Merge pull request #3119 from freqtrade/new_release

New release 2020.3

.github/workflows/ci.yml

@@ -7,6 +7,8 @@ on:
       - develop
       - github_actions_tests
     tags:
+    release:
+      types: [published]
   pull_request:
   schedule:
     - cron:  '0 5 * * 4'
@@ -18,10 +20,10 @@ jobs:
     strategy:
       matrix:
         os: [ ubuntu-18.04, macos-latest ]
-        python-version: [3.7]
+        python-version: [3.7, 3.8]
 
     steps:
-    - uses: actions/checkout@v1
+    - uses: actions/checkout@v2
 
     - name: Set up Python
       uses: actions/setup-python@v1
@@ -68,7 +70,7 @@ jobs:
         pytest --random-order --cov=freqtrade --cov-config=.coveragerc
 
     - name: Coveralls
-      if: startsWith(matrix.os, 'ubuntu')
+      if: (startsWith(matrix.os, 'ubuntu') && matrix.python-version == '3.8')
       env:
         # Coveralls token. Not used as secret due to github not providing secrets to forked repositories
         COVERALLS_REPO_TOKEN: 6D1m0xupS3FgutfuGao8keFf9Hc0FpIXu
@@ -113,10 +115,10 @@ jobs:
     strategy:
       matrix:
         os: [ windows-latest ]
-        python-version: [3.7]
+        python-version: [3.7, 3.8]
 
     steps:
-    - uses: actions/checkout@v1
+    - uses: actions/checkout@v2
 
     - name: Set up Python
       uses: actions/setup-python@v1
@@ -128,8 +130,7 @@ jobs:
       if: startsWith(runner.os, 'Windows')
       with:
         path: ~\AppData\Local\pip\Cache
-        key: ${{ runner.os }}-pip
-        restore-keys: ${{ runner.os }}-pip
+        key: ${{ matrix.os }}-${{ matrix.python-version }}-pip
 
     - name: Installation
       run: |
@@ -173,7 +174,7 @@ jobs:
   docs_check:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v1
+    - uses: actions/checkout@v2
 
     - name: Documentation syntax
       run: |
@@ -191,15 +192,40 @@ jobs:
   deploy:
     needs: [ build, build_windows, docs_check ]
     runs-on: ubuntu-18.04
-    if: (github.event_name == 'push' || github.event_name == 'schedule') && github.repository == 'freqtrade/freqtrade'
+    if: (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'release') && github.repository == 'freqtrade/freqtrade'
     steps:
-    - uses: actions/checkout@v1
+    - uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v1
+      with:
+        python-version: 3.8
 
     - name: Extract branch name
       shell: bash
       run: echo "##[set-output name=branch;]$(echo ${GITHUB_REF#refs/heads/})"
       id: extract_branch
 
+    - name: Build distribution
+      run: |
+        pip install -U setuptools wheel
+        python setup.py sdist bdist_wheel
+
+    - name: Publish to PyPI (Test)
+      uses: pypa/gh-action-pypi-publish@master
+      if: (steps.extract_branch.outputs.branch == 'master' || github.event_name == 'release')
+      with:
+        user: __token__
+        password: ${{ secrets.pypi_test_password }}
+        repository_url: https://test.pypi.org/legacy/
+
+    - name: Publish to PyPI
+      uses: pypa/gh-action-pypi-publish@master
+      if: (steps.extract_branch.outputs.branch == 'master' || github.event_name == 'release')
+      with:
+        user: __token__
+        password: ${{ secrets.pypi_password }}
+
     - name: Build and test and push docker image
       env:
         IMAGE_NAME: freqtradeorg/freqtrade

.gitignore

@@ -6,7 +6,6 @@ user_data/*
 !user_data/strategy/sample_strategy.py
 !user_data/notebooks
 user_data/notebooks/*
-!user_data/notebooks/*example.ipynb
 freqtrade-plot.html
 freqtrade-profit-plot.html
 

.travis.yml

@@ -1,4 +1,3 @@
-sudo: true
 os:
 - linux
 dist: xenial
@@ -11,10 +10,10 @@ env:
   global:
     - IMAGE_NAME=freqtradeorg/freqtrade
 install:
-- cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
+- cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies; cd ..
 - export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
 - export TA_LIBRARY_PATH=${HOME}/dependencies/lib
-- export TA_INCLUDE_PATH=${HOME}/dependencies/lib/include
+- export TA_INCLUDE_PATH=${HOME}/dependencies/include
 - pip install -r requirements-dev.txt
 - pip install -e .
 jobs:

CONTRIBUTING.md

@@ -48,7 +48,7 @@ pytest tests/test_<file_name>.py::test_<method_name>
 #### Run Flake8
 
 ```bash
-flake8 freqtrade
+flake8 freqtrade tests scripts
 ```
 
 We receive a lot of code that fails the `flake8` checks.
@@ -109,11 +109,11 @@ Exceptions:
 
 Contributors may be given commit privileges. Preference will be given to those with:
 
-1. Past contributions to FreqTrade and other related open-source projects. Contributions to FreqTrade include both code (both accepted and pending) and friendly participation in the issue tracker and Pull request reviews. Quantity and quality are considered.
+1. Past contributions to Freqtrade and other related open-source projects. Contributions to Freqtrade include both code (both accepted and pending) and friendly participation in the issue tracker and Pull request reviews. Quantity and quality are considered.
 1. A coding style that the other core committers find simple, minimal, and clean.
 1. Access to resources for cross-platform development and testing.
 1. Time to devote to the project regularly.
 
-Being a Committer does not grant write permission on `develop` or `master` for security reasons (Users trust FreqTrade with their Exchange API keys).
+Being a Committer does not grant write permission on `develop` or `master` for security reasons (Users trust Freqtrade with their Exchange API keys).
 
 After being Committer for some time, a Committer may be named Core Committer and given full repository access.

Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.7.6-slim-stretch
+FROM python:3.8.2-slim-buster
 
 RUN apt-get update \
     && apt-get -y install curl build-essential libssl-dev \

MANIFEST.in

@@ -2,3 +2,4 @@ include LICENSE
 include README.md
 include config.json.example
 recursive-include freqtrade *.py
+recursive-include freqtrade/templates/ *.j2 *.ipynb

README.md

@@ -1,6 +1,6 @@
 # Freqtrade
 
-[![Build Status](https://travis-ci.org/freqtrade/freqtrade.svg?branch=develop)](https://travis-ci.org/freqtrade/freqtrade)
+[![Freqtrade CI](https://github.com/freqtrade/freqtrade/workflows/Freqtrade%20CI/badge.svg)](https://github.com/freqtrade/freqtrade/actions/)
 [![Coverage Status](https://coveralls.io/repos/github/freqtrade/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/freqtrade/freqtrade?branch=develop)
 [![Documentation](https://readthedocs.org/projects/freqtrade/badge/)](https://www.freqtrade.io)
 [![Maintainability](https://api.codeclimate.com/v1/badges/5737e6d668200b7518ff/maintainability)](https://codeclimate.com/github/freqtrade/freqtrade/maintainability)
@@ -25,7 +25,8 @@ hesitate to read the source code and understand the mechanism of this bot.
 ## Exchange marketplaces supported
 
 - [X] [Bittrex](https://bittrex.com/)
-- [X] [Binance](https://www.binance.com/) ([*Note for binance users](#a-note-on-binance))
+- [X] [Binance](https://www.binance.com/) ([*Note for binance users](docs/exchanges.md#blacklists))
+- [X] [Kraken](https://kraken.com/)
 - [ ] [113 others to tests](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
 
 ## Documentation

bin/freqtrade

@@ -1,11 +1,11 @@
 #!/usr/bin/env python3
 
 import sys
-import warnings
+import logging
 
-from freqtrade.main import main
+logger = logging.getLogger(__name__)
 
-warnings.warn(
-    "Deprecated - To continue to run the bot like this, please run `pip install -e .` again.",
-    DeprecationWarning)
-main(sys.argv[1:])
+
+logger.error("DEPRECATED installation detected, please run `pip install -e .` again.")
+
+sys.exit(2)

build_helpers/install_windows.ps1

@@ -3,7 +3,15 @@
 # Invoke-WebRequest -Uri "https://download.lfd.uci.edu/pythonlibs/xxxxxxx/TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl" -OutFile "TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl"
 
 python -m pip install --upgrade pip
-pip install build_helpers\TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl
+
+$pyv = python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')"
+
+if ($pyv -eq '3.7') {
+    pip install build_helpers\TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl
+}
+if ($pyv -eq '3.8') {
+    pip install build_helpers\TA_Lib-0.4.17-cp38-cp38-win_amd64.whl
+}
 
 pip install -r requirements-dev.txt
 pip install -e .

build_helpers/publish_docker.sh

@@ -23,7 +23,7 @@ if [ $? -ne 0 ]; then
 fi
 
 # Run backtest
-docker run --rm -v $(pwd)/config.json.example:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG} backtesting --datadir /tests/testdata --strategy DefaultStrategy
+docker run --rm -v $(pwd)/config.json.example:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG} backtesting --datadir /tests/testdata --strategy-path /tests/strategy/strats/ --strategy DefaultStrategy
 
 if [ $? -ne 0 ]; then
     echo "failed running backtest"

config.json.example

@@ -4,7 +4,7 @@
     "stake_amount": 0.05,
     "tradable_balance_ratio": 0.99,
     "fiat_display_currency": "USD",
-    "ticker_interval" : "5m",
+    "ticker_interval": "5m",
     "dry_run": false,
     "trailing_stop": false,
     "unfilledtimeout": {
@@ -23,7 +23,7 @@
     "ask_strategy":{
         "use_order_book": false,
         "order_book_min": 1,
-        "order_book_max": 9,
+        "order_book_max": 1,
         "use_sell_signal": true,
         "sell_profit_only": false,
         "ignore_roi_if_buy_signal": false
@@ -44,7 +44,7 @@
             "DASH/BTC",
             "ZEC/BTC",
             "XLM/BTC",
-            "NXT/BTC",
+            "XRP/BTC",
             "TRX/BTC",
             "ADA/BTC",
             "XMR/BTC"

config_binance.json.example

@@ -4,7 +4,7 @@
     "stake_amount": 0.05,
     "tradable_balance_ratio": 0.99,
     "fiat_display_currency": "USD",
-    "ticker_interval" : "5m",
+    "ticker_interval": "5m",
     "dry_run": true,
     "trailing_stop": false,
     "unfilledtimeout": {
@@ -23,7 +23,7 @@
     "ask_strategy":{
         "use_order_book": false,
         "order_book_min": 1,
-        "order_book_max": 9,
+        "order_book_max": 1,
         "use_sell_signal": true,
         "sell_profit_only": false,
         "ignore_roi_if_buy_signal": false

config_full.json.example

@@ -4,7 +4,7 @@
     "stake_amount": 0.05,
     "tradable_balance_ratio": 0.99,
     "fiat_display_currency": "USD",
-    "amount_reserve_percent" : 0.05,
+    "amount_reserve_percent": 0.05,
     "amend_last_stake_amount": false,
     "last_stake_amount_min_ratio": 0.5,
     "dry_run": false,
@@ -25,6 +25,7 @@
         "sell": 30
     },
     "bid_strategy": {
+        "price_side": "bid",
         "use_order_book": false,
         "ask_last_balance": 0.0,
         "order_book_top": 1,
@@ -34,9 +35,10 @@
         }
     },
     "ask_strategy":{
+        "price_side": "ask",
         "use_order_book": false,
         "order_book_min": 1,
-        "order_book_max": 9,
+        "order_book_max": 1,
         "use_sell_signal": true,
         "sell_profit_only": false,
         "ignore_roi_if_buy_signal": false
@@ -62,8 +64,8 @@
             "refresh_period": 1800
         },
         {"method": "PrecisionFilter"},
-        {"method": "PriceFilter", "low_price_ratio": 0.01
-        }
+        {"method": "PriceFilter", "low_price_ratio": 0.01},
+        {"method": "SpreadFilter", "max_spread_ratio": 0.005}
     ],
     "exchange": {
         "name": "bittrex",
@@ -129,5 +131,7 @@
         "heartbeat_interval": 60
     },
     "strategy": "DefaultStrategy",
-    "strategy_path": "user_data/strategies/"
+    "strategy_path": "user_data/strategies/",
+    "dataformat_ohlcv": "json",
+    "dataformat_trades": "jsongz"
 }

config_kraken.json.example

@@ -4,7 +4,7 @@
     "stake_amount": 10,
     "tradable_balance_ratio": 0.99,
     "fiat_display_currency": "EUR",
-    "ticker_interval" : "5m",
+    "ticker_interval": "5m",
     "dry_run": true,
     "trailing_stop": false,
     "unfilledtimeout": {
@@ -23,7 +23,7 @@
     "ask_strategy":{
         "use_order_book": false,
         "order_book_min": 1,
-        "order_book_max": 9,
+        "order_book_max": 1,
         "use_sell_signal": true,
         "sell_profit_only": false,
         "ignore_roi_if_buy_signal": false

docker-compose.yml

@@ -3,6 +3,18 @@ version: '3'
 services:
   freqtrade:
     image: freqtradeorg/freqtrade:master
+    # Build step - only needed when additional dependencies are needed
+    # build:
+    #   context: .
+    #   dockerfile: "./Dockerfile.technical"
+    restart: unless-stopped
+    container_name: freqtrade
     volumes:
       - "./user_data:/freqtrade/user_data"
-      - "./config.json:/freqtrade/config.json"
+    # Default command used when running `docker compose up`
+    command: >
+      trade
+      --logfile /freqtrade/user_data/freqtrade.log
+      --db-url sqlite:////freqtrade/user_data/tradesv3.sqlite
+      --config /freqtrade/user_data/config.json
+      --strategy SampleStrategy

docs/advanced-hyperopt.md

@@ -4,6 +4,34 @@ This page explains some advanced Hyperopt topics that may require higher
 coding skills and Python knowledge than creation of an ordinal hyperoptimization
 class.
 
+## Derived hyperopt classes
+
+Custom hyperop classes can be derived in the same way [it can be done for strategies](strategy-customization.md#derived-strategies).
+
+Applying to hyperoptimization, as an example, you may override how dimensions are defined in your optimization hyperspace:
+
+```python
+class MyAwesomeHyperOpt(IHyperOpt):
+    ...
+    # Uses default stoploss dimension
+
+class MyAwesomeHyperOpt2(MyAwesomeHyperOpt):
+    @staticmethod
+    def stoploss_space() -> List[Dimension]:
+        # Override boundaries for stoploss
+        return [
+            Real(-0.33, -0.01, name='stoploss'),
+        ]
+```
+
+and then quickly switch between hyperopt classes, running optimization process with hyperopt class you need in each particular case:
+
+```
+$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt ...
+or
+$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt2 ...
+```
+
 ## Creating and using a custom loss function
 
 To use a custom loss function class, make sure that the function `hyperopt_loss_function` is defined in your custom hyperopt loss class.

docs/backtesting.md

@@ -11,8 +11,8 @@ Now you have good Buy and Sell strategies and some historic data, you want to te
 real data. This is what we call
 [backtesting](https://en.wikipedia.org/wiki/Backtesting).
 
-Backtesting will use the crypto-currencies (pairs) from your config file and load ticker data from `user_data/data/<exchange>` by default.
-If no data is available for the exchange / pair / ticker interval combination, backtesting will ask you to download them first using `freqtrade download-data`.
+Backtesting will use the crypto-currencies (pairs) from your config file and load historical candle (OHCLV) data from `user_data/data/<exchange>` by default.
+If no data is available for the exchange / pair / timeframe (ticker interval) combination, backtesting will ask you to download them first using `freqtrade download-data`.
 For details on downloading, please refer to the [Data Downloading](data-download.md) section in the documentation.
 
 The result of backtesting will confirm if your bot has better odds of making a profit than a loss.
@@ -22,19 +22,19 @@ The result of backtesting will confirm if your bot has better odds of making a p
 
 ### Run a backtesting against the currencies listed in your config file
 
-#### With 5 min tickers (Per default)
+#### With 5 min candle (OHLCV) data (per default)
 
 ```bash
 freqtrade backtesting
 ```
 
-#### With 1 min tickers
+#### With 1 min candle (OHLCV) data
 
 ```bash
 freqtrade backtesting --ticker-interval 1m
 ```
 
-#### Using a different on-disk ticker-data source
+#### Using a different on-disk historical candle (OHLCV) data source
 
 Assume you downloaded the history data from the Bittrex exchange and kept it in the `user_data/data/bittrex-20180101` directory. 
 You can then use this data for backtesting as follows:
@@ -119,40 +119,40 @@ A backtesting result will look like that:
 
 ```
 ========================================================= BACKTESTING REPORT ========================================================
-| pair     |   buy count |   avg profit % |   cum profit % |   tot profit BTC |   tot profit % | avg duration   |   profit |   loss |
-|:---------|------------:|---------------:|---------------:|-----------------:|---------------:|:---------------|---------:|-------:|
-| ADA/BTC  |          35 |          -0.11 |          -3.88 |      -0.00019428 |          -1.94 | 4:35:00        |       14 |     21 |
-| ARK/BTC  |          11 |          -0.41 |          -4.52 |      -0.00022647 |          -2.26 | 2:03:00        |        3 |      8 |
-| BTS/BTC  |          32 |           0.31 |           9.78 |       0.00048938 |           4.89 | 5:05:00        |       18 |     14 |
-| DASH/BTC |          13 |          -0.08 |          -1.07 |      -0.00005343 |          -0.53 | 4:39:00        |        6 |      7 |
-| ENG/BTC  |          18 |           1.36 |          24.54 |       0.00122807 |          12.27 | 2:50:00        |        8 |     10 |
-| EOS/BTC  |          36 |           0.08 |           3.06 |       0.00015304 |           1.53 | 3:34:00        |       16 |     20 |
-| ETC/BTC  |          26 |           0.37 |           9.51 |       0.00047576 |           4.75 | 6:14:00        |       11 |     15 |
-| ETH/BTC  |          33 |           0.30 |           9.96 |       0.00049856 |           4.98 | 7:31:00        |       16 |     17 |
-| IOTA/BTC |          32 |           0.03 |           1.09 |       0.00005444 |           0.54 | 3:12:00        |       14 |     18 |
-| LSK/BTC  |          15 |           1.75 |          26.26 |       0.00131413 |          13.13 | 2:58:00        |        6 |      9 |
-| LTC/BTC  |          32 |          -0.04 |          -1.38 |      -0.00006886 |          -0.69 | 4:49:00        |       11 |     21 |
-| NANO/BTC |          17 |           1.26 |          21.39 |       0.00107058 |          10.70 | 1:55:00        |       10 |      7 |
-| NEO/BTC  |          23 |           0.82 |          18.97 |       0.00094936 |           9.48 | 2:59:00        |       10 |     13 |
-| REQ/BTC  |           9 |           1.17 |          10.54 |       0.00052734 |           5.27 | 3:47:00        |        4 |      5 |
-| XLM/BTC  |          16 |           1.22 |          19.54 |       0.00097800 |           9.77 | 3:15:00        |        7 |      9 |
-| XMR/BTC  |          23 |          -0.18 |          -4.13 |      -0.00020696 |          -2.07 | 5:30:00        |       12 |     11 |
-| XRP/BTC  |          35 |           0.66 |          22.96 |       0.00114897 |          11.48 | 3:49:00        |       12 |     23 |
-| ZEC/BTC  |          22 |          -0.46 |         -10.18 |      -0.00050971 |          -5.09 | 2:22:00        |        7 |     15 |
-| TOTAL    |         429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00        |      186 |    243 |
+| Pair     |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration   |  Wins |  Draws |  Losses |
+|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|--------:|
+| ADA/BTC  |     35 |          -0.11 |          -3.88 |      -0.00019428 |          -1.94 | 4:35:00        |    14 |      0 |      21 |
+| ARK/BTC  |     11 |          -0.41 |          -4.52 |      -0.00022647 |          -2.26 | 2:03:00        |     3 |      0 |       8 |
+| BTS/BTC  |     32 |           0.31 |           9.78 |       0.00048938 |           4.89 | 5:05:00        |    18 |      0 |      14 |
+| DASH/BTC |     13 |          -0.08 |          -1.07 |      -0.00005343 |          -0.53 | 4:39:00        |     6 |      0 |       7 |
+| ENG/BTC  |     18 |           1.36 |          24.54 |       0.00122807 |          12.27 | 2:50:00        |     8 |      0 |      10 |
+| EOS/BTC  |     36 |           0.08 |           3.06 |       0.00015304 |           1.53 | 3:34:00        |    16 |      0 |      20 |
+| ETC/BTC  |     26 |           0.37 |           9.51 |       0.00047576 |           4.75 | 6:14:00        |    11 |      0 |      15 |
+| ETH/BTC  |     33 |           0.30 |           9.96 |       0.00049856 |           4.98 | 7:31:00        |    16 |      0 |      17 |
+| IOTA/BTC |     32 |           0.03 |           1.09 |       0.00005444 |           0.54 | 3:12:00        |    14 |      0 |      18 |
+| LSK/BTC  |     15 |           1.75 |          26.26 |       0.00131413 |          13.13 | 2:58:00        |     6 |      0 |       9 |
+| LTC/BTC  |     32 |          -0.04 |          -1.38 |      -0.00006886 |          -0.69 | 4:49:00        |    11 |      0 |      21 |
+| NANO/BTC |     17 |           1.26 |          21.39 |       0.00107058 |          10.70 | 1:55:00        |    10 |      0 |       7 |
+| NEO/BTC  |     23 |           0.82 |          18.97 |       0.00094936 |           9.48 | 2:59:00        |    10 |      0 |      13 |
+| REQ/BTC  |      9 |           1.17 |          10.54 |       0.00052734 |           5.27 | 3:47:00        |     4 |      0 |       5 |
+| XLM/BTC  |     16 |           1.22 |          19.54 |       0.00097800 |           9.77 | 3:15:00        |     7 |      0 |       9 |
+| XMR/BTC  |     23 |          -0.18 |          -4.13 |      -0.00020696 |          -2.07 | 5:30:00        |    12 |      0 |      11 |
+| XRP/BTC  |     35 |           0.66 |          22.96 |       0.00114897 |          11.48 | 3:49:00        |    12 |      0 |      23 |
+| ZEC/BTC  |     22 |          -0.46 |         -10.18 |      -0.00050971 |          -5.09 | 2:22:00        |     7 |      0 |      15 |
+| TOTAL    |    429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00        |   186 |      0 |     243 |
 ========================================================= SELL REASON STATS =========================================================
-| Sell Reason        |   Count |   Profit |   Loss |
-|:-------------------|--------:|---------:|-------:|
-| trailing_stop_loss |     205 |      150 |     55 |
-| stop_loss          |     166 |        0 |    166 |
-| sell_signal        |      56 |       36 |     20 |
-| force_sell         |       2 |        0 |      2 |
+| Sell Reason        |   Sells |  Wins |  Draws |  Losses |
+|:-------------------|--------:|------:|-------:|--------:|
+| trailing_stop_loss |     205 |   150 |      0 |      55 |
+| stop_loss          |     166 |     0 |      0 |     166 |
+| sell_signal        |      56 |    36 |      0 |      20 |
+| force_sell         |       2 |     0 |      0 |       2 |
 ====================================================== LEFT OPEN TRADES REPORT ======================================================
-| pair     |   buy count |   avg profit % |   cum profit % |   tot profit BTC |   tot profit % | avg duration   |   profit |   loss |
-|:---------|------------:|---------------:|---------------:|-----------------:|---------------:|:---------------|---------:|-------:|
-| ADA/BTC  |           1 |           0.89 |           0.89 |       0.00004434 |           0.44 | 6:00:00        |        1 |      0 |
-| LTC/BTC  |           1 |           0.68 |           0.68 |       0.00003421 |           0.34 | 2:00:00        |        1 |      0 |
-| TOTAL    |           2 |           0.78 |           1.57 |       0.00007855 |           0.78 | 4:00:00        |        2 |      0 |
+| Pair     |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration   |  Wins |  Draws |  Losses |
+|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|--------:|
+| ADA/BTC  |      1 |           0.89 |           0.89 |       0.00004434 |           0.44 | 6:00:00        |     1 |      0 |       0 |
+| LTC/BTC  |      1 |           0.68 |           0.68 |       0.00003421 |           0.34 | 2:00:00        |     1 |      0 |       0 |
+| TOTAL    |      2 |           0.78 |           1.57 |       0.00007855 |           0.78 | 4:00:00        |     2 |      0 |       0 |
 ```
 
 The 1st table contains all trades the bot made, including "left open trades".
@@ -223,7 +223,7 @@ You can then load the trades to perform further analysis as shown in our [data a
 
 To compare multiple strategies, a list of Strategies can be provided to backtesting.
 
-This is limited to 1 ticker-interval per run, however, data is only loaded once from disk so if you have multiple
+This is limited to 1 timeframe (ticker interval) value per run. However, data is only loaded once from disk so if you have multiple
 strategies you'd like to compare, this will give a nice runtime boost.
 
 All listed Strategies need to be in the same directory.
@@ -237,11 +237,11 @@ There will be an additional table comparing win/losses of the different strategi
 Detailed output for all strategies one after the other will be available, so make sure to scroll up to see the details per strategy.
 
 ```
-=========================================================== Strategy Summary ===========================================================
-| Strategy    |   buy count |   avg profit % |   cum profit % |   tot profit BTC |   tot profit % | avg duration   |   profit |   loss |
-|:------------|------------:|---------------:|---------------:|-----------------:|---------------:|:---------------|---------:|-------:|
-| Strategy1   |         429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00        |      186 |    243 |
-| Strategy2   |        1487 |          -0.13 |        -197.58 |      -0.00988917 |         -98.79 | 4:43:00        |      662 |    825 |
+=========================================================== STRATEGY SUMMARY ===========================================================
+| Strategy    |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration   |  Wins |  Draws | Losses |
+|:------------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|-------:|
+| Strategy1   |    429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00        |   186 |      0 |    243 |
+| Strategy2   |   1487 |          -0.13 |        -197.58 |      -0.00988917 |         -98.79 | 4:43:00        |   662 |      0 |    825 |
 ```
 
 ## Next step

docs/bot-usage.md

@@ -58,9 +58,10 @@ Common arguments:
                         details.
   -V, --version         show program's version number and exit
   -c PATH, --config PATH
-                        Specify configuration file (default: `config.json`).
-                        Multiple --config options may be used. Can be set to
-                        `-` to read config from stdin.
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
   -d PATH, --datadir PATH
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
@@ -71,6 +72,7 @@ Strategy arguments:
                         Specify strategy class name which will be used by the
                         bot.
   --strategy-path PATH  Specify additional strategy lookup path.
+.
 
 ```
 
@@ -242,12 +244,15 @@ optional arguments:
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
-  --logfile FILE        Log to the file specified.
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
   -V, --version         show program's version number and exit
   -c PATH, --config PATH
-                        Specify configuration file (default: `config.json`).
-                        Multiple --config options may be used. Can be set to
-                        `-` to read config from stdin.
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
   -d PATH, --datadir PATH
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
@@ -270,7 +275,7 @@ Check the corresponding [Data Downloading](data-download.md) section for more de
 ## Hyperopt commands
 
 To optimize your strategy, you can use hyperopt parameter hyperoptimization
-to find optimal parameter values for your stategy.
+to find optimal parameter values for your strategy.
 
 ```
 usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
@@ -280,7 +285,7 @@ usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
                           [--stake-amount STAKE_AMOUNT] [--fee FLOAT]
                           [--hyperopt NAME] [--hyperopt-path PATH] [--eps]
                           [-e INT]
-                          [--spaces {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]]
+                          [--spaces {all,buy,sell,roi,stoploss,trailing,default} [{all,buy,sell,roi,stoploss,trailing,default} ...]]
                           [--dmmp] [--print-all] [--no-color] [--print-json]
                           [-j JOBS] [--random-state INT] [--min-trades INT]
                           [--continue] [--hyperopt-loss NAME]
@@ -308,9 +313,9 @@ optional arguments:
                         Allow buying the same pair multiple times (position
                         stacking).
   -e INT, --epochs INT  Specify number of epochs (default: 100).
-  --spaces {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]
+  --spaces {all,buy,sell,roi,stoploss,trailing,default} [{all,buy,sell,roi,stoploss,trailing,default} ...]
                         Specify which parameters to hyperopt. Space-separated
-                        list. Default: `all`.
+                        list.
   --dmmp, --disable-max-market-positions
                         Disable applying `max_open_trades` during backtest
                         (same as setting `max_open_trades` to a very high
@@ -318,7 +323,7 @@ optional arguments:
   --print-all           Print all results, not only the best ones.
   --no-color            Disable colorization of hyperopt results. May be
                         useful if you are redirecting output to a file.
-  --print-json          Print best result detailization in JSON format.
+  --print-json          Print best results in JSON format.
   -j JOBS, --job-workers JOBS
                         The number of concurrently running jobs for
                         hyperoptimization (hyperopt worker processes). If -1
@@ -336,18 +341,23 @@ optional arguments:
                         class (IHyperOptLoss). Different functions can
                         generate completely different results, since the
                         target for optimization is different. Built-in
-                        Hyperopt-loss-functions are: DefaultHyperOptLoss,
-                        OnlyProfitHyperOptLoss, SharpeHyperOptLoss (default:
-                        `DefaultHyperOptLoss`).
+                        Hyperopt-loss-functions are:
+                        DefaultHyperOptLoss, OnlyProfitHyperOptLoss,
+                        SharpeHyperOptLoss, SharpeHyperOptLossDaily,
+                        SortinoHyperOptLoss, SortinoHyperOptLossDaily.
+                        (default: `DefaultHyperOptLoss`).
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
-  --logfile FILE        Log to the file specified.
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
   -V, --version         show program's version number and exit
   -c PATH, --config PATH
-                        Specify configuration file (default: `config.json`).
-                        Multiple --config options may be used. Can be set to
-                        `-` to read config from stdin.
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
   -d PATH, --datadir PATH
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
@@ -358,6 +368,7 @@ Strategy arguments:
                         Specify strategy class name which will be used by the
                         bot.
   --strategy-path PATH  Specify additional strategy lookup path.
+
 ```
 
 ## Edge commands
@@ -394,12 +405,15 @@ optional arguments:
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
-  --logfile FILE        Log to the file specified.
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
   -V, --version         show program's version number and exit
   -c PATH, --config PATH
-                        Specify configuration file (default: `config.json`).
-                        Multiple --config options may be used. Can be set to
-                        `-` to read config from stdin.
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
   -d PATH, --datadir PATH
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
@@ -410,6 +424,7 @@ Strategy arguments:
                         Specify strategy class name which will be used by the
                         bot.
   --strategy-path PATH  Specify additional strategy lookup path.
+
 ```
 
 To understand edge and how to read the results, please read the [edge documentation](edge.md).

docs/configuration.md

@@ -40,75 +40,81 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 
 |  Parameter | Description |
 |------------|-------------|
-| `max_open_trades` | **Required.** Number of trades open your bot will have. If -1 then it is ignored (i.e. potentially unlimited open trades). [More information below](#configuring-amount-per-trade).<br> ***Datatype:*** *Positive integer or -1.*
-| `stake_currency` | **Required.** Crypto-currency used for trading. [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *String*
-| `stake_amount` | **Required.** Amount of crypto-currency your bot will use for each trade. Set it to `"unlimited"` to allow the bot to use all available balance. [More information below](#configuring-amount-per-trade). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *Positive float or `"unlimited"`.*
-| `tradable_balance_ratio` | Ratio of the total account balance the bot is allowed to trade. [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.99` 99%).*<br> ***Datatype:*** *Positive float between `0.1` and `1.0`.*
-| `amend_last_stake_amount` | Use reduced last stake amount if necessary. [More information below](#configuring-amount-per-trade). <br>*Defaults to `false`.* <br> ***Datatype:*** *Boolean*
-| `last_stake_amount_min_ratio` | Defines minimum stake amount that has to be left and executed. Applies only to the last stake amount when it's amended to a reduced value (i.e. if `amend_last_stake_amount` is set to `true`). [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.5`.* <br> ***Datatype:*** *Float (as ratio)*
-| `amount_reserve_percent` | Reserve some amount in min pair stake amount. The bot will reserve `amount_reserve_percent` + stoploss value when calculating min pair stake amount in order to avoid possible trade refusals. <br>*Defaults to `0.05` (5%).* <br> ***Datatype:*** *Positive Float as ratio.*
-| `ticker_interval` | The ticker interval to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *String*
-| `fiat_display_currency` | Fiat currency used to show your profits. [More information below](#what-values-can-be-used-for-fiat_display_currency). <br> ***Datatype:*** *String*
-| `dry_run` | **Required.** Define if the bot must be in Dry Run or production mode. <br>*Defaults to `true`.* <br> ***Datatype:*** *Boolean*
-| `dry_run_wallet` | Define the starting amount in stake currency for the simulated wallet used by the bot running in the Dry Run mode.<br>*Defaults to `1000`.* <br> ***Datatype:*** *Float*
-| `process_only_new_candles` | Enable processing of indicators only when new candles arrive. 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. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*  <br> ***Datatype:*** *Boolean*
-| `minimal_roi` | **Required.** Set the threshold in percent the bot will use to sell a trade. [More information below](#understand-minimal_roi). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *Dict*
-| `stoploss` |  **Required.** Value of the stoploss in percent used by the bot. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy).  <br> ***Datatype:*** *Float (as ratio)*
-| `trailing_stop` | Enables trailing stoploss (based on `stoploss` in either configuration or strategy file). More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *Boolean*
-| `trailing_stop_positive` | Changes stoploss once profit has been reached. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *Float*
-| `trailing_stop_positive_offset` | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0` (no offset).* <br> ***Datatype:*** *Float*
-| `trailing_only_offset_is_reached` | Only apply trailing stoploss when the offset is reached. [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*  <br> ***Datatype:*** *Boolean*
-| `unfilledtimeout.buy` | **Required.** How long (in minutes) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled. [Strategy Override](#parameters-in-the-strategy).<br> ***Datatype:*** *Integer*
-| `unfilledtimeout.sell` | **Required.** How long (in minutes) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled. [Strategy Override](#parameters-in-the-strategy).<br> ***Datatype:*** *Integer*
-| `bid_strategy.ask_last_balance` | **Required.** Set the bidding price. More information [below](#buy-price-without-orderbook). 
-| `bid_strategy.use_order_book` | Enable buying using the rates in [Order Book Bids](#buy-price-with-orderbook-enabled). <br> ***Datatype:*** *Boolean*
-| `bid_strategy.order_book_top` | Bot will use the top N rate in Order Book Bids to buy. I.e. a value of 2 will allow the bot to pick the 2nd bid rate in [Order Book Bids](#buy-price-with-orderbook-enabled). <br>*Defaults to `1`.*  <br> ***Datatype:*** *Positive Integer*
-| `bid_strategy. check_depth_of_market.enabled` | Do not buy if the difference of buy orders and sell orders is met in Order Book. [Check market depth](#check-depth-of-market). <br>*Defaults to `false`.* <br> ***Datatype:*** *Boolean*
-| `bid_strategy. check_depth_of_market.bids_to_ask_delta` | The difference ratio of buy orders and sell orders found in Order Book. A value below 1 means sell order size is greater, while value greater than 1 means buy order size is higher. [Check market depth](#check-depth-of-market) <br> *Defaults to `0`.*  <br> ***Datatype:*** *Float (as ratio)*
-| `ask_strategy.use_order_book` | Enable selling of open trades using [Order Book Asks](#sell-price-with-orderbook-enabled). <br> ***Datatype:*** *Boolean*
-| `ask_strategy.order_book_min` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> ***Datatype:*** *Positive Integer*
-| `ask_strategy.order_book_max` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> ***Datatype:*** *Positive Integer*
-| `ask_strategy.use_sell_signal` | Use sell signals produced by the strategy in addition to the `minimal_roi`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `true`.* <br> ***Datatype:*** *Boolean*
-| `ask_strategy.sell_profit_only` | Wait until the bot makes a positive profit before taking a sell decision. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> ***Datatype:*** *Boolean*
-| `ask_strategy.ignore_roi_if_buy_signal` | Do not sell if the buy signal is still active. This setting takes preference over `minimal_roi` and `use_sell_signal`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> ***Datatype:*** *Boolean*
-| `order_types` | Configure order-types depending on the action (`"buy"`, `"sell"`, `"stoploss"`, `"stoploss_on_exchange"`). [More information below](#understand-order_types). [Strategy Override](#parameters-in-the-strategy).<br> ***Datatype:*** *Dict*
-| `order_time_in_force` | Configure time in force for buy and sell orders. [More information below](#understand-order_time_in_force). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *Dict*
-| `exchange.name` | **Required.** Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename). <br> ***Datatype:*** *String*
-| `exchange.sandbox` | Use the 'sandbox' version of the exchange, where the exchange provides a sandbox for risk-free integration. See [here](sandbox-testing.md) in more details.<br> ***Datatype:*** *Boolean*
-| `exchange.key` | API key to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
-| `exchange.secret` | API secret to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
-| `exchange.password` | API password to use for the exchange. Only required when you are in production mode and for exchanges that use password for API requests.<br>**Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
-| `exchange.pair_whitelist` | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Not used by VolumePairList (see [below](#dynamic-pairlists)). <br> ***Datatype:*** *List*
-| `exchange.pair_blacklist` | List of pairs the bot must absolutely avoid for trading and backtesting (see [below](#dynamic-pairlists)). <br> ***Datatype:*** *List*
-| `exchange.ccxt_config` | Additional CCXT parameters passed to the regular ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> ***Datatype:*** *Dict*
-| `exchange.ccxt_async_config` | Additional CCXT parameters passed to the async ccxt instance. Parameters may differ from exchange to exchange  and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> ***Datatype:*** *Dict*
-| `exchange.markets_refresh_interval` | The interval in minutes in which markets are reloaded. <br>*Defaults to `60` minutes.* <br> ***Datatype:*** *Positive Integer*
+| `max_open_trades` | **Required.** Number of open trades your bot is allowed to have. Only one open trade per pair is possible, so the length of your pairlist is another limitation which can apply. If -1 then it is ignored (i.e. potentially unlimited open trades, limited by the pairlist). [More information below](#configuring-amount-per-trade).<br> **Datatype:** Positive integer or -1.
+| `stake_currency` | **Required.** Crypto-currency used for trading. [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
+| `stake_amount` | **Required.** Amount of crypto-currency your bot will use for each trade. Set it to `"unlimited"` to allow the bot to use all available balance. [More information below](#configuring-amount-per-trade). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Positive float or `"unlimited"`.
+| `tradable_balance_ratio` | Ratio of the total account balance the bot is allowed to trade. [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.99` 99%).*<br> **Datatype:** Positive float between `0.1` and `1.0`.
+| `amend_last_stake_amount` | Use reduced last stake amount if necessary. [More information below](#configuring-amount-per-trade). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
+| `last_stake_amount_min_ratio` | Defines minimum stake amount that has to be left and executed. Applies only to the last stake amount when it's amended to a reduced value (i.e. if `amend_last_stake_amount` is set to `true`). [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.5`.* <br> **Datatype:** Float (as ratio)
+| `amount_reserve_percent` | Reserve some amount in min pair stake amount. The bot will reserve `amount_reserve_percent` + stoploss value when calculating min pair stake amount in order to avoid possible trade refusals. <br>*Defaults to `0.05` (5%).* <br> **Datatype:** Positive Float as ratio.
+| `ticker_interval` | The timeframe (ticker interval) to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
+| `fiat_display_currency` | Fiat currency used to show your profits. [More information below](#what-values-can-be-used-for-fiat_display_currency). <br> **Datatype:** String
+| `dry_run` | **Required.** Define if the bot must be in Dry Run or production mode. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
+| `dry_run_wallet` | Define the starting amount in stake currency for the simulated wallet used by the bot running in the Dry Run mode.<br>*Defaults to `1000`.* <br> **Datatype:** Float
+| `process_only_new_candles` | Enable processing of indicators only when new candles arrive. 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. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*  <br> **Datatype:** Boolean
+| `minimal_roi` | **Required.** Set the threshold in percent the bot will use to sell a trade. [More information below](#understand-minimal_roi). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
+| `stoploss` |  **Required.** Value of the stoploss in percent used by the bot. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy).  <br> **Datatype:** Float (as ratio)
+| `trailing_stop` | Enables trailing stoploss (based on `stoploss` in either configuration or strategy file). More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Boolean
+| `trailing_stop_positive` | Changes stoploss once profit has been reached. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Float
+| `trailing_stop_positive_offset` | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0` (no offset).* <br> **Datatype:** Float
+| `trailing_only_offset_is_reached` | Only apply trailing stoploss when the offset is reached. [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*  <br> **Datatype:** Boolean
+| `unfilledtimeout.buy` | **Required.** How long (in minutes) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
+| `unfilledtimeout.sell` | **Required.** How long (in minutes) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
+| `bid_strategy.price_side` | Select the side of the spread the bot should look at to get the buy rate. [More information below](#buy-price-side).<br> *Defaults to `bid`.* <br> **Datatype:** String (either `ask` or `bid`).
+| `bid_strategy.ask_last_balance` | **Required.** Set the bidding price. More information [below](#buy-price-without-orderbook-enabled).
+| `bid_strategy.use_order_book` | Enable buying using the rates in [Order Book Bids](#buy-price-with-orderbook-enabled). <br> **Datatype:** Boolean
+| `bid_strategy.order_book_top` | Bot will use the top N rate in Order Book Bids to buy. I.e. a value of 2 will allow the bot to pick the 2nd bid rate in [Order Book Bids](#buy-price-with-orderbook-enabled). <br>*Defaults to `1`.*  <br> **Datatype:** Positive Integer
+| `bid_strategy. check_depth_of_market.enabled` | Do not buy if the difference of buy orders and sell orders is met in Order Book. [Check market depth](#check-depth-of-market). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
+| `bid_strategy. check_depth_of_market.bids_to_ask_delta` | The difference ratio of buy orders and sell orders found in Order Book. A value below 1 means sell order size is greater, while value greater than 1 means buy order size is higher. [Check market depth](#check-depth-of-market) <br> *Defaults to `0`.*  <br> **Datatype:** Float (as ratio)
+| `ask_strategy.price_side` | Select the side of the spread the bot should look at to get the sell rate. [More information below](#sell-price-side).<br> *Defaults to `ask`.* <br> **Datatype:** String (either `ask` or `bid`).
+| `ask_strategy.use_order_book` | Enable selling of open trades using [Order Book Asks](#sell-price-with-orderbook-enabled). <br> **Datatype:** Boolean
+| `ask_strategy.order_book_min` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
+| `ask_strategy.order_book_max` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
+| `ask_strategy.use_sell_signal` | Use sell signals produced by the strategy in addition to the `minimal_roi`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `true`.* <br> **Datatype:** Boolean
+| `ask_strategy.sell_profit_only` | Wait until the bot makes a positive profit before taking a sell decision. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
+| `ask_strategy.ignore_roi_if_buy_signal` | Do not sell if the buy signal is still active. This setting takes preference over `minimal_roi` and `use_sell_signal`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
+| `order_types` | Configure order-types depending on the action (`"buy"`, `"sell"`, `"stoploss"`, `"stoploss_on_exchange"`). [More information below](#understand-order_types). [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Dict
+| `order_time_in_force` | Configure time in force for buy and sell orders. [More information below](#understand-order_time_in_force). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
+| `exchange.name` | **Required.** Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename). <br> **Datatype:** String
+| `exchange.sandbox` | Use the 'sandbox' version of the exchange, where the exchange provides a sandbox for risk-free integration. See [here](sandbox-testing.md) in more details.<br> **Datatype:** Boolean
+| `exchange.key` | API key to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
+| `exchange.secret` | API secret to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
+| `exchange.password` | API password to use for the exchange. Only required when you are in production mode and for exchanges that use password for API requests.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
+| `exchange.pair_whitelist` | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Not used by VolumePairList (see [below](#dynamic-pairlists)). <br> **Datatype:** List
+| `exchange.pair_blacklist` | List of pairs the bot must absolutely avoid for trading and backtesting (see [below](#dynamic-pairlists)). <br> **Datatype:** List
+| `exchange.ccxt_config` | Additional CCXT parameters passed to the regular ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
+| `exchange.ccxt_async_config` | Additional CCXT parameters passed to the async ccxt instance. Parameters may differ from exchange to exchange  and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
+| `exchange.markets_refresh_interval` | The interval in minutes in which markets are reloaded. <br>*Defaults to `60` minutes.* <br> **Datatype:** Positive Integer
 | `edge.*` | Please refer to [edge configuration document](edge.md) for detailed explanation.
-| `experimental.block_bad_exchanges` | Block exchanges known to not work with freqtrade. Leave on default unless you want to test if that exchange works now. <br>*Defaults to `true`.* <br> ***Datatype:*** *Boolean*
-| `pairlists` | Define one or more pairlists to be used. [More information below](#dynamic-pairlists). <br>*Defaults to `StaticPairList`.*  <br> ***Datatype:*** *List of Dicts*
-| `telegram.enabled` | Enable the usage of Telegram. <br> ***Datatype:*** *Boolean*
-| `telegram.token` | Your Telegram bot token. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
-| `telegram.chat_id` | Your personal Telegram account id. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
-| `webhook.enabled` | Enable usage of Webhook notifications <br> ***Datatype:*** *Boolean*
-| `webhook.url` | URL for the webhook. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> ***Datatype:*** *String*
-| `webhook.webhookbuy` | Payload to send on buy. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> ***Datatype:*** *String*
-| `webhook.webhooksell` | Payload to send on sell. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> ***Datatype:*** *String*
-| `webhook.webhookstatus` | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> ***Datatype:*** *String*
-| `api_server.enabled` | Enable usage of API Server. See the [API Server documentation](rest-api.md) for more details. <br> ***Datatype:*** *Boolean*
-| `api_server.listen_ip_address` | Bind IP address. See the [API Server documentation](rest-api.md) for more details. <br> ***Datatype:*** *IPv4*
-| `api_server.listen_port` | Bind Port. See the [API Server documentation](rest-api.md) for more details. <br>***Datatype:*** *Integer between 1024 and 65535*
-| `api_server.username` | Username for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> ***Datatype:*** *String*
-| `api_server.password` | Password for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> ***Datatype:*** *String*
-| `db_url` | Declares database URL to use. NOTE: This defaults to `sqlite:///tradesv3.dryrun.sqlite` if `dry_run` is `true`, and to `sqlite:///tradesv3.sqlite` for production instances. <br> ***Datatype:*** *String, SQLAlchemy connect string*
-| `initial_state` | Defines the initial application state. More information below. <br>*Defaults to `stopped`.* <br> ***Datatype:*** *Enum, either `stopped` or `running`*
-| `forcebuy_enable` | Enables the RPC Commands to force a buy. More information below. <br> ***Datatype:*** *Boolean*
-| `strategy` | **Required** Defines Strategy class to use. Recommended to be set via `--strategy NAME`. <br> ***Datatype:*** *ClassName*
-| `strategy_path` | Adds an additional strategy lookup path (must be a directory). <br> ***Datatype:*** *String*
-| `internals.process_throttle_secs` | Set the process throttle. Value in second. <br>*Defaults to `5` seconds.* <br> ***Datatype:*** *Positive Integer*
-| `internals.heartbeat_interval` | Print heartbeat message every N seconds. Set to 0 to disable heartbeat messages. <br>*Defaults to `60` seconds.* <br> ***Datatype:*** *Positive Integer or 0*
-| `internals.sd_notify` | Enables use of the sd_notify protocol to tell systemd service manager about changes in the bot state and issue keep-alive pings. See [here](installation.md#7-optional-configure-freqtrade-as-a-systemd-service) for more details. <br> ***Datatype:*** *Boolean*
-| `logfile` | Specifies logfile name. Uses a rolling strategy for log file rotation for 10 files with the 1MB limit per file. <br> ***Datatype:*** *String*
-| `user_data_dir` | Directory containing user data. <br> *Defaults to `./user_data/`*. <br> ***Datatype:*** *String*
+| `experimental.block_bad_exchanges` | Block exchanges known to not work with freqtrade. Leave on default unless you want to test if that exchange works now. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
+| `pairlists` | Define one or more pairlists to be used. [More information below](#dynamic-pairlists). <br>*Defaults to `StaticPairList`.*  <br> **Datatype:** List of Dicts
+| `telegram.enabled` | Enable the usage of Telegram. <br> **Datatype:** Boolean
+| `telegram.token` | Your Telegram bot token. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
+| `telegram.chat_id` | Your personal Telegram account id. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
+| `webhook.enabled` | Enable usage of Webhook notifications <br> **Datatype:** Boolean
+| `webhook.url` | URL for the webhook. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhookbuy` | Payload to send on buy. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhookbuycancel` | Payload to send on buy order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhooksell` | Payload to send on sell. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhooksellcancel` | Payload to send on sell order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhookstatus` | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `api_server.enabled` | Enable usage of API Server. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** Boolean
+| `api_server.listen_ip_address` | Bind IP address. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** IPv4
+| `api_server.listen_port` | Bind Port. See the [API Server documentation](rest-api.md) for more details. <br>**Datatype:** Integer between 1024 and 65535
+| `api_server.username` | Username for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
+| `api_server.password` | Password for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
+| `db_url` | Declares database URL to use. NOTE: This defaults to `sqlite:///tradesv3.dryrun.sqlite` if `dry_run` is `true`, and to `sqlite:///tradesv3.sqlite` for production instances. <br> **Datatype:** String, SQLAlchemy connect string
+| `initial_state` | Defines the initial application state. More information below. <br>*Defaults to `stopped`.* <br> **Datatype:** Enum, either `stopped` or `running`
+| `forcebuy_enable` | Enables the RPC Commands to force a buy. More information below. <br> **Datatype:** Boolean
+| `strategy` | **Required** Defines Strategy class to use. Recommended to be set via `--strategy NAME`. <br> **Datatype:** ClassName
+| `strategy_path` | Adds an additional strategy lookup path (must be a directory). <br> **Datatype:** String
+| `internals.process_throttle_secs` | Set the process throttle. Value in second. <br>*Defaults to `5` seconds.* <br> **Datatype:** Positive Intege
+| `internals.heartbeat_interval` | Print heartbeat message every N seconds. Set to 0 to disable heartbeat messages. <br>*Defaults to `60` seconds.* <br> **Datatype:** Positive Integer or 0
+| `internals.sd_notify` | Enables use of the sd_notify protocol to tell systemd service manager about changes in the bot state and issue keep-alive pings. See [here](installation.md#7-optional-configure-freqtrade-as-a-systemd-service) for more details. <br> **Datatype:** Boolean
+| `logfile` | Specifies logfile name. Uses a rolling strategy for log file rotation for 10 files with the 1MB limit per file. <br> **Datatype:** String
+| `user_data_dir` | Directory containing user data. <br> *Defaults to `./user_data/`*. <br> **Datatype:** String
+| `dataformat_ohlcv` | Data format to use to store historical candle (OHLCV) data. <br> *Defaults to `json`*. <br> **Datatype:** String
+| `dataformat_trades` | Data format to use to store historical trades data. <br> *Defaults to `jsongz`*. <br> **Datatype:** String
 
 ### Parameters in the strategy
 
@@ -278,7 +284,7 @@ If this is configured, the following 4 values (`buy`, `sell`, `stoploss` and
 The below is the default which is used if this is not configured in either strategy or configuration file.
 
