Version bump to 2022.2.2

New release 2022.2.1

.github/FUNDING.yml

@@ -0,0 +1,3 @@
+# These are supported funding model platforms
+
+github: [xmatthias]

.github/ISSUE_TEMPLATE/bug_report.md

@@ -9,7 +9,7 @@ assignees: ''
 <!-- 
 Have you searched for similar issues before posting it?
 
-If you have discovered a bug in the bot, please [search our issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue). 
+If you have discovered a bug in the bot, please [search the issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue). 
 If it hasn't been reported, please create a new issue.
 
 Please do not use bug reports to request new features.

.github/ISSUE_TEMPLATE/question.md

@@ -22,4 +22,4 @@ Please do not use the question template to report bugs or to request new feature
   
 ## Your question
 
-*Ask the question you have not been able to find an answer in our [Documentation](https://www.freqtrade.io/en/latest/)*
+*Ask the question you have not been able to find an answer in the [Documentation](https://www.freqtrade.io/en/latest/)*

.github/dependabot.yml

@@ -5,9 +5,17 @@ updates:
   schedule:
     interval: daily
   open-pull-requests-limit: 10
+
 - package-ecosystem: pip
   directory: "/"
   schedule:
     interval: weekly
   open-pull-requests-limit: 10
   target-branch: develop
+
+- package-ecosystem: "github-actions"
+  directory: "/"
+  schedule:
+    interval: "weekly"
+  open-pull-requests-limit: 10
+  target-branch: develop

.github/workflows/ci.yml

@@ -3,9 +3,9 @@ name: Freqtrade CI
 on:
   push:
     branches:
-      - master
       - stable
       - develop
+      - ci/*
     tags:
   release:
     types: [published]
@@ -20,7 +20,7 @@ jobs:
     strategy:
       matrix:
         os: [ ubuntu-18.04, ubuntu-20.04 ]
-        python-version: [3.7, 3.8, 3.9]
+        python-version: ["3.8", "3.9", "3.10"]
 
     steps:
     - uses: actions/checkout@v2
@@ -39,7 +39,7 @@ jobs:
 
     - name: pip cache (linux)
       uses: actions/cache@v2
-      if: startsWith(matrix.os, 'ubuntu')
+      if: runner.os == 'Linux'
       with:
         path: ~/.cache/pip
         key: test-${{ matrix.os }}-${{ matrix.python-version }}-pip
@@ -50,8 +50,9 @@ jobs:
         cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
 
     - name: Installation - *nix
+      if: runner.os == 'Linux'
       run: |
-        python -m pip install --upgrade pip
+        python -m pip install --upgrade pip wheel
         export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
         export TA_LIBRARY_PATH=${HOME}/dependencies/lib
         export TA_INCLUDE_PATH=${HOME}/dependencies/include
@@ -69,7 +70,7 @@ jobs:
       if: matrix.python-version == '3.9'
 
     - name: Coveralls
-      if: (startsWith(matrix.os, 'ubuntu-20') && matrix.python-version == '3.8')
+      if: (runner.os == 'Linux' && matrix.python-version == '3.8')
       env:
         # Coveralls token. Not used as secret due to github not providing secrets to forked repositories
         COVERALLS_REPO_TOKEN: 6D1m0xupS3FgutfuGao8keFf9Hc0FpIXu
@@ -101,23 +102,20 @@ jobs:
       run: |
         mypy freqtrade scripts
 
-    - name: Slack Notification
-      uses: lazy-actions/slatify@v3.0.0
+    - name: Discord notification
+      uses: rjstone/discord-webhook-notify@v1
       if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
       with:
-        type: ${{ job.status }}
-        job_name: '*Freqtrade CI ${{ matrix.os }}*'
-        mention: 'here'
-        mention_if: 'failure'
-        channel: '#notifications'
-        url: ${{ secrets.SLACK_WEBHOOK }}
+          severity: error
+          details: Freqtrade CI failed on ${{ matrix.os }}
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
   build_macos:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
         os: [ macos-latest ]
-        python-version: [3.7, 3.8, 3.9]
+        python-version: ["3.8", "3.9", "3.10"]
 
     steps:
     - uses: actions/checkout@v2
@@ -136,7 +134,7 @@ jobs:
 
     - name: pip cache (macOS)
       uses: actions/cache@v2
-      if: startsWith(matrix.os, 'macOS')
+      if: runner.os == 'macOS'
       with:
         path: ~/Library/Caches/pip
         key: test-${{ matrix.os }}-${{ matrix.python-version }}-pip
@@ -147,10 +145,11 @@ jobs:
         cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
 
     - name: Installation - macOS
+      if: runner.os == 'macOS'
       run: |
         brew update
         brew install hdf5 c-blosc
-        python -m pip install --upgrade pip
+        python -m pip install --upgrade pip wheel
         export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
         export TA_LIBRARY_PATH=${HOME}/dependencies/lib
         export TA_INCLUDE_PATH=${HOME}/dependencies/include
@@ -162,7 +161,7 @@ jobs:
         pytest --random-order --cov=freqtrade --cov-config=.coveragerc
 
     - name: Coveralls
-      if: (startsWith(matrix.os, 'ubuntu-20') && matrix.python-version == '3.8')
+      if: (runner.os == 'Linux' && matrix.python-version == '3.8')
       env:
         # Coveralls token. Not used as secret due to github not providing secrets to forked repositories
         COVERALLS_REPO_TOKEN: 6D1m0xupS3FgutfuGao8keFf9Hc0FpIXu
@@ -194,17 +193,13 @@ jobs:
       run: |
         mypy freqtrade scripts
 
-    - name: Slack Notification
-      uses: lazy-actions/slatify@v3.0.0
+    - name: Discord notification
+      uses: rjstone/discord-webhook-notify@v1
       if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
       with:
-        type: ${{ job.status }}
-        job_name: '*Freqtrade CI ${{ matrix.os }}*'
-        mention: 'here'
-        mention_if: 'failure'
-        channel: '#notifications'
-        url: ${{ secrets.SLACK_WEBHOOK }}
-
+          severity: info
+          details: Test Succeeded!
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
   build_windows:
 
@@ -212,7 +207,7 @@ jobs:
     strategy:
       matrix:
         os: [ windows-latest ]
-        python-version: [3.7, 3.8]
+        python-version: ["3.8", "3.9", "3.10"]
 
     steps:
     - uses: actions/checkout@v2
@@ -224,7 +219,6 @@ jobs:
 
     - name: Pip cache (Windows)
       uses: actions/cache@preview
-      if: startsWith(runner.os, 'Windows')
       with:
         path: ~\AppData\Local\pip\Cache
         key: ${{ matrix.os }}-${{ matrix.python-version }}-pip
@@ -257,16 +251,13 @@ jobs:
       run: |
         mypy freqtrade scripts
 
-    - name: Slack Notification
-      uses: lazy-actions/slatify@v3.0.0
+    - name: Discord notification
+      uses: rjstone/discord-webhook-notify@v1
       if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
       with:
-        type: ${{ job.status }}
-        job_name: '*Freqtrade CI windows*'
-        mention: 'here'
-        mention_if: 'failure'
-        channel: '#notifications'
-        url: ${{ secrets.SLACK_WEBHOOK }}
+          severity: error
+          details: Test Failed
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
   docs_check:
     runs-on: ubuntu-20.04
@@ -288,14 +279,13 @@ jobs:
         pip install mkdocs
         mkdocs build
 
