Merge pull request #3228 from freqtrade/new_release2020.4

New release 2020.4

.github/workflows/ci.yml

@@ -23,7 +23,7 @@ jobs:
         python-version: [3.7, 3.8]
 
     steps:
-    - uses: actions/checkout@v1
+    - uses: actions/checkout@v2
 
     - name: Set up Python
       uses: actions/setup-python@v1
@@ -115,10 +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
@@ -130,8 +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: |
@@ -175,7 +174,7 @@ jobs:
   docs_check:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v1
+    - uses: actions/checkout@v2
 
     - name: Documentation syntax
       run: |
@@ -195,7 +194,7 @@ jobs:
     runs-on: ubuntu-18.04
     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

.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:

Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.8.1-slim-buster
+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

@@ -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

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 .

config.json.example

@@ -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_binance.json.example

@@ -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

@@ -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

config_kraken.json.example

@@ -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 +3,7 @@ version: '3'
 services:
   freqtrade:
     image: freqtradeorg/freqtrade:master
+    # image: freqtradeorg/freqtrade:develop
     # Build step - only needed when additional dependencies are needed
     # build:
     #   context: .
@@ -14,7 +15,7 @@ services:
     # Default command used when running `docker compose up`
     command: >
       trade
-      --logfile /freqtrade/user_data/freqtrade.log
+      --logfile /freqtrade/user_data/logs/freqtrade.log
       --db-url sqlite:////freqtrade/user_data/tradesv3.sqlite
       --config /freqtrade/user_data/config.json
       --strategy SampleStrategy

docs/advanced-setup.md

@@ -37,30 +37,30 @@ as the watchdog.
 
 ## Advanced Logging
 
-On many Linux systems the bot can be configured to send its log messages to `syslog` or `journald` system services. Logging to a remote `syslog` server is also available on Windows. The special values for the `--logfilename` command line option can be used for this.
+On many Linux systems the bot can be configured to send its log messages to `syslog` or `journald` system services. Logging to a remote `syslog` server is also available on Windows. The special values for the `--logfile` command line option can be used for this.
 
 ### Logging to syslog
 
-To send Freqtrade log messages to a local or remote `syslog` service use the `--logfilename` command line option with the value in the following format:
+To send Freqtrade log messages to a local or remote `syslog` service use the `--logfile` command line option with the value in the following format:
 
-* `--logfilename syslog:<syslog_address>` -- send log messages to `syslog` service using the `<syslog_address>` as the syslog address.
+* `--logfile syslog:<syslog_address>` -- send log messages to `syslog` service using the `<syslog_address>` as the syslog address.
 
 The syslog address can be either a Unix domain socket (socket filename) or a UDP socket specification, consisting of IP address and UDP port, separated by the `:` character.
 
 So, the following are the examples of possible usages:
 
-* `--logfilename syslog:/dev/log` -- log to syslog (rsyslog) using the `/dev/log` socket, suitable for most systems.
-* `--logfilename syslog` -- same as above, the shortcut for `/dev/log`.
-* `--logfilename syslog:/var/run/syslog` -- log to syslog (rsyslog) using the `/var/run/syslog` socket. Use this on MacOS.
-* `--logfilename syslog:localhost:514` -- log to local syslog using UDP socket, if it listens on port 514.
-* `--logfilename syslog:<ip>:514` -- log to remote syslog at IP address and port 514. This may be used on Windows for remote logging to an external syslog server.
+* `--logfile syslog:/dev/log` -- log to syslog (rsyslog) using the `/dev/log` socket, suitable for most systems.
+* `--logfile syslog` -- same as above, the shortcut for `/dev/log`.
+* `--logfile syslog:/var/run/syslog` -- log to syslog (rsyslog) using the `/var/run/syslog` socket. Use this on MacOS.
+* `--logfile syslog:localhost:514` -- log to local syslog using UDP socket, if it listens on port 514.
+* `--logfile syslog:<ip>:514` -- log to remote syslog at IP address and port 514. This may be used on Windows for remote logging to an external syslog server.
 
 Log messages are send to `syslog` with the `user` facility. So you can see them with the following commands:
 
 * `tail -f /var/log/user`, or 
 * install a comprehensive graphical viewer (for instance, 'Log File Viewer' for Ubuntu).
 
-On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfilename syslog` or `--logfilename journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.
+On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfile syslog` or `--logfile journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.
 
 For `rsyslog` the messages from the bot can be redirected into a separate dedicated log file. To achieve this, add
 ```
@@ -78,9 +78,9 @@ $RepeatedMsgReduction on
 
 This needs the `systemd` python package installed as the dependency, which is not available on Windows. Hence, the whole journald logging functionality is not available for a bot running on Windows.
 
-To send Freqtrade log messages to `journald` system service use the `--logfilename` command line option with the value in the following format:
+To send Freqtrade log messages to `journald` system service use the `--logfile` command line option with the value in the following format:
 
-* `--logfilename journald` -- send log messages to `journald`.
+* `--logfile journald` -- send log messages to `journald`.
 
 Log messages are send to `journald` with the `user` facility. So you can see them with the following commands:
 
@@ -89,4 +89,4 @@ Log messages are send to `journald` with the `user` facility. So you can see the
 
 There are many other options in the `journalctl` utility to filter the messages, see manual pages for this utility.
 
-On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfilename syslog` or `--logfilename journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.
+On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfile syslog` or `--logfile journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.

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:
@@ -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.

docs/bot-usage.md

@@ -144,10 +144,10 @@ It is recommended to use version control to keep track of changes to your strate
 ### How to use **--strategy**?
 
 This parameter will allow you to load your custom strategy class.
-Per default without `--strategy` or `-s` the bot will load the
-`DefaultStrategy` included with the bot (`freqtrade/strategy/default_strategy.py`).
+To test the bot installation, you can use the `SampleStrategy` installed by the `create-userdir` subcommand (usually `user_data/strategy/sample_strategy.py`).
 
-The bot will search your strategy file within `user_data/strategies` and `freqtrade/strategy`.
+The bot will search your strategy file within `user_data/strategies`.
+To use other directories, please read the next section about `--strategy-path`.
 
 To load a strategy, simply pass the class name (e.g.: `CustomStrategy`) in this parameter.
 
@@ -275,7 +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]
@@ -323,7 +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
@@ -341,10 +341,11 @@ 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,
-                        SharpeHyperOptLossDaily.(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).

docs/configuration.md

@@ -34,20 +34,20 @@ The prevelance for all Options is as follows:
 
 - CLI arguments override any other option
 - Configuration files are used in sequence (last file wins), and override Strategy configurations.
-- Strategy configurations are only used if they are not set via configuration or via command line arguments. These options are market with [Strategy Override](#parameters-in-the-strategy) in the below table.
+- Strategy configurations are only used if they are not set via configuration or via command line arguments. These options are marked with [Strategy Override](#parameters-in-the-strategy) in the below table.
 
 Mandatory parameters are marked as **Required**, which means that they are required to be set in one of the possible ways.
 
 |  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.
+| `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 ticker interval to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
+| `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
@@ -60,11 +60,13 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `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.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
@@ -111,8 +113,8 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `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 OHLCV historic data. <br> *Defaults to `json`*. <br> **Datatype:** String
-| `dataformat_trades` | Data format to use to store trades historic data. <br> *Defaults to `jsongz`*. <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
 
@@ -340,7 +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.
 
@@ -370,16 +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:
@@ -409,7 +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": {
@@ -461,34 +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
 
@@ -532,6 +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",

docs/data-download.md

@@ -33,7 +33,7 @@ optional arguments:
                         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 ohlcv data. (default: `json`).
+                        Storage format for downloaded candle (OHLCV) data. (default: `json`).
   --data-format-trades {json,jsongz}
                         Storage format for downloaded trades data. (default: `jsongz`).
 
@@ -105,7 +105,7 @@ Common arguments:
 
 ##### Example converting data
 
-The following command will convert all ohlcv (candle) data available in `~/.freqtrade/data/binance` from json to jsongz, saving diskspace in the process.
+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
@@ -192,15 +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

@@ -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/edge.md

@@ -79,7 +79,7 @@ So lets say your Win rate is 28% and your Risk Reward Ratio is 5:
 Expectancy = (5 X 0.28) – 0.72 = 0.68
 ```
 
-Superficially, this means that on average you expect this strategy’s trades to return .68 times the size of your loses. This is important for two reasons: First, it may seem obvious, but you know right away that you have a positive return. Second, you now have a number you can compare to other candidate systems to make decisions about which ones you employ.
+Superficially, this means that on average you expect this strategy’s trades to return 1.68 times the size of your loses. Said another way, you can expect to win $1.68 for every $1 you lose. This is important for two reasons: First, it may seem obvious, but you know right away that you have a positive return. Second, you now have a number you can compare to other candidate systems to make decisions about which ones you employ.
 
 It is important to remember that any system with an expectancy greater than 0 is profitable using past data. The key is finding one that will be profitable in the future.
 
@@ -156,7 +156,7 @@ Edge module has following configuration options:
 | `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
+| `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

@@ -62,6 +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:
@@ -69,23 +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

@@ -100,7 +100,7 @@ $ tail -f /path/to/mylogfile.log | grep 'something'
 ```
 from a separate terminal window.
 
-On Windows, the `--logfilename` option is also supported by Freqtrade and you can use the `findstr` command to search the log for the string of interest:
+On Windows, the `--logfile` option is also supported by Freqtrade and you can use the `findstr` command to search the log for the string of interest:
 ```
 > type \path\to\mylogfile.log | findstr "something"
 ```

docs/hyperopt.md

@@ -6,9 +6,7 @@ algorithms included in the `scikit-optimize` package to accomplish this. The
 search will burn all your CPU cores, make your laptop sound like a fighter jet
 and still take a long time.
 
-In general, the search for best parameters starts with a few random combinations and then uses Bayesian search with a
-ML regressor algorithm (currently ExtraTreesRegressor) to quickly find a combination of parameters in the search hyperspace
-that minimizes the value of the [loss function](#loss-functions).
+In general, the search for best parameters starts with a few random combinations (see [below](#reproducible-results) for more details) and then uses Bayesian search with a ML regressor algorithm (currently ExtraTreesRegressor) to quickly find a combination of parameters in the search hyperspace that minimizes the value of the [loss function](#loss-functions).
 
 Hyperopt requires historic data to be available, just as backtesting does.
 To learn how to get data for the pairs and exchange you're interested in, head over to the [Data Downloading](data-download.md) section of the documentation.
@@ -16,6 +14,24 @@ To learn how to get data for the pairs and exchange you're interested in, head o
 !!! Bug
     Hyperopt can crash when used with only 1 CPU Core as found out in [Issue #1133](https://github.com/freqtrade/freqtrade/issues/1133)
 
+## Install hyperopt dependencies
+
+Since Hyperopt dependencies are not needed to run the bot itself, are heavy, can not be easily built on some platforms (like Raspberry PI), they are not installed by default. Before you run Hyperopt, you need to install the corresponding dependencies, as described in this section below.
+
+!!! Note
+    Since Hyperopt is a resource intensive process, running it on a Raspberry Pi is not recommended nor supported.
+
+### Docker
+
+The docker-image includes hyperopt dependencies, no further action needed.
+
+### Easy installation script (setup.sh) / Manual installation
+
+```bash
+source .env/bin/activate
+pip install -r requirements-hyperopt.txt
+```
+
 ## Prepare Hyperopting
 
 Before we start digging into Hyperopt, we recommend you to take a look at
@@ -31,9 +47,9 @@ 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.
@@ -47,6 +63,9 @@ Optional - can also be loaded from a strategy:
 !!! Note
     Assuming the optional methods are not in your hyperopt file, please use `--strategy AweSomeStrategy` which contains these methods so hyperopt can use these methods instead.
 
+!!! Note
+    You always have to provide a strategy to Hyperopt, even if your custom Hyperopt class contains all methods.
+
 Rarely you may also need to override:
 
 * `roi_space` - for custom ROI optimization (if you need the ranges for the ROI parameters in the optimization hyperspace that differ from default)
@@ -81,11 +100,11 @@ There are two places you need to change in your hyperopt file to add a new buy h
 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 +122,10 @@ 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
@@ -159,6 +179,9 @@ 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 +195,7 @@ 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
@@ -191,8 +214,10 @@ 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)
-* `SharpeHyperOptLossDaily` (optimizes Sharpe Ratio calculated on daily 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.
 
@@ -220,11 +245,11 @@ 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
 
@@ -272,7 +297,7 @@ 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`
@@ -287,7 +312,7 @@ You can also enable position stacking in the configuration file by explicitly se
 
 ### Reproducible results
 
-The search for optimal parameters starts with a few (currently 30) random combinations in the hyperspace of parameters, random Hyperopt epochs. These random epochs are marked with a leading asterisk sign at the Hyperopt output.
+The search for optimal parameters starts with a few (currently 30) random combinations in the hyperspace of parameters, random Hyperopt epochs. These random epochs are marked with an asterisk character (`*`) in the first column in the Hyperopt output.
 
 The initial state for generation of these random values (random state) is controlled by the value of the `--random-state` command line option. You can set it to some arbitrary value of your choice to obtain reproducible results.
 
@@ -378,7 +403,7 @@ As stated in the comment, you can also use it as the value of the `minimal_roi`
 
 #### 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           |                   |
 | ------ | ------ | ----------------- | -------- | ----------- | ---------- | ----------------- | ------------ | ----------------- |
@@ -387,7 +412,7 @@ If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace f
 | 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.
 

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==4.6.3
+mkdocs-material==5.1.3
 mdx_truly_sane_lists==1.2

docs/sql_cheatsheet.md

@@ -67,22 +67,32 @@ SELECT * FROM trades;
 
 !!! Warning
   	Manually selling a pair on the exchange will not be detected by the bot and it will try to sell anyway. Whenever possible, forcesell <tradeid> should be used to accomplish the same thing.  
-		It is strongly advised to backup your database file before making any manual changes.
+	It is strongly advised to backup your database file before making any manual changes.
 
 !!! Note
   	This should not be necessary after /forcesell, as forcesell orders are closed automatically by the bot on the next iteration.
 