 Since `stoploss_on_exchange` uses limit orders, the exchange needs 2 prices, the stoploss_price and the Limit price.
-`stoploss` defines the stop-price - and limit should be slightly below this. This defaults to 0.99 / 1%.
+`stoploss` defines the stop-price - and limit should be slightly below this. This defaults to 0.99 / 1% (configurable via `stoploss_on_exchange_limit_ratio`).
 Calculation example: we bought the asset at 100$.
 Stop-price is 95$, then limit would be `95 * 0.99 = 94.05$` - so the stoploss will happen between 95$ and 94.05$.
 
@@ -336,7 +342,7 @@ This is most of the time the default time in force. It means the order will rema
 on exchange till it is canceled by user. It can be fully or partially fulfilled.
 If partially fulfilled, the remaining will stay on the exchange till cancelled.
 
-**FOK (Full Or Kill):**
+**FOK (Fill Or Kill):**
 
 It means if the order is not executed immediately AND fully then it is canceled by the exchange.
 
@@ -366,16 +372,18 @@ The possible values are: `gtc` (default), `fok` or `ioc`.
 
 Freqtrade is based on [CCXT library](https://github.com/ccxt/ccxt) that supports over 100 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:
+[CCXT repo homepage](https://github.com/ccxt/ccxt/tree/master/python).
+ However, the bot was tested by the development team with only Bittrex, Binance and Kraken,
+ so the these are the only officially supported exhanges:
 
 - [Bittrex](https://bittrex.com/): "bittrex"
 - [Binance](https://www.binance.com/): "binance"
+- [Kraken](https://kraken.com/): "kraken"
 
 Feel free to test other exchanges and submit your PR to improve the bot.
 
+Some exchanges require special configuration, which can be found on the [Exchange-specific Notes](exchanges.md) documentation page.
+
 #### Sample exchange configuration
 
 A exchange configuration for "binance" would look as follows:
@@ -405,7 +413,7 @@ Advanced options can be configured using the `_ft_has_params` setting, which wil
 
 Available options are listed in the exchange-class as `_ft_has_default`.
 
-For example, to test the order type `FOK` with Kraken, and modify candle_limit to 200 (so you only get 200 candles per call):
+For example, to test the order type `FOK` with Kraken, and modify candle limit to 200 (so you only get 200 candles per API call):
 
 ```json
 "exchange": {
@@ -457,34 +465,89 @@ Orderbook `bid` (buy) side depth is then divided by the orderbook `ask` (sell) s
 !!! Note
     A delta value below 1 means that `ask` (sell) orderbook side depth is greater than the depth of the `bid` (buy) orderbook side, while a value greater than 1 means opposite (depth of the buy side is higher than the depth of the sell side).
 
+#### Buy price side
+
+The configuration setting `bid_strategy.price_side` defines the side of the spread the bot looks for when buying.
+
+The following displays an orderbook.
+
+``` explanation
+...
+103
+102
+101  # ask
+-------------Current spread
+99   # bid
+98
+97
+...
+```
+
+If `bid_strategy.price_side` is set to `"bid"`, then the bot will use 99 as buying price.  
+In line with that, if `bid_strategy.price_side` is set to `"ask"`, then the bot will use 101 as buying price.
+
+Using `ask` price often guarantees quicker filled orders, but the bot can also end up paying more than what would have been necessary.
+Taker fees instead of maker fees will most likely apply even when using limit buy orders.
+Also, prices at the "ask" side of the spread are higher than prices at the "bid" side in the orderbook, so the order behaves similar to a market order (however with a maximum price).
+
 #### Buy price with Orderbook enabled
 
-When buying with the orderbook enabled (`bid_strategy.use_order_book=True`), Freqtrade fetches the `bid_strategy.order_book_top` entries from the orderbook and then uses the entry specified as `bid_strategy.order_book_top` on the `bid` (buy) side of the orderbook. 1 specifies the topmost entry in the orderbook, while 2 would use the 2nd entry in the orderbook, and so on.
+When buying with the orderbook enabled (`bid_strategy.use_order_book=True`), Freqtrade fetches the `bid_strategy.order_book_top` entries from the orderbook and then uses the entry specified as `bid_strategy.order_book_top` on the configured side (`bid_strategy.price_side`) of the orderbook. 1 specifies the topmost entry in the orderbook, while 2 would use the 2nd entry in the orderbook, and so on.
 
 #### Buy price without Orderbook enabled
 
-When not using orderbook (`bid_strategy.use_order_book=False`), Freqtrade uses the best `ask` (sell) price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `ask` price is not below the `last` price), it calculates a rate between `ask` and `last` price.
+The following section uses `side` as the configured `bid_strategy.price_side`.
 
-The `bid_strategy.ask_last_balance` configuration parameter controls this. A value of `0.0` will use `ask` price, while `1.0` will use the `last` price and values between those interpolate between ask and last price.
+When not using orderbook (`bid_strategy.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `side` price is above the `last` price), it calculates a rate between `side` and `last` price.
 
-Using `ask` price often guarantees quicker success in the bid, but the bot can also end up paying more than what would have been necessary.
+The `bid_strategy.ask_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the `last` price and values between those interpolate between ask and last price.
 
 ### Sell price
 
+#### Sell price side
+
+The configuration setting `ask_strategy.price_side` defines the side of the spread the bot looks for when selling.
+
+The following displays an orderbook:
+
+``` explanation
+...
+103
+102
+101  # ask
+-------------Current spread
+99   # bid
+98
+97
+...
+```
+
+If `ask_strategy.price_side` is set to `"ask"`, then the bot will use 101 as selling price.  
+In line with that, if `ask_strategy.price_side` is set to `"bid"`, then the bot will use 99 as selling price.
+
 #### Sell price with Orderbook enabled
 
-When selling with the orderbook enabled (`ask_strategy.use_order_book=True`), Freqtrade fetches the `ask_strategy.order_book_max` entries in the orderbook. Then each of the orderbook steps between `ask_strategy.order_book_min` and `ask_strategy.order_book_max` on the `ask` orderbook side are validated for a profitable sell-possibility based on the strategy configuration and the sell order is placed at the first profitable spot.
+When selling with the orderbook enabled (`ask_strategy.use_order_book=True`), Freqtrade fetches the `ask_strategy.order_book_max` entries in the orderbook. Then each of the orderbook steps between `ask_strategy.order_book_min` and `ask_strategy.order_book_max` on the configured orderbook side are validated for a profitable sell-possibility based on the strategy configuration (`minimal_roi` conditions) and the sell order is placed at the first profitable spot.
+
+!!! Note
+    Using `order_book_max` higher than `order_book_min` only makes sense when ask_strategy.price_side is set to `"ask"`.
 
 The idea here is to place the sell order early, to be ahead in the queue.
 
 A fixed slot (mirroring `bid_strategy.order_book_top`) can be defined by setting `ask_strategy.order_book_min` and `ask_strategy.order_book_max` to the same number.
 
-!!! Warning "Orderbook and stoploss_on_exchange"
-    Using `ask_strategy.order_book_max` higher than 1 may increase the risk, since an eventual [stoploss on exchange](#understand-order_types) will be needed to be cancelled as soon as the order is placed.
+!!! Warning "Order_book_max > 1 - increased risks for stoplosses!"
+    Using `ask_strategy.order_book_max` higher than 1 will increase the risk the stoploss on exchange is cancelled too early, since an eventual [stoploss on exchange](#understand-order_types) will be cancelled as soon as the order is placed.
+    Also, the sell order will remain on the exchange for `unfilledtimeout.sell` (or until it's filled) - which can lead to missed stoplosses (with or without using stoploss on exchange).
+
+!!! Warning "Order_book_max > 1 in dry-run"
+    Using `ask_strategy.order_book_max` higher than 1 will result in improper dry-run results (significantly better than real orders executed on exchange), since dry-run assumes orders to be filled almost instantly.
+    It is therefore advised to not use this setting for dry-runs.
+
 
 #### Sell price without Orderbook enabled
 
-When not using orderbook (`ask_strategy.use_order_book=False`), the `bid` price from the ticker will be used as the sell price.
+When not using orderbook (`ask_strategy.use_order_book=False`), the price at the `ask_strategy.price_side` side (defaults to `"ask"`) from the ticker will be used as the sell price.
 
 ## Pairlists
 
@@ -503,6 +566,7 @@ Inactive markets and blacklisted pairs are always removed from the resulting `pa
 * [`VolumePairList`](#volume-pair-list)
 * [`PrecisionFilter`](#precision-filter)
 * [`PriceFilter`](#price-pair-filter)
+* [`SpreadFilter`](#spread-filter)
 
 !!! Tip "Testing pairlists"
     Pairlist configurations can be quite tricky to get right. Best use the [`test-pairlist`](utils.md#test-pairlist) subcommand to test your configuration quickly.
@@ -527,6 +591,12 @@ It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklis
 
 `refresh_period` allows setting the period (in seconds), at which the pairlist will be refreshed. Defaults to 1800s (30 minutes).
 
+`VolumePairList` is based on the ticker data, as reported by the ccxt library:
+
+* The `bidVolume` is the volume (amount) of current best bid in the orderbook.
+* The `askVolume` is the volume (amount) of current best ask in the orderbook.
+* The `quoteVolume` is the amount of quote (stake) currency traded (bought or sold) in last 24 hours.
+
 ```json
 "pairlists": [{
         "method": "VolumePairList",
@@ -551,6 +621,11 @@ Min price precision is 8 decimals. If price is 0.00000011 - one step would be 0.
 
 These pairs are dangerous since it may be impossible to place the desired stoploss - and often result in high losses.
 
+#### Spread Filter
+Removes pairs that have a difference between asks and bids above the specified ratio (default `0.005`).
+Example:
+If `DOGE/BTC` maximum bid is 0.00000026 and minimum ask is 0.00000027 the ratio is calculated as: `1 - bid/ask ~= 0.037` which is `> 0.005` 
+
 ### Full Pairlist example
 
 The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets, sorting by `quoteVolume` and applies both [`PrecisionFilter`](#precision-filter) and [`PriceFilter`](#price-pair-filter), filtering all assets where 1 priceunit is > 1%.
@@ -602,12 +677,25 @@ Once you will be happy with your bot performance running in the Dry-run mode, yo
 !!! Note
     A simulated wallet is available during dry-run mode, and will assume a starting capital of `dry_run_wallet` (defaults to 1000).
 
+### Considerations for dry-run
+
+* API-keys may or may not be provided. Only Read-Only operations (i.e. operations that do not alter account state) on the exchange are performed in the dry-run mode.
+* Wallets (`/balance`) are simulated.
+* Orders are simulated, and will not be posted to the exchange.
+* In combination with `stoploss_on_exchange`, the stop_loss price is assumed to be filled.
+* Open orders (not trades, which are stored in the database) are reset on bot restart.
+
 ## Switch to production mode
 
 In production mode, the bot will engage your money. Be careful, since a wrong
 strategy can lose all your money. Be aware of what you are doing when
 you run it in production mode.
 
+### Setup your exchange account
+
+You will need to create API Keys (usually you get `key` and `secret`, some exchanges require an additional `password`) from the Exchange website and you'll need to insert this into the appropriate fields in the configuration or when asked by the `freqtrade new-config` command.
+API Keys are usually only required for live trading (trading for real money, bot running in "production mode", executing real orders on the exchange) and are not required for the bot running in dry-run (trade simulation) mode. When you setup the bot in dry-run mode, you may fill these fields with empty values.
+
 ### To switch your bot in production mode
 
 **Edit your `config.json`  file.**
@@ -629,9 +717,6 @@ you run it in production mode.
 }
 ```
 
-!!! Note
-    If you have an exchange API key yet, [see our tutorial](installation.md#setup-your-exchange-account).
-
 You should also make sure to read the [Exchanges](exchanges.md) section of the documentation to be aware of potential configuration details specific to your exchange.
 
 ### Using proxy with Freqtrade
@@ -656,7 +741,7 @@ freqtrade
 
 ## Embedding Strategies
 
-FreqTrade provides you with with an easy way to embed the strategy into your configuration file.
+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.
 

docs/data-download.md

@@ -12,6 +12,152 @@ Otherwise `--exchange` becomes mandatory.
     If you already have backtesting data available in your data-directory and would like to refresh this data up to today, use `--days xx` with a number slightly higher than the missing number of days. Freqtrade will keep the available data and only download the missing data.
     Be carefull though: If the number is too small (which would result in a few missing days), the whole dataset will be removed and only xx days will be downloaded.
 
+### Usage
+
+```
+usage: freqtrade download-data [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [-p PAIRS [PAIRS ...]]
+                               [--pairs-file FILE] [--days INT] [--dl-trades] [--exchange EXCHANGE]
+                               [-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]]
+                               [--erase] [--data-format-ohlcv {json,jsongz}] [--data-format-trades {json,jsongz}]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Show profits for only these pairs. Pairs are space-separated.
+  --pairs-file FILE     File containing a list of pairs to download.
+  --days INT            Download data for given number of days.
+  --dl-trades           Download trades instead of OHLCV data. The bot will resample trades to the desired timeframe as specified as
+                        --timeframes/-t.
+  --exchange EXCHANGE   Exchange name (default: `bittrex`). Only valid if no config is provided.
+  -t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]
+                        Specify which tickers to download. Space-separated list. Default: `1m 5m`.
+  --erase               Clean all existing data for the selected exchange/pairs/timeframes.
+  --data-format-ohlcv {json,jsongz}
+                        Storage format for downloaded candle (OHLCV) data. (default: `json`).
+  --data-format-trades {json,jsongz}
+                        Storage format for downloaded trades data. (default: `jsongz`).
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are: 'syslog', 'journald'. See the documentation for more details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`). Multiple --config options may be used. Can be set to `-`
+                        to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+```
+
+### Data format
+
+Freqtrade currently supports 2 dataformats, `json` (plain "text" json files) and `jsongz` (a gzipped version of json files).
+By default, OHLCV data is stored as `json` data, while trades data is stored as `jsongz` data.
+
+This can be changed via the `--data-format-ohlcv` and `--data-format-trades` parameters respectivly.
+
+If the default dataformat has been changed during download, then the keys `dataformat_ohlcv` and `dataformat_trades` in the configuration file need to be adjusted to the selected dataformat as well.
+
+!!! Note
+    You can convert between data-formats using the [convert-data](#subcommand-convert-data) and [convert-trade-data](#subcommand-convert-trade-data) methods.
+
+#### Subcommand convert data
+
+```
+usage: freqtrade convert-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                              [-d PATH] [--userdir PATH]
+                              [-p PAIRS [PAIRS ...]] --format-from
+                              {json,jsongz} --format-to {json,jsongz}
+                              [--erase]
+                              [-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Show profits for only these pairs. Pairs are space-
+                        separated.
+  --format-from {json,jsongz}
+                        Source format for data conversion.
+  --format-to {json,jsongz}
+                        Destination format for data conversion.
+  --erase               Clean all existing data for the selected
+                        exchange/pairs/timeframes.
+  -t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]
+                        Specify which tickers to download. Space-separated
+                        list. Default: `1m 5m`.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`).
+                        Multiple --config options may be used. Can be set to
+                        `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+```
+
+##### Example converting data
+
+The following command will convert all candle (OHLCV) data available in `~/.freqtrade/data/binance` from json to jsongz, saving diskspace in the process.
+It'll also remove original json data files (`--erase` parameter).
+
+``` bash
+freqtrade convert-data --format-from json --format-to jsongz --data-dir ~/.freqtrade/data/binance -t 5m 15m --erase
+```
+
+#### Subcommand convert-trade data
+
+```
+usage: freqtrade convert-trade-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                                    [-d PATH] [--userdir PATH]
+                                    [-p PAIRS [PAIRS ...]] --format-from
+                                    {json,jsongz} --format-to {json,jsongz}
+                                    [--erase]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Show profits for only these pairs. Pairs are space-
+                        separated.
+  --format-from {json,jsongz}
+                        Source format for data conversion.
+  --format-to {json,jsongz}
+                        Destination format for data conversion.
+  --erase               Clean all existing data for the selected
+                        exchange/pairs/timeframes.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`).
+                        Multiple --config options may be used. Can be set to
+                        `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+```
+
+##### Example converting trades
+
+The following command will convert all available trade-data in `~/.freqtrade/data/kraken` from jsongz to json.
+It'll also remove original jsongz data files (`--erase` parameter).
+
+``` bash
+freqtrade convert-trade-data --format-from jsongz --format-to json --data-dir ~/.freqtrade/data/kraken --erase
+```
+
 ### Pairs file
 
 In alternative to the whitelist from `config.json`, a `pairs.json` file can be used.
@@ -46,15 +192,15 @@ Then run:
 freqtrade download-data --exchange binance
 ```
 
-This will download ticker data for all the currency pairs you defined in `pairs.json`.
+This will download historical candle (OHLCV) data for all the currency pairs you defined in `pairs.json`.
 
 ### Other Notes
 
 - To use a different directory than the exchange specific default, use `--datadir user_data/data/some_directory`.
-- To change the exchange used to download the tickers, please use a different configuration file (you'll probably need to adjust ratelimits etc.)
+- To change the exchange used to download the historical data from, please use a different configuration file (you'll probably need to adjust ratelimits etc.)
 - To use `pairs.json` from some other directory, use `--pairs-file some_other_dir/pairs.json`.
-- To download ticker data for only 10 days, use `--days 10` (defaults to 30 days).
-- Use `--timeframes` to specify which tickers to download. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute tickers.
+- To download historical candle (OHLCV) data for only 10 days, use `--days 10` (defaults to 30 days).
+- Use `--timeframes` to specify what timeframe download the historical candle (OHLCV) data for. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute data.
 - To use exchange, timeframe and list of pairs as defined in your configuration file, use the `-c/--config` option. With this, the script uses the whitelist defined in the config as the list of currency pairs to download data for and does not require the pairs.json file. You can combine `-c/--config` with most other options.
 
 ### Trades (tick) data

docs/developer.md

@@ -1,6 +1,6 @@
 # Development Help
 
-This page is intended for developers of FreqTrade, people who want to contribute to the FreqTrade codebase or documentation, or people who want to understand the source code of the application they're running.
+This page is intended for developers of Freqtrade, people who want to contribute to the Freqtrade codebase or documentation, or people who want to understand the source code of the application they're running.
 
 All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. We [track issues](https://github.com/freqtrade/freqtrade/issues) on [GitHub](https://github.com) and also have a dev channel in [slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) where you can ask questions.
 
@@ -153,7 +153,7 @@ In VolumePairList, this implements different methods of sorting, does early vali
 ## Implement a new Exchange (WIP)
 
 !!! Note
-    This section is a Work in Progress and is not a complete guide on how to test a new exchange with FreqTrade.
+    This section is a Work in Progress and is not a complete guide on how to test a new exchange with Freqtrade.
 
 Most exchanges supported by CCXT should work out of the box.
 
@@ -165,7 +165,7 @@ Since CCXT does not provide unification for Stoploss On Exchange yet, we'll need
 
 ### Incomplete candles
 
-While fetching OHLCV data, we're may end up getting incomplete candles (Depending on the exchange).
+While fetching candle (OHLCV) data, we may end up getting incomplete candles (depending on the exchange).
 To demonstrate this, we'll use daily candles (`"1d"`) to keep things simple.
 We query the api (`ct.fetch_ohlcv()`) for the timeframe and look at the date of the last entry. If this entry changes or shows the date of a "incomplete" candle, then we should drop this since having incomplete candles is problematic because indicators assume that only complete candles are passed to them, and will generate a lot of false buy signals. By default, we're therefore removing the last candle assuming it's incomplete.
 
@@ -174,14 +174,14 @@ To check how the new exchange behaves, you can use the following snippet:
 ``` python
 import ccxt
 from datetime import datetime
-from freqtrade.data.converter import parse_ticker_dataframe
+from freqtrade.data.converter import ohlcv_to_dataframe
 ct = ccxt.binance()
 timeframe = "1d"
 pair = "XLM/BTC"  # Make sure to use a pair that exists on that exchange!
 raw = ct.fetch_ohlcv(pair, timeframe=timeframe)
 
 # convert to dataframe
-df1 = parse_ticker_dataframe(raw, timeframe, pair=pair, drop_incomplete=False)
+df1 = ohlcv_to_dataframe(raw, timeframe, pair=pair, drop_incomplete=False)
 
 print(df1.tail(1))
 print(datetime.utcnow())
@@ -234,7 +234,7 @@ git checkout -b new_release <commitid>
 
 Determine if crucial bugfixes have been made between this commit and the current state, and eventually cherry-pick these.
 
-* Edit `freqtrade/__init__.py` and add the version matching the current date (for example `2019.7` for July 2019). Minor versions can be `2019.7-1` should we need to do a second release that month.
+* Edit `freqtrade/__init__.py` and add the version matching the current date (for example `2019.7` for July 2019). Minor versions can be `2019.7.1` should we need to do a second release that month. Version numbers must follow allowed versions from PEP0440 to avoid failures pushing to pypi.
 * Commit this part
 * push that branch to the remote and create a PR against the master branch
 
@@ -268,11 +268,6 @@ Once the PR against master is merged (best right after merging):
 * Use "master" as reference (this step comes after the above PR is merged).
 * Use the above changelog as release comment (as codeblock)
 
-### After-release
-
-* Update version in develop by postfixing that with `-dev` (`2019.6 -> 2019.6-dev`).
-* Create a PR against develop to update that branch.
-
 ## Releases
 
 ### pypi

docs/docker.md

@@ -1,4 +1,4 @@
-# Using FreqTrade with Docker
+# Using Freqtrade with Docker
 
 ## Install Docker
 
@@ -8,13 +8,141 @@ Start by downloading and installing Docker CE for your platform:
 * [Windows](https://docs.docker.com/docker-for-windows/install/)
 * [Linux](https://docs.docker.com/install/)
 
+Optionally, [docker-compose](https://docs.docker.com/compose/install/) should be installed and available to follow the [docker quick start guide](#docker-quick-start).
+
 Once you have Docker installed, simply prepare the config file (e.g. `config.json`) and run the image for `freqtrade` as explained below.
 
-## Download the official FreqTrade docker image
+## Freqtrade with docker-compose
+
+Freqtrade provides an official Docker image on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/), as well as a [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) ready for usage.
+
+!!! Note
+    The following section assumes that docker and docker-compose is installed and available to the logged in user.
+
+!!! Note
+    All below comands use relative directories and will have to be executed from the directory containing the `docker-compose.yml` file.
+
+### Docker quick start
+
+Create a new directory and place the [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) in this directory.
+
+``` bash
+mkdir ft_userdata
+cd ft_userdata/
+# Download the docker-compose file from the repository
+curl https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docker-compose.yml -o docker-compose.yml
+
+# Pull the freqtrade image
+docker-compose pull
+
+# Create user directory structure
+docker-compose run --rm freqtrade create-userdir --userdir user_data
+
+# Create configuration - Requires answering interactive questions
+docker-compose run --rm freqtrade new-config --config user_data/config.json
+```
+
+The above snippet creates a new directory called "ft_userdata", downloads the latest compose file and pulls the freqtrade image.
+The last 2 steps in the snippet create the directory with user-data, as well as (interactively) the default configuration based on your selections.
+
+!!! Note
+    You can edit the configuration at any time, which is available as `user_data/config.json` (within the directory `ft_userdata`) when using the above configuration.
+
+#### Adding your strategy
+
+The configuration is now available as `user_data/config.json`.
+You should now copy your strategy to `user_data/strategies/` - and add the Strategy class name to the `docker-compose.yml` file, replacing `SampleStrategy`. If you wish to run the bot with the SampleStrategy, just leave it as it is.
+
+!!! Warning
+    The `SampleStrategy` is there for your reference and give you ideas for your own strategy.
+    Please always backtest the strategy and use dry-run for some time before risking real money!
+
+Once this is done, you're ready to launch the bot in trading mode (Dry-run or Live-trading, depending on your answer to the corresponding question you made above).
+
+``` bash
+docker-compose up -d
+```
+
+#### Docker-compose logs
+
+Logs will be written to `user_data/freqtrade.log`. 
+Alternatively, you can check the latest logs using `docker-compose logs -f`.
+
+#### Database
+
+The database will be in the user_data directory as well, and will be called `user_data/tradesv3.sqlite`.
+
+#### Updating freqtrade with docker-compose
+
+To update freqtrade when using docker-compose is as simple as running the following 2 commands:
+
+``` bash
+# Download the latest image
+docker-compose pull
+# Restart the image
+docker-compose up -d
+```
+
+This will first pull the latest image, and will then restart the container with the just pulled version.
+
+!!! Note
+    You should always check the changelog for breaking changes / manual interventions required and make sure the bot starts correctly after the update.
+
+#### Going from here
+
+Advanced users may edit the docker-compose file further to include all possible options or arguments.
+
+All possible freqtrade arguments will be available by running `docker-compose run --rm freqtrade <command> <optional arguments>`.
+
+!!! Note "`docker-compose run --rm`"
+    Including `--rm` will clean up the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
+
+##### Example: Download data with docker-compose
+
+Download backtesting data for 5 days for the pair ETH/BTC and 1h timeframe from Binance. The data will be stored in the directory `user_data/data/` on the host.
+
+``` bash
+docker-compose run --rm freqtrade download-data --pairs ETH/BTC --exchange binance --days 5 -t 1h
+```
+
+Head over to the [Data Downloading Documentation](data-download.md) for more details on downloading data.
+
+##### Example: Backtest with docker-compose
+
+Run backtesting in docker-containers for SampleStrategy and specified timerange of historical data, on 5m timeframe:
+
+``` bash
+docker-compose run --rm freqtrade backtesting --config user_data/config.json --strategy SampleStrategy --timerange 20190801-20191001 -i 5m
+```
+
+Head over to the [Backtesting Documentation](backtesting.md) to learn more.
+
+#### Additional dependencies with docker-compose
+
+If your strategy requires dependencies not included in the default image (like [technical](https://github.com/freqtrade/technical)) - it will be necessary to build the image on your host.
+For this, please create a Dockerfile containing installation steps for the additional dependencies (have a look at [Dockerfile.technical](https://github.com/freqtrade/freqtrade/blob/develop/Dockerfile.technical) for an example).
+
+You'll then also need to modify the `docker-compose.yml` file and uncomment the build step, as well as rename the image to avoid naming collisions.
+
+``` yaml
+    image: freqtrade_custom
+    build:
+      context: .
+      dockerfile: "./Dockerfile.<yourextension>"
+```
+
+You can then run `docker-compose build` to build the docker image, and run it using the commands described above.
+
+## Freqtrade with docker without docker-compose
+
+!!! Warning
+    The below documentation is provided for completeness and assumes that you are somewhat familiar with running docker containers. If you're just starting out with docker, we recommend to follow the [Freqtrade with docker-compose](#freqtrade-with-docker-compose) instructions.
+
+### Download the official Freqtrade docker image
 
 Pull the image from docker hub.
 
-Branches / tags available can be checked out on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/tags/).
+Branches / tags available can be checked out on [Dockerhub tags page](https://hub.docker.com/r/freqtradeorg/freqtrade/tags/).
 
 ```bash
 docker pull freqtradeorg/freqtrade:develop

docs/edge.md

@@ -145,19 +145,19 @@ Edge module has following configuration options:
 
 |  Parameter | Description |
 |------------|-------------|
-| `enabled` | If true, then Edge will run periodically. <br>*Defaults to `false`.* <br> ***Datatype:*** *Boolean*
-| `process_throttle_secs` | How often should Edge run in seconds. <br>*Defaults to `3600` (once per hour).* <br> ***Datatype:*** *Integer*
-| `calculate_since_number_of_days` | Number of days of data against which Edge calculates Win Rate, Risk Reward and Expectancy. <br> **Note** that it downloads historical data so increasing this number would lead to slowing down the bot. <br>*Defaults to `7`.* <br> ***Datatype:*** *Integer*
-| `capital_available_percentage` | **DEPRECATED - [replaced with `tradable_balance_ratio`](configuration.md#Available balance)** This is the percentage of the total capital on exchange in stake currency. <br>As an example if you have 10 ETH available in your wallet on the exchange and this value is 0.5 (which is 50%), then the bot will use a maximum amount of 5 ETH for trading and considers it as available capital. <br>*Defaults to `0.5`.* <br> ***Datatype:*** *Float*
-| `allowed_risk` | Ratio of allowed risk per trade. <br>*Defaults to `0.01` (1%)).* <br> ***Datatype:*** *Float*
-| `stoploss_range_min` | Minimum stoploss. <br>*Defaults to `-0.01`.* <br> ***Datatype:*** *Float*
-| `stoploss_range_max` | Maximum stoploss. <br>*Defaults to `-0.10`.* <br> ***Datatype:*** *Float*
-| `stoploss_range_step` | As an example if this is set to -0.01 then Edge will test the strategy for `[-0.01, -0,02, -0,03 ..., -0.09, -0.10]` ranges. <br> **Note** than having a smaller step means having a bigger range which could lead to slow calculation. <br> If you set this parameter to -0.001, you then slow down the Edge calculation by a factor of 10. <br>*Defaults to `-0.001`.* <br> ***Datatype:*** *Float*
-| `minimum_winrate` | It filters out pairs which don't have at least minimum_winrate. <br>This comes handy if you want to be conservative and don't comprise win rate in favour of risk reward ratio. <br>*Defaults to `0.60`.* <br> ***Datatype:*** *Float*
-| `minimum_expectancy` | It filters out pairs which have the expectancy lower than this number. <br>Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ return. <br>*Defaults to `0.20`.* <br> ***Datatype:*** *Float*
-| `min_trade_number` | When calculating *W*, *R* and *E* (expectancy) against historical data, you always want to have a minimum number of trades. The more this number is the more Edge is reliable. <br>Having a win rate of 100% on a single trade doesn't mean anything at all. But having a win rate of 70% over past 100 trades means clearly something. <br>*Defaults to `10` (it is highly recommended not to decrease this number).* <br> ***Datatype:*** *Integer*
-| `max_trade_duration_minute` | Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.<br>**NOTICE:** While configuring this value, you should take into consideration your ticker interval. As an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. Default value is set assuming your strategy interval is relatively small (1m or 5m, etc.).<br>*Defaults to `1440` (one day).* <br> ***Datatype:*** *Integer*
-| `remove_pumps` | Edge will remove sudden pumps in a given market while going through historical data. However, given that pumps happen very often in crypto markets, we recommend you keep this off.<br>*Defaults to `false`.* <br> ***Datatype:*** *Boolean*
+| `enabled` | If true, then Edge will run periodically. <br>*Defaults to `false`.* <br> **Datatype:** Boolean
+| `process_throttle_secs` | How often should Edge run in seconds. <br>*Defaults to `3600` (once per hour).* <br> **Datatype:** Integer
+| `calculate_since_number_of_days` | Number of days of data against which Edge calculates Win Rate, Risk Reward and Expectancy. <br> **Note** that it downloads historical data so increasing this number would lead to slowing down the bot. <br>*Defaults to `7`.* <br> **Datatype:** Integer
+| `capital_available_percentage` | **DEPRECATED - [replaced with `tradable_balance_ratio`](configuration.md#Available balance)** This is the percentage of the total capital on exchange in stake currency. <br>As an example if you have 10 ETH available in your wallet on the exchange and this value is 0.5 (which is 50%), then the bot will use a maximum amount of 5 ETH for trading and considers it as available capital. <br>*Defaults to `0.5`.* <br> **Datatype:** Float
+| `allowed_risk` | Ratio of allowed risk per trade. <br>*Defaults to `0.01` (1%)).* <br> **Datatype:** Float
+| `stoploss_range_min` | Minimum stoploss. <br>*Defaults to `-0.01`.* <br> **Datatype:** Float
+| `stoploss_range_max` | Maximum stoploss. <br>*Defaults to `-0.10`.* <br> **Datatype:** Float
+| `stoploss_range_step` | As an example if this is set to -0.01 then Edge will test the strategy for `[-0.01, -0,02, -0,03 ..., -0.09, -0.10]` ranges. <br> **Note** than having a smaller step means having a bigger range which could lead to slow calculation. <br> If you set this parameter to -0.001, you then slow down the Edge calculation by a factor of 10. <br>*Defaults to `-0.001`.* <br> **Datatype:** Float
+| `minimum_winrate` | It filters out pairs which don't have at least minimum_winrate. <br>This comes handy if you want to be conservative and don't comprise win rate in favour of risk reward ratio. <br>*Defaults to `0.60`.* <br> **Datatype:** Float
+| `minimum_expectancy` | It filters out pairs which have the expectancy lower than this number. <br>Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ return. <br>*Defaults to `0.20`.* <br> **Datatype:** Float
+| `min_trade_number` | When calculating *W*, *R* and *E* (expectancy) against historical data, you always want to have a minimum number of trades. The more this number is the more Edge is reliable. <br>Having a win rate of 100% on a single trade doesn't mean anything at all. But having a win rate of 70% over past 100 trades means clearly something. <br>*Defaults to `10` (it is highly recommended not to decrease this number).* <br> **Datatype:** Integer
+| `max_trade_duration_minute` | Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.<br>**NOTICE:** While configuring this value, you should take into consideration your timeframe (ticker interval). As an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. Default value is set assuming your strategy interval is relatively small (1m or 5m, etc.).<br>*Defaults to `1440` (one day).* <br> **Datatype:** Integer
+| `remove_pumps` | Edge will remove sudden pumps in a given market while going through historical data. However, given that pumps happen very often in crypto markets, we recommend you keep this off.<br>*Defaults to `false`.* <br> **Datatype:** Boolean
 
 ## Running Edge independently
 

docs/exchanges.md

@@ -5,7 +5,7 @@ This page combines common gotchas and informations which are exchange-specific a
 ## Binance
 
 !!! Tip "Stoploss on Exchange"
-    Binance is currently the only exchange supporting `stoploss_on_exchange`. It provides great advantages, so we recommend to benefit from it.
+    Binance supports `stoploss_on_exchange` and uses stop-loss-limit orders. It provides great advantages, so we recommend to benefit from it.
 
 ### Blacklists
 
@@ -22,6 +22,9 @@ Binance has been split into 3, and users must use the correct ccxt exchange ID f
 
 ## Kraken
 
+!!! Tip "Stoploss on Exchange"
+    Kraken supports `stoploss_on_exchange` and uses stop-loss-market orders. It provides great advantages, so we recommend to benefit from it, however since the resulting order is a stoploss-market order, sell-rates are not guaranteed, which makes this feature less secure than on other exchanges. This limitation is based on kraken's policy [source](https://blog.kraken.com/post/1234/announcement-delisting-pairs-and-temporary-suspension-of-advanced-order-types/) and [source2](https://blog.kraken.com/post/1494/kraken-enables-advanced-orders-and-adds-10-currency-pairs/) - which has stoploss-limit orders disabled.
+
 ### Historic Kraken data
 
 The Kraken API does only provide 720 historic candles, which is sufficient for Freqtrade dry-run and live trade modes, but is a problem for backtesting.
@@ -29,6 +32,10 @@ To download data for the Kraken exchange, using `--dl-trades` is mandatory, othe
 
 ## Bittrex
 
+### Order types
+
+Bittrex does not support market orders. If you have a message at the bot startup about this, you should change order type values set in your configuration and/or in the strategy from `"market"` to `"limit"`. See some more details on this [here in the FAQ](faq.md#im-getting-the-exchange-bittrex-does-not-support-market-orders-message-and-cannot-run-my-strategy).
+
 ### Restricted markets
 
 Bittrex split its exchange into US and International versions.
@@ -55,6 +62,11 @@ res = [ f"{x['MarketCurrency']}/{x['BaseCurrency']}" for x in ct.publicGetMarket
 print(res)
 ```
 
+## All exchanges
+
+Should you experience constant errors with Nonce (like `InvalidNonce`), it is best to regenerate the API keys. Resetting Nonce is difficult and it's usually easier to regenerate the API keys.
+
+
 ## Random notes for other exchanges
 
 * The Ocean (exchange id: `theocean`) exchange uses Web3 functionality and requires `web3` python package to be installed:
@@ -62,23 +74,13 @@ print(res)
 $ pip3 install web3
 ```
 
-### Send incomplete candles to the strategy
+### Getting latest price / Incomplete candles
 
-Most exchanges return incomplete candles via their ohlcv / klines interface.
-By default, Freqtrade assumes that incomplete candles are returned and removes the last candle assuming it's an incomplete candle.
+Most exchanges return current incomplete candle via their OHLCV/klines API interface.
+By default, Freqtrade assumes that incomplete candle is fetched from the exchange and removes the last candle assuming it's the incomplete candle.
 
 Whether your exchange returns incomplete candles or not can be checked using [the helper script](developer.md#Incomplete-candles) from the Contributor documentation.
 
-If the exchange does return incomplete candles and you would like to have incomplete candles in your strategy, you can set the following parameter in the configuration file.
+Due to the danger of repainting, Freqtrade does not allow you to use this incomplete candle.
 
-``` json
-{
-
-    "exchange": {
-        "_ft_has_params": {"ohlcv_partial_candle": false}
-    }
-}
-```
-
-!!! Warning "Danger of repainting"
-    Changing this parameter makes the strategy responsible to avoid repainting and handle this accordingly. Doing this is therefore not recommended, and should only be performed by experienced users who are fully aware of the impact this setting has.
+However, if it is based on the need for the latest price for your strategy - then this requirement can be acquired using the [data provider](strategy-customization.md#possible-options-for-dataprovider) from within the strategy.

docs/faq.md

@@ -45,12 +45,28 @@ the tutorial [here|Testing-new-strategies-with-Hyperopt](bot-usage.md#hyperopt-c
 
 You can use the `/forcesell all` command from Telegram.
 
-### I get the message "RESTRICTED_MARKET"
+### I'm getting the "RESTRICTED_MARKET" message in the log
 
 Currently known to happen for US Bittrex users.  
 
 Read [the Bittrex section about restricted markets](exchanges.md#restricted-markets) for more information.
 
+### I'm getting the "Exchange Bittrex does not support market orders." message and cannot run my strategy
+
+As the message says, Bittrex does not support market orders and you have one of the [order types](configuration.md/#understand-order_types) set to "market". Probably your strategy was written with other exchanges in mind and sets "market" orders for "stoploss" orders, which is correct and preferable for most of the exchanges supporting market orders (but not for Bittrex).
+
+To fix it for Bittrex, redefine order types in the strategy to use "limit" instead of "market":
+
+```
+    order_types = {
+        ...
+        'stoploss': 'limit',
+        ...
+    }
+```
+
+Same fix should be done in the configuration file, if order types are defined in your custom config rather than in the strategy.
+
 ### How do I search the bot logs for something?
 
 By default, the bot writes its log into stderr stream. This is implemented this way so that you can easily separate the bot's diagnostics messages from Backtesting, Edge and Hyperopt results, output from other various Freqtrade utility subcommands, as well as from the output of your custom `print()`'s you may have inserted into your strategy. So if you need to search the log messages with the grep utility, you need to redirect stderr to stdout and disregard stdout.

docs/hyperopt.md

@@ -31,9 +31,9 @@ This will create a new hyperopt file from a template, which will be located unde
 Depending on the space you want to optimize, only some of the below are required:
 
 * fill `buy_strategy_generator` - for buy signal optimization
-* fill `indicator_space` - for buy signal optimzation
+* fill `indicator_space` - for buy signal optimization
 * fill `sell_strategy_generator` - for sell signal optimization
-* fill `sell_indicator_space` - for sell signal optimzation
+* fill `sell_indicator_space` - for sell signal optimization
 
 !!! Note
     `populate_indicators` needs to create all indicators any of thee spaces may use, otherwise hyperopt will not work.
@@ -57,7 +57,7 @@ Rarely you may also need to override:
 !!! Tip "Quickly optimize ROI, stoploss and trailing stoploss"
     You can quickly optimize the spaces `roi`, `stoploss` and `trailing` without changing anything (i.e. without creation of a "complete" Hyperopt class with dimensions, parameters, triggers and guards, as described in this document) from the default hyperopt template by relying on your strategy to do most of the calculations.
 
-    ``` python
+    ```python
     # Have a working strategy at hand.
     freqtrade new-hyperopt --hyperopt EmptyHyperopt
 
@@ -75,17 +75,17 @@ Copy the file `user_data/hyperopts/sample_hyperopt.py` into `user_data/hyperopts
 
 There are two places you need to change in your hyperopt file to add a new buy hyperopt for testing:
 
-- Inside `indicator_space()` - the parameters hyperopt shall be optimizing.
-- Inside `populate_buy_trend()` - applying the parameters.
+* Inside `indicator_space()` - the parameters hyperopt shall be optimizing.
+* Inside `populate_buy_trend()` - applying the parameters.
 
 There you have two different types 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".
+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".
 
 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
+"*buy exactly when close price touches lower Bollinger band, BUT only if
 ADX > 10*".
 
 If you have updated the buy strategy, i.e. changed the contents of
@@ -103,9 +103,10 @@ Place the corresponding settings into the following methods
 The configuration and rules are the same than for buy signals.
 To avoid naming collisions in the search-space, please prefix all sell-spaces with `sell-`.
 
-#### Using ticker-interval as part of the Strategy
+#### Using timeframe as a part of the Strategy
 
-The Strategy exposes the ticker-interval as `self.ticker_interval`. The same value is available as class-attribute `HyperoptName.ticker_interval`.
+The Strategy class exposes the timeframe (ticker interval) value as the `self.ticker_interval` attribute.
+The same value is available as class-attribute `HyperoptName.ticker_interval`.
 In the case of the linked sample-value this would be `SampleHyperOpt.ticker_interval`.
 
 ## Solving a Mystery
@@ -141,7 +142,7 @@ 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:
 
-``` python
+```python
         def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
             conditions = []
             # GUARDS AND TRENDS
@@ -159,6 +160,9 @@ So let's write the buy strategy using these values:
                         dataframe['macd'], dataframe['macdsignal']
                     ))
 
+            # Check that volume is not 0
+            conditions.append(dataframe['volume'] > 0)
+
             if conditions:
                 dataframe.loc[
                     reduce(lambda x, y: x & y, conditions),
@@ -172,7 +176,7 @@ So let's write the buy strategy using these values:
 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.
+it will end with telling you which parameter combination produced the best profits.
 
 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
@@ -182,7 +186,7 @@ add it to the `populate_indicators()` method in your custom hyperopt file.
 
 Each hyperparameter tuning requires a target. This is usually defined as a loss function (sometimes also called objective function), which should decrease for more desirable results, and increase for bad results.
 
-By default, FreqTrade uses a loss function, which has been with freqtrade since the beginning and optimizes mostly for short trade duration and avoiding losses.
+By default, Freqtrade uses a loss function, which has been with freqtrade since the beginning and optimizes mostly for short trade duration and avoiding losses.
 
 A different loss function can be specified by using the `--hyperopt-loss <Class-name>` argument.
 This class should be in its own file within the `user_data/hyperopts/` directory.
@@ -191,7 +195,10 @@ Currently, the following loss functions are builtin:
 
 * `DefaultHyperOptLoss` (default legacy Freqtrade hyperoptimization loss function)
 * `OnlyProfitHyperOptLoss` (which takes only amount of profit into consideration)
-* `SharpeHyperOptLoss` (optimizes Sharpe Ratio calculated on the trade returns)
+* `SharpeHyperOptLoss` (optimizes Sharpe Ratio calculated on trade returns relative to standard deviation)
+* `SharpeHyperOptLossDaily` (optimizes Sharpe Ratio calculated on **daily** trade returns relative to standard deviation)
+* `SortinoHyperOptLoss` (optimizes Sortino Ratio calculated on trade returns relative to **downside** standard deviation)
+* `SortinoHyperOptLossDaily` (optimizes Sortino Ratio calculated on **daily** trade returns relative to **downside** standard deviation)
 
 Creation of a custom loss function is covered in the [Advanced Hyperopt](advanced-hyperopt.md) part of the documentation.
 
@@ -206,7 +213,7 @@ We strongly recommend to use `screen` or `tmux` to prevent any connection loss.
 freqtrade hyperopt --config config.json --hyperopt <hyperoptname> -e 5000 --spaces all
 ```
 
-Use  `<hyperoptname>` as the name of the custom hyperopt used.
+Use `<hyperoptname>` as the name of the custom hyperopt used.
 
 The `-e` option will set how many evaluations hyperopt will do. We recommend
 running at least several thousand evaluations.
@@ -219,11 +226,11 @@ The `--spaces all` option determines that all possible parameters should be opti
 !!! Warning
     When switching parameters or changing configuration options, make sure to not use the argument `--continue` so temporary results can be removed.
 
-### Execute Hyperopt with Different Ticker-Data Source
+### Execute Hyperopt with different historical 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`.
+If you would like to hyperopt parameters using an alternate historical data set that
+you have on-disk, use the `--datadir PATH` option. By default, hyperopt
+uses data from directory `user_data/data`.
 
 ### Running Hyperopt with Smaller Testset
 
@@ -271,7 +278,7 @@ In some situations, you may need to run Hyperopt (and Backtesting) with the
 By default, hyperopt emulates the behavior of the Freqtrade Live Run/Dry Run, where only one
 open trade is allowed for every traded pair. The total number of trades open for all pairs
 is also limited by the `max_open_trades` setting. During Hyperopt/Backtesting this may lead to
-some potential trades to be hidden (or masked) by previosly open trades.
+some potential trades to be hidden (or masked) by previously open trades.
 
 The `--eps`/`--enable-position-stacking` argument allows emulation of buying the same pair multiple times,
 while `--dmmp`/`--disable-max-market-positions` disables applying `max_open_trades`
@@ -323,7 +330,7 @@ method, what those values match to.
 
 So for example you had `rsi-value: 29.0` so we would look at `rsi`-block, that translates to the following code block:
 
-``` python
+```python
 (dataframe['rsi'] < 29.0)
 ```
 
@@ -372,20 +379,21 @@ In order to use this best ROI table found by Hyperopt in backtesting and for liv
         118: 0
     }
 ```
+
 As stated in the comment, you can also use it as the value of the `minimal_roi` setting in the configuration file.
 
 #### Default ROI Search Space
 
-If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps). Hyperopt implements adaptive ranges for ROI tables with ranges for values in the ROI steps that depend on the ticker_interval used. By default the values vary in the following ranges (for some of the most used ticker intervals, values are rounded to 5 digits after the decimal point):
+If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps). Hyperopt implements adaptive ranges for ROI tables with ranges for values in the ROI steps that depend on the ticker_interval used. By default the values vary in the following ranges (for some of the most used timeframes, values are rounded to 5 digits after the decimal point):
 
-| # step | 1m |  | 5m |  | 1h |  | 1d |  |
-|---|---|---|---|---|---|---|---|---|
-| 1 | 0 | 0.01161...0.11992 | 0 | 0.03...0.31 | 0 | 0.06883...0.71124 | 0 | 0.12178...1.25835 |
-| 2 | 2...8 | 0.00774...0.04255 | 10...40 | 0.02...0.11 | 120...480 | 0.04589...0.25238 | 2880...11520 | 0.08118...0.44651 |
-| 3 | 4...20 | 0.00387...0.01547 | 20...100 | 0.01...0.04 | 240...1200 | 0.02294...0.09177 | 5760...28800 | 0.04059...0.16237 |
-| 4 | 6...44 | 0.0 | 30...220 | 0.0 | 360...2640 | 0.0 | 8640...63360 | 0.0 |
+| # step | 1m     |                   | 5m       |             | 1h         |                   | 1d           |                   |
+| ------ | ------ | ----------------- | -------- | ----------- | ---------- | ----------------- | ------------ | ----------------- |
+| 1      | 0      | 0.01161...0.11992 | 0        | 0.03...0.31 | 0          | 0.06883...0.71124 | 0            | 0.12178...1.25835 |
+| 2      | 2...8  | 0.00774...0.04255 | 10...40  | 0.02...0.11 | 120...480  | 0.04589...0.25238 | 2880...11520 | 0.08118...0.44651 |
+| 3      | 4...20 | 0.00387...0.01547 | 20...100 | 0.01...0.04 | 240...1200 | 0.02294...0.09177 | 5760...28800 | 0.04059...0.16237 |
+| 4      | 6...44 | 0.0               | 30...220 | 0.0         | 360...2640 | 0.0               | 8640...63360 | 0.0               |
 
-These ranges should be sufficient in most cases. The minutes in the steps (ROI dict keys) are scaled linearly depending on the ticker interval used. The ROI values in the steps (ROI dict values) are scaled logarithmically depending on the ticker interval used.
+These ranges should be sufficient in most cases. The minutes in the steps (ROI dict keys) are scaled linearly depending on the timeframe (ticker interval) used. The ROI values in the steps (ROI dict values) are scaled logarithmically depending on the timeframe used.
 
 If you have the `generate_roi_table()` and `roi_space()` methods in your custom hyperopt file, remove them in order to utilize these adaptive ROI tables and the ROI hyperoptimization space generated by Freqtrade by default.
 
@@ -416,6 +424,7 @@ In order to use this best stoploss value found by Hyperopt in backtesting and fo
     # This attribute will be overridden if the config file contains "stoploss"
     stoploss = -0.27996
 ```
+
 As stated in the comment, you can also use it as the value of the `stoploss` setting in the configuration file.
 
