol='BTC', fiat_symbol='US Dollar')
 
     # Check the value return by the method
-    assert len(fiat_convert._pairs) == 0
+    pair_len = len(fiat_convert._pairs)
+    assert pair_len == 0
     assert fiat_convert.get_price(crypto_symbol='BTC', fiat_symbol='USD') == 28000.0
     assert fiat_convert._pairs[0].crypto_symbol == 'BTC'
     assert fiat_convert._pairs[0].fiat_symbol == 'USD'
@@ -116,10 +130,92 @@ def test_fiat_convert_get_price(mocker):
     assert fiat_convert._pairs[0]._expiration is not expiration
 
 
-def test_fiat_convert_without_network(mocker):
-    pymarketcap = MagicMock(side_effect=ImportError('Oh boy, you have no network!'))
-    mocker.patch('freqtrade.fiat_convert.Pymarketcap', pymarketcap)
+def test_fiat_convert_same_currencies(mocker):
+    patch_coinmarketcap(mocker)
+    fiat_convert = CryptoToFiatConverter()
+
+    assert fiat_convert.get_price(crypto_symbol='USD', fiat_symbol='USD') == 1.0
+
+
+def test_fiat_convert_two_FIAT(mocker):
+    patch_coinmarketcap(mocker)
+    fiat_convert = CryptoToFiatConverter()
+
+    assert fiat_convert.get_price(crypto_symbol='USD', fiat_symbol='EUR') == 0.0
+
+
+def test_loadcryptomap(mocker):
+    patch_coinmarketcap(mocker)
 
     fiat_convert = CryptoToFiatConverter()
+    assert len(fiat_convert._cryptomap) == 2
+
+    assert fiat_convert._cryptomap["BTC"] == "1"
+
+
+def test_fiat_init_network_exception(mocker):
+    # Because CryptoToFiatConverter is a Singleton we reset the listings
+    listmock = MagicMock(side_effect=RequestException)
+    mocker.patch.multiple(
+        'freqtrade.fiat_convert.Market',
+        listings=listmock,
+    )
+    # with pytest.raises(RequestEsxception):
+    fiat_convert = CryptoToFiatConverter()
+    fiat_convert._cryptomap = {}
+    fiat_convert._load_cryptomap()
+
+    length_cryptomap = len(fiat_convert._cryptomap)
+    assert length_cryptomap == 0
+
+
+def test_fiat_convert_without_network(mocker):
+    # Because CryptoToFiatConverter is a Singleton we reset the value of _coinmarketcap
+    patch_coinmarketcap(mocker)
+
+    fiat_convert = CryptoToFiatConverter()
+
+    cmc_temp = CryptoToFiatConverter._coinmarketcap
+    CryptoToFiatConverter._coinmarketcap = None
+
     assert fiat_convert._coinmarketcap is None
     assert fiat_convert._find_price(crypto_symbol='BTC', fiat_symbol='USD') == 0.0
+    CryptoToFiatConverter._coinmarketcap = cmc_temp
+
+
+def test_fiat_invalid_response(mocker, caplog):
+    # Because CryptoToFiatConverter is a Singleton we reset the listings
+    listmock = MagicMock(return_value="{'novalidjson':DEADBEEFf}")
+    mocker.patch.multiple(
+        'freqtrade.fiat_convert.Market',
+        listings=listmock,
+    )
+    # with pytest.raises(RequestEsxception):
+    fiat_convert = CryptoToFiatConverter()
+    fiat_convert._cryptomap = {}
+    fiat_convert._load_cryptomap()
+
+    length_cryptomap = len(fiat_convert._cryptomap)
+    assert length_cryptomap == 0
+    assert log_has('Could not load FIAT Cryptocurrency map for the following problem: TypeError',
+                   caplog.record_tuples)
+
+
+def test_convert_amount(mocker):
+    patch_coinmarketcap(mocker)
+    mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter.get_price', return_value=12345.0)
+
+    fiat_convert = CryptoToFiatConverter()
+    result = fiat_convert.convert_amount(
+        crypto_amount=1.23,
+        crypto_symbol="BTC",
+        fiat_symbol="USD"
+    )
+    assert result == 15184.35
+
+    result = fiat_convert.convert_amount(
+        crypto_amount=1.23,
+        crypto_symbol="BTC",
+        fiat_symbol="BTC"
+    )
+    assert result == 1.23

freqtrade/tests/test_indicator_helpers.py

@@ -0,0 +1,15 @@
+# pragma pylint: disable=missing-docstring
+
+import pandas as pd
+
+from freqtrade.indicator_helpers import went_down, went_up
+
+
+def test_went_up():
+    series = pd.Series([1, 2, 3, 1])
+    assert went_up(series).equals(pd.Series([False, True, True, False]))
+
+
+def test_went_down():
+    series = pd.Series([1, 2, 3, 1])
+    assert went_down(series).equals(pd.Series([False, False, False, True]))

freqtrade/tests/test_main.py

@@ -1,676 +1,171 @@
-# pragma pylint: disable=missing-docstring,C0103
-import copy
-import logging
+# pragma pylint: disable=missing-docstring
+
+from copy import deepcopy
 from unittest.mock import MagicMock
 
-import arrow
 import pytest
-import requests
-from sqlalchemy import create_engine
 
-import freqtrade.main as main
-from freqtrade import DependencyException, OperationalException
-from freqtrade.analyze import SignalType
-from freqtrade.exchange import Exchanges
-from freqtrade.main import (_process, check_handle_timedout, create_trade,
-                            execute_sell, get_target_bid, handle_trade, init)
-from freqtrade.misc import State, get_state
-from freqtrade.persistence import Trade
+from freqtrade import OperationalException
+from freqtrade.arguments import Arguments
+from freqtrade.freqtradebot import FreqtradeBot
+from freqtrade.main import main, reconfigure
+from freqtrade.state import State
+from freqtrade.tests.conftest import log_has, patch_exchange
 
 
-def test_parse_args_backtesting(mocker):
-    """ Test that main() can start backtesting or hyperopt.
-        and also ensure we can pass some specific arguments
-        argument parsing is done in test_misc.py """
-    backtesting_mock = mocker.patch(
-        'freqtrade.optimize.backtesting.start', MagicMock())
-    with pytest.raises(SystemExit, match=r'0'):
-        main.main(['backtesting'])
+def test_parse_args_backtesting(mocker) -> None:
+    """
+    Test that main() can start backtesting and also ensure we can pass some specific arguments
+    further argument parsing is done in test_arguments.py
+    """
+    backtesting_mock = mocker.patch('freqtrade.optimize.backtesting.start', MagicMock())
+    main(['backtesting'])
     assert backtesting_mock.call_count == 1
     call_args = backtesting_mock.call_args[0][0]
     assert call_args.config == 'config.json'
     assert call_args.live is False
-    assert call_args.loglevel == 20
+    assert call_args.loglevel == 0
     assert call_args.subparser == 'backtesting'
     assert call_args.func is not None
-    assert call_args.ticker_interval == 5
+    assert call_args.ticker_interval is None
 
 
-def test_main_start_hyperopt(mocker):
-    hyperopt_mock = mocker.patch(
-        'freqtrade.optimize.hyperopt.start', MagicMock())
-    with pytest.raises(SystemExit, match=r'0'):
-        main.main(['hyperopt'])
+def test_main_start_hyperopt(mocker) -> None:
+    hyperopt_mock = mocker.patch('freqtrade.optimize.hyperopt.start', MagicMock())
+    main(['hyperopt'])
     assert hyperopt_mock.call_count == 1
     call_args = hyperopt_mock.call_args[0][0]
     assert call_args.config == 'config.json'
-    assert call_args.loglevel == 20
+    assert call_args.loglevel == 0
     assert call_args.subparser == 'hyperopt'
     assert call_args.func is not None
 