 ```sql
 UPDATE trades
-SET is_open=0, close_date=<close_date>, close_rate=<close_rate>, close_profit=close_rate/open_rate-1, sell_reason=<sell_reason>  
+SET is_open=0,
+  close_date=<close_date>,
+  close_rate=<close_rate>,
+  close_profit=close_rate/open_rate-1,
+  close_profit_abs = (amount * <close_rate> * (1 - fee_close) - (amount * open_rate * 1 - fee_open),
+  sell_reason=<sell_reason>
 WHERE id=<trade_ID_to_update>;
 ```
 
-##### Example
+### Example
 
 ```sql
 UPDATE trades
-SET is_open=0, close_date='2017-12-20 03:08:45.103418', close_rate=0.19638016, close_profit=0.0496, sell_reason='force_sell'  
+SET is_open=0,
+  close_date='2017-12-20 03:08:45.103418',
+  close_rate=0.19638016,
+  close_profit=0.0496,
+  close_profit_abs = (amount * 0.19638016 * (1 - fee_close) - (amount * open_rate * 1 - fee_open)
+  sell_reason='force_sell'  
 WHERE id=31;
 ```
 
@@ -99,10 +109,3 @@ VALUES ('bittrex', 'ETH/BTC', 1, 0.0025, 0.0025, <open_rate>, <stake_amount>, <a
 INSERT INTO trades (exchange, pair, is_open, fee_open, fee_close, open_rate, stake_amount, amount, open_date)
 VALUES ('bittrex', 'ETH/BTC', 1, 0.0025, 0.0025, 0.00258580, 0.002, 0.7715262081, '2017-11-28 12:44:24.000000')
 ```
-
-## Fix wrong fees in the table
-If your DB was created before [PR#200](https://github.com/freqtrade/freqtrade/pull/200) was merged (before 12/23/17).
-
-```sql
-UPDATE trades SET fee=0.0025 WHERE fee=0.005;
-```

docs/strategy-advanced.md

@@ -0,0 +1,91 @@
+# Advanced Strategies
+
+This page explains some advanced concepts available for strategies.
+If you're just getting started, please be familiar with the methods described in the [Strategy Customization](strategy-customization.md) documentation first.
+
+## Custom order timeout rules
+
+Simple, timebased order-timeouts can be configured either via strategy or in the configuration in the `unfilledtimeout` section.
+
+However, freqtrade also offers a custom callback for both ordertypes, which allows you to decide based on custom criteria if a order did time out or not.
+
+!!! Note
+    Unfilled order timeouts are not relevant during backtesting or hyperopt, and are only relevant during real (live) trading. Therefore these methods are only called in these circumstances.
+
+### Custom order timeout example
+
+A simple example, which applies different unfilled-timeouts depending on the price of the asset can be seen below.
+It applies a tight timeout for higher priced assets, while allowing more time to fill on cheap coins.
+
+The function must return either `True` (cancel order) or `False` (keep order alive).
+
+``` python
+from datetime import datetime, timestamp
+from freqtrade.persistence import Trade
+
+class Awesomestrategy(IStrategy):
+
+    # ... populate_* methods
+
+    # Set unfilledtimeout to 25 hours, since our maximum timeout from below is 24 hours.
+    unfilledtimeout = {
+        'buy': 60 * 25,
+        'sell': 60 * 25
+    }
+
+    def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
+        if trade.open_rate > 100 and trade.open_date < datetime.utcnow() - timedelta(minutes=5):
+            return True
+        elif trade.open_rate > 10 and trade.open_date < datetime.utcnow() - timedelta(minutes=3):
+            return True
+        elif trade.open_rate < 1 and trade.open_date < datetime.utcnow() - timedelta(hours=24):
+           return True
+        return False
+
+
+    def check_sell_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
+        if trade.open_rate > 100 and trade.open_date < datetime.utcnow() - timedelta(minutes=5):
+            return True
+        elif trade.open_rate > 10 and trade.open_date < datetime.utcnow() - timedelta(minutes=3):
+            return True
+        elif trade.open_rate < 1 and trade.open_date < datetime.utcnow() - timedelta(hours=24):
+           return True
+        return False
+```
+
+!!! Note
+    For the above example, `unfilledtimeout` must be set to something bigger than 24h, otherwise that type of timeout will apply first.
+
+### Custom order timeout example (using additional data)
+
+``` python
+from datetime import datetime, timestamp
+from freqtrade.persistence import Trade
+
+class Awesomestrategy(IStrategy):
+
+    # ... populate_* methods
+
+    # Set unfilledtimeout to 25 hours, since our maximum timeout from below is 24 hours.
+    unfilledtimeout = {
+        'buy': 60 * 25,
+        'sell': 60 * 25
+    }
+
+    def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
+        ob = self.dp.orderbook(pair, 1)
+        current_price = ob['bids'][0][0]
+        # Cancel buy order if price is more than 2% above the order.
+        if current_price > order['price'] * 1.02:
+            return True
+        return False
+
+
+    def check_sell_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
+        ob = self.dp.orderbook(pair, 1)
+        current_price = ob['asks'][0][0]
+        # Cancel sell order if price is more than 2% below the order.
+        if current_price < order['price'] * 0.98:
+            return True
+        return False
+```

docs/strategy-customization.md

@@ -1,7 +1,6 @@
 # Strategy Customization
 
-This page explains where to customize your strategies, and add new
-indicators.
+This page explains where to customize your strategies, and add new indicators.
 
 ## Install a custom strategy file
 
@@ -84,7 +83,7 @@ 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 +248,23 @@ 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 +283,14 @@ 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 +335,14 @@ 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:
@@ -360,8 +377,8 @@ 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

docs/strategy_analysis_example.md

@@ -121,7 +121,6 @@ from freqtrade.data.btanalysis import analyze_trade_parallelism
 # Analyze the above
 parallel_trades = analyze_trade_parallelism(trades, '5m')
 
-
 parallel_trades.plot()
 ```
 
@@ -134,11 +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/utils.md

@@ -61,8 +61,8 @@ $ 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'): 5
-? Please insert your ticker interval: 15m
+? 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
@@ -77,7 +77,7 @@ Results will be located in `user_data/strategies/<strategyclassname>.py`.
 
 ``` output
 usage: freqtrade new-strategy [-h] [--userdir PATH] [-s NAME]
-                              [--template {full,minimal}]
+                              [--template {full,minimal,advanced}]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -86,10 +86,10 @@ optional arguments:
   -s NAME, --strategy NAME
                         Specify strategy class name which will be used by the
                         bot.
-  --template {full,minimal}
-                        Use a template which is either `minimal` or `full`
-                        (containing multiple sample indicators). Default:
-                        `full`.
+  --template {full,minimal,advanced}
+                        Use a template which is either `minimal`, `full`
+                        (containing multiple sample indicators) or `advanced`.
+                        Default: `full`.
 
 ```
 
@@ -105,6 +105,12 @@ With custom user directory
 freqtrade new-strategy --userdir ~/.freqtrade/ --strategy AwesomeStrategy
 ```
 
+Using the advanced template (populates all optional functions and methods)
+
+```bash
+freqtrade new-strategy --strategy AwesomeStrategy --template advanced
+```
+
 ## Create new hyperopt
 
 Creates a new hyperopt from a template similar to SampleHyperopt.
@@ -114,7 +120,7 @@ Results will be located in `user_data/hyperopts/<classname>.py`.
 
 ``` output
 usage: freqtrade new-hyperopt [-h] [--userdir PATH] [--hyperopt NAME]
-                              [--template {full,minimal}]
+                              [--template {full,minimal,advanced}]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -122,10 +128,10 @@ optional arguments:
                         Path to userdata directory.
   --hyperopt NAME       Specify hyperopt class name which will be used by the
                         bot.
-  --template {full,minimal}
-                        Use a template which is either `minimal` or `full`
-                        (containing multiple sample indicators). Default:
-                        `full`.
+  --template {full,minimal,advanced}
+                        Use a template which is either `minimal`, `full`
+                        (containing multiple sample indicators) or `advanced`.
+                        Default: `full`.
 ```
 
 ### Sample usage of new-hyperopt
@@ -258,7 +264,7 @@ 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] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [--exchange EXCHANGE] [-1]
@@ -429,6 +435,7 @@ usage: freqtrade hyperopt-list [-h] [-v] [--logfile FILE] [-V] [-c PATH]
                                [--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
@@ -450,6 +457,8 @@ optional arguments:
                         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).
@@ -458,9 +467,10 @@ 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

docs/webhook-config.md

@@ -23,12 +23,12 @@ Sample configuration (tested using IFTTT).
         "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}"
+            "value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
         },
         "webhookstatus": {
             "value1": "Status: {status}",
@@ -87,7 +87,7 @@ Possible parameters are:
 * `open_rate`
 * `current_rate`
 * `profit_amount`
-* `profit_percent`
+* `profit_ratio`
 * `stake_currency`
 * `fiat_currency`
 * `sell_reason`
@@ -108,7 +108,7 @@ Possible parameters are:
 * `open_rate`
 * `current_rate`
 * `profit_amount`
-* `profit_percent`
+* `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

@@ -1,5 +1,5 @@
 """ Freqtrade bot """
-__version__ = '2020.02'
+__version__ = '2020.4'
 
 if __version__ == 'develop':
 
@@ -24,4 +24,11 @@ if __version__ == 'develop':
         #     stderr=subprocess.DEVNULL).decode("utf-8").rstrip().strip('"')
     except Exception:
         # git not available, ignore
-        pass
+        try:
+            # Try Fallback to freqtrade_commit file (created by CI while building docker image)
+            from pathlib import Path
+            versionfile = Path('./freqtrade_commit')
+            if versionfile.is_file():
+                __version__ = f"docker-{versionfile.read_text()[:8]}"
+        except Exception:
+            pass

freqtrade/commands/arguments.py

@@ -59,7 +59,7 @@ ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "download_trades", "exchang
 
 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"]
@@ -69,7 +69,8 @@ ARGS_HYPEROPT_LIST = ["hyperopt_list_best", "hyperopt_list_profitable",
                       "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"]
+                      "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"]
@@ -296,7 +297,7 @@ class Arguments:
         # Add convert-data subcommand
         convert_data_cmd = subparsers.add_parser(
             'convert-data',
-            help='Convert OHLCV data from one format to another.',
+            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))
@@ -305,7 +306,7 @@ class Arguments:
         # Add convert-trade-data subcommand
         convert_trade_data_cmd = subparsers.add_parser(
             'convert-trade-data',
-            help='Convert trade-data from one format to another.',
+            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))

freqtrade/commands/build_config_commands.py

@@ -76,7 +76,7 @@ def ask_user_config() -> Dict[str, Any]:
         {
             "type": "text",
             "name": "ticker_interval",
-            "message": "Please insert your ticker interval:",
+            "message": "Please insert your timeframe (ticker interval):",
             "default": "5m",
         },
         {

freqtrade/commands/cli_options.py

@@ -221,6 +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 '
@@ -257,7 +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, SharpeHyperOptLossDaily.'
+        'DefaultHyperOptLoss, OnlyProfitHyperOptLoss, SharpeHyperOptLoss, SharpeHyperOptLossDaily, '
+        'SortinoHyperOptLoss, SortinoHyperOptLossDaily.'
         '(default: `%(default)s`).',
         metavar='NAME',
         default=constants.DEFAULT_HYPEROPT_LOSS,
@@ -347,7 +355,7 @@ AVAILABLE_CLI_OPTIONS = {
     ),
     "dataformat_ohlcv": Arg(
         '--data-format-ohlcv',
-        help='Storage format for downloaded ohlcv data. (default: `%(default)s`).',
+        help='Storage format for downloaded candle (OHLCV) data. (default: `%(default)s`).',
         choices=constants.AVAILABLE_DATAHANDLERS,
         default='json'
     ),
@@ -379,9 +387,9 @@ AVAILABLE_CLI_OPTIONS = {
     # Templating options
     "template": Arg(
         '--template',
-        help='Use a template which is either `minimal` or '
-        '`full` (containing multiple sample indicators). Default: `%(default)s`.',
-        choices=['full', 'minimal'],
+        help='Use a template which is either `minimal`, '
+        '`full` (containing multiple sample indicators) or `advanced`. Default: `%(default)s`.',
+        choices=['full', 'minimal', 'advanced'],
         default='full',
     ),
     # Plot dataframe
@@ -405,6 +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)) '

freqtrade/commands/deploy_commands.py

@@ -8,7 +8,7 @@ from freqtrade.configuration.directory_operations import (copy_sample_files,
                                                           create_userdata_dir)
 from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGIES
 from freqtrade.exceptions import OperationalException
-from freqtrade.misc import render_template
+from freqtrade.misc import render_template, render_template_with_fallback
 from freqtrade.state import RunMode
 
 logger = logging.getLogger(__name__)
@@ -32,10 +32,27 @@ def deploy_new_strategy(strategy_name: str, strategy_path: Path, subtemplate: st
     """
     Deploy new strategy from template to strategy_path
     """
-    indicators = render_template(templatefile=f"subtemplates/indicators_{subtemplate}.j2",)
-    buy_trend = render_template(templatefile=f"subtemplates/buy_trend_{subtemplate}.j2",)
-    sell_trend = render_template(templatefile=f"subtemplates/sell_trend_{subtemplate}.j2",)
-    plot_config = render_template(templatefile=f"subtemplates/plot_config_{subtemplate}.j2",)
+    fallback = 'full'
+    indicators = render_template_with_fallback(
+        templatefile=f"subtemplates/indicators_{subtemplate}.j2",
+        templatefallbackfile=f"subtemplates/indicators_{fallback}.j2",
+        )
+    buy_trend = render_template_with_fallback(
+        templatefile=f"subtemplates/buy_trend_{subtemplate}.j2",
+        templatefallbackfile=f"subtemplates/buy_trend_{fallback}.j2",
+        )
+    sell_trend = render_template_with_fallback(
+        templatefile=f"subtemplates/sell_trend_{subtemplate}.j2",
+        templatefallbackfile=f"subtemplates/sell_trend_{fallback}.j2",
+        )
+    plot_config = render_template_with_fallback(
+        templatefile=f"subtemplates/plot_config_{subtemplate}.j2",
+        templatefallbackfile=f"subtemplates/plot_config_{fallback}.j2",
+    )
+    additional_methods = render_template_with_fallback(
+        templatefile=f"subtemplates/strategy_methods_{subtemplate}.j2",
+        templatefallbackfile=f"subtemplates/strategy_methods_empty.j2",
+    )
 