 #### Default Stoploss Search Space
@@ -452,6 +461,7 @@ In order to use these best trailing stop parameters found by Hyperopt in backtes
     trailing_stop_positive_offset = 0.06038
     trailing_only_offset_is_reached = True
 ```
+
 As stated in the comment, you can also use it as the values of the corresponding settings in the configuration file.
 
 #### Default Trailing Stop Search Space

docs/index.md

@@ -1,5 +1,5 @@
 # Freqtrade
-[![Build Status](https://travis-ci.org/freqtrade/freqtrade.svg?branch=develop)](https://travis-ci.org/freqtrade/freqtrade)
+[![Freqtrade CI](https://github.com/freqtrade/freqtrade/workflows/Freqtrade%20CI/badge.svg)](https://github.com/freqtrade/freqtrade/actions/)
 [![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)
 
@@ -51,12 +51,15 @@ To run this bot we recommend you a cloud instance with a minimum of:
 
 ### Software requirements
 
+- Docker (Recommended)
+
+Alternatively
+
 - Python 3.6.x
 - pip (pip3)
 - git
 - TA-Lib
 - virtualenv (Recommended)
-- Docker (Recommended)
 
 ## Support
 
@@ -67,4 +70,4 @@ Click [here](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODc
 
 ## Ready to try?
 
-Begin by reading our installation guide [here](installation).
+Begin by reading our installation guide [for docker](docker.md), or for [installation without docker](installation.md).

docs/installation.md

@@ -2,6 +2,8 @@
 
 This page explains how to prepare your environment for running the bot.
 
+Please consider using the prebuilt [docker images](docker.md) to get started quickly while trying out freqtrade evaluating how it operates.
+
 ## Prerequisite
 
 ### Requirements
@@ -14,15 +16,7 @@ Click each one for install guide:
 * [virtualenv](https://virtualenv.pypa.io/en/stable/installation/) (Recommended)
 * [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html) (install instructions below)
 
-### API keys
-
-Before running your bot in production you will need to setup few
-external API. In production mode, the bot will require valid Exchange API
-credentials. We also recommend a [Telegram bot](telegram-usage.md#setup-your-telegram-bot) (optional but recommended).
-
-### Setup your exchange account
-
-You will need to create API Keys (Usually you get `key` and `secret`) from the Exchange website and insert this into the appropriate fields in the configuration or when asked by the installation script.
+ We also recommend a [Telegram bot](telegram-usage.md#setup-your-telegram-bot), which is optional but recommended.
 
 ## Quick start
 
@@ -31,7 +25,7 @@ Freqtrade provides the Linux/MacOS Easy Installation script to install all depen
 !!! Note
     Windows installation is explained [here](#windows).
 
-The easiest way to install and run Freqtrade is to clone the bot GitHub repository and then run the Easy Installation script, if it's available for your platform.
+The easiest way to install and run Freqtrade is to clone the bot Github repository and then run the Easy Installation script, if it's available for your platform.
 
 !!! Note "Version considerations"
     When cloning the repository the default working branch has the name `develop`. This branch contains all last features (can be considered as relatively stable, thanks to automated tests). The `master` branch contains the code of the last release (done usually once per month on an approximately one week old snapshot of the `develop` branch to prevent packaging bugs, so potentially it's more stable).
@@ -42,11 +36,12 @@ The easiest way to install and run Freqtrade is to clone the bot GitHub reposito
 This can be achieved with the following commands:
 
 ```bash
-git clone git@github.com:freqtrade/freqtrade.git
+git clone https://github.com/freqtrade/freqtrade.git
 cd freqtrade
 git checkout master  # Optional, see (1)
 ./setup.sh --install
 ```
+
 (1) This command switches the cloned repository to the use of the `master` branch. It's not needed if you wish to stay on the `develop` branch. You may later switch between branches at any time with the `git checkout master`/`git checkout develop` commands.
 
 ## Easy Installation Script (Linux/MacOS)
@@ -64,11 +59,11 @@ usage:
 
 ** --install **
 
-With this option, the script will install everything you need to run the bot:
+With this option, the script will install the bot and most dependencies:
+You will need to have git and python3.6+ installed beforehand for this to work.
 
 * Mandatory software as: `ta-lib`
-* Setup your virtualenv
-* Configure your `config.json` file
+* Setup your virtualenv under `.env/`
 
 This option is a combination of installation tasks, `--reset` and `--config`.
 
@@ -82,7 +77,7 @@ This option will hard reset your branch (only if you are on either `master` or `
 
 ** --config **
 
-Use this option to configure the `config.json` configuration file. The script will interactively ask you questions to setup your bot and create your `config.json`.
+DEPRECATED - use `freqtrade new-config -c config.json` instead.
 
 ------
 
@@ -129,6 +124,17 @@ bash setup.sh -i
 
 #### 1. Install TA-Lib
 
+Use the provided ta-lib installation script
+
+```bash
+sudo ./build_helpers/install_ta-lib.sh
+```
+
+!!! Note
+    This will use the ta-lib tar.gz included in this repository.
+
+##### TA-Lib manual installation
+
 Official webpage: https://mrjbq7.github.io/ta-lib/install.html
 
 ```bash
@@ -184,7 +190,8 @@ python3 -m pip install -e .
 # Initialize the user_directory
 freqtrade create-userdir --userdir user_data/
 
-cp config.json.example config.json
+# Create a new configuration file
+freqtrade new-config --config config.json
 ```
 
 > *To edit the config please refer to [Bot Configuration](configuration.md).*

docs/plotting.md

@@ -23,44 +23,64 @@ The `freqtrade plot-dataframe` subcommand shows an interactive graph with three
 Possible arguments:
 
 ```
-usage: freqtrade plot-dataframe [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [-s NAME]
-                                [--strategy-path PATH] [-p PAIRS [PAIRS ...]] [--indicators1 INDICATORS1 [INDICATORS1 ...]]
-                                [--indicators2 INDICATORS2 [INDICATORS2 ...]] [--plot-limit INT] [--db-url PATH]
-                                [--trade-source {DB,file}] [--export EXPORT] [--export-filename PATH] [--timerange TIMERANGE]
-                                [-i TICKER_INTERVAL]
+usage: freqtrade plot-dataframe [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                                [-d PATH] [--userdir PATH] [-s NAME]
+                                [--strategy-path PATH] [-p PAIRS [PAIRS ...]]
+                                [--indicators1 INDICATORS1 [INDICATORS1 ...]]
+                                [--indicators2 INDICATORS2 [INDICATORS2 ...]]
+                                [--plot-limit INT] [--db-url PATH]
+                                [--trade-source {DB,file}] [--export EXPORT]
+                                [--export-filename PATH]
+                                [--timerange TIMERANGE] [-i TICKER_INTERVAL]
+                                [--no-trades]
 
 optional arguments:
   -h, --help            show this help message and exit
   -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
-                        Show profits for only these pairs. Pairs are space-separated.
+                        Show profits for only these pairs. Pairs are space-
+                        separated.
   --indicators1 INDICATORS1 [INDICATORS1 ...]
-                        Set indicators from your strategy you want in the first row of the graph. Space-separated list. Example:
+                        Set indicators from your strategy you want in the
+                        first row of the graph. Space-separated list. Example:
                         `ema3 ema5`. Default: `['sma', 'ema3', 'ema5']`.
   --indicators2 INDICATORS2 [INDICATORS2 ...]
-                        Set indicators from your strategy you want in the third row of the graph. Space-separated list. Example:
+                        Set indicators from your strategy you want in the
+                        third row of the graph. Space-separated list. Example:
                         `fastd fastk`. Default: `['macd', 'macdsignal']`.
-  --plot-limit INT      Specify tick limit for plotting. Notice: too high values cause huge files. Default: 750.
-  --db-url PATH         Override trades database URL, this is useful in custom deployments (default: `sqlite:///tradesv3.sqlite`
-                        for Live Run mode, `sqlite:///tradesv3.dryrun.sqlite` for Dry Run).
+  --plot-limit INT      Specify tick limit for plotting. Notice: too high
+                        values cause huge files. Default: 750.
+  --db-url PATH         Override trades database URL, this is useful in custom
+                        deployments (default: `sqlite:///tradesv3.sqlite` for
+                        Live Run mode, `sqlite:///tradesv3.dryrun.sqlite` for
+                        Dry Run).
   --trade-source {DB,file}
-                        Specify the source for trades (Can be DB or file (backtest file)) Default: file
-  --export EXPORT       Export backtest results, argument are: trades. Example: `--export=trades`
+                        Specify the source for trades (Can be DB or file
+                        (backtest file)) Default: file
+  --export EXPORT       Export backtest results, argument are: trades.
+                        Example: `--export=trades`
   --export-filename PATH
-                        Save backtest results to the file with this filename. Requires `--export` to be set as well. Example:
-                        `--export-filename=user_data/backtest_results/backtest_today.json`
+                        Save backtest results to the file with this filename.
+                        Requires `--export` to be set as well. Example:
+                        `--export-filename=user_data/backtest_results/backtest
+                        _today.json`
   --timerange TIMERANGE
                         Specify what timerange of data to use.
   -i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
-                        Specify ticker interval (`1m`, `5m`, `30m`, `1h`, `1d`).
+                        Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
+                        `1d`).
+  --no-trades           Skip using trades from backtesting file and DB.
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
-  --logfile FILE        Log to the file specified. Special values are: 'syslog', 'journald'. See the documentation for more
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
                         details.
   -V, --version         show program's version number and exit
   -c PATH, --config PATH
-                        Specify configuration file (default: `config.json`). Multiple --config options may be used. Can be set to
-                        `-` to read config from stdin.
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
   -d PATH, --datadir PATH
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
@@ -68,9 +88,9 @@ Common arguments:
 
 Strategy arguments:
   -s NAME, --strategy NAME
-                        Specify strategy class name which will be used by the bot.
+                        Specify strategy class name which will be used by the
+                        bot.
   --strategy-path PATH  Specify additional strategy lookup path.
-
 ```
 
 Example:
@@ -196,6 +216,7 @@ The first graph is good to get a grip of how the overall market progresses.
 
 The second graph will show if your algorithm works or doesn't.
 Perhaps you want an algorithm that steadily makes small profits, or one that acts less often, but makes big swings.
+This graph will also highlight the start (and end) of the Max drawdown period.
 
 The third graph can be useful to spot outliers, events in pairs that cause profit spikes.
 

docs/requirements-docs.txt

@@ -1,2 +1,2 @@
-mkdocs-material==4.6.0
+mkdocs-material==4.6.3
 mdx_truly_sane_lists==1.2

docs/rest-api.md

@@ -74,7 +74,7 @@ docker run -d \
 ## Consuming the API
 
 You can consume the API by using the script `scripts/rest_client.py`.
-The client script only requires the `requests` module, so FreqTrade does not need to be installed on the system.
+The client script only requires the `requests` module, so Freqtrade does not need to be installed on the system.
 
 ``` bash
 python3 scripts/rest_client.py <command> [optional parameters]

docs/stoploss.md

@@ -27,7 +27,7 @@ So this parameter will tell the bot how often it should update the stoploss orde
 This same logic will reapply a stoploss order on the exchange should you cancel it accidentally.
 
 !!! Note
-    Stoploss on exchange is only supported for Binance as of now.
+    Stoploss on exchange is only supported for Binance (stop-loss-limit) and Kraken (stop-loss-market) as of now.
 
 ## Static Stop Loss
 

docs/strategy-customization.md

@@ -84,7 +84,7 @@ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> 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 dataframe: Dataframe with data from the exchange
     :param metadata: Additional information, like the currently traded pair
     :return: a Dataframe with all mandatory indicators for the strategies
     """
@@ -249,6 +249,23 @@ minimal_roi = {
 
 While technically not completely disabled, this would sell once the trade reaches 10000% Profit.
 
+To use times based on candle duration (ticker_interval or timeframe), the following snippet can be handy.
+This will allow you to change the ticket_interval for the strategy, and ROI times will still be set as candles (e.g. after 3 candles ...)
+
+``` python
+from freqtrade.exchange import timeframe_to_minutes
+
+class AwesomeStrategy(IStrategy):
+
+    ticker_interval = "1d"
+    ticker_interval_mins = timeframe_to_minutes(ticker_interval)
+    minimal_roi = {
+        "0": 0.05,                             # 5% for the first 3 candles
+        str(ticker_interval_mins * 3)): 0.02,  # 2% after 3 candles
+        str(ticker_interval_mins * 6)): 0.01,  # 1% After 6 candles
+    }
+```
+
 ### Stoploss
 
 Setting a stoploss is highly recommended to protect your capital from strong moves against you.
@@ -267,13 +284,14 @@ If your exchange supports it, it's recommended to also set `"stoploss_on_exchang
 
 For more information on order_types please look [here](configuration.md#understand-order_types).
 
-### Ticker interval
+### Timeframe (ticker interval)
 
 This is the set of candles the bot should download and use for the analysis.
 Common values are `"1m"`, `"5m"`, `"15m"`, `"1h"`, however all values supported by your exchange should work.
 
-Please note that the same buy/sell signals may work with one interval, but not the other.
-This setting is accessible within the strategy by using `self.ticker_interval`.
+Please note that the same buy/sell signals may work well with one timeframe, but not with the others.
+
+This setting is accessible within the strategy methods as the `self.ticker_interval` attribute.
 
 ### Metadata dict
 
@@ -318,14 +336,14 @@ Please always check the mode of operation to select the correct method to get da
 #### Possible options for DataProvider
 
 - `available_pairs` - Property with tuples listing cached pairs with their intervals (pair, interval).
-- `ohlcv(pair, timeframe)` - Currently cached ticker data for the pair, returns DataFrame or empty DataFrame.
+- `ohlcv(pair, timeframe)` - Currently cached candle (OHLCV) data for the pair, returns DataFrame or empty DataFrame.
 - `historic_ohlcv(pair, timeframe)` - Returns historical data stored on disk.
 - `get_pair_dataframe(pair, timeframe)` - This is a universal method, which returns either historical data (for backtesting) or cached live data (for the Dry-Run and Live-Run modes).
 - `orderbook(pair, maximum)` - Returns latest orderbook data for the pair, a dict with bids/asks with a total of `maximum` entries.
 - `market(pair)` - Returns market data for the pair: fees, limits, precisions, activity flag, etc. See [ccxt documentation](https://github.com/ccxt/ccxt/wiki/Manual#markets) for more details on Market data structure.
 - `runmode` - Property containing the current runmode.
 
-#### Example: fetch live ohlcv / historic data for the first informative pair
+#### Example: fetch live / historical candle (OHLCV) data for the first informative pair
 
 ``` python
 if self.dp:
@@ -346,7 +364,7 @@ if self.dp:
 
 ``` python
 if self.dp:
-    if self.dp.runmode in ('live', 'dry_run'):
+    if self.dp.runmode.value in ('live', 'dry_run'):
         ob = self.dp.orderbook(metadata['pair'], 1)
         dataframe['best_bid'] = ob['bids'][0][0]
         dataframe['best_ask'] = ob['asks'][0][0]
@@ -360,8 +378,8 @@ if self.dp:
 
 ``` python
 if self.dp:
-    for pair, ticker in self.dp.available_pairs:
-        print(f"available {pair}, {ticker}")
+    for pair, timeframe in self.dp.available_pairs:
+        print(f"available {pair}, {timeframe}")
 ```
 
 #### Get data for non-tradeable pairs
@@ -422,7 +440,7 @@ from freqtrade.persistence import Trade
 The following example queries for the current pair and trades from today, however other filters can easily be added.
 
 ``` python
-if self.config['runmode'] in ('live', 'dry_run'):
+if self.config['runmode'].value in ('live', 'dry_run'):
     trades = Trade.get_trades([Trade.pair == metadata['pair'],
                                Trade.open_date > datetime.utcnow() - timedelta(days=1),
                                Trade.is_open == False,
@@ -434,7 +452,7 @@ if self.config['runmode'] in ('live', 'dry_run'):
 Get amount of stake_currency currently invested in Trades:
 
 ``` python
-if self.config['runmode'] in ('live', 'dry_run'):
+if self.config['runmode'].value in ('live', 'dry_run'):
     total_stakes = Trade.total_open_trades_stakes()
 ```
 
@@ -442,7 +460,7 @@ Retrieve performance per pair.
 Returns a List of dicts per pair.
 
 ``` python
-if self.config['runmode'] in ('live', 'dry_run'):
+if self.config['runmode'].value in ('live', 'dry_run'):
     performance = Trade.get_overall_performance()
 ```
 
@@ -487,7 +505,7 @@ from datetime import timedelta, datetime, timezone
 # --------
 
 # Within populate indicators (or populate_buy):
-if self.config['runmode'] in ('live', 'dry_run'):
+if self.config['runmode'].value in ('live', 'dry_run'):
    # fetch closed trades for the last 2 days
     trades = Trade.get_trades([Trade.pair == metadata['pair'],
                                Trade.open_date > datetime.utcnow() - timedelta(days=2),
@@ -532,6 +550,27 @@ If you want to use a strategy from a different directory you can pass `--strateg
 freqtrade trade --strategy AwesomeStrategy --strategy-path /some/directory
 ```
 
+### Derived strategies
+
+The strategies can be derived from other strategies. This avoids duplication of your custom strategy code. You can use this technique to override small parts of your main strategy, leaving the rest untouched:
+
+``` python
+class MyAwesomeStrategy(IStrategy):
+    ...
+    stoploss = 0.13
+    trailing_stop = False
+    # All other attributes and methods are here as they
+    # should be in any custom strategy...
+    ...
+
+class MyAwesomeStrategy2(MyAwesomeStrategy):
+    # Override something
+    stoploss = 0.08
+    trailing_stop = True
+```
+
+Both attributes and methods may be overriden, altering behavior of the original strategy in a way you need.
+
 ### Common mistakes when developing strategies
 
 Backtesting analyzes the whole time-range at once for performance reasons. Because of this, strategy authors need to make sure that strategies do not look-ahead into the future.

docs/strategy_analysis_example.md

@@ -1,24 +1,28 @@
 # Strategy analysis example
 
-Debugging a strategy can be time-consuming. FreqTrade offers helper functions to visualize raw data.
+Debugging a strategy can be time-consuming. Freqtrade offers helper functions to visualize raw data.
+The following assumes you work with SampleStrategy, data for 5m timeframe from Binance and have downloaded them into the data directory in the default location.
 
 ## Setup
 
 
 ```python
 from pathlib import Path
+from freqtrade.configuration import Configuration
+
 # Customize these according to your needs.
 
+# Initialize empty configuration object
+config = Configuration.from_files([])
+# Optionally, use existing configuration file
+# config = Configuration.from_files(["config.json"])
+
 # Define some constants
-timeframe = "5m"
+config["ticker_interval"] = "5m"
 # Name of the strategy class
-strategy_name = 'SampleStrategy'
-# Path to user data
-user_data_dir = Path('user_data')
-# Location of the strategy
-strategy_location = user_data_dir / 'strategies'
+config["strategy"] = "SampleStrategy"
 # Location of the data
-data_location = Path(user_data_dir, 'data', 'binance')
+data_location = Path(config['user_data_dir'], 'data', 'binance')
 # Pair to analyze - Only use one pair here
 pair = "BTC_USDT"
 ```
@@ -29,7 +33,7 @@ pair = "BTC_USDT"
 from freqtrade.data.history import load_pair_history
 
 candles = load_pair_history(datadir=data_location,
-                            timeframe=timeframe,
+                            timeframe=config["ticker_interval"],
                             pair=pair)
 
 # Confirm success
@@ -44,9 +48,7 @@ candles.head()
 ```python
 # Load strategy using values set above
 from freqtrade.resolvers import StrategyResolver
-strategy = StrategyResolver.load_strategy({'strategy': strategy_name,
-                                          'user_data_dir': user_data_dir,
-                                          'strategy_path': strategy_location})
+strategy = StrategyResolver.load_strategy(config)
 
 # Generate buy/sell signals using strategy
 df = strategy.analyze_ticker(candles, {'pair': pair})
@@ -86,7 +88,7 @@ Analyze a trades dataframe (also used below for plotting)
 from freqtrade.data.btanalysis import load_backtest_data
 
 # Load backtest results
-trades = load_backtest_data(user_data_dir / "backtest_results/backtest-result.json")
+trades = load_backtest_data(config["user_data_dir"] / "backtest_results/backtest-result.json")
 
 # Show value-counts per pair
 trades.groupby("pair")["sell_reason"].value_counts()
@@ -119,7 +121,6 @@ from freqtrade.data.btanalysis import analyze_trade_parallelism
 # Analyze the above
 parallel_trades = analyze_trade_parallelism(trades, '5m')
 
-
 parallel_trades.plot()
 ```
 
@@ -132,11 +133,14 @@ Freqtrade offers interactive plotting capabilities based on plotly.
 from freqtrade.plot.plotting import  generate_candlestick_graph
 # Limit graph period to keep plotly quick and reactive
 
+# Filter trades to one pair
+trades_red = trades.loc[trades['pair'] == pair]
+
 data_red = data['2019-06-01':'2019-06-10']
 # Generate candlestick graph
 graph = generate_candlestick_graph(pair=pair,
                                    data=data_red,
-                                   trades=trades,
+                                   trades=trades_red,
                                    indicators1=['sma20', 'ema50', 'ema55'],
                                    indicators2=['rsi', 'macd', 'macdsignal', 'macdhist']
                                   )

docs/telegram-usage.md

@@ -55,7 +55,7 @@ official commands. You can ask at any moment for help with `/help`.
 | `/reload_conf` | | Reloads the configuration file
 | `/show_config` | | Shows part of the current configuration with relevant settings to operation
 | `/status` | | Lists all open trades
-| `/status table` | | List all open trades in a table format
+| `/status table` | | List all open trades in a table format. Pending buy orders are marked with an asterisk (*) Pending sell orders are marked with a double asterisk (**)
 | `/count` | | Displays number of trades used and available
 | `/profit` | | Display a summary of your profit/loss from close trades and some stats about your performance
 | `/forcesell <trade_id>` | | Instantly sells the given trade  (Ignoring `minimum_roi`).

docs/utils.md

@@ -36,6 +36,38 @@ optional arguments:
     └── sample_strategy.py
 ```
 
+## Create new config
+
+Creates a new configuration file, asking some questions which are important selections for a configuration.
+
+```
+usage: freqtrade new-config [-h] [-c PATH]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`). Multiple --config options may be used. Can be set to `-`
+                        to read config from stdin.
+```
+
+!!! Warning
+    Only vital questions are asked. Freqtrade offers a lot more configuration possibilities, which are listed in the [Configuration documentation](configuration.md#configuration-parameters)
+
+### Create config examples
+
+```
+$ freqtrade new-config --config config_binance.json
+
+? Do you want to enable Dry-run (simulated trades)?  Yes
+? Please insert your stake currency: BTC
+? Please insert your stake amount: 0.05
+? Please insert max_open_trades (Integer or 'unlimited'): 3
+? Please insert your timeframe (ticker interval): 5m
+? Please insert your display Currency (for reporting): USD
+? Select exchange  binance
+? Do you want to enable Telegram?  No
+```
+
 ## Create new strategy
 
 Creates a new strategy from a template similar to SampleStrategy.
@@ -108,26 +140,62 @@ With custom user directory
 freqtrade new-hyperopt --userdir ~/.freqtrade/ --hyperopt AwesomeHyperopt
 ```
 
-## List Strategies
+## List Strategies and List Hyperopts
 
-Use the `list-strategies` subcommand to see all strategies in one particular directory.
+Use the `list-strategies` subcommand to see all strategies in one particular directory and the `list-hyperopts` subcommand to list custom Hyperopts.
+
+These subcommands are useful for finding problems in your environment with loading strategies or hyperopt classes: modules with strategies or hyperopt classes that contain errors and failed to load are printed in red (LOAD FAILED), while strategies or hyperopt classes with duplicate names are printed in yellow (DUPLICATE NAME).
 
 ```
-freqtrade list-strategies --help
-usage: freqtrade list-strategies [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [--strategy-path PATH] [-1]
+usage: freqtrade list-strategies [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                                 [-d PATH] [--userdir PATH]
+                                 [--strategy-path PATH] [-1] [--no-color]
 
 optional arguments:
   -h, --help            show this help message and exit
   --strategy-path PATH  Specify additional strategy lookup path.
   -1, --one-column      Print output in one column.
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
-  --logfile FILE        Log to the file specified. Special values are: 'syslog', 'journald'. See the documentation for more details.
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
   -V, --version         show program's version number and exit
   -c PATH, --config PATH
-                        Specify configuration file (default: `config.json`). Multiple --config options may be used. Can be set to `-`
-                        to read config from stdin.
+                        Specify configuration file (default: `config.json`).
+                        Multiple --config options may be used. Can be set to
+                        `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+```
+```
+usage: freqtrade list-hyperopts [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                                [-d PATH] [--userdir PATH]
+                                [--hyperopt-path PATH] [-1] [--no-color]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --hyperopt-path PATH  Specify additional lookup path for Hyperopt and
+                        Hyperopt Loss functions.
+  -1, --one-column      Print output in one column.
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`).
+                        Multiple --config options may be used. Can be set to
+                        `-` to read config from stdin.
   -d PATH, --datadir PATH
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
@@ -135,20 +203,34 @@ Common arguments:
 ```
 
 !!! Warning
-    Using this command will try to load all python files from a directory. This can be a security risk if untrusted files reside in this directory, since all module-level code is executed.
+    Using these commands will try to load all python files from a directory. This can be a security risk if untrusted files reside in this directory, since all module-level code is executed.
 
-Example: search default strategy directory within userdir
+Example: Search default strategies and hyperopts directories (within the default userdir).
+
+``` bash
+freqtrade list-strategies
+freqtrade list-hyperopts
+```
+
+Example: Search strategies and hyperopts directory within the userdir.
 
 ``` bash
 freqtrade list-strategies --userdir ~/.freqtrade/
+freqtrade list-hyperopts --userdir ~/.freqtrade/
 ```
 
-Example: search dedicated strategy path
+Example: Search dedicated strategy path.
 
 ``` bash
 freqtrade list-strategies --strategy-path ~/.freqtrade/strategies/
 ```
 
+Example: Search dedicated hyperopt path.
+
+``` bash
+freqtrade list-hyperopt --hyperopt-path ~/.freqtrade/hyperopts/
+```
+
 ## List Exchanges
 
 Use the `list-exchanges` subcommand to see the exchanges available for the bot.
@@ -176,23 +258,34 @@ All exchanges supported by the ccxt library: _1btcxe, acx, adara, allcoin, anxpr
 
 ## List Timeframes
 
-Use the `list-timeframes` subcommand to see the list of ticker intervals (timeframes) available for the exchange.
+Use the `list-timeframes` subcommand to see the list of timeframes (ticker intervals) available for the exchange.
 
 ```
-usage: freqtrade list-timeframes [-h] [--exchange EXCHANGE] [-1]
+usage: freqtrade list-timeframes [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [--exchange EXCHANGE] [-1]
 
 optional arguments:
-  -h, --help           show this help message and exit
-  --exchange EXCHANGE  Exchange name (default: `bittrex`). Only valid if no
-                       config is provided.
-  -1, --one-column     Print output in one column.
+  -h, --help            show this help message and exit
+  --exchange EXCHANGE   Exchange name (default: `bittrex`). Only valid if no config is provided.
+  -1, --one-column      Print output in one column.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are: 'syslog', 'journald'. See the documentation for more details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`). Multiple --config options may be used. Can be set to `-`
+                        to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
 
 ```
 
 * Example: see the timeframes for the 'binance' exchange, set in the configuration file:
 
 ```
-$ freqtrade -c config_binance.json list-timeframes
+$ freqtrade list-timeframes -c config_binance.json
 ...
 Timeframes available for the exchange `binance`: 1m, 3m, 5m, 15m, 30m, 1h, 2h, 4h, 6h, 8h, 12h, 1d, 3d, 1w, 1M
 ```
@@ -216,14 +309,16 @@ You can print info about any pair/market with these subcommands - and you can fi
 These subcommands have same usage and same set of available options:
 
 ```
-usage: freqtrade list-markets [-h] [--exchange EXCHANGE] [--print-list]
-                              [--print-json] [-1] [--print-csv]
+usage: freqtrade list-markets [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                              [-d PATH] [--userdir PATH] [--exchange EXCHANGE]
+                              [--print-list] [--print-json] [-1] [--print-csv]
                               [--base BASE_CURRENCY [BASE_CURRENCY ...]]
                               [--quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]]
                               [-a]
 
-usage: freqtrade list-pairs [-h] [--exchange EXCHANGE] [--print-list]
-                            [--print-json] [-1] [--print-csv]
+usage: freqtrade list-pairs [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                            [-d PATH] [--userdir PATH] [--exchange EXCHANGE]
+                            [--print-list] [--print-json] [-1] [--print-csv]
                             [--base BASE_CURRENCY [BASE_CURRENCY ...]]
                             [--quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]] [-a]
 
@@ -242,6 +337,22 @@ optional arguments:
                         Specify quote currency(-ies). Space-separated list.
   -a, --all             Print all pairs or market symbols. By default only
                         active ones are shown.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`).
+                        Multiple --config options may be used. Can be set to
+                        `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
 ```
 
 By default, only active pairs/markets are shown. Active pairs/markets are those that can currently be traded
@@ -263,7 +374,7 @@ $ freqtrade list-pairs --quote USD --print-json
 human-readable list with summary:
 
 ```
-$ freqtrade -c config_binance.json list-pairs --all --base BTC ETH --quote USDT USD --print-list
+$ freqtrade list-pairs -c config_binance.json --all --base BTC ETH --quote USDT USD --print-list
 ```
 
 * Print all markets on exchange "Kraken", in the tabular format:
@@ -311,17 +422,53 @@ You can list the hyperoptimization epochs the Hyperopt module evaluated previous
 ```
 usage: freqtrade hyperopt-list [-h] [-v] [--logfile FILE] [-V] [-c PATH]
                                [-d PATH] [--userdir PATH] [--best]
-                               [--profitable] [--no-color] [--print-json]
-                               [--no-details]
+                               [--profitable] [--min-trades INT]
+                               [--max-trades INT] [--min-avg-time FLOAT]
+                               [--max-avg-time FLOAT] [--min-avg-profit FLOAT]
+                               [--max-avg-profit FLOAT]
+                               [--min-total-profit FLOAT]
+                               [--max-total-profit FLOAT] [--no-color]
+                               [--print-json] [--no-details]
+                               [--export-csv FILE]
 
 optional arguments:
   -h, --help            show this help message and exit
   --best                Select only best epochs.
   --profitable          Select only profitable epochs.
+  --min-trades INT      Select epochs with more than INT trades.
+  --max-trades INT      Select epochs with less than INT trades.
+  --min-avg-time FLOAT  Select epochs on above average time.
+  --max-avg-time FLOAT  Select epochs on under average time.
+  --min-avg-profit FLOAT
+                        Select epochs on above average profit.
+  --max-avg-profit FLOAT
+                        Select epochs on below average profit.
+  --min-total-profit FLOAT
+                        Select epochs on above total profit.
+  --max-total-profit FLOAT
+                        Select epochs on below total profit.
   --no-color            Disable colorization of hyperopt results. May be
                         useful if you are redirecting output to a file.
   --print-json          Print best result detailization in JSON format.
   --no-details          Do not print best epoch details.
+  --export-csv FILE     Export to CSV-File. This will disable table print.
+                        Example: --export-csv hyperopt.csv
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
 ```
  
 ### Examples

docs/webhook-config.md

@@ -15,10 +15,20 @@ Sample configuration (tested using IFTTT).
             "value2": "limit {limit:8f}",
             "value3": "{stake_amount:8f} {stake_currency}"
         },
+        "webhookbuycancel": {
+            "value1": "Cancelling Open Buy Order for {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}"
+            "value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
+        },
+        "webhooksellcancel": {
+            "value1": "Cancelling Open Sell Order for {pair}",
+            "value2": "limit {limit:8f}",
+            "value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
         },
         "webhookstatus": {
             "value1": "Status: {status}",
@@ -40,10 +50,29 @@ Possible parameters are:
 * `exchange`
 * `pair`
 * `limit`
+* `amount`
+* `open_date`
 * `stake_amount`
 * `stake_currency`
 * `fiat_currency`
 * `order_type`
+* `current_rate`
+
+### Webhookbuycancel
+
+The fields in `webhook.webhookbuycancel` are filled when the bot cancels a buy order. Parameters are filled using string.format.
+Possible parameters are:
+
+* `exchange`
+* `pair`
+* `limit`
+* `amount`
+* `open_date`
+* `stake_amount`
+* `stake_currency`
+* `fiat_currency`
+* `order_type`
+* `current_rate`
 
 ### Webhooksell
 
@@ -58,7 +87,28 @@ Possible parameters are:
 * `open_rate`
 * `current_rate`
 * `profit_amount`
-* `profit_percent`
+* `profit_ratio`
+* `stake_currency`
+* `fiat_currency`
+* `sell_reason`
+* `order_type`
+* `open_date`
+* `close_date`
+
+### Webhooksellcancel
+
+The fields in `webhook.webhooksellcancel` are filled when the bot cancels a sell order. Parameters are filled using string.format.
+Possible parameters are:
+
+* `exchange`
+* `pair`
+* `gain`
+* `limit`
+* `amount`
+* `open_rate`
+* `current_rate`
+* `profit_amount`
+* `profit_ratio`
 * `stake_currency`
 * `fiat_currency`
 * `sell_reason`

environment.yml

@@ -45,7 +45,7 @@ dependencies:
   - pip:
     # Required for app
     - cython
-    - coinmarketcap
+    - pycoingecko
     - ccxt
     - TA-Lib
     - py_find_1st

freqtrade/__init__.py

@@ -1,13 +1,27 @@
-""" FreqTrade bot """
-__version__ = '2020.01'
+""" Freqtrade bot """
+__version__ = '2020.3'
 
 if __version__ == 'develop':
 
     try:
         import subprocess
+
         __version__ = 'develop-' + subprocess.check_output(
             ['git', 'log', '--format="%h"', '-n 1'],
             stderr=subprocess.DEVNULL).decode("utf-8").rstrip().strip('"')
+
+        # from datetime import datetime
+        # last_release = subprocess.check_output(
+        #     ['git', 'tag']
+        # ).decode('utf-8').split()[-1].split(".")
+        # # Releases are in the format "2020.1" - we increment the latest version for dev.
+        # prefix = f"{last_release[0]}.{int(last_release[1]) + 1}"
+        # dev_version = int(datetime.now().timestamp() // 1000)
+        # __version__ = f"{prefix}.dev{dev_version}"
+
+        #  subprocess.check_output(
+        #     ['git', 'log', '--format="%h"', '-n 1'],
+        #     stderr=subprocess.DEVNULL).decode("utf-8").rstrip().strip('"')
     except Exception:
         # git not available, ignore
         pass

freqtrade/commands/__init__.py

@@ -7,13 +7,16 @@ Note: Be careful with file-scoped imports in these subfiles.
     as they are parsed on startup, nothing containing optional modules should be loaded.
 """
 from freqtrade.commands.arguments import Arguments
-from freqtrade.commands.data_commands import start_download_data
+from freqtrade.commands.build_config_commands import start_new_config
+from freqtrade.commands.data_commands import (start_convert_data,
+                                              start_download_data)
 from freqtrade.commands.deploy_commands import (start_create_userdir,
                                                 start_new_hyperopt,
                                                 start_new_strategy)
 from freqtrade.commands.hyperopt_commands import (start_hyperopt_list,
                                                   start_hyperopt_show)
 from freqtrade.commands.list_commands import (start_list_exchanges,
+                                              start_list_hyperopts,
                                               start_list_markets,
                                               start_list_strategies,
                                               start_list_timeframes)

freqtrade/commands/arguments.py

@@ -6,8 +6,8 @@ from functools import partial
 from pathlib import Path
 from typing import Any, Dict, List, Optional
 
-from freqtrade import constants
 from freqtrade.commands.cli_options import AVAILABLE_CLI_OPTIONS
+from freqtrade.constants import DEFAULT_CONFIG
 
 ARGS_COMMON = ["verbosity", "logfile", "version", "config", "datadir", "user_data_dir"]
 
@@ -30,7 +30,9 @@ ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
 
 ARGS_EDGE = ARGS_COMMON_OPTIMIZE + ["stoploss_range"]
 
-ARGS_LIST_STRATEGIES = ["strategy_path", "print_one_column"]
+ARGS_LIST_STRATEGIES = ["strategy_path", "print_one_column", "print_colorized"]
+
+ARGS_LIST_HYPEROPTS = ["hyperopt_path", "print_one_column", "print_colorized"]
 
 ARGS_LIST_EXCHANGES = ["print_one_column", "list_exchanges_all"]
 
@@ -43,29 +45,40 @@ ARGS_TEST_PAIRLIST = ["config", "quote_currencies", "print_one_column", "list_pa
 
 ARGS_CREATE_USERDIR = ["user_data_dir", "reset"]
 
+ARGS_BUILD_CONFIG = ["config"]
+
 ARGS_BUILD_STRATEGY = ["user_data_dir", "strategy", "template"]
 
 ARGS_BUILD_HYPEROPT = ["user_data_dir", "hyperopt", "template"]
 
+ARGS_CONVERT_DATA = ["pairs", "format_from", "format_to", "erase"]
+ARGS_CONVERT_DATA_OHLCV = ARGS_CONVERT_DATA + ["timeframes"]
+
 ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "download_trades", "exchange",
-                      "timeframes", "erase"]
+                      "timeframes", "erase", "dataformat_ohlcv", "dataformat_trades"]
 
 ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",
                        "db_url", "trade_source", "export", "exportfilename",
-                       "timerange", "ticker_interval"]
+                       "timerange", "ticker_interval", "no_trades"]
 
 ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
                     "trade_source", "ticker_interval"]
 
-ARGS_HYPEROPT_LIST = ["hyperopt_list_best", "hyperopt_list_profitable", "print_colorized",
-                      "print_json", "hyperopt_list_no_details"]
+ARGS_HYPEROPT_LIST = ["hyperopt_list_best", "hyperopt_list_profitable",
+                      "hyperopt_list_min_trades", "hyperopt_list_max_trades",
+                      "hyperopt_list_min_avg_time", "hyperopt_list_max_avg_time",
+                      "hyperopt_list_min_avg_profit", "hyperopt_list_max_avg_profit",
+                      "hyperopt_list_min_total_profit", "hyperopt_list_max_total_profit",
+                      "print_colorized", "print_json", "hyperopt_list_no_details",
+                      "export_csv"]
 
 ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperopt_show_index",
                       "print_json", "hyperopt_show_no_header"]
 
-NO_CONF_REQURIED = ["download-data", "list-timeframes", "list-markets", "list-pairs",
-                    "list-strategies", "hyperopt-list", "hyperopt-show", "plot-dataframe",
-                    "plot-profit"]
+NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
+                    "list-markets", "list-pairs", "list-strategies",
+                    "list-hyperopts", "hyperopt-list", "hyperopt-show",
+                    "plot-dataframe", "plot-profit"]
 
 NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-hyperopt", "new-strategy"]
 
@@ -99,10 +112,23 @@ class Arguments:
         # Workaround issue in argparse with action='append' and default value
         # (see https://bugs.python.org/issue16399)
         # Allow no-config for certain commands (like downloading / plotting)
-        if ('config' in parsed_arg and parsed_arg.config is None and
-            ((Path.cwd() / constants.DEFAULT_CONFIG).is_file() or
-             not ('command' in parsed_arg and parsed_arg.command in NO_CONF_REQURIED))):
-            parsed_arg.config = [constants.DEFAULT_CONFIG]
+        if ('config' in parsed_arg and parsed_arg.config is None):
+            conf_required = ('command' in parsed_arg and parsed_arg.command in NO_CONF_REQURIED)
+
+            if 'user_data_dir' in parsed_arg and parsed_arg.user_data_dir is not None:
+                user_dir = parsed_arg.user_data_dir
+            else:
+                # Default case
+                user_dir = 'user_data'
+                # Try loading from "user_data/config.json"
+            cfgfile = Path(user_dir) / DEFAULT_CONFIG
+            if cfgfile.is_file():
+                parsed_arg.config = [str(cfgfile)]
+            else:
+                # Else use "config.json".
+                cfgfile = Path.cwd() / DEFAULT_CONFIG
+                if cfgfile.is_file() or not conf_required:
+                    parsed_arg.config = [DEFAULT_CONFIG]
 
         return parsed_arg
 
@@ -130,11 +156,13 @@ class Arguments:
         self.parser = argparse.ArgumentParser(description='Free, open source crypto trading bot')
         self._build_args(optionlist=['version'], parser=self.parser)
 
-        from freqtrade.commands import (start_create_userdir, start_download_data,
+        from freqtrade.commands import (start_create_userdir, start_convert_data,
+                                        start_download_data,
                                         start_hyperopt_list, start_hyperopt_show,
-                                        start_list_exchanges, start_list_markets,
-                                        start_list_strategies, start_new_hyperopt,
-                                        start_new_strategy, start_list_timeframes,
+                                        start_list_exchanges, start_list_hyperopts,
+                                        start_list_markets, start_list_strategies,
+                                        start_list_timeframes, start_new_config,
+                                        start_new_hyperopt, start_new_strategy,
                                         start_plot_dataframe, start_plot_profit,
                                         start_backtesting, start_hyperopt, start_edge,
                                         start_test_pairlist, start_trading)
@@ -177,6 +205,12 @@ class Arguments:
         create_userdir_cmd.set_defaults(func=start_create_userdir)
         self._build_args(optionlist=ARGS_CREATE_USERDIR, parser=create_userdir_cmd)
 
+        # add new-config subcommand
+        build_config_cmd = subparsers.add_parser('new-config',
+                                                 help="Create new config")
+        build_config_cmd.set_defaults(func=start_new_config)
+        self._build_args(optionlist=ARGS_BUILD_CONFIG, parser=build_config_cmd)
+
         # add new-strategy subcommand
         build_strategy_cmd = subparsers.add_parser('new-strategy',
                                                    help="Create new strategy")
@@ -198,6 +232,15 @@ class Arguments:
         list_strategies_cmd.set_defaults(func=start_list_strategies)
         self._build_args(optionlist=ARGS_LIST_STRATEGIES, parser=list_strategies_cmd)
 
+        # Add list-hyperopts subcommand
+        list_hyperopts_cmd = subparsers.add_parser(
+            'list-hyperopts',
+            help='Print available hyperopt classes.',
+            parents=[_common_parser],
+        )
+        list_hyperopts_cmd.set_defaults(func=start_list_hyperopts)
+        self._build_args(optionlist=ARGS_LIST_HYPEROPTS, parser=list_hyperopts_cmd)
+
         # Add list-exchanges subcommand
         list_exchanges_cmd = subparsers.add_parser(
             'list-exchanges',
@@ -251,6 +294,24 @@ class Arguments:
         download_data_cmd.set_defaults(func=start_download_data)
         self._build_args(optionlist=ARGS_DOWNLOAD_DATA, parser=download_data_cmd)
 