-    - name: Slack Notification
-      uses: lazy-actions/slatify@v3.0.0
+    - name: Discord notification
+      uses: rjstone/discord-webhook-notify@v1
       if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
       with:
-        type: ${{ job.status }}
-        job_name: '*Freqtrade Docs*'
-        channel: '#notifications'
-        url: ${{ secrets.SLACK_WEBHOOK }}
+          severity: error
+          details: Freqtrade doc test failed!
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
   cleanup-prior-runs:
     runs-on: ubuntu-20.04
@@ -306,7 +296,7 @@ jobs:
       env:
         GITHUB_TOKEN: "${{ secrets.GITHUB_TOKEN }}"
 
-  # Notify on slack only once - when CI completes (and after deploy) in case it's successfull
+  # Notify only once - when CI completes (and after deploy) in case it's successfull
   notify-complete:
     needs: [ build_linux, build_macos, build_windows, docs_check ]
     runs-on: ubuntu-20.04
@@ -320,14 +310,13 @@ jobs:
       env:
         GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
 
-    - name: Slack Notification
-      uses: lazy-actions/slatify@v3.0.0
+    - name: Discord notification
+      uses: rjstone/discord-webhook-notify@v1
       if: always() && steps.check.outputs.has-permission && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
       with:
-        type: ${{ job.status }}
-        job_name: '*Freqtrade CI*'
-        channel: '#notifications'
-        url: ${{ secrets.SLACK_WEBHOOK }}
+          severity: info
+          details: Test Completed!
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
   deploy:
     needs: [ build_linux, build_macos, build_windows, docs_check ]
@@ -385,7 +374,7 @@ jobs:
 
     - name: Set up Docker Buildx
       id: buildx
-      uses: crazy-max/ghaction-docker-buildx@v1
+      uses: crazy-max/ghaction-docker-buildx@v3.3.1
       with:
         buildx-version: latest
         qemu-version: latest
@@ -400,17 +389,13 @@ jobs:
       run: |
         build_helpers/publish_docker_multi.sh
 
-
-    - name: Slack Notification
-      uses: lazy-actions/slatify@v3.0.0
+    - name: Discord notification
+      uses: rjstone/discord-webhook-notify@v1
       if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
       with:
-        type: ${{ job.status }}
-        job_name: '*Freqtrade CI Deploy*'
-        mention: 'here'
-        mention_if: 'failure'
-        channel: '#notifications'
-        url: ${{ secrets.SLACK_WEBHOOK }}
+          severity: info
+          details: Deploy Succeeded!
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
 
   deploy_arm:

.github/workflows/docker_update_readme.yml

@@ -10,7 +10,7 @@ jobs:
     steps:
     - uses: actions/checkout@v1
     - name: Docker Hub Description
-      uses: peter-evans/dockerhub-description@v2.1.0
+      uses: peter-evans/dockerhub-description@v2.4.3
       env:
         DOCKERHUB_USERNAME: ${{ secrets.DOCKER_USERNAME }}
         DOCKERHUB_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}

.gitignore

@@ -1,6 +1,8 @@
 # Freqtrade rules
 config*.json
 *.sqlite
+*.sqlite-shm
+*.sqlite-wal
 logfile.txt
 user_data/*
 !user_data/strategy/sample_strategy.py
@@ -10,6 +12,9 @@ freqtrade-plot.html
 freqtrade-profit-plot.html
 freqtrade/rpc/api_server/ui/*
 
+# Macos related
+.DS_Store
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

.travis.yml

@@ -1,55 +0,0 @@
-os:
-- linux
-dist: bionic
-language: python
-python:
-- 3.8
-services:
-  - docker
-env:
-  global:
-    - IMAGE_NAME=freqtradeorg/freqtrade
-install:
-- 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/include
-- pip install -r requirements-dev.txt
-- pip install -e .
-jobs:
-
-  include:
-    - stage: tests
-      script:
-      - pytest --random-order --cov=freqtrade --cov-config=.coveragerc
-      # Allow failure for coveralls
-      # - coveralls || true
-      name: pytest
-    - script:
-      - cp config_examples/config_bittrex.example.json config.json
-      - freqtrade create-userdir --userdir user_data
-      - freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
-      name: backtest
-    - script:
-      - cp config_examples/config_bittrex.example.json config.json
-      - freqtrade create-userdir --userdir user_data
-      - freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily
-      name: hyperopt
-    - script: flake8
-      name: flake8
-    - script:
-      # Test Documentation boxes -
-      # !!! <TYPE>: is not allowed!
-      # !!! <TYPE> "title" - Title needs to be quoted!
-      - grep -Er '^!{3}\s\S+:|^!{3}\s\S+\s[^"]' docs/*; test $? -ne 0
-      name: doc syntax
-    - script: mypy freqtrade scripts
-      name: mypy
-
-notifications:
-  slack:
-    secure: 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
-cache:
-  pip: True
-  directories:
-    - $HOME/dependencies

CONTRIBUTING.md

@@ -56,6 +56,13 @@ To help with that, we encourage you to install the git pre-commit
 hook that will warn you when you try to commit code that fails these checks. 
 Guide for installing them is [here](http://flake8.pycqa.org/en/latest/user/using-hooks.html).
 
+##### Additional styles applied
+
+* Have docstrings on all public methods
+* Use double-quotes for docstrings
+* Multiline docstrings should be indented to the level of the first quote
+* Doc-strings should follow the reST format (`:param xxx: ...`, `:return: ...`, `:raises KeyError: ... `)
+
 ### 3. Test if all type-hints are correct
 
 #### Run mypy

Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.9.7-slim-buster as base
+FROM python:3.9.9-slim-bullseye as base
 
 # Setup env
 ENV LANG C.UTF-8

README.md

@@ -5,10 +5,14 @@
 [![Documentation](https://readthedocs.org/projects/freqtrade/badge/)](https://www.freqtrade.io)
 [![Maintainability](https://api.codeclimate.com/v1/badges/5737e6d668200b7518ff/maintainability)](https://codeclimate.com/github/freqtrade/freqtrade/maintainability)
 
-Freqtrade is a free and open source crypto trading bot written in Python. It is designed to support all major exchanges and be controlled via Telegram. It contains backtesting, plotting and money management tools as well as strategy optimization by machine learning.
+Freqtrade is a free and open source crypto trading bot written in Python. It is designed to support all major exchanges and be controlled via Telegram or webUI. It contains backtesting, plotting and money management tools as well as strategy optimization by machine learning.
 
 ![freqtrade](https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docs/assets/freqtrade-screenshot.png)
 
+## Sponsored promotion
+
+[![tokenbot-promo](https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docs/assets/TokenBot-Freqtrade-banner.png)](https://tokenbot.com/?utm_source=github&utm_medium=freqtrade&utm_campaign=algodevs)
+
 ## Disclaimer
 
 This software is for educational purposes only. Do not risk money which
@@ -28,9 +32,10 @@ Please read the [exchange specific notes](docs/exchanges.md) to learn about even
 
 - [X] [Binance](https://www.binance.com/) ([*Note for binance users](docs/exchanges.md#binance-blacklist))
 - [X] [Bittrex](https://bittrex.com/)
-- [X] [Kraken](https://kraken.com/)
 - [X] [FTX](https://ftx.com)
 - [X] [Gate.io](https://www.gate.io/ref/6266643)
+- [X] [Kraken](https://kraken.com/)
+- [X] [OKX](https://www.okx.com/)
 - [ ] [potentially many others](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
 
 ### Community tested
@@ -44,11 +49,11 @@ Exchanges confirmed working by the community:
 
 We invite you to read the bot documentation to ensure you understand how the bot is working.
 
-Please find the complete documentation on our [website](https://www.freqtrade.io).
+Please find the complete documentation on the [freqtrade website](https://www.freqtrade.io).
 