     strategy_text = render_template(templatefile='base_strategy.py.j2',
                                     arguments={"strategy": strategy_name,
@@ -43,6 +60,7 @@ def deploy_new_strategy(strategy_name: str, strategy_path: Path, subtemplate: st
                                                "buy_trend": buy_trend,
                                                "sell_trend": sell_trend,
                                                "plot_config": plot_config,
+                                               "additional_methods": additional_methods,
                                                })
 
     logger.info(f"Writing strategy to `{strategy_path}`.")
@@ -73,14 +91,23 @@ def deploy_new_hyperopt(hyperopt_name: str, hyperopt_path: Path, subtemplate: st
     """
     Deploys a new hyperopt template to hyperopt_path
     """
-    buy_guards = render_template(
-        templatefile=f"subtemplates/hyperopt_buy_guards_{subtemplate}.j2",)
-    sell_guards = render_template(
-        templatefile=f"subtemplates/hyperopt_sell_guards_{subtemplate}.j2",)
-    buy_space = render_template(
-        templatefile=f"subtemplates/hyperopt_buy_space_{subtemplate}.j2",)
-    sell_space = render_template(
-        templatefile=f"subtemplates/hyperopt_sell_space_{subtemplate}.j2",)
+    fallback = 'full'
+    buy_guards = render_template_with_fallback(
+        templatefile=f"subtemplates/hyperopt_buy_guards_{subtemplate}.j2",
+        templatefallbackfile=f"subtemplates/hyperopt_buy_guards_{fallback}.j2",
+        )
+    sell_guards = render_template_with_fallback(
+        templatefile=f"subtemplates/hyperopt_sell_guards_{subtemplate}.j2",
+        templatefallbackfile=f"subtemplates/hyperopt_sell_guards_{fallback}.j2",
+        )
+    buy_space = render_template_with_fallback(
+        templatefile=f"subtemplates/hyperopt_buy_space_{subtemplate}.j2",
+        templatefallbackfile=f"subtemplates/hyperopt_buy_space_{fallback}.j2",
+        )
+    sell_space = render_template_with_fallback(
+        templatefile=f"subtemplates/hyperopt_sell_space_{subtemplate}.j2",
+        templatefallbackfile=f"subtemplates/hyperopt_sell_space_{fallback}.j2",
+        )
 
     strategy_text = render_template(templatefile='base_hyperopt.py.j2',
                                     arguments={"hyperopt": hyperopt_name,

freqtrade/commands/hyperopt_commands.py

@@ -21,6 +21,7 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
 
     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
 
@@ -46,26 +47,26 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
 
     trials = _hyperopt_filter_trials(trials, filteroptions)
 
-    # TODO: fetch the interval for epochs to print from the cli option
-    epoch_start, epoch_stop = 0, None
-
     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 filteroptions['only_best'], print_colorized)
-
-    except KeyboardInterrupt:
-        print('User interrupted..')
+    if not export_csv:
+        try:
+            print(Hyperopt.get_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:
     """
@@ -75,6 +76,12 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
 
     config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
 
+    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),
@@ -87,10 +94,6 @@ def start_hyperopt_show(args: Dict[str, Any]) -> 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)
     }
-    no_header = config.get('hyperopt_show_no_header', False)
-
-    trials_file = (config['user_data_dir'] /
-                   'hyperopt_results' / 'hyperopt_results.pickle')
 
     # Previous evaluations
     trials = Hyperopt.load_previous_results(trials_file)
@@ -99,20 +102,17 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
     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,
@@ -129,52 +129,52 @@ def _hyperopt_filter_trials(trials: List, filteroptions: dict) -> List:
         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']
-                 ]
+            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']
-                 ]
+            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']
-                 ]
+            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']
-                 ]
+            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']
-                 ]
+            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']
-                 ]
+            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']
-                 ]
+            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']
-                 ]
+            x for x in trials
+            if x['results_metrics']['profit'] < filteroptions['filter_max_total_profit']
+        ]
 
     logger.info(f"{len(trials)} " +
                 ("best " if filteroptions['only_best'] else "") +

freqtrade/commands/list_commands.py

@@ -58,7 +58,7 @@ def _print_objs_tabular(objs: List, print_colorized: bool) -> None:
                    else yellow + "DUPLICATE NAME" + reset)
     } for s in objs]
 
-    print(tabulate(objss_to_print, headers='keys', tablefmt='pipe'))
+    print(tabulate(objss_to_print, headers='keys', tablefmt='psql', stralign='right'))
 
 
 def start_list_strategies(args: Dict[str, Any]) -> None:
@@ -192,7 +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/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: {}')
 
@@ -359,6 +359,9 @@ 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: {}')
 
@@ -376,10 +379,14 @@ class Configuration:
 
     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/directory_operations.py

@@ -33,8 +33,8 @@ def create_userdata_dir(directory: str, create_dir: bool = False) -> Path:
     :param create_dir: Create directory if it does not exist.
     :return: Path object containing the directory
     """
-    sub_dirs = ["backtest_results", "data", "hyperopts", "hyperopt_results", "notebooks",
-                "plot", "strategies", ]
+    sub_dirs = ["backtest_results", "data", "hyperopts", "hyperopt_results", "logs",
+                "notebooks", "plot", "strategies", ]
     folder = Path(directory)
     if not folder.is_dir():
         if create_dir:

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

@@ -45,7 +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

freqtrade/constants.py

@@ -15,6 +15,7 @@ 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',
@@ -42,7 +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 = {
@@ -113,15 +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']
@@ -129,6 +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},
@@ -251,7 +254,6 @@ CONF_SCHEMA = {
                     'type': 'array',
                     'items': {
                         'type': 'string',
-                        'pattern': '^[0-9A-Z]+/[0-9A-Z]+$'
                     },
                     'uniqueItems': True
                 },
@@ -259,7 +261,6 @@ CONF_SCHEMA = {
                     'type': 'array',
                     'items': {
                         'type': 'string',
-                        'pattern': '^[0-9A-Z]+/[0-9A-Z]+$'
                     },
                     'uniqueItems': True
                 },
@@ -301,6 +302,7 @@ SCHEMA_TRADE_REQUIRED = [
     'last_stake_amount_min_ratio',
     'dry_run',
     'dry_run_wallet',
+    'ask_strategy',
     'bid_strategy',
     'unfilledtimeout',
     'stoploss',

freqtrade/data/btanalysis.py

@@ -3,7 +3,7 @@ Helpers when analyzing backtest data
 """
 import logging
 from pathlib import Path
-from typing import Dict, Union
+from typing import Dict, Union, Tuple
 
 import numpy as np
 import pandas as pd
@@ -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,
@@ -129,39 +129,56 @@ 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:
+def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame,
+                             date_index=False) -> pd.DataFrame:
     """
     Compare trades and backtested pair DataFrames to get trades performed on backtested period
     :return: the DataFrame of a trades of period
     """
-    trades = trades.loc[(trades['open_time'] >= dataframe.iloc[0]['date']) &
-                        (trades['close_time'] <= dataframe.iloc[-1]['date'])]
+    if date_index:
+        trades_start = dataframe.index[0]
+        trades_stop = dataframe.index[-1]
+    else:
+        trades_start = dataframe.iloc[0]['date']
+        trades_stop = dataframe.iloc[-1]['date']
+    trades = trades.loc[(trades['open_time'] >= trades_start) &
+                        (trades['close_time'] <= trades_stop)]
     return trades
 
 
-def combine_tickers_with_mean(tickers: Dict[str, pd.DataFrame],
-                              column: str = "close") -> pd.DataFrame:
+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)
 
@@ -188,3 +205,30 @@ 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).reset_index(drop=True)
+    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']
+
+    idxmin = max_drawdown_df['drawdown'].idxmin()
+    if idxmin == 0:
+        raise ValueError("No losing trade, therefore no drawdown.")
+    high_date = profit_results.loc[max_drawdown_df.iloc[:idxmin]['high_value'].idxmax(), date_col]
+    low_date = profit_results.loc[idxmin, date_col]
+    return abs(min(max_drawdown_df['drawdown'])), high_date, low_date

freqtrade/data/converter.py

@@ -13,12 +13,12 @@ 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
@@ -26,21 +26,18 @@ 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")
+    logger.debug(f"Converting candle (OHLCV) data to dataframe for pair {pair}.")
     cols = DEFAULT_DATAFRAME_COLUMNS
-    frame = DataFrame(ticker, columns=cols)
+    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'})
-    return clean_ohlcv_dataframe(frame, timeframe, pair,
+    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)
 
@@ -49,11 +46,11 @@ def clean_ohlcv_dataframe(data: DataFrame, timeframe: str, pair: str, *,
                           fill_missing: bool = True,
                           drop_incomplete: bool = True) -> DataFrame:
     """
-    Clense a ohlcv dataframe by
+    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 ohlcv data.
+    :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
@@ -88,16 +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')
@@ -159,20 +156,20 @@ def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
 
 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 Dataframe.
+    :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 = 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()
@@ -206,7 +203,7 @@ def convert_trades_format(config: Dict[str, Any], convert_from: str, convert_to:
 
 def convert_ohlcv_format(config: Dict[str, Any], convert_from: str, convert_to: str, erase: bool):
     """
-    Convert ohlcv from one format to another format.
+    Convert OHLCV from one format to another
     :param config: Config dictionary
     :param convert_from: Source format
     :param convert_to: Target format
@@ -216,7 +213,7 @@ def convert_ohlcv_format(config: Dict[str, Any], convert_from: str, convert_to:
     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 OHLCV for timeframe {timeframes}")
+    logger.info(f"Converting candle (OHLCV) for timeframe {timeframes}")
 
     if 'pairs' not in config:
         config['pairs'] = []
@@ -224,7 +221,7 @@ def convert_ohlcv_format(config: Dict[str, Any], convert_from: str, convert_to:
         for timeframe in timeframes:
             config['pairs'].extend(src.ohlcv_get_pairs(config['datadir'],
                                                        timeframe))
-    logger.info(f"Converting OHLCV for {config['pairs']}")
+    logger.info(f"Converting candle (OHLCV) data for {config['pairs']}")
 
     for timeframe in timeframes:
         for pair in config['pairs']:

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/history_utils.py

@@ -9,7 +9,7 @@ from pandas import DataFrame
 
 from freqtrade.configuration import TimeRange
 from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
-from freqtrade.data.converter import parse_ticker_dataframe, trades_to_ohlcv
+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
@@ -28,10 +28,10 @@ def load_pair_history(pair: str,
                       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
@@ -63,10 +63,10 @@ def load_data(datadir: Path,
               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
@@ -104,10 +104,10 @@ def refresh_data(datadir: Path,
                  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
@@ -165,7 +165,7 @@ 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
     """
@@ -194,8 +194,8 @@ def _download_pair_history(datadir: Path,
                                                    days=-30).float_timestamp) * 1000
                                                )
         # TODO: Maybe move parsing to exchange class (?)
-        new_dataframe = parse_ticker_dataframe(new_data, timeframe, pair,
-                                               fill_missing=False, drop_incomplete=True)
+        new_dataframe = ohlcv_to_dataframe(new_data, timeframe, pair,
+                                           fill_missing=False, drop_incomplete=True)
         if data.empty:
             data = new_dataframe
         else:
@@ -362,7 +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

@@ -55,7 +55,7 @@ class IDataHandler(ABC):
         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: Ticker timeframe (e.g. "5m")
+        :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.
@@ -67,7 +67,7 @@ class IDataHandler(ABC):
         """
         Remove data for this pair
         :param pair: Delete data for this pair.
-        :param timeframe: Ticker timeframe (e.g. "5m")
+        :param timeframe: Timeframe (e.g. "5m")
         :return: True when deleted, false if file did not exist.
         """
 
@@ -129,10 +129,10 @@ class IDataHandler(ABC):
                    warn_no_data: bool = True
                    ) -> DataFrame:
         """
-        Load cached ticker history for the given pair.
+        Load cached candle (OHLCV) data 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 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.
@@ -147,12 +147,7 @@ class IDataHandler(ABC):
 
         pairdf = self._ohlcv_load(pair, timeframe,
                                   timerange=timerange_startup)
-        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'
-                )
+        if self._check_empty_df(pairdf, pair, timeframe, warn_no_data):
             return pairdf
         else:
             enddate = pairdf.iloc[-1]['date']
@@ -160,13 +155,30 @@ class IDataHandler(ABC):
             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.
-            return clean_ohlcv_dataframe(pairdf, timeframe,
-                                         pair=pair,
-                                         fill_missing=fill_missing,
-                                         drop_incomplete=(drop_incomplete and
-                                                          enddate == pairdf.iloc[-1]['date']))
+            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):
         """

freqtrade/data/history/jsondatahandler.py

@@ -60,7 +60,7 @@ class JsonDataHandler(IDataHandler):
         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: Ticker timeframe (e.g. "5m")
+        :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.
@@ -83,7 +83,7 @@ class JsonDataHandler(IDataHandler):
         """
         Remove data for this pair
         :param pair: Delete data for this pair.
-        :param timeframe: Ticker timeframe (e.g. "5m")
+        :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)

freqtrade/edge/edge_positioning.py

@@ -8,10 +8,10 @@ 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,
@@ -104,7 +104,7 @@ class Edge:
                 timerange=self._timerange,
             )
 
-        data = history.load_data(
+        data = load_data(
             datadir=self.config['datadir'],
             pairs=pairs,
             timeframe=self.strategy.ticker_interval,
@@ -119,10 +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(),
@@ -137,10 +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:
@@ -246,7 +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
@@ -269,8 +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']
@@ -316,7 +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
@@ -358,11 +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:
@@ -399,9 +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(
@@ -441,7 +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/exceptions.py

@@ -35,3 +35,10 @@ class TemporaryError(FreqtradeException):
     This could happen when an exchange is congested, unavailable, or the user
     has networking problems. Usually resolves itself after a time.
     """
+
+
+class StrategyError(FreqtradeException):
+    """
+    Errors with custom user-code deteced.
+    Usually caused by errors in the strategy.
+    """

freqtrade/exchange/exchange.py

@@ -18,7 +18,7 @@ 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
@@ -226,6 +226,18 @@ class Exchange:
         markets = self.markets
         return sorted(set([x['quote'] for _, x in markets.items()]))
 
+    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]
@@ -298,7 +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
@@ -320,6 +332,13 @@ 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: str, curr_2: str) -> str:
         """
@@ -332,7 +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
@@ -345,7 +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(
@@ -433,6 +452,17 @@ class Exchange:
                 price = ceil(big_price) / pow(10, symbol_prec)
         return price
 