+        # Add convert-data subcommand
+        convert_data_cmd = subparsers.add_parser(
+            'convert-data',
+            help='Convert candle (OHLCV) data from one format to another.',
+            parents=[_common_parser],
+        )
+        convert_data_cmd.set_defaults(func=partial(start_convert_data, ohlcv=True))
+        self._build_args(optionlist=ARGS_CONVERT_DATA_OHLCV, parser=convert_data_cmd)
+
+        # Add convert-trade-data subcommand
+        convert_trade_data_cmd = subparsers.add_parser(
+            'convert-trade-data',
+            help='Convert trade data from one format to another.',
+            parents=[_common_parser],
+        )
+        convert_trade_data_cmd.set_defaults(func=partial(start_convert_data, ohlcv=False))
+        self._build_args(optionlist=ARGS_CONVERT_DATA, parser=convert_trade_data_cmd)
+
         # Add Plotting subcommand
         plot_dataframe_cmd = subparsers.add_parser(
             'plot-dataframe',

freqtrade/commands/build_config_commands.py

@@ -0,0 +1,193 @@
+import logging
+from pathlib import Path
+from typing import Any, Dict
+
+from questionary import Separator, prompt
+
+from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
+from freqtrade.exchange import available_exchanges, MAP_EXCHANGE_CHILDCLASS
+from freqtrade.misc import render_template
+from freqtrade.exceptions import OperationalException
+logger = logging.getLogger(__name__)
+
+
+def validate_is_int(val):
+    try:
+        _ = int(val)
+        return True
+    except Exception:
+        return False
+
+
+def validate_is_float(val):
+    try:
+        _ = float(val)
+        return True
+    except Exception:
+        return False
+
+
+def ask_user_overwrite(config_path: Path) -> bool:
+    questions = [
+        {
+            "type": "confirm",
+            "name": "overwrite",
+            "message": f"File {config_path} already exists. Overwrite?",
+            "default": False,
+        },
+    ]
+    answers = prompt(questions)
+    return answers['overwrite']
+
+
+def ask_user_config() -> Dict[str, Any]:
+    """
+    Ask user a few questions to build the configuration.
+    Interactive questions built using https://github.com/tmbo/questionary
+    :returns: Dict with keys to put into template
+    """
+    questions = [
+        {
+            "type": "confirm",
+            "name": "dry_run",
+            "message": "Do you want to enable Dry-run (simulated trades)?",
+            "default": True,
+        },
+        {
+            "type": "text",
+            "name": "stake_currency",
+            "message": "Please insert your stake currency:",
+            "default": 'BTC',
+        },
+        {
+            "type": "text",
+            "name": "stake_amount",
+            "message": "Please insert your stake amount:",
+            "default": "0.01",
+            "validate": lambda val: val == UNLIMITED_STAKE_AMOUNT or validate_is_float(val),
+        },
+        {
+            "type": "text",
+            "name": "max_open_trades",
+            "message": f"Please insert max_open_trades (Integer or '{UNLIMITED_STAKE_AMOUNT}'):",
+            "default": "3",
+            "validate": lambda val: val == UNLIMITED_STAKE_AMOUNT or validate_is_int(val)
+        },
+        {
+            "type": "text",
+            "name": "ticker_interval",
+            "message": "Please insert your timeframe (ticker interval):",
+            "default": "5m",
+        },
+        {
+            "type": "text",
+            "name": "fiat_display_currency",
+            "message": "Please insert your display Currency (for reporting):",
+            "default": 'USD',
+        },
+        {
+            "type": "select",
+            "name": "exchange_name",
+            "message": "Select exchange",
+            "choices": [
+                "binance",
+                "binanceje",
+                "binanceus",
+                "bittrex",
+                "kraken",
+                Separator(),
+                "other",
+            ],
+        },
+        {
+            "type": "autocomplete",
+            "name": "exchange_name",
+            "message": "Type your exchange name (Must be supported by ccxt)",
+            "choices": available_exchanges(),
+            "when": lambda x: x["exchange_name"] == 'other'
+        },
+        {
+            "type": "password",
+            "name": "exchange_key",
+            "message": "Insert Exchange Key",
+            "when": lambda x: not x['dry_run']
+        },
+        {
+            "type": "password",
+            "name": "exchange_secret",
+            "message": "Insert Exchange Secret",
+            "when": lambda x: not x['dry_run']
+        },
+        {
+            "type": "confirm",
+            "name": "telegram",
+            "message": "Do you want to enable Telegram?",
+            "default": False,
+        },
+        {
+            "type": "password",
+            "name": "telegram_token",
+            "message": "Insert Telegram token",
+            "when": lambda x: x['telegram']
+        },
+        {
+            "type": "text",
+            "name": "telegram_chat_id",
+            "message": "Insert Telegram chat id",
+            "when": lambda x: x['telegram']
+        },
+    ]
+    answers = prompt(questions)
+
+    if not answers:
+        # Interrupted questionary sessions return an empty dict.
+        raise OperationalException("User interrupted interactive questions.")
+
+    return answers
+
+
+def deploy_new_config(config_path: Path, selections: Dict[str, Any]) -> None:
+    """
+    Applies selections to the template and writes the result to config_path
+    :param config_path: Path object for new config file. Should not exist yet
+    :param selecions: Dict containing selections taken by the user.
+    """
+    from jinja2.exceptions import TemplateNotFound
+    try:
+        exchange_template = MAP_EXCHANGE_CHILDCLASS.get(
+            selections['exchange_name'], selections['exchange_name'])
+
+        selections['exchange'] = render_template(
+            templatefile=f"subtemplates/exchange_{exchange_template}.j2",
+            arguments=selections
+            )
+    except TemplateNotFound:
+        selections['exchange'] = render_template(
+            templatefile=f"subtemplates/exchange_generic.j2",
+            arguments=selections
+        )
+
+    config_text = render_template(templatefile='base_config.json.j2',
+                                  arguments=selections)
+
+    logger.info(f"Writing config to `{config_path}`.")
+    config_path.write_text(config_text)
+
+
+def start_new_config(args: Dict[str, Any]) -> None:
+    """
+    Create a new strategy from a template
+    Asking the user questions to fill out the templateaccordingly.
+    """
+
+    config_path = Path(args['config'][0])
+    if config_path.exists():
+        overwrite = ask_user_overwrite(config_path)
+        if overwrite:
+            config_path.unlink()
+        else:
+            raise OperationalException(
+                f"Configuration file `{config_path}` already exists. "
+                "Please delete it or use a different configuration file name.")
+    selections = ask_user_config()
+    deploy_new_config(config_path, selections)

freqtrade/commands/cli_options.py

@@ -59,7 +59,8 @@ AVAILABLE_CLI_OPTIONS = {
     ),
     "config": Arg(
         '-c', '--config',
-        help=f'Specify configuration file (default: `{constants.DEFAULT_CONFIG}`). '
+        help=f'Specify configuration file (default: `userdir/{constants.DEFAULT_CONFIG}` '
+        f'or `config.json` whichever exists). '
         f'Multiple --config options may be used. '
         f'Can be set to `-` to read config from stdin.',
         action='append',
@@ -220,6 +221,13 @@ AVAILABLE_CLI_OPTIONS = {
         action='store_true',
         default=False,
     ),
+    "export_csv": Arg(
+        '--export-csv',
+        help='Export to CSV-File.'
+        ' This will disable table print.'
+        ' Example: --export-csv hyperopt.csv',
+        metavar='FILE',
+    ),
     "hyperopt_jobs": Arg(
         '-j', '--job-workers',
         help='The number of concurrently running jobs for hyperoptimization '
@@ -256,7 +264,8 @@ AVAILABLE_CLI_OPTIONS = {
         help='Specify the class name of the hyperopt loss function class (IHyperOptLoss). '
         'Different functions can generate completely different results, '
         'since the target for optimization is different. Built-in Hyperopt-loss-functions are: '
-        'DefaultHyperOptLoss, OnlyProfitHyperOptLoss, SharpeHyperOptLoss.'
+        'DefaultHyperOptLoss, OnlyProfitHyperOptLoss, SharpeHyperOptLoss, SharpeHyperOptLossDaily, '
+        'SortinoHyperOptLoss, SortinoHyperOptLossDaily.'
         '(default: `%(default)s`).',
         metavar='NAME',
         default=constants.DEFAULT_HYPEROPT_LOSS,
@@ -332,6 +341,30 @@ AVAILABLE_CLI_OPTIONS = {
              'desired timeframe as specified as --timeframes/-t.',
         action='store_true',
     ),
+    "format_from": Arg(
+        '--format-from',
+        help='Source format for data conversion.',
+        choices=constants.AVAILABLE_DATAHANDLERS,
+        required=True,
+    ),
+    "format_to": Arg(
+        '--format-to',
+        help='Destination format for data conversion.',
+        choices=constants.AVAILABLE_DATAHANDLERS,
+        required=True,
+    ),
+    "dataformat_ohlcv": Arg(
+        '--data-format-ohlcv',
+        help='Storage format for downloaded candle (OHLCV) data. (default: `%(default)s`).',
+        choices=constants.AVAILABLE_DATAHANDLERS,
+        default='json'
+    ),
+    "dataformat_trades": Arg(
+        '--data-format-trades',
+        help='Storage format for downloaded trades data. (default: `%(default)s`).',
+        choices=constants.AVAILABLE_DATAHANDLERS,
+        default='jsongz'
+    ),
     "exchange": Arg(
         '--exchange',
         help=f'Exchange name (default: `{constants.DEFAULT_EXCHANGE}`). '
@@ -380,6 +413,11 @@ AVAILABLE_CLI_OPTIONS = {
         metavar='INT',
         default=750,
     ),
+    "no_trades": Arg(
+        '--no-trades',
+        help='Skip using trades from backtesting file and DB.',
+        action='store_true',
+    ),
     "trade_source": Arg(
         '--trade-source',
         help='Specify the source for trades (Can be DB or file (backtest file)) '
@@ -398,6 +436,54 @@ AVAILABLE_CLI_OPTIONS = {
         help='Select only best epochs.',
         action='store_true',
     ),
+    "hyperopt_list_min_trades": Arg(
+        '--min-trades',
+        help='Select epochs with more than INT trades.',
+        type=check_int_positive,
+        metavar='INT',
+    ),
+    "hyperopt_list_max_trades": Arg(
+        '--max-trades',
+        help='Select epochs with less than INT trades.',
+        type=check_int_positive,
+        metavar='INT',
+    ),
+    "hyperopt_list_min_avg_time": Arg(
+        '--min-avg-time',
+        help='Select epochs on above average time.',
+        type=float,
+        metavar='FLOAT',
+    ),
+    "hyperopt_list_max_avg_time": Arg(
+        '--max-avg-time',
+        help='Select epochs on under average time.',
+        type=float,
+        metavar='FLOAT',
+    ),
+    "hyperopt_list_min_avg_profit": Arg(
+        '--min-avg-profit',
+        help='Select epochs on above average profit.',
+        type=float,
+        metavar='FLOAT',
+    ),
+    "hyperopt_list_max_avg_profit": Arg(
+        '--max-avg-profit',
+        help='Select epochs on below average profit.',
+        type=float,
+        metavar='FLOAT',
+    ),
+    "hyperopt_list_min_total_profit": Arg(
+        '--min-total-profit',
+        help='Select epochs on above total profit.',
+        type=float,
+        metavar='FLOAT',
+    ),
+    "hyperopt_list_max_total_profit": Arg(
+        '--max-total-profit',
+        help='Select epochs on below total profit.',
+        type=float,
+        metavar='FLOAT',
+    ),
     "hyperopt_list_no_details": Arg(
         '--no-details',
         help='Do not print best epoch details.',

freqtrade/commands/data_commands.py

@@ -5,6 +5,8 @@ from typing import Any, Dict, List
 import arrow
 
 from freqtrade.configuration import TimeRange, setup_utils_configuration
+from freqtrade.data.converter import (convert_ohlcv_format,
+                                      convert_trades_format)
 from freqtrade.data.history import (convert_trades_to_ohlcv,
                                     refresh_backtest_ohlcv_data,
                                     refresh_backtest_trades_data)
@@ -37,22 +39,32 @@ def start_download_data(args: Dict[str, Any]) -> None:
     pairs_not_available: List[str] = []
 
     # Init exchange
-    exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config)
+    exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
+    # Manual validations of relevant settings
+    exchange.validate_pairs(config['pairs'])
+    for timeframe in config['timeframes']:
+        exchange.validate_timeframes(timeframe)
+
     try:
 
         if config.get('download_trades'):
             pairs_not_available = refresh_backtest_trades_data(
                 exchange, pairs=config["pairs"], datadir=config['datadir'],
-                timerange=timerange, erase=config.get("erase"))
+                timerange=timerange, erase=bool(config.get("erase")),
+                data_format=config['dataformat_trades'])
 
             # Convert downloaded trade data to different timeframes
             convert_trades_to_ohlcv(
                 pairs=config["pairs"], timeframes=config["timeframes"],
-                datadir=config['datadir'], timerange=timerange, erase=config.get("erase"))
+                datadir=config['datadir'], timerange=timerange, erase=bool(config.get("erase")),
+                data_format_ohlcv=config['dataformat_ohlcv'],
+                data_format_trades=config['dataformat_trades'],
+                )
         else:
             pairs_not_available = refresh_backtest_ohlcv_data(
                 exchange, pairs=config["pairs"], timeframes=config["timeframes"],
-                datadir=config['datadir'], timerange=timerange, erase=config.get("erase"))
+                datadir=config['datadir'], timerange=timerange, erase=bool(config.get("erase")),
+                data_format=config['dataformat_ohlcv'])
 
     except KeyboardInterrupt:
         sys.exit("SIGINT received, aborting ...")
@@ -61,3 +73,18 @@ def start_download_data(args: Dict[str, Any]) -> None:
         if pairs_not_available:
             logger.info(f"Pairs [{','.join(pairs_not_available)}] not available "
                         f"on exchange {exchange.name}.")
+
+
+def start_convert_data(args: Dict[str, Any], ohlcv: bool = True) -> None:
+    """
+    Convert data from one format to another
+    """
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+    if ohlcv:
+        convert_ohlcv_format(config,
+                             convert_from=args['format_from'], convert_to=args['format_to'],
+                             erase=args['erase'])
+    else:
+        convert_trades_format(config,
+                              convert_from=args['format_from'], convert_to=args['format_to'],
+                              erase=args['erase'])

freqtrade/commands/deploy_commands.py

@@ -6,7 +6,7 @@ from typing import Any, Dict
 from freqtrade.configuration import setup_utils_configuration
 from freqtrade.configuration.directory_operations import (copy_sample_files,
                                                           create_userdata_dir)
-from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGY
+from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGIES
 from freqtrade.exceptions import OperationalException
 from freqtrade.misc import render_template
 from freqtrade.state import RunMode
@@ -28,7 +28,7 @@ def start_create_userdir(args: Dict[str, Any]) -> None:
         sys.exit(1)
 
 
-def deploy_new_strategy(strategy_name, strategy_path: Path, subtemplate: str):
+def deploy_new_strategy(strategy_name: str, strategy_path: Path, subtemplate: str) -> None:
     """
     Deploy new strategy from template to strategy_path
     """
@@ -57,7 +57,7 @@ def start_new_strategy(args: Dict[str, Any]) -> None:
         if args["strategy"] == "DefaultStrategy":
             raise OperationalException("DefaultStrategy is not allowed as name.")
 
-        new_path = config['user_data_dir'] / USERPATH_STRATEGY / (args["strategy"] + ".py")
+        new_path = config['user_data_dir'] / USERPATH_STRATEGIES / (args["strategy"] + ".py")
 
         if new_path.exists():
             raise OperationalException(f"`{new_path}` already exists. "
@@ -69,7 +69,7 @@ def start_new_strategy(args: Dict[str, Any]) -> None:
         raise OperationalException("`new-strategy` requires --strategy to be set.")
 
 
-def deploy_new_hyperopt(hyperopt_name, hyperopt_path: Path, subtemplate: str):
+def deploy_new_hyperopt(hyperopt_name: str, hyperopt_path: Path, subtemplate: str) -> None:
     """
     Deploys a new hyperopt template to hyperopt_path
     """

freqtrade/commands/hyperopt_commands.py

@@ -19,13 +19,25 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
 
     config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
 
-    only_best = config.get('hyperopt_list_best', False)
-    only_profitable = config.get('hyperopt_list_profitable', False)
     print_colorized = config.get('print_colorized', False)
     print_json = config.get('print_json', False)
+    export_csv = config.get('export_csv', None)
     no_details = config.get('hyperopt_list_no_details', False)
     no_header = False
 
+    filteroptions = {
+        'only_best': config.get('hyperopt_list_best', False),
+        'only_profitable': config.get('hyperopt_list_profitable', False),
+        'filter_min_trades': config.get('hyperopt_list_min_trades', 0),
+        'filter_max_trades': config.get('hyperopt_list_max_trades', 0),
+        'filter_min_avg_time': config.get('hyperopt_list_min_avg_time', None),
+        'filter_max_avg_time': config.get('hyperopt_list_max_avg_time', None),
+        'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit', None),
+        'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit', None),
+        'filter_min_total_profit': config.get('hyperopt_list_min_total_profit', None),
+        'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None)
+    }
+
     trials_file = (config['user_data_dir'] /
                    'hyperopt_results' / 'hyperopt_results.pickle')
 
@@ -33,27 +45,28 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
     trials = Hyperopt.load_previous_results(trials_file)
     total_epochs = len(trials)
 
-    trials = _hyperopt_filter_trials(trials, only_best, only_profitable)
-
-    # TODO: fetch the interval for epochs to print from the cli option
-    epoch_start, epoch_stop = 0, None
+    trials = _hyperopt_filter_trials(trials, filteroptions)
 
     if print_colorized:
         colorama_init(autoreset=True)
 
-    try:
-        # Human-friendly indexes used here (starting from 1)
-        for val in trials[epoch_start:epoch_stop]:
-            Hyperopt.print_results_explanation(val, total_epochs, not only_best, print_colorized)
-
-    except KeyboardInterrupt:
-        print('User interrupted..')
+    if not export_csv:
+        try:
+            Hyperopt.print_result_table(config, trials, total_epochs,
+                                        not filteroptions['only_best'], print_colorized, 0)
+        except KeyboardInterrupt:
+            print('User interrupted..')
 
     if trials and not no_details:
         sorted_trials = sorted(trials, key=itemgetter('loss'))
         results = sorted_trials[0]
         Hyperopt.print_epoch_details(results, total_epochs, print_json, no_header)
 
+    if trials and export_csv:
+        Hyperopt.export_csv_file(
+            config, trials, total_epochs, not filteroptions['only_best'], export_csv
+        )
+
 
 def start_hyperopt_show(args: Dict[str, Any]) -> None:
     """
@@ -63,52 +76,109 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
 
     config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
 
-    only_best = config.get('hyperopt_list_best', False)
-    only_profitable = config.get('hyperopt_list_profitable', False)
+    print_json = config.get('print_json', False)
     no_header = config.get('hyperopt_show_no_header', False)
-
     trials_file = (config['user_data_dir'] /
                    'hyperopt_results' / 'hyperopt_results.pickle')
+    n = config.get('hyperopt_show_index', -1)
+
+    filteroptions = {
+        'only_best': config.get('hyperopt_list_best', False),
+        'only_profitable': config.get('hyperopt_list_profitable', False),
+        'filter_min_trades': config.get('hyperopt_list_min_trades', 0),
+        'filter_max_trades': config.get('hyperopt_list_max_trades', 0),
+        'filter_min_avg_time': config.get('hyperopt_list_min_avg_time', None),
+        'filter_max_avg_time': config.get('hyperopt_list_max_avg_time', None),
+        'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit', None),
+        'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit', None),
+        'filter_min_total_profit': config.get('hyperopt_list_min_total_profit', None),
+        'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None)
+    }
 
     # Previous evaluations
     trials = Hyperopt.load_previous_results(trials_file)
     total_epochs = len(trials)
 
-    trials = _hyperopt_filter_trials(trials, only_best, only_profitable)
+    trials = _hyperopt_filter_trials(trials, filteroptions)
     trials_epochs = len(trials)
 
-    n = config.get('hyperopt_show_index', -1)
     if n > trials_epochs:
         raise OperationalException(
-                f"The index of the epoch to show should be less than {trials_epochs + 1}.")
+            f"The index of the epoch to show should be less than {trials_epochs + 1}.")
     if n < -trials_epochs:
         raise OperationalException(
-                f"The index of the epoch to show should be greater than {-trials_epochs - 1}.")
+            f"The index of the epoch to show should be greater than {-trials_epochs - 1}.")
 
     # Translate epoch index from human-readable format to pythonic
     if n > 0:
         n -= 1
 
-    print_json = config.get('print_json', False)
-
     if trials:
         val = trials[n]
         Hyperopt.print_epoch_details(val, total_epochs, print_json, no_header,
                                      header_str="Epoch details")
 
 
-def _hyperopt_filter_trials(trials: List, only_best: bool, only_profitable: bool) -> List:
+def _hyperopt_filter_trials(trials: List, filteroptions: dict) -> List:
     """
     Filter our items from the list of hyperopt results
     """
-    if only_best:
+    if filteroptions['only_best']:
         trials = [x for x in trials if x['is_best']]
-    if only_profitable:
+    if filteroptions['only_profitable']:
         trials = [x for x in trials if x['results_metrics']['profit'] > 0]
+    if filteroptions['filter_min_trades'] > 0:
+        trials = [
+            x for x in trials
+            if x['results_metrics']['trade_count'] > filteroptions['filter_min_trades']
+        ]
+    if filteroptions['filter_max_trades'] > 0:
+        trials = [
+            x for x in trials
+            if x['results_metrics']['trade_count'] < filteroptions['filter_max_trades']
+        ]
+    if filteroptions['filter_min_avg_time'] is not None:
+        trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
+        trials = [
+            x for x in trials
+            if x['results_metrics']['duration'] > filteroptions['filter_min_avg_time']
+        ]
+    if filteroptions['filter_max_avg_time'] is not None:
+        trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
+        trials = [
+            x for x in trials
+            if x['results_metrics']['duration'] < filteroptions['filter_max_avg_time']
+        ]
+    if filteroptions['filter_min_avg_profit'] is not None:
+        trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
+        trials = [
+            x for x in trials
+            if x['results_metrics']['avg_profit']
+            > filteroptions['filter_min_avg_profit']
+        ]
+    if filteroptions['filter_max_avg_profit'] is not None:
+        trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
+        trials = [
+            x for x in trials
+            if x['results_metrics']['avg_profit']
+            < filteroptions['filter_max_avg_profit']
+        ]
+    if filteroptions['filter_min_total_profit'] is not None:
+        trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
+        trials = [
+            x for x in trials
+            if x['results_metrics']['profit'] > filteroptions['filter_min_total_profit']
+        ]
+    if filteroptions['filter_max_total_profit'] is not None:
+        trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
+        trials = [
+            x for x in trials
+            if x['results_metrics']['profit'] < filteroptions['filter_max_total_profit']
+        ]
 
     logger.info(f"{len(trials)} " +
-                ("best " if only_best else "") +
-                ("profitable " if only_profitable else "") +
+                ("best " if filteroptions['only_best'] else "") +
+                ("profitable " if filteroptions['only_profitable'] else "") +
                 "epochs found.")
 
     return trials

freqtrade/commands/list_commands.py

@@ -3,13 +3,15 @@ import logging
 import sys
 from collections import OrderedDict
 from pathlib import Path
-from typing import Any, Dict
+from typing import Any, Dict, List
 
+from colorama import init as colorama_init
+from colorama import Fore, Style
 import rapidjson
 from tabulate import tabulate
 
 from freqtrade.configuration import setup_utils_configuration
-from freqtrade.constants import USERPATH_STRATEGY
+from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGIES
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import (available_exchanges, ccxt_exchanges,
                                 market_is_active, symbol_is_pair)
@@ -36,22 +38,63 @@ def start_list_exchanges(args: Dict[str, Any]) -> None:
             print(f"Exchanges available for Freqtrade: {', '.join(exchanges)}")
 
 
+def _print_objs_tabular(objs: List, print_colorized: bool) -> None:
+    if print_colorized:
+        colorama_init(autoreset=True)
+        red = Fore.RED
+        yellow = Fore.YELLOW
+        reset = Style.RESET_ALL
+    else:
+        red = ''
+        yellow = ''
+        reset = ''
+
+    names = [s['name'] for s in objs]
+    objss_to_print = [{
+        'name': s['name'] if s['name'] else "--",
+        'location': s['location'].name,
+        'status': (red + "LOAD FAILED" + reset if s['class'] is None
+                   else "OK" if names.count(s['name']) == 1
+                   else yellow + "DUPLICATE NAME" + reset)
+    } for s in objs]
+
+    print(tabulate(objss_to_print, headers='keys', tablefmt='psql', stralign='right'))
+
+
 def start_list_strategies(args: Dict[str, Any]) -> None:
     """
-    Print Strategies available in a directory
+    Print files with Strategy custom classes available in the directory
     """
     config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
 
-    directory = Path(config.get('strategy_path', config['user_data_dir'] / USERPATH_STRATEGY))
-    strategies = StrategyResolver.search_all_objects(directory)
+    directory = Path(config.get('strategy_path', config['user_data_dir'] / USERPATH_STRATEGIES))
+    strategy_objs = StrategyResolver.search_all_objects(directory, not args['print_one_column'])
     # Sort alphabetically
-    strategies = sorted(strategies, key=lambda x: x['name'])
-    strats_to_print = [{'name': s['name'], 'location': s['location'].name} for s in strategies]
+    strategy_objs = sorted(strategy_objs, key=lambda x: x['name'])
 
     if args['print_one_column']:
-        print('\n'.join([s['name'] for s in strategies]))
+        print('\n'.join([s['name'] for s in strategy_objs]))
     else:
-        print(tabulate(strats_to_print, headers='keys', tablefmt='pipe'))
+        _print_objs_tabular(strategy_objs, config.get('print_colorized', False))
+
+
+def start_list_hyperopts(args: Dict[str, Any]) -> None:
+    """
+    Print files with HyperOpt custom classes available in the directory
+    """
+    from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    directory = Path(config.get('hyperopt_path', config['user_data_dir'] / USERPATH_HYPEROPTS))
+    hyperopt_objs = HyperOptResolver.search_all_objects(directory, not args['print_one_column'])
+    # Sort alphabetically
+    hyperopt_objs = sorted(hyperopt_objs, key=lambda x: x['name'])
+
+    if args['print_one_column']:
+        print('\n'.join([s['name'] for s in hyperopt_objs]))
+    else:
+        _print_objs_tabular(hyperopt_objs, config.get('print_colorized', False))
 
 
 def start_list_timeframes(args: Dict[str, Any]) -> None:
@@ -149,7 +192,7 @@ def start_list_markets(args: Dict[str, Any], pairs_only: bool = False) -> None:
             else:
                 # print data as a table, with the human-readable summary
                 print(f"{summary_str}:")
-                print(tabulate(tabular_data, headers='keys', tablefmt='pipe'))
+                print(tabulate(tabular_data, headers='keys', tablefmt='psql', stralign='right'))
         elif not (args.get('print_one_column', False) or
                   args.get('list_pairs_print_json', False) or
                   args.get('print_csv', False)):

freqtrade/commands/optimize_commands.py

@@ -17,10 +17,15 @@ def setup_optimize_configuration(args: Dict[str, Any], method: RunMode) -> Dict[
     """
     config = setup_utils_configuration(args, method)
 
-    if method == RunMode.BACKTEST:
-        if config['stake_amount'] == constants.UNLIMITED_STAKE_AMOUNT:
-            raise DependencyException('stake amount could not be "%s" for backtesting' %
-                                      constants.UNLIMITED_STAKE_AMOUNT)
+    no_unlimited_runmodes = {
+        RunMode.BACKTEST: 'backtesting',
+        RunMode.HYPEROPT: 'hyperoptimization',
+    }
+    if (method in no_unlimited_runmodes.keys() and
+            config['stake_amount'] == constants.UNLIMITED_STAKE_AMOUNT):
+        raise DependencyException(
+            f'The value of `stake_amount` cannot be set as "{constants.UNLIMITED_STAKE_AMOUNT}" '
+            f'for {no_unlimited_runmodes[method]}')
 
     return config
 

freqtrade/commands/plot_commands.py

@@ -5,7 +5,7 @@ from freqtrade.exceptions import OperationalException
 from freqtrade.state import RunMode
 
 
-def validate_plot_args(args: Dict[str, Any]):
+def validate_plot_args(args: Dict[str, Any]) -> None:
     if not args.get('datadir') and not args.get('config'):
         raise OperationalException(
             "You need to specify either `--datadir` or `--config` "

freqtrade/configuration/check_exchange.py

@@ -10,7 +10,7 @@ from freqtrade.state import RunMode
 logger = logging.getLogger(__name__)
 
 
-def remove_credentials(config: Dict[str, Any]):
+def remove_credentials(config: Dict[str, Any]) -> None:
     """
     Removes exchange keys from the configuration and specifies dry-run
     Used for backtesting / hyperopt / edge and utils.

freqtrade/configuration/config_validation.py

@@ -150,15 +150,3 @@ def _validate_whitelist(conf: Dict[str, Any]) -> None:
         if (pl.get('method') == 'StaticPairList'
                 and not conf.get('exchange', {}).get('pair_whitelist')):
             raise OperationalException("StaticPairList requires pair_whitelist to be set.")
-
-        if pl.get('method') == 'StaticPairList':
-            stake = conf['stake_currency']
-            invalid_pairs = []
-            for pair in conf['exchange'].get('pair_whitelist'):
-                if not pair.endswith(f'/{stake}'):
-                    invalid_pairs.append(pair)
-
-            if invalid_pairs:
-                raise OperationalException(
-                    f"Stake-currency '{stake}' not compatible with pair-whitelist. "
-                    f"Please remove the following pairs: {invalid_pairs}")

freqtrade/configuration/configuration.py

@@ -96,6 +96,8 @@ class Configuration:
         # Keep a copy of the original configuration file
         config['original_config'] = deepcopy(config)
 
+        self._process_logging_options(config)
+
         self._process_runmode(config)
 
         self._process_common_options(config)
@@ -146,8 +148,6 @@ class Configuration:
 
     def _process_common_options(self, config: Dict[str, Any]) -> None:
 
-        self._process_logging_options(config)
-
         # Set strategy if not specified in config and or if it's non default
         if self.args.get("strategy") or not config.get('strategy'):
             config.update({'strategy': self.args.get("strategy")})
@@ -167,10 +167,6 @@ class Configuration:
         if 'sd_notify' in self.args and self.args["sd_notify"]:
             config['internals'].update({'sd_notify': True})
 
-        self._args_to_config(config, argname='dry_run',
-                             logstring='Parameter --dry-run detected, '
-                             'overriding dry_run to: {} ...')
-
     def _process_datadir_options(self, config: Dict[str, Any]) -> None:
         """
         Extract information for sys.argv and load directory configurations
@@ -200,6 +196,7 @@ class Configuration:
         if self.args.get('exportfilename'):
             self._args_to_config(config, argname='exportfilename',
                                  logstring='Storing backtest results to {} ...')
+            config['exportfilename'] = Path(config['exportfilename'])
         else:
             config['exportfilename'] = (config['user_data_dir']
                                         / 'backtest_results/backtest-result.json')
@@ -286,6 +283,9 @@ class Configuration:
         self._args_to_config(config, argname='print_json',
                              logstring='Parameter --print-json detected ...')
 
+        self._args_to_config(config, argname='export_csv',
+                             logstring='Parameter --export-csv detected: {}')
+
         self._args_to_config(config, argname='hyperopt_jobs',
                              logstring='Parameter -j/--job-workers detected: {}')
 
@@ -310,6 +310,30 @@ class Configuration:
         self._args_to_config(config, argname='hyperopt_list_profitable',
                              logstring='Parameter --profitable detected: {}')
 
+        self._args_to_config(config, argname='hyperopt_list_min_trades',
+                             logstring='Parameter --min-trades detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_max_trades',
+                             logstring='Parameter --max-trades detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_min_avg_time',
+                             logstring='Parameter --min-avg-time detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_max_avg_time',
+                             logstring='Parameter --max-avg-time detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_min_avg_profit',
+                             logstring='Parameter --min-avg-profit detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_max_avg_profit',
+                             logstring='Parameter --max-avg-profit detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_min_total_profit',
+                             logstring='Parameter --min-total-profit detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_max_total_profit',
+                             logstring='Parameter --max-total-profit detected: {}')
+
         self._args_to_config(config, argname='hyperopt_list_no_details',
                              logstring='Parameter --no-details detected: {}')
 
@@ -335,20 +359,34 @@ class Configuration:
         self._args_to_config(config, argname='erase',
                              logstring='Erase detected. Deleting existing data.')
 
+        self._args_to_config(config, argname='no_trades',
+                             logstring='Parameter --no-trades detected.')
+
         self._args_to_config(config, argname='timeframes',
                              logstring='timeframes --timeframes: {}')
 
         self._args_to_config(config, argname='days',
                              logstring='Detected --days: {}')
+
         self._args_to_config(config, argname='download_trades',
                              logstring='Detected --dl-trades: {}')
 
+        self._args_to_config(config, argname='dataformat_ohlcv',
+                             logstring='Using "{}" to store OHLCV data.')
+
+        self._args_to_config(config, argname='dataformat_trades',
+                             logstring='Using "{}" to store trades data.')
+
     def _process_runmode(self, config: Dict[str, Any]) -> None:
 
+        self._args_to_config(config, argname='dry_run',
+                             logstring='Parameter --dry-run detected, '
+                             'overriding dry_run to: {} ...')
+
         if not self.runmode:
             # Handle real mode, infer dry/live from config
             self.runmode = RunMode.DRY_RUN if config.get('dry_run', True) else RunMode.LIVE
-            logger.info(f"Runmode set to {self.runmode}.")
+            logger.info(f"Runmode set to {self.runmode.value}.")
 
         config.update({'runmode': self.runmode})
 

freqtrade/configuration/deprecated_settings.py

@@ -13,7 +13,7 @@ logger = logging.getLogger(__name__)
 
 def check_conflicting_settings(config: Dict[str, Any],
                                section1: str, name1: str,
-                               section2: str, name2: str):
+                               section2: str, name2: str) -> None:
     section1_config = config.get(section1, {})
     section2_config = config.get(section2, {})
     if name1 in section1_config and name2 in section2_config:
@@ -28,7 +28,7 @@ def check_conflicting_settings(config: Dict[str, Any],
 
 def process_deprecated_setting(config: Dict[str, Any],
                                section1: str, name1: str,
-                               section2: str, name2: str):
+                               section2: str, name2: str) -> None:
     section2_config = config.get(section2, {})
 
     if name2 in section2_config:

freqtrade/configuration/directory_operations.py

@@ -23,7 +23,7 @@ def create_datadir(config: Dict[str, Any], datadir: Optional[str] = None) -> Pat
     return folder
 
 
-def create_userdata_dir(directory: str, create_dir=False) -> Path:
+def create_userdata_dir(directory: str, create_dir: bool = False) -> Path:
     """
     Create userdata directory structure.
     if create_dir is True, then the parent-directory will be created if it does not exist.

freqtrade/configuration/load_config.py

@@ -1,13 +1,15 @@
 """
 This module contain functions to load the configuration file
 """
-import rapidjson
 import logging
+import re
 import sys
+from pathlib import Path
 from typing import Any, Dict
 
-from freqtrade.exceptions import OperationalException
+import rapidjson
 
+from freqtrade.exceptions import OperationalException
 
 logger = logging.getLogger(__name__)
 
@@ -15,6 +17,26 @@ logger = logging.getLogger(__name__)
 CONFIG_PARSE_MODE = rapidjson.PM_COMMENTS | rapidjson.PM_TRAILING_COMMAS
 
 
+def log_config_error_range(path: str, errmsg: str) -> str:
+    """
+    Parses configuration file and prints range around error
+    """
+    if path != '-':
+        offsetlist = re.findall(r'(?<=Parse\serror\sat\soffset\s)\d+', errmsg)
+        if offsetlist:
+            offset = int(offsetlist[0])
+            text = Path(path).read_text()
+            # Fetch an offset of 80 characters around the error line
+            subtext = text[offset-min(80, offset):offset+80]
+            segments = subtext.split('\n')
+            if len(segments) > 3:
+                # Remove first and last lines, to avoid odd truncations
+                return '\n'.join(segments[1:-1])
+            else:
+                return subtext
+    return ''
+
+
 def load_config_file(path: str) -> Dict[str, Any]:
     """
     Loads a config file from the given path
@@ -29,5 +51,12 @@ def load_config_file(path: str) -> Dict[str, Any]:
         raise OperationalException(
             f'Config file "{path}" not found!'
             ' Please create a config file or check whether it exists.')
+    except rapidjson.JSONDecodeError as e:
+        err_range = log_config_error_range(path, str(e))
+        raise OperationalException(
+            f'{e}\n'
+            f'Please verify the following segment of your configuration:\n{err_range}'
+            if err_range else 'Please verify your configuration file for syntax errors.'
+        )
 
     return config

freqtrade/configuration/timerange.py

@@ -7,6 +7,7 @@ from typing import Optional
 
 import arrow
 
+
 logger = logging.getLogger(__name__)
 
 
@@ -30,7 +31,7 @@ class TimeRange:
         return (self.starttype == other.starttype and self.stoptype == other.stoptype
                 and self.startts == other.startts and self.stopts == other.stopts)
 
-    def subtract_start(self, seconds) -> None:
+    def subtract_start(self, seconds: int) -> None:
         """
         Subtracts <seconds> from startts if startts is set.
         :param seconds: Seconds to subtract from starttime
@@ -44,7 +45,7 @@ class TimeRange:
         """
         Adjust startts by <startup_candles> candles.
         Applies only if no startup-candles have been available.
-        :param timeframe_secs: Ticker timeframe in seconds e.g. `timeframe_to_seconds('5m')`
+        :param timeframe_secs: Timeframe in seconds e.g. `timeframe_to_seconds('5m')`
         :param startup_candles: Number of candles to move start-date forward
         :param min_date: Minimum data date loaded. Key kriterium to decide if start-time
                          has to be moved
@@ -59,7 +60,7 @@ class TimeRange:
             self.starttype = 'date'
 
     @staticmethod
-    def parse_timerange(text: Optional[str]):
+    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

freqtrade/constants.py

@@ -15,22 +15,27 @@ UNLIMITED_STAKE_AMOUNT = 'unlimited'
 DEFAULT_AMOUNT_RESERVE_PERCENT = 0.05
 REQUIRED_ORDERTIF = ['buy', 'sell']
 REQUIRED_ORDERTYPES = ['buy', 'sell', 'stoploss', 'stoploss_on_exchange']
+ORDERBOOK_SIDES = ['ask', 'bid']
 ORDERTYPE_POSSIBILITIES = ['limit', 'market']
 ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
-AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList', 'PrecisionFilter', 'PriceFilter']
+AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
+                       'PrecisionFilter', 'PriceFilter', 'SpreadFilter']
+AVAILABLE_DATAHANDLERS = ['json', 'jsongz']
 DRY_RUN_WALLET = 1000
 MATH_CLOSE_PREC = 1e-14  # Precision used for float comparisons
+DEFAULT_DATAFRAME_COLUMNS = ['date', 'open', 'high', 'low', 'close', 'volume']
 
 USERPATH_HYPEROPTS = 'hyperopts'
-USERPATH_STRATEGY = 'strategies'
+USERPATH_STRATEGIES = 'strategies'
+USERPATH_NOTEBOOKS = 'notebooks'
 
 # Soure files with destination directories within user-directory
 USER_DATA_FILES = {
-    'sample_strategy.py': USERPATH_STRATEGY,
+    'sample_strategy.py': USERPATH_STRATEGIES,
     'sample_hyperopt_advanced.py': USERPATH_HYPEROPTS,
     'sample_hyperopt_loss.py': USERPATH_HYPEROPTS,
     'sample_hyperopt.py': USERPATH_HYPEROPTS,
-    'strategy_analysis_example.ipynb': 'notebooks',
+    'strategy_analysis_example.ipynb': USERPATH_NOTEBOOKS,
 }
 
 SUPPORTED_FIAT = [
@@ -38,7 +43,7 @@ SUPPORTED_FIAT = [
     "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"
+    "BTC", "ETH", "XRP", "LTC", "BCH"
 ]
 
 MINIMAL_CONFIG = {
@@ -76,7 +81,7 @@ CONF_SCHEMA = {
         'amend_last_stake_amount': {'type': 'boolean', 'default': False},
         'last_stake_amount_min_ratio': {
             'type': 'number', 'minimum': 0.0, 'maximum': 1.0, 'default': 0.5
-            },
+        },
         'fiat_display_currency': {'type': 'string', 'enum': SUPPORTED_FIAT},
         'dry_run': {'type': 'boolean'},
         'dry_run_wallet': {'type': 'number', 'default': DRY_RUN_WALLET},
@@ -109,15 +114,16 @@ CONF_SCHEMA = {
                     'minimum': 0,
                     'maximum': 1,
                     'exclusiveMaximum': False,
-                    'use_order_book': {'type': 'boolean'},
-                    'order_book_top': {'type': 'integer', 'maximum': 20, 'minimum': 1},
-                    'check_depth_of_market': {
-                        'type': 'object',
-                        'properties': {
-                            'enabled': {'type': 'boolean'},
-                            'bids_to_ask_delta': {'type': 'number', 'minimum': 0},
-                        }
-                    },
+                },
+                'price_side': {'type': 'string', 'enum': ORDERBOOK_SIDES, 'default': 'bid'},
+                'use_order_book': {'type': 'boolean'},
+                'order_book_top': {'type': 'integer', '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']
@@ -125,6 +131,7 @@ CONF_SCHEMA = {
         'ask_strategy': {
             'type': 'object',
             'properties': {
+                'price_side': {'type': 'string', 'enum': ORDERBOOK_SIDES, 'default': 'ask'},
                 'use_order_book': {'type': 'boolean'},
                 'order_book_min': {'type': 'integer', 'minimum': 1},
                 'order_book_max': {'type': 'integer', 'minimum': 1, 'maximum': 50},
@@ -189,7 +196,9 @@ CONF_SCHEMA = {
             'properties': {
                 'enabled': {'type': 'boolean'},
                 'webhookbuy': {'type': 'object'},
+                'webhookbuycancel': {'type': 'object'},
                 'webhooksell': {'type': 'object'},
+                'webhooksellcancel': {'type': 'object'},
                 'webhookstatus': {'type': 'object'},
             },
         },
@@ -213,11 +222,22 @@ CONF_SCHEMA = {
         'forcebuy_enable': {'type': 'boolean'},
         'internals': {
             'type': 'object',
+            'default': {},
             'properties': {
                 'process_throttle_secs': {'type': 'integer'},
                 'interval': {'type': 'integer'},
                 'sd_notify': {'type': 'boolean'},
             }
+        },
+        'dataformat_ohlcv': {
+            'type': 'string',
+                    'enum': AVAILABLE_DATAHANDLERS,
+                    'default': 'json'
+        },
+        'dataformat_trades': {
+            'type': 'string',
+                    'enum': AVAILABLE_DATAHANDLERS,
+                    'default': 'jsongz'
         }
     },
     'definitions': {
@@ -234,7 +254,6 @@ CONF_SCHEMA = {
                     'type': 'array',
                     'items': {
                         'type': 'string',
-                        'pattern': '^[0-9A-Z]+/[0-9A-Z]+$'
                     },
                     'uniqueItems': True
                 },
@@ -242,7 +261,6 @@ CONF_SCHEMA = {
                     'type': 'array',
                     'items': {
                         'type': 'string',
-                        'pattern': '^[0-9A-Z]+/[0-9A-Z]+$'
                     },
                     'uniqueItems': True
                 },
@@ -284,13 +302,19 @@ SCHEMA_TRADE_REQUIRED = [
     'last_stake_amount_min_ratio',
     'dry_run',
     'dry_run_wallet',
+    'ask_strategy',
     'bid_strategy',
     'unfilledtimeout',
     'stoploss',
     'minimal_roi',
+    'internals',
+    'dataformat_ohlcv',
+    'dataformat_trades',
 ]
 
 SCHEMA_MINIMAL_REQUIRED = [
     'exchange',
     'dry_run',
+    'dataformat_ohlcv',
+    'dataformat_trades',
 ]

freqtrade/data/btanalysis.py

@@ -3,7 +3,7 @@ Helpers when analyzing backtest data
 """
 import logging
 from pathlib import Path
-from typing import Dict
+from typing import Dict, Union, Tuple
 
 import numpy as np
 import pandas as pd
@@ -20,7 +20,7 @@ BT_DATA_COLUMNS = ["pair", "profitperc", "open_time", "close_time", "index", "du
                    "open_rate", "close_rate", "open_at_end", "sell_reason"]
 
 
-def load_backtest_data(filename) -> pd.DataFrame:
+def load_backtest_data(filename: Union[Path, str]) -> pd.DataFrame:
     """
     Load backtest data file.
     :param filename: pathlib.Path object, or string pointing to the file.
@@ -111,7 +111,7 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
                             t.calc_profit(), t.calc_profit_ratio(),
                             t.open_rate, t.close_rate, t.amount,
                             (round((t.close_date.timestamp() - t.open_date.timestamp()) / 60, 2)
-                                if t.close_date else None),
+                             if t.close_date else None),
                             t.sell_reason,
                             t.fee_open, t.fee_close,
                             t.open_rate_requested,
@@ -129,16 +129,26 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
     return trades
 
 
-def load_trades(source: str, db_url: str, exportfilename: str) -> pd.DataFrame:
+def load_trades(source: str, db_url: str, exportfilename: Path,
+                no_trades: bool = False) -> pd.DataFrame:
     """
     Based on configuration option "trade_source":
     * loads data from DB (using `db_url`)
     * loads data from backtestfile (using `exportfilename`)
+    :param source: "DB" or "file" - specify source to load from
+    :param db_url: sqlalchemy formatted url to a database
+    :param exportfilename: Json file generated by backtesting
+    :param no_trades: Skip using trades, only return backtesting data columns
+    :return: DataFrame containing trades
     """
+    if no_trades:
+        df = pd.DataFrame(columns=BT_DATA_COLUMNS)
+        return df
+
     if source == "DB":
         return load_trades_from_db(db_url)
     elif source == "file":
-        return load_backtest_data(Path(exportfilename))
+        return load_backtest_data(exportfilename)
 
 
 def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame) -> pd.DataFrame:
@@ -151,16 +161,17 @@ def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame) -> p
     return trades
 
 
-def combine_tickers_with_mean(tickers: Dict[str, pd.DataFrame], column: str = "close"):
+def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
+                                 column: str = "close") -> pd.DataFrame:
     """
     Combine multiple dataframes "column"
-    :param tickers: Dict of Dataframes, dict key should be pair.
+    :param data: Dict of Dataframes, dict key should be pair.
     :param column: Column in the original dataframes to use
     :return: DataFrame with the column renamed to the dict key, and a column
         named mean, containing the mean of all pairs.
     """
-    df_comb = pd.concat([tickers[pair].set_index('date').rename(
-        {column: pair}, axis=1)[pair] for pair in tickers], axis=1)
+    df_comb = pd.concat([data[pair].set_index('date').rename(
+        {column: pair}, axis=1)[pair] for pair in data], axis=1)
 
     df_comb['mean'] = df_comb.mean(axis=1)
 
@@ -187,3 +198,28 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
     # FFill to get continuous
     df[col_name] = df[col_name].ffill()
     return df
+
+
+def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_time',
+                           value_col: str = 'profitperc'
+                           ) -> Tuple[float, pd.Timestamp, pd.Timestamp]:
+    """
+    Calculate max drawdown and the corresponding close dates
+    :param trades: DataFrame containing trades (requires columns close_time and profitperc)
+    :param date_col: Column in DataFrame to use for dates (defaults to 'close_time')
+    :param value_col: Column in DataFrame to use for values (defaults to 'profitperc')
+    :return: Tuple (float, highdate, lowdate) with absolute max drawdown, high and low time
+    :raise: ValueError if trade-dataframe was found empty.
+    """
+    if len(trades) == 0:
+        raise ValueError("Trade dataframe empty.")
+    profit_results = trades.sort_values(date_col)
+    max_drawdown_df = pd.DataFrame()
+    max_drawdown_df['cumulative'] = profit_results[value_col].cumsum()
+    max_drawdown_df['high_value'] = max_drawdown_df['cumulative'].cummax()
+    max_drawdown_df['drawdown'] = max_drawdown_df['cumulative'] - max_drawdown_df['high_value']
+
+    high_date = profit_results.loc[max_drawdown_df['high_value'].idxmax(), date_col]
+    low_date = profit_results.loc[max_drawdown_df['drawdown'].idxmin(), date_col]
+
+    return abs(min(max_drawdown_df['drawdown'])), high_date, low_date

freqtrade/data/converter.py

@@ -2,20 +2,23 @@
 Functions to convert data from one format to another
 """
 import logging
+from datetime import datetime, timezone
+from typing import Any, Dict
 
 import pandas as pd
 from pandas import DataFrame, to_datetime
 
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
 
 logger = logging.getLogger(__name__)
 
 
-def parse_ticker_dataframe(ticker: list, timeframe: str, pair: str, *,
-                           fill_missing: bool = True,
-                           drop_incomplete: bool = True) -> DataFrame:
+def ohlcv_to_dataframe(ohlcv: list, timeframe: str, pair: str, *,
+                       fill_missing: bool = True, drop_incomplete: bool = True) -> DataFrame:
     """
-    Converts a ticker-list (format ccxt.fetch_ohlcv) to a Dataframe
-    :param ticker: ticker list, as returned by exchange.async_get_candle_history
+    Converts a list with candle (OHLCV) data (in format returned by ccxt.fetch_ohlcv)
+    to a Dataframe
+    :param ohlcv: list with candle (OHLCV) data, as returned by exchange.async_get_candle_history
     :param timeframe: timeframe (e.g. 5m). Used to fill up eventual missing data
     :param pair: Pair this data is for (used to warn if fillup was necessary)
     :param fill_missing: fill up missing candles with 0 candles
@@ -23,23 +26,40 @@ def parse_ticker_dataframe(ticker: list, timeframe: str, pair: str, *,
     :param drop_incomplete: Drop the last candle of the dataframe, assuming it's incomplete
     :return: DataFrame
     """
-    logger.debug("Parsing tickerlist to dataframe")
-    cols = ['date', 'open', 'high', 'low', 'close', 'volume']
-    frame = DataFrame(ticker, columns=cols)
+    logger.debug(f"Converting candle (OHLCV) data to dataframe for pair {pair}.")
+    cols = DEFAULT_DATAFRAME_COLUMNS
+    df = DataFrame(ohlcv, columns=cols)
 
-    frame['date'] = to_datetime(frame['date'],
-                                unit='ms',
-                                utc=True,
-                                infer_datetime_format=True)
+    df['date'] = to_datetime(df['date'], unit='ms', utc=True, infer_datetime_format=True)
 
-    # Some exchanges return int values for volume and even for ohlc.
+    # Some exchanges return int values for Volume and even for OHLC.
     # Convert them since TA-LIB indicators used in the strategy assume floats
     # and fail with exception...
-    frame = frame.astype(dtype={'open': 'float', 'high': 'float', 'low': 'float', 'close': 'float',
-                                'volume': 'float'})
+    df = df.astype(dtype={'open': 'float', 'high': 'float', 'low': 'float', 'close': 'float',
+                          'volume': 'float'})
+    return clean_ohlcv_dataframe(df, timeframe, pair,
+                                 fill_missing=fill_missing,
+                                 drop_incomplete=drop_incomplete)
 
+
+def clean_ohlcv_dataframe(data: DataFrame, timeframe: str, pair: str, *,
+                          fill_missing: bool = True,
+                          drop_incomplete: bool = True) -> DataFrame:
+    """
+    Clense a OHLCV dataframe by
+      * Grouping it by date (removes duplicate tics)
+      * dropping last candles if requested
+      * Filling up missing data (if requested)
+    :param data: DataFrame containing candle (OHLCV) data.
+    :param timeframe: timeframe (e.g. 5m). Used to fill up eventual missing data
+    :param pair: Pair this data is for (used to warn if fillup was necessary)
+    :param fill_missing: fill up missing candles with 0 candles
+                         (see ohlcv_fill_up_missing_data for details)
+    :param drop_incomplete: Drop the last candle of the dataframe, assuming it's incomplete
+    :return: DataFrame
+    """
     # group by index and aggregate results to eliminate duplicate ticks
-    frame = frame.groupby(by='date', as_index=False, sort=True).agg({
+    data = data.groupby(by='date', as_index=False, sort=True).agg({
         'open': 'first',
         'high': 'max',
         'low': 'min',
@@ -48,13 +68,13 @@ def parse_ticker_dataframe(ticker: list, timeframe: str, pair: str, *,
     })
     # eliminate partial candle
     if drop_incomplete:
-        frame.drop(frame.tail(1).index, inplace=True)
+        data.drop(data.tail(1).index, inplace=True)
         logger.debug('Dropping last candle')
 
     if fill_missing:
-        return ohlcv_fill_up_missing_data(frame, timeframe, pair)
+        return ohlcv_fill_up_missing_data(data, timeframe, pair)
     else:
-        return frame
+        return data
 
 
 def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str) -> DataFrame:
@@ -65,16 +85,16 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str)
     """
     from freqtrade.exchange import timeframe_to_minutes
 
-    ohlc_dict = {
+    ohlcv_dict = {
         'open': 'first',
         'high': 'max',
         'low': 'min',
         'close': 'last',
         'volume': 'sum'
     }
-    ticker_minutes = timeframe_to_minutes(timeframe)
+    timeframe_minutes = timeframe_to_minutes(timeframe)
     # Resample to create "NAN" values
-    df = dataframe.resample(f'{ticker_minutes}min', on='date').agg(ohlc_dict)
+    df = dataframe.resample(f'{timeframe_minutes}min', on='date').agg(ohlcv_dict)
 
     # Forwardfill close for missing columns
     df['close'] = df['close'].fillna(method='ffill')
@@ -92,8 +112,26 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str)
     return df
 
 
+def trim_dataframe(df: DataFrame, timerange, df_date_col: str = 'date') -> DataFrame:
+    """
+    Trim dataframe based on given timerange
+    :param df: Dataframe to trim
+    :param timerange: timerange (use start and end date if available)
+    :param: df_date_col: Column in the dataframe to use as Date column
+    :return: trimmed dataframe
+    """
+    if timerange.starttype == 'date':
+        start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
+        df = df.loc[df[df_date_col] >= start, :]
+    if timerange.stoptype == 'date':
+        stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
+        df = df.loc[df[df_date_col] <= stop, :]
+    return df
+
+
 def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
     """
+    TODO: This should get a dedicated test
     Gets order book list, returns dataframe with below format per suggested by creslin
     -------------------------------------------------------------------
      b_sum       b_size       bids       asks       a_size       a_sum
@@ -116,23 +154,84 @@ def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
     return frame
 