 
-def test_process_trade_creation(default_conf, ticker, limit_buy_order, health, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          get_wallet_health=health,
-                          buy=MagicMock(return_value='mocked_limit_buy'),
-                          get_order=MagicMock(return_value=limit_buy_order))
-    init(default_conf, create_engine('sqlite://'))
-
-    trades = Trade.query.filter(Trade.is_open.is_(True)).all()
-    assert not trades
-
-    result = _process()
-    assert result is True
-
-    trades = Trade.query.filter(Trade.is_open.is_(True)).all()
-    assert len(trades) == 1
-    trade = trades[0]
-    assert trade is not None
-    assert trade.stake_amount == default_conf['stake_amount']
-    assert trade.is_open
-    assert trade.open_date is not None
-    assert trade.exchange == Exchanges.BITTREX.name
-    assert trade.open_rate == 0.00001099
-    assert trade.amount == 90.99181073703367
-
-
-def test_process_exchange_failures(default_conf, ticker, health, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
-    sleep_mock = mocker.patch('time.sleep', side_effect=lambda _: None)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          get_wallet_health=health,
-                          buy=MagicMock(side_effect=requests.exceptions.RequestException))
-    init(default_conf, create_engine('sqlite://'))
-    result = _process()
-    assert result is False
-    assert sleep_mock.has_calls()
-
-
-def test_process_operational_exception(default_conf, ticker, health, mocker):
-    msg_mock = MagicMock()
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=msg_mock)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          get_wallet_health=health,
-                          buy=MagicMock(side_effect=OperationalException))
-    init(default_conf, create_engine('sqlite://'))
-    assert get_state() == State.RUNNING
-
-    result = _process()
-    assert result is False
-    assert get_state() == State.STOPPED
-    assert 'OperationalException' in msg_mock.call_args_list[-1][0][0]
-
-
-def test_process_trade_handling(default_conf, ticker, limit_buy_order, health, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch('freqtrade.main.get_signal',
-                 side_effect=lambda *args: False if args[1] == SignalType.SELL else True)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          get_wallet_health=health,
-                          buy=MagicMock(return_value='mocked_limit_buy'),
-                          get_order=MagicMock(return_value=limit_buy_order))
-    init(default_conf, create_engine('sqlite://'))
-
-    trades = Trade.query.filter(Trade.is_open.is_(True)).all()
-    assert not trades
-    result = _process()
-    assert result is True
-    trades = Trade.query.filter(Trade.is_open.is_(True)).all()
-    assert len(trades) == 1
-
-    result = _process()
-    assert result is False
-
-
-def test_create_trade(default_conf, ticker, limit_buy_order, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          buy=MagicMock(return_value='mocked_limit_buy'))
-    # Save state of current whitelist
-    whitelist = copy.deepcopy(default_conf['exchange']['pair_whitelist'])
-
-    init(default_conf, create_engine('sqlite://'))
-    create_trade(0.001)
-
-    trade = Trade.query.first()
-    assert trade is not None
-    assert trade.stake_amount == 0.001
-    assert trade.is_open
-    assert trade.open_date is not None
-    assert trade.exchange == Exchanges.BITTREX.name
-
-    # Simulate fulfilled LIMIT_BUY order for trade
-    trade.update(limit_buy_order)
-
-    assert trade.open_rate == 0.00001099
-    assert trade.amount == 90.99181073
-
-    assert whitelist == default_conf['exchange']['pair_whitelist']
-
-
-def test_create_trade_minimal_amount(default_conf, ticker, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
-    buy_mock = mocker.patch(
-        'freqtrade.main.exchange.buy', MagicMock(return_value='mocked_limit_buy')
+def test_main_fatal_exception(mocker, default_conf, caplog) -> None:
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        'freqtrade.freqtradebot.FreqtradeBot',
+        _init_modules=MagicMock(),
+        worker=MagicMock(side_effect=Exception),
+        cleanup=MagicMock(),
     )
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker)
-    init(default_conf, create_engine('sqlite://'))
-    min_stake_amount = 0.0005
-    create_trade(min_stake_amount)
-    rate, amount = buy_mock.call_args[0][1], buy_mock.call_args[0][2]
-    assert rate * amount >= min_stake_amount
+    mocker.patch(
+        'freqtrade.configuration.Configuration._load_config_file',
+        lambda *args, **kwargs: default_conf
+    )
+    mocker.patch('freqtrade.freqtradebot.RPCManager', MagicMock())
+
+    args = ['-c', 'config.json.example']
+
+    # Test Main + the KeyboardInterrupt exception
+    with pytest.raises(SystemExit):
+        main(args)
+    assert log_has('Using config: config.json.example ...', caplog.record_tuples)
+    assert log_has('Fatal exception!', caplog.record_tuples)
 
 
-def test_create_trade_no_stake_amount(default_conf, ticker, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          buy=MagicMock(return_value='mocked_limit_buy'),
-                          get_balance=MagicMock(return_value=default_conf['stake_amount'] * 0.5))
-    with pytest.raises(DependencyException, match=r'.*stake amount.*'):
-        create_trade(default_conf['stake_amount'])
+def test_main_keyboard_interrupt(mocker, default_conf, caplog) -> None:
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        'freqtrade.freqtradebot.FreqtradeBot',
+        _init_modules=MagicMock(),
+        worker=MagicMock(side_effect=KeyboardInterrupt),
+        cleanup=MagicMock(),
+    )
+    mocker.patch(
+        'freqtrade.configuration.Configuration._load_config_file',
+        lambda *args, **kwargs: default_conf
+    )
+    mocker.patch('freqtrade.freqtradebot.RPCManager', MagicMock())
+
+    args = ['-c', 'config.json.example']
+
+    # Test Main + the KeyboardInterrupt exception
+    with pytest.raises(SystemExit):
+        main(args)
+    assert log_has('Using config: config.json.example ...', caplog.record_tuples)
+    assert log_has('SIGINT received, aborting ...', caplog.record_tuples)
 
 
-def test_create_trade_no_pairs(default_conf, ticker, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          buy=MagicMock(return_value='mocked_limit_buy'))
+def test_main_operational_exception(mocker, default_conf, caplog) -> None:
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        'freqtrade.freqtradebot.FreqtradeBot',
+        _init_modules=MagicMock(),
+        worker=MagicMock(side_effect=OperationalException('Oh snap!')),
+        cleanup=MagicMock(),
+    )
+    mocker.patch(
+        'freqtrade.configuration.Configuration._load_config_file',
+        lambda *args, **kwargs: default_conf
+    )
+    mocker.patch('freqtrade.freqtradebot.RPCManager', MagicMock())
 
-    with pytest.raises(DependencyException, match=r'.*No pair in whitelist.*'):
-        conf = copy.deepcopy(default_conf)
-        conf['exchange']['pair_whitelist'] = []
-        mocker.patch.dict('freqtrade.main._CONF', conf)
-        create_trade(default_conf['stake_amount'])
+    args = ['-c', 'config.json.example']
+
+    # Test Main + the KeyboardInterrupt exception
+    with pytest.raises(SystemExit):
+        main(args)
+    assert log_has('Using config: config.json.example ...', caplog.record_tuples)
+    assert log_has('Oh snap!', caplog.record_tuples)
 
 
-def test_create_trade_no_pairs_after_blacklist(default_conf, ticker, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          buy=MagicMock(return_value='mocked_limit_buy'))
+def test_main_reload_conf(mocker, default_conf, caplog) -> None:
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        'freqtrade.freqtradebot.FreqtradeBot',
+        _init_modules=MagicMock(),
+        worker=MagicMock(return_value=State.RELOAD_CONF),
+        cleanup=MagicMock(),
+    )
+    mocker.patch(
+        'freqtrade.configuration.Configuration._load_config_file',
+        lambda *args, **kwargs: default_conf
+    )
+    mocker.patch('freqtrade.freqtradebot.RPCManager', MagicMock())
 