+    def price_get_one_pip(self, pair: str, price: float) -> float:
+        """
+        Get's the "1 pip" value for this pair.
+        Used in PriceFilter to calculate the 1pip movements.
+        """
+        precision = self.markets[pair]['precision']['price']
+        if self.precisionMode == TICK_SIZE:
+            return precision
+        else:
+            return 1 / pow(10, precision)
+
     def dry_run_order(self, pair: str, ordertype: str, side: str, amount: float,
                       rate: float, params: Dict = {}) -> Dict[str, Any]:
         order_id = f'dry_run_{side}_{randint(0, 10**6)}'
@@ -580,7 +610,7 @@ 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(
@@ -604,13 +634,13 @@ class Exchange:
     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,
@@ -630,26 +660,27 @@ 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 = []
 
@@ -660,15 +691,15 @@ 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
@@ -679,13 +710,14 @@ 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)
@@ -695,11 +727,11 @@ 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...",
@@ -709,9 +741,9 @@ 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]:
@@ -724,14 +756,15 @@ 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 for pair {pair} due to '
-                                 f'{e.__class__.__name__}. Message: {e}') from e
+            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 for pair {pair}. '
-                                       f'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 +897,14 @@ 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.")
@@ -880,10 +913,18 @@ class Exchange:
             self._async_get_trade_history(pair=pair, since=since,
                                           until=until, from_id=from_id))
 
+    def check_order_canceled_empty(self, order: Dict) -> bool:
+        """
+        Verify if an order has been cancelled without being partially filled
+        :param order: Order dict as returned from get_order()
+        :return: True if order has been cancelled without being filled, False otherwise.
+        """
+        return order.get('status') in ('closed', 'canceled') and order.get('filled') == 0.0
+
     @retrier
-    def cancel_order(self, order_id: str, pair: str) -> None:
+    def cancel_order(self, order_id: str, pair: str) -> Dict:
         if self._config['dry_run']:
-            return
+            return {}
 
         try:
             return self._api.cancel_order(order_id, pair)
@@ -896,6 +937,37 @@ class Exchange:
         except ccxt.BaseError as e:
             raise OperationalException(e) from e
 
+    def is_cancel_order_result_suitable(self, corder) -> bool:
+        if not isinstance(corder, dict):
+            return False
+
+        required = ('fee', 'status', 'amount')
+        return all(k in corder for k in required)
+
+    def cancel_order_with_result(self, order_id: str, pair: str, amount: float) -> Dict:
+        """
+        Cancel order returning a result.
+        Creates a fake result if cancel order returns a non-usable result
+        and get_order does not work (certain exchanges don't return cancelled orders)
+        :param order_id: Orderid to cancel
+        :param pair: Pair corresponding to order_id
+        :param amount: Amount to use for fake response
+        :return: Result from either cancel_order if usable, or fetch_order
+        """
+        try:
+            corder = self.cancel_order(order_id, pair)
+            if self.is_cancel_order_result_suitable(corder):
+                return corder
+        except InvalidOrderException:
+            logger.warning(f"Could not cancel order {order_id}.")
+        try:
+            order = self.get_order(order_id, pair)
+        except InvalidOrderException:
+            logger.warning(f"Could not fetch cancelled order {order_id}.")
+            order = {'fee': {}, 'status': 'canceled', 'amount': amount, 'info': {}}
+
+        return order
+
     @retrier
     def get_order(self, order_id: str, pair: str) -> Dict:
         if self._config['dry_run']:
@@ -1005,7 +1077,7 @@ def is_exchange_known_ccxt(exchange_name: str, ccxt_module: CcxtModuleType = Non
 
 
 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: CcxtModuleType = None) -> List[str]:

freqtrade/freqtradebot.py

@@ -20,12 +20,14 @@ from freqtrade.data.dataprovider import DataProvider
 from freqtrade.edge import Edge
 from freqtrade.exceptions import DependencyException, InvalidOrderException
 from freqtrade.exchange import timeframe_to_minutes, timeframe_to_next_date
+from freqtrade.misc import safe_value_fallback
 from freqtrade.pairlist.pairlistmanager import PairListManager
 from freqtrade.persistence import Trade
 from freqtrade.resolvers import ExchangeResolver, StrategyResolver
 from freqtrade.rpc import RPCManager, RPCMessageType
 from freqtrade.state import State
 from freqtrade.strategy.interface import IStrategy, SellType
+from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
 from freqtrade.wallets import Wallets
 
 logger = logging.getLogger(__name__)
@@ -143,6 +145,10 @@ class FreqtradeBot:
         self.dataprovider.refresh(self._create_pair_whitelist(self.active_pair_whitelist),
                                   self.strategy.informative_pairs())
 
+        with self._sell_lock:
+            # Check and handle any timed out open orders
+            self.check_handle_timedout()
+
         # Protect from collisions with forcesell.
         # Without this, freqtrade my try to recreate stoploss_on_exchange orders
         # while selling is in process, since telegram messages arrive in an different thread.
@@ -154,8 +160,6 @@ class FreqtradeBot:
         if self.get_free_open_trades():
             self.enter_positions()
 
-        # Check and handle any timed out open orders
-        self.check_handle_timedout()
         Trade.session.flush()
 
     def _refresh_whitelist(self, trades: List[Trade] = []) -> List[str]:
@@ -172,8 +176,8 @@ 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
 
@@ -242,25 +246,25 @@ class FreqtradeBot:
                 logger.info(f"Using cached buy rate for {pair}.")
                 return rate
 
-        config_bid_strategy = self.config.get('bid_strategy', {})
-        if 'use_order_book' in config_bid_strategy and\
-                config_bid_strategy.get('use_order_book', False):
-            logger.info('Getting price from order book')
-            order_book_top = config_bid_strategy.get('order_book_top', 1)
+        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:
-            logger.info('Using Last Ask / Last Price')
+            logger.info(f"Using Last {bid_strategy['price_side'].capitalize()} / Last Price")
             ticker = self.exchange.fetch_ticker(pair)
-            if ticker['ask'] < ticker['last']:
-                ticker_rate = ticker['ask']
-            else:
+            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
@@ -394,16 +398,18 @@ 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(f"Stake amount is 0, ignoring possible trade for {pair}.")
@@ -598,7 +604,6 @@ class FreqtradeBot:
         trades_closed = 0
         for trade in trades:
             try:
-                self.update_trade_state(trade)
 
                 if (self.strategy.order_types.get('stoploss_on_exchange') and
                         self.handle_stoploss_on_exchange(trade)):
@@ -617,9 +622,18 @@ 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.
@@ -634,15 +648,16 @@ class FreqtradeBot:
                 logger.info(f"Using cached sell rate for {pair}.")
                 return 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')
-
-            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)['bid']
+            rate = self.exchange.fetch_ticker(pair)[ask_strategy['price_side']]
         self._sell_rate_cache[pair] = rate
         return rate
 
@@ -661,7 +676,7 @@ 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))
@@ -672,12 +687,13 @@ class FreqtradeBot:
             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.debug('  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
@@ -847,30 +863,35 @@ class FreqtradeBot:
                     continue
                 order = self.exchange.get_order(trade.open_order_id, trade.pair)
             except (RequestException, DependencyException, InvalidOrderException):
-                logger.info(
-                    'Cannot query order for %s due to %s',
-                    trade,
-                    traceback.format_exc())
+                logger.info('Cannot query order for %s due to %s', trade, traceback.format_exc())
                 continue
 
-            # Check if trade is still actually open
-            if float(order.get('remaining', 0.0)) == 0.0:
-                self.wallets.update()
-                continue
+            trade_state_update = self.update_trade_state(trade, order)
+
+            if (order['side'] == 'buy' and (
+                    trade_state_update
+                    or self._check_timed_out('buy', order)
+                    or strategy_safe_wrapper(self.strategy.check_buy_timeout,
+                                             default_retval=False)(pair=trade.pair,
+                                                                   trade=trade,
+                                                                   order=order))):
 
-            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)
+            elif (order['side'] == 'sell' and (
+                  trade_state_update
+                  or self._check_timed_out('sell', order)
+                  or strategy_safe_wrapper(self.strategy.check_sell_timeout,
+                                           default_retval=False)(pair=trade.pair,
+                                                                 trade=trade,
+                                                                 order=order))):
+                reason = self.handle_timedout_limit_sell(trade, order)
                 self.wallets.update()
                 order_type = self.strategy.order_types['sell']
-                self._notify_sell_cancel(trade, order_type)
+                self._notify_sell_cancel(trade, order_type, reason)
 
     def handle_timedout_limit_buy(self, trade: Trade, order: Dict) -> bool:
         """
@@ -879,15 +900,17 @@ class FreqtradeBot:
         """
         if order['status'] != 'canceled':
             reason = "cancelled due to timeout"
-            corder = self.exchange.cancel_order(trade.open_order_id, trade.pair)
-            logger.info('Buy order %s for %s.', reason, trade)
+            corder = self.exchange.cancel_order_with_result(trade.open_order_id, trade.pair,
+                                                            trade.amount)
         else:
             # Order was cancelled already, so we can reuse the existing dict
             corder = order
             reason = "cancelled on exchange"
-            logger.info('Buy order %s for %s.', reason, trade)
 
-        if corder.get('remaining', order['remaining']) == order['amount']:
+        logger.info('Buy order %s for %s.', reason, trade)
+
+        if safe_value_fallback(corder, order, 'remaining', 'remaining') == order['amount']:
+            logger.info('Buy order fully cancelled. Removing %s from database.', trade)
             # if trade is not partially completed, just delete the trade
             Trade.session.delete(trade)
             Trade.session.flush()
@@ -898,19 +921,10 @@ class FreqtradeBot:
         # cancel_order may not contain the full order dict, so we need to fallback
         # to the order dict aquired before cancelling.
         # we need to fall back to the values from order if corder does not contain these keys.
-        trade.amount = order['amount'] - corder.get('remaining', order['remaining'])
+        trade.amount = order['amount'] - safe_value_fallback(corder, order,
+                                                             'remaining', 'remaining')
         trade.stake_amount = trade.amount * trade.open_rate
-        # verify if fees were taken from amount to avoid problems during selling
-        try:
-            new_amount = self.get_real_amount(trade, corder if 'fee' in corder else order,
-                                              trade.amount)
-            if not isclose(order['amount'], new_amount, abs_tol=constants.MATH_CLOSE_PREC):
-                trade.amount = new_amount
-                # Fee was applied, so set to 0
-                trade.fee_open = 0
-                trade.recalc_open_trade_price()
-        except DependencyException as e:
-            logger.warning("Could not update trade amount: %s", e)
+        self.update_trade_state(trade, corder, trade.amount)
 
         trade.open_order_id = None
         logger.info('Partial buy order timeout for %s.', trade)
@@ -920,14 +934,14 @@ class FreqtradeBot:
         })
         return False
 
-    def handle_timedout_limit_sell(self, trade: Trade, order: Dict) -> bool:
+    def handle_timedout_limit_sell(self, trade: Trade, order: Dict) -> str:
         """
         Sell timeout - cancel order and update trade
-        :return: True if order was fully cancelled
+        :return: Reason for cancel
         """
         # if trade is not partially completed, just cancel the trade
-        if order['remaining'] == order['amount']:
-            if order["status"] != "canceled":
+        if order['remaining'] == order['amount'] or order.get('filled') == 0.0:
+            if not self.exchange.check_order_canceled_empty(order):
                 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)
@@ -937,15 +951,17 @@ class FreqtradeBot:
                 logger.info('Sell order %s for %s.', reason, trade)
 
             trade.close_rate = None
+            trade.close_rate_requested = None
             trade.close_profit = None
+            trade.close_profit_abs = None
             trade.close_date = None
             trade.is_open = True
             trade.open_order_id = None
 
-            return True
+            return reason
 
         # TODO: figure out how to handle partially complete sell orders
-        return False
+        return 'partially filled - keeping order open'
 
     def _safe_sell_amount(self, pair: str, amount: float) -> float:
         """
@@ -960,8 +976,8 @@ 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
@@ -1032,10 +1048,10 @@ class FreqtradeBot:
         """
         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,
@@ -1048,7 +1064,7 @@ class FreqtradeBot:
             '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(),
@@ -1064,15 +1080,15 @@ class FreqtradeBot:
         # Send the message
         self.rpc.send_msg(msg)
 
-    def _notify_sell_cancel(self, trade: Trade, order_type: str) -> None:
+    def _notify_sell_cancel(self, trade: Trade, order_type: str, reason: 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_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_CANCEL_NOTIFICATION,
@@ -1085,12 +1101,13 @@ class FreqtradeBot:
             '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,
             'stake_currency': self.config['stake_currency'],
             'fiat_currency': self.config.get('fiat_display_currency', None),
+            'reason': reason,
         }
 
         if 'fiat_display_currency' in self.config:
@@ -1105,9 +1122,12 @@ class FreqtradeBot:
 # Common update trade state methods
 #
 
-    def update_trade_state(self, trade: Trade, action_order: dict = None) -> None:
+    def update_trade_state(self, trade: Trade, action_order: dict = None,
+                           order_amount: float = None) -> bool:
         """
         Checks trades with open orders and updates the amount if necessary
+        Handles closing both buy and sell orders.
+        :return: True if order has been cancelled without being filled partially, False otherwise
         """
         # Get order details for actual price per unit
         if trade.open_order_id:
@@ -1117,25 +1137,31 @@ class FreqtradeBot:
                 order = action_order or self.exchange.get_order(trade.open_order_id, trade.pair)
             except InvalidOrderException as exception:
                 logger.warning('Unable to fetch order %s: %s', trade.open_order_id, exception)
-                return
+                return False
             # Try update amount (binance-fix)
             try:
-                new_amount = self.get_real_amount(trade, order)
+                new_amount = self.get_real_amount(trade, order, order_amount)
                 if not isclose(order['amount'], new_amount, abs_tol=constants.MATH_CLOSE_PREC):
                     order['amount'] = new_amount
+                    order.pop('filled', None)
                     # Fee was applied, so set to 0
                     trade.fee_open = 0
                     trade.recalc_open_trade_price()
-
             except DependencyException as exception:
                 logger.warning("Could not update trade amount: %s", exception)
 
+            if self.exchange.check_order_canceled_empty(order):
+                # Trade has been cancelled on exchange
+                # Handling of this will happen in check_handle_timeout.
+                return True
             trade.update(order)
 