 
-def trades_to_ohlcv(trades: list, timeframe: str) -> list:
+def trades_to_ohlcv(trades: list, timeframe: str) -> DataFrame:
     """
-    Converts trades list to ohlcv list
+    Converts trades list to OHLCV list
+    TODO: This should get a dedicated test
     :param trades: List of trades, as returned by ccxt.fetch_trades.
-    :param timeframe: Ticker timeframe to resample data to
-    :return: ohlcv timeframe as list (as returned by ccxt.fetch_ohlcv)
+    :param timeframe: Timeframe to resample data to
+    :return: OHLCV Dataframe.
     """
     from freqtrade.exchange import timeframe_to_minutes
-    ticker_minutes = timeframe_to_minutes(timeframe)
+    timeframe_minutes = timeframe_to_minutes(timeframe)
     df = pd.DataFrame(trades)
     df['datetime'] = pd.to_datetime(df['datetime'])
     df = df.set_index('datetime')
 
-    df_new = df['price'].resample(f'{ticker_minutes}min').ohlc()
-    df_new['volume'] = df['amount'].resample(f'{ticker_minutes}min').sum()
-    df_new['date'] = df_new.index.astype("int64") // 10 ** 6
+    df_new = df['price'].resample(f'{timeframe_minutes}min').ohlc()
+    df_new['volume'] = df['amount'].resample(f'{timeframe_minutes}min').sum()
+    df_new['date'] = df_new.index
     # Drop 0 volume rows
     df_new = df_new.dropna()
-    columns = ["date", "open", "high", "low", "close", "volume"]
-    return list(zip(*[df_new[x].values.tolist() for x in columns]))
+    return df_new[DEFAULT_DATAFRAME_COLUMNS]
+
+
+def convert_trades_format(config: Dict[str, Any], convert_from: str, convert_to: str, erase: bool):
+    """
+    Convert trades from one format to another format.
+    :param config: Config dictionary
+    :param convert_from: Source format
+    :param convert_to: Target format
+    :param erase: Erase souce data (does not apply if source and target format are identical)
+    """
+    from freqtrade.data.history.idatahandler import get_datahandler
+    src = get_datahandler(config['datadir'], convert_from)
+    trg = get_datahandler(config['datadir'], convert_to)
+
+    if 'pairs' not in config:
+        config['pairs'] = src.trades_get_pairs(config['datadir'])
+    logger.info(f"Converting trades for {config['pairs']}")
+
+    for pair in config['pairs']:
+        data = src.trades_load(pair=pair)
+        logger.info(f"Converting {len(data)} trades for {pair}")
+        trg.trades_store(pair, data)
+        if erase and convert_from != convert_to:
+            logger.info(f"Deleting source Trade data for {pair}.")
+            src.trades_purge(pair=pair)
+
+
+def convert_ohlcv_format(config: Dict[str, Any], convert_from: str, convert_to: str, erase: bool):
+    """
+    Convert OHLCV from one format to another
+    :param config: Config dictionary
+    :param convert_from: Source format
+    :param convert_to: Target format
+    :param erase: Erase souce data (does not apply if source and target format are identical)
+    """
+    from freqtrade.data.history.idatahandler import get_datahandler
+    src = get_datahandler(config['datadir'], convert_from)
+    trg = get_datahandler(config['datadir'], convert_to)
+    timeframes = config.get('timeframes', [config.get('ticker_interval')])
+    logger.info(f"Converting candle (OHLCV) for timeframe {timeframes}")
+
+    if 'pairs' not in config:
+        config['pairs'] = []
+        # Check timeframes or fall back to ticker_interval.
+        for timeframe in timeframes:
+            config['pairs'].extend(src.ohlcv_get_pairs(config['datadir'],
+                                                       timeframe))
+    logger.info(f"Converting candle (OHLCV) data for {config['pairs']}")
+
+    for timeframe in timeframes:
+        for pair in config['pairs']:
+            data = src.ohlcv_load(pair=pair, timeframe=timeframe,
+                                  timerange=None,
+                                  fill_missing=False,
+                                  drop_incomplete=False,
+                                  startup_candles=0)
+            logger.info(f"Converting {len(data)} candles for {pair}")
+            trg.ohlcv_store(pair=pair, timeframe=timeframe, data=data)
+            if erase and convert_from != convert_to:
+                logger.info(f"Deleting source data for {pair} / {timeframe}")
+                src.ohlcv_purge(pair=pair, timeframe=timeframe)

freqtrade/data/dataprovider.py

@@ -1,7 +1,7 @@
 """
 Dataprovider
 Responsible to provide data to the bot
-including Klines, tickers, historic data
+including ticker and orderbook data, live and historical candle (OHLCV) data
 Common Interface for bot and strategy to access data.
 """
 import logging
@@ -43,10 +43,10 @@ class DataProvider:
 
     def ohlcv(self, pair: str, timeframe: str = None, copy: bool = True) -> DataFrame:
         """
-        Get ohlcv data for the given pair as DataFrame
+        Get candle (OHLCV) data for the given pair as DataFrame
         Please use the `available_pairs` method to verify which pairs are currently cached.
         :param pair: pair to get the data for
-        :param timeframe: Ticker timeframe to get data for
+        :param timeframe: Timeframe to get data for
         :param copy: copy dataframe before returning if True.
                      Use False only for read-only operations (where the dataframe is not modified)
         """
@@ -58,7 +58,7 @@ class DataProvider:
 
     def historic_ohlcv(self, pair: str, timeframe: str = None) -> DataFrame:
         """
-        Get stored historic ohlcv data
+        Get stored historical candle (OHLCV) data
         :param pair: pair to get the data for
         :param timeframe: timeframe to get data for
         """
@@ -69,17 +69,17 @@ class DataProvider:
 
     def get_pair_dataframe(self, pair: str, timeframe: str = None) -> DataFrame:
         """
-        Return pair ohlcv data, either live or cached historical -- depending
+        Return pair candle (OHLCV) data, either live or cached historical -- depending
         on the runmode.
         :param pair: pair to get the data for
         :param timeframe: timeframe to get data for
         :return: Dataframe for this pair
         """
         if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
-            # Get live ohlcv data.
+            # Get live OHLCV data.
             data = self.ohlcv(pair=pair, timeframe=timeframe)
         else:
-            # Get historic ohlcv data (cached on disk).
+            # Get historical OHLCV data (cached on disk).
             data = self.historic_ohlcv(pair=pair, timeframe=timeframe)
         if len(data) == 0:
             logger.warning(f"No data found for ({pair}, {timeframe}).")

freqtrade/data/history/__init__.py

@@ -0,0 +1,14 @@
+"""
+Handle historic data (ohlcv).
+
+Includes:
+* load data for a pair (or a list of pairs) from disk
+* download data from exchange and store to disk
+"""
+
+from .history_utils import (convert_trades_to_ohlcv,  # noqa: F401
+                            get_timerange, load_data, load_pair_history,
+                            refresh_backtest_ohlcv_data,
+                            refresh_backtest_trades_data, refresh_data,
+                            validate_backtest_data)
+from .idatahandler import get_datahandler  # noqa: F401

freqtrade/data/history/history_utils.py

@@ -1,200 +1,93 @@
-"""
-Handle historic data (ohlcv).
-
-Includes:
-* load data for a pair (or a list of pairs) from disk
-* download data from exchange and store to disk
-"""
-
 import logging
 import operator
-from copy import deepcopy
 from datetime import datetime, timezone
 from pathlib import Path
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Dict, List, Optional, Tuple
 
 import arrow
 from pandas import DataFrame
 
-from freqtrade import misc
 from freqtrade.configuration import TimeRange
-from freqtrade.data.converter import parse_ticker_dataframe, trades_to_ohlcv
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
+from freqtrade.data.converter import ohlcv_to_dataframe, trades_to_ohlcv
+from freqtrade.data.history.idatahandler import IDataHandler, get_datahandler
 from freqtrade.exceptions import OperationalException
-from freqtrade.exchange import (Exchange, timeframe_to_minutes,
-                                timeframe_to_seconds)
+from freqtrade.exchange import Exchange
 
 logger = logging.getLogger(__name__)
 
 
-def trim_tickerlist(tickerlist: List[Dict], timerange: TimeRange) -> List[Dict]:
-    """
-    Trim tickerlist based on given timerange
-    """
-    if not tickerlist:
-        return tickerlist
-
-    start_index = 0
-    stop_index = len(tickerlist)
-
-    if timerange.starttype == 'date':
-        while (start_index < len(tickerlist) and
-               tickerlist[start_index][0] < timerange.startts * 1000):
-            start_index += 1
-
-    if 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 trim_dataframe(df: DataFrame, timerange: TimeRange, df_date_col: str = 'date') -> DataFrame:
-    """
-    Trim dataframe based on given timerange
-    :param df: Dataframe to trim
-    :param timerange: timerange (use start and end date if available)
-    :param: df_date_col: Column in the dataframe to use as Date column
-    :return: trimmed dataframe
-    """
-    if timerange.starttype == 'date':
-        start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
-        df = df.loc[df[df_date_col] >= start, :]
-    if timerange.stoptype == 'date':
-        stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
-        df = df.loc[df[df_date_col] <= stop, :]
-    return df
-
-
-def load_tickerdata_file(datadir: Path, pair: str, timeframe: str,
-                         timerange: Optional[TimeRange] = None) -> List[Dict]:
-    """
-    Load a pair from file, either .json.gz or .json
-    :return: tickerlist or None if unsuccessful
-    """
-    filename = pair_data_filename(datadir, pair, timeframe)
-    pairdata = misc.file_load_json(filename)
-    if not pairdata:
-        return []
-
-    if timerange:
-        pairdata = trim_tickerlist(pairdata, timerange)
-    return pairdata
-
-
-def store_tickerdata_file(datadir: Path, pair: str,
-                          timeframe: str, data: list, is_zip: bool = False):
-    """
-    Stores tickerdata to file
-    """
-    filename = pair_data_filename(datadir, pair, timeframe)
-    misc.file_dump_json(filename, data, is_zip=is_zip)
-
-
-def load_trades_file(datadir: Path, pair: str,
-                     timerange: Optional[TimeRange] = None) -> List[Dict]:
-    """
-    Load a pair from file, either .json.gz or .json
-    :return: tradelist or empty list if unsuccesful
-    """
-    filename = pair_trades_filename(datadir, pair)
-    tradesdata = misc.file_load_json(filename)
-    if not tradesdata:
-        return []
-
-    return tradesdata
-
-
-def store_trades_file(datadir: Path, pair: str,
-                      data: list, is_zip: bool = True):
-    """
-    Stores tickerdata to file
-    """
-    filename = pair_trades_filename(datadir, pair)
-    misc.file_dump_json(filename, data, is_zip=is_zip)
-
-
-def _validate_pairdata(pair, pairdata, timerange: TimeRange):
-    if timerange.starttype == 'date' and pairdata[0][0] > timerange.startts * 1000:
-        logger.warning('Missing data at start for pair %s, data starts at %s',
-                       pair, arrow.get(pairdata[0][0] // 1000).strftime('%Y-%m-%d %H:%M:%S'))
-    if timerange.stoptype == 'date' and pairdata[-1][0] < timerange.stopts * 1000:
-        logger.warning('Missing data at end for pair %s, data ends at %s',
-                       pair, arrow.get(pairdata[-1][0] // 1000).strftime('%Y-%m-%d %H:%M:%S'))
-
-
 def load_pair_history(pair: str,
                       timeframe: str,
-                      datadir: Path,
+                      datadir: Path, *,
                       timerange: Optional[TimeRange] = None,
                       fill_up_missing: bool = True,
                       drop_incomplete: bool = True,
                       startup_candles: int = 0,
+                      data_format: str = None,
+                      data_handler: IDataHandler = None,
                       ) -> DataFrame:
     """
-    Load cached ticker history for the given pair.
+    Load cached ohlcv history for the given pair.
 
     :param pair: Pair to load data for
-    :param timeframe: Ticker timeframe (e.g. "5m")
+    :param timeframe: Timeframe (e.g. "5m")
     :param datadir: Path to the data storage location.
+    :param data_format: Format of the data. Ignored if data_handler is set.
     :param timerange: Limit data to be loaded to this timerange
     :param fill_up_missing: Fill missing values with "No action"-candles
     :param drop_incomplete: Drop last candle assuming it may be incomplete.
     :param startup_candles: Additional candles to load at the start of the period
+    :param data_handler: Initialized data-handler to use.
+                         Will be initialized from data_format if not set
     :return: DataFrame with ohlcv data, or empty DataFrame
     """
-    timerange_startup = deepcopy(timerange)
-    if startup_candles > 0 and timerange_startup:
-        timerange_startup.subtract_start(timeframe_to_seconds(timeframe) * startup_candles)
+    data_handler = get_datahandler(datadir, data_format, data_handler)
 
-    pairdata = load_tickerdata_file(datadir, pair, timeframe, timerange=timerange_startup)
-
-    if pairdata:
-        if timerange_startup:
-            _validate_pairdata(pair, pairdata, timerange_startup)
-        return parse_ticker_dataframe(pairdata, timeframe, pair=pair,
-                                      fill_missing=fill_up_missing,
-                                      drop_incomplete=drop_incomplete)
-    else:
-        logger.warning(
-            f'No history data for pair: "{pair}", timeframe: {timeframe}. '
-            'Use `freqtrade download-data` to download the data'
-        )
-        return DataFrame()
+    return data_handler.ohlcv_load(pair=pair,
+                                   timeframe=timeframe,
+                                   timerange=timerange,
+                                   fill_missing=fill_up_missing,
+                                   drop_incomplete=drop_incomplete,
+                                   startup_candles=startup_candles,
+                                   )
 
 
 def load_data(datadir: Path,
               timeframe: str,
-              pairs: List[str],
+              pairs: List[str], *,
               timerange: Optional[TimeRange] = None,
               fill_up_missing: bool = True,
               startup_candles: int = 0,
-              fail_without_data: bool = False
+              fail_without_data: bool = False,
+              data_format: str = 'json',
               ) -> Dict[str, DataFrame]:
     """
-    Load ticker history data for a list of pairs.
+    Load ohlcv history data for a list of pairs.
 
     :param datadir: Path to the data storage location.
-    :param timeframe: Ticker Timeframe (e.g. "5m")
+    :param timeframe: Timeframe (e.g. "5m")
     :param pairs: List of pairs to load
     :param timerange: Limit data to be loaded to this timerange
     :param fill_up_missing: Fill missing values with "No action"-candles
     :param startup_candles: Additional candles to load at the start of the period
     :param fail_without_data: Raise OperationalException if no data is found.
+    :param data_format: Data format which should be used. Defaults to json
     :return: dict(<pair>:<Dataframe>)
     """
     result: Dict[str, DataFrame] = {}
     if startup_candles > 0 and timerange:
         logger.info(f'Using indicator startup period: {startup_candles} ...')
 
+    data_handler = get_datahandler(datadir, data_format)
+
     for pair in pairs:
         hist = load_pair_history(pair=pair, timeframe=timeframe,
                                  datadir=datadir, timerange=timerange,
                                  fill_up_missing=fill_up_missing,
-                                 startup_candles=startup_candles)
+                                 startup_candles=startup_candles,
+                                 data_handler=data_handler
+                                 )
         if not hist.empty:
             result[pair] = hist
 
@@ -207,81 +100,62 @@ def refresh_data(datadir: Path,
                  timeframe: str,
                  pairs: List[str],
                  exchange: Exchange,
+                 data_format: str = None,
                  timerange: Optional[TimeRange] = None,
                  ) -> None:
     """
-    Refresh ticker history data for a list of pairs.
+    Refresh ohlcv history data for a list of pairs.
 
     :param datadir: Path to the data storage location.
-    :param timeframe: Ticker Timeframe (e.g. "5m")
+    :param timeframe: Timeframe (e.g. "5m")
     :param pairs: List of pairs to load
     :param exchange: Exchange object
     :param timerange: Limit data to be loaded to this timerange
     """
+    data_handler = get_datahandler(datadir, data_format)
     for pair in pairs:
         _download_pair_history(pair=pair, timeframe=timeframe,
                                datadir=datadir, timerange=timerange,
-                               exchange=exchange)
+                               exchange=exchange, data_handler=data_handler)
 
 
-def pair_data_filename(datadir: Path, pair: str, timeframe: str) -> Path:
-    pair_s = pair.replace("/", "_")
-    filename = datadir.joinpath(f'{pair_s}-{timeframe}.json')
-    return filename
-
-
-def pair_trades_filename(datadir: Path, pair: str) -> Path:
-    pair_s = pair.replace("/", "_")
-    filename = datadir.joinpath(f'{pair_s}-trades.json.gz')
-    return filename
-
-
-def _load_cached_data_for_updating(datadir: Path, pair: str, timeframe: str,
-                                   timerange: Optional[TimeRange]) -> Tuple[List[Any],
-                                                                            Optional[int]]:
+def _load_cached_data_for_updating(pair: str, timeframe: str, timerange: Optional[TimeRange],
+                                   data_handler: IDataHandler) -> Tuple[DataFrame, Optional[int]]:
     """
     Load cached data to download more data.
     If timerange is passed in, checks whether data from an before the stored data will be
     downloaded.
     If that's the case then what's available should be completely overwritten.
-    Only used by download_pair_history().
+    Otherwise downloads always start at the end of the available data to avoid data gaps.
+    Note: Only used by download_pair_history().
     """
-
-    since_ms = None
-
-    # user sets timerange, so find the start time
+    start = None
     if timerange:
         if timerange.starttype == 'date':
-            since_ms = timerange.startts * 1000
-        elif timerange.stoptype == 'line':
-            num_minutes = timerange.stopts * timeframe_to_minutes(timeframe)
-            since_ms = arrow.utcnow().shift(minutes=num_minutes).timestamp * 1000
+            # TODO: convert to date for conversion
+            start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
 
-    # read the cached file
     # Intentionally don't pass timerange in - since we need to load the full dataset.
-    data = load_tickerdata_file(datadir, pair, timeframe)
-    # remove the last item, could be incomplete candle
-    if data:
-        data.pop()
-    else:
-        data = []
-
-    if data:
-        if since_ms and since_ms < data[0][0]:
+    data = data_handler.ohlcv_load(pair, timeframe=timeframe,
+                                   timerange=None, fill_missing=False,
+                                   drop_incomplete=True, warn_no_data=False)
+    if not data.empty:
+        if start and start < data.iloc[0]['date']:
             # Earlier data than existing data requested, redownload all
-            data = []
+            data = DataFrame(columns=DEFAULT_DATAFRAME_COLUMNS)
         else:
-            # a part of the data was already downloaded, so download unexist data only
-            since_ms = data[-1][0] + 1
+            start = data.iloc[-1]['date']
 
-    return (data, since_ms)
+    start_ms = int(start.timestamp() * 1000) if start else None
+    return data, start_ms
 
 
 def _download_pair_history(datadir: Path,
                            exchange: Exchange,
-                           pair: str,
+                           pair: str, *,
                            timeframe: str = '5m',
-                           timerange: Optional[TimeRange] = None) -> bool:
+                           timerange: Optional[TimeRange] = None,
+                           data_handler: IDataHandler = None) -> bool:
     """
     Download latest candles from the exchange for the pair and timeframe passed in parameters
     The data is downloaded starting from the last correct data that
@@ -291,20 +165,26 @@ def _download_pair_history(datadir: Path,
     Based on @Rybolov work: https://github.com/rybolov/freqtrade-data
 
     :param pair: pair to download
-    :param timeframe: Ticker Timeframe (e.g 5m)
+    :param timeframe: Timeframe (e.g "5m")
     :param timerange: range of time to download
     :return: bool with success state
     """
+    data_handler = get_datahandler(datadir, data_handler=data_handler)
+
     try:
         logger.info(
             f'Download history data for pair: "{pair}", timeframe: {timeframe} '
             f'and store in {datadir}.'
         )
 
-        data, since_ms = _load_cached_data_for_updating(datadir, pair, timeframe, timerange)
+        # data, since_ms = _load_cached_data_for_updating_old(datadir, pair, timeframe, timerange)
+        data, since_ms = _load_cached_data_for_updating(pair, timeframe, timerange,
+                                                        data_handler=data_handler)
 
-        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')
+        logger.debug("Current Start: %s",
+                     f"{data.iloc[0]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')
+        logger.debug("Current End: %s",
+                     f"{data.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')
 
         # Default since_ms to 30 days if nothing is given
         new_data = exchange.get_historic_ohlcv(pair=pair,
@@ -313,12 +193,20 @@ def _download_pair_history(datadir: Path,
                                                int(arrow.utcnow().shift(
                                                    days=-30).float_timestamp) * 1000
                                                )
-        data.extend(new_data)
+        # TODO: Maybe move parsing to exchange class (?)
+        new_dataframe = ohlcv_to_dataframe(new_data, timeframe, pair,
+                                           fill_missing=False, drop_incomplete=True)
+        if data.empty:
+            data = new_dataframe
+        else:
+            data = data.append(new_dataframe)
 
-        logger.debug("New Start: %s", misc.format_ms_time(data[0][0]))
-        logger.debug("New End: %s", misc.format_ms_time(data[-1][0]))
+        logger.debug("New  Start: %s",
+                     f"{data.iloc[0]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')
+        logger.debug("New End: %s",
+                     f"{data.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')
 
-        store_tickerdata_file(datadir, pair, timeframe, data=data)
+        data_handler.ohlcv_store(pair, timeframe, data=data)
         return True
 
     except Exception as e:
@@ -331,13 +219,14 @@ def _download_pair_history(datadir: Path,
 
 def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes: List[str],
                                 datadir: Path, timerange: Optional[TimeRange] = None,
-                                erase=False) -> List[str]:
+                                erase: bool = False, data_format: str = None) -> List[str]:
     """
     Refresh stored ohlcv data for backtesting and hyperopt operations.
     Used by freqtrade download-data subcommand.
     :return: List of pairs that are not available.
     """
     pairs_not_available = []
+    data_handler = get_datahandler(datadir, data_format)
     for pair in pairs:
         if pair not in exchange.markets:
             pairs_not_available.append(pair)
@@ -345,23 +234,23 @@ def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes
             continue
         for timeframe in timeframes:
 
-            dl_file = pair_data_filename(datadir, pair, timeframe)
-            if erase and dl_file.exists():
-                logger.info(
-                    f'Deleting existing data for pair {pair}, interval {timeframe}.')
-                dl_file.unlink()
+            if erase:
+                if data_handler.ohlcv_purge(pair, timeframe):
+                    logger.info(
+                        f'Deleting existing data for pair {pair}, interval {timeframe}.')
 
             logger.info(f'Downloading pair {pair}, interval {timeframe}.')
             _download_pair_history(datadir=datadir, exchange=exchange,
                                    pair=pair, timeframe=str(timeframe),
-                                   timerange=timerange)
+                                   timerange=timerange, data_handler=data_handler)
     return pairs_not_available
 
 
-def _download_trades_history(datadir: Path,
-                             exchange: Exchange,
-                             pair: str,
-                             timerange: Optional[TimeRange] = None) -> bool:
+def _download_trades_history(exchange: Exchange,
+                             pair: str, *,
+                             timerange: Optional[TimeRange] = None,
+                             data_handler: IDataHandler
+                             ) -> bool:
     """
     Download trade history from the exchange.
     Appends to previously downloaded trades data.
@@ -370,7 +259,7 @@ def _download_trades_history(datadir: Path,
 
         since = timerange.startts * 1000 if timerange and timerange.starttype == 'date' else None
 
-        trades = load_trades_file(datadir, pair)
+        trades = data_handler.trades_load(pair)
 
         from_id = trades[-1]['id'] if trades else None
 
@@ -385,7 +274,7 @@ def _download_trades_history(datadir: Path,
                                                   from_id=from_id,
                                                   )
         trades.extend(new_trades[1])
-        store_trades_file(datadir, pair, trades)
+        data_handler.trades_store(pair, data=trades)
 
         logger.debug("New Start: %s", trades[0]['datetime'])
         logger.debug("New End: %s", trades[-1]['datetime'])
@@ -401,47 +290,52 @@ def _download_trades_history(datadir: Path,
 
 
 def refresh_backtest_trades_data(exchange: Exchange, pairs: List[str], datadir: Path,
-                                 timerange: TimeRange, erase=False) -> List[str]:
+                                 timerange: TimeRange, erase: bool = False,
+                                 data_format: str = 'jsongz') -> List[str]:
     """
     Refresh stored trades data for backtesting and hyperopt operations.
     Used by freqtrade download-data subcommand.
     :return: List of pairs that are not available.
     """
     pairs_not_available = []
+    data_handler = get_datahandler(datadir, data_format=data_format)
     for pair in pairs:
         if pair not in exchange.markets:
             pairs_not_available.append(pair)
             logger.info(f"Skipping pair {pair}...")
             continue
 
-        dl_file = pair_trades_filename(datadir, pair)
-        if erase and dl_file.exists():
-            logger.info(
-                f'Deleting existing data for pair {pair}.')
-            dl_file.unlink()
+        if erase:
+            if data_handler.trades_purge(pair):
+                logger.info(f'Deleting existing data for pair {pair}.')
 
         logger.info(f'Downloading trades for pair {pair}.')
-        _download_trades_history(datadir=datadir, exchange=exchange,
+        _download_trades_history(exchange=exchange,
                                  pair=pair,
-                                 timerange=timerange)
+                                 timerange=timerange,
+                                 data_handler=data_handler)
     return pairs_not_available
 
 
 def convert_trades_to_ohlcv(pairs: List[str], timeframes: List[str],
-                            datadir: Path, timerange: TimeRange, erase=False) -> None:
+                            datadir: Path, timerange: TimeRange, erase: bool = False,
+                            data_format_ohlcv: str = 'json',
+                            data_format_trades: str = 'jsongz') -> None:
     """
     Convert stored trades data to ohlcv data
     """
+    data_handler_trades = get_datahandler(datadir, data_format=data_format_trades)
+    data_handler_ohlcv = get_datahandler(datadir, data_format=data_format_ohlcv)
+
     for pair in pairs:
-        trades = load_trades_file(datadir, pair)
+        trades = data_handler_trades.trades_load(pair)
         for timeframe in timeframes:
-            ohlcv_file = pair_data_filename(datadir, pair, timeframe)
-            if erase and ohlcv_file.exists():
-                logger.info(f'Deleting existing data for pair {pair}, interval {timeframe}.')
-                ohlcv_file.unlink()
+            if erase:
+                if data_handler_ohlcv.ohlcv_purge(pair, timeframe):
+                    logger.info(f'Deleting existing data for pair {pair}, interval {timeframe}.')
             ohlcv = trades_to_ohlcv(trades, timeframe)
             # Store ohlcv
-            store_tickerdata_file(datadir, pair, timeframe, data=ohlcv)
+            data_handler_ohlcv.ohlcv_store(pair, timeframe, data=ohlcv)
 
 
 def get_timerange(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
@@ -468,7 +362,7 @@ def validate_backtest_data(data: DataFrame, pair: str, min_date: datetime,
     :param pair: pair used for log output.
     :param min_date: start-date of the data
     :param max_date: end-date of the data
-    :param timeframe_min: ticker Timeframe in minutes
+    :param timeframe_min: Timeframe in minutes
     """
     # total difference in minutes / timeframe-minutes
     expected_frames = int((max_date - min_date).total_seconds() // 60 // timeframe_min)

freqtrade/data/history/idatahandler.py

@@ -0,0 +1,232 @@
+"""
+Abstract datahandler interface.
+It's subclasses handle and storing data from disk.
+
+"""
+import logging
+from abc import ABC, abstractclassmethod, abstractmethod
+from copy import deepcopy
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Dict, List, Optional, Type
+
+from pandas import DataFrame
+
+from freqtrade.configuration import TimeRange
+from freqtrade.data.converter import clean_ohlcv_dataframe, trim_dataframe
+from freqtrade.exchange import timeframe_to_seconds
+
+logger = logging.getLogger(__name__)
+
+
+class IDataHandler(ABC):
+
+    def __init__(self, datadir: Path) -> None:
+        self._datadir = datadir
+
+    @abstractclassmethod
+    def ohlcv_get_pairs(cls, datadir: Path, timeframe: str) -> List[str]:
+        """
+        Returns a list of all pairs with ohlcv data available in this datadir
+        for the specified timeframe
+        :param datadir: Directory to search for ohlcv files
+        :param timeframe: Timeframe to search pairs for
+        :return: List of Pairs
+        """
+
+    @abstractmethod
+    def ohlcv_store(self, pair: str, timeframe: str, data: DataFrame) -> None:
+        """
+        Store data in json format "values".
+            format looks as follows:
+            [[<date>,<open>,<high>,<low>,<close>]]
+        :param pair: Pair - used to generate filename
+        :timeframe: Timeframe - used to generate filename
+        :data: Dataframe containing OHLCV data
+        :return: None
+        """
+
+    @abstractmethod
+    def _ohlcv_load(self, pair: str, timeframe: str,
+                    timerange: Optional[TimeRange] = None,
+                    ) -> DataFrame:
+        """
+        Internal method used to load data for one pair from disk.
+        Implements the loading and conversion to a Pandas dataframe.
+        Timerange trimming and dataframe validation happens outside of this method.
+        :param pair: Pair to load data
+        :param timeframe: Timeframe (e.g. "5m")
+        :param timerange: Limit data to be loaded to this timerange.
+                        Optionally implemented by subclasses to avoid loading
+                        all data where possible.
+        :return: DataFrame with ohlcv data, or empty DataFrame
+        """
+
+    @abstractmethod
+    def ohlcv_purge(self, pair: str, timeframe: str) -> bool:
+        """
+        Remove data for this pair
+        :param pair: Delete data for this pair.
+        :param timeframe: Timeframe (e.g. "5m")
+        :return: True when deleted, false if file did not exist.
+        """
+
+    @abstractmethod
+    def ohlcv_append(self, pair: str, timeframe: str, data: DataFrame) -> None:
+        """
+        Append data to existing data structures
+        :param pair: Pair
+        :param timeframe: Timeframe this ohlcv data is for
+        :param data: Data to append.
+        """
+
+    @abstractclassmethod
+    def trades_get_pairs(cls, datadir: Path) -> List[str]:
+        """
+        Returns a list of all pairs for which trade data is available in this
+        :param datadir: Directory to search for ohlcv files
+        :return: List of Pairs
+        """
+
+    @abstractmethod
+    def trades_store(self, pair: str, data: List[Dict]) -> None:
+        """
+        Store trades data (list of Dicts) to file
+        :param pair: Pair - used for filename
+        :param data: List of Dicts containing trade data
+        """
+
+    @abstractmethod
+    def trades_append(self, pair: str, data: List[Dict]):
+        """
+        Append data to existing files
+        :param pair: Pair - used for filename
+        :param data: List of Dicts containing trade data
+        """
+
+    @abstractmethod
+    def trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> List[Dict]:
+        """
+        Load a pair from file, either .json.gz or .json
+        :param pair: Load trades for this pair
+        :param timerange: Timerange to load trades for - currently not implemented
+        :return: List of trades
+        """
+
+    @abstractmethod
+    def trades_purge(self, pair: str) -> bool:
+        """
+        Remove data for this pair
+        :param pair: Delete data for this pair.
+        :return: True when deleted, false if file did not exist.
+        """
+
+    def ohlcv_load(self, pair, timeframe: str,
+                   timerange: Optional[TimeRange] = None,
+                   fill_missing: bool = True,
+                   drop_incomplete: bool = True,
+                   startup_candles: int = 0,
+                   warn_no_data: bool = True
+                   ) -> DataFrame:
+        """
+        Load cached candle (OHLCV) data for the given pair.
+
+        :param pair: Pair to load data for
+        :param timeframe: Timeframe (e.g. "5m")
+        :param timerange: Limit data to be loaded to this timerange
+        :param fill_missing: Fill missing values with "No action"-candles
+        :param drop_incomplete: Drop last candle assuming it may be incomplete.
+        :param startup_candles: Additional candles to load at the start of the period
+        :param warn_no_data: Log a warning message when no data is found
+        :return: DataFrame with ohlcv data, or empty DataFrame
+        """
+        # Fix startup period
+        timerange_startup = deepcopy(timerange)
+        if startup_candles > 0 and timerange_startup:
+            timerange_startup.subtract_start(timeframe_to_seconds(timeframe) * startup_candles)
+
+        pairdf = self._ohlcv_load(pair, timeframe,
+                                  timerange=timerange_startup)
+        if self._check_empty_df(pairdf, pair, timeframe, warn_no_data):
+            return pairdf
+        else:
+            enddate = pairdf.iloc[-1]['date']
+
+            if timerange_startup:
+                self._validate_pairdata(pair, pairdf, timerange_startup)
+                pairdf = trim_dataframe(pairdf, timerange_startup)
+                if self._check_empty_df(pairdf, pair, timeframe, warn_no_data):
+                    return pairdf
+
+            # incomplete candles should only be dropped if we didn't trim the end beforehand.
+            pairdf = clean_ohlcv_dataframe(pairdf, timeframe,
+                                           pair=pair,
+                                           fill_missing=fill_missing,
+                                           drop_incomplete=(drop_incomplete and
+                                                            enddate == pairdf.iloc[-1]['date']))
+            self._check_empty_df(pairdf, pair, timeframe, warn_no_data)
+            return pairdf
+
+    def _check_empty_df(self, pairdf: DataFrame, pair: str, timeframe: str, warn_no_data: bool):
+        """
+        Warn on empty dataframe
+        """
+        if pairdf.empty:
+            if warn_no_data:
+                logger.warning(
+                    f'No history data for pair: "{pair}", timeframe: {timeframe}. '
+                    'Use `freqtrade download-data` to download the data'
+                )
+            return True
+        return False
+
+    def _validate_pairdata(self, pair, pairdata: DataFrame, timerange: TimeRange):
+        """
+        Validates pairdata for missing data at start end end and logs warnings.
+        :param pairdata: Dataframe to validate
+        :param timerange: Timerange specified for start and end dates
+        """
+
+        if timerange.starttype == 'date':
+            start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
+            if pairdata.iloc[0]['date'] > start:
+                logger.warning(f"Missing data at start for pair {pair}, "
+                               f"data starts at {pairdata.iloc[0]['date']:%Y-%m-%d %H:%M:%S}")
+        if timerange.stoptype == 'date':
+            stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
+            if pairdata.iloc[-1]['date'] < stop:
+                logger.warning(f"Missing data at end for pair {pair}, "
+                               f"data ends at {pairdata.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}")
+
+
+def get_datahandlerclass(datatype: str) -> Type[IDataHandler]:
+    """
+    Get datahandler class.
+    Could be done using Resolvers, but since this may be called often and resolvers
+    are rather expensive, doing this directly should improve performance.
+    :param datatype: datatype to use.
+    :return: Datahandler class
+    """
+
+    if datatype == 'json':
+        from .jsondatahandler import JsonDataHandler
+        return JsonDataHandler
+    elif datatype == 'jsongz':
+        from .jsondatahandler import JsonGzDataHandler
+        return JsonGzDataHandler
+    else:
+        raise ValueError(f"No datahandler for datatype {datatype} available.")
+
+
+def get_datahandler(datadir: Path, data_format: str = None,
+                    data_handler: IDataHandler = None) -> IDataHandler:
+    """
+    :param datadir: Folder to save data
+    :data_format: dataformat to use
+    :data_handler: returns this datahandler if it exists or initializes a new one
+    """
+
+    if not data_handler:
+        HandlerClass = get_datahandlerclass(data_format or 'json')
+        data_handler = HandlerClass(datadir)
+    return data_handler

freqtrade/data/history/jsondatahandler.py

@@ -0,0 +1,179 @@
+import re
+from pathlib import Path
+from typing import Dict, List, Optional
+
+import numpy as np
+from pandas import DataFrame, read_json, to_datetime
+
+from freqtrade import misc
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
+
+from .idatahandler import IDataHandler
+
+
+class JsonDataHandler(IDataHandler):
+
+    _use_zip = False
+    _columns = DEFAULT_DATAFRAME_COLUMNS
+
+    @classmethod
+    def ohlcv_get_pairs(cls, datadir: Path, timeframe: str) -> List[str]:
+        """
+        Returns a list of all pairs with ohlcv data available in this datadir
+        for the specified timeframe
+        :param datadir: Directory to search for ohlcv files
+        :param timeframe: Timeframe to search pairs for
+        :return: List of Pairs
+        """
+
+        _tmp = [re.search(r'^(\S+)(?=\-' + timeframe + '.json)', p.name)
+                for p in datadir.glob(f"*{timeframe}.{cls._get_file_extension()}")]
+        # Check if regex found something and only return these results
+        return [match[0].replace('_', '/') for match in _tmp if match]
+
+    def ohlcv_store(self, pair: str, timeframe: str, data: DataFrame) -> None:
+        """
+        Store data in json format "values".
+            format looks as follows:
+            [[<date>,<open>,<high>,<low>,<close>]]
+        :param pair: Pair - used to generate filename
+        :timeframe: Timeframe - used to generate filename
+        :data: Dataframe containing OHLCV data
+        :return: None
+        """
+        filename = self._pair_data_filename(self._datadir, pair, timeframe)
+        _data = data.copy()
+        # Convert date to int
+        _data['date'] = _data['date'].astype(np.int64) // 1000 // 1000
+
+        # Reset index, select only appropriate columns and save as json
+        _data.reset_index(drop=True).loc[:, self._columns].to_json(
+            filename, orient="values",
+            compression='gzip' if self._use_zip else None)
+
+    def _ohlcv_load(self, pair: str, timeframe: str,
+                    timerange: Optional[TimeRange] = None,
+                    ) -> DataFrame:
+        """
+        Internal method used to load data for one pair from disk.
+        Implements the loading and conversion to a Pandas dataframe.
+        Timerange trimming and dataframe validation happens outside of this method.
+        :param pair: Pair to load data
+        :param timeframe: Timeframe (e.g. "5m")
+        :param timerange: Limit data to be loaded to this timerange.
+                        Optionally implemented by subclasses to avoid loading
+                        all data where possible.
+        :return: DataFrame with ohlcv data, or empty DataFrame
+        """
+        filename = self._pair_data_filename(self._datadir, pair, timeframe)
+        if not filename.exists():
+            return DataFrame(columns=self._columns)
+        pairdata = read_json(filename, orient='values')
+        pairdata.columns = self._columns
+        pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
+                                          'low': 'float', 'close': 'float', 'volume': 'float'})
+        pairdata['date'] = to_datetime(pairdata['date'],
+                                       unit='ms',
+                                       utc=True,
+                                       infer_datetime_format=True)
+        return pairdata
+
+    def ohlcv_purge(self, pair: str, timeframe: str) -> bool:
+        """
+        Remove data for this pair
+        :param pair: Delete data for this pair.
+        :param timeframe: Timeframe (e.g. "5m")
+        :return: True when deleted, false if file did not exist.
+        """
+        filename = self._pair_data_filename(self._datadir, pair, timeframe)
+        if filename.exists():
+            filename.unlink()
+            return True
+        return False
+
+    def ohlcv_append(self, pair: str, timeframe: str, data: DataFrame) -> None:
+        """
+        Append data to existing data structures
+        :param pair: Pair
+        :param timeframe: Timeframe this ohlcv data is for
+        :param data: Data to append.
+        """
+        raise NotImplementedError()
+
+    @classmethod
+    def trades_get_pairs(cls, datadir: Path) -> List[str]:
+        """
+        Returns a list of all pairs for which trade data is available in this
+        :param datadir: Directory to search for ohlcv files
+        :return: List of Pairs
+        """
+        _tmp = [re.search(r'^(\S+)(?=\-trades.json)', p.name)
+                for p in datadir.glob(f"*trades.{cls._get_file_extension()}")]
+        # Check if regex found something and only return these results to avoid exceptions.
+        return [match[0].replace('_', '/') for match in _tmp if match]
+
+    def trades_store(self, pair: str, data: List[Dict]) -> None:
+        """
+        Store trades data (list of Dicts) to file
+        :param pair: Pair - used for filename
+        :param data: List of Dicts containing trade data
+        """
+        filename = self._pair_trades_filename(self._datadir, pair)
+        misc.file_dump_json(filename, data, is_zip=self._use_zip)
+
+    def trades_append(self, pair: str, data: List[Dict]):
+        """
+        Append data to existing files
+        :param pair: Pair - used for filename
+        :param data: List of Dicts containing trade data
+        """
+        raise NotImplementedError()
+
+    def trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> List[Dict]:
+        """
+        Load a pair from file, either .json.gz or .json
+        # TODO: respect timerange ...
+        :param pair: Load trades for this pair
+        :param timerange: Timerange to load trades for - currently not implemented
+        :return: List of trades
+        """
+        filename = self._pair_trades_filename(self._datadir, pair)
+        tradesdata = misc.file_load_json(filename)
+        if not tradesdata:
+            return []
+
+        return tradesdata
+
+    def trades_purge(self, pair: str) -> bool:
+        """
+        Remove data for this pair
+        :param pair: Delete data for this pair.
+        :return: True when deleted, false if file did not exist.
+        """
+        filename = self._pair_trades_filename(self._datadir, pair)
+        if filename.exists():
+            filename.unlink()
+            return True
+        return False
+
+    @classmethod
+    def _pair_data_filename(cls, datadir: Path, pair: str, timeframe: str) -> Path:
+        pair_s = misc.pair_to_filename(pair)
+        filename = datadir.joinpath(f'{pair_s}-{timeframe}.{cls._get_file_extension()}')
+        return filename
+
+    @classmethod
+    def _get_file_extension(cls):
+        return "json.gz" if cls._use_zip else "json"
+
+    @classmethod
+    def _pair_trades_filename(cls, datadir: Path, pair: str) -> Path:
+        pair_s = misc.pair_to_filename(pair)
+        filename = datadir.joinpath(f'{pair_s}-trades.{cls._get_file_extension()}')
+        return filename
+
+
+class JsonGzDataHandler(JsonDataHandler):
+
+    _use_zip = True

freqtrade/edge/edge_positioning.py

@@ -1,17 +1,17 @@
 # pragma pylint: disable=W0603
 """ Edge positioning package """
 import logging
-from typing import Any, Dict, NamedTuple
+from typing import Any, Dict, List, NamedTuple
 
 import arrow
 import numpy as np
 import utils_find_1st as utf1st
 from pandas import DataFrame
 
-from freqtrade import constants
 from freqtrade.configuration import TimeRange
-from freqtrade.data import history
+from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
 from freqtrade.exceptions import OperationalException
+from freqtrade.data.history import get_timerange, load_data, refresh_data
 from freqtrade.strategy.interface import SellType
 
 logger = logging.getLogger(__name__)
@@ -54,7 +54,7 @@ class Edge:
         if self.config['max_open_trades'] != float('inf'):
             logger.critical('max_open_trades should be -1 in config !')
 
-        if self.config['stake_amount'] != constants.UNLIMITED_STAKE_AMOUNT:
+        if self.config['stake_amount'] != UNLIMITED_STAKE_AMOUNT:
             raise OperationalException('Edge works only with unlimited stake amount')
 
         # Deprecated capital_available_percentage. Will use tradable_balance_ratio in the future.
@@ -96,7 +96,7 @@ class Edge:
         logger.info('Using local backtesting data (using whitelist in given config) ...')
 
         if self._refresh_pairs:
-            history.refresh_data(
+            refresh_data(
                 datadir=self.config['datadir'],
                 pairs=pairs,
                 exchange=self.exchange,
@@ -104,12 +104,13 @@ class Edge:
                 timerange=self._timerange,
             )
 
-        data = history.load_data(
+        data = load_data(
             datadir=self.config['datadir'],
             pairs=pairs,
             timeframe=self.strategy.ticker_interval,
             timerange=self._timerange,
             startup_candles=self.strategy.startup_candle_count,
+            data_format=self.config.get('dataformat_ohlcv', 'json'),
         )
 
         if not data:
@@ -118,10 +119,10 @@ class Edge:
             logger.critical("No data found. Edge is stopped ...")
             return False
 
-        preprocessed = self.strategy.tickerdata_to_dataframe(data)
+        preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
 
         # Print timeframe
-        min_date, max_date = history.get_timerange(preprocessed)
+        min_date, max_date = get_timerange(preprocessed)
         logger.info(
             'Measuring data from %s up to %s (%s days) ...',
             min_date.isoformat(),
@@ -136,10 +137,10 @@ class Edge:
             pair_data = pair_data.sort_values(by=['date'])
             pair_data = pair_data.reset_index(drop=True)
 
-            ticker_data = self.strategy.advise_sell(
+            df_analyzed = self.strategy.advise_sell(
                 self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
 
-            trades += self._find_trades_for_stoploss_range(ticker_data, pair, self._stoploss_range)
+            trades += self._find_trades_for_stoploss_range(df_analyzed, pair, self._stoploss_range)
 
         # If no trade found then exit
         if len(trades) == 0:
@@ -181,7 +182,7 @@ class Edge:
                            'strategy stoploss is returned instead.')
             return self.strategy.stoploss
 
-    def adjust(self, pairs) -> list:
+    def adjust(self, pairs: List[str]) -> list:
         """
         Filters out and sorts "pairs" according to Edge calculated pairs
         """
@@ -245,7 +246,8 @@ class Edge:
 
         # we set stake amount to an arbitrary amount.
         # as it doesn't change the calculation.
-        # all returned values are relative. they are percentages.
+        # all returned values are relative.
+        # they are defined as ratios.
         stake = 0.015
         fee = self.fee
         open_fee = fee / 2
@@ -268,8 +270,8 @@ class Edge:
         result['sell_fee'] = result['sell_sum'] * close_fee
         result['sell_take'] = result['sell_sum'] - result['sell_fee']
 
-        # profit_percent
-        result['profit_percent'] = (result['sell_take'] - result['buy_spend']) / result['buy_spend']
+        # profit_ratio
+        result['profit_ratio'] = (result['sell_take'] - result['buy_spend']) / result['buy_spend']
 
         # Absolute profit
         result['profit_abs'] = result['sell_take'] - result['buy_spend']
@@ -315,7 +317,7 @@ class Edge:
         }
 
         # Group by (pair and stoploss) by applying above aggregator
-        df = results.groupby(['pair', 'stoploss'])['profit_abs', 'trade_duration'].agg(
+        df = results.groupby(['pair', 'stoploss'])[['profit_abs', 'trade_duration']].agg(
             groupby_aggregator).reset_index(col_level=1)
 
         # Dropping level 0 as we don't need it
@@ -357,11 +359,11 @@ class Edge:
         # Returning a list of pairs in order of "expectancy"
         return final
 
-    def _find_trades_for_stoploss_range(self, ticker_data, pair, stoploss_range):
-        buy_column = ticker_data['buy'].values
-        sell_column = ticker_data['sell'].values
-        date_column = ticker_data['date'].values
-        ohlc_columns = ticker_data[['open', 'high', 'low', 'close']].values
+    def _find_trades_for_stoploss_range(self, df, pair, stoploss_range):
+        buy_column = df['buy'].values
+        sell_column = df['sell'].values
+        date_column = df['date'].values
+        ohlc_columns = df[['open', 'high', 'low', 'close']].values
 
         result: list = []
         for stoploss in stoploss_range:
@@ -398,9 +400,8 @@ class Edge:
                 # trade opens in reality on the next candle
                 open_trade_index += 1
 
-            stop_price_percentage = stoploss + 1
             open_price = ohlc_columns[open_trade_index, 0]
-            stop_price = (open_price * stop_price_percentage)
+            stop_price = (open_price * (stoploss + 1))
 
             # Searching for the index where stoploss is hit
             stop_index = utf1st.find_1st(
@@ -440,7 +441,7 @@ class Edge:
 
             trade = {'pair': pair,
                      'stoploss': stoploss,
-                     'profit_percent': '',
+                     'profit_ratio': '',
                      'profit_abs': '',
                      'open_time': date_column[open_trade_index],
                      'close_time': date_column[exit_index],

freqtrade/exchange/__init__.py

@@ -1,18 +1,20 @@
-from freqtrade.exchange.common import MAP_EXCHANGE_CHILDCLASS  # noqa: F401
-from freqtrade.exchange.exchange import Exchange  # noqa: F401
-from freqtrade.exchange.exchange import (get_exchange_bad_reason,  # noqa: F401
+# flake8: noqa: F401
+from freqtrade.exchange.common import MAP_EXCHANGE_CHILDCLASS
+from freqtrade.exchange.exchange import Exchange
+from freqtrade.exchange.exchange import (get_exchange_bad_reason,
                                          is_exchange_bad,
                                          is_exchange_known_ccxt,
                                          is_exchange_officially_supported,
                                          ccxt_exchanges,
                                          available_exchanges)
-from freqtrade.exchange.exchange import (timeframe_to_seconds,  # noqa: F401
+from freqtrade.exchange.exchange import (timeframe_to_seconds,
                                          timeframe_to_minutes,
                                          timeframe_to_msecs,
                                          timeframe_to_next_date,
                                          timeframe_to_prev_date)
-from freqtrade.exchange.exchange import (market_is_active,  # noqa: F401
+from freqtrade.exchange.exchange import (market_is_active,
                                          symbol_is_pair)
-from freqtrade.exchange.kraken import Kraken  # noqa: F401
-from freqtrade.exchange.binance import Binance  # noqa: F401
-from freqtrade.exchange.bibox import Bibox  # noqa: F401
+from freqtrade.exchange.kraken import Kraken
+from freqtrade.exchange.binance import Binance
+from freqtrade.exchange.bibox import Bibox
+from freqtrade.exchange.ftx import Ftx

freqtrade/exchange/binance.py

@@ -32,13 +32,23 @@ class Binance(Exchange):
 
         return super().get_order_book(pair, limit)
 
-    def stoploss_limit(self, pair: str, amount: float, stop_price: float, rate: float) -> Dict:
+    def stoploss_adjust(self, stop_loss: float, order: Dict) -> bool:
+        """
+        Verify stop_loss against stoploss-order value (limit or price)
+        Returns True if adjustment is necessary.
+        """
+        return order['type'] == 'stop_loss_limit' and stop_loss > float(order['info']['stopPrice'])
+
+    def stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict) -> Dict:
         """
         creates a stoploss limit order.
         this stoploss-limit is binance-specific.
         It may work with a limited number of other exchanges, but this has not been tested yet.
-
         """
+        # Limit price threshold: As limit price should always be below stop-price
+        limit_price_pct = order_types.get('stoploss_on_exchange_limit_ratio', 0.99)
+        rate = stop_price * limit_price_pct
+
         ordertype = "stop_loss_limit"
 
         stop_price = self.price_to_precision(pair, stop_price)
@@ -61,8 +71,8 @@ class Binance(Exchange):
 
             rate = self.price_to_precision(pair, rate)
 
-            order = self._api.create_order(pair, ordertype, 'sell',
-                                           amount, rate, params)
+            order = self._api.create_order(symbol=pair, type=ordertype, side='sell',
+                                           amount=amount, price=stop_price, params=params)
             logger.info('stoploss limit order added for %s. '
                         'stop price: %s. limit: %s', pair, stop_price, rate)
             return order

freqtrade/exchange/exchange.py

@@ -18,12 +18,16 @@ from ccxt.base.decimal_to_precision import (ROUND_DOWN, ROUND_UP, TICK_SIZE,
                                             TRUNCATE, decimal_to_precision)
 from pandas import DataFrame
 
-from freqtrade.data.converter import parse_ticker_dataframe
+from freqtrade.data.converter import ohlcv_to_dataframe
 from freqtrade.exceptions import (DependencyException, InvalidOrderException,
                                   OperationalException, TemporaryError)
 from freqtrade.exchange.common import BAD_EXCHANGES, retrier, retrier_async
 from freqtrade.misc import deep_merge_dicts
 
+
+CcxtModuleType = Any
+
+
 logger = logging.getLogger(__name__)
 
 
@@ -51,7 +55,7 @@ class Exchange:
     }
     _ft_has: Dict = {}
 
-    def __init__(self, config: dict, validate: bool = True) -> None:
+    def __init__(self, config: Dict[str, Any], validate: bool = True) -> None:
         """
         Initializes this module with the given config,
         it does basic validation whether the specified exchange and pairs are valid.
@@ -62,8 +66,6 @@ class Exchange:
 
         self._config.update(config)
 
-        self._cached_ticker: Dict[str, Any] = {}
-
         # Holds last candle refreshed time of each pair
         self._pairs_last_refresh_time: Dict[Tuple[str, str], int] = {}
         # Timestamp of last markets refresh
@@ -135,7 +137,7 @@ class Exchange:
         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,
+    def _init_ccxt(self, exchange_config: Dict[str, Any], ccxt_module: CcxtModuleType = ccxt,
                    ccxt_kwargs: dict = None) -> ccxt.Exchange:
         """
         Initialize ccxt with given config and return valid
@@ -224,13 +226,25 @@ class Exchange:
         markets = self.markets
         return sorted(set([x['quote'] for _, x in markets.items()]))
 
-    def klines(self, pair_interval: Tuple[str, str], copy=True) -> DataFrame:
+    def get_pair_quote_currency(self, pair: str) -> str:
+        """
+        Return a pair's quote currency
+        """
+        return self.markets.get(pair, {}).get('quote', '')
+
+    def get_pair_base_currency(self, pair: str) -> str:
+        """
+        Return a pair's quote currency
+        """
+        return self.markets.get(pair, {}).get('base', '')
+
+    def klines(self, pair_interval: Tuple[str, str], copy: bool = True) -> DataFrame:
         if pair_interval in self._klines:
             return self._klines[pair_interval].copy() if copy else self._klines[pair_interval]
         else:
             return DataFrame()
 
-    def set_sandbox(self, api, exchange_config: dict, name: str):
+    def set_sandbox(self, api: ccxt.Exchange, exchange_config: dict, name: str) -> None:
         if exchange_config.get('sandbox'):
             if api.urls.get('test'):
                 api.urls['api'] = api.urls['test']
@@ -240,7 +254,7 @@ class Exchange:
                                      "Please check your config.json")
                 raise OperationalException(f'Exchange {name} does not provide a sandbox api')
 
-    def _load_async_markets(self, reload=False) -> None:
+    def _load_async_markets(self, reload: bool = False) -> None:
         try:
             if self._api_async:
                 asyncio.get_event_loop().run_until_complete(
@@ -273,7 +287,7 @@ class Exchange:
         except ccxt.BaseError:
             logger.exception("Could not reload markets.")
 