 ## Features
 
-- [x] **Based on Python 3.7+**: For botting on any operating system - Windows, macOS and Linux.
+- [x] **Based on Python 3.8+**: For botting on any operating system - Windows, macOS and Linux.
 - [x] **Persistence**: Persistence is achieved through sqlite.
 - [x] **Dry-run**: Run the bot without paying money.
 - [x] **Backtesting**: Run a simulation of your buy/sell strategy.
@@ -56,9 +61,9 @@ Please find the complete documentation on our [website](https://www.freqtrade.io
 - [x] **Edge position sizing** Calculate your win rate, risk reward ratio, the best stoploss and adjust your position size before taking a position for each specific market. [Learn more](https://www.freqtrade.io/en/stable/edge/).
 - [x] **Whitelist crypto-currencies**: Select which crypto-currency you want to trade or use dynamic whitelists.
 - [x] **Blacklist crypto-currencies**: Select which crypto-currency you want to avoid.
+- [x] **Builtin WebUI**: Builtin web UI to manage your bot.
 - [x] **Manageable via Telegram**: Manage the bot with Telegram.
-- [x] **Display profit/loss in fiat**: Display your profit/loss in 33 fiat.
-- [x] **Daily summary of profit/loss**: Provide a daily summary of your profit/loss.
+- [x] **Display profit/loss in fiat**: Display your profit/loss in fiat currency.
 - [x] **Performance status report**: Provide a performance status of your current trades.
 
 ## Quick start
@@ -121,7 +126,7 @@ optional arguments:
 
 ### Telegram RPC commands
 
-Telegram is not mandatory. However, this is a great way to control your bot. More details and the full command list on our [documentation](https://www.freqtrade.io/en/latest/telegram-usage/)
+Telegram is not mandatory. However, this is a great way to control your bot. More details and the full command list on the [documentation](https://www.freqtrade.io/en/latest/telegram-usage/)
 
 - `/start`: Starts the trader.
 - `/stop`: Stops the trader.
@@ -152,10 +157,10 @@ For any questions not covered by the documentation or for further information ab
 ### [Bugs / Issues](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)
 
 If you discover a bug in the bot, please
-[search our issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)
+[search the issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)
 first. If it hasn't been reported, please
 [create a new issue](https://github.com/freqtrade/freqtrade/issues/new/choose) and
-ensure you follow the template guide so that our team can assist you as
+ensure you follow the template guide so that the team can assist you as
 quickly as possible.
 
 ### [Feature Requests](https://github.com/freqtrade/freqtrade/labels/enhancement)
@@ -169,13 +174,13 @@ in the bug reports.
 
 ### [Pull Requests](https://github.com/freqtrade/freqtrade/pulls)
 
-Feel like our bot is missing a feature? We welcome your pull requests!
+Feel like the bot is missing a feature? We welcome your pull requests!
 
-Please read our
+Please read the
 [Contributing document](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)
 to understand the requirements before sending your pull-requests.
 
-Coding is not a necessity to contribute - maybe start with improving our documentation?
+Coding is not a necessity to contribute - maybe start with improving the documentation?
 Issues labeled [good first issue](https://github.com/freqtrade/freqtrade/labels/good%20first%20issue) can be good first contributions, and will help get you familiar with the codebase.
 
 **Note** before starting any major new feature work, *please open an issue describing what you are planning to do* or talk to us on [discord](https://discord.gg/p7nuUNVfP7) (please use the #dev channel for this). This will ensure that interested parties can give valuable feedback on the feature, and let others know that you are working on it.
@@ -196,7 +201,7 @@ To run this bot we recommend you a cloud instance with a minimum of:
 
 ### Software requirements
 
-- [Python 3.7.x](http://docs.python-guide.org/en/latest/starting/installation/)
+- [Python >= 3.8](http://docs.python-guide.org/en/latest/starting/installation/)
 - [pip](https://pip.pypa.io/en/stable/installing/)
 - [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
 - [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html)

build_helpers/install_windows.ps1

@@ -1,19 +1,18 @@
 # Downloads don't work automatically, since the URL is regenerated via javascript.
 # Downloaded from https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib
 
-python -m pip install --upgrade pip
+python -m pip install --upgrade pip wheel
 
 $pyv = python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')"
 
-if ($pyv -eq '3.7') {
-    pip install build_helpers\TA_Lib-0.4.21-cp37-cp37m-win_amd64.whl
-}
 if ($pyv -eq '3.8') {
-    pip install build_helpers\TA_Lib-0.4.21-cp38-cp38-win_amd64.whl
+    pip install build_helpers\TA_Lib-0.4.24-cp38-cp38-win_amd64.whl
 }
 if ($pyv -eq '3.9') {
-    pip install build_helpers\TA_Lib-0.4.21-cp39-cp39-win_amd64.whl
+    pip install build_helpers\TA_Lib-0.4.24-cp39-cp39-win_amd64.whl
+}
+if ($pyv -eq '3.10') {
+    pip install build_helpers\TA_Lib-0.4.24-cp310-cp310-win_amd64.whl
 }
-
 pip install -r requirements-dev.txt
 pip install -e .

config_examples/config_binance.example.json

@@ -9,7 +9,9 @@
     "cancel_open_orders_on_exit": false,
     "unfilledtimeout": {
         "buy": 10,
-        "sell": 30
+        "sell": 10,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
     },
     "bid_strategy": {
         "ask_last_balance": 0.0,

config_examples/config_bittrex.example.json

@@ -9,7 +9,9 @@
     "cancel_open_orders_on_exit": false,
     "unfilledtimeout": {
         "buy": 10,
-        "sell": 30
+        "sell": 10,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
     },
     "bid_strategy": {
         "use_order_book": true,

config_examples/config_ftx.example.json

@@ -9,7 +9,9 @@
     "cancel_open_orders_on_exit": false,
     "unfilledtimeout": {
         "buy": 10,
-        "sell": 30
+        "sell": 10,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
     },
     "bid_strategy": {
         "ask_last_balance": 0.0,

config_examples/config_full.example.json

@@ -8,6 +8,7 @@
     "amend_last_stake_amount": false,
     "last_stake_amount_min_ratio": 0.5,
     "dry_run": true,
+    "dry_run_wallet": 1000,
     "cancel_open_orders_on_exit": false,
     "timeframe": "5m",
     "trailing_stop": false,
@@ -18,6 +19,7 @@
     "sell_profit_only": false,
     "sell_profit_offset": 0.0,
     "ignore_roi_if_buy_signal": false,
+    "ignore_buying_expired_candle_after": 300,
     "minimal_roi": {
         "40":  0.0,
         "30":  0.01,
@@ -27,7 +29,8 @@
     "stoploss": -0.10,
     "unfilledtimeout": {
         "buy": 10,
-        "sell": 30,
+        "sell": 10,
+        "exit_timeout_count": 0,
         "unit": "minutes"
     },
     "bid_strategy": {
@@ -84,6 +87,7 @@
         "key": "your_exchange_key",
         "secret": "your_exchange_secret",
         "password": "",
+        "log_responses": false,
         "ccxt_config": {},
         "ccxt_async_config": {},
         "pair_whitelist": [

config_examples/config_kraken.example.json

@@ -9,7 +9,9 @@
     "cancel_open_orders_on_exit": false,
     "unfilledtimeout": {
         "buy": 10,
-        "sell": 30
+        "sell": 10,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
     },
     "bid_strategy": {
         "use_order_book": true,

docker/Dockerfile.armhf

@@ -1,4 +1,4 @@
-FROM python:3.7.10-slim-buster as base
+FROM python:3.9.9-slim-bullseye as base
 