             # Updating wallets when order is closed
             if not trade.is_open:
                 self.wallets.update()
 
+        return False
+
     def get_real_amount(self, trade: Trade, order: Dict, order_amount: float = None) -> float:
         """
         Get real amount for the trade
@@ -1147,12 +1173,13 @@ 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)
@@ -1174,7 +1201,7 @@ 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/loggers.py

@@ -18,13 +18,13 @@ def _set_loggers(verbosity: int = 0) -> None:
     """
 
     logging.getLogger('requests').setLevel(
-            logging.INFO if verbosity <= 1 else logging.DEBUG
+        logging.INFO if verbosity <= 1 else logging.DEBUG
     )
     logging.getLogger("urllib3").setLevel(
-            logging.INFO if verbosity <= 1 else logging.DEBUG
+        logging.INFO if verbosity <= 1 else logging.DEBUG
     )
     logging.getLogger('ccxt.base.exchange').setLevel(
-            logging.INFO if verbosity <= 2 else logging.DEBUG
+        logging.INFO if verbosity <= 2 else logging.DEBUG
     )
     logging.getLogger('telegram').setLevel(logging.INFO)
 

freqtrade/misc.py

@@ -81,13 +81,13 @@ 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
@@ -134,6 +134,21 @@ def round_dict(d, n):
     return {k: (round(v, n) if isinstance(v, float) else v) for k, v in d.items()}
 
 
+def safe_value_fallback(dict1: dict, dict2: dict, key1: str, key2: str, default_value=None):
+    """
+    Search a value in dict1, return this if it's not None.
+    Fall back to dict2 - return key2 from dict2 if it's not None.
+    Else falls back to None.
+
+    """
+    if key1 in dict1 and dict1[key1] is not None:
+        return dict1[key1]
+    else:
+        if key2 in dict2 and dict2[key2] is not None:
+            return dict2[key2]
+    return default_value
+
+
 def plural(num: float, singular: str, plural: str = None) -> str:
     return singular if (num == 1 or num == -1) else plural or singular + 's'
 
@@ -148,3 +163,15 @@ def render_template(templatefile: str, arguments: dict = {}) -> str:
     )
     template = env.get_template(templatefile)
     return template.render(**arguments)
+
+
+def render_template_with_fallback(templatefile: str, templatefallbackfile: str,
+                                  arguments: dict = {}) -> str:
+    """
+    Use templatefile if possible, otherwise fall back to templatefallbackfile
+    """
+    from jinja2.exceptions import TemplateNotFound
+    try:
+        return render_template(templatefile, arguments)
+    except TemplateNotFound:
+        return render_template(templatefallbackfile, arguments)

freqtrade/optimize/backtesting.py

@@ -6,8 +6,7 @@ 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
@@ -19,10 +18,8 @@ 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
@@ -88,8 +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)
 
@@ -108,7 +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')))
 
@@ -134,49 +131,33 @@ 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) -> 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.loc[:, 'buy'].shift(1)
+            df_analyzed.loc[:, 'sell'] = df_analyzed.loc[:, '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: SellCheckTuple,
                         trade_dur: int) -> float:
@@ -220,7 +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(
@@ -235,7 +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
@@ -259,9 +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),
@@ -308,8 +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.
@@ -319,12 +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
@@ -352,7 +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)
 
@@ -395,7 +377,7 @@ 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():
@@ -416,35 +398,7 @@ class Backtesting:
                 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,
-                                                  stake_currency=self.config['stake_currency'],
-                                                  max_open_trades=self.config['max_open_trades'],
-                                                  results=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

@@ -7,8 +7,8 @@ This module contains the hyperopt logic
 import locale
 import logging
 import random
-import sys
 import warnings
+from math import ceil
 from collections import OrderedDict
 from operator import itemgetter
 from pathlib import Path
@@ -17,10 +17,13 @@ from typing import Any, Dict, List, Optional
 
 import rapidjson
 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 progressbar
+import tabulate
+from os import path
+import io
 
 from freqtrade.data.converter import trim_dataframe
 from freqtrade.data.history import get_timerange
@@ -39,7 +42,8 @@ with warnings.catch_warnings():
     from skopt import Optimizer
     from skopt.space import Dimension
 
-
+progressbar.streams.wrap_stderr()
+progressbar.streams.wrap_stdout()
 logger = logging.getLogger(__name__)
 
 
@@ -73,8 +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
@@ -115,6 +119,7 @@ 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)
 
@@ -127,7 +132,7 @@ class Hyperopt:
         """
         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}`.")
@@ -152,7 +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:
@@ -261,33 +266,16 @@ class Hyperopt:
         Log results if it is better than any previous evaluation
         """
         is_best = results['is_best']
-        if not self.print_all:
-            # Print '\n' after each 100th epoch to separate dots from the log messages.
-            # Otherwise output is messy on a terminal.
-            print('.', end='' if results['current_epoch'] % 100 != 0 else None)  # type: ignore
-            sys.stdout.flush()
 
         if self.print_all or is_best:
-            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)
-
-    @staticmethod
-    def print_results_explanation(results, total_epochs, highlight_best: bool,
-                                  print_colorized: bool) -> None:
-        """
-        Log results explanation string
-        """
-        explanation_str = Hyperopt._format_explanation_string(results, total_epochs)
-        # Colorize output
-        if print_colorized:
-            if results['total_profit'] > 0:
-                explanation_str = Fore.GREEN + explanation_str
-            if highlight_best and results['is_best']:
-                explanation_str = Style.BRIGHT + explanation_str
-        print(explanation_str)
+            print(
+                self.get_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 _format_explanation_string(results, total_epochs) -> str:
@@ -296,6 +284,144 @@ class Hyperopt:
                 f"{results['results_explanation']} " +
                 f"Objective: {results['loss']:.5f}")
 
+    @staticmethod
+    def get_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
+                         print_colorized: bool, remove_header: int) -> str:
+        """
+        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['is_initial_point'] & 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"
+            )
+        return 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['is_initial_point'] & 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
@@ -369,7 +495,7 @@ 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)
 
@@ -482,10 +608,10 @@ 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():
@@ -496,7 +622,7 @@ 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
@@ -510,43 +636,75 @@ class Hyperopt:
 
         self.dimensions: List[Dimension] = self.hyperopt_space()
         self.opt = self.get_optimizer(self.dimensions, config_jobs)
-
-        if self.print_colorized:
-            colorama_init(autoreset=True)
-
         try:
             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)
-                for i in range(EVALS):
-                    asked = self.opt.ask(n_points=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):
-                        # 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}")
 
-                        is_best = self.is_best_loss(val, self.current_best_loss)
-                        # This value is assigned here and not in the optimization method
-                        # to keep proper order in the list of results. That's because
-                        # evaluations can take different time. Here they are aligned in the
-                        # order they will be shown to the user.
-                        val['is_best'] = is_best
+                # Define progressbar
+                if self.print_colorized:
+                    widgets = [
+                        ' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
+                        ' (', progressbar.Percentage(), ')] ',
+                        progressbar.Bar(marker=progressbar.AnimatedMarker(
+                            fill='\N{FULL BLOCK}',
+                            fill_wrap=Fore.GREEN + '{}' + Fore.RESET,
+                            marker_wrap=Style.BRIGHT + '{}' + Style.RESET_ALL,
+                        )),
+                        ' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
+                    ]
+                else:
+                    widgets = [
+                        ' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
+                        ' (', progressbar.Percentage(), ')] ',
+                        progressbar.Bar(marker=progressbar.AnimatedMarker(
+                            fill='\N{FULL BLOCK}',
+                        )),
+                        ' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
+                    ]
+                with progressbar.ProgressBar(
+                         maxval=self.total_epochs, redirect_stdout=False, redirect_stderr=False,
+                         widgets=widgets
+                     ) as pbar:
+                    EVALS = ceil(self.total_epochs / jobs)
+                    for i in range(EVALS):
+                        # 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
 
-                        self.print_results(val)
+                        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()
+
+                        # Calculate progressbar outputs
+                        for j, val in enumerate(f_val):
+                            # Use human-friendly indexes here (starting from 1)
+                            current = i * jobs + j + 1
+                            val['current_epoch'] = current
+                            val['is_initial_point'] = current <= INITIAL_POINTS
+
+                            logger.debug(f"Optimizer epoch evaluated: {val}")
+
+                            is_best = self.is_best_loss(val, self.current_best_loss)
+                            # This value is assigned here and not in the optimization method
+                            # to keep proper order in the list of results. That's because
+                            # evaluations can take different time. Here they are aligned in the
+                            # order they will be shown to the user.
+                            val['is_best'] = is_best
+                            self.print_results(val)
+
+                            if is_best:
+                                self.current_best_loss = val['loss']
+                            self.trials.append(val)
+
+                            # Save results after each best epoch and every 100 epochs
+                            if is_best or current % 100 == 0:
+                                self.save_trials()
+
+                            pbar.update(current)
 
-                        if is_best:
-                            self.current_best_loss = val['loss']
-                        self.trials.append(val)
-                        # Save results after each best epoch and every 100 epochs
-                        if is_best or current % 100 == 0:
-                            self.save_trials()
         except KeyboardInterrupt:
             print('User interrupted..')
 

freqtrade/optimize/hyperopt_loss_sharpe.py

@@ -36,7 +36,7 @@ class SharpeHyperOptLoss(IHyperOptLoss):
         expected_returns_mean = total_profit.sum() / days_period
         up_stdev = np.std(total_profit)
 
-        if (np.std(total_profit) != 0.):
+        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.

freqtrade/optimize/hyperopt_loss_sharpe_daily.py

@@ -51,7 +51,7 @@ class SharpeHyperOptLossDaily(IHyperOptLoss):
         expected_returns_mean = total_profit.mean()
         up_stdev = total_profit.std()
 
-        if (up_stdev != 0.):
+        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.

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 +1,38 @@
+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:
+            filename = recordfilename
+            if len(all_results) > 1:
+                # Inject strategy to filename
+                filename = Path.joinpath(
+                    recordfilename.parent,
+                    f'{recordfilename.stem}-{strategy}').with_suffix(recordfilename.suffix)
+            logger.info(f'Dumping backtest results to {filename}')
+            file_dump_json(filename, records)
+
 
 def generate_text_table(data: Dict[str, Dict], stake_currency: str, max_open_trades: int,
                         results: DataFrame, skip_nan: bool = False) -> str:
@@ -66,15 +95,15 @@ def generate_text_table(data: Dict[str, Dict], stake_currency: str, max_open_tra
     ])
     # 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], stake_currency: str, max_open_trades: int, 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
     """
@@ -112,7 +141,7 @@ def generate_text_table_sell_reason(
                 profit_percent_tot,
             ]
         )
-    return tabulate(tabular_data, headers=headers, tablefmt="pipe")
+    return tabulate(tabular_data, headers=headers, tablefmt="orgtbl", stralign="right")
 
 
 def generate_text_table_strategy(stake_currency: str, max_open_trades: str,
@@ -146,7 +175,7 @@ def generate_text_table_strategy(stake_currency: str, max_open_trades: 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 generate_edge_table(results: dict) -> str:
@@ -172,4 +201,44 @@ 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

@@ -9,6 +9,8 @@ from abc import ABC, abstractmethod, abstractproperty
 from copy import deepcopy
 from typing import Any, Dict, List
 
+from cachetools import TTLCache, cached
+
 from freqtrade.exchange import market_is_active
 
 logger = logging.getLogger(__name__)
@@ -31,6 +33,9 @@ class IPairList(ABC):
         self._config = config
         self._pairlistconfig = pairlistconfig
         self._pairlist_pos = pairlist_pos
+        self.refresh_period = self._pairlistconfig.get('refresh_period', 1800)
+        self._last_refresh = 0
+        self._log_cache = TTLCache(maxsize=1024, ttl=self.refresh_period)
 
     @property
     def name(self) -> str:
@@ -40,6 +45,24 @@ class IPairList(ABC):
         """
         return self.__class__.__name__
 
+    def log_on_refresh(self, logmethod, message: str) -> None:
+        """
+        Logs message - not more often than "refresh_period" to avoid log spamming
+        Logs the log-message as debug as well to simplify debugging.
+        :param logmethod: Function that'll be called. Most likely `logger.info`.
+        :param message: String containing the message to be sent to the function.
+        :return: None.
+        """
+
+        @cached(cache=self._log_cache)
+        def _log_on_refresh(message: str):
+            logmethod(message)
+
+        # Log as debug first
+        logger.debug(message)
+        # Call hidden function.
+        _log_on_refresh(message)
+
     @abstractproperty
     def needstickers(self) -> bool:
         """
@@ -67,21 +90,37 @@ 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]:
         """
@@ -99,7 +138,8 @@ 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
@@ -112,6 +152,5 @@ 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

@@ -39,8 +39,9 @@ class PrecisionFilter(IPairList):
         stop_gap_price = self._exchange.price_to_precision(ticker["symbol"], stop_price * 0.99)
         logger.debug(f"{ticker['symbol']} - {sp} : {stop_gap_price}")
         if sp <= stop_gap_price:
-            logger.info(f"Removed {ticker['symbol']} from whitelist, "
-                        f"because stop price {sp} would be <= stop limit {stop_gap_price}")
+            self.log_on_refresh(logger.info,
+                                f"Removed {ticker['symbol']} from whitelist, "
+                                f"because stop price {sp} would be <= stop limit {stop_gap_price}")
             return False
         return True
 

freqtrade/pairlist/PriceFilter.py

@@ -35,21 +35,24 @@ class PriceFilter(IPairList):
         """
         Check if if one price-step (pip) is > than a certain barrier.
         :param ticker: ticker dict as returned from ccxt.load_markets()
-        :param precision: Precision
         :return: True if the pair can stay, false if it should be removed
         """
-        precision = self._exchange.markets[ticker['symbol']]['precision']['price']
+        if ticker['last'] is None:
 
-        compare = ticker['last'] + 1 / pow(10, precision)
+            self.log_on_refresh(logger.info,
+                                f"Removed {ticker['symbol']} from whitelist, because "
+                                "ticker['last'] is empty (Usually no trade in the last 24h).")
+            return False
+        compare = ticker['last'] + self._exchange.price_get_one_pip(ticker['symbol'],
+                                                                    ticker['last'])
         changeperc = (compare - ticker['last']) / ticker['last']
         if changeperc > self._low_price_ratio:
-            logger.info(f"Removed {ticker['symbol']} from whitelist, "
-                        f"because 1 unit is {changeperc * 100:.3f}%")
+            self.log_on_refresh(logger.info, f"Removed {ticker['symbol']} from whitelist, "
+                                             f"because 1 unit is {changeperc * 100:.3f}%")
             return False
         return True
 