-    def validate_stakecurrency(self, stake_currency) -> None:
+    def validate_stakecurrency(self, stake_currency: str) -> None:
         """
         Checks stake-currency against available currencies on the exchange.
         :param stake_currency: Stake-currency to validate
@@ -282,8 +296,8 @@ class Exchange:
         quote_currencies = self.get_quote_currencies()
         if stake_currency not in quote_currencies:
             raise OperationalException(
-                    f"{stake_currency} is not available as stake on {self.name}. "
-                    f"Available currencies are: {', '.join(quote_currencies)}")
+                f"{stake_currency} is not available as stake on {self.name}. "
+                f"Available currencies are: {', '.join(quote_currencies)}")
 
     def validate_pairs(self, pairs: List[str]) -> None:
         """
@@ -296,7 +310,7 @@ class Exchange:
         if not self.markets:
             logger.warning('Unable to validate pairs (assuming they are correct).')
             return
-
+        invalid_pairs = []
         for pair in pairs:
             # Note: ccxt has BaseCurrency/QuoteCurrency format for pairs
             # TODO: add a support for having coins in BTC/USDT format
@@ -318,8 +332,15 @@ class Exchange:
                 logger.warning(f"Pair {pair} is restricted for some users on this exchange."
                                f"Please check if you are impacted by this restriction "
                                f"on the exchange and eventually remove {pair} from your whitelist.")
+            if (self._config['stake_currency'] and
+                    self.get_pair_quote_currency(pair) != self._config['stake_currency']):
+                invalid_pairs.append(pair)
+        if invalid_pairs:
+            raise OperationalException(
+                f"Stake-currency '{self._config['stake_currency']}' not compatible with "
+                f"pair-whitelist. Please remove the following pairs: {invalid_pairs}")
 
-    def get_valid_pair_combination(self, curr_1, curr_2) -> str:
+    def get_valid_pair_combination(self, curr_1: str, curr_2: str) -> str:
         """
         Get valid pair combination of curr_1 and curr_2 by trying both combinations.
         """
@@ -330,7 +351,7 @@ class Exchange:
 
     def validate_timeframes(self, timeframe: Optional[str]) -> None:
         """
-        Checks if ticker interval from config is a supported timeframe on the exchange
+        Check if timeframe from config is a supported timeframe on the exchange
         """
         if not hasattr(self._api, "timeframes") or self._api.timeframes is None:
             # If timeframes attribute is missing (or is None), the exchange probably
@@ -343,7 +364,7 @@ class Exchange:
 
         if timeframe and (timeframe not in self.timeframes):
             raise OperationalException(
-                f"Invalid ticker interval '{timeframe}'. This exchange supports: {self.timeframes}")
+                f"Invalid timeframe '{timeframe}'. This exchange supports: {self.timeframes}")
 
         if timeframe and timeframe_to_minutes(timeframe) < 1:
             raise OperationalException(
@@ -373,7 +394,7 @@ class Exchange:
             raise OperationalException(
                 f'Time in force policies are not supported for {self.name} yet.')
 
-    def validate_required_startup_candles(self, startup_candles) -> None:
+    def validate_required_startup_candles(self, startup_candles: int) -> None:
         """
         Checks if required startup_candles is more than ohlcv_candle_limit.
         Requires a grace-period of 5 candles - so a startup-period up to 494 is allowed by default.
@@ -392,7 +413,7 @@ class Exchange:
         """
         return endpoint in self._api.has and self._api.has[endpoint]
 
-    def amount_to_precision(self, pair, amount: float) -> float:
+    def amount_to_precision(self, pair: str, amount: float) -> float:
         '''
         Returns the amount to buy or sell to a precision the Exchange accepts
         Reimplementation of ccxt internal methods - ensuring we can test the result is correct
@@ -406,7 +427,7 @@ class Exchange:
 
         return amount
 
-    def price_to_precision(self, pair, price: float) -> float:
+    def price_to_precision(self, pair: str, price: float) -> float:
         '''
         Returns the price rounded up to the precision the Exchange accepts.
         Partial Reimplementation of ccxt internal method decimal_to_precision(),
@@ -460,7 +481,7 @@ class Exchange:
                 "status": "closed",
                 "filled": closed_order["amount"],
                 "remaining": 0
-                })
+            })
         if closed_order["type"] in ["stop_loss_limit"]:
             closed_order["info"].update({"stopPrice": closed_order["price"]})
         self._dry_run_open_orders[closed_order["id"]] = closed_order
@@ -494,7 +515,7 @@ class Exchange:
             raise OperationalException(e) from e
 
     def buy(self, pair: str, ordertype: str, amount: float,
-            rate: float, time_in_force) -> Dict:
+            rate: float, time_in_force: str) -> Dict:
 
         if self._config['dry_run']:
             dry_order = self.dry_run_order(pair, ordertype, "buy", amount, rate)
@@ -507,7 +528,7 @@ class Exchange:
         return self.create_order(pair, ordertype, 'buy', amount, rate, params)
 
     def sell(self, pair: str, ordertype: str, amount: float,
-             rate: float, time_in_force='gtc') -> Dict:
+             rate: float, time_in_force: str = 'gtc') -> Dict:
 
         if self._config['dry_run']:
             dry_order = self.dry_run_order(pair, ordertype, "sell", amount, rate)
@@ -519,9 +540,17 @@ class Exchange:
 
         return self.create_order(pair, ordertype, 'sell', amount, rate, params)
 
-    def stoploss_limit(self, pair: str, amount: float, stop_price: float, rate: float) -> Dict:
+    def stoploss_adjust(self, stop_loss: float, order: Dict) -> bool:
         """
-        creates a stoploss limit order.
+        Verify stop_loss against stoploss-order value (limit or price)
+        Returns True if adjustment is necessary.
+        """
+        raise OperationalException(f"stoploss is not implemented for {self.name}.")
+
+    def stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict) -> Dict:
+        """
+        creates a stoploss order.
+        The precise ordertype is determined by the order_types dict or exchange default.
         Since ccxt does not unify stoploss-limit orders yet, this needs to be implemented in each
         exchange's subclass.
         The exception below should never raise, since we disallow
@@ -529,7 +558,7 @@ class Exchange:
         Note: Changes to this interface need to be applied to all sub-classes too.
         """
 
-        raise OperationalException(f"stoploss_limit is not implemented for {self.name}.")
+        raise OperationalException(f"stoploss is not implemented for {self.name}.")
 
     @retrier
     def get_balance(self, currency: str) -> float:
@@ -570,7 +599,7 @@ class Exchange:
             return self._api.fetch_tickers()
         except ccxt.NotSupported as e:
             raise OperationalException(
-                f'Exchange {self._api.name} does not support fetching tickers in batch.'
+                f'Exchange {self._api.name} does not support fetching tickers in batch. '
                 f'Message: {e}') from e
         except (ccxt.NetworkError, ccxt.ExchangeError) as e:
             raise TemporaryError(
@@ -579,39 +608,28 @@ class Exchange:
             raise OperationalException(e) from e
 
     @retrier
-    def fetch_ticker(self, pair: str, refresh: Optional[bool] = True) -> dict:
-        if refresh or pair not in self._cached_ticker.keys():
-            try:
-                if pair not in self._api.markets or not self._api.markets[pair].get('active'):
-                    raise DependencyException(f"Pair {pair} not available")
-                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}') from e
-            except ccxt.BaseError as e:
-                raise OperationalException(e) from e
-        else:
-            logger.info("returning cached ticker-data for %s", pair)
-            return self._cached_ticker[pair]
+    def fetch_ticker(self, pair: str) -> dict:
+        try:
+            if pair not in self._api.markets or not self._api.markets[pair].get('active'):
+                raise DependencyException(f"Pair {pair} not available")
+            data = self._api.fetch_ticker(pair)
+            return data
+        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f'Could not load ticker due to {e.__class__.__name__}. Message: {e}') from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
 
     def get_historic_ohlcv(self, pair: str, timeframe: str,
                            since_ms: int) -> List:
         """
-        Gets candle history using asyncio and returns the list of candles.
-        Handles all async doing.
-        Async over one pair, assuming we get `_ohlcv_candle_limit` candles per call.
+        Get candle history using asyncio and returns the list of candles.
+        Handles all async work for this.
+        Async over one pair, assuming we get `self._ohlcv_candle_limit` candles per call.
         :param pair: Pair to download
-        :param timeframe: Ticker Timeframe to get
+        :param timeframe: Timeframe to get data for
         :param since_ms: Timestamp in milliseconds to get history from
-        :returns List of tickers
+        :returns List with candle (OHLCV) data
         """
         return asyncio.get_event_loop().run_until_complete(
             self._async_get_historic_ohlcv(pair=pair, timeframe=timeframe,
@@ -631,26 +649,27 @@ class Exchange:
             pair, timeframe, since) for since in
             range(since_ms, arrow.utcnow().timestamp * 1000, one_call)]
 
-        tickers = await asyncio.gather(*input_coroutines, return_exceptions=True)
+        results = await asyncio.gather(*input_coroutines, return_exceptions=True)
 
-        # Combine tickers
+        # Combine gathered results
         data: List = []
-        for p, timeframe, ticker in tickers:
+        for p, timeframe, res in results:
             if p == pair:
-                data.extend(ticker)
+                data.extend(res)
         # Sort data again after extending the result - above calls return in "async order"
         data = sorted(data, key=lambda x: x[0])
-        logger.info("downloaded %s with length %s.", pair, len(data))
+        logger.info("Downloaded data for %s with length %s.", pair, len(data))
         return data
 
     def refresh_latest_ohlcv(self, pair_list: List[Tuple[str, str]]) -> List[Tuple[str, List]]:
         """
-        Refresh in-memory ohlcv asynchronously and set `_klines` with the result
+        Refresh in-memory OHLCV asynchronously and set `_klines` with the result
         Loops asynchronously over pair_list and downloads all pairs async (semi-parallel).
+        Only used in the dataprovider.refresh() method.
         :param pair_list: List of 2 element tuples containing pair, interval to refresh
-        :return: Returns a List of ticker-dataframes.
+        :return: TODO: return value is only used in the tests, get rid of it
         """
-        logger.debug("Refreshing ohlcv data for %d pairs", len(pair_list))
+        logger.debug("Refreshing candle (OHLCV) data for %d pairs", len(pair_list))
 
         input_coroutines = []
 
@@ -661,15 +680,15 @@ class Exchange:
                 input_coroutines.append(self._async_get_candle_history(pair, timeframe))
             else:
                 logger.debug(
-                    "Using cached ohlcv data for pair %s, timeframe %s ...",
+                    "Using cached candle (OHLCV) data for pair %s, timeframe %s ...",
                     pair, timeframe
                 )
 
-        tickers = asyncio.get_event_loop().run_until_complete(
+        results = asyncio.get_event_loop().run_until_complete(
             asyncio.gather(*input_coroutines, return_exceptions=True))
 
         # handle caching
-        for res in tickers:
+        for res in results:
             if isinstance(res, Exception):
                 logger.warning("Async code raised an exception: %s", res.__class__.__name__)
                 continue
@@ -680,13 +699,14 @@ class Exchange:
             if ticks:
                 self._pairs_last_refresh_time[(pair, timeframe)] = ticks[-1][0] // 1000
             # keeping parsed dataframe in cache
-            self._klines[(pair, timeframe)] = parse_ticker_dataframe(
+            self._klines[(pair, timeframe)] = ohlcv_to_dataframe(
                 ticks, timeframe, pair=pair, fill_missing=True,
                 drop_incomplete=self._ohlcv_partial_candle)
-        return tickers
+
+        return results
 
     def _now_is_time_to_refresh(self, pair: str, timeframe: str) -> bool:
-        # Calculating ticker interval in seconds
+        # Timeframe in seconds
         interval_in_sec = timeframe_to_seconds(timeframe)
 
         return not ((self._pairs_last_refresh_time.get((pair, timeframe), 0)
@@ -696,11 +716,11 @@ class Exchange:
     async def _async_get_candle_history(self, pair: str, timeframe: str,
                                         since_ms: Optional[int] = None) -> Tuple[str, str, List]:
         """
-        Asynchronously gets candle histories using fetch_ohlcv
+        Asynchronously get candle history data using fetch_ohlcv
         returns tuple: (pair, timeframe, ohlcv_list)
         """
         try:
-            # fetch ohlcv asynchronously
+            # Fetch OHLCV asynchronously
             s = '(' + arrow.get(since_ms // 1000).isoformat() + ') ' if since_ms is not None else ''
             logger.debug(
                 "Fetching pair %s, interval %s, since %s %s...",
@@ -710,9 +730,9 @@ class Exchange:
             data = await self._api_async.fetch_ohlcv(pair, timeframe=timeframe,
                                                      since=since_ms)
 
-            # 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)
+            # Some exchanges sort OHLCV in ASC order and others in DESC.
+            # Ex: Bittrex returns the list of OHLCV in ASC order (oldest first, newest last)
+            # while GDAX returns the list of OHLCV in DESC order (newest first, oldest last)
             # Only sort if necessary to save computing time
             try:
                 if data and data[0][0] > data[-1][0]:
@@ -725,13 +745,15 @@ class Exchange:
 
         except ccxt.NotSupported as e:
             raise OperationalException(
-                f'Exchange {self._api.name} does not support fetching historical candlestick data.'
-                f'Message: {e}') from e
+                f'Exchange {self._api.name} does not support fetching historical '
+                f'candle (OHLCV) data. Message: {e}') from e
         except (ccxt.NetworkError, ccxt.ExchangeError) as e:
-            raise TemporaryError(f'Could not load ticker history due to {e.__class__.__name__}. '
+            raise TemporaryError(f'Could not fetch historical candle (OHLCV) data '
+                                 f'for pair {pair} due to {e.__class__.__name__}. '
                                  f'Message: {e}') from e
         except ccxt.BaseError as e:
-            raise OperationalException(f'Could not fetch ticker data. Msg: {e}') from e
+            raise OperationalException(f'Could not fetch historical candle (OHLCV) data '
+                                       f'for pair {pair}. Message: {e}') from e
 
     @retrier_async
     async def _async_fetch_trades(self, pair: str,
@@ -864,14 +886,14 @@ class Exchange:
                             until: Optional[int] = None,
                             from_id: Optional[str] = None) -> Tuple[str, List]:
         """
-        Gets candle history using asyncio and returns the list of candles.
-        Handles all async doing.
-        Async over one pair, assuming we get `_ohlcv_candle_limit` candles per call.
+        Get trade history data using asyncio.
+        Handles all async work and returns the list of candles.
+        Async over one pair, assuming we get `self._ohlcv_candle_limit` candles per call.
         :param pair: Pair to download
         :param since: Timestamp in milliseconds to get history from
         :param until: Timestamp in milliseconds. Defaults to current timestamp if not defined.
         :param from_id: Download data starting with ID (if id is known)
-        :returns List of tickers
+        :returns List of trade data
         """
         if not self.exchange_has("fetchTrades"):
             raise OperationalException("This exchange does not suport downloading Trades.")
@@ -976,8 +998,8 @@ class Exchange:
             raise OperationalException(e) from e
 
     @retrier
-    def get_fee(self, symbol, type='', side='', amount=1,
-                price=1, taker_or_maker='maker') -> float:
+    def get_fee(self, symbol: str, type: str = '', side: str = '', amount: float = 1,
+                price: float = 1, taker_or_maker: str = '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:
@@ -1000,22 +1022,22 @@ def get_exchange_bad_reason(exchange_name: str) -> str:
     return BAD_EXCHANGES.get(exchange_name, "")
 
 
-def is_exchange_known_ccxt(exchange_name: str, ccxt_module=None) -> bool:
+def is_exchange_known_ccxt(exchange_name: str, ccxt_module: CcxtModuleType = None) -> bool:
     return exchange_name in ccxt_exchanges(ccxt_module)
 
 
 def is_exchange_officially_supported(exchange_name: str) -> bool:
-    return exchange_name in ['bittrex', 'binance']
+    return exchange_name in ['bittrex', 'binance', 'kraken']
 
 
-def ccxt_exchanges(ccxt_module=None) -> List[str]:
+def ccxt_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:
     """
     Return the list of all exchanges known to ccxt
     """
     return ccxt_module.exchanges if ccxt_module is not None else ccxt.exchanges
 
 
-def available_exchanges(ccxt_module=None) -> List[str]:
+def available_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:
     """
     Return exchanges available to the bot, i.e. non-bad exchanges in the ccxt list
     """
@@ -1075,7 +1097,8 @@ def timeframe_to_next_date(timeframe: str, date: datetime = None) -> datetime:
     return datetime.fromtimestamp(new_timestamp, tz=timezone.utc)
 
 
-def symbol_is_pair(market_symbol: str, base_currency: str = None, quote_currency: str = None):
+def symbol_is_pair(market_symbol: str, base_currency: str = None,
+                   quote_currency: str = None) -> bool:
     """
     Check if the market symbol is a pair, i.e. that its symbol consists of the base currency and the
     quote currency separated by '/' character. If base_currency and/or quote_currency is passed,
@@ -1088,7 +1111,7 @@ def symbol_is_pair(market_symbol: str, base_currency: str = None, quote_currency
             (symbol_parts[1] == quote_currency if quote_currency else len(symbol_parts[1]) > 0))
 
 
-def market_is_active(market):
+def market_is_active(market: Dict) -> bool:
     """
     Return True if the market is active.
     """

freqtrade/exchange/ftx.py

@@ -0,0 +1,14 @@
+""" FTX exchange subclass """
+import logging
+from typing import Dict
+
+from freqtrade.exchange import Exchange
+
+logger = logging.getLogger(__name__)
+
+
+class Ftx(Exchange):
+
+    _ft_has: Dict = {
+        "ohlcv_candle_limit": 1500,
+    }

freqtrade/exchange/kraken.py

@@ -4,7 +4,8 @@ from typing import Dict
 
 import ccxt
 
-from freqtrade.exceptions import OperationalException, TemporaryError
+from freqtrade.exceptions import (DependencyException, InvalidOrderException,
+                                  OperationalException, TemporaryError)
 from freqtrade.exchange import Exchange
 from freqtrade.exchange.exchange import retrier
 
@@ -15,6 +16,7 @@ class Kraken(Exchange):
 
     _params: Dict = {"trading_agreement": "agree"}
     _ft_has: Dict = {
+        "stoploss_on_exchange": True,
         "trades_pagination": "id",
         "trades_pagination_arg": "since",
     }
@@ -48,3 +50,51 @@ class Kraken(Exchange):
                 f'Could not get balance due to {e.__class__.__name__}. Message: {e}') from e
         except ccxt.BaseError as e:
             raise OperationalException(e) from e
+
+    def stoploss_adjust(self, stop_loss: float, order: Dict) -> bool:
+        """
+        Verify stop_loss against stoploss-order value (limit or price)
+        Returns True if adjustment is necessary.
+        """
+        return order['type'] == 'stop-loss' and stop_loss > float(order['price'])
+
+    def stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict) -> Dict:
+        """
+        Creates a stoploss market order.
+        Stoploss market orders is the only stoploss type supported by kraken.
+        """
+
+        ordertype = "stop-loss"
+
+        stop_price = self.price_to_precision(pair, stop_price)
+
+        if self._config['dry_run']:
+            dry_order = self.dry_run_order(
+                pair, ordertype, "sell", amount, stop_price)
+            return dry_order
+
+        try:
+            params = self._params.copy()
+
+            amount = self.amount_to_precision(pair, amount)
+
+            order = self._api.create_order(symbol=pair, type=ordertype, side='sell',
+                                           amount=amount, price=stop_price, params=params)
+            logger.info('stoploss order added for %s. '
+                        'stop price: %s.', pair, stop_price)
+            return order
+        except ccxt.InsufficientFunds as e:
+            raise DependencyException(
+                f'Insufficient funds to create {ordertype} sell order on market {pair}.'
+                f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
+                f'Message: {e}') from e
+        except ccxt.InvalidOrder as e:
+            raise InvalidOrderException(
+                f'Could not create {ordertype} sell order on market {pair}. '
+                f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
+                f'Message: {e}') from e
+        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f'Could not place sell order due to {e.__class__.__name__}. Message: {e}') from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e

freqtrade/freqtradebot.py

@@ -6,11 +6,11 @@ import logging
 import traceback
 from datetime import datetime
 from math import isclose
-from os import getpid
 from threading import Lock
 from typing import Any, Dict, List, Optional, Tuple
 
 import arrow
+from cachetools import TTLCache
 from requests.exceptions import RequestException
 
 from freqtrade import __version__, constants, persistence
@@ -52,9 +52,8 @@ class FreqtradeBot:
         # Init objects
         self.config = config
 
-        self._heartbeat_msg = 0
-
-        self.heartbeat_interval = self.config.get('internals', {}).get('heartbeat_interval', 60)
+        self._sell_rate_cache = TTLCache(maxsize=100, ttl=5)
+        self._buy_rate_cache = TTLCache(maxsize=100, ttl=5)
 
         self.strategy: IStrategy = StrategyResolver.load_strategy(self.config)
 
@@ -159,11 +158,6 @@ class FreqtradeBot:
         self.check_handle_timedout()
         Trade.session.flush()
 
-        if (self.heartbeat_interval
-                and (arrow.utcnow().timestamp - self._heartbeat_msg > self.heartbeat_interval)):
-            logger.info(f"Bot heartbeat. PID={getpid()}")
-            self._heartbeat_msg = arrow.utcnow().timestamp
-
     def _refresh_whitelist(self, trades: List[Trade] = []) -> List[str]:
         """
         Refresh whitelist from pairlist or edge and extend it with trades.
@@ -178,8 +172,8 @@ class FreqtradeBot:
             _whitelist = self.edge.adjust(_whitelist)
 
         if trades:
-            # Extend active-pair whitelist with pairs from open trades
-            # It ensures that tickers are downloaded for open trades
+            # Extend active-pair whitelist with pairs of open trades
+            # It ensures that candle (OHLCV) data are downloaded for open trades as well
             _whitelist.extend([trade.pair for trade in trades if trade.pair not in _whitelist])
         return _whitelist
 
@@ -234,38 +228,46 @@ class FreqtradeBot:
 
         return trades_created
 
-    def get_buy_rate(self, pair: str, tick: Dict = None) -> float:
+    def get_buy_rate(self, pair: str, refresh: bool) -> float:
         """
         Calculates bid target between current ask price and last price
+        :param pair: Pair to get rate for
+        :param refresh: allow cached data
         :return: float: Price
         """
-        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)
+        if not refresh:
+            rate = self._buy_rate_cache.get(pair)
+            # Check if cache has been invalidated
+            if rate:
+                logger.info(f"Using cached buy rate for {pair}.")
+                return rate
+
+        bid_strategy = self.config.get('bid_strategy', {})
+        if 'use_order_book' in bid_strategy and bid_strategy.get('use_order_book', False):
+            logger.info(
+                f"Getting price from order book {bid_strategy['price_side'].capitalize()} side."
+            )
+            order_book_top = 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]
-            logger.info('...top %s order book buy rate %0.8f', order_book_top, order_book_rate)
+            order_book_rate = order_book[f"{bid_strategy['price_side']}s"][order_book_top - 1][0]
+            logger.info(f'...top {order_book_top} order book buy rate {order_book_rate:.8f}')
             used_rate = order_book_rate
         else:
-            if not tick:
-                logger.info('Using Last Ask / Last Price')
-                ticker = self.exchange.fetch_ticker(pair)
-            else:
-                ticker = tick
-            if ticker['ask'] < ticker['last']:
-                ticker_rate = ticker['ask']
-            else:
+            logger.info(f"Using Last {bid_strategy['price_side'].capitalize()} / Last Price")
+            ticker = self.exchange.fetch_ticker(pair)
+            ticker_rate = ticker[bid_strategy['price_side']]
+            if ticker['last'] and ticker_rate > ticker['last']:
                 balance = self.config['bid_strategy']['ask_last_balance']
-                ticker_rate = ticker['ask'] + balance * (ticker['last'] - ticker['ask'])
+                ticker_rate = ticker_rate + balance * (ticker['last'] - ticker_rate)
             used_rate = ticker_rate
 
+        self._buy_rate_cache[pair] = used_rate
+
         return used_rate
 
-    def get_trade_stake_amount(self, pair) -> float:
+    def get_trade_stake_amount(self, pair: str) -> float:
         """
         Calculate stake amount for the trade
         :return: float: Stake amount
@@ -392,19 +394,21 @@ class FreqtradeBot:
             logger.info(f"Pair {pair} is currently locked.")
             return False
 
+        # get_free_open_trades is checked before create_trade is called
+        # but it is still used here to prevent opening too many trades within one iteration
+        if not self.get_free_open_trades():
+            logger.debug(f"Can't open a new trade for {pair}: max number of trades is reached.")
+            return False
+
         # running get_signal on historical data fetched
         (buy, sell) = self.strategy.get_signal(
             pair, self.strategy.ticker_interval,
             self.dataprovider.ohlcv(pair, self.strategy.ticker_interval))
 
         if buy and not sell:
-            if not self.get_free_open_trades():
-                logger.debug("Can't open a new trade: max number of trades is reached.")
-                return False
-
             stake_amount = self.get_trade_stake_amount(pair)
             if not stake_amount:
-                logger.debug("Stake amount is 0, ignoring possible trade for {pair}.")
+                logger.debug(f"Stake amount is 0, ignoring possible trade for {pair}.")
                 return False
 
             logger.info(f"Buy signal found: about create a new trade with stake_amount: "
@@ -414,10 +418,12 @@ class FreqtradeBot:
             if ((bid_check_dom.get('enabled', False)) and
                     (bid_check_dom.get('bids_to_ask_delta', 0) > 0)):
                 if self._check_depth_of_market_buy(pair, bid_check_dom):
+                    logger.info(f'Executing Buy for {pair}.')
                     return self.execute_buy(pair, stake_amount)
                 else:
                     return False
 
+            logger.info(f'Executing Buy for {pair}')
             return self.execute_buy(pair, stake_amount)
         else:
             return False
@@ -427,23 +433,30 @@ class FreqtradeBot:
         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)
+        logger.info(f"Checking depth of market for {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)
+        logger.info(
+            f"Bids: {order_book_bids}, Asks: {order_book_asks}, Delta: {bids_ask_delta}, "
+            f"Bid Price: {order_book['bids'][0][0]}, Ask Price: {order_book['asks'][0][0]}, "
+            f"Immediate Bid Quantity: {order_book['bids'][0][1]}, "
+            f"Immediate Ask Quantity: {order_book['asks'][0][1]}."
+        )
         if bids_ask_delta >= conf_bids_to_ask_delta:
+            logger.info(f"Bids to asks delta for {pair} DOES satisfy condition.")
             return True
-        return False
+        else:
+            logger.info(f"Bids to asks delta for {pair} does not satisfy condition.")
+            return False
 
     def execute_buy(self, pair: str, stake_amount: float, price: Optional[float] = None) -> bool:
         """
         Executes a limit buy for the given pair
         :param pair: pair for which we want to create a LIMIT_BUY
-        :return: None
+        :return: True if a buy order is created, false if it fails.
         """
         time_in_force = self.strategy.order_time_in_force['buy']
 
@@ -451,7 +464,7 @@ class FreqtradeBot:
             buy_limit_requested = price
         else:
             # Calculate price
-            buy_limit_requested = self.get_buy_rate(pair)
+            buy_limit_requested = self.get_buy_rate(pair, True)
 
         min_stake_amount = self._get_min_pair_stake_amount(pair, buy_limit_requested)
         if min_stake_amount is not None and min_stake_amount > stake_amount:
@@ -518,8 +531,6 @@ class FreqtradeBot:
             ticker_interval=timeframe_to_minutes(self.config['ticker_interval'])
         )
 
-        self._notify_buy(trade, order_type)
-
         # Update fees if order is closed
         if order_status == 'closed':
             self.update_trade_state(trade, order)
@@ -530,9 +541,11 @@ class FreqtradeBot:
         # Updating wallets
         self.wallets.update()
 
+        self._notify_buy(trade, order_type)
+
         return True
 
-    def _notify_buy(self, trade: Trade, order_type: str):
+    def _notify_buy(self, trade: Trade, order_type: str) -> None:
         """
         Sends rpc notification when a buy occured.
         """
@@ -545,6 +558,32 @@ class FreqtradeBot:
             'stake_amount': trade.stake_amount,
             'stake_currency': self.config['stake_currency'],
             'fiat_currency': self.config.get('fiat_display_currency', None),
+            'amount': trade.amount,
+            'open_date': trade.open_date or datetime.utcnow(),
+            'current_rate': trade.open_rate_requested,
+        }
+
+        # Send the message
+        self.rpc.send_msg(msg)
+
+    def _notify_buy_cancel(self, trade: Trade, order_type: str) -> None:
+        """
+        Sends rpc notification when a buy cancel occured.
+        """
+        current_rate = self.get_buy_rate(trade.pair, False)
+
+        msg = {
+            'type': RPCMessageType.BUY_CANCEL_NOTIFICATION,
+            'exchange': self.exchange.name.capitalize(),
+            'pair': trade.pair,
+            'limit': trade.open_rate,
+            'order_type': order_type,
+            'stake_amount': trade.stake_amount,
+            'stake_currency': self.config['stake_currency'],
+            'fiat_currency': self.config.get('fiat_display_currency', None),
+            'amount': trade.amount,
+            'open_date': trade.open_date,
+            'current_rate': current_rate,
         }
 
         # Send the message
@@ -580,23 +619,43 @@ class FreqtradeBot:
 
         return trades_closed
 
+    def _order_book_gen(self, pair: str, side: str, order_book_max: int = 1,
+                        order_book_min: int = 1):
+        """
+        Helper generator to query orderbook in loop (used for early sell-order placing)
+        """
+        order_book = self.exchange.get_order_book(pair, order_book_max)
+        for i in range(order_book_min, order_book_max + 1):
+            yield order_book[side][i - 1][0]
+
     def get_sell_rate(self, pair: str, refresh: bool) -> float:
         """
-        Get sell rate - either using get-ticker bid or first bid based on orderbook
+        Get sell rate - either using ticker bid or first bid based on orderbook
         The orderbook portion is only used for rpc messaging, which would otherwise fail
         for BitMex (has no bid/ask in fetch_ticker)
         or remain static in any other case since it's not updating.
+        :param pair: Pair to get rate for
+        :param refresh: allow cached data
         :return: Bid rate
         """
-        config_ask_strategy = self.config.get('ask_strategy', {})
-        if config_ask_strategy.get('use_order_book', False):
-            logger.debug('Using order book to get sell rate')
+        if not refresh:
+            rate = self._sell_rate_cache.get(pair)
+            # Check if cache has been invalidated
+            if rate:
+                logger.info(f"Using cached sell rate for {pair}.")
+                return rate
 
-            order_book = self.exchange.get_order_book(pair, 1)
-            rate = order_book['bids'][0][0]
+        ask_strategy = self.config.get('ask_strategy', {})
+        if ask_strategy.get('use_order_book', False):
+            # This code is only used for notifications, selling uses the generator directly
+            logger.info(
+                f"Getting price from order book {ask_strategy['price_side'].capitalize()} side."
+            )
+            rate = next(self._order_book_gen(pair, f"{ask_strategy['price_side']}s"))
 
         else:
-            rate = self.exchange.fetch_ticker(pair, refresh)['bid']
+            rate = self.exchange.fetch_ticker(pair)[ask_strategy['price_side']]
+        self._sell_rate_cache[pair] = rate
         return rate
 
     def handle_trade(self, trade: Trade) -> bool:
@@ -614,23 +673,24 @@ class FreqtradeBot:
         config_ask_strategy = self.config.get('ask_strategy', {})
 
         if (config_ask_strategy.get('use_sell_signal', True) or
-                config_ask_strategy.get('ignore_roi_if_buy_signal')):
+                config_ask_strategy.get('ignore_roi_if_buy_signal', False)):
             (buy, sell) = self.strategy.get_signal(
                 trade.pair, self.strategy.ticker_interval,
                 self.dataprovider.ohlcv(trade.pair, self.strategy.ticker_interval))
 
         if config_ask_strategy.get('use_order_book', False):
-            logger.info('Using order book for selling...')
+            logger.debug(f'Using order book for selling {trade.pair}...')
             # 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)
-
+            order_book = self._order_book_gen(trade.pair, f"{config_ask_strategy['price_side']}s",
+                                              order_book_min=order_book_min,
+                                              order_book_max=order_book_max)
             for i in range(order_book_min, order_book_max + 1):
-                order_book_rate = order_book['asks'][i - 1][0]
-                logger.info('  order book asks top %s: %0.8f', i, order_book_rate)
-                sell_rate = order_book_rate
+                sell_rate = next(order_book)
+                logger.debug(f"  order book {config_ask_strategy['price_side']} top {i}: "
+                             f"{sell_rate:0.8f}")
 
                 if self._check_and_execute_sell(trade, sell_rate, buy, sell):
                     return True
@@ -651,13 +711,10 @@ class FreqtradeBot:
         Force-sells the pair (using EmergencySell reason) in case of Problems creating the order.
         :return: True if the order succeeded, and False in case of problems.
         """
-        # Limit price threshold: As limit price should always be below stop-price
-        LIMIT_PRICE_PCT = self.strategy.order_types.get('stoploss_on_exchange_limit_ratio', 0.99)
-
         try:
-            stoploss_order = self.exchange.stoploss_limit(pair=trade.pair, amount=trade.amount,
-                                                          stop_price=stop_price,
-                                                          rate=rate * LIMIT_PRICE_PCT)
+            stoploss_order = self.exchange.stoploss(pair=trade.pair, amount=trade.amount,
+                                                    stop_price=stop_price,
+                                                    order_types=self.strategy.order_types)
             trade.stoploss_order_id = str(stoploss_order['id'])
             return True
         except InvalidOrderException as e:
@@ -689,8 +746,24 @@ class FreqtradeBot:
         except InvalidOrderException as exception:
             logger.warning('Unable to fetch stoploss order: %s', exception)
 
+        # We check if stoploss order is fulfilled
+        if stoploss_order and stoploss_order['status'] == 'closed':
+            trade.sell_reason = SellType.STOPLOSS_ON_EXCHANGE.value
+            trade.update(stoploss_order)
+            # Lock pair for one candle to prevent immediate rebuys
+            self.strategy.lock_pair(trade.pair,
+                                    timeframe_to_next_date(self.config['ticker_interval']))
+            self._notify_sell(trade, "stoploss")
+            return True
+
+        if trade.open_order_id or not trade.is_open:
+            # Trade has an open Buy or Sell order, Stoploss-handling can't happen in this case
+            # as the Amount on the exchange is tied up in another trade.
+            # The trade can be closed already (sell-order fill confirmation came in this iteration)
+            return False
+
         # If buy order is fulfilled but there is no stoploss, we add a stoploss on exchange
-        if (not trade.open_order_id and not stoploss_order):
+        if (not stoploss_order):
 
             stoploss = self.edge.stoploss(pair=trade.pair) if self.edge else self.strategy.stoploss
 
@@ -709,16 +782,6 @@ class FreqtradeBot:
                 trade.stoploss_order_id = None
                 logger.warning('Stoploss order was cancelled, but unable to recreate one.')
 
-        # We check if stoploss order is fulfilled
-        if stoploss_order and stoploss_order['status'] == 'closed':
-            trade.sell_reason = SellType.STOPLOSS_ON_EXCHANGE.value
-            trade.update(stoploss_order)
-            # Lock pair for one candle to prevent immediate rebuys
-            self.strategy.lock_pair(trade.pair,
-                                    timeframe_to_next_date(self.config['ticker_interval']))
-            self._notify_sell(trade, "stoploss")
-            return True
-
         # Finally we check if stoploss on exchange should be moved up because of trailing.
         if stoploss_order and self.config.get('trailing_stop', False):
             # if trailing stoploss is enabled we check if stoploss value has changed
@@ -728,7 +791,7 @@ class FreqtradeBot:
 
         return False
 
-    def handle_trailing_stoploss_on_exchange(self, trade: Trade, order):
+    def handle_trailing_stoploss_on_exchange(self, trade: Trade, order: dict) -> None:
         """
         Check to see if stoploss on exchange should be updated
         in case of trailing stoploss on exchange
@@ -736,13 +799,12 @@ class FreqtradeBot:
         :param order: Current on exchange stoploss order
         :return: None
         """
-
-        if trade.stop_loss > float(order['info']['stopPrice']):
+        if self.exchange.stoploss_adjust(trade.stop_loss, order):
             # we check if the update is neccesary
             update_beat = self.strategy.order_types.get('stoploss_on_exchange_interval', 60)
             if (datetime.utcnow() - trade.stoploss_last_update).total_seconds() >= update_beat:
                 # cancelling the current stoploss on exchange first
-                logger.info('Trailing stoploss: cancelling current stoploss on exchange (id:{%s})'
+                logger.info('Trailing stoploss: cancelling current stoploss on exchange (id:{%s}) '
                             'in order to add another one ...', order['id'])
                 try:
                     self.exchange.cancel_order(order['id'], trade.pair)
@@ -751,10 +813,8 @@ class FreqtradeBot:
                                      f"for pair {trade.pair}")
 
                 # Create new stoploss order
-                if self.create_stoploss_order(trade=trade, stop_price=trade.stop_loss,
-                                              rate=trade.stop_loss):
-                    return False
-                else:
+                if not self.create_stoploss_order(trade=trade, stop_price=trade.stop_loss,
+                                                  rate=trade.stop_loss):
                     logger.warning(f"Could not create trailing stoploss order "
                                    f"for pair {trade.pair}.")
 
@@ -769,8 +829,8 @@ class FreqtradeBot:
         )
 
         if should_sell.sell_flag:
+            logger.info(f'Executing Sell for {trade.pair}. Reason: {should_sell.sell_type}')
             self.execute_sell(trade, sell_rate, should_sell.sell_type)
-            logger.info('executed sell, reason: %s', should_sell.sell_type)
             return True
         return False
 
@@ -813,41 +873,40 @@ class FreqtradeBot:
 
             if ((order['side'] == 'buy' and order['status'] == 'canceled')
                     or (self._check_timed_out('buy', order))):
-
                 self.handle_timedout_limit_buy(trade, order)
                 self.wallets.update()
+                order_type = self.strategy.order_types['buy']
+                self._notify_buy_cancel(trade, order_type)
 
             elif ((order['side'] == 'sell' and order['status'] == 'canceled')
                   or (self._check_timed_out('sell', order))):
                 self.handle_timedout_limit_sell(trade, order)
                 self.wallets.update()
-
-    def handle_buy_order_full_cancel(self, trade: Trade, reason: str) -> None:
-        """Close trade in database and send message"""
-        Trade.session.delete(trade)
-        Trade.session.flush()
-        logger.info('Buy order %s for %s.', reason, trade)
-        self.rpc.send_msg({
-            'type': RPCMessageType.STATUS_NOTIFICATION,
-            'status': f'Unfilled buy order for {trade.pair} {reason}'
-        })
+                order_type = self.strategy.order_types['sell']
+                self._notify_sell_cancel(trade, order_type)
 
     def handle_timedout_limit_buy(self, trade: Trade, order: Dict) -> bool:
         """
         Buy timeout - cancel order
         :return: True if order was fully cancelled
         """
-        reason = "cancelled due to timeout"
         if order['status'] != 'canceled':
+            reason = "cancelled due to timeout"
             corder = self.exchange.cancel_order(trade.open_order_id, trade.pair)
+            # Some exchanges don't return a dict here.
+            if not isinstance(corder, dict):
+                corder = {}
+            logger.info('Buy order %s for %s.', reason, trade)
         else:
             # Order was cancelled already, so we can reuse the existing dict
             corder = order
-            reason = "canceled on Exchange"
+            reason = "cancelled on exchange"
+            logger.info('Buy order %s for %s.', reason, trade)
 
         if corder.get('remaining', order['remaining']) == order['amount']:
             # if trade is not partially completed, just delete the trade
-            self.handle_buy_order_full_cancel(trade, reason)
+            Trade.session.delete(trade)
+            Trade.session.flush()
             return True
 
         # if trade is partially complete, edit the stake details for the trade
@@ -882,24 +941,23 @@ class FreqtradeBot:
         Sell timeout - cancel order and update trade
         :return: True if order was fully cancelled
         """
+        # if trade is not partially completed, just cancel the trade
         if order['remaining'] == order['amount']:
-            # if trade is not partially completed, just cancel the trade
             if order["status"] != "canceled":
-                reason = "due to timeout"
+                reason = "cancelled due to timeout"
+                # if trade is not partially completed, just delete the trade
                 self.exchange.cancel_order(trade.open_order_id, trade.pair)
-                logger.info('Sell order timeout for %s.', trade)
+                logger.info('Sell order %s for %s.', reason, trade)
             else:
-                reason = "on exchange"
-                logger.info('Sell order canceled on exchange for %s.', trade)
+                reason = "cancelled on exchange"
+                logger.info('Sell order %s for %s.', reason, trade)
+
             trade.close_rate = None
             trade.close_profit = None
+            trade.close_profit_abs = 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 {trade.pair} cancelled {reason}'
-            })
 
             return True
 
@@ -919,8 +977,8 @@ class FreqtradeBot:
         """
         # Update wallets to ensure amounts tied up in a stoploss is now free!
         self.wallets.update()
-
-        wallet_amount = self.wallets.get_free(pair.split('/')[0])
+        trade_base_currency = self.exchange.get_pair_base_currency(pair)
+        wallet_amount = self.wallets.get_free(trade_base_currency)
         logger.debug(f"{pair} - Wallet: {wallet_amount} - Trade-amount: {amount}")
         if wallet_amount >= amount:
             return amount
@@ -931,13 +989,13 @@ class FreqtradeBot:
             raise DependencyException(
                 f"Not enough amount to sell. Trade-amount: {amount}, Wallet: {wallet_amount}")
 
-    def execute_sell(self, trade: Trade, limit: float, sell_reason: SellType) -> None:
+    def execute_sell(self, trade: Trade, limit: float, sell_reason: SellType) -> bool:
         """
         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
+        :return: True if it succeeds (supported) False (not supported)
         """
         sell_type = 'sell'
         if sell_reason in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
@@ -958,7 +1016,7 @@ class FreqtradeBot:
 
         order_type = self.strategy.order_types[sell_type]
         if sell_reason == SellType.EMERGENCY_SELL:
-            # Emergencysells (default to market!)
+            # Emergency sells (default to market!)
             order_type = self.strategy.order_types.get("emergencysell", "market")
 
         amount = self._safe_sell_amount(trade.pair, trade.amount)
@@ -983,33 +1041,73 @@ class FreqtradeBot:
 
         self._notify_sell(trade, order_type)
 
-    def _notify_sell(self, trade: Trade, order_type: str):
+        return True
+
+    def _notify_sell(self, trade: Trade, order_type: str) -> None:
         """
         Sends rpc notification when a sell occured.
         """
         profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
         profit_trade = trade.calc_profit(rate=profit_rate)
-        # Use cached ticker here - it was updated seconds ago.
+        # Use cached rates here - it was updated seconds ago.
         current_rate = self.get_sell_rate(trade.pair, False)
-        profit_percent = trade.calc_profit_ratio(profit_rate)
-        gain = "profit" if profit_percent > 0 else "loss"
+        profit_ratio = trade.calc_profit_ratio(profit_rate)
+        gain = "profit" if profit_ratio > 0 else "loss"
 
         msg = {
             'type': RPCMessageType.SELL_NOTIFICATION,
             'exchange': trade.exchange.capitalize(),
             'pair': trade.pair,
             'gain': gain,
-            'limit': trade.close_rate_requested,
+            'limit': profit_rate,
             'order_type': order_type,
             'amount': trade.amount,
             'open_rate': trade.open_rate,
             'current_rate': current_rate,
             'profit_amount': profit_trade,
-            'profit_percent': profit_percent,
+            'profit_ratio': profit_ratio,
             'sell_reason': trade.sell_reason,
             'open_date': trade.open_date,
             'close_date': trade.close_date or datetime.utcnow(),
             'stake_currency': self.config['stake_currency'],
+            'fiat_currency': self.config.get('fiat_display_currency', None),
+        }
+
+        if 'fiat_display_currency' in self.config:
+            msg.update({
+                'fiat_currency': self.config['fiat_display_currency'],
+            })
+
+        # Send the message
+        self.rpc.send_msg(msg)
+
+    def _notify_sell_cancel(self, trade: Trade, order_type: str) -> None:
+        """
+        Sends rpc notification when a sell cancel occured.
+        """
+        profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
+        profit_trade = trade.calc_profit(rate=profit_rate)
+        current_rate = self.get_sell_rate(trade.pair, False)
+        profit_ratio = trade.calc_profit_ratio(profit_rate)
+        gain = "profit" if profit_ratio > 0 else "loss"
+
+        msg = {
+            'type': RPCMessageType.SELL_CANCEL_NOTIFICATION,
+            'exchange': trade.exchange.capitalize(),
+            'pair': trade.pair,
+            'gain': gain,
+            'limit': profit_rate,
+            'order_type': order_type,
+            'amount': trade.amount,
+            'open_rate': trade.open_rate,
+            'current_rate': current_rate,
+            'profit_amount': profit_trade,
+            'profit_ratio': profit_ratio,
+            'sell_reason': trade.sell_reason,
+            'open_date': trade.open_date,
+            'close_date': trade.close_date,
+            'stake_currency': self.config['stake_currency'],
+            'fiat_currency': self.config.get('fiat_display_currency', None),
         }
 
         if 'fiat_display_currency' in self.config:
@@ -1024,7 +1122,7 @@ class FreqtradeBot:
 # Common update trade state methods
 #
 