 # Setup env
 ENV LANG C.UTF-8

docs/advanced-hyperopt.md

@@ -13,7 +13,7 @@ A sample of this can be found below, which is identical to the Default Hyperopt
 
 ``` python
 from datetime import datetime
-from typing import Dict
+from typing import Any, Dict
 
 from pandas import DataFrame
 
@@ -105,7 +105,7 @@ You can define your own estimator for Hyperopt by implementing `generate_estimat
 ```python
 class MyAwesomeStrategy(IStrategy):
     class HyperOpt:
-        def generate_estimator():
+        def generate_estimator(dimensions: List['Dimension'], **kwargs):
             return "RF"
 
 ```
@@ -119,13 +119,34 @@ Example for `ExtraTreesRegressor` ("ET") with additional parameters:
 ```python
 class MyAwesomeStrategy(IStrategy):
     class HyperOpt:
-        def generate_estimator():
+        def generate_estimator(dimensions: List['Dimension'], **kwargs):
             from skopt.learning import ExtraTreesRegressor
             # Corresponds to "ET" - but allows additional parameters.
             return ExtraTreesRegressor(n_estimators=100)
 
 ```
 
+The `dimensions` parameter is the list of `skopt.space.Dimension` objects corresponding to the parameters to be optimized. It can be used to create isotropic kernels for the `skopt.learning.GaussianProcessRegressor` estimator. Here's an example:
+
+```python
+class MyAwesomeStrategy(IStrategy):
+    class HyperOpt:
+        def generate_estimator(dimensions: List['Dimension'], **kwargs):
+            from skopt.utils import cook_estimator
+            from skopt.learning.gaussian_process.kernels import (Matern, ConstantKernel)
+            kernel_bounds = (0.0001, 10000)
+            kernel = (
+                ConstantKernel(1.0, kernel_bounds) * 
+                Matern(length_scale=np.ones(len(dimensions)), length_scale_bounds=[kernel_bounds for d in dimensions], nu=2.5)
+            )
+            kernel += (
+                ConstantKernel(1.0, kernel_bounds) * 
+                Matern(length_scale=np.ones(len(dimensions)), length_scale_bounds=[kernel_bounds for d in dimensions], nu=1.5)
+            )
+
+            return cook_estimator("GP", space=dimensions, kernel=kernel, n_restarts_optimizer=2)
+```
+
 !!! Note
     While custom estimators can be provided, it's up to you as User to do research on possible parameters and analyze / understand which ones should be used.
     If you're unsure about this, best use one of the Defaults (`"ET"` has proven to be the most versatile) without further parameters.

docs/advanced-setup.md

@@ -176,12 +176,15 @@ Log messages are send to `syslog` with the `user` facility. So you can see them
 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
+
 ```
 if $programname startswith "freqtrade" then -/var/log/freqtrade.log
 ```
+
 to one of the rsyslog configuration files, for example at the end of the `/etc/rsyslog.d/50-default.conf`.
 
 For `syslog` (`rsyslog`), the reduction mode can be switched on. This will reduce the number of repeating messages. For instance, multiple bot Heartbeat messages will be reduced to a single message when nothing else happens with the bot. To achieve this, set in `/etc/rsyslog.conf`:
+
 ```
 # Filter duplicated messages
 $RepeatedMsgReduction on

docs/backtesting.md

@@ -22,6 +22,7 @@ usage: freqtrade backtesting [-h] [-v] [--logfile FILE] [-V] [-c PATH]
                              [--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
                              [--export {none,trades}] [--export-filename PATH]
                              [--breakdown {day,week,month} [{day,week,month} ...]]
+                             [--cache {none,day,week,month}]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -76,6 +77,9 @@ optional arguments:
                         _today.json`
   --breakdown {day,week,month} [{day,week,month} ...]
                         Show backtesting breakdown per [day, week, month].
+  --cache {none,day,week,month}
+                        Load a cached backtest result no older than specified
+                        age (default: day).
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
@@ -115,7 +119,7 @@ The result of backtesting will confirm if your bot has better odds of making a p
 All profit calculations include fees, and freqtrade will use the exchange's default fees for the calculation.
 
 !!! Warning "Using dynamic pairlists for backtesting"
-    Using dynamic pairlists is possible, however it relies on the current market conditions - which will not reflect the historic status of the pairlist.
+    Using dynamic pairlists is possible (not all of the handlers are allowed to be used in backtest mode), however it relies on the current market conditions - which will not reflect the historic status of the pairlist.
     Also, when using pairlists other than StaticPairlist, reproducibility of backtesting-results cannot be guaranteed.
     Please read the [pairlists documentation](plugins.md#pairlists) for more information.
 
@@ -309,10 +313,11 @@ A backtesting result will look like that:
 | Avg. Duration Winners | 4:23:00             |
 | Avg. Duration Loser   | 6:55:00             |
 | Rejected Buy signals  | 3089                |
+| Entry/Exit Timeouts   | 0 / 0               |
 |                       |                     |
 | Min balance           | 0.00945123 BTC      |
 | Max balance           | 0.01846651 BTC      |
-| Drawdown              | 50.63%              |
+| Drawdown (Account)    | 13.33%              |
 | Drawdown              | 0.0015 BTC          |
 | Drawdown high         | 0.0013 BTC          |
 | Drawdown low          | -0.0002 BTC         |
@@ -396,10 +401,11 @@ It contains some useful key metrics about performance of your strategy on backte
 | Avg. Duration Winners | 4:23:00             |
 | Avg. Duration Loser   | 6:55:00             |
 | Rejected Buy signals  | 3089                |
+| Entry/Exit Timeouts   | 0 / 0               |
 |                       |                     |
 | Min balance           | 0.00945123 BTC      |
 | Max balance           | 0.01846651 BTC      |
-| Drawdown              | 50.63%              |
+| Drawdown (Account)    | 13.33%              |
 | Drawdown              | 0.0015 BTC          |
 | Drawdown high         | 0.0013 BTC          |
 | Drawdown low          | -0.0002 BTC         |
@@ -425,8 +431,10 @@ It contains some useful key metrics about performance of your strategy on backte
 - `Days win/draw/lose`: Winning / Losing days (draws are usually days without closed trade).
 - `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.
 - `Rejected Buy signals`: Buy signals that could not be acted upon due to max_open_trades being reached.
+- `Entry/Exit Timeouts`: Entry/exit orders which did not fill (only applicable if custom pricing is used).
 - `Min balance` / `Max balance`: Lowest and Highest Wallet balance during the backtest period.
-- `Drawdown`: Maximum drawdown experienced. For example, the value of 50% means that from highest to subsequent lowest point, a 50% drop was experienced).
+- `Drawdown (Account)`: Maximum Account Drawdown experienced. Calculated as $(Absolute Drawdown) / (DrawdownHigh + startingBalance)$.
+- `Drawdown`: Maximum, absolute drawdown experienced. Difference between Drawdown High and Subsequent Low point.
 - `Drawdown high` / `Drawdown low`: Profit at the beginning and end of the largest drawdown period. A negative low value means initial capital lost.
 - `Drawdown Start` / `Drawdown End`: Start and end datetime for this largest drawdown (can also be visualized via the `plot-dataframe` sub-command).
 - `Market change`: Change of the market during the backtest period. Calculated as average of all pairs changes from the first to the last candle using the "close" column.
@@ -456,10 +464,18 @@ freqtrade backtesting --strategy MyAwesomeStrategy --breakdown day month
 
 The output will show a table containing the realized absolute Profit (in stake currency) for the given timeperiod, as well as wins, draws and losses that materialized (closed) on this day.
 