-    with pytest.raises(DependencyException, match=r'.*No pair in whitelist.*'):
-        conf = copy.deepcopy(default_conf)
-        conf['exchange']['pair_whitelist'] = ["BTC_ETH"]
-        conf['exchange']['pair_blacklist'] = ["BTC_ETH"]
-        mocker.patch.dict('freqtrade.main._CONF', conf)
-        create_trade(default_conf['stake_amount'])
-
-
-def test_handle_trade(default_conf, limit_buy_order, limit_sell_order, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=MagicMock(return_value={
-                              'bid': 0.00001172,
-                              'ask': 0.00001173,
-                              'last': 0.00001172
-                          }),
-                          buy=MagicMock(return_value='mocked_limit_buy'),
-                          sell=MagicMock(return_value='mocked_limit_sell'))
-    mocker.patch.multiple('freqtrade.fiat_convert.Pymarketcap',
-                          ticker=MagicMock(return_value={'price_usd': 15000.0}),
-                          _cache_symbols=MagicMock(return_value={'BTC': 1}))
-    init(default_conf, create_engine('sqlite://'))
-    create_trade(0.001)
-
-    trade = Trade.query.first()
-    assert trade
-
-    trade.update(limit_buy_order)
-    assert trade.is_open is True
-
-    handle_trade(trade)
-    assert trade.open_order_id == 'mocked_limit_sell'
-
-    # Simulate fulfilled LIMIT_SELL order for trade
-    trade.update(limit_sell_order)
-
-    assert trade.close_rate == 0.00001173
-    assert trade.close_profit == 0.06201057
-    assert trade.calc_profit() == 0.00006217
-    assert trade.close_date is not None
-
-
-def test_handle_trade_roi(default_conf, ticker, mocker, caplog):
-    default_conf.update({'experimental': {'use_sell_signal': True}})
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          buy=MagicMock(return_value='mocked_limit_buy'))
-    mocker.patch('freqtrade.main.min_roi_reached', return_value=True)
-
-    init(default_conf, create_engine('sqlite://'))
-    create_trade(0.001)
-
-    trade = Trade.query.first()
-    trade.is_open = True
-
-    # FIX: sniffing logs, suggest handle_trade should not execute_sell
-    #      instead that responsibility should be moved out of handle_trade(),
-    #      we might just want to check if we are in a sell condition without
-    #      executing
-    # if ROI is reached we must sell
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: False)
-    assert handle_trade(trade)
-    assert ('freqtrade', logging.DEBUG, 'Executing sell due to ROI ...') in caplog.record_tuples
-    # if ROI is reached we must sell even if sell-signal is not signalled
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
-    assert handle_trade(trade)
-    assert ('freqtrade', logging.DEBUG, 'Executing sell due to ROI ...') in caplog.record_tuples
-
-
-def test_handle_trade_experimental(default_conf, ticker, mocker, caplog):
-    default_conf.update({'experimental': {'use_sell_signal': True}})
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          buy=MagicMock(return_value='mocked_limit_buy'))
-    mocker.patch('freqtrade.main.min_roi_reached', return_value=False)
-
-    init(default_conf, create_engine('sqlite://'))
-    create_trade(0.001)
-
-    trade = Trade.query.first()
-    trade.is_open = True
-
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: False)
-    value_returned = handle_trade(trade)
-    assert ('freqtrade', logging.DEBUG, 'Checking sell_signal ...') in caplog.record_tuples
-    assert value_returned is False
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
-    assert handle_trade(trade)
-    s = 'Executing sell due to sell signal ...'
-    assert ('freqtrade', logging.DEBUG, s) in caplog.record_tuples
-
-
-def test_close_trade(default_conf, ticker, limit_buy_order, limit_sell_order, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          buy=MagicMock(return_value='mocked_limit_buy'))
-
-    # Create trade and sell it
-    init(default_conf, create_engine('sqlite://'))
-    create_trade(0.001)
-
-    trade = Trade.query.first()
-    assert trade
-
-    trade.update(limit_buy_order)
-    trade.update(limit_sell_order)
-    assert trade.is_open is False
-
-    with pytest.raises(ValueError, match=r'.*closed trade.*'):
-        handle_trade(trade)
-
-
-def test_check_handle_timedout_buy(default_conf, ticker, limit_buy_order_old, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    cancel_order_mock = MagicMock()
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          get_order=MagicMock(return_value=limit_buy_order_old),
-                          cancel_order=cancel_order_mock)
-    init(default_conf, create_engine('sqlite://'))
-
-    trade_buy = Trade(
-        pair='BTC_ETH',
-        open_rate=0.00001099,
-        exchange='BITTREX',
-        open_order_id='123456789',
-        amount=90.99181073,
-        fee=0.0,
-        stake_amount=1,
-        open_date=arrow.utcnow().shift(minutes=-601).datetime,
-        is_open=True
+    # Raise exception as side effect to avoid endless loop
+    reconfigure_mock = mocker.patch(
+        'freqtrade.main.reconfigure', MagicMock(side_effect=Exception)
     )
 
-    Trade.session.add(trade_buy)
+    with pytest.raises(SystemExit):
+        main(['-c', 'config.json.example'])
 
-    # check it does cancel buy orders over the time limit
-    check_handle_timedout(600)
-    assert cancel_order_mock.call_count == 1
-    trades = Trade.query.filter(Trade.open_order_id.is_(trade_buy.open_order_id)).all()
-    assert len(trades) == 0
+    assert reconfigure_mock.call_count == 1
+    assert log_has('Using config: config.json.example ...', caplog.record_tuples)
 
 
-def test_check_handle_timedout_sell(default_conf, ticker, limit_sell_order_old, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    cancel_order_mock = MagicMock()
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          get_order=MagicMock(return_value=limit_sell_order_old),
-                          cancel_order=cancel_order_mock)
-    init(default_conf, create_engine('sqlite://'))
+def test_reconfigure(mocker, default_conf) -> None:
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        'freqtrade.freqtradebot.FreqtradeBot',
+        _init_modules=MagicMock(),
+        worker=MagicMock(side_effect=OperationalException('Oh snap!')),
+        cleanup=MagicMock(),
+    )
+    mocker.patch(
+        'freqtrade.configuration.Configuration._load_config_file',
+        lambda *args, **kwargs: default_conf
+    )
+    mocker.patch('freqtrade.freqtradebot.RPCManager', MagicMock())
 
-    trade_sell = Trade(
-        pair='BTC_ETH',
-        open_rate=0.00001099,
-        exchange='BITTREX',
-        open_order_id='123456789',
-        amount=90.99181073,
-        fee=0.0,
-        stake_amount=1,
-        open_date=arrow.utcnow().shift(hours=-5).datetime,
-        close_date=arrow.utcnow().shift(minutes=-601).datetime,
-        is_open=False
+    freqtrade = FreqtradeBot(default_conf)
+
+    # Renew mock to return modified data
+    conf = deepcopy(default_conf)
+    conf['stake_amount'] += 1
+    mocker.patch(
+        'freqtrade.configuration.Configuration._load_config_file',
+        lambda *args, **kwargs: conf
     )
 
-    Trade.session.add(trade_sell)
-
-    # check it does cancel sell orders over the time limit
-    check_handle_timedout(600)
-    assert cancel_order_mock.call_count == 1
-    assert trade_sell.is_open is True
-
-
-def test_check_handle_timedout_partial(default_conf, ticker, limit_buy_order_old_partial,
-                                       mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    cancel_order_mock = MagicMock()
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          get_order=MagicMock(return_value=limit_buy_order_old_partial),
-                          cancel_order=cancel_order_mock)
-    init(default_conf, create_engine('sqlite://'))
-
-    trade_buy = Trade(
-        pair='BTC_ETH',
-        open_rate=0.00001099,
-        exchange='BITTREX',
-        open_order_id='123456789',
-        amount=90.99181073,
-        fee=0.0,
-        stake_amount=1,
-        open_date=arrow.utcnow().shift(minutes=-601).datetime,
-        is_open=True
+    # reconfigure should return a new instance
+    freqtrade2 = reconfigure(
+        freqtrade,
+        Arguments(['-c', 'config.json.example'], '').get_parsed_arg()
     )
 