-    def update_trade_state(self, trade, action_order: dict = None):
+    def update_trade_state(self, trade: Trade, action_order: dict = None) -> None:
         """
         Checks trades with open orders and updates the amount if necessary
         """
@@ -1066,12 +1164,13 @@ class FreqtradeBot:
         if trade.fee_open == 0 or order['status'] == 'open':
             return order_amount
 
+        trade_base_currency = self.exchange.get_pair_base_currency(trade.pair)
         # use fee from order-dict if possible
         if ('fee' in order and order['fee'] is not None and
                 (order['fee'].keys() >= {'currency', 'cost'})):
             if (order['fee']['currency'] is not None and
                     order['fee']['cost'] is not None and
-                    trade.pair.startswith(order['fee']['currency'])):
+                    trade_base_currency == 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)
@@ -1093,7 +1192,7 @@ class FreqtradeBot:
                 # only applies if fee is in quote currency!
                 if (exectrade['fee']['currency'] is not None and
                         exectrade['fee']['cost'] is not None and
-                        trade.pair.startswith(exectrade['fee']['currency'])):
+                        trade_base_currency == exectrade['fee']['currency']):
                     fee_abs += exectrade['fee']['cost']
 
         if not isclose(amount, order_amount, abs_tol=constants.MATH_CLOSE_PREC):

freqtrade/main.py

@@ -38,8 +38,8 @@ def main(sysargv: List[str] = None) -> None:
             # No subcommand was issued.
             raise OperationalException(
                 "Usage of Freqtrade requires a subcommand to be specified.\n"
-                "To have the previous behavior (bot executing trades in live/dry-run modes, "
-                "depending on the value of the `dry_run` setting in the config), run freqtrade "
+                "To have the bot executing trades in live/dry-run modes, "
+                "depending on the value of the `dry_run` setting in the config, run Freqtrade "
                 "as `freqtrade trade [options...]`.\n"
                 "To see the full list of options available, please use "
                 "`freqtrade --help` or `freqtrade <command> --help`."

freqtrade/misc.py

@@ -6,6 +6,7 @@ import logging
 import re
 from datetime import datetime
 from pathlib import Path
+from typing import Any
 from typing.io import IO
 
 import numpy as np
@@ -40,28 +41,30 @@ def datesarray_to_datetimearray(dates: np.ndarray) -> np.ndarray:
     return dates.dt.to_pydatetime()
 
 
-def file_dump_json(filename: Path, data, is_zip=False) -> None:
+def file_dump_json(filename: Path, data: Any, is_zip: bool = False) -> None:
     """
     Dump JSON data into a file
     :param filename: file to create
     :param data: JSON Data to save
     :return:
     """
-    logger.info(f'dumping json to "{filename}"')
 
     if is_zip:
         if filename.suffix != '.gz':
             filename = filename.with_suffix('.gz')
+        logger.info(f'dumping json to "{filename}"')
+
         with gzip.open(filename, 'w') as fp:
             rapidjson.dump(data, fp, default=str, number_mode=rapidjson.NM_NATIVE)
     else:
+        logger.info(f'dumping json to "{filename}"')
         with open(filename, 'w') as fp:
             rapidjson.dump(data, fp, default=str, number_mode=rapidjson.NM_NATIVE)
 
     logger.debug(f'done json to "{filename}"')
 
 
-def json_load(datafile: IO):
+def json_load(datafile: IO) -> Any:
     """
     load data with rapidjson
     Use this to have a consistent experience,
@@ -78,18 +81,24 @@ def file_load_json(file):
         gzipfile = file
     # Try gzip file first, otherwise regular json file.
     if gzipfile.is_file():
-        logger.debug('Loading ticker data from file %s', gzipfile)
-        with gzip.open(gzipfile) as tickerdata:
-            pairdata = json_load(tickerdata)
+        logger.debug(f"Loading historical data from file {gzipfile}")
+        with gzip.open(gzipfile) as datafile:
+            pairdata = json_load(datafile)
     elif file.is_file():
-        logger.debug('Loading ticker data from file %s', file)
-        with open(file) as tickerdata:
-            pairdata = json_load(tickerdata)
+        logger.debug(f"Loading historical data from file {file}")
+        with open(file) as datafile:
+            pairdata = json_load(datafile)
     else:
         return None
     return pairdata
 
 
+def pair_to_filename(pair: str) -> str:
+    for ch in ['/', '-', ' ', '.', '@', '$', '+', ':']:
+        pair = pair.replace(ch, '_')
+    return pair
+
+
 def format_ms_time(date: int) -> str:
     """
     convert MS date to readable format.
@@ -125,11 +134,11 @@ def round_dict(d, n):
     return {k: (round(v, n) if isinstance(v, float) else v) for k, v in d.items()}
 
 
-def plural(num, singular: str, plural: str = None) -> str:
+def plural(num: float, singular: str, plural: str = None) -> str:
     return singular if (num == 1 or num == -1) else plural or singular + 's'
 
 
-def render_template(templatefile: str, arguments: dict = {}):
+def render_template(templatefile: str, arguments: dict = {}) -> str:
 
     from jinja2 import Environment, PackageLoader, select_autoescape
 
@@ -138,5 +147,4 @@ def render_template(templatefile: str, arguments: dict = {}):
         autoescape=select_autoescape(['html', 'xml'])
     )
     template = env.get_template(templatefile)
-
     return template.render(**arguments)

freqtrade/optimize/backtesting.py

@@ -6,25 +6,24 @@ This module contains the backtesting logic
 import logging
 from copy import deepcopy
 from datetime import datetime, timedelta
-from pathlib import Path
-from typing import Any, Dict, List, NamedTuple, Optional
+from typing import Any, Dict, List, NamedTuple, Optional, Tuple
 
+import arrow
 from pandas import DataFrame
 
 from freqtrade.configuration import (TimeRange, remove_credentials,
                                      validate_config_consistency)
 from freqtrade.data import history
+from freqtrade.data.converter import trim_dataframe
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
-from freqtrade.misc import file_dump_json
-from freqtrade.optimize.optimize_reports import (
-    generate_text_table, generate_text_table_sell_reason,
-    generate_text_table_strategy)
+from freqtrade.optimize.optimize_reports import (show_backtest_results,
+                                                 store_backtest_result)
 from freqtrade.persistence import Trade
 from freqtrade.resolvers import ExchangeResolver, StrategyResolver
 from freqtrade.state import RunMode
-from freqtrade.strategy.interface import IStrategy, SellType
+from freqtrade.strategy.interface import IStrategy, SellCheckTuple, SellType
 
 logger = logging.getLogger(__name__)
 
@@ -86,8 +85,8 @@ class Backtesting:
             validate_config_consistency(self.config)
 
         if "ticker_interval" not in self.config:
-            raise OperationalException("Ticker-interval needs to be set in either configuration "
-                                       "or as cli argument `--ticker-interval 5m`")
+            raise OperationalException("Timeframe (ticker interval) needs to be set in either "
+                                       "configuration or as cli argument `--ticker-interval 5m`")
         self.timeframe = str(self.config.get('ticker_interval'))
         self.timeframe_min = timeframe_to_minutes(self.timeframe)
 
@@ -106,7 +105,7 @@ class Backtesting:
         # And the regular "stoploss" function would not apply to that case
         self.strategy.order_types['stoploss_on_exchange'] = False
 
-    def load_bt_data(self):
+    def load_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]:
         timerange = TimeRange.parse_timerange(None if self.config.get(
             'timerange') is None else str(self.config.get('timerange')))
 
@@ -117,6 +116,7 @@ class Backtesting:
             timerange=timerange,
             startup_candles=self.required_startup,
             fail_without_data=True,
+            data_format=self.config.get('dataformat_ohlcv', 'json'),
         )
 
         min_date, max_date = history.get_timerange(data)
@@ -131,51 +131,36 @@ class Backtesting:
 
         return data, timerange
 
-    def _store_backtest_result(self, recordfilename: Path, 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
-                recordfilename = Path.joinpath(
-                    recordfilename.parent,
-                    f'{recordfilename.stem}-{strategyname}').with_suffix(recordfilename.suffix)
-            logger.info(f'Dumping backtest results to {recordfilename}')
-            file_dump_json(recordfilename, records)
-
-    def _get_ticker_list(self, processed) -> Dict[str, DataFrame]:
+    def _get_ohlcv_as_lists(self, processed: Dict) -> Dict[str, DataFrame]:
         """
-        Helper function to convert a processed tickerlist into a list for performance reasons.
+        Helper function to convert a processed dataframes into lists for performance reasons.
 
         Used by backtest() - so keep this optimized for performance.
         """
         headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
-        ticker: Dict = {}
-        # Create ticker dict
+        data: Dict = {}
+        # Create dict with data
         for pair, pair_data in processed.items():
             pair_data.loc[:, 'buy'] = 0  # cleanup from previous run
             pair_data.loc[:, 'sell'] = 0  # cleanup from previous run
 
-            ticker_data = self.strategy.advise_sell(
+            df_analyzed = self.strategy.advise_sell(
                 self.strategy.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)
+            # To avoid using data from future, we use buy/sell signals shifted
+            # from the previous candle
+            df_analyzed.loc[:, 'buy'] = df_analyzed['buy'].shift(1)
+            df_analyzed.loc[:, 'sell'] = df_analyzed['sell'].shift(1)
 
-            ticker_data.drop(ticker_data.head(1).index, inplace=True)
+            df_analyzed.drop(df_analyzed.head(1).index, inplace=True)
 
             # Convert from Pandas to list for performance reasons
             # (Looping Pandas is slow.)
-            ticker[pair] = [x for x in ticker_data.itertuples()]
-        return ticker
+            data[pair] = [x for x in df_analyzed.itertuples()]
+        return data
 
-    def _get_close_rate(self, sell_row, trade: Trade, sell, trade_dur) -> float:
+    def _get_close_rate(self, sell_row, trade: Trade, sell: SellCheckTuple,
+                        trade_dur: int) -> float:
         """
         Get close rate for backtesting result
         """
@@ -216,7 +201,7 @@ class Backtesting:
 
     def _get_sell_trade_entry(
             self, pair: str, buy_row: DataFrame,
-            partial_ticker: List, trade_count_lock: Dict,
+            partial_ohlcv: List, trade_count_lock: Dict,
             stake_amount: float, max_open_trades: int) -> Optional[BacktestResult]:
 
         trade = Trade(
@@ -231,7 +216,7 @@ class Backtesting:
         )
         logger.debug(f"{pair} - Backtesting emulates creation of new trade: {trade}.")
         # calculate win/lose forwards from buy point
-        for sell_row in partial_ticker:
+        for sell_row in partial_ohlcv:
             if max_open_trades > 0:
                 # Increase trade_count_lock for every iteration
                 trade_count_lock[sell_row.date] = trade_count_lock.get(sell_row.date, 0) + 1
@@ -255,9 +240,9 @@ class Backtesting:
                                       close_rate=closerate,
                                       sell_reason=sell.sell_type
                                       )
-        if partial_ticker:
+        if partial_ohlcv:
             # no sell condition found - trade stil open at end of backtest period
-            sell_row = partial_ticker[-1]
+            sell_row = partial_ohlcv[-1]
             bt_res = BacktestResult(pair=pair,
                                     profit_percent=trade.calc_profit_ratio(rate=sell_row.open),
                                     profit_abs=trade.calc_profit(rate=sell_row.open),
@@ -280,7 +265,7 @@ class Backtesting:
         return None
 
     def backtest(self, processed: Dict, stake_amount: float,
-                 start_date, end_date,
+                 start_date: arrow.Arrow, end_date: arrow.Arrow,
                  max_open_trades: int = 0, position_stacking: bool = False) -> DataFrame:
         """
         Implement backtesting functionality
@@ -304,8 +289,9 @@ class Backtesting:
         trades = []
         trade_count_lock: Dict = {}
 
-        # Dict of ticker-lists for performance (looping lists is a lot faster than dataframes)
-        ticker: Dict = self._get_ticker_list(processed)
+        # Use dict of lists with data for performance
+        # (looping lists is a lot faster than pandas DataFrames)
+        data: Dict = self._get_ohlcv_as_lists(processed)
 
         lock_pair_until: Dict = {}
         # Indexes per pair, so some pairs are allowed to have a missing start.
@@ -315,12 +301,12 @@ class Backtesting:
         # Loop timerange and get candle for each pair at that point in time
         while tmp < end_date:
 
-            for i, pair in enumerate(ticker):
+            for i, pair in enumerate(data):
                 if pair not in indexes:
                     indexes[pair] = 0
 
                 try:
-                    row = ticker[pair][indexes[pair]]
+                    row = data[pair][indexes[pair]]
                 except IndexError:
                     # missing Data for one pair at the end.
                     # Warnings for this are shown during data loading
@@ -348,7 +334,7 @@ class Backtesting:
 
                 # since indexes has been incremented before, we need to go one step back to
                 # also check the buying candle for sell conditions.
-                trade_entry = self._get_sell_trade_entry(pair, row, ticker[pair][indexes[pair]-1:],
+                trade_entry = self._get_sell_trade_entry(pair, row, data[pair][indexes[pair]-1:],
                                                          trade_count_lock, stake_amount,
                                                          max_open_trades)
 
@@ -391,11 +377,11 @@ class Backtesting:
             self._set_strategy(strat)
 
             # need to reprocess data every time to populate signals
-            preprocessed = self.strategy.tickerdata_to_dataframe(data)
+            preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
 
             # Trim startup period from analyzed dataframe
             for pair, df in preprocessed.items():
-                preprocessed[pair] = history.trim_dataframe(df, timerange)
+                preprocessed[pair] = trim_dataframe(df, timerange)
             min_date, max_date = history.get_timerange(preprocessed)
 
             logger.info(
@@ -404,40 +390,15 @@ class Backtesting:
             )
             # Execute backtest and print results
             all_results[self.strategy.get_strategy_name()] = self.backtest(
-                    processed=preprocessed,
-                    stake_amount=self.config['stake_amount'],
-                    start_date=min_date,
-                    end_date=max_date,
-                    max_open_trades=max_open_trades,
-                    position_stacking=position_stacking,
+                processed=preprocessed,
+                stake_amount=self.config['stake_amount'],
+                start_date=min_date,
+                end_date=max_date,
+                max_open_trades=max_open_trades,
+                position_stacking=position_stacking,
             )
 
-        for strategy, results in all_results.items():
-
-            if self.config.get('export', False):
-                self._store_backtest_result(Path(self.config['exportfilename']), results,
-                                            strategy if len(self.strategylist) > 1 else None)
-
-            print(f"Result for strategy {strategy}")
-            print(' BACKTESTING REPORT '.center(133, '='))
-            print(generate_text_table(data,
-                                      stake_currency=self.config['stake_currency'],
-                                      max_open_trades=self.config['max_open_trades'],
-                                      results=results))
-
-            print(' SELL REASON STATS '.center(133, '='))
-            print(generate_text_table_sell_reason(data, results))
-
-            print(' LEFT OPEN TRADES REPORT '.center(133, '='))
-            print(generate_text_table(data,
-                                      stake_currency=self.config['stake_currency'],
-                                      max_open_trades=self.config['max_open_trades'],
-                                      results=results.loc[results.open_at_end], skip_nan=True))
-            print()
-        if len(all_results) > 1:
-            # Print Strategy summary table
-            print(' Strategy Summary '.center(133, '='))
-            print(generate_text_table_strategy(self.config['stake_currency'],
-                                               self.config['max_open_trades'],
-                                               all_results=all_results))
-            print('\nFor more details, please look at the detail tables above')
+        if self.config.get('export', False):
+            store_backtest_result(self.config['exportfilename'], all_results)
+        # Show backtest results
+        show_backtest_results(self.config, data, all_results)

freqtrade/optimize/hyperopt.py

@@ -9,6 +9,7 @@ import logging
 import random
 import sys
 import warnings
+from math import ceil
 from collections import OrderedDict
 from operator import itemgetter
 from pathlib import Path
@@ -20,9 +21,13 @@ from colorama import Fore, Style
 from colorama import init as colorama_init
 from joblib import (Parallel, cpu_count, delayed, dump, load,
                     wrap_non_picklable_objects)
-from pandas import DataFrame
+from pandas import DataFrame, json_normalize, isna
+import tabulate
+from os import path
+import io
 
-from freqtrade.data.history import get_timerange, trim_dataframe
+from freqtrade.data.converter import trim_dataframe
+from freqtrade.data.history import get_timerange
 from freqtrade.exceptions import OperationalException
 from freqtrade.misc import plural, round_dict
 from freqtrade.optimize.backtesting import Backtesting
@@ -59,6 +64,7 @@ class Hyperopt:
     hyperopt = Hyperopt(config)
     hyperopt.start()
     """
+
     def __init__(self, config: Dict[str, Any]) -> None:
         self.config = config
 
@@ -71,8 +77,8 @@ class Hyperopt:
 
         self.trials_file = (self.config['user_data_dir'] /
                             'hyperopt_results' / 'hyperopt_results.pickle')
-        self.tickerdata_pickle = (self.config['user_data_dir'] /
-                                  'hyperopt_results' / 'hyperopt_tickerdata.pkl')
+        self.data_pickle_file = (self.config['user_data_dir'] /
+                                 'hyperopt_results' / 'hyperopt_tickerdata.pkl')
         self.total_epochs = config.get('epochs', 0)
 
         self.current_best_loss = 100
@@ -90,13 +96,13 @@ class Hyperopt:
         # Populate functions here (hasattr is slow so should not be run during "regular" operations)
         if hasattr(self.custom_hyperopt, 'populate_indicators'):
             self.backtesting.strategy.advise_indicators = \
-                    self.custom_hyperopt.populate_indicators  # type: ignore
+                self.custom_hyperopt.populate_indicators  # type: ignore
         if hasattr(self.custom_hyperopt, 'populate_buy_trend'):
             self.backtesting.strategy.advise_buy = \
-                    self.custom_hyperopt.populate_buy_trend  # type: ignore
+                self.custom_hyperopt.populate_buy_trend  # type: ignore
         if hasattr(self.custom_hyperopt, 'populate_sell_trend'):
             self.backtesting.strategy.advise_sell = \
-                    self.custom_hyperopt.populate_sell_trend  # type: ignore
+                self.custom_hyperopt.populate_sell_trend  # type: ignore
 
         # Use max_open_trades for hyperopt as well, except --disable-max-market-positions is set
         if self.config.get('use_max_market_positions', True):
@@ -113,19 +119,20 @@ class Hyperopt:
             self.config['ask_strategy']['use_sell_signal'] = True
 
         self.print_all = self.config.get('print_all', False)
+        self.hyperopt_table_header = 0
         self.print_colorized = self.config.get('print_colorized', False)
         self.print_json = self.config.get('print_json', False)
 
     @staticmethod
-    def get_lock_filename(config) -> str:
+    def get_lock_filename(config: Dict[str, Any]) -> str:
 
         return str(config['user_data_dir'] / 'hyperopt.lock')
 
-    def clean_hyperopt(self):
+    def clean_hyperopt(self) -> None:
         """
         Remove hyperopt pickle files to restart hyperopt.
         """
-        for f in [self.tickerdata_pickle, self.trials_file]:
+        for f in [self.data_pickle_file, self.trials_file]:
             p = Path(f)
             if p.is_file():
                 logger.info(f"Removing `{p}`.")
@@ -150,7 +157,7 @@ class Hyperopt:
         """
         num_trials = len(self.trials)
         if num_trials > self.num_trials_saved:
-            logger.info(f"Saving {num_trials} {plural(num_trials, 'epoch')}.")
+            logger.debug(f"Saving {num_trials} {plural(num_trials, 'epoch')}.")
             dump(self.trials, self.trials_file)
             self.num_trials_saved = num_trials
         if final:
@@ -158,7 +165,7 @@ class Hyperopt:
                         f"saved to '{self.trials_file}'.")
 
     @staticmethod
-    def _read_trials(trials_file) -> List:
+    def _read_trials(trials_file: Path) -> List:
         """
         Read hyperopt trials file
         """
@@ -189,7 +196,7 @@ class Hyperopt:
         return result
 
     @staticmethod
-    def print_epoch_details(results, total_epochs, print_json: bool,
+    def print_epoch_details(results, total_epochs: int, print_json: bool,
                             no_header: bool = False, header_str: str = None) -> None:
         """
         Display details of the hyperopt result
@@ -218,7 +225,7 @@ class Hyperopt:
             Hyperopt._params_pretty_print(params, 'trailing', "Trailing stop:")
 
     @staticmethod
-    def _params_update_for_json(result_dict, params, space: str):
+    def _params_update_for_json(result_dict, params, space: str) -> None:
         if space in params:
             space_params = Hyperopt._space_params(params, space)
             if space in ['buy', 'sell']:
@@ -235,7 +242,7 @@ class Hyperopt:
                 result_dict.update(space_params)
 
     @staticmethod
-    def _params_pretty_print(params, space: str, header: str):
+    def _params_pretty_print(params, space: str, header: str) -> None:
         if space in params:
             space_params = Hyperopt._space_params(params, space, 5)
             if space == 'stoploss':
@@ -251,7 +258,7 @@ class Hyperopt:
         return round_dict(d, r) if r else d
 
     @staticmethod
-    def is_best_loss(results, current_best_loss) -> bool:
+    def is_best_loss(results, current_best_loss: float) -> bool:
         return results['loss'] < current_best_loss
 
     def print_results(self, results) -> None:
@@ -269,8 +276,10 @@ class Hyperopt:
             if not self.print_all:
                 # Separate the results explanation string from dots
                 print("\n")
-            self.print_results_explanation(results, self.total_epochs, self.print_all,
-                                           self.print_colorized)
+            self.print_result_table(self.config, results, self.total_epochs,
+                                    self.print_all, self.print_colorized,
+                                    self.hyperopt_table_header)
+            self.hyperopt_table_header = 2
 
     @staticmethod
     def print_results_explanation(results, total_epochs, highlight_best: bool,
@@ -294,6 +303,142 @@ class Hyperopt:
                 f"{results['results_explanation']} " +
                 f"Objective: {results['loss']:.5f}")
 
+    @staticmethod
+    def print_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
+                           print_colorized: bool, remove_header: int) -> None:
+        """
+        Log result table
+        """
+        if not results:
+            return
+
+        tabulate.PRESERVE_WHITESPACE = True
+
+        trials = json_normalize(results, max_level=1)
+        trials['Best'] = ''
+        trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
+                         'results_metrics.avg_profit', 'results_metrics.total_profit',
+                         'results_metrics.profit', 'results_metrics.duration',
+                         'loss', 'is_initial_point', 'is_best']]
+        trials.columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit',
+                          'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
+        trials['is_profit'] = False
+        trials.loc[trials['is_initial_point'], 'Best'] = '*'
+        trials.loc[trials['is_best'], 'Best'] = 'Best'
+        trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
+        trials['Trades'] = trials['Trades'].astype(str)
+
+        trials['Epoch'] = trials['Epoch'].apply(
+            lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
+        )
+        trials['Avg profit'] = trials['Avg profit'].apply(
+            lambda x: '{:,.2f}%'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
+        )
+        trials['Avg duration'] = trials['Avg duration'].apply(
+            lambda x: '{:,.1f} m'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
+        )
+        trials['Objective'] = trials['Objective'].apply(
+            lambda x: '{:,.5f}'.format(x).rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
+        )
+
+        trials['Profit'] = trials.apply(
+            lambda x: '{:,.8f} {} {}'.format(
+                x['Total profit'], config['stake_currency'],
+                '({:,.2f}%)'.format(x['Profit']).rjust(10, ' ')
+            ).rjust(25+len(config['stake_currency']))
+            if x['Total profit'] != 0.0 else '--'.rjust(25+len(config['stake_currency'])),
+            axis=1
+        )
+        trials = trials.drop(columns=['Total profit'])
+
+        if print_colorized:
+            for i in range(len(trials)):
+                if trials.loc[i]['is_profit']:
+                    for j in range(len(trials.loc[i])-3):
+                        trials.iat[i, j] = "{}{}{}".format(Fore.GREEN,
+                                                           str(trials.loc[i][j]), Fore.RESET)
+                if trials.loc[i]['is_best'] and highlight_best:
+                    for j in range(len(trials.loc[i])-3):
+                        trials.iat[i, j] = "{}{}{}".format(Style.BRIGHT,
+                                                           str(trials.loc[i][j]), Style.RESET_ALL)
+
+        trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
+        if remove_header > 0:
+            table = tabulate.tabulate(
+                trials.to_dict(orient='list'), tablefmt='orgtbl',
+                headers='keys', stralign="right"
+            )
+
+            table = table.split("\n", remove_header)[remove_header]
+        elif remove_header < 0:
+            table = tabulate.tabulate(
+                trials.to_dict(orient='list'), tablefmt='psql',
+                headers='keys', stralign="right"
+            )
+            table = "\n".join(table.split("\n")[0:remove_header])
+        else:
+            table = tabulate.tabulate(
+                trials.to_dict(orient='list'), tablefmt='psql',
+                headers='keys', stralign="right"
+            )
+        print(table)
+
+    @staticmethod
+    def export_csv_file(config: dict, results: list, total_epochs: int, highlight_best: bool,
+                        csv_file: str) -> None:
+        """
+        Log result to csv-file
+        """
+        if not results:
+            return
+
+        # Verification for overwrite
+        if path.isfile(csv_file):
+            logger.error("CSV-File already exists!")
+            return
+
+        try:
+            io.open(csv_file, 'w+').close()
+        except IOError:
+            logger.error("Filed to create CSV-File!")
+            return
+
+        trials = json_normalize(results, max_level=1)
+        trials['Best'] = ''
+        trials['Stake currency'] = config['stake_currency']
+        trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
+                         'results_metrics.avg_profit', 'results_metrics.total_profit',
+                         'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
+                         'loss', 'is_initial_point', 'is_best']]
+        trials.columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit', 'Stake currency',
+                          'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
+        trials['is_profit'] = False
+        trials.loc[trials['is_initial_point'], 'Best'] = '*'
+        trials.loc[trials['is_best'], 'Best'] = 'Best'
+        trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
+        trials['Epoch'] = trials['Epoch'].astype(str)
+        trials['Trades'] = trials['Trades'].astype(str)
+
+        trials['Total profit'] = trials['Total profit'].apply(
+            lambda x: '{:,.8f}'.format(x) if x != 0.0 else ""
+        )
+        trials['Profit'] = trials['Profit'].apply(
+            lambda x: '{:,.2f}'.format(x) if not isna(x) else ""
+        )
+        trials['Avg profit'] = trials['Avg profit'].apply(
+            lambda x: '{:,.2f}%'.format(x) if not isna(x) else ""
+        )
+        trials['Avg duration'] = trials['Avg duration'].apply(
+            lambda x: '{:,.1f} m'.format(x) if not isna(x) else ""
+        )
+        trials['Objective'] = trials['Objective'].apply(
+            lambda x: '{:,.5f}'.format(x) if x != 100000 else ""
+        )
+
+        trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
+        trials.to_csv(csv_file, index=False, header=True, mode='w', encoding='UTF-8')
+        print("CSV-File created!")
+
     def has_space(self, space: str) -> bool:
         """
         Tell if the space value is contained in the configuration
@@ -345,15 +490,15 @@ class Hyperopt:
 
         if self.has_space('roi'):
             self.backtesting.strategy.minimal_roi = \
-                    self.custom_hyperopt.generate_roi_table(params_dict)
+                self.custom_hyperopt.generate_roi_table(params_dict)
 
         if self.has_space('buy'):
             self.backtesting.strategy.advise_buy = \
-                    self.custom_hyperopt.buy_strategy_generator(params_dict)
+                self.custom_hyperopt.buy_strategy_generator(params_dict)
 
         if self.has_space('sell'):
             self.backtesting.strategy.advise_sell = \
-                    self.custom_hyperopt.sell_strategy_generator(params_dict)
+                self.custom_hyperopt.sell_strategy_generator(params_dict)
 
         if self.has_space('stoploss'):
             self.backtesting.strategy.stoploss = params_dict['stoploss']
@@ -367,17 +512,17 @@ class Hyperopt:
             self.backtesting.strategy.trailing_only_offset_is_reached = \
                 d['trailing_only_offset_is_reached']
 
-        processed = load(self.tickerdata_pickle)
+        processed = load(self.data_pickle_file)
 
         min_date, max_date = get_timerange(processed)
 
         backtesting_results = self.backtesting.backtest(
-                processed=processed,
-                stake_amount=self.config['stake_amount'],
-                start_date=min_date,
-                end_date=max_date,
-                max_open_trades=self.max_open_trades,
-                position_stacking=self.position_stacking,
+            processed=processed,
+            stake_amount=self.config['stake_amount'],
+            start_date=min_date,
+            end_date=max_date,
+            max_open_trades=self.max_open_trades,
+            position_stacking=self.position_stacking,
         )
         return self._get_results_dict(backtesting_results, min_date, max_date,
                                       params_dict, params_details)
@@ -438,7 +583,7 @@ class Hyperopt:
             random_state=self.random_state,
         )
 
-    def fix_optimizer_models_list(self):
+    def fix_optimizer_models_list(self) -> None:
         """
         WORKAROUND: Since skopt is not actively supported, this resolves problems with skopt
         memory usage, see also: https://github.com/scikit-optimize/scikit-optimize/pull/746
@@ -460,7 +605,7 @@ class Hyperopt:
                         wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked)
 
     @staticmethod
-    def load_previous_results(trials_file) -> List:
+    def load_previous_results(trials_file: Path) -> List:
         """
         Load data for epochs from the file if we have one
         """
@@ -469,8 +614,8 @@ class Hyperopt:
             trials = Hyperopt._read_trials(trials_file)
             if trials[0].get('is_best') is None:
                 raise OperationalException(
-                        "The file with Hyperopt results is incompatible with this version "
-                        "of Freqtrade and cannot be loaded.")
+                    "The file with Hyperopt results is incompatible with this version "
+                    "of Freqtrade and cannot be loaded.")
             logger.info(f"Loaded {len(trials)} previous evaluations from disk.")
         return trials
 
@@ -480,10 +625,10 @@ class Hyperopt:
     def start(self) -> None:
         self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))
         logger.info(f"Using optimizer random state: {self.random_state}")
-
+        self.hyperopt_table_header = -1
         data, timerange = self.backtesting.load_bt_data()
 
-        preprocessed = self.backtesting.strategy.tickerdata_to_dataframe(data)
+        preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
 
         # Trim startup period from analyzed dataframe
         for pair, df in preprocessed.items():
@@ -494,7 +639,7 @@ class Hyperopt:
             'Hyperopting with data from %s up to %s (%s days)..',
             min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
         )
-        dump(preprocessed, self.tickerdata_pickle)
+        dump(preprocessed, self.data_pickle_file)
 
         # We don't need exchange instance anymore while running hyperopt
         self.backtesting.exchange = None  # type: ignore
@@ -516,16 +661,21 @@ class Hyperopt:
             with Parallel(n_jobs=config_jobs) as parallel:
                 jobs = parallel._effective_n_jobs()
                 logger.info(f'Effective number of parallel workers used: {jobs}')
-                EVALS = max(self.total_epochs // jobs, 1)
+                EVALS = ceil(self.total_epochs / jobs)
                 for i in range(EVALS):
-                    asked = self.opt.ask(n_points=jobs)
+                    # Correct the number of epochs to be processed for the last
+                    # iteration (should not exceed self.total_epochs in total)
+                    n_rest = (i + 1) * jobs - self.total_epochs
+                    current_jobs = jobs - n_rest if n_rest > 0 else jobs
+
+                    asked = self.opt.ask(n_points=current_jobs)
                     f_val = self.run_optimizer_parallel(parallel, asked, i)
                     self.opt.tell(asked, [v['loss'] for v in f_val])
                     self.fix_optimizer_models_list()
-                    for j in range(jobs):
+
+                    for j, val in enumerate(f_val):
                         # Use human-friendly indexes here (starting from 1)
                         current = i * jobs + j + 1
-                        val = f_val[j]
                         val['current_epoch'] = current
                         val['is_initial_point'] = current <= INITIAL_POINTS
                         logger.debug(f"Optimizer epoch evaluated: {val}")

freqtrade/optimize/hyperopt_interface.py

@@ -207,7 +207,7 @@ class IHyperOpt(ABC):
             # so this intermediate parameter is used as the value of the difference between
             # them. The value of the 'trailing_stop_positive_offset' is constructed in the
             # generate_trailing_params() method.
-            # # This is similar to the hyperspace dimensions used for constructing the ROI tables.
+            # This is similar to the hyperspace dimensions used for constructing the ROI tables.
             Real(0.001, 0.1, name='trailing_stop_positive_offset_p1'),
 
             Categorical([True, False], name='trailing_only_offset_is_reached'),

freqtrade/optimize/hyperopt_loss_sharpe.py

@@ -28,18 +28,19 @@ class SharpeHyperOptLoss(IHyperOptLoss):
 
         Uses Sharpe Ratio calculation.
         """
-        total_profit = results.profit_percent
+        total_profit = results["profit_percent"]
         days_period = (max_date - min_date).days
 
         # adding slippage of 0.1% per trade
         total_profit = total_profit - 0.0005
-        expected_yearly_return = total_profit.sum() / days_period
+        expected_returns_mean = total_profit.sum() / days_period
+        up_stdev = np.std(total_profit)
 
-        if (np.std(total_profit) != 0.):
-            sharp_ratio = expected_yearly_return / np.std(total_profit) * np.sqrt(365)
+        if up_stdev != 0:
+            sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
         else:
             # Define high (negative) sharpe ratio to be clear that this is NOT optimal.
             sharp_ratio = -20.
 
-        # print(expected_yearly_return, np.std(total_profit), sharp_ratio)
+        # print(expected_returns_mean, up_stdev, sharp_ratio)
         return -sharp_ratio

freqtrade/optimize/hyperopt_loss_sharpe_daily.py

@@ -0,0 +1,62 @@
+"""
+SharpeHyperOptLossDaily
+
+This module defines the alternative HyperOptLoss class which can be used for
+Hyperoptimization.
+"""
+import math
+from datetime import datetime
+
+from pandas import DataFrame, date_range
+
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+class SharpeHyperOptLossDaily(IHyperOptLoss):
+    """
+    Defines the loss function for hyperopt.
+
+    This implementation uses the Sharpe Ratio calculation.
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(results: DataFrame, trade_count: int,
+                               min_date: datetime, max_date: datetime,
+                               *args, **kwargs) -> float:
+        """
+        Objective function, returns smaller number for more optimal results.
+
+        Uses Sharpe Ratio calculation.
+        """
+        resample_freq = '1D'
+        slippage_per_trade_ratio = 0.0005
+        days_in_year = 365
+        annual_risk_free_rate = 0.0
+        risk_free_rate = annual_risk_free_rate / days_in_year
+
+        # apply slippage per trade to profit_percent
+        results.loc[:, 'profit_percent_after_slippage'] = \
+            results['profit_percent'] - slippage_per_trade_ratio
+
+        # create the index within the min_date and end max_date
+        t_index = date_range(start=min_date, end=max_date, freq=resample_freq,
+                             normalize=True)
+
+        sum_daily = (
+            results.resample(resample_freq, on='close_time').agg(
+                {"profit_percent_after_slippage": sum}).reindex(t_index).fillna(0)
+        )
+
+        total_profit = sum_daily["profit_percent_after_slippage"] - risk_free_rate
+        expected_returns_mean = total_profit.mean()
+        up_stdev = total_profit.std()
+
+        if up_stdev != 0:
+            sharp_ratio = expected_returns_mean / up_stdev * math.sqrt(days_in_year)
+        else:
+            # Define high (negative) sharpe ratio to be clear that this is NOT optimal.
+            sharp_ratio = -20.
+
+        # print(t_index, sum_daily, total_profit)
+        # print(risk_free_rate, expected_returns_mean, up_stdev, sharp_ratio)
+        return -sharp_ratio

freqtrade/optimize/hyperopt_loss_sortino.py

@@ -0,0 +1,49 @@
+"""
+SortinoHyperOptLoss
+
+This module defines the alternative HyperOptLoss class which can be used for
+Hyperoptimization.
+"""
+from datetime import datetime
+
+from pandas import DataFrame
+import numpy as np
+
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+class SortinoHyperOptLoss(IHyperOptLoss):
+    """
+    Defines the loss function for hyperopt.
+
+    This implementation uses the Sortino Ratio calculation.
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(results: DataFrame, trade_count: int,
+                               min_date: datetime, max_date: datetime,
+                               *args, **kwargs) -> float:
+        """
+        Objective function, returns smaller number for more optimal results.
+
+        Uses Sortino Ratio calculation.
+        """
+        total_profit = results["profit_percent"]
+        days_period = (max_date - min_date).days
+
+        # adding slippage of 0.1% per trade
+        total_profit = total_profit - 0.0005
+        expected_returns_mean = total_profit.sum() / days_period
+
+        results['downside_returns'] = 0
+        results.loc[total_profit < 0, 'downside_returns'] = results['profit_percent']
+        down_stdev = np.std(results['downside_returns'])
+
+        if down_stdev != 0:
+            sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
+        else:
+            # Define high (negative) sortino ratio to be clear that this is NOT optimal.
+            sortino_ratio = -20.
+
+        # print(expected_returns_mean, down_stdev, sortino_ratio)
+        return -sortino_ratio

freqtrade/optimize/hyperopt_loss_sortino_daily.py

@@ -0,0 +1,70 @@
+"""
+SortinoHyperOptLossDaily
+
+This module defines the alternative HyperOptLoss class which can be used for
+Hyperoptimization.
+"""
+import math
+from datetime import datetime
+
+from pandas import DataFrame, date_range
+
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+class SortinoHyperOptLossDaily(IHyperOptLoss):
+    """
+    Defines the loss function for hyperopt.
+
+    This implementation uses the Sortino Ratio calculation.
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(results: DataFrame, trade_count: int,
+                               min_date: datetime, max_date: datetime,
+                               *args, **kwargs) -> float:
+        """
+        Objective function, returns smaller number for more optimal results.
+
+        Uses Sortino Ratio calculation.
+
+        Sortino Ratio calculated as described in
+        http://www.redrockcapital.com/Sortino__A__Sharper__Ratio_Red_Rock_Capital.pdf
+        """
+        resample_freq = '1D'
+        slippage_per_trade_ratio = 0.0005
+        days_in_year = 365
+        minimum_acceptable_return = 0.0
+
+        # apply slippage per trade to profit_percent
+        results.loc[:, 'profit_percent_after_slippage'] = \
+            results['profit_percent'] - slippage_per_trade_ratio
+
+        # create the index within the min_date and end max_date
+        t_index = date_range(start=min_date, end=max_date, freq=resample_freq,
+                             normalize=True)
+
+        sum_daily = (
+            results.resample(resample_freq, on='close_time').agg(
+                {"profit_percent_after_slippage": sum}).reindex(t_index).fillna(0)
+        )
+
+        total_profit = sum_daily["profit_percent_after_slippage"] - minimum_acceptable_return
+        expected_returns_mean = total_profit.mean()
+
+        sum_daily['downside_returns'] = 0
+        sum_daily.loc[total_profit < 0, 'downside_returns'] = total_profit
+        total_downside = sum_daily['downside_returns']
+        # Here total_downside contains min(0, P - MAR) values,
+        # where P = sum_daily["profit_percent_after_slippage"]
+        down_stdev = math.sqrt((total_downside**2).sum() / len(total_downside))
+
+        if down_stdev != 0:
+            sortino_ratio = expected_returns_mean / down_stdev * math.sqrt(days_in_year)
+        else:
+            # Define high (negative) sortino ratio to be clear that this is NOT optimal.
+            sortino_ratio = -20.
+
+        # print(t_index, sum_daily, total_profit)
+        # print(minimum_acceptable_return, expected_returns_mean, down_stdev, sortino_ratio)
+        return -sortino_ratio

freqtrade/optimize/optimize_reports.py

@@ -1,9 +1,37 @@
+import logging
 from datetime import timedelta
+from pathlib import Path
 from typing import Dict
 
 from pandas import DataFrame
 from tabulate import tabulate
 
+from freqtrade.misc import file_dump_json
+
+logger = logging.getLogger(__name__)
+
+
+def store_backtest_result(recordfilename: Path, all_results: Dict[str, DataFrame]) -> None:
+    """
+    Stores backtest results to file (one file per strategy)
+    :param recordfilename: Destination filename
+    :param all_results: Dict of Dataframes, one results dataframe per strategy
+    """
+    for strategy, results in all_results.items():
+        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 len(all_results) > 1:
+                # Inject strategy to filename
+                recordfilename = Path.joinpath(
+                    recordfilename.parent,
+                    f'{recordfilename.stem}-{strategy}').with_suffix(recordfilename.suffix)
+            logger.info(f'Dumping backtest results to {recordfilename}')
+            file_dump_json(recordfilename, records)
+
 
 def generate_text_table(data: Dict[str, Dict], stake_currency: str, max_open_trades: int,
                         results: DataFrame, skip_nan: bool = False) -> str:
@@ -19,9 +47,18 @@ def generate_text_table(data: Dict[str, Dict], stake_currency: str, max_open_tra
 
     floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
     tabular_data = []
-    headers = ['pair', 'buy count', 'avg profit %', 'cum profit %',
-               f'tot profit {stake_currency}', 'tot profit %', 'avg duration',
-               'profit', 'loss']
+    headers = [
+        'Pair',
+        'Buys',
+        'Avg Profit %',
+        'Cum Profit %',
+        f'Tot Profit {stake_currency}',
+        'Tot Profit %',
+        'Avg Duration',
+        'Wins',
+        'Draws',
+        'Losses'
+    ]
     for pair in data:
         result = results[results.pair == pair]
         if skip_nan and result.profit_abs.isnull().all():
@@ -37,6 +74,7 @@ def generate_text_table(data: Dict[str, Dict], stake_currency: str, max_open_tra
             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]),
             len(result[result.profit_abs < 0])
         ])
 
@@ -51,29 +89,58 @@ def generate_text_table(data: Dict[str, Dict], stake_currency: str, max_open_tra
         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]),
         len(results[results.profit_abs < 0])
     ])
     # Ignore type as floatfmt does allow tuples but mypy does not know that
     return tabulate(tabular_data, headers=headers,
-                    floatfmt=floatfmt, tablefmt="pipe")  # type: ignore
+                    floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")  # type: ignore
 
 
-def generate_text_table_sell_reason(data: Dict[str, Dict], results: DataFrame) -> str:
+def generate_text_table_sell_reason(stake_currency: str, max_open_trades: int,
+                                    results: DataFrame) -> str:
     """
     Generate small table outlining Backtest results
-    :param data: Dict of <pair: dataframe> containing data that was used during backtesting.
+    :param stake_currency: Stakecurrency used
+    :param max_open_trades: Max_open_trades parameter
     :param results: Dataframe containing the backtest results
     :return: pretty printed table with tabulate as string
     """
     tabular_data = []
-    headers = ['Sell Reason', 'Count', 'Profit', 'Loss', 'Profit %']
+    headers = [
+        "Sell Reason",
+        "Sells",
+        "Wins",
+        "Draws",
+        "Losses",
+        "Avg Profit %",
+        "Cum Profit %",
+        f"Tot Profit {stake_currency}",
+        "Tot Profit %",
+    ]
     for reason, count in results['sell_reason'].value_counts().iteritems():
         result = results.loc[results['sell_reason'] == reason]
-        profit = len(result[result['profit_abs'] >= 0])
+        wins = len(result[result['profit_abs'] > 0])
+        draws = len(result[result['profit_abs'] == 0])
         loss = len(result[result['profit_abs'] < 0])
         profit_mean = round(result['profit_percent'].mean() * 100.0, 2)
-        tabular_data.append([reason.value, count, profit, loss, profit_mean])
-    return tabulate(tabular_data, headers=headers, tablefmt="pipe")
+        profit_sum = round(result["profit_percent"].sum() * 100.0, 2)
+        profit_tot = result['profit_abs'].sum()
+        profit_percent_tot = round(result['profit_percent'].sum() * 100.0 / max_open_trades, 2)
+        tabular_data.append(
+            [
+                reason.value,
+                count,
+                wins,
+                draws,
+                loss,
+                profit_mean,
+                profit_sum,
+                profit_tot,
+                profit_percent_tot,
+            ]
+        )
+    return tabulate(tabular_data, headers=headers, tablefmt="orgtbl", stralign="right")
 
 
 def generate_text_table_strategy(stake_currency: str, max_open_trades: str,
@@ -88,9 +155,9 @@ def generate_text_table_strategy(stake_currency: str, max_open_trades: str,
 
     floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
     tabular_data = []
-    headers = ['Strategy', 'buy count', 'avg profit %', 'cum profit %',
-               f'tot profit {stake_currency}', 'tot profit %', 'avg duration',
-               'profit', 'loss']
+    headers = ['Strategy', 'Buys', 'Avg Profit %', 'Cum Profit %',
+               f'Tot Profit {stake_currency}', 'Tot Profit %', 'Avg Duration',
+               'Wins', 'Draws', 'Losses']
     for strategy, results in all_results.items():
         tabular_data.append([
             strategy,
@@ -102,20 +169,21 @@ def generate_text_table_strategy(stake_currency: str, max_open_trades: str,
             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]),
             len(results[results.profit_abs < 0])
         ])
     # Ignore type as floatfmt does allow tuples but mypy does not know that
     return tabulate(tabular_data, headers=headers,
-                    floatfmt=floatfmt, tablefmt="pipe")  # type: ignore
+                    floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")  # type: ignore
 
 
 def generate_edge_table(results: dict) -> str:
 
     floatfmt = ('s', '.10g', '.2f', '.2f', '.2f', '.2f', 'd', '.d')
     tabular_data = []
-    headers = ['pair', 'stoploss', 'win rate', 'risk reward ratio',
-               'required risk reward', 'expectancy', 'total number of trades',
-               'average duration (min)']
+    headers = ['Pair', 'Stoploss', 'Win Rate', 'Risk Reward Ratio',
+               'Required Risk Reward', 'Expectancy', 'Total Number of Trades',
+               'Average Duration (min)']
 
     for result in results.items():
         if result[1].nb_trades > 0:
@@ -132,4 +200,44 @@ def generate_edge_table(results: dict) -> str:
 
     # Ignore type as floatfmt does allow tuples but mypy does not know that
     return tabulate(tabular_data, headers=headers,
-                    floatfmt=floatfmt, tablefmt="pipe")  # type: ignore
+                    floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")  # type: ignore
+
+
+def show_backtest_results(config: Dict, btdata: Dict[str, DataFrame],
+                          all_results: Dict[str, DataFrame]):
+    for strategy, results in all_results.items():
+
+        print(f"Result for strategy {strategy}")
+        table = generate_text_table(btdata, stake_currency=config['stake_currency'],
+                                    max_open_trades=config['max_open_trades'],
+                                    results=results)
+        if isinstance(table, str):
+            print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
+        print(table)
+
+        table = generate_text_table_sell_reason(stake_currency=config['stake_currency'],
+                                                max_open_trades=config['max_open_trades'],
+                                                results=results)
+        if isinstance(table, str):
+            print(' SELL REASON STATS '.center(len(table.splitlines()[0]), '='))
+        print(table)
+
+        table = generate_text_table(btdata,
+                                    stake_currency=config['stake_currency'],
+                                    max_open_trades=config['max_open_trades'],
+                                    results=results.loc[results.open_at_end], skip_nan=True)
+        if isinstance(table, str):
+            print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
+        print(table)
+        if isinstance(table, str):
+            print('=' * len(table.splitlines()[0]))
+        print()
+    if len(all_results) > 1:
+        # Print Strategy summary table
+        table = generate_text_table_strategy(config['stake_currency'],
+                                             config['max_open_trades'],
+                                             all_results=all_results)
+        print(' STRATEGY SUMMARY '.center(len(table.splitlines()[0]), '='))
+        print(table)
+        print('=' * len(table.splitlines()[0]))
+        print('\nFor more details, please look at the detail tables above')

freqtrade/pairlist/IPairList.py

@@ -7,7 +7,7 @@ Provides lists as configured in config.json
 import logging
 from abc import ABC, abstractmethod, abstractproperty
 from copy import deepcopy
-from typing import Dict, List
+from typing import Any, Dict, List
 
 from freqtrade.exchange import market_is_active
 
@@ -16,7 +16,8 @@ logger = logging.getLogger(__name__)
 
 class IPairList(ABC):
 
-    def __init__(self, exchange, pairlistmanager, config, pairlistconfig: dict,
+    def __init__(self, exchange, pairlistmanager,
+                 config: Dict[str, Any], pairlistconfig: Dict[str, Any],
                  pairlist_pos: int) -> None:
         """
         :param exchange: Exchange instance
@@ -66,21 +67,37 @@ class IPairList(ABC):
         """
 
     @staticmethod
-    def verify_blacklist(pairlist: List[str], blacklist: List[str]) -> List[str]:
+    def verify_blacklist(pairlist: List[str], blacklist: List[str],
+                         aswarning: bool) -> List[str]:
         """
         Verify and remove items from pairlist - returning a filtered pairlist.
+        Logs a warning or info depending on `aswarning`.
+        Pairlists explicitly using this method shall use `aswarning=False`!
+        :param pairlist: Pairlist to validate
+        :param blacklist: Blacklist to validate pairlist against
+        :param aswarning: Log message as Warning or info
+        :return: pairlist - blacklisted pairs
         """
         for pair in deepcopy(pairlist):
             if pair in blacklist:
-                logger.warning(f"Pair {pair} in your blacklist. Removing it from whitelist...")
+                if aswarning:
+                    logger.warning(f"Pair {pair} in your blacklist. Removing it from whitelist...")
+                else:
+                    logger.info(f"Pair {pair} in your blacklist. Removing it from whitelist...")
                 pairlist.remove(pair)
         return pairlist
 