+### Backtest result caching
+
+To save time, by default backtest will reuse a cached result from within the last day when the backtested strategy and config match that of a previous backtest. To force a new backtest despite existing result for an identical run specify `--cache none` parameter.
+
+!!! Warning
+    Caching is automatically disabled for open-ended timeranges (`--timerange 20210101-`), as freqtrade cannot ensure reliably that the underlying data didn't change. It can also use cached results where it shouldn't if the original backtest had missing data at the end, which was fixed by downloading more data.
+    In this instance, please use `--cache none` once to force a fresh backtest.
+
 ### Further backtest-result analysis
 
 To further analyze your backtest results, you can [export the trades](#exporting-trades-to-file).
-You can then load the trades to perform further analysis as shown in our [data analysis](data-analysis.md#backtesting) backtesting section.
+You can then load the trades to perform further analysis as shown in the [data analysis](data-analysis.md#backtesting) backtesting section.
 
 ## Assumptions made by backtesting
 
@@ -478,13 +494,14 @@ Since backtesting lacks some detailed information about what happens within a ca
 - Low happens before high for stoploss, protecting capital first
 - Trailing stoploss
   - Trailing Stoploss is only adjusted if it's below the candle's low (otherwise it would be triggered)
+  - On trade entry candles that trigger trailing stoploss, the "minimum offset" (`stop_positive_offset`) is assumed (instead of high) - and the stop is calculated from this point
   - High happens first - adjusting stoploss
   - Low uses the adjusted stoploss (so sells with large high-low difference are backtested correctly)
   - ROI applies before trailing-stop, ensuring profits are "top-capped" at ROI if both ROI and trailing stop applies
 - Sell-reason does not explain if a trade was positive or negative, just what triggered the sell (this can look odd if negative ROI values are used)
 - Evaluation sequence (if multiple signals happen on the same candle)
-  - ROI (if not stoploss)
   - Sell-signal
+  - ROI (if not stoploss)
   - Stoploss
 
 Taking these assumptions, backtesting tries to mirror real trading as closely as possible. However, backtesting will **never** replace running a strategy in dry-run mode.

docs/bot-basics.md

@@ -38,6 +38,7 @@ By default, loop runs every few seconds (`internals.process_throttle_secs`) and
   * Considers stoploss, ROI and sell-signal, `custom_sell()` and `custom_stoploss()`.
   * Determine sell-price based on `ask_strategy` configuration setting or by using the `custom_exit_price()` callback.
   * Before a sell order is placed, `confirm_trade_exit()` strategy callback is called.
+* Check position adjustments for open trades if enabled by calling `adjust_trade_position()` and place additional order if required.
 * Check if trade-slots are still available (if `max_open_trades` is reached).
 * Verifies buy signal trying to enter new positions.
   * Determine buy-price based on `bid_strategy` configuration setting, or by using the `custom_entry_price()` callback.
@@ -56,7 +57,12 @@ This loop will be repeated again and again until the bot is stopped.
 * Calculate buy / sell signals (calls `populate_buy_trend()` and `populate_sell_trend()` once per pair).
 * Loops per candle simulating entry and exit points.
   * Confirm trade buy / sell (calls `confirm_trade_entry()` and `confirm_trade_exit()` if implemented in the strategy).
+  * Call `custom_entry_price()` (if implemented in the strategy) to determine entry price (Prices are moved to be within the opening candle).
+  * Determine stake size by calling the `custom_stake_amount()` callback.
+  * Check position adjustments for open trades if enabled and call `adjust_trade_position()` to determine if an additional order is requested.
   * Call `custom_stoploss()` and `custom_sell()` to find custom exit points.
+  * For sells based on sell-signal and custom-sell: Call `custom_exit_price()` to determine exit price (Prices are moved to be within the closing candle).
+  * Check for Order timeouts, either via the `unfilledtimeout` configuration, or via `check_buy_timeout()` / `check_sell_timeout()` strategy callbacks.
 * Generate backtest report output
 
 !!! Note

docs/configuration.md

@@ -37,6 +37,15 @@ Using this scheme, all configuration settings will also be available as environm
 
 Please note that Environment variables will overwrite corresponding settings in your configuration, but command line Arguments will always win.
 
+Common example:
+
+```
+FREQTRADE__TELEGRAM__CHAT_ID=<telegramchatid>
+FREQTRADE__TELEGRAM__TOKEN=<telegramToken>
+FREQTRADE__EXCHANGE__KEY=<yourExchangeKey>
+FREQTRADE__EXCHANGE__SECRET=<yourExchangeSecret>
+```
+
 !!! Note
     Environment variables detected are logged at startup - so if you can't find why a value is not what you think it should be based on the configuration, make sure it's not loaded from an environment variable.
 
@@ -93,6 +102,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `unfilledtimeout.buy` | **Required.** How long (in minutes or seconds) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
 | `unfilledtimeout.sell` | **Required.** How long (in minutes or seconds) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
 | `unfilledtimeout.unit` | Unit to use in unfilledtimeout setting. Note: If you set unfilledtimeout.unit to "seconds", "internals.process_throttle_secs" must be inferior or equal to timeout [Strategy Override](#parameters-in-the-strategy). <br> *Defaults to `minutes`.* <br> **Datatype:** String
+| `unfilledtimeout.exit_timeout_count` | How many times can exit orders time out. Once this number of timeouts is reached, an emergency sell is triggered. 0 to disable and allow unlimited order cancels. [Strategy Override](#parameters-in-the-strategy).<br>*Defaults to `0`.* <br> **Datatype:** Integer
 | `bid_strategy.price_side` | Select the side of the spread the bot should look at to get the buy rate. [More information below](#buy-price-side).<br> *Defaults to `bid`.* <br> **Datatype:** String (either `ask` or `bid`).
 | `bid_strategy.ask_last_balance` | **Required.** Interpolate 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
@@ -116,14 +126,16 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `exchange.key` | API key to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
 | `exchange.secret` | API secret to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
 | `exchange.password` | API password to use for the exchange. Only required when you are in production mode and for exchanges that use password for API requests.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
+| `exchange.uid` | API uid to use for the exchange. Only required when you are in production mode and for exchanges that use uid for API requests.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
 | `exchange.pair_whitelist` | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Supports regex pairs as `.*/BTC`. Not used by VolumePairList. [More information](plugins.md#pairlists-and-pairlist-handlers). <br> **Datatype:** List
 | `exchange.pair_blacklist` | List of pairs the bot must absolutely avoid for trading and backtesting. [More information](plugins.md#pairlists-and-pairlist-handlers). <br> **Datatype:** List
-| `exchange.ccxt_config` | Additional CCXT parameters passed to both ccxt instances (sync and async). This is usually the correct place for ccxt configurations. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
+| `exchange.ccxt_config` | Additional CCXT parameters passed to both ccxt instances (sync and async). This is usually the correct place for additional ccxt configurations. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation). Please avoid adding exchange secrets here (use the dedicated fields instead), as they may be contained in logs. <br> **Datatype:** Dict
 | `exchange.ccxt_sync_config` | Additional CCXT parameters passed to the regular (sync) ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
 | `exchange.ccxt_async_config` | Additional CCXT parameters passed to the async ccxt instance. Parameters may differ from exchange to exchange  and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
 | `exchange.markets_refresh_interval` | The interval in minutes in which markets are reloaded. <br>*Defaults to `60` minutes.* <br> **Datatype:** Positive Integer
 | `exchange.skip_pair_validation` | Skip pairlist validation on startup.<br>*Defaults to `false`<br> **Datatype:** Boolean
 | `exchange.skip_open_order_update` | Skips open order updates on startup should the exchange cause problems. Only relevant in live conditions.<br>*Defaults to `false`<br> **Datatype:** Boolean
+| `exchange.unknown_fee_rate` | Fallback value to use when calculating trading fees. This can be useful for exchanges which have fees in non-tradable currencies. The value provided here will be multiplied with the "fee cost".<br>*Defaults to `None`<br> **Datatype:** float
 | `exchange.log_responses` | Log relevant exchange responses. For debug mode only - use with care.<br>*Defaults to `false`<br> **Datatype:** Boolean
 | `edge.*` | Please refer to [edge configuration document](edge.md) for detailed explanation.
 | `experimental.block_bad_exchanges` | Block exchanges known to not work with freqtrade. Leave on default unless you want to test if that exchange works now. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
@@ -160,6 +172,8 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `user_data_dir` | Directory containing user data. <br> *Defaults to `./user_data/`*. <br> **Datatype:** String
 | `dataformat_ohlcv` | Data format to use to store historical candle (OHLCV) data. <br> *Defaults to `json`*. <br> **Datatype:** String
 | `dataformat_trades` | Data format to use to store historical trades data. <br> *Defaults to `jsongz`*. <br> **Datatype:** String
+| `position_adjustment_enable` | Enables the strategy to use position adjustments (additional buys or sells). [More information here](strategy-callbacks.md#adjust-trade-position). <br> [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*<br> **Datatype:** Boolean
+| `max_entry_position_adjustment` | Maximum additional order(s) for each open trade on top of the first entry Order. Set it to `-1` for unlimited additional orders. [More information here](strategy-callbacks.md#adjust-trade-position). <br> [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `-1`.*<br> **Datatype:** Positive Integer or -1
 