-    Trade.session.add(trade_buy)
-
-    # check it does cancel buy orders over the time limit
-    # note this is for a partially-complete buy order
-    check_handle_timedout(600)
-    assert cancel_order_mock.call_count == 1
-    trades = Trade.query.filter(Trade.open_order_id.is_(trade_buy.open_order_id)).all()
-    assert len(trades) == 1
-    assert trades[0].amount == 23.0
-    assert trades[0].stake_amount == trade_buy.open_rate * trades[0].amount
-
-
-def test_balance_fully_ask_side(mocker):
-    mocker.patch.dict('freqtrade.main._CONF', {'bid_strategy': {'ask_last_balance': 0.0}})
-    assert get_target_bid({'ask': 20, 'last': 10}) == 20
-
-
-def test_balance_fully_last_side(mocker):
-    mocker.patch.dict('freqtrade.main._CONF', {'bid_strategy': {'ask_last_balance': 1.0}})
-    assert get_target_bid({'ask': 20, 'last': 10}) == 10
-
-
-def test_balance_bigger_last_ask(mocker):
-    mocker.patch.dict('freqtrade.main._CONF', {'bid_strategy': {'ask_last_balance': 1.0}})
-    assert get_target_bid({'ask': 5, 'last': 10}) == 5
-
-
-def test_execute_sell_up(default_conf, ticker, ticker_sell_up, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
-    mocker.patch('freqtrade.rpc.init', MagicMock())
-    rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker)
-    mocker.patch.multiple('freqtrade.fiat_convert.Pymarketcap',
-                          ticker=MagicMock(return_value={'price_usd': 15000.0}),
-                          _cache_symbols=MagicMock(return_value={'BTC': 1}))
-    init(default_conf, create_engine('sqlite://'))
-
-    # Create some test data
-    create_trade(0.001)
-
-    trade = Trade.query.first()
-    assert trade
-
-    # Increase the price and sell it
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker_sell_up)
-
-    execute_sell(trade=trade, limit=ticker_sell_up()['bid'])
-
-    assert rpc_mock.call_count == 2
-    assert 'Selling [BTC/ETH]' in rpc_mock.call_args_list[-1][0][0]
-    assert '0.00001172' in rpc_mock.call_args_list[-1][0][0]
-    assert 'profit: 6.11%, 0.00006126' in rpc_mock.call_args_list[-1][0][0]
-    assert '0.919 USD' in rpc_mock.call_args_list[-1][0][0]
-
-
-def test_execute_sell_down(default_conf, ticker, ticker_sell_down, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
-    mocker.patch('freqtrade.rpc.init', MagicMock())
-    rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock(),
-                          send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker)
-    mocker.patch.multiple('freqtrade.fiat_convert.Pymarketcap',
-                          ticker=MagicMock(return_value={'price_usd': 15000.0}),
-                          _cache_symbols=MagicMock(return_value={'BTC': 1}))
-    init(default_conf, create_engine('sqlite://'))
-
-    # Create some test data
-    create_trade(0.001)
-
-    trade = Trade.query.first()
-    assert trade
-
-    # Decrease the price and sell it
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker_sell_down)
-
-    execute_sell(trade=trade, limit=ticker_sell_down()['bid'])
-
-    assert rpc_mock.call_count == 2
-    assert 'Selling [BTC/ETH]' in rpc_mock.call_args_list[-1][0][0]
-    assert '0.00001044' in rpc_mock.call_args_list[-1][0][0]
-    assert 'loss: -5.48%, -0.00005492' in rpc_mock.call_args_list[-1][0][0]
-    assert '-0.824 USD' in rpc_mock.call_args_list[-1][0][0]
-
-
-def test_execute_sell_without_conf(default_conf, ticker, ticker_sell_up, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
-    mocker.patch('freqtrade.rpc.init', MagicMock())
-    rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker)
-    init(default_conf, create_engine('sqlite://'))
-
-    # Create some test data
-    create_trade(0.001)
-
-    trade = Trade.query.first()
-    assert trade
-
-    # Increase the price and sell it
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker_sell_up)
-    mocker.patch('freqtrade.main._CONF', {})
-
-    execute_sell(trade=trade, limit=ticker_sell_up()['bid'])
-
-    assert rpc_mock.call_count == 2
-    assert 'Selling [BTC/ETH]' in rpc_mock.call_args_list[-1][0][0]
-    assert '0.00001172' in rpc_mock.call_args_list[-1][0][0]
-    assert '(profit: 6.11%, 0.00006126)' in rpc_mock.call_args_list[-1][0][0]
-    assert 'USD' not in rpc_mock.call_args_list[-1][0][0]
-
-
-def test_sell_profit_only_enable_profit(default_conf, limit_buy_order, mocker):
-    default_conf['experimental'] = {
-        'use_sell_signal': True,
-        'sell_profit_only': True,
-    }
-
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.min_roi_reached', return_value=False)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=MagicMock(return_value={
-                              'bid': 0.00002172,
-                              'ask': 0.00002173,
-                              'last': 0.00002172
-                          }),
-                          buy=MagicMock(return_value='mocked_limit_buy'))
-
-    init(default_conf, create_engine('sqlite://'))
-    create_trade(0.001)
-
-    trade = Trade.query.first()
-    trade.update(limit_buy_order)
-    assert handle_trade(trade) is True
-
-
-def test_sell_profit_only_disable_profit(default_conf, limit_buy_order, mocker):
-    default_conf['experimental'] = {
-        'use_sell_signal': True,
-        'sell_profit_only': False,
-    }
-
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.min_roi_reached', return_value=False)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=MagicMock(return_value={
-                              'bid': 0.00002172,
-                              'ask': 0.00002173,
-                              'last': 0.00002172
-                          }),
-                          buy=MagicMock(return_value='mocked_limit_buy'))
-
-    init(default_conf, create_engine('sqlite://'))
-    create_trade(0.001)
-
-    trade = Trade.query.first()
-    trade.update(limit_buy_order)
-    assert handle_trade(trade) is True
-
-
-def test_sell_profit_only_enable_loss(default_conf, limit_buy_order, mocker):
-    default_conf['experimental'] = {
-        'use_sell_signal': True,
-        'sell_profit_only': True,
-    }
-
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.min_roi_reached', return_value=False)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=MagicMock(return_value={
-                              'bid': 0.00000172,
-                              'ask': 0.00000173,
-                              'last': 0.00000172
-                          }),
-                          buy=MagicMock(return_value='mocked_limit_buy'))
-
-    init(default_conf, create_engine('sqlite://'))
-    create_trade(0.001)
-
-    trade = Trade.query.first()
-    trade.update(limit_buy_order)
-    assert handle_trade(trade) is False
-
-
-def test_sell_profit_only_disable_loss(default_conf, limit_buy_order, mocker):
-    default_conf['experimental'] = {
-        'use_sell_signal': True,
-        'sell_profit_only': False,
-    }
-
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.min_roi_reached', return_value=False)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=MagicMock(return_value={
-                              'bid': 0.00000172,
-                              'ask': 0.00000173,
-                              'last': 0.00000172
-                          }),
-                          buy=MagicMock(return_value='mocked_limit_buy'))
-
-    init(default_conf, create_engine('sqlite://'))
-    create_trade(0.001)
-
-    trade = Trade.query.first()
-    trade.update(limit_buy_order)
-    assert handle_trade(trade) is True
+    # Verify we have a new instance with the new config
+    assert freqtrade is not freqtrade2
+    assert freqtrade.config['stake_amount'] + 1 == freqtrade2.config['stake_amount']

freqtrade/tests/test_misc.py

@@ -1,164 +1,70 @@
 # pragma pylint: disable=missing-docstring,C0103
-import argparse
-import json
-import time
-from copy import deepcopy
 
-import pytest
-from jsonschema import ValidationError
+import datetime
+from unittest.mock import MagicMock
 
-from freqtrade.misc import (common_args_parser, load_config, parse_args,
-                            throttle)
+from freqtrade.exchange.exchange_helpers import parse_ticker_dataframe
+from freqtrade.misc import (common_datearray, datesarray_to_datetimearray,
+                            file_dump_json, format_ms_time, shorten_date)
+from freqtrade.optimize.__init__ import load_tickerdata_file
+from freqtrade.strategy.default_strategy import DefaultStrategy
 