-    def _verify_blacklist(self, pairlist: List[str]) -> List[str]:
+    def _verify_blacklist(self, pairlist: List[str], aswarning: bool = True) -> List[str]:
         """
         Proxy method to verify_blacklist for easy access for child classes.
+        Logs a warning or info depending on `aswarning`.
+        Pairlists explicitly using this method shall use aswarning=False!
+        :param pairlist: Pairlist to validate
+        :param aswarning: Log message as Warning or info.
+        :return: pairlist - blacklisted pairs
         """
-        return IPairList.verify_blacklist(pairlist, self._pairlistmanager.blacklist)
+        return IPairList.verify_blacklist(pairlist, self._pairlistmanager.blacklist,
+                                          aswarning=aswarning)
 
     def _whitelist_for_active_markets(self, pairlist: List[str]) -> List[str]:
         """
@@ -98,7 +115,8 @@ class IPairList(ABC):
                 logger.warning(f"Pair {pair} is not compatible with exchange "
                                f"{self._exchange.name}. Removing it from whitelist..")
                 continue
-            if not pair.endswith(self._config['stake_currency']):
+
+            if self._exchange.get_pair_quote_currency(pair) != self._config['stake_currency']:
                 logger.warning(f"Pair {pair} is not compatible with your stake currency "
                                f"{self._config['stake_currency']}. Removing it from whitelist..")
                 continue
@@ -111,6 +129,5 @@ class IPairList(ABC):
             if pair not in sanitized_whitelist:
                 sanitized_whitelist.append(pair)
 
-        sanitized_whitelist = self._verify_blacklist(sanitized_whitelist)
         # We need to remove pairs that are unknown
         return sanitized_whitelist

freqtrade/pairlist/PrecisionFilter.py

@@ -48,10 +48,10 @@ class PrecisionFilter(IPairList):
         """
         Filters and sorts pairlists and assigns and returns them again.
         """
-        stoploss = None
-        if self._config.get('stoploss') is not None:
+        stoploss = self._config.get('stoploss')
+        if stoploss is not None:
             # Precalculate sanitized stoploss value to avoid recalculation for every pair
-            stoploss = 1 - abs(self._config.get('stoploss'))
+            stoploss = 1 - abs(stoploss)
         # Copy list since we're modifying this list
         for p in deepcopy(pairlist):
             ticker = tickers.get(p)

freqtrade/pairlist/PriceFilter.py

@@ -1,6 +1,6 @@
 import logging
 from copy import deepcopy
-from typing import Dict, List
+from typing import Any, Dict, List
 
 from freqtrade.pairlist.IPairList import IPairList
 
@@ -9,7 +9,8 @@ logger = logging.getLogger(__name__)
 
 class PriceFilter(IPairList):
 
-    def __init__(self, exchange, pairlistmanager, config, pairlistconfig: dict,
+    def __init__(self, exchange, pairlistmanager,
+                 config: Dict[str, Any], pairlistconfig: Dict[str, Any],
                  pairlist_pos: int) -> None:
         super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
 

freqtrade/pairlist/SpreadFilter.py

@@ -0,0 +1,59 @@
+import logging
+from copy import deepcopy
+from typing import Dict, List
+
+from freqtrade.pairlist.IPairList import IPairList
+
+logger = logging.getLogger(__name__)
+
+
+class SpreadFilter(IPairList):
+
+    def __init__(self, exchange, pairlistmanager, config, pairlistconfig: dict,
+                 pairlist_pos: int) -> None:
+        super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
+
+        self._max_spread_ratio = pairlistconfig.get('max_spread_ratio', 0.005)
+
+    @property
+    def needstickers(self) -> bool:
+        """
+        Boolean property defining if tickers are necessary.
+        If no Pairlist requries tickers, an empty List is passed
+        as tickers argument to filter_pairlist
+        """
+        return True
+
+    def short_desc(self) -> str:
+        """
+        Short whitelist method description - used for startup-messages
+        """
+        return (f"{self.name} - Filtering pairs with ask/bid diff above "
+                f"{self._max_spread_ratio * 100}%.")
+
+    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
+
+        """
+        Filters and sorts pairlist and returns the whitelist again.
+        Called on each bot iteration - please use internal caching if necessary
+        :param pairlist: pairlist to filter or sort
+        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
+        :return: new whitelist
+        """
+        # Copy list since we're modifying this list
+
+        spread = None
+        for p in deepcopy(pairlist):
+            ticker = tickers.get(p)
+            assert ticker is not None
+            if 'bid' in ticker and 'ask' in ticker:
+                spread = 1 - ticker['bid'] / ticker['ask']
+                if not ticker or spread > self._max_spread_ratio:
+                    logger.info(f"Removed {ticker['symbol']} from whitelist, "
+                                f"because spread {spread * 100:.3f}% >"
+                                f"{self._max_spread_ratio * 100}%")
+                    pairlist.remove(p)
+            else:
+                pairlist.remove(p)
+
+        return pairlist

freqtrade/pairlist/VolumePairList.py

@@ -6,7 +6,7 @@ Provides lists as configured in config.json
  """
 import logging
 from datetime import datetime
-from typing import Dict, List
+from typing import Any, Dict, List
 
 from freqtrade.exceptions import OperationalException
 from freqtrade.pairlist.IPairList import IPairList
@@ -18,7 +18,7 @@ SORT_VALUES = ['askVolume', 'bidVolume', 'quoteVolume']
 
 class VolumePairList(IPairList):
 
-    def __init__(self, exchange, pairlistmanager, config, pairlistconfig: dict,
+    def __init__(self, exchange, pairlistmanager, config: Dict[str, Any], pairlistconfig: dict,
                  pairlist_pos: int) -> None:
         super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
 
@@ -28,6 +28,7 @@ class VolumePairList(IPairList):
                 'for "pairlist.config.number_assets"')
         self._number_pairs = self._pairlistconfig['number_assets']
         self._sort_key = self._pairlistconfig.get('sort_key', 'quoteVolume')
+        self._min_value = self._pairlistconfig.get('min_value', 0)
         self.refresh_period = self._pairlistconfig.get('refresh_period', 1800)
 
         if not self._exchange.exchange_has('fetchTickers'):
@@ -73,11 +74,13 @@ class VolumePairList(IPairList):
                                             tickers,
                                             self._config['stake_currency'],
                                             self._sort_key,
+                                            self._min_value
                                             )
         else:
             return pairlist
 
-    def _gen_pair_whitelist(self, pairlist, tickers, base_currency: str, key: str) -> List[str]:
+    def _gen_pair_whitelist(self, pairlist: List[str], tickers: Dict,
+                            base_currency: str, key: str, min_val: int) -> List[str]:
         """
         Updates the whitelist with with a dynamically generated list
         :param base_currency: base currency as str
@@ -88,19 +91,22 @@ class VolumePairList(IPairList):
 
         if self._pairlist_pos == 0:
             # If VolumePairList is the first in the list, use fresh pairlist
-            # check length so that we make sure that '/' is actually in the string
+            # Check if pair quote currency equals to the stake currency.
             filtered_tickers = [v for k, v in tickers.items()
-                                if (len(k.split('/')) == 2 and k.split('/')[1] == base_currency
+                                if (self._exchange.get_pair_quote_currency(k) == base_currency
                                     and v[key] is not None)]
         else:
             # If other pairlist is in front, use the incomming pairlist.
             filtered_tickers = [v for k, v in tickers.items() if k in pairlist]
 
+        if min_val > 0:
+            filtered_tickers = list(filter(lambda t: t[key] > min_val, filtered_tickers))
+
         sorted_tickers = sorted(filtered_tickers, reverse=True, key=lambda t: t[key])
 
         # Validate whitelist to only have active market pairs
         pairs = self._whitelist_for_active_markets([s['symbol'] for s in sorted_tickers])
-        pairs = self._verify_blacklist(pairs)
+        pairs = self._verify_blacklist(pairs, aswarning=False)
         # Limit to X number of pairs
         pairs = pairs[:self._number_pairs]
         logger.info(f"Searching {self._number_pairs} pairs: {pairs}")

freqtrade/pairlist/pairlistmanager.py

@@ -91,6 +91,6 @@ class PairListManager():
             pairlist = pl.filter_pairlist(pairlist, tickers)
 
         # Validation against blacklist happens after the pairlists to ensure blacklist is respected.
-        pairlist = IPairList.verify_blacklist(pairlist, self.blacklist)
+        pairlist = IPairList.verify_blacklist(pairlist, self.blacklist, True)
 
         self._whitelist = pairlist

freqtrade/persistence.py

@@ -64,11 +64,11 @@ def init(db_url: str, clean_open_orders: bool = False) -> None:
         clean_dry_run_db()
 
 
-def has_column(columns, searchname: str) -> bool:
+def has_column(columns: List, searchname: str) -> bool:
     return len(list(filter(lambda x: x["name"] == searchname, columns))) == 1
 
 
-def get_column_def(columns, column: str, default: str) -> str:
+def get_column_def(columns: List, column: str, default: str) -> str:
     return default if not has_column(columns, column) else column
 
 
@@ -86,7 +86,7 @@ def check_migrate(engine) -> None:
         logger.debug(f'trying {table_back_name}')
 
     # Check for latest column
-    if not has_column(cols, 'open_trade_price'):
+    if not has_column(cols, 'close_profit_abs'):
         logger.info(f'Running database migration - backup available as {table_back_name}')
 
         fee_open = get_column_def(cols, 'fee_open', 'fee')
@@ -106,6 +106,9 @@ def check_migrate(engine) -> None:
         ticker_interval = get_column_def(cols, 'ticker_interval', 'null')
         open_trade_price = get_column_def(cols, 'open_trade_price',
                                           f'amount * open_rate * (1 + {fee_open})')
+        close_profit_abs = get_column_def(
+            cols, 'close_profit_abs',
+            f"(amount * close_rate * (1 - {fee_close})) - {open_trade_price}")
 
         # Schema migration necessary
         engine.execute(f"alter table trades rename to {table_back_name}")
@@ -123,7 +126,7 @@ def check_migrate(engine) -> None:
                 stop_loss, stop_loss_pct, initial_stop_loss, initial_stop_loss_pct,
                 stoploss_order_id, stoploss_last_update,
                 max_rate, min_rate, sell_reason, strategy,
-                ticker_interval, open_trade_price
+                ticker_interval, open_trade_price, close_profit_abs
                 )
             select id, lower(exchange),
                 case
@@ -143,7 +146,7 @@ def check_migrate(engine) -> None:
                 {stoploss_order_id} stoploss_order_id, {stoploss_last_update} stoploss_last_update,
                 {max_rate} max_rate, {min_rate} min_rate, {sell_reason} sell_reason,
                 {strategy} strategy, {ticker_interval} ticker_interval,
-                {open_trade_price} open_trade_price
+                {open_trade_price} open_trade_price, {close_profit_abs} close_profit_abs
                 from {table_back_name}
              """)
 
@@ -190,6 +193,7 @@ class Trade(_DECL_BASE):
     close_rate = Column(Float)
     close_rate_requested = Column(Float)
     close_profit = Column(Float)
+    close_profit_abs = Column(Float)
     stake_amount = Column(Float, nullable=False)
     amount = Column(Float)
     open_date = Column(DateTime, nullable=False, default=datetime.utcnow)
@@ -246,14 +250,15 @@ class Trade(_DECL_BASE):
                                       if self.initial_stop_loss_pct else None),
         }
 
-    def adjust_min_max_rates(self, current_price: float):
+    def adjust_min_max_rates(self, current_price: float) -> None:
         """
         Adjust the max_rate and min_rate.
         """
         self.max_rate = max(current_price, self.max_rate or self.open_rate)
         self.min_rate = min(current_price, self.min_rate or self.open_rate)
 
-    def adjust_stop_loss(self, current_price: float, stoploss: float, initial: bool = False):
+    def adjust_stop_loss(self, current_price: float, stoploss: float,
+                         initial: bool = False) -> None:
         """
         This adjusts the stop loss to it's most recently observed setting
         :param current_price: Current rate the asset is traded
@@ -317,10 +322,10 @@ class Trade(_DECL_BASE):
         elif order_type in ('market', 'limit') and order['side'] == 'sell':
             self.close(order['price'])
             logger.info('%s_SELL has been fulfilled for %s.', order_type.upper(), self)
-        elif order_type == 'stop_loss_limit':
+        elif order_type in ('stop_loss_limit', 'stop-loss'):
             self.stoploss_order_id = None
             self.close_rate_requested = self.stop_loss
-            logger.info('STOP_LOSS_LIMIT is hit for %s.', self)
+            logger.info('%s is hit for %s.', order_type.upper(), self)
             self.close(order['average'])
         else:
             raise ValueError(f'Unknown order type: {order_type}')
@@ -333,6 +338,7 @@ class Trade(_DECL_BASE):
         """
         self.close_rate = Decimal(rate)
         self.close_profit = self.calc_profit_ratio()
+        self.close_profit_abs = self.calc_profit()
         self.close_date = datetime.utcnow()
         self.is_open = False
         self.open_order_id = None
@@ -404,8 +410,8 @@ class Trade(_DECL_BASE):
             rate=(rate or self.close_rate),
             fee=(fee or self.fee_close)
         )
-        profit_percent = (close_trade_price / self.open_trade_price) - 1
-        return float(f"{profit_percent:.8f}")
+        profit_ratio = (close_trade_price / self.open_trade_price) - 1
+        return float(f"{profit_ratio:.8f}")
 
     @staticmethod
     def get_trades(trade_filter=None) -> Query:

freqtrade/plot/plotting.py

@@ -3,11 +3,15 @@ from pathlib import Path
 from typing import Any, Dict, List
 
 import pandas as pd
+
 from freqtrade.configuration import TimeRange
-from freqtrade.data import history
-from freqtrade.data.btanalysis import (combine_tickers_with_mean,
+from freqtrade.data.btanalysis import (calculate_max_drawdown,
+                                       combine_dataframes_with_mean,
                                        create_cum_profit,
                                        extract_trades_of_period, load_trades)
+from freqtrade.data.converter import trim_dataframe
+from freqtrade.data.history import load_data
+from freqtrade.misc import pair_to_filename
 from freqtrade.resolvers import StrategyResolver
 
 logger = logging.getLogger(__name__)
@@ -25,7 +29,7 @@ except ImportError:
 def init_plotscript(config):
     """
     Initialize objects needed for plotting
-    :return: Dict with tickers, trades and pairs
+    :return: Dict with candle (OHLCV) data, trades and pairs
     """
 
     if "pairs" in config:
@@ -36,19 +40,30 @@ def init_plotscript(config):
     # Set timerange to use
     timerange = TimeRange.parse_timerange(config.get("timerange"))
 
-    tickers = history.load_data(
+    data = load_data(
         datadir=config.get("datadir"),
         pairs=pairs,
         timeframe=config.get('ticker_interval', '5m'),
         timerange=timerange,
+        data_format=config.get('dataformat_ohlcv', 'json'),
     )
 
-    trades = load_trades(config['trade_source'],
-                         db_url=config.get('db_url'),
-                         exportfilename=config.get('exportfilename'),
-                         )
-    trades = history.trim_dataframe(trades, timerange, 'open_time')
-    return {"tickers": tickers,
+    no_trades = False
+    if config.get('no_trades', False):
+        no_trades = True
+    elif not config['exportfilename'].is_file() and config['trade_source'] == 'file':
+        logger.warning("Backtest file is missing skipping trades.")
+        no_trades = True
+
+    trades = load_trades(
+        config['trade_source'],
+        db_url=config.get('db_url'),
+        exportfilename=config.get('exportfilename'),
+        no_trades=no_trades
+    )
+    trades = trim_dataframe(trades, timerange, 'open_time')
+
+    return {"ohlcv": data,
             "trades": trades,
             "pairs": pairs,
             }
@@ -107,6 +122,36 @@ def add_profit(fig, row, data: pd.DataFrame, column: str, name: str) -> make_sub
     return fig
 
 
+def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame) -> make_subplots:
+    """
+    Add scatter points indicating max drawdown
+    """
+    try:
+        max_drawdown, highdate, lowdate = calculate_max_drawdown(trades)
+
+        drawdown = go.Scatter(
+            x=[highdate, lowdate],
+            y=[
+                df_comb.loc[highdate, 'cum_profit'],
+                df_comb.loc[lowdate, 'cum_profit'],
+            ],
+            mode='markers',
+            name=f"Max drawdown {max_drawdown:.2f}%",
+            text=f"Max drawdown {max_drawdown:.2f}%",
+            marker=dict(
+                symbol='square-open',
+                size=9,
+                line=dict(width=2),
+                color='green'
+
+            )
+        )
+        fig.add_trace(drawdown, row, 1)
+    except ValueError:
+        logger.warning("No trades found - not plotting max drawdown.")
+    return fig
+
+
 def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
     """
     Add trades to "fig"
@@ -333,10 +378,10 @@ def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFra
     return fig
 
 
-def generate_profit_graph(pairs: str, tickers: Dict[str, pd.DataFrame],
+def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
                           trades: pd.DataFrame, timeframe: str) -> go.Figure:
     # Combine close-values for all pairs, rename columns to "pair"
-    df_comb = combine_tickers_with_mean(tickers, "close")
+    df_comb = combine_dataframes_with_mean(data, "close")
 
     # Add combined cumulative profit
     df_comb = create_cum_profit(df_comb, trades, 'cum_profit', timeframe)
@@ -360,6 +405,7 @@ def generate_profit_graph(pairs: str, tickers: Dict[str, pd.DataFrame],
 
     fig.add_trace(avgclose, 1, 1)
     fig = add_profit(fig, 2, df_comb, 'cum_profit', 'Profit')
+    fig = add_max_drawdown(fig, 2, trades, df_comb)
 
     for pair in pairs:
         profit_col = f'cum_profit_{pair}'
@@ -370,12 +416,12 @@ def generate_profit_graph(pairs: str, tickers: Dict[str, pd.DataFrame],
     return fig
 
 
-def generate_plot_filename(pair, timeframe) -> str:
+def generate_plot_filename(pair: str, timeframe: str) -> str:
     """
     Generate filenames per pair/timeframe to be used for storing plots
     """
-    pair_name = pair.replace("/", "_")
-    file_name = 'freqtrade-plot-' + pair_name + '-' + timeframe + '.html'
+    pair_s = pair_to_filename(pair)
+    file_name = 'freqtrade-plot-' + pair_s + '-' + timeframe + '.html'
 
     logger.info('Generate plot file for %s', pair)
 
@@ -403,7 +449,7 @@ def load_and_plot_trades(config: Dict[str, Any]):
     """
     From configuration provided
     - Initializes plot-script
-    - Get tickers data
+    - Get candle (OHLCV) data
     - Generate Dafaframes populated with indicators and signals based on configured strategy
     - Load trades excecuted during the selected period
     - Generate Plotly plot objects
@@ -415,19 +461,17 @@ def load_and_plot_trades(config: Dict[str, Any]):
     plot_elements = init_plotscript(config)
     trades = plot_elements['trades']
     pair_counter = 0
-    for pair, data in plot_elements["tickers"].items():
+    for pair, data in plot_elements["ohlcv"].items():
         pair_counter += 1
         logger.info("analyse pair %s", pair)
-        tickers = {}
-        tickers[pair] = data
 
-        dataframe = strategy.analyze_ticker(tickers[pair], {'pair': pair})
+        df_analyzed = strategy.analyze_ticker(data, {'pair': pair})
         trades_pair = trades.loc[trades['pair'] == pair]
-        trades_pair = extract_trades_of_period(dataframe, trades_pair)
+        trades_pair = extract_trades_of_period(df_analyzed, trades_pair)
 
         fig = generate_candlestick_graph(
             pair=pair,
-            data=dataframe,
+            data=df_analyzed,
             trades=trades_pair,
             indicators1=config.get("indicators1", []),
             indicators2=config.get("indicators2", []),
@@ -458,7 +502,7 @@ def plot_profit(config: Dict[str, Any]) -> None:
 
     # Create an average close price of all the pairs that were involved.
     # this could be useful to gauge the overall market trend
-    fig = generate_profit_graph(plot_elements["pairs"], plot_elements["tickers"],
+    fig = generate_profit_graph(plot_elements["pairs"], plot_elements["ohlcv"],
                                 trades, config.get('ticker_interval', '5m'))
     store_plot_file(fig, filename='freqtrade-profit-plot.html',
                     directory=config['user_data_dir'] / "plot", auto_open=True)

freqtrade/resolvers/iresolver.py

@@ -7,7 +7,7 @@ import importlib.util
 import inspect
 import logging
 from pathlib import Path
-from typing import Any, Dict, Generator, List, Optional, Tuple, Type, Union
+from typing import Any, Dict, Iterator, List, Optional, Tuple, Type, Union
 
 from freqtrade.exceptions import OperationalException
 
@@ -22,13 +22,15 @@ class IResolver:
     object_type: Type[Any]
     object_type_str: str
     user_subdir: Optional[str] = None
-    initial_search_path: Path
+    initial_search_path: Optional[Path]
 
     @classmethod
-    def build_search_paths(cls, config, user_subdir: Optional[str] = None,
+    def build_search_paths(cls, config: Dict[str, Any], user_subdir: Optional[str] = None,
                            extra_dir: Optional[str] = None) -> List[Path]:
 
-        abs_paths: List[Path] = [cls.initial_search_path]
+        abs_paths: List[Path] = []
+        if cls.initial_search_path:
+            abs_paths.append(cls.initial_search_path)
 
         if user_subdir:
             abs_paths.insert(0, config['user_data_dir'].joinpath(user_subdir))
@@ -40,12 +42,14 @@ class IResolver:
         return abs_paths
 
     @classmethod
-    def _get_valid_object(cls, module_path: Path,
-                          object_name: Optional[str]) -> Generator[Any, None, None]:
+    def _get_valid_object(cls, module_path: Path, object_name: Optional[str],
+                          enum_failed: bool = False) -> Iterator[Any]:
         """
         Generator returning objects with matching object_type and object_name in the path given.
         :param module_path: absolute path to the module
         :param object_name: Class name of the object
+        :param enum_failed: If True, will return None for modules which fail.
+            Otherwise, failing modules are skipped.
         :return: generator containing matching objects
         """
 
@@ -58,10 +62,13 @@ class IResolver:
         except (ModuleNotFoundError, SyntaxError) as err:
             # Catch errors in case a specific module is not installed
             logger.warning(f"Could not import {module_path} due to '{err}'")
+            if enum_failed:
+                return iter([None])
 
         valid_objects_gen = (
             obj for name, obj in inspect.getmembers(module, inspect.isclass)
-            if (object_name is None or object_name == name) and cls.object_type in obj.__bases__
+            if ((object_name is None or object_name == name) and
+                issubclass(obj, cls.object_type) and obj is not cls.object_type)
         )
         return valid_objects_gen
 
@@ -135,10 +142,13 @@ class IResolver:
         )
 
     @classmethod
-    def search_all_objects(cls, directory: Path) -> List[Dict[str, Any]]:
+    def search_all_objects(cls, directory: Path,
+                           enum_failed: bool) -> List[Dict[str, Any]]:
         """
         Searches a directory for valid objects
         :param directory: Path to search
+        :param enum_failed: If True, will return None for modules which fail.
+            Otherwise, failing modules are skipped.
         :return: List of dicts containing 'name', 'class' and 'location' entires
         """
         logger.debug(f"Searching for {cls.object_type.__name__} '{directory}'")
@@ -150,9 +160,10 @@ class IResolver:
                 continue
             module_path = entry.resolve()
             logger.debug(f"Path {module_path}")
-            for obj in cls._get_valid_object(module_path, object_name=None):
+            for obj in cls._get_valid_object(module_path, object_name=None,
+                                             enum_failed=enum_failed):
                 objects.append(
-                    {'name': obj.__name__,
+                    {'name': obj.__name__ if obj is not None else '',
                      'class': obj,
                      'location': entry,
                      })

freqtrade/resolvers/strategy_resolver.py

@@ -9,10 +9,10 @@ from base64 import urlsafe_b64decode
 from collections import OrderedDict
 from inspect import getfullargspec
 from pathlib import Path
-from typing import Dict, Optional
+from typing import Any, Dict, Optional
 
 from freqtrade.constants import (REQUIRED_ORDERTIF, REQUIRED_ORDERTYPES,
-                                 USERPATH_STRATEGY)
+                                 USERPATH_STRATEGIES)
 from freqtrade.exceptions import OperationalException
 from freqtrade.resolvers import IResolver
 from freqtrade.strategy.interface import IStrategy
@@ -26,11 +26,11 @@ class StrategyResolver(IResolver):
     """
     object_type = IStrategy
     object_type_str = "Strategy"
-    user_subdir = USERPATH_STRATEGY
-    initial_search_path = Path(__file__).parent.parent.joinpath('strategy').resolve()
+    user_subdir = USERPATH_STRATEGIES
+    initial_search_path = None
 
     @staticmethod
-    def load_strategy(config: Optional[Dict] = None) -> IStrategy:
+    def load_strategy(config: Dict[str, Any] = None) -> IStrategy:
         """
         Load the custom class from config parameter
         :param config: configuration dictionary or None
@@ -96,7 +96,8 @@ class StrategyResolver(IResolver):
         return strategy
 
     @staticmethod
-    def _override_attribute_helper(strategy, config, attribute: str, default):
+    def _override_attribute_helper(strategy, config: Dict[str, Any],
+                                   attribute: str, default: Any):
         """
         Override attributes in the strategy.
         Prevalence:
@@ -140,7 +141,7 @@ class StrategyResolver(IResolver):
         """
 
         abs_paths = StrategyResolver.build_search_paths(config,
-                                                        user_subdir=USERPATH_STRATEGY,
+                                                        user_subdir=USERPATH_STRATEGIES,
                                                         extra_dir=extra_dir)
 
         if ":" in strategy_name:

freqtrade/rpc/fiat_convert.py

@@ -7,7 +7,7 @@ import logging
 import time
 from typing import Dict, List
 
-from coinmarketcap import Market
+from pycoingecko import CoinGeckoAPI
 
 from freqtrade.constants import SUPPORTED_FIAT
 
@@ -38,8 +38,8 @@ class CryptoFiat:
         # Private attributes
         self._expiration = 0.0
 
-        self.crypto_symbol = crypto_symbol.upper()
-        self.fiat_symbol = fiat_symbol.upper()
+        self.crypto_symbol = crypto_symbol.lower()
+        self.fiat_symbol = fiat_symbol.lower()
         self.set_price(price=price)
 
     def set_price(self, price: float) -> None:
@@ -67,17 +67,20 @@ class CryptoToFiatConverter:
     This object is also a Singleton
     """
     __instance = None
-    _coinmarketcap: Market = None
+    _coingekko: CoinGeckoAPI = None
 
     _cryptomap: Dict = {}
 
     def __new__(cls):
+        """
+        This class is a singleton - cannot be instantiated twice.
+        """
         if CryptoToFiatConverter.__instance is None:
             CryptoToFiatConverter.__instance = object.__new__(cls)
             try:
-                CryptoToFiatConverter._coinmarketcap = Market()
+                CryptoToFiatConverter._coingekko = CoinGeckoAPI()
             except BaseException:
-                CryptoToFiatConverter._coinmarketcap = None
+                CryptoToFiatConverter._coingekko = None
         return CryptoToFiatConverter.__instance
 
     def __init__(self) -> None:
@@ -86,14 +89,12 @@ class CryptoToFiatConverter:
 
     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:
+            coinlistings = self._coingekko.get_coins_list()
+            # Create mapping table from synbol to coingekko_id
+            self._cryptomap = {x['symbol']: x['id'] for x in coinlistings}
+        except (Exception) as exception:
             logger.error(
-                "Could not load FIAT Cryptocurrency map for the following problem: %s",
-                type(exception).__name__
-            )
+                f"Could not load FIAT Cryptocurrency map for the following problem: {exception}")
 
     def convert_amount(self, crypto_amount: float, crypto_symbol: str, fiat_symbol: str) -> float:
         """
@@ -115,8 +116,8 @@ class CryptoToFiatConverter:
         :param fiat_symbol: FIAT currency you want to convert to (e.g USD)
         :return: Price in FIAT
         """
-        crypto_symbol = crypto_symbol.upper()
-        fiat_symbol = fiat_symbol.upper()
+        crypto_symbol = crypto_symbol.lower()
+        fiat_symbol = fiat_symbol.lower()
 
         # Check if the fiat convertion you want is supported
         if not self._is_supported_fiat(fiat=fiat_symbol):
@@ -170,15 +171,13 @@ class CryptoToFiatConverter:
         :return: bool, True supported, False not supported
         """
 
-        fiat = fiat.upper()
-
-        return fiat in SUPPORTED_FIAT
+        return fiat.upper() in SUPPORTED_FIAT
 
     def _find_price(self, crypto_symbol: str, fiat_symbol: str) -> float:
         """
-        Call CoinMarketCap API to retrieve the price in the FIAT
-        :param crypto_symbol: Crypto-currency you want to convert (e.g BTC)
-        :param fiat_symbol: FIAT currency you want to convert to (e.g USD)
+        Call CoinGekko API to retrieve the price in the FIAT
+        :param crypto_symbol: Crypto-currency you want to convert (e.g btc)
+        :param fiat_symbol: FIAT currency you want to convert to (e.g usd)
         :return: float, price of the crypto-currency in Fiat
         """
         # Check if the fiat convertion you want is supported
@@ -195,12 +194,13 @@ class CryptoToFiatConverter:
             return 0.0
 
         try:
+            _gekko_id = self._cryptomap[crypto_symbol]
             return float(
-                self._coinmarketcap.ticker(
-                    currency=self._cryptomap[crypto_symbol],
-                    convert=fiat_symbol
-                )['data']['quotes'][fiat_symbol.upper()]['price']
+                self._coingekko.get_price(
+                    ids=_gekko_id,
+                    vs_currencies=fiat_symbol
+                )[_gekko_id][fiat_symbol]
             )
-        except BaseException as exception:
+        except Exception as exception:
             logger.error("Error in _find_price: %s", exception)
             return 0.0

freqtrade/rpc/rpc.py

@@ -26,7 +26,9 @@ class RPCMessageType(Enum):
     WARNING_NOTIFICATION = 'warning'
     CUSTOM_NOTIFICATION = 'custom'
     BUY_NOTIFICATION = 'buy'
+    BUY_CANCEL_NOTIFICATION = 'buy_cancel'
     SELL_NOTIFICATION = 'sell'
+    SELL_CANCEL_NOTIFICATION = 'sell_cancel'
 
     def __repr__(self):
         return self.value
@@ -39,6 +41,7 @@ class RPCException(Exception):
 
     raise RPCException('*Status:* `no active trade`')
     """
+
     def __init__(self, message: str) -> None:
         super().__init__(self)
         self.message = message
@@ -139,7 +142,8 @@ class RPC:
                 results.append(trade_dict)
             return results
 
-    def _rpc_status_table(self, stake_currency, fiat_display_currency: str) -> Tuple[List, List]:
+    def _rpc_status_table(self, stake_currency: str,
+                          fiat_display_currency: str) -> Tuple[List, List]:
         trades = Trade.get_open_trades()
         if not trades:
             raise RPCException('no active trade')
@@ -151,20 +155,22 @@ class RPC:
                     current_rate = self._freqtrade.get_sell_rate(trade.pair, False)
                 except DependencyException:
                     current_rate = NAN
-                trade_perc = (100 * trade.calc_profit_ratio(current_rate))
+                trade_percent = (100 * trade.calc_profit_ratio(current_rate))
                 trade_profit = trade.calc_profit(current_rate)
-                profit_str = f'{trade_perc:.2f}%'
+                profit_str = f'{trade_percent:.2f}%'
                 if self._fiat_converter:
                     fiat_profit = self._fiat_converter.convert_amount(
-                            trade_profit,
-                            stake_currency,
-                            fiat_display_currency
-                        )
+                        trade_profit,
+                        stake_currency,
+                        fiat_display_currency
+                    )
                     if fiat_profit and not isnan(fiat_profit):
                         profit_str += f" ({fiat_profit:.2f})"
                 trades_list.append([
                     trade.id,
-                    trade.pair,
+                    trade.pair + ('*' if (trade.open_order_id is not None
+                                          and trade.close_rate_requested is None) else '')
+                               + ('**' if (trade.close_rate_requested is not None) else ''),
                     shorten_date(arrow.get(trade.open_date).humanize(only_distance=True)),
                     profit_str
                 ])
@@ -191,7 +197,7 @@ class RPC:
                 Trade.close_date >= profitday,
                 Trade.close_date < (profitday + timedelta(days=1))
             ]).order_by(Trade.close_date).all()
-            curdayprofit = sum(trade.calc_profit() for trade in trades)
+            curdayprofit = sum(trade.close_profit_abs for trade in trades)
             profit_days[profitday] = {
                 'amount': f'{curdayprofit:.8f}',
                 'trades': len(trades)
@@ -226,9 +232,9 @@ class RPC:
         trades = Trade.get_trades().order_by(Trade.id).all()
 
         profit_all_coin = []
-        profit_all_perc = []
+        profit_all_ratio = []
         profit_closed_coin = []
-        profit_closed_perc = []
+        profit_closed_ratio = []
         durations = []
 
         for trade in trades:
@@ -240,21 +246,21 @@ class RPC:
                 durations.append((trade.close_date - trade.open_date).total_seconds())
 
             if not trade.is_open:
-                profit_percent = trade.calc_profit_ratio()
-                profit_closed_coin.append(trade.calc_profit())
-                profit_closed_perc.append(profit_percent)
+                profit_ratio = trade.close_profit
+                profit_closed_coin.append(trade.close_profit_abs)
+                profit_closed_ratio.append(profit_ratio)
             else:
                 # Get current rate
                 try:
                     current_rate = self._freqtrade.get_sell_rate(trade.pair, False)
                 except DependencyException:
                     current_rate = NAN
-                profit_percent = trade.calc_profit_ratio(rate=current_rate)
+                profit_ratio = trade.calc_profit_ratio(rate=current_rate)
 
             profit_all_coin.append(
                 trade.calc_profit(rate=trade.close_rate or current_rate)
             )
-            profit_all_perc.append(profit_percent)
+            profit_all_ratio.append(profit_ratio)
 
         best_pair = Trade.get_best_pair()
 
@@ -265,7 +271,7 @@ class RPC:
 
         # Prepare data to display
         profit_closed_coin_sum = round(sum(profit_closed_coin), 8)
-        profit_closed_percent = (round(mean(profit_closed_perc) * 100, 2) if profit_closed_perc
+        profit_closed_percent = (round(mean(profit_closed_ratio) * 100, 2) if profit_closed_ratio
                                  else 0.0)
         profit_closed_fiat = self._fiat_converter.convert_amount(
             profit_closed_coin_sum,
@@ -274,7 +280,7 @@ class RPC:
         ) if self._fiat_converter else 0
 
         profit_all_coin_sum = round(sum(profit_all_coin), 8)
-        profit_all_percent = round(mean(profit_all_perc) * 100, 2) if profit_all_perc else 0.0
+        profit_all_percent = round(mean(profit_all_ratio) * 100, 2) if profit_all_ratio else 0.0
         profit_all_fiat = self._fiat_converter.convert_amount(
             profit_all_coin_sum,
             stake_currency,
@@ -385,7 +391,7 @@ class RPC:
 
         return {'status': 'No more buy will occur from now. Run /reload_conf to reset.'}
 
-    def _rpc_forcesell(self, trade_id) -> Dict[str, str]:
+    def _rpc_forcesell(self, trade_id: str) -> Dict[str, str]:
         """
         Handler for forcesell <id>.
         Sells the given trade at current price
@@ -454,9 +460,9 @@ class RPC:
         if self._freqtrade.state != State.RUNNING:
             raise RPCException('trader is not running')
 
-        # Check pair is in stake currency
+        # Check if pair quote currency equals to the stake currency.
         stake_currency = self._freqtrade.config.get('stake_currency')
-        if not pair.endswith(stake_currency):
+        if not self._freqtrade.exchange.get_pair_quote_currency(pair) == stake_currency:
             raise RPCException(
                 f'Wrong pair selected. Please pairs with stake {stake_currency} pairs only')
         # check if valid pair
@@ -511,7 +517,7 @@ class RPC:
         if add:
             stake_currency = self._freqtrade.config.get('stake_currency')
             for pair in add:
-                if (pair.endswith(stake_currency)
+                if (self._freqtrade.exchange.get_pair_quote_currency(pair) == stake_currency
                         and pair not in self._freqtrade.pairlists.blacklist):
                     self._freqtrade.pairlists.blacklist.append(pair)
 

freqtrade/rpc/rpc_manager.py

@@ -61,7 +61,7 @@ class RPCManager:
             except NotImplementedError:
                 logger.error(f"Message type {msg['type']} not implemented by handler {mod.name}.")
 
-    def startup_messages(self, config, pairlist) -> None:
+    def startup_messages(self, config: Dict[str, Any], pairlist) -> None:
         if config['dry_run']:
             self.send_msg({
                 'type': RPCMessageType.WARNING_NOTIFICATION,

freqtrade/rpc/telegram.py

@@ -134,25 +134,30 @@ class Telegram(RPC):
                 msg['stake_amount_fiat'] = 0
 
             message = ("*{exchange}:* Buying {pair}\n"
-                       "at rate `{limit:.8f}\n"
-                       "({stake_amount:.6f} {stake_currency}").format(**msg)
+                       "*Amount:* `{amount:.8f}`\n"
+                       "*Open Rate:* `{limit:.8f}`\n"
+                       "*Current Rate:* `{current_rate:.8f}`\n"
+                       "*Total:* `({stake_amount:.6f} {stake_currency}").format(**msg)
 
             if msg.get('fiat_currency', None):
-                message += ",{stake_amount_fiat:.3f} {fiat_currency}".format(**msg)
+                message += ", {stake_amount_fiat:.3f} {fiat_currency}".format(**msg)
             message += ")`"
 
+        elif msg['type'] == RPCMessageType.BUY_CANCEL_NOTIFICATION:
+            message = "*{exchange}:* Cancelling Open Buy Order for {pair}".format(**msg)
+
         elif msg['type'] == RPCMessageType.SELL_NOTIFICATION:
             msg['amount'] = round(msg['amount'], 8)
-            msg['profit_percent'] = round(msg['profit_percent'] * 100, 2)
+            msg['profit_percent'] = round(msg['profit_ratio'] * 100, 2)
             msg['duration'] = msg['close_date'].replace(
                 microsecond=0) - msg['open_date'].replace(microsecond=0)
             msg['duration_min'] = msg['duration'].total_seconds() / 60
 
             message = ("*{exchange}:* Selling {pair}\n"
-                       "*Rate:* `{limit:.8f}`\n"
                        "*Amount:* `{amount:.8f}`\n"
                        "*Open Rate:* `{open_rate:.8f}`\n"
                        "*Current Rate:* `{current_rate:.8f}`\n"
+                       "*Close Rate:* `{limit:.8f}`\n"
                        "*Sell Reason:* `{sell_reason}`\n"
                        "*Duration:* `{duration} ({duration_min:.1f} min)`\n"
                        "*Profit:* `{profit_percent:.2f}%`").format(**msg)
@@ -163,8 +168,11 @@ class Telegram(RPC):
                and self._fiat_converter):
                 msg['profit_fiat'] = self._fiat_converter.convert_amount(
                     msg['profit_amount'], msg['stake_currency'], msg['fiat_currency'])
-                message += ('` ({gain}: {profit_amount:.8f} {stake_currency}`'
-                            '` / {profit_fiat:.3f} {fiat_currency})`').format(**msg)
+                message += (' `({gain}: {profit_amount:.8f} {stake_currency}'
+                            ' / {profit_fiat:.3f} {fiat_currency})`').format(**msg)
+
+        elif msg['type'] == RPCMessageType.SELL_CANCEL_NOTIFICATION:
+            message = "*{exchange}:* Cancelling Open Sell Order for {pair}".format(**msg)
 
         elif msg['type'] == RPCMessageType.STATUS_NOTIFICATION:
             message = '*Status:* `{status}`'.format(**msg)
@@ -553,6 +561,8 @@ class Telegram(RPC):
                   "*/stop:* `Stops the trader`\n" \
                   "*/status [table]:* `Lists all open trades`\n" \
                   "         *table :* `will display trades in a table`\n" \
+                  "                `pending buy orders are marked with an asterisk (*)`\n" \
+                  "                `pending sell orders are marked with a double asterisk (**)`\n" \
                   "*/profit:* `Lists cumulative profit from all finished trades`\n" \
                   "*/forcesell <trade_id>|all:* `Instantly sells the given trade or all trades, " \
                   "regardless of profit`\n" \

freqtrade/rpc/webhook.py

@@ -41,8 +41,12 @@ class Webhook(RPC):
 
             if msg['type'] == RPCMessageType.BUY_NOTIFICATION:
                 valuedict = self._config['webhook'].get('webhookbuy', None)
+            elif msg['type'] == RPCMessageType.BUY_CANCEL_NOTIFICATION:
+                valuedict = self._config['webhook'].get('webhookbuycancel', None)
             elif msg['type'] == RPCMessageType.SELL_NOTIFICATION:
                 valuedict = self._config['webhook'].get('webhooksell', None)
+            elif msg['type'] == RPCMessageType.SELL_CANCEL_NOTIFICATION:
+                valuedict = self._config['webhook'].get('webhooksellcancel', None)
             elif msg['type'] in(RPCMessageType.STATUS_NOTIFICATION,
                                 RPCMessageType.CUSTOM_NOTIFICATION,
                                 RPCMessageType.WARNING_NOTIFICATION):

freqtrade/strategy/interface.py

@@ -59,7 +59,7 @@ class IStrategy(ABC):
     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
+        ticker_interval -> str: value of the timeframe (ticker interval) to use with the strategy
     """
     # Strategy interface version
     # Default to version 2
@@ -125,7 +125,7 @@ class IStrategy(ABC):
     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 dataframe: DataFrame with data from the exchange
         :param metadata: Additional information, like the currently traded pair
         :return: a Dataframe with all mandatory indicators for the strategies
         """
@@ -180,7 +180,7 @@ class IStrategy(ABC):
         if pair not in self._pair_locked_until or self._pair_locked_until[pair] < until:
             self._pair_locked_until[pair] = until
 
-    def unlock_pair(self, pair) -> None:
+    def unlock_pair(self, pair: str) -> None:
         """
         Unlocks a pair previously locked using lock_pair.
         Not used by freqtrade itself, but intended to be used if users lock pairs
@@ -200,11 +200,11 @@ class IStrategy(ABC):
 
     def analyze_ticker(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
-        Parses the given ticker history and returns a populated DataFrame
+        Parses the given candle (OHLCV) data and returns a populated DataFrame
         add several TA indicators and buy signal to it
-        :param dataframe: Dataframe containing ticker data
+        :param dataframe: Dataframe containing data from exchange
         :param metadata: Metadata dictionary with additional data (e.g. 'pair')
-        :return: DataFrame with ticker data and indicator data
+        :return: DataFrame of candle (OHLCV) data with indicator data and signals added
         """
         logger.debug("TA Analysis Launched")
         dataframe = self.advise_indicators(dataframe, metadata)
@@ -214,12 +214,12 @@ class IStrategy(ABC):
 
     def _analyze_ticker_internal(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
-        Parses the given ticker history and returns a populated DataFrame
+        Parses the given candle (OHLCV) data and returns a populated DataFrame
         add several TA indicators and buy signal to it
         WARNING: Used internally only, may skip analysis if `process_only_new_candles` is set.
-        :param dataframe: Dataframe containing ticker data
+        :param dataframe: Dataframe containing data from exchange
         :param metadata: Metadata dictionary with additional data (e.g. 'pair')
-        :return: DataFrame with ticker data and indicator data
+        :return: DataFrame of candle (OHLCV) data with indicator data and signals added
         """
         pair = str(metadata.get('pair'))
 
@@ -251,21 +251,21 @@ class IStrategy(ABC):
         :return: (Buy, Sell) A bool-tuple indicating buy/sell signal
         """
         if not isinstance(dataframe, DataFrame) or dataframe.empty:
-            logger.warning('Empty ticker history for pair %s', pair)
+            logger.warning('Empty candle (OHLCV) data for pair %s', pair)
             return False, False
 
         try:
             dataframe = self._analyze_ticker_internal(dataframe, {'pair': pair})
         except ValueError as error:
             logger.warning(
-                'Unable to analyze ticker for pair %s: %s',
+                'Unable to analyze candle (OHLCV) data 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',
+                'Unexpected error when analyzing candle (OHLCV) data for pair %s: %s',
                 pair,
                 str(error)
             )
@@ -364,7 +364,7 @@ class IStrategy(ABC):
         """
         Based on current profit of the trade and configured (trailing) stoploss,
         decides to sell or not
-        :param current_profit: current profit in percent
+        :param current_profit: current profit as ratio
         """
         stop_loss_value = force_stoploss if force_stoploss else self.stoploss
 
@@ -427,8 +427,9 @@ class IStrategy(ABC):
 
     def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool:
         """
-        Based on trade duration, current price and ROI configuration, decides whether bot should
-        sell. Requires current_profit to be in percent!!
+        Based on trade duration, current profit of the trade and ROI configuration,
+        decides whether bot should sell.
+        :param current_profit: current profit as ratio
         :return: True if bot should sell at current rate
         """
         # Check if time matches and current rate is above threshold
@@ -439,19 +440,19 @@ class IStrategy(ABC):
         else:
             return current_profit > roi
 
-    def tickerdata_to_dataframe(self, tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
+    def ohlcvdata_to_dataframe(self, data: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
         """
-        Creates a dataframe and populates indicators for given ticker data
+        Creates a dataframe and populates indicators for given candle (OHLCV) data
         Used by optimize operations only, not during dry / live runs.
         """
         return {pair: self.advise_indicators(pair_data, {'pair': pair})
-                for pair, pair_data in tickerdata.items()}
+                for pair, pair_data in data.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 dataframe: Dataframe with data from the exchange
         :param metadata: Additional information, like the currently traded pair
         :return: a Dataframe with all mandatory indicators for the strategies
         """

freqtrade/templates/base_config.json.j2

@@ -0,0 +1,60 @@
+{
+    "max_open_trades": {{ max_open_trades }},
+    "stake_currency": "{{ stake_currency }}",
+    "stake_amount": {{ stake_amount }},
+    "tradable_balance_ratio": 0.99,
+    "fiat_display_currency": "{{ fiat_display_currency }}",
+    "ticker_interval": "{{ ticker_interval }}",
+    "dry_run": {{ dry_run | lower }},
+    "unfilledtimeout": {
+        "buy": 10,
+        "sell": 30
+    },
+    "bid_strategy": {
+        "price_side": "bid",
+        "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": {
+        "price_side": "ask",
+        "use_order_book": false,
+        "order_book_min": 1,
+        "order_book_max": 1,
+        "use_sell_signal": true,
+        "sell_profit_only": false,
+        "ignore_roi_if_buy_signal": false
+    },
+    {{ exchange | indent(4) }},
+    "pairlists": [
+        {"method": "StaticPairList"}
+    ],
+    "edge": {
+        "enabled": false,
+        "process_throttle_secs": 3600,
+        "calculate_since_number_of_days": 7,
+        "allowed_risk": 0.01,
+        "stoploss_range_min": -0.01,
+        "stoploss_range_max": -0.1,
+        "stoploss_range_step": -0.01,
+        "minimum_winrate": 0.60,
+        "minimum_expectancy": 0.20,
+        "min_trade_number": 10,
+        "max_trade_duration_minute": 1440,
+        "remove_pumps": false
+    },
+    "telegram": {
+        "enabled": {{ telegram | lower }},
+        "token": "{{ telegram_token }}",
+        "chat_id": "{{ telegram_chat_id }}"
+    },
+    "initial_state": "running",
+    "forcebuy_enable": false,
+    "internals": {
+        "process_throttle_secs": 5
+    }
+}

freqtrade/templates/base_hyperopt.py.j2

@@ -21,7 +21,7 @@ class {{ hyperopt }}(IHyperOpt):
     """
     This is a Hyperopt template to get you started.
 
-    More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
+    More information in the documentation: https://www.freqtrade.io/en/latest/hyperopt/
 
     You should:
     - Add any lib you need to build your hyperopt.
@@ -29,11 +29,14 @@ class {{ hyperopt }}(IHyperOpt):
     You must keep:
     - The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
 
-    The roi_space, generate_roi_table, stoploss_space methods are no longer required to be
-    copied in every custom hyperopt. However, you may override them if you need the
-    'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade.
-    Sample implementation of these methods can be found in
-    https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py
+    The methods roi_space, generate_roi_table and stoploss_space are not required
+    and are provided by default.
+    However, you may override them if you need 'roi' and 'stoploss' spaces that
+    differ from the defaults offered by Freqtrade.
+    Sample implementation of these methods will be copied to `user_data/hyperopts` when
+    creating the user-data directory using `freqtrade create-userdir --userdir user_data`,
+    or is available online under the following URL:
+    https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py.
     """
 
     @staticmethod
@@ -63,6 +66,9 @@ class {{ hyperopt }}(IHyperOpt):
                         dataframe['close'], dataframe['sar']
                     ))
 
+            # Check that the candle had volume
+            conditions.append(dataframe['volume'] > 0)
+
             if conditions:
                 dataframe.loc[
                     reduce(lambda x, y: x & y, conditions),
@@ -108,6 +114,9 @@ class {{ hyperopt }}(IHyperOpt):
                         dataframe['sar'], dataframe['close']
                     ))
 
+            # Check that the candle had volume
+            conditions.append(dataframe['volume'] > 0)
+
             if conditions:
                 dataframe.loc[
                     reduce(lambda x, y: x & y, conditions),