     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

freqtrade/pairlist/SpreadFilter.py

@@ -49,9 +49,9 @@ class SpreadFilter(IPairList):
             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}%")
+                    self.log_on_refresh(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)

freqtrade/pairlist/VolumePairList.py

@@ -39,7 +39,6 @@ class VolumePairList(IPairList):
         if not self._validate_keys(self._sort_key):
             raise OperationalException(
                 f'key {self._sort_key} not in {SORT_VALUES}')
-        self._last_refresh = 0
 
     @property
     def needstickers(self) -> bool:
@@ -68,16 +67,18 @@ class VolumePairList(IPairList):
         :return: new whitelist
         """
         # Generate dynamic whitelist
-        if self._last_refresh + self.refresh_period < datetime.now().timestamp():
+        # Must always run if this pairlist is not the first in the list.
+        if (self._pairlist_pos != 0 or
+                (self._last_refresh + self.refresh_period < datetime.now().timestamp())):
+
             self._last_refresh = int(datetime.now().timestamp())
-            return self._gen_pair_whitelist(pairlist,
-                                            tickers,
-                                            self._config['stake_currency'],
-                                            self._sort_key,
-                                            self._min_value
-                                            )
+            pairs = self._gen_pair_whitelist(pairlist, tickers,
+                                             self._config['stake_currency'],
+                                             self._sort_key, self._min_value)
         else:
-            return pairlist
+            pairs = pairlist
+        self.log_on_refresh(logger.info, f"Searching {self._number_pairs} pairs: {pairs}")
+        return pairs
 
     def _gen_pair_whitelist(self, pairlist: List[str], tickers: Dict,
                             base_currency: str, key: str, min_val: int) -> List[str]:
@@ -88,12 +89,11 @@ class VolumePairList(IPairList):
         :param tickers: Tickers (from exchange.get_tickers()).
         :return: List of pairs
         """
-
         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.
@@ -106,9 +106,8 @@ class VolumePairList(IPairList):
 
         # 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}")
 
         return 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

@@ -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}
              """)
 
@@ -185,11 +188,12 @@ class Trade(_DECL_BASE):
     fee_close = Column(Float, nullable=False, default=0.0)
     open_rate = Column(Float)
     open_rate_requested = Column(Float)
-    # open_trade_price - calcuated via _calc_open_trade_price
+    # open_trade_price - calculated via _calc_open_trade_price
     open_trade_price = Column(Float)
     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)
@@ -229,6 +233,9 @@ class Trade(_DECL_BASE):
         return {
             'trade_id': self.id,
             'pair': self.pair,
+            'is_open': self.is_open,
+            'fee_open': self.fee_open,
+            'fee_close': self.fee_close,
             'open_date_hum': arrow.get(self.open_date).humanize(),
             'open_date': self.open_date.strftime("%Y-%m-%d %H:%M:%S"),
             'close_date_hum': (arrow.get(self.close_date).humanize()
@@ -236,14 +243,24 @@ class Trade(_DECL_BASE):
             'close_date': (self.close_date.strftime("%Y-%m-%d %H:%M:%S")
                            if self.close_date else None),
             'open_rate': self.open_rate,
+            'open_rate_requested': self.open_rate_requested,
+            'open_trade_price': self.open_trade_price,
             'close_rate': self.close_rate,
+            'close_rate_requested': self.close_rate_requested,
             'amount': round(self.amount, 8),
             'stake_amount': round(self.stake_amount, 8),
+            'close_profit': self.close_profit,
+            'sell_reason': self.sell_reason,
             'stop_loss': self.stop_loss,
             'stop_loss_pct': (self.stop_loss_pct * 100) if self.stop_loss_pct else None,
             'initial_stop_loss': self.initial_stop_loss,
             'initial_stop_loss_pct': (self.initial_stop_loss_pct * 100
                                       if self.initial_stop_loss_pct else None),
+            'min_rate': self.min_rate,
+            'max_rate': self.max_rate,
+            'strategy': self.strategy,
+            'ticker_interval': self.ticker_interval,
+            'open_order_id': self.open_order_id,
         }
 
     def adjust_min_max_rates(self, current_price: float) -> None:
@@ -311,7 +328,7 @@ class Trade(_DECL_BASE):
         if order_type in ('market', 'limit') and order['side'] == 'buy':
             # Update open rate and actual amount
             self.open_rate = Decimal(order['price'])
-            self.amount = Decimal(order['amount'])
+            self.amount = Decimal(order.get('filled', order['amount']))
             self.recalc_open_trade_price()
             logger.info('%s_BUY has been fulfilled for %s.', order_type.upper(), self)
             self.open_order_id = None
@@ -334,6 +351,7 @@ 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
@@ -405,8 +423,8 @@ 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

@@ -5,10 +5,12 @@ from typing import Any, Dict, List
 import pandas as pd
 
 from freqtrade.configuration import TimeRange
-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.exchange import timeframe_to_prev_date
 from freqtrade.data.history import load_data
 from freqtrade.misc import pair_to_filename
 from freqtrade.resolvers import StrategyResolver
@@ -28,7 +30,7 @@ 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:
@@ -39,7 +41,7 @@ def init_plotscript(config):
     # Set timerange to use
     timerange = TimeRange.parse_timerange(config.get("timerange"))
 
-    tickers = load_data(
+    data = load_data(
         datadir=config.get("datadir"),
         pairs=pairs,
         timeframe=config.get('ticker_interval', '5m'),
@@ -47,12 +49,22 @@ def init_plotscript(config):
         data_format=config.get('dataformat_ohlcv', 'json'),
     )
 
-    trades = load_trades(config['trade_source'],
-                         db_url=config.get('db_url'),
-                         exportfilename=config.get('exportfilename'),
-                         )
+    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 {"tickers": tickers,
+
+    return {"ohlcv": data,
             "trades": trades,
             "pairs": pairs,
             }
@@ -111,6 +123,37 @@ 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,
+                     timeframe: str) -> 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[timeframe_to_prev_date(timeframe, highdate), 'cum_profit'],
+                df_comb.loc[timeframe_to_prev_date(timeframe, lowdate), 'cum_profit'],
+            ],
+            mode='markers',
+            name=f"Max drawdown {max_drawdown * 100:.2f}%",
+            text=f"Max drawdown {max_drawdown * 100:.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"
@@ -337,10 +380,13 @@ 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")
+
+    # Trim trades to available OHLCV data
+    trades = extract_trades_of_period(df_comb, trades, date_index=True)
 
     # Add combined cumulative profit
     df_comb = create_cum_profit(df_comb, trades, 'cum_profit', timeframe)
@@ -364,6 +410,7 @@ 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, timeframe)
 
     for pair in pairs:
         profit_col = f'cum_profit_{pair}'
@@ -407,7 +454,7 @@ 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
@@ -419,19 +466,17 @@ 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", []),
@@ -462,7 +507,7 @@ 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/rpc/api_server.py

@@ -173,7 +173,8 @@ class ApiServer(RPC):
                               view_func=self._show_config, methods=['GET'])
         self.app.add_url_rule(f'{BASE_URI}/ping', 'ping',
                               view_func=self._ping, methods=['GET'])
-
+        self.app.add_url_rule(f'{BASE_URI}/trades', 'trades',
+                              view_func=self._trades, methods=['GET'])
         # Combined actions and infos
         self.app.add_url_rule(f'{BASE_URI}/blacklist', 'blacklist', view_func=self._blacklist,
                               methods=['GET', 'POST'])
@@ -358,6 +359,18 @@ class ApiServer(RPC):
                                     self._config.get('fiat_display_currency', ''))
         return self.rest_dump(results)
 
+    @require_login
+    @rpc_catch_errors
+    def _trades(self):
+        """
+        Handler for /trades.
+
+        Returns the X last trades in json format
+        """
+        limit = int(request.args.get('limit', 0))
+        results = self._rpc_trade_history(limit)
+        return self.rest_dump(results)
+
     @require_login
     @rpc_catch_errors
     def _whitelist(self):

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

@@ -155,9 +155,9 @@ 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,
@@ -197,7 +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)
@@ -226,15 +226,29 @@ class RPC:
             for key, value in profit_days.items()
         ]
 
+    def _rpc_trade_history(self, limit: int) -> Dict:
+        """ Returns the X last trades """
+        if limit > 0:
+            trades = Trade.get_trades().order_by(Trade.id.desc()).limit(limit)
+        else:
+            trades = Trade.get_trades().order_by(Trade.id.desc()).all()
+
+        output = [trade.to_json() for trade in trades]
+
+        return {
+            "trades": output,
+            "trades_count": len(output)
+        }
+
     def _rpc_trade_statistics(
             self, stake_currency: str, fiat_display_currency: str) -> Dict[str, Any]:
         """ Returns cumulative profit statistics """
         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:
@@ -246,21 +260,21 @@ 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()
 
@@ -271,7 +285,7 @@ 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,
@@ -280,7 +294,7 @@ 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,
@@ -460,9 +474,9 @@ 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
@@ -517,7 +531,7 @@ 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/telegram.py

@@ -148,7 +148,7 @@ class Telegram(RPC):
 
         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
@@ -172,7 +172,8 @@ class Telegram(RPC):
                             ' / {profit_fiat:.3f} {fiat_currency})`').format(**msg)
 
         elif msg['type'] == RPCMessageType.SELL_CANCEL_NOTIFICATION:
-            message = "*{exchange}:* Cancelling Open Sell Order for {pair}".format(**msg)
+            message = ("*{exchange}:* Cancelling Open Sell Order "
+                       "for {pair}. Reason: {reason}").format(**msg)
 
         elif msg['type'] == RPCMessageType.STATUS_NOTIFICATION:
             message = '*Status:* `{status}`'.format(**msg)

freqtrade/strategy/interface.py

@@ -3,21 +3,22 @@ IStrategy interface
 This module defines the interface to apply for strategies
 """
 import logging
+import warnings
 from abc import ABC, abstractmethod
 from datetime import datetime, timezone
 from enum import Enum
 from typing import Dict, List, NamedTuple, Optional, Tuple
-import warnings
 
 import arrow
 from pandas import DataFrame
 
 from freqtrade.data.dataprovider import DataProvider
+from freqtrade.exceptions import StrategyError
 from freqtrade.exchange import timeframe_to_minutes
 from freqtrade.persistence import Trade
+from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
 from freqtrade.wallets import Wallets
 
-
 logger = logging.getLogger(__name__)
 
 
@@ -59,7 +60,7 @@ 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 +126,7 @@ 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
         """
@@ -148,6 +149,42 @@ class IStrategy(ABC):
         :return: DataFrame with sell column
         """
 
+    def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
+        """
+        Check buy timeout function callback.
+        This method can be used to override the buy-timeout.
+        It is called whenever a limit buy order has been created,
+        and is not yet fully filled.
+        Configuration options in `unfilledtimeout` will be verified before this,
+        so ensure to set these timeouts high enough.
+
+        When not implemented by a strategy, this simply returns False.
+        :param pair: Pair the trade is for
+        :param trade: trade object.
+        :param order: Order dictionary as returned from CCXT.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return bool: When True is returned, then the buy-order is cancelled.
+        """
+        return False
+
+    def check_sell_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
+        """
+        Check sell timeout function callback.
+        This method can be used to override the sell-timeout.
+        It is called whenever a limit sell order has been created,
+        and is not yet fully filled.
+        Configuration options in `unfilledtimeout` will be verified before this,
+        so ensure to set these timeouts high enough.
+
+        When not implemented by a strategy, this simply returns False.
+        :param pair: Pair the trade is for
+        :param trade: trade object.
+        :param order: Order dictionary as returned from CCXT.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return bool: When True is returned, then the sell-order is cancelled.
+        """
+        return False
+
     def informative_pairs(self) -> List[Tuple[str, str]]:
         """
         Define additional, informative pair/interval combinations to be cached from the exchange.
@@ -200,11 +237,11 @@ 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 +251,12 @@ 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'))
 
@@ -241,8 +278,25 @@ class IStrategy(ABC):
 
         return dataframe
 
-    def get_signal(self, pair: str, interval: str,
-                   dataframe: DataFrame) -> Tuple[bool, bool]:
+    @staticmethod
+    def preserve_df(dataframe: DataFrame) -> Tuple[int, float, datetime]:
+        """ keep some data for dataframes """
+        return len(dataframe), dataframe["close"].iloc[-1], dataframe["date"].iloc[-1]
+
+    @staticmethod
+    def assert_df(dataframe: DataFrame, df_len: int, df_close: float, df_date: datetime):
+        """ make sure data is unmodified """
+        message = ""
+        if df_len != len(dataframe):
+            message = "length"
+        elif df_close != dataframe["close"].iloc[-1]:
+            message = "last close price"
+        elif df_date != dataframe["date"].iloc[-1]:
+            message = "last date"
+        if message:
+            raise StrategyError(f"Dataframe returned from strategy has mismatching {message}.")
+
+    def get_signal(self, pair: str, interval: str, dataframe: DataFrame) -> Tuple[bool, bool]:
         """
         Calculates current signal based several technical analysis indicators
         :param pair: pair in format ANT/BTC
@@ -251,31 +305,26 @@ 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
 
+        latest_date = dataframe['date'].max()
         try:
-            dataframe = self._analyze_ticker_internal(dataframe, {'pair': pair})
-        except ValueError as error:
-            logger.warning(
-                'Unable to analyze ticker for pair %s: %s',
-                pair,
-                str(error)
-            )
-            return False, False
-        except Exception as error:
-            logger.exception(
-                'Unexpected error when analyzing ticker for pair %s: %s',
-                pair,
-                str(error)
-            )
+            df_len, df_close, df_date = self.preserve_df(dataframe)
+            dataframe = strategy_safe_wrapper(
+                self._analyze_ticker_internal, message=""
+                )(dataframe, {'pair': pair})
+            self.assert_df(dataframe, df_len, df_close, df_date)
+        except StrategyError as error:
+            logger.warning(f"Unable to analyze candle (OHLCV) data for pair {pair}: {error}")
+
             return False, False
 
         if dataframe.empty:
             logger.warning('Empty dataframe for pair %s', pair)
             return False, False
 
-        latest = dataframe.iloc[-1]
+        latest = dataframe.loc[dataframe['date'] == latest_date].iloc[-1]
 