 
-def test_throttle():
-
-    def func():
-        return 42
-
-    start = time.time()
-    result = throttle(func, min_secs=0.1)
-    end = time.time()
-
-    assert result == 42
-    assert end - start > 0.1
-
-    result = throttle(func, min_secs=-1)
-    assert result == 42
+def test_shorten_date() -> None:
+    str_data = '1 day, 2 hours, 3 minutes, 4 seconds ago'
+    str_shorten_data = '1 d, 2 h, 3 min, 4 sec ago'
+    assert shorten_date(str_data) == str_shorten_data
 
 
-def test_throttle_with_assets():
+def test_datesarray_to_datetimearray(ticker_history):
+    dataframes = parse_ticker_dataframe(ticker_history)
+    dates = datesarray_to_datetimearray(dataframes['date'])
 
-    def func(nb_assets=-1):
-        return nb_assets
+    assert isinstance(dates[0], datetime.datetime)
+    assert dates[0].year == 2017
+    assert dates[0].month == 11
+    assert dates[0].day == 26
+    assert dates[0].hour == 8
+    assert dates[0].minute == 50
 
-    result = throttle(func, min_secs=0.1, nb_assets=666)
-    assert result == 666
-
-    result = throttle(func, min_secs=0.1)
-    assert result == -1
+    date_len = len(dates)
+    assert date_len == 2
 
 
-# Parse common command-line-arguments. Used for all tools
+def test_common_datearray(default_conf) -> None:
+    strategy = DefaultStrategy(default_conf)
+    tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
+    tickerlist = {'UNITTEST/BTC': tick}
+    dataframes = strategy.tickerdata_to_dataframe(tickerlist)
 
-def test_parse_args_none():
-    args = common_args_parser('')
-    assert isinstance(args, argparse.ArgumentParser)
+    dates = common_datearray(dataframes)
+
+    assert dates.size == dataframes['UNITTEST/BTC']['date'].size
+    assert dates[0] == dataframes['UNITTEST/BTC']['date'][0]
+    assert dates[-1] == dataframes['UNITTEST/BTC']['date'][-1]
 
 
-def test_parse_args_defaults():
-    args = parse_args([], '')
-    assert args.config == 'config.json'
-    assert args.dynamic_whitelist is None
-    assert args.loglevel == 20
+def test_file_dump_json(mocker) -> None:
+    file_open = mocker.patch('freqtrade.misc.open', MagicMock())
+    json_dump = mocker.patch('json.dump', MagicMock())
+    file_dump_json('somefile', [1, 2, 3])
+    assert file_open.call_count == 1
+    assert json_dump.call_count == 1
+    file_open = mocker.patch('freqtrade.misc.gzip.open', MagicMock())
+    json_dump = mocker.patch('json.dump', MagicMock())
+    file_dump_json('somefile', [1, 2, 3], True)
+    assert file_open.call_count == 1
+    assert json_dump.call_count == 1
 
 
-def test_parse_args_config():
-    args = parse_args(['-c', '/dev/null'], '')
-    assert args.config == '/dev/null'
-
-    args = parse_args(['--config', '/dev/null'], '')
-    assert args.config == '/dev/null'
-
-
-def test_parse_args_verbose():
-    args = parse_args(['-v'], '')
-    assert args.loglevel == 10
-
-    args = parse_args(['--verbose'], '')
-    assert args.loglevel == 10
-
-
-def test_parse_args_version():
-    with pytest.raises(SystemExit, match=r'0'):
-        parse_args(['--version'], '')
-
-
-def test_parse_args_invalid():
-    with pytest.raises(SystemExit, match=r'2'):
-        parse_args(['-c'], '')
-
-
-# Parse command-line-arguments
-# used for main, backtesting and hyperopt
-
-
-def test_parse_args_dynamic_whitelist():
-    args = parse_args(['--dynamic-whitelist'], '')
-    assert args.dynamic_whitelist == 20
-
-
-def test_parse_args_dynamic_whitelist_10():
-    args = parse_args(['--dynamic-whitelist', '10'], '')
-    assert args.dynamic_whitelist == 10
-
-
-def test_parse_args_dynamic_whitelist_invalid_values():
-    with pytest.raises(SystemExit, match=r'2'):
-        parse_args(['--dynamic-whitelist', 'abc'], '')
-
-
-def test_parse_args_backtesting_invalid():
-    with pytest.raises(SystemExit, match=r'2'):
-        parse_args(['backtesting --ticker-interval'], '')
-
-    with pytest.raises(SystemExit, match=r'2'):
-        parse_args(['backtesting --ticker-interval', 'abc'], '')
-
-
-def test_parse_args_backtesting_custom():
-    args = [
-        '-c', 'test_conf.json',
-        'backtesting',
-        '--live',
-        '--ticker-interval', '1',
-        '--refresh-pairs-cached']
-    call_args = parse_args(args, '')
-    assert call_args.config == 'test_conf.json'
-    assert call_args.live is True
-    assert call_args.loglevel == 20
-    assert call_args.subparser == 'backtesting'
-    assert call_args.func is not None
-    assert call_args.ticker_interval == 1
-    assert call_args.refresh_pairs is True
-
-
-def test_parse_args_hyperopt_custom(mocker):
-    args = ['-c', 'test_conf.json', 'hyperopt', '--epochs', '20']
-    call_args = parse_args(args, '')
-    assert call_args.config == 'test_conf.json'
-    assert call_args.epochs == 20
-    assert call_args.loglevel == 20
-    assert call_args.subparser == 'hyperopt'
-    assert call_args.func is not None
-
-
-def test_load_config(default_conf, mocker):
-    file_mock = mocker.patch('freqtrade.misc.open', mocker.mock_open(
-        read_data=json.dumps(default_conf)
-    ))
-    validated_conf = load_config('somefile')
-    assert file_mock.call_count == 1
-    assert validated_conf.items() >= default_conf.items()
-
-
-def test_load_config_invalid_pair(default_conf, mocker):
-    conf = deepcopy(default_conf)
-    conf['exchange']['pair_whitelist'].append('BTC-ETH')
-    mocker.patch(
-        'freqtrade.misc.open',
-        mocker.mock_open(
-            read_data=json.dumps(conf)))
-    with pytest.raises(ValidationError, match=r'.*does not match.*'):
-        load_config('somefile')
-
-
-def test_load_config_missing_attributes(default_conf, mocker):
-    conf = deepcopy(default_conf)
-    conf.pop('exchange')
-    mocker.patch(
-        'freqtrade.misc.open',
-        mocker.mock_open(
-            read_data=json.dumps(conf)))
-    with pytest.raises(ValidationError, match=r'.*\'exchange\' is a required property.*'):
-        load_config('somefile')
+def test_format_ms_time() -> None:
+    # Date 2018-04-10 18:02:01
+    date_in_epoch_ms = 1523383321000
+    date = format_ms_time(date_in_epoch_ms)
+    assert type(date) is str
+    res = datetime.datetime(2018, 4, 10, 18, 2, 1, tzinfo=datetime.timezone.utc)
+    assert date == res.astimezone(None).strftime('%Y-%m-%dT%H:%M:%S')
+    res = datetime.datetime(2017, 12, 13, 8, 2, 1, tzinfo=datetime.timezone.utc)
+    # Date 2017-12-13 08:02:01
+    date_in_epoch_ms = 1513152121000
+    assert format_ms_time(date_in_epoch_ms) == res.astimezone(None).strftime('%Y-%m-%dT%H:%M:%S')

freqtrade/tests/test_persistence.py

@@ -1,95 +1,68 @@
-# pragma pylint: disable=missing-docstring
-import os
+# pragma pylint: disable=missing-docstring, C0103
+from unittest.mock import MagicMock
+import logging
 
 import pytest
+from sqlalchemy import create_engine
 
-from freqtrade.exchange import Exchanges
-from freqtrade.persistence import Trade, init
+from freqtrade import OperationalException, constants
+from freqtrade.persistence import Trade, clean_dry_run_db, init
+from freqtrade.tests.conftest import log_has
 
 
-def test_init_create_session(default_conf, mocker):
-    mocker.patch.dict('freqtrade.persistence._CONF', default_conf)
+@pytest.fixture(scope='function')
+def init_persistence(default_conf):
+    init(default_conf)
 