 ### Parameters in the strategy
 
@@ -184,6 +198,8 @@ Values set in the configuration file always overwrite values set in the strategy
 * `sell_profit_offset`
 * `ignore_roi_if_buy_signal`
 * `ignore_buying_expired_candle_after`
+* `position_adjustment_enable`
+* `max_entry_position_adjustment`
 
 ### Configuring amount per trade
 
@@ -192,9 +208,8 @@ There are several methods to configure how much of the stake currency the bot wi
 #### Minimum trade stake
 
 The minimum stake amount will depend on exchange and pair and is usually listed in the exchange support pages.
-Assuming the minimum tradable amount for XRP/USD is 20 XRP (given by the exchange), and the price is 0.6$.
 
-The minimum stake amount to buy this pair is, therefore, `20 * 0.6 ~= 12`.
+Assuming the minimum tradable amount for XRP/USD is 20 XRP (given by the exchange), and the price is 0.6$, the minimum stake amount to buy this pair is `20 * 0.6 ~= 12`.
 This exchange has also a limit on USD - where all orders must be > 10$ - which however does not apply in this case.
 
 To guarantee safe execution, freqtrade will not allow buying with a stake-amount of 10.1$, instead, it'll make sure that there's enough space to place a stoploss below the pair (+ an offset, defined by `amount_reserve_percent`, which defaults to 5%).
@@ -204,7 +219,7 @@ With a reserve of 5%, the minimum stake amount would be ~12.6$ (`12 * (1 + 0.05)
 To limit this calculation in case of large stoploss values, the calculated minimum stake-limit will never be more than 50% above the real limit.
 
 !!! Warning
-    Since the limits on exchanges are usually stable and are not updated often, some pairs can show pretty high minimum limits, simply because the price increased a lot since the last limit adjustment by the exchange.
+    Since the limits on exchanges are usually stable and are not updated often, some pairs can show pretty high minimum limits, simply because the price increased a lot since the last limit adjustment by the exchange. Freqtrade adjusts the stake-amount to this value, unless it's > 30% more than the calculated/desired stake-amount - in which case the trade is rejected.
 
 #### Tradable balance
 
@@ -291,6 +306,15 @@ To allow the bot to trade all the available `stake_currency` in your account (mi
     When using `"stake_amount" : "unlimited",` in combination with Dry-Run, Backtesting or Hyperopt, the balance will be simulated starting with a stake of `dry_run_wallet` which will evolve.
     It is therefore important to set `dry_run_wallet` to a sensible value (like 0.05 or 0.01 for BTC and 1000 or 100 for USDT, for example), otherwise, it may simulate trades with 100 BTC (or more) or 0.05 USDT (or less) at once - which may not correspond to your real available balance or is less than the exchange minimal limit for the order amount for the stake currency.
 
+#### Dynamic stake amount with position adjustment
+
+When you want to use position adjustment with unlimited stakes, you must also implement `custom_stake_amount` to a return a value depending on your strategy.
+Typical value would be in the range of 25% - 50% of the proposed stakes, but depends highly on your strategy and how much you wish to leave into the wallet as position adjustment buffer.
+
+For example if your position adjustment assumes it can do 2 additional buys with the same stake amounts then your buffer should be 66.6667% of the initially proposed unlimited stake amount.
+
+Or another example if your position adjustment assumes it can do 1 additional buy with 3x the original stake amount then `custom_stake_amount` should return 25% of proposed stake amount and leave 75% for possible later position adjustments.
+
 --8<-- "includes/pricing.md"
 
 ### Understand minimal_roi

docs/data-analysis.md

@@ -1,6 +1,6 @@
-# Analyzing bot data with Jupyter notebooks  
+# Analyzing bot data with Jupyter notebooks
 
-You can analyze the results of backtests and trading history easily using Jupyter notebooks. Sample notebooks are located at `user_data/notebooks/` after initializing the user directory with `freqtrade create-userdir --userdir user_data`.  
+You can analyze the results of backtests and trading history easily using Jupyter notebooks. Sample notebooks are located at `user_data/notebooks/` after initializing the user directory with `freqtrade create-userdir --userdir user_data`.
 
 ## Quick start with docker
 
@@ -41,32 +41,35 @@ ipython kernel install --user --name=freqtrade
 !!! Warning
     Some tasks don't work especially well in notebooks. For example, anything using asynchronous execution is a problem for Jupyter. Also, freqtrade's primary entry point is the shell cli, so using pure python in a notebook bypasses arguments that provide required objects and parameters to helper functions. You may need to set those values or create expected objects manually.
 
-## Recommended workflow  
+## Recommended workflow
 
-| Task | Tool |  
-  --- | ---  
-Bot operations | CLI  
+| Task | Tool |
+  --- | ---
+Bot operations | CLI
 Repetitive tasks | Shell scripts
-Data analysis & visualization | Notebook  
+Data analysis & visualization | Notebook
 
 1. Use the CLI to
+
     * download historical data
     * run a backtest
     * run with real-time data
-    * export results  
+    * export results
 
 1. Collect these actions in shell scripts
+
     * save complicated commands with arguments
-    * execute multi-step operations  
+    * execute multi-step operations
     * automate testing strategies and preparing data for analysis
 
 1. Use a notebook to
+
     * visualize data
-    * munge and plot to generate insights
+    * mangle and plot to generate insights
 
-## Example utility snippets  
+## Example utility snippets
 
-### Change directory to root  
+### Change directory to root
 
 Jupyter notebooks execute from the notebook directory. The following snippet searches for the project root, so relative paths remain consistent.
 

docs/deprecated.md

@@ -15,8 +15,8 @@ This command line option was deprecated in 2019.7-dev (develop branch) and remov
 
 ### The **--dynamic-whitelist** command line option
 
-This command line option was deprecated in 2018 and removed freqtrade 2019.6-dev (develop branch)
-and in freqtrade 2019.7.
+This command line option was deprecated in 2018 and removed freqtrade 2019.6-dev (develop branch) and in freqtrade 2019.7.
+Please refer to [pairlists](plugins.md#pairlists-and-pairlist-handlers) instead.
 
 ### the `--live` command line option
 

docs/developer.md

@@ -26,6 +26,8 @@ Alternatively (e.g. if your system is not supported by the setup.sh script), fol
 
 This will install all required tools for development, including `pytest`, `flake8`, `mypy`, and `coveralls`.
 