         # Check if dataframe is out of date
         signal_date = arrow.get(latest['date'])
@@ -364,7 +413,7 @@ 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 +476,9 @@ 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 +489,22 @@ class IStrategy(ABC):
         else:
             return current_profit > roi
 
-    def tickerdata_to_dataframe(self, tickerdata: Dict[str, DataFrame]) -> 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.
+        Using .copy() to get a fresh copy of the dataframe for every strategy run.
+        Has positive effects on memory usage for whatever reason - also when
+        using only one strategy.
         """
-        return {pair: self.advise_indicators(pair_data, {'pair': pair})
-                for pair, pair_data in tickerdata.items()}
+        return {pair: self.advise_indicators(pair_data.copy(), {'pair': pair})
+                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/strategy/strategy_wrapper.py

@@ -0,0 +1,35 @@
+import logging
+
+from freqtrade.exceptions import StrategyError
+
+logger = logging.getLogger(__name__)
+
+
+def strategy_safe_wrapper(f, message: str = "", default_retval=None):
+    """
+    Wrapper around user-provided methods and functions.
+    Caches all exceptions and returns either the default_retval (if it's not None) or raises
+    a StrategyError exception, which then needs to be handled by the calling method.
+    """
+    def wrapper(*args, **kwargs):
+        try:
+            return f(*args, **kwargs)
+        except ValueError as error:
+            logger.warning(
+                f"{message}"
+                f"Strategy caused the following exception: {error}"
+                f"{f}"
+            )
+            if default_retval is None:
+                raise StrategyError(str(error)) from error
+            return default_retval
+        except Exception as error:
+            logger.exception(
+                f"{message}"
+                f"Unexpected error {error} calling {f}"
+            )
+            if default_retval is None:
+                raise StrategyError(str(error)) from error
+            return default_retval
+
+    return wrapper

freqtrade/templates/base_config.json.j2

@@ -11,6 +11,7 @@
         "sell": 30
     },
     "bid_strategy": {
+        "price_side": "bid",
         "ask_last_balance": 0.0,
         "use_order_book": false,
         "order_book_top": 1,
@@ -20,9 +21,10 @@
         }
     },
     "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

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),

freqtrade/templates/base_strategy.py.j2

@@ -99,7 +99,7 @@ class {{ strategy }}(IStrategy):
         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
         """
@@ -137,3 +137,4 @@ class {{ strategy }}(IStrategy):
             ),
             'sell'] = 1
         return dataframe
+    {{ additional_methods | indent(4) }}

freqtrade/templates/sample_hyperopt.py

@@ -20,23 +20,28 @@ import freqtrade.vendor.qtpylib.indicators as qtpylib
 class SampleHyperOpt(IHyperOpt):
     """
     This is a sample Hyperopt to inspire you.
-    Feel free to customize it.
 
-    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:
     - Rename the class name to some unique name.
     - Add any methods you want to build your hyperopt.
     - Add any lib you need to build your hyperopt.
 
+    An easier way to get a new hyperopt file is by using
+    `freqtrade new-hyperopt --hyperopt MyCoolHyperopt`.
+
     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
@@ -73,6 +78,9 @@ class SampleHyperOpt(IHyperOpt):
                         dataframe['close'], dataframe['sar']
                     ))
 
+            # Check that volume is not 0
+            conditions.append(dataframe['volume'] > 0)
+
             if conditions:
                 dataframe.loc[
                     reduce(lambda x, y: x & y, conditions),
@@ -133,6 +141,9 @@ class SampleHyperOpt(IHyperOpt):
                         dataframe['sar'], dataframe['close']
                     ))
 
+            # Check that volume is not 0
+            conditions.append(dataframe['volume'] > 0)
+
             if conditions:
                 dataframe.loc[
                     reduce(lambda x, y: x & y, conditions),

freqtrade/templates/sample_hyperopt_advanced.py

@@ -22,7 +22,7 @@ class AdvancedSampleHyperOpt(IHyperOpt):
     This is a sample hyperopt to inspire you.
     Feel free to customize it.
 
-    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:
     - Rename the class name to some unique name.
@@ -32,8 +32,9 @@ class AdvancedSampleHyperOpt(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
+    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 the
     'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade.
 
     This sample illustrates how to override these methods.
@@ -92,6 +93,9 @@ class AdvancedSampleHyperOpt(IHyperOpt):
                         dataframe['close'], dataframe['sar']
                     ))
 
+            # Check that volume is not 0
+            conditions.append(dataframe['volume'] > 0)
+
             if conditions:
                 dataframe.loc[
                     reduce(lambda x, y: x & y, conditions),
@@ -152,6 +156,9 @@ class AdvancedSampleHyperOpt(IHyperOpt):
                         dataframe['sar'], dataframe['close']
                     ))
 
+            # Check that volume is not 0
+            conditions.append(dataframe['volume'] > 0)
+
             if conditions:
                 dataframe.loc[
                     reduce(lambda x, y: x & y, conditions),

freqtrade/templates/sample_strategy.py

@@ -116,7 +116,7 @@ class SampleStrategy(IStrategy):
         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
         """

freqtrade/templates/strategy_analysis_example.ipynb

@@ -190,7 +190,6 @@
     "# Analyze the above\n",
     "parallel_trades = analyze_trade_parallelism(trades, '5m')\n",
     "\n",
-    "\n",
     "parallel_trades.plot()"
    ]
   },
@@ -212,11 +211,14 @@
     "from freqtrade.plot.plotting import  generate_candlestick_graph\n",
     "# Limit graph period to keep plotly quick and reactive\n",
     "\n",
+    "# Filter trades to one pair\n",
+    "trades_red = trades.loc[trades['pair'] == pair]\n",
+    "\n",
     "data_red = data['2019-06-01':'2019-06-10']\n",
     "# Generate candlestick graph\n",
     "graph = generate_candlestick_graph(pair=pair,\n",
     "                                   data=data_red,\n",
-    "                                   trades=trades,\n",
+    "                                   trades=trades_red,\n",
     "                                   indicators1=['sma20', 'ema50', 'ema55'],\n",
     "                                   indicators2=['rsi', 'macd', 'macdsignal', 'macdhist']\n",
     "                                  )\n",

freqtrade/templates/subtemplates/strategy_methods_advanced.j2

@@ -0,0 +1,40 @@
+
+def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
+    """
+    Check buy timeout function callback.
+    This method can be used to override the buy-timeout.
+    It is called whenever a limit buy order has been created,
+    and is not yet fully filled.
+    Configuration options in `unfilledtimeout` will be verified before this,
+    so ensure to set these timeouts high enough.
+
+    For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+    When not implemented by a strategy, this simply returns False.
+    :param pair: Pair the trade is for
+    :param trade: trade object.
+    :param order: Order dictionary as returned from CCXT.
+    :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+    :return bool: When True is returned, then the buy-order is cancelled.
+    """
+    return False
+
+def check_sell_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
+    """
+    Check sell timeout function callback.
+    This method can be used to override the sell-timeout.
+    It is called whenever a limit sell order has been created,
+    and is not yet fully filled.
+    Configuration options in `unfilledtimeout` will be verified before this,
+    so ensure to set these timeouts high enough.
+
+    For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+    When not implemented by a strategy, this simply returns False.
+    :param pair: Pair the trade is for
+    :param trade: trade object.
+    :param order: Order dictionary as returned from CCXT.
+    :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+    :return bool: When True is returned, then the sell-order is cancelled.
+    """
+    return False

freqtrade/wallets.py

@@ -74,7 +74,7 @@ class Wallets:
         )
 
         for trade in open_trades:
-            curr = trade.pair.split('/')[0]
+            curr = self._exchange.get_pair_base_currency(trade.pair)
             _wallets[curr] = Wallet(
                 curr,
                 trade.amount,

mkdocs.yml

@@ -24,6 +24,7 @@ nav:
         - Plotting: plotting.md
     - SQL Cheatsheet: sql_cheatsheet.md
     - Advanced Post-installation Tasks: advanced-setup.md
+    - Advanced Strategy: strategy-advanced.md
     - Advanced Hyperopt: advanced-hyperopt.md
     - Sandbox Testing: sandbox-testing.md
     - Deprecated Features: deprecated.md

requirements-common.txt

@@ -1,18 +1,18 @@
 # requirements without requirements installable via conda
 # mainly used for Raspberry pi installs
-ccxt==1.22.95
-SQLAlchemy==1.3.13
-python-telegram-bot==12.4.2
+ccxt==1.27.1
+SQLAlchemy==1.3.16
+python-telegram-bot==12.6.1
 arrow==0.15.5
-cachetools==4.0.0
+cachetools==4.1.0
 requests==2.23.0
-urllib3==1.25.8
-wrapt==1.12.0
+urllib3==1.25.9
+wrapt==1.12.1
 jsonschema==3.2.0
 TA-Lib==0.4.17
-tabulate==0.8.6
-coinmarketcap==5.0.3
-jinja2==2.11.1
+tabulate==0.8.7
+pycoingecko==1.2.0
+jinja2==2.11.2
 
 # find first, C search in arrays
 py_find_1st==1.1.4
@@ -24,10 +24,10 @@ python-rapidjson==0.9.1
 sdnotify==0.3.2
 
 # Api server
-flask==1.1.1
+flask==1.1.2
 
 # Support for colorized terminal output
 colorama==0.4.3
 # Building config files interactively
-questionary==1.5.1
-prompt-toolkit==3.0.3
+questionary==1.5.2
+prompt-toolkit==3.0.5

requirements-dev.txt

@@ -3,15 +3,15 @@
 -r requirements-plot.txt
 -r requirements-hyperopt.txt
 
-coveralls==1.11.1
+coveralls==2.0.0
 flake8==3.7.9
 flake8-type-annotations==0.1.0
-flake8-tidy-imports==4.0.0
-mypy==0.761
-pytest==5.3.5
-pytest-asyncio==0.10.0
+flake8-tidy-imports==4.1.0
+mypy==0.770
+pytest==5.4.1
+pytest-asyncio==0.11.0
 pytest-cov==2.8.1
-pytest-mock==2.0.0
+pytest-mock==3.1.0
 pytest-random-order==1.0.4
 
 # Convert jupyter notebooks to markdown documents

requirements-hyperopt.txt

@@ -3,7 +3,8 @@
 
 # Required for hyperopt
 scipy==1.4.1
-scikit-learn==0.22.1
+scikit-learn==0.22.2.post1
 scikit-optimize==0.7.4
 filelock==3.0.12
 joblib==0.14.1
+progressbar2==3.51.0

requirements-plot.txt

@@ -1,5 +1,5 @@
 # Include all requirements to run the bot.
 -r requirements.txt
 
-plotly==4.5.1
+plotly==4.6.0
 

requirements.txt

@@ -1,5 +1,5 @@
 # Load common requirements
 -r requirements-common.txt
 
-numpy==1.18.1
-pandas==1.0.1
+numpy==1.18.3
+pandas==1.0.3

scripts/rest_client.py

@@ -156,6 +156,14 @@ class FtRestClient():
         """
         return self._get("show_config")
 
+    def trades(self, limit=None):
+        """Return trades history.
+
+        :param limit: Limits trades to the X last trades. No limit to get all the trades.
+        :return: json object
+        """
+        return self._get("trades", params={"limit": limit} if limit else 0)
+
     def whitelist(self):
         """Show the current whitelist.
 

setup.py

@@ -24,6 +24,7 @@ hyperopt = [
     'scikit-optimize',
     'filelock',
     'joblib',
+    'progressbar2',
     ]
 
 develop = [
@@ -73,7 +74,7 @@ setup(name='freqtrade',
           'jsonschema',
           'TA-Lib',
           'tabulate',
-          'coinmarketcap',
+          'pycoingecko',
           'py_find_1st',
           'python-rapidjson',
           'sdnotify',

setup.sh

@@ -252,7 +252,9 @@ function install() {
     echo "-------------------------"
     echo "Run the bot !"
     echo "-------------------------"
-    echo "You can now use the bot by executing 'source .env/bin/activate; freqtrade trade'."
+    echo "You can now use the bot by executing 'source .env/bin/activate; freqtrade <subcommand>'."
+    echo "You can see the list of available bot subcommands by executing 'source .env/bin/activate; freqtrade --help'."
+    echo "You verify that freqtrade is installed successfully by running 'source .env/bin/activate; freqtrade --version'."
 }
 
 function plot() {

tests/commands/test_commands.py

@@ -217,8 +217,9 @@ def test_list_markets(mocker, markets, capsys):
     ]
     start_list_markets(get_args(args), False)
     captured = capsys.readouterr()
-    assert ("Exchange Bittrex has 9 active markets: "
-            "BLK/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/USD, NEO/BTC, TKN/BTC, XLTCUSDT, XRP/BTC.\n"
+    assert ("Exchange Bittrex has 10 active markets: "
+            "BLK/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/ETH, LTC/USD, NEO/BTC, "
+            "TKN/BTC, XLTCUSDT, XRP/BTC.\n"
             in captured.out)
 
     patch_exchange(mocker, api_mock=api_mock, id="binance")
@@ -231,7 +232,7 @@ def test_list_markets(mocker, markets, capsys):
     pargs['config'] = None
     start_list_markets(pargs, False)
     captured = capsys.readouterr()
-    assert re.match("\nExchange Binance has 9 active markets:\n",
+    assert re.match("\nExchange Binance has 10 active markets:\n",
                     captured.out)
 
     patch_exchange(mocker, api_mock=api_mock, id="bittrex")
@@ -243,8 +244,8 @@ def test_list_markets(mocker, markets, capsys):
     ]
     start_list_markets(get_args(args), False)
     captured = capsys.readouterr()
-    assert ("Exchange Bittrex has 11 markets: "
-            "BLK/BTC, BTT/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/USD, LTC/USDT, NEO/BTC, "
+    assert ("Exchange Bittrex has 12 markets: "
+            "BLK/BTC, BTT/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/ETH, LTC/USD, LTC/USDT, NEO/BTC, "
             "TKN/BTC, XLTCUSDT, XRP/BTC.\n"
             in captured.out)
 
@@ -256,8 +257,8 @@ def test_list_markets(mocker, markets, capsys):
     ]
     start_list_markets(get_args(args), True)
     captured = capsys.readouterr()
-    assert ("Exchange Bittrex has 8 active pairs: "
-            "BLK/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/USD, NEO/BTC, TKN/BTC, XRP/BTC.\n"
+    assert ("Exchange Bittrex has 9 active pairs: "
+            "BLK/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/ETH, LTC/USD, NEO/BTC, TKN/BTC, XRP/BTC.\n"
             in captured.out)
 