+
+def test_init_create_session(default_conf):
     # Check if init create a session
     init(default_conf)
     assert hasattr(Trade, 'session')
-    assert type(Trade.session).__name__ is 'Session'
+    assert 'Session' in type(Trade.session).__name__
 
 
-def test_init_dry_run_db(default_conf, mocker):
-    default_conf.update({'dry_run_db': True})
-    mocker.patch.dict('freqtrade.persistence._CONF', default_conf)
+def test_init_custom_db_url(default_conf, mocker):
+    # Update path to a value other than default, but still in-memory
+    default_conf.update({'db_url': 'sqlite:///tmp/freqtrade2_test.sqlite'})
+    create_engine_mock = mocker.patch('freqtrade.persistence.create_engine', MagicMock())
 
-    # First, protect the existing 'tradesv3.dry_run.sqlite' (Do not delete user data)
-    dry_run_db = 'tradesv3.dry_run.sqlite'
-    dry_run_db_swp = dry_run_db + '.swp'
-
-    if os.path.isfile(dry_run_db):
-        os.rename(dry_run_db, dry_run_db_swp)
-
-    # Check if the new tradesv3.dry_run.sqlite was created
     init(default_conf)
-    assert os.path.isfile(dry_run_db) is True
-
-    # Delete the file made for this unitest and rollback to the previous
-    # tradesv3.dry_run.sqlite file
-
-    # 1. Delete file from the test
-    if os.path.isfile(dry_run_db):
-        os.remove(dry_run_db)
-
-    # 2. Rollback to the initial file
-    if os.path.isfile(dry_run_db_swp):
-        os.rename(dry_run_db_swp, dry_run_db)
+    assert create_engine_mock.call_count == 1
+    assert create_engine_mock.mock_calls[0][1][0] == 'sqlite:///tmp/freqtrade2_test.sqlite'
 
 
-def test_init_dry_run_without_db(default_conf, mocker):
-    default_conf.update({'dry_run_db': False})
-    mocker.patch.dict('freqtrade.persistence._CONF', default_conf)
-
-    # First, protect the existing 'tradesv3.dry_run.sqlite' (Do not delete user data)
-    dry_run_db = 'tradesv3.dry_run.sqlite'
-    dry_run_db_swp = dry_run_db + '.swp'
-
-    if os.path.isfile(dry_run_db):
-        os.rename(dry_run_db, dry_run_db_swp)
-
-    # Check if the new tradesv3.dry_run.sqlite was created
-    init(default_conf)
-    assert os.path.isfile(dry_run_db) is False
-
-    # Rollback to the initial 'tradesv3.dry_run.sqlite' file
-    if os.path.isfile(dry_run_db_swp):
-        os.rename(dry_run_db_swp, dry_run_db)
+def test_init_invalid_db_url(default_conf):
+    # Update path to a value other than default, but still in-memory
+    default_conf.update({'db_url': 'unknown:///some.url'})
+    with pytest.raises(OperationalException, match=r'.*no valid database URL*'):
+        init(default_conf)
 
 
 def test_init_prod_db(default_conf, mocker):
     default_conf.update({'dry_run': False})
-    mocker.patch.dict('freqtrade.persistence._CONF', default_conf)
+    default_conf.update({'db_url': constants.DEFAULT_DB_PROD_URL})
 
-    # First, protect the existing 'tradesv3.sqlite' (Do not delete user data)
-    prod_db = 'tradesv3.sqlite'
-    prod_db_swp = prod_db + '.swp'
+    create_engine_mock = mocker.patch('freqtrade.persistence.create_engine', MagicMock())
 
-    if os.path.isfile(prod_db):
-        os.rename(prod_db, prod_db_swp)
-
-    # Check if the new tradesv3.sqlite was created
     init(default_conf)
-    assert os.path.isfile(prod_db) is True
-
-    # Delete the file made for this unitest and rollback to the previous tradesv3.sqlite file
-
-    # 1. Delete file from the test
-    if os.path.isfile(prod_db):
-        os.remove(prod_db)
-
-    # Rollback to the initial 'tradesv3.sqlite' file
-    if os.path.isfile(prod_db_swp):
-        os.rename(prod_db_swp, prod_db)
+    assert create_engine_mock.call_count == 1
+    assert create_engine_mock.mock_calls[0][1][0] == 'sqlite:///tradesv3.sqlite'
 
 
-def test_update_with_bittrex(limit_buy_order, limit_sell_order):
+def test_init_dryrun_db(default_conf, mocker):
+    default_conf.update({'dry_run': True})
+    default_conf.update({'db_url': constants.DEFAULT_DB_DRYRUN_URL})
+
+    create_engine_mock = mocker.patch('freqtrade.persistence.create_engine', MagicMock())
+
+    init(default_conf)
+    assert create_engine_mock.call_count == 1
+    assert create_engine_mock.mock_calls[0][1][0] == 'sqlite://'
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_update_with_bittrex(limit_buy_order, limit_sell_order, fee):
     """
     On this test we will buy and sell a crypto currency.
 
@@ -118,10 +91,11 @@ def test_update_with_bittrex(limit_buy_order, limit_sell_order):
     """
 
     trade = Trade(
-        pair='BTC_ETH',
+        pair='ETH/BTC',
         stake_amount=0.001,
-        fee=0.0025,
-        exchange=Exchanges.BITTREX,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange='bittrex',
     )
     assert trade.open_order_id is None
     assert trade.open_rate is None
@@ -143,12 +117,14 @@ def test_update_with_bittrex(limit_buy_order, limit_sell_order):
     assert trade.close_date is not None
 
 
-def test_calc_open_close_trade_price(limit_buy_order, limit_sell_order):
+@pytest.mark.usefixtures("init_persistence")
+def test_calc_open_close_trade_price(limit_buy_order, limit_sell_order, fee):
     trade = Trade(
-        pair='BTC_ETH',
+        pair='ETH/BTC',
         stake_amount=0.001,
-        fee=0.0025,
-        exchange=Exchanges.BITTREX,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange='bittrex',
     )
 
     trade.open_order_id = 'something'
@@ -165,12 +141,14 @@ def test_calc_open_close_trade_price(limit_buy_order, limit_sell_order):
     assert trade.calc_profit_percent() == 0.06201057
 
 
-def test_calc_close_trade_price_exception(limit_buy_order):
+@pytest.mark.usefixtures("init_persistence")
+def test_calc_close_trade_price_exception(limit_buy_order, fee):
     trade = Trade(
-        pair='BTC_ETH',
+        pair='ETH/BTC',
         stake_amount=0.001,
-        fee=0.0025,
-        exchange=Exchanges.BITTREX,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange='bittrex',
     )
 
     trade.open_order_id = 'something'
@@ -178,12 +156,14 @@ def test_calc_close_trade_price_exception(limit_buy_order):
     assert trade.calc_close_trade_price() == 0.0
 
 
+@pytest.mark.usefixtures("init_persistence")
 def test_update_open_order(limit_buy_order):
     trade = Trade(
-        pair='BTC_ETH',
+        pair='ETH/BTC',
         stake_amount=1.00,
-        fee=0.1,
-        exchange=Exchanges.BITTREX,
+        fee_open=0.1,
+        fee_close=0.1,
+        exchange='bittrex',
     )
 
     assert trade.open_order_id is None
@@ -191,7 +171,7 @@ def test_update_open_order(limit_buy_order):
     assert trade.close_profit is None
     assert trade.close_date is None
 
-    limit_buy_order['closed'] = False
+    limit_buy_order['status'] = 'open'
     trade.update(limit_buy_order)
 
     assert trade.open_order_id is None
@@ -200,24 +180,28 @@ def test_update_open_order(limit_buy_order):
     assert trade.close_date is None
 