+Before opening a pull request, please familiarize yourself with our [Contributing Guidelines](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md).
+
 ### Devcontainer setup
 
 The fastest and easiest way to get started is to use [VSCode](https://code.visualstudio.com/) with the Remote container extension.
@@ -250,7 +252,23 @@ Most exchanges supported by CCXT should work out of the box.
 To quickly test the public endpoints of an exchange, add a configuration for your exchange to `test_ccxt_compat.py` and run these tests with `pytest --longrun tests/exchange/test_ccxt_compat.py`.
 Completing these tests successfully a good basis point (it's a requirement, actually), however these won't guarantee correct exchange functioning, as this only tests public endpoints, but no private endpoint (like generate order or similar).
 
-Also try to use `freqtrade download-data` for an extended timerange and verify that the data downloaded correctly (no holes, the specified timerange was actually downloaded).
+Also try to use `freqtrade download-data` for an extended timerange (multiple months) and verify that the data downloaded correctly (no holes, the specified timerange was actually downloaded).
+
+These are prerequisites to have an exchange listed as either Supported or Community tested (listed on the homepage).
+The below are "extras", which will make an exchange better (feature-complete) - but are not absolutely necessary for either of the 2 categories.
+
+Additional tests / steps to complete:
+
+* Verify data provided by `fetch_ohlcv()` - and eventually adjust `ohlcv_candle_limit` for this exchange
+* Check L2 orderbook limit range (API documentation) - and eventually set as necessary
+* Check if balance shows correctly (*)
+* Create market order (*)
+* Create limit order (*)
+* Complete trade (buy + sell) (*)
+  * Compare result calculation between exchange and bot
+  * Ensure fees are applied correctly (check the database against the exchange)
+
+(*) Requires API keys and Balance on the exchange.
 
 ### Stoploss On Exchange
 
@@ -306,9 +324,8 @@ jupyter nbconvert --ClearOutputPreprocessor.enabled=True --to markdown freqtrade
 This documents some decisions taken for the CI Pipeline.
 
 * CI runs on all OS variants, Linux (ubuntu), macOS and Windows.
-* Docker images are build for the branches `stable` and `develop`.
+* Docker images are build for the branches `stable` and `develop`, and are built as multiarch builds, supporting multiple platforms via the same tag.
 * Docker images containing Plot dependencies are also available as `stable_plot` and `develop_plot`.
-* Raspberry PI Docker images are postfixed with `_pi` - so tags will be `:stable_pi` and `develop_pi`.
 * Docker images contain a file, `/freqtrade/freqtrade_commit` containing the commit this image is based of.
 * Full docker image rebuilds are run once a week via schedule.
 * Deployments run on ubuntu.

docs/docker_quickstart.md

@@ -126,6 +126,12 @@ All freqtrade arguments will be available by running `docker-compose run --rm fr
 !!! Note "`docker-compose run --rm`"
     Including `--rm` will remove the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
 
+??? Note "Using docker without docker-compose"
+    "`docker-compose run --rm`" will require a compose file to be provided.
+    Some freqtrade commands that don't require authentication such as `list-pairs` can be run with "`docker run --rm`" instead.  
+    For example `docker run --rm freqtradeorg/freqtrade:stable list-pairs --exchange binance --quote BTC --print-json`.  
+    This can be useful for fetching exchange information to add to your `config.json` without affecting your running containers.
+
 #### Example: Download data with docker-compose
 
 Download backtesting data for 5 days for the pair ETH/BTC and 1h timeframe from Binance. The data will be stored in the directory `user_data/data/` on the host.

docs/exchanges.md

@@ -46,7 +46,7 @@ In case of problems related to rate-limits (usually DDOS Exceptions in your logs
 ```
 
 This configuration enables kraken, as well as rate-limiting to avoid bans from the exchange.
-`"rateLimit": 3100` defines a wait-event of 0.2s between each call. This can also be completely disabled by setting `"enableRateLimit"` to false.
+`"rateLimit": 3100` defines a wait-event of 3.1s between each call. This can also be completely disabled by setting `"enableRateLimit"` to false.
 
 !!! Note
     Optimal settings for rate-limiting depend on the exchange and the size of the whitelist, so an ideal parameter will vary on many other settings.
@@ -182,6 +182,28 @@ Kucoin supports [time_in_force](configuration.md#understand-order_time_in_force)
 For Kucoin, please add `"KCS/<STAKE>"` to your blacklist to avoid issues.
 Accounts having KCS accounts use this to pay for fees - if your first trade happens to be on `KCS`, further trades will consume this position and make the initial KCS trade unsellable as the expected amount is not there anymore.
 
+## OKX
+
+OKX requires a passphrase for each api key, you will therefore need to add this key into the configuration so your exchange section looks as follows:
+
+```json
+"exchange": {
+    "name": "okx",
+    "key": "your_exchange_key",
+    "secret": "your_exchange_secret",
+    "password": "your_exchange_api_key_password",
+    // ...
+}
+```
+
+!!! Warning
+    OKX only provides 100 candles per api call. Therefore, the strategy will only have a pretty low amount of data available in backtesting mode.
+
+## Gate.io
+
+Gate.io allows the use of `POINT` to pay for fees. As this is not a tradable currency (no regular market available), automatic fee calculations will fail (and default to a fee of 0).
+The configuration parameter `exchange.unknown_fee_rate` can be used to specify the exchange rate between Point and the stake currency. Obviously, changing the stake-currency will also require changes to this value.
+
 ## 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.

docs/faq.md

@@ -42,7 +42,7 @@ position for a trade. Be patient!
 ### I have made 12 trades already, why is my total profit negative?
 
 I understand your disappointment but unfortunately 12 trades is just
-not enough to say anything. If you run backtesting, you can see that our
+not enough to say anything. If you run backtesting, you can see that the
 current algorithm does leave you on the plus side, but that is after
 thousands of trades and even there, you will be left with losses on
 specific coins that you have traded tens if not hundreds of times. We
@@ -54,6 +54,21 @@ you can't say much from few trades.
 
 Yes. You can edit your config and use the `/reload_config` command to reload the configuration. The bot will stop, reload the configuration and strategy and will restart with the new configuration and strategy.
 
+### Why does my bot not sell everything it bought?
+
+This is called "coin dust" and can happen on all exchanges.
+It happens because many exchanges subtract fees from the "receiving currency" - so you buy 100 COIN - but you only get 99.9 COIN.
+As COIN is trading in full lot sizes (1COIN steps), you cannot sell 0.9 COIN (or 99.9 COIN) - but you need to round down to 99 COIN.
+
+This is not a bot-problem, but will also happen while manual trading.
+
+While freqtrade can handle this (it'll sell 99 COIN), fees are often below the minimum tradable lot-size (you can only trade full COIN, not 0.9 COIN).
+Leaving the dust (0.9 COIN) on the exchange makes usually sense, as the next time freqtrade buys COIN, it'll eat into the remaining small balance, this time selling everything it bought, and therefore slowly declining the dust balance (although it most likely will never reach exactly 0).
+
+Where possible (e.g. on binance), the use of the exchange's dedicated fee currency will fix this.  
+On binance, it's sufficient to have BNB in your account, and have "Pay fees in BNB" enabled in your profile. Your BNB balance will slowly decline (as it's used to pay fees) - but you'll no longer encounter dust (Freqtrade will include the fees in the profit calculations).
+Other exchanges don't offer such possibilities, where it's simply something you'll have to accept or move to a different exchange.
+
 ### I want to use incomplete candles
 
 Freqtrade will not provide incomplete candles to strategies. Using incomplete candles will lead to repainting and consequently to strategies with "ghost" buys, which are impossible to both backtest, and verify after they happened.
@@ -78,6 +93,18 @@ If this happens for all pairs in the pairlist, this might indicate a recent exch
 
 Irrespectively of the reason, Freqtrade will fill up these candles with "empty" candles, where open, high, low and close are set to the previous candle close - and volume is empty. In a chart, this will look like a `_` - and is aligned with how exchanges usually represent 0 volume candles.
 
+### I'm getting "Outdated history for pair xxx" in the log
+
+The bot is trying to tell you that it got an outdated last candle (not the last complete candle).
+As a consequence, Freqtrade will not enter a trade for this pair - as trading on old information is usually not what is desired.
+
+This warning can point to one of the below problems:
+
+* Exchange downtime -> Check your exchange status page / blog / twitter feed for details.
+* Wrong system time -> Ensure your system-time is correct.
+* Barely traded pair -> Check the pair on the exchange webpage, look at the timeframe your strategy uses. If the pair does not have any volume in some candles (usually visualized with a "volume 0" bar, and a "_" as candle), this pair did not have any trades in this timeframe. These pairs should ideally be avoided, as they can cause problems with order-filling.
+* API problem -> API returns wrong data (this only here for completeness, and should not happen with supported exchanges).
+
 ### I'm getting the "RESTRICTED_MARKET" message in the log
 
 Currently known to happen for US Bittrex users.  
@@ -161,12 +188,12 @@ There is however nothing preventing you from using GPU-enabled indicators within
 Per default Hyperopt called without the `-e`/`--epochs` command line option will only
 run 100 epochs, means 100 evaluations of your triggers, guards, ... Too few
 to find a great result (unless if you are very lucky), so you probably
-have to run it for 10.000 or more. But it will take an eternity to
+have to run it for 10000 or more. But it will take an eternity to
 compute.
 
 Since hyperopt uses Bayesian search, running for too many epochs may not produce greater results.
 
-It's therefore recommended to run between 500-1000 epochs over and over until you hit at least 10.000 epochs in total (or are satisfied with the result). You can best judge by looking at the results - if the bot keeps discovering better strategies, it's best to keep on going.
+It's therefore recommended to run between 500-1000 epochs over and over until you hit at least 10000 epochs in total (or are satisfied with the result). You can best judge by looking at the results - if the bot keeps discovering better strategies, it's best to keep on going.
 