     # Test list-pairs subcommand with --all: all pairs
@@ -268,8 +269,8 @@ def test_list_markets(mocker, markets, capsys):
     ]
     start_list_markets(get_args(args), True)
     captured = capsys.readouterr()
-    assert ("Exchange Bittrex has 10 pairs: "
-            "BLK/BTC, BTT/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/USD, LTC/USDT, NEO/BTC, "
+    assert ("Exchange Bittrex has 11 pairs: "
+            "BLK/BTC, BTT/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/ETH, LTC/USD, LTC/USDT, NEO/BTC, "
             "TKN/BTC, XRP/BTC.\n"
             in captured.out)
 
@@ -282,8 +283,8 @@ def test_list_markets(mocker, markets, capsys):
     ]
     start_list_markets(get_args(args), False)
     captured = capsys.readouterr()
-    assert ("Exchange Bittrex has 5 active markets with ETH, LTC as base currencies: "
-            "ETH/BTC, ETH/USDT, LTC/BTC, LTC/USD, XLTCUSDT.\n"
+    assert ("Exchange Bittrex has 6 active markets with ETH, LTC as base currencies: "
+            "ETH/BTC, ETH/USDT, LTC/BTC, LTC/ETH, LTC/USD, XLTCUSDT.\n"
             in captured.out)
 
     # active markets, base=LTC
@@ -295,8 +296,8 @@ def test_list_markets(mocker, markets, capsys):
     ]
     start_list_markets(get_args(args), False)
     captured = capsys.readouterr()
-    assert ("Exchange Bittrex has 3 active markets with LTC as base currency: "
-            "LTC/BTC, LTC/USD, XLTCUSDT.\n"
+    assert ("Exchange Bittrex has 4 active markets with LTC as base currency: "
+            "LTC/BTC, LTC/ETH, LTC/USD, XLTCUSDT.\n"
             in captured.out)
 
     # active markets, quote=USDT, USD
@@ -384,7 +385,7 @@ def test_list_markets(mocker, markets, capsys):
     ]
     start_list_markets(get_args(args), False)
     captured = capsys.readouterr()
-    assert ("Exchange Bittrex has 9 active markets:\n"
+    assert ("Exchange Bittrex has 10 active markets:\n"
             in captured.out)
 
     # Test tabular output, no markets found
@@ -407,7 +408,7 @@ def test_list_markets(mocker, markets, capsys):
     ]
     start_list_markets(get_args(args), False)
     captured = capsys.readouterr()
-    assert ('["BLK/BTC","ETH/BTC","ETH/USDT","LTC/BTC","LTC/USD","NEO/BTC",'
+    assert ('["BLK/BTC","ETH/BTC","ETH/USDT","LTC/BTC","LTC/ETH","LTC/USD","NEO/BTC",'
             '"TKN/BTC","XLTCUSDT","XRP/BTC"]'
             in captured.out)
 
@@ -446,11 +447,6 @@ def test_create_datadir_failed(caplog):
 
 
 def test_create_datadir(caplog, mocker):
-    # Ensure that caplog is empty before starting ...
-    # Should prevent random failures.
-    caplog.clear()
-    # Added assert here to analyze random test-failures ...
-    assert len(caplog.record_tuples) == 0
 
     cud = mocker.patch("freqtrade.commands.deploy_commands.create_userdata_dir", MagicMock())
     csf = mocker.patch("freqtrade.commands.deploy_commands.copy_sample_files", MagicMock())
@@ -463,7 +459,6 @@ def test_create_datadir(caplog, mocker):
 
     assert cud.call_count == 1
     assert csf.call_count == 1
-    assert len(caplog.record_tuples) == 0
 
 
 def test_start_new_strategy(mocker, caplog):
@@ -778,6 +773,20 @@ def test_hyperopt_list(mocker, capsys, hyperopt_results):
     assert all(x not in captured.out
                for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
                          " 11/12", " 12/12"])
+    args = [
+        "hyperopt-list",
+        "--profitable"
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(x in captured.out
+               for x in [" 2/12", " 10/12", "Best result:", "Buy hyperspace params",
+                         "Sell hyperspace params", "ROI table", "Stoploss"])
+    assert all(x not in captured.out
+               for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
+                         " 11/12", " 12/12"])
     args = [
         "hyperopt-list",
         "--no-details",
@@ -893,6 +902,21 @@ def test_hyperopt_list(mocker, capsys, hyperopt_results):
     assert all(x not in captured.out
                for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 7/12", " 8/12"
                          " 9/12", " 10/12", " 11/12", " 12/12"])
+    args = [
+        "hyperopt-list",
+        "--no-details",
+        "--export-csv", "test_file.csv"
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(x in captured.out
+               for x in ["CSV-File created!"])
+    f = Path("test_file.csv")
+    assert 'Best,1,2,-1.25%,-0.00125625,,-2.51,"3,930.0 m",0.43662' in f.read_text()
+    assert f.is_file()
+    f.unlink()
 
 
 def test_hyperopt_show(mocker, capsys, hyperopt_results):

tests/conftest.py

@@ -15,7 +15,7 @@ from telegram import Chat, Message, Update
 
 from freqtrade import constants, persistence
 from freqtrade.commands import Arguments
-from freqtrade.data.converter import parse_ticker_dataframe
+from freqtrade.data.converter import ohlcv_to_dataframe
 from freqtrade.edge import Edge, PairInfo
 from freqtrade.exchange import Exchange
 from freqtrade.freqtradebot import FreqtradeBot
@@ -166,24 +166,70 @@ def patch_get_signal(freqtrade: FreqtradeBot, value=(True, False)) -> None:
     freqtrade.exchange.refresh_latest_ohlcv = lambda p: None
 
 
-@pytest.fixture(autouse=True)
-def patch_coinmarketcap(mocker) -> None:
+def create_mock_trades(fee):
     """
-    Mocker to coinmarketcap to speed up tests
-    :param mocker: mocker to patch coinmarketcap class
+    Create some fake trades ...
+    """
+    # Simulate dry_run entries
+    trade = Trade(
+        pair='ETH/BTC',
+        stake_amount=0.001,
+        amount=123.0,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_rate=0.123,
+        exchange='bittrex',
+        open_order_id='dry_run_buy_12345'
+    )
+    Trade.session.add(trade)
+
+    trade = Trade(
+        pair='ETC/BTC',
+        stake_amount=0.001,
+        amount=123.0,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_rate=0.123,
+        close_rate=0.128,
+        close_profit=0.005,
+        exchange='bittrex',
+        is_open=False,
+        open_order_id='dry_run_sell_12345'
+    )
+    Trade.session.add(trade)
+
+    # Simulate prod entry
+    trade = Trade(
+        pair='ETC/BTC',
+        stake_amount=0.001,
+        amount=123.0,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_rate=0.123,
+        exchange='bittrex',
+        open_order_id='prod_buy_12345'
+    )
+    Trade.session.add(trade)
+
+
+@pytest.fixture(autouse=True)
+def patch_coingekko(mocker) -> None:
+    """
+    Mocker to coingekko to speed up tests
+    :param mocker: mocker to patch coingekko class
     :return: None
     """
 
-    tickermock = MagicMock(return_value={'price_usd': 12345.0})
-    listmock = MagicMock(return_value={'data': [{'id': 1, 'name': 'Bitcoin', 'symbol': 'BTC',
-                                                 'website_slug': 'bitcoin'},
-                                                {'id': 1027, 'name': 'Ethereum', 'symbol': 'ETH',
-                                                 'website_slug': 'ethereum'}
-                                                ]})
+    tickermock = MagicMock(return_value={'bitcoin': {'usd': 12345.0}, 'ethereum': {'usd': 12345.0}})
+    listmock = MagicMock(return_value=[{'id': 'bitcoin', 'name': 'Bitcoin', 'symbol': 'btc',
+                                        'website_slug': 'bitcoin'},
+                                       {'id': 'ethereum', 'name': 'Ethereum', 'symbol': 'eth',
+                                        'website_slug': 'ethereum'}
+                                       ])
     mocker.patch.multiple(
-        'freqtrade.rpc.fiat_convert.Market',
-        ticker=tickermock,
-        listings=listmock,
+        'freqtrade.rpc.fiat_convert.CoinGeckoAPI',
+        get_price=tickermock,
+        get_coins_list=listmock,
 
     )
 
@@ -575,7 +621,34 @@ def get_markets():
                 }
             },
             'info': {},
-        }
+        },
+        'LTC/ETH': {
+            'id': 'LTCETH',
+            'symbol': 'LTC/ETH',
+            'base': 'LTC',
+            'quote': 'ETH',
+            'active': True,
+            'precision': {
+                'base': 8,
+                'quote': 8,
+                'amount': 3,
+                'price': 5
+            },
+            'limits': {
+                'amount': {
+                    'min': 0.001,
+                    'max': 10000000.0
+                },
+                'price': {
+                    'min': 1e-05,
+                    'max': 1000.0
+                },
+                'cost': {
+                    'min': 0.01,
+                    'max': None
+                }
+            },
+        },
     }
 
 
@@ -666,6 +739,31 @@ def shitcoinmarkets(markets):
             "future": False,
             "active": True
         },
+        'ADAHALF/USDT': {
+            "percentage": True,
+            "tierBased": False,
+            "taker": 0.001,
+            "maker": 0.001,
+            "precision": {
+                "base": 8,
+                "quote": 8,
+                "amount": 2,
+                "price": 4
+            },
+            "limits": {
+            },
+            "id": "ADAHALFUSDT",
+            "symbol": "ADAHALF/USDT",
+            "base": "ADAHALF",
+            "quote": "USDT",
+            "baseId": "ADAHALF",
+            "quoteId": "USDT",
+            "info": {},
+            "type": "spot",
+            "spot": True,
+            "future": False,
+            "active": True
+    },
         })
     return shitmarkets
 
@@ -685,6 +783,7 @@ def limit_buy_order():
         'datetime': arrow.utcnow().isoformat(),
         'price': 0.00001099,
         'amount': 90.99181073,
+        'filled': 90.99181073,
         'remaining': 0.0,
         'status': 'closed'
     }
@@ -700,6 +799,7 @@ def market_buy_order():
         'datetime': arrow.utcnow().isoformat(),
         'price': 0.00004099,
         'amount': 91.99181073,
+        'filled': 91.99181073,
         'remaining': 0.0,
         'status': 'closed'
     }
@@ -715,6 +815,7 @@ def market_sell_order():
         'datetime': arrow.utcnow().isoformat(),
         'price': 0.00004173,
         'amount': 91.99181073,
+        'filled': 91.99181073,
         'remaining': 0.0,
         'status': 'closed'
     }
@@ -730,6 +831,7 @@ def limit_buy_order_old():
         'datetime': str(arrow.utcnow().shift(minutes=-601).datetime),
         'price': 0.00001099,
         'amount': 90.99181073,
+        'filled': 0.0,
         'remaining': 90.99181073,
         'status': 'open'
     }
@@ -745,6 +847,7 @@ def limit_sell_order_old():
         'datetime': arrow.utcnow().shift(minutes=-601).isoformat(),
         'price': 0.00001099,
         'amount': 90.99181073,
+        'filled': 0.0,
         'remaining': 90.99181073,
         'status': 'open'
     }
@@ -760,6 +863,7 @@ def limit_buy_order_old_partial():
         'datetime': arrow.utcnow().shift(minutes=-601).isoformat(),
         'price': 0.00001099,
         'amount': 90.99181073,
+        'filled': 23.0,
         'remaining': 67.99181073,
         'status': 'open'
     }
@@ -783,6 +887,7 @@ def limit_sell_order():
         'datetime': arrow.utcnow().isoformat(),
         'price': 0.00001173,
         'amount': 90.99181073,
+        'filled': 90.99181073,
         'remaining': 0.0,
         'status': 'closed'
     }
@@ -822,15 +927,15 @@ def order_book_l2():
 
 
 @pytest.fixture
-def ticker_history_list():
+def ohlcv_history_list():
     return [
         [
             1511686200000,  # unix timestamp ms
-            8.794e-05,  # open
-            8.948e-05,  # high
-            8.794e-05,  # low
-            8.88e-05,  # close
-            0.0877869,  # volume (in quote currency)
+            8.794e-05,      # open
+            8.948e-05,      # high
+            8.794e-05,      # low
+            8.88e-05,       # close
+            0.0877869,      # volume (in quote currency)
         ],
         [
             1511686500000,
@@ -852,8 +957,9 @@ def ticker_history_list():
 
 
 @pytest.fixture
-def ticker_history(ticker_history_list):
-    return parse_ticker_dataframe(ticker_history_list, "5m", pair="UNITTEST/BTC", fill_missing=True)
+def ohlcv_history(ohlcv_history_list):
+    return ohlcv_to_dataframe(ohlcv_history_list, "5m", pair="UNITTEST/BTC",
+                              fill_missing=True)
 
 
 @pytest.fixture
@@ -1162,14 +1268,37 @@ def tickers():
             "quoteVolume": 323652.075405,
             "info": {}
         },
+        # Example of leveraged pair with incomplete info
+        "ADAHALF/USDT": {
+            "symbol": "ADAHALF/USDT",
+            "timestamp": 1580469388244,
+            "datetime": "2020-01-31T11:16:28.244Z",
+            "high": None,
+            "low": None,
+            "bid": 0.7305,
+            "bidVolume": None,
+            "ask": 0.7342,
+            "askVolume": None,
+            "vwap": None,
+            "open": None,
+            "close": None,
+            "last": None,
+            "previousClose": None,
+            "change": None,
+            "percentage": 2.628,
+            "average": None,
+            "baseVolume": 0.0,
+            "quoteVolume": 0.0,
+            "info": {}
+        },
     })
 
 
 @pytest.fixture
 def result(testdatadir):
     with (testdatadir / 'UNITTEST_BTC-1m.json').open('r') as data_file:
-        return parse_ticker_dataframe(json.load(data_file), '1m', pair="UNITTEST/BTC",
-                                      fill_missing=True)
+        return ohlcv_to_dataframe(json.load(data_file), '1m', pair="UNITTEST/BTC",
+                                  fill_missing=True)
 
 
 @pytest.fixture(scope="function")