 
+@pytest.mark.usefixtures("init_persistence")
 def test_update_invalid_order(limit_buy_order):
     trade = Trade(
-        pair='BTC_ETH',
+        pair='ETH/BTC',
         stake_amount=1.00,
-        fee=0.1,
-        exchange=Exchanges.BITTREX,
+        fee_open=0.1,
+        fee_close=0.1,
+        exchange='bittrex',
     )
     limit_buy_order['type'] = 'invalid'
     with pytest.raises(ValueError, match=r'Unknown order type'):
         trade.update(limit_buy_order)
 
 
-def test_calc_open_trade_price(limit_buy_order):
+@pytest.mark.usefixtures("init_persistence")
+def test_calc_open_trade_price(limit_buy_order, fee):
     trade = Trade(
-        pair='BTC_ETH',
+        pair='ETH/BTC',
         stake_amount=0.001,
-        fee=0.0025,
-        exchange=Exchanges.BITTREX,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange='bittrex',
     )
     trade.open_order_id = 'open_trade'
     trade.update(limit_buy_order)  # Buy @ 0.00001099
@@ -229,12 +213,14 @@ def test_calc_open_trade_price(limit_buy_order):
     assert trade.calc_open_trade_price(fee=0.003) == 0.001003000
 
 
-def test_calc_close_trade_price(limit_buy_order, limit_sell_order):
+@pytest.mark.usefixtures("init_persistence")
+def test_calc_close_trade_price(limit_buy_order, limit_sell_order, fee):
     trade = Trade(
-        pair='BTC_ETH',
+        pair='ETH/BTC',
         stake_amount=0.001,
-        fee=0.0025,
-        exchange=Exchanges.BITTREX,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange='bittrex',
     )
     trade.open_order_id = 'close_trade'
     trade.update(limit_buy_order)  # Buy @ 0.00001099
@@ -250,12 +236,14 @@ def test_calc_close_trade_price(limit_buy_order, limit_sell_order):
     assert trade.calc_close_trade_price(fee=0.005) == 0.0010619972
 
 
-def test_calc_profit(limit_buy_order, limit_sell_order):
+@pytest.mark.usefixtures("init_persistence")
+def test_calc_profit(limit_buy_order, limit_sell_order, fee):
     trade = Trade(
-        pair='BTC_ETH',
+        pair='ETH/BTC',
         stake_amount=0.001,
-        fee=0.0025,
-        exchange=Exchanges.BITTREX,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange='bittrex',
     )
     trade.open_order_id = 'profit_percent'
     trade.update(limit_buy_order)  # Buy @ 0.00001099
@@ -272,10 +260,6 @@ def test_calc_profit(limit_buy_order, limit_sell_order):
     # Lower than open rate
     assert trade.calc_profit(rate=0.00000123, fee=0.003) == -0.00089092
 
-    # Only custom fee without sell order applied
-    with pytest.raises(TypeError):
-        trade.calc_profit(fee=0.003)
-
     # Test when we apply a Sell order. Sell higher than open rate @ 0.00001173
     trade.update(limit_sell_order)
     assert trade.calc_profit() == 0.00006217
@@ -284,12 +268,14 @@ def test_calc_profit(limit_buy_order, limit_sell_order):
     assert trade.calc_profit(fee=0.003) == 0.00006163
 
 
-def test_calc_profit_percent(limit_buy_order, limit_sell_order):
+@pytest.mark.usefixtures("init_persistence")
+def test_calc_profit_percent(limit_buy_order, limit_sell_order, fee):
     trade = Trade(
-        pair='BTC_ETH',
+        pair='ETH/BTC',
         stake_amount=0.001,
-        fee=0.0025,
-        exchange=Exchanges.BITTREX,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange='bittrex',
     )
     trade.open_order_id = 'profit_percent'
     trade.update(limit_buy_order)  # Buy @ 0.00001099
@@ -300,13 +286,300 @@ def test_calc_profit_percent(limit_buy_order, limit_sell_order):
     # Get percent of profit with a custom rate (Lower than open rate)
     assert trade.calc_profit_percent(rate=0.00000123) == -0.88863827
 
-    # Only custom fee without sell order applied
-    with pytest.raises(TypeError):
-        trade.calc_profit_percent(fee=0.003)
-
     # Test when we apply a Sell order. Sell higher than open rate @ 0.00001173
     trade.update(limit_sell_order)
     assert trade.calc_profit_percent() == 0.06201057
 
     # Test with a custom fee rate on the close trade
     assert trade.calc_profit_percent(fee=0.003) == 0.0614782
+
+
+def test_clean_dry_run_db(default_conf, fee):
+    init(default_conf)
+
+    # Simulate dry_run entries
+    trade = Trade(
+        pair='ETH/BTC',
+        stake_amount=0.001,
+        amount=123.0,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_rate=0.123,
+        exchange='bittrex',
+        open_order_id='dry_run_buy_12345'
+    )
+    Trade.session.add(trade)
+
+    trade = Trade(
+        pair='ETC/BTC',
+        stake_amount=0.001,
+        amount=123.0,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_rate=0.123,
+        exchange='bittrex',
+        open_order_id='dry_run_sell_12345'
+    )
+    Trade.session.add(trade)
+
+    # Simulate prod entry
+    trade = Trade(
+        pair='ETC/BTC',
+        stake_amount=0.001,
+        amount=123.0,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_rate=0.123,
+        exchange='bittrex',
+        open_order_id='prod_buy_12345'
+    )
+    Trade.session.add(trade)
+
+    # We have 3 entries: 2 dry_run, 1 prod
+    assert len(Trade.query.filter(Trade.open_order_id.isnot(None)).all()) == 3
+
+    clean_dry_run_db()
+
+    # We have now only the prod
+    assert len(Trade.query.filter(Trade.open_order_id.isnot(None)).all()) == 1
+
+
+def test_migrate_old(mocker, default_conf, fee):
+    """
+    Test Database migration(starting with old pairformat)
+    """
+    amount = 103.223
+    create_table_old = """CREATE TABLE IF NOT EXISTS "trades" (
+                                id INTEGER NOT NULL,
+                                exchange VARCHAR NOT NULL,
+                                pair VARCHAR NOT NULL,
+                                is_open BOOLEAN NOT NULL,
+                                fee FLOAT NOT NULL,
+                                open_rate FLOAT,
+                                close_rate FLOAT,
+                                close_profit FLOAT,
+                                stake_amount FLOAT NOT NULL,
+                                amount FLOAT,
+                                open_date DATETIME NOT NULL,
+                                close_date DATETIME,
+                                open_order_id VARCHAR,
+                                PRIMARY KEY (id),
+                                CHECK (is_open IN (0, 1))
+                                );"""
+    insert_table_old = """INSERT INTO trades (exchange, pair, is_open, fee,
+                          open_rate, stake_amount, amount, open_date)
+                          VALUES ('BITTREX', 'BTC_ETC', 1, {fee},
+                          0.00258580, {stake}, {amount},
+                          '2017-11-28 12:44:24.000000')
+                          """.format(fee=fee.return_value,
+                                     stake=default_conf.get("stake_amount"),
+                                     amount=amount
+                                     )
+    engine = create_engine('sqlite://')
+    mocker.patch('freqtrade.persistence.create_engine', lambda *args, **kwargs: engine)
+
+    # Create table using the old format
+    engine.execute(create_table_old)
+    engine.execute(insert_table_old)
+    # Run init to test migration
+    init(default_conf)
+
+    assert len(Trade.query.filter(Trade.id == 1).all()) == 1
+    trade = Trade.query.filter(Trade.id == 1).first()
+    assert trade.fee_open == fee.return_value
+    assert trade.fee_close == fee.return_value
+    assert trade.open_rate_requested is None
+    assert trade.close_rate_requested is None
+    assert trade.is_open == 1
+    assert trade.amount == amount
+    assert trade.stake_amount == default_conf.get("stake_amount")
+    assert trade.pair == "ETC/BTC"
+    assert trade.exchange == "bittrex"
+    assert trade.max_rate == 0.0
+    assert trade.stop_loss == 0.0
+    assert trade.initial_stop_loss == 0.0
+
+
+def test_migrate_new(mocker, default_conf, fee, caplog):
+    """
+    Test Database migration (starting with new pairformat)
+    """
+    caplog.set_level(logging.DEBUG)
+    amount = 103.223
+    # Always create all columns apart from the last!
+    create_table_old = """CREATE TABLE IF NOT EXISTS "trades" (
+                                id INTEGER NOT NULL,
+                                exchange VARCHAR NOT NULL,
+                                pair VARCHAR NOT NULL,
+                                is_open BOOLEAN NOT NULL,
+                                fee FLOAT NOT NULL,
+                                open_rate FLOAT,
+                                close_rate FLOAT,
+                                close_profit FLOAT,
+                                stake_amount FLOAT NOT NULL,
+                                amount FLOAT,
+                                open_date DATETIME NOT NULL,
+                                close_date DATETIME,
+                                open_order_id VARCHAR,
+                                stop_loss FLOAT,
+                                initial_stop_loss FLOAT,
+                                max_rate FLOAT,
+                                sell_reason VARCHAR,
+                                strategy VARCHAR,
+                                PRIMARY KEY (id),
+                                CHECK (is_open IN (0, 1))
+                                );"""
+    insert_table_old = """INSERT INTO trades (exchange, pair, is_open, fee,
+                          open_rate, stake_amount, amount, open_date,
+                          stop_loss, initial_stop_loss, max_rate)
+                          VALUES ('binance', 'ETC/BTC', 1, {fee},
+                          0.00258580, {stake}, {amount},
+                          '2019-11-28 12:44:24.000000',
+                          0.0, 0.0, 0.0)
+                          """.format(fee=fee.return_value,
+                                     stake=default_conf.get("stake_amount"),
+                                     amount=amount
+                                     )
+    engine = create_engine('sqlite://')
+    mocker.patch('freqtrade.persistence.create_engine', lambda *args, **kwargs: engine)
+
+    # Create table using the old format
+    engine.execute(create_table_old)
+    engine.execute(insert_table_old)
+
+    # fake previous backup
+    engine.execute("create table trades_bak as select * from trades")
+
+    engine.execute("create table trades_bak1 as select * from trades")
+    # Run init to test migration
+    init(default_conf)
+
+    assert len(Trade.query.filter(Trade.id == 1).all()) == 1
+    trade = Trade.query.filter(Trade.id == 1).first()
+    assert trade.fee_open == fee.return_value
+    assert trade.fee_close == fee.return_value
+    assert trade.open_rate_requested is None
+    assert trade.close_rate_requested is None
+    assert trade.is_open == 1
+    assert trade.amount == amount
+    assert trade.stake_amount == default_conf.get("stake_amount")
+    assert trade.pair == "ETC/BTC"
+    assert trade.exchange == "binance"
+    assert trade.max_rate == 0.0
+    assert trade.stop_loss == 0.0
+    assert trade.initial_stop_loss == 0.0
+    assert trade.sell_reason is None
+    assert trade.strategy is None
+    assert trade.ticker_interval is None
+    assert log_has("trying trades_bak1", caplog.record_tuples)
+    assert log_has("trying trades_bak2", caplog.record_tuples)
+    assert log_has("Running database migration - backup available as trades_bak2",
+                   caplog.record_tuples)
+
+
+def test_migrate_mid_state(mocker, default_conf, fee, caplog):
+    """
+    Test Database migration (starting with new pairformat)
+    """
+    caplog.set_level(logging.DEBUG)
+    amount = 103.223
+    create_table_old = """CREATE TABLE IF NOT EXISTS "trades" (
+                                id INTEGER NOT NULL,
+                                exchange VARCHAR NOT NULL,
+                                pair VARCHAR NOT NULL,
+                                is_open BOOLEAN NOT NULL,
+                                fee_open FLOAT NOT NULL,
+                                fee_close FLOAT NOT NULL,
+                                open_rate FLOAT,
+                                close_rate FLOAT,
+                                close_profit FLOAT,
+                                stake_amount FLOAT NOT NULL,
+                                amount FLOAT,
+                                open_date DATETIME NOT NULL,
+                                close_date DATETIME,
+                                open_order_id VARCHAR,
+                                PRIMARY KEY (id),
+                                CHECK (is_open IN (0, 1))
+                                );"""
+    insert_table_old = """INSERT INTO trades (exchange, pair, is_open, fee_open, fee_close,
+                          open_rate, stake_amount, amount, open_date)
+                          VALUES ('binance', 'ETC/BTC', 1, {fee}, {fee},
+                          0.00258580, {stake}, {amount},
+                          '2019-11-28 12:44:24.000000')
+                          """.format(fee=fee.return_value,
+                                     stake=default_conf.get("stake_amount"),
+                                     amount=amount
+                                     )
+    engine = create_engine('sqlite://')
+    mocker.patch('freqtrade.persistence.create_engine', lambda *args, **kwargs: engine)
+
+    # Create table using the old format
+    engine.execute(create_table_old)
+    engine.execute(insert_table_old)
+
+    # Run init to test migration
+    init(default_conf)
+
+    assert len(Trade.query.filter(Trade.id == 1).all()) == 1
+    trade = Trade.query.filter(Trade.id == 1).first()
+    assert trade.fee_open == fee.return_value
+    assert trade.fee_close == fee.return_value
+    assert trade.open_rate_requested is None
+    assert trade.close_rate_requested is None
+    assert trade.is_open == 1
+    assert trade.amount == amount
+    assert trade.stake_amount == default_conf.get("stake_amount")
+    assert trade.pair == "ETC/BTC"
+    assert trade.exchange == "binance"
+    assert trade.max_rate == 0.0
+    assert trade.stop_loss == 0.0
+    assert trade.initial_stop_loss == 0.0
+    assert log_has("trying trades_bak0", caplog.record_tuples)
+    assert log_has("Running database migration - backup available as trades_bak0",
+                   caplog.record_tuples)
+
+
+def test_adjust_stop_loss(limit_buy_order, limit_sell_order, fee):
+    trade = Trade(
+        pair='ETH/BTC',
+        stake_amount=0.001,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange='bittrex',
+        open_rate=1,
+    )
+
+    trade.adjust_stop_loss(trade.open_rate, 0.05, True)
+    assert trade.stop_loss == 0.95
+    assert trade.max_rate == 1
+    assert trade.initial_stop_loss == 0.95
+
+    # Get percent of profit with a lowre rate
+    trade.adjust_stop_loss(0.96, 0.05)
+    assert trade.stop_loss == 0.95
+    assert trade.max_rate == 1
+    assert trade.initial_stop_loss == 0.95
+
+    # Get percent of profit with a custom rate (Higher than open rate)
+    trade.adjust_stop_loss(1.3, -0.1)
+    assert round(trade.stop_loss, 8) == 1.17
+    assert trade.max_rate == 1.3
+    assert trade.initial_stop_loss == 0.95
+
+    # current rate lower again ... should not change
+    trade.adjust_stop_loss(1.2, 0.1)
+    assert round(trade.stop_loss, 8) == 1.17
+    assert trade.max_rate == 1.3
+    assert trade.initial_stop_loss == 0.95
+
+    # current rate higher... should raise stoploss
+    trade.adjust_stop_loss(1.4, 0.1)
+    assert round(trade.stop_loss, 8) == 1.26
+    assert trade.max_rate == 1.4
+    assert trade.initial_stop_loss == 0.95
+
+    #  Initial is true but stop_loss set - so doesn't do anything
+    trade.adjust_stop_loss(1.7, 0.1, True)
+    assert round(trade.stop_loss, 8) == 1.26
+    assert trade.max_rate == 1.4
+    assert trade.initial_stop_loss == 0.95

freqtrade/tests/test_talib.py

@@ -0,0 +1,16 @@
+
+
+import talib.abstract as ta
+import pandas as pd
+
+
+def test_talib_bollingerbands_near_zero_values():
+    inputs = pd.DataFrame([
+        {'close': 0.00000010},
+        {'close': 0.00000011},
+        {'close': 0.00000012},
+        {'close': 0.00000013},
+        {'close': 0.00000014}
+    ])
+    bollinger = ta.BBANDS(inputs, matype=0, timeperiod=2)
+    assert (bollinger['upperband'][3] != bollinger['middleband'][3])