 ```bash
 freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --strategy SampleStrategy -e 1000
@@ -190,9 +217,9 @@ already 8\*10^9\*10 evaluations. A roughly total of 80 billion evaluations.
 Did you run 100 000 evaluations? Congrats, you've done roughly 1 / 100 000 th
 of the search space, assuming that the bot never tests the same parameters more than once.
 
-* The time it takes to run 1000 hyperopt epochs depends on things like: The available cpu, hard-disk, ram, timeframe, timerange, indicator settings, indicator count, amount of coins that hyperopt test strategies on and the resulting trade count - which can be 650 trades in a year or 10.0000 trades depending if the strategy aims for big profits by trading rarely or for many low profit trades. 
+* The time it takes to run 1000 hyperopt epochs depends on things like: The available cpu, hard-disk, ram, timeframe, timerange, indicator settings, indicator count, amount of coins that hyperopt test strategies on and the resulting trade count - which can be 650 trades in a year or 100000 trades depending if the strategy aims for big profits by trading rarely or for many low profit trades. 
 
-Example: 4% profit 650 times vs 0,3% profit a trade 10.000 times in a year. If we assume you set the --timerange to 365 days. 
+Example: 4% profit 650 times vs 0,3% profit a trade 10000 times in a year. If we assume you set the --timerange to 365 days. 
 
 Example:
 `freqtrade --config config.json --strategy SampleStrategy --hyperopt SampleHyperopt -e 1000 --timerange 20190601-20200601`

docs/hyperopt.md

@@ -116,7 +116,7 @@ optional arguments:
                         ShortTradeDurHyperOptLoss, OnlyProfitHyperOptLoss,
                         SharpeHyperOptLoss, SharpeHyperOptLossDaily,
                         SortinoHyperOptLoss, SortinoHyperOptLossDaily,
-                        MaxDrawDownHyperOptLoss
+                        CalmarHyperOptLoss, MaxDrawDownHyperOptLoss, ProfitDrawDownHyperOptLoss
   --disable-param-export
                         Disable automatic hyperopt parameter export.
   --ignore-missing-spaces, --ignore-unparameterized-spaces
@@ -508,6 +508,46 @@ class MyAwesomeStrategy(IStrategy):
 
 You will then obviously also change potential interesting entries to parameters to allow hyper-optimization.
 
+### Optimizing `max_entry_position_adjustment`
+
+While `max_entry_position_adjustment` is not a separate space, it can still be used in hyperopt by using the property approach shown above.
+
+``` python
+from pandas import DataFrame
+from functools import reduce
+
+import talib.abstract as ta
+
+from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, 
+                                IStrategy, IntParameter)
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+class MyAwesomeStrategy(IStrategy):
+    stoploss = -0.05
+    timeframe = '15m'
+
+    # Define the parameter spaces
+    max_epa = CategoricalParameter([-1, 0, 1, 3, 5, 10], default=1, space="buy", optimize=True)
+
+    @property
+    def max_entry_position_adjustment(self):
+        return self.max_epa.value
+        
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # ...
+```
+
+??? Tip "Using `IntParameter`"
+    You can also use the `IntParameter` for this optimization, but you must explicitly return an integer:
+    ``` python
+    max_epa = IntParameter(-1, 10, default=1, space="buy", optimize=True)
+
+    @property
+    def max_entry_position_adjustment(self):
+        return int(self.max_epa.value)
+    ```
+
 ## Loss-functions
 
 Each hyperparameter tuning requires a target. This is usually defined as a loss function (sometimes also called objective function), which should decrease for more desirable results, and increase for bad results.
@@ -524,6 +564,8 @@ Currently, the following loss functions are builtin:
 * `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.
 * `MaxDrawDownHyperOptLoss` - Optimizes Maximum drawdown.
+* `CalmarHyperOptLoss` - Optimizes Calmar Ratio calculated on trade returns relative to max drawdown.
+* `ProfitDrawDownHyperOptLoss` - Optimizes by max Profit & min Drawdown objective. `DRAWDOWN_MULT` variable within the hyperoptloss file can be adjusted to be stricter or more flexible on drawdown purposes.
 
 Creation of a custom loss function is covered in the [Advanced Hyperopt](advanced-hyperopt.md) part of the documentation.
 

docs/includes/pairlists.md

@@ -196,9 +196,9 @@ Trade count is used as a tie breaker.
 You can use the `minutes` parameter to only consider performance of the past X minutes (rolling window).
 Not defining this parameter (or setting it to 0) will use all-time performance.
 
-The optional `min_profit` parameter defines the minimum profit a pair must have to be considered.
+The optional `min_profit` (as ratio -> a setting of `0.01` corresponds to 1%) parameter defines the minimum profit a pair must have to be considered.
 Pairs below this level will be filtered out.
-Using this parameter without `minutes` is highly discouraged, as it can lead to an empty pairlist without without a way to recover.
+Using this parameter without `minutes` is highly discouraged, as it can lead to an empty pairlist without a way to recover.
 
 ```json
 "pairlists": [
@@ -206,11 +206,13 @@ Using this parameter without `minutes` is highly discouraged, as it can lead to
     {
         "method": "PerformanceFilter",
         "minutes": 1440,  // rolling 24h
-        "min_profit": 0.01
+        "min_profit": 0.01  // minimal profit 1%
     }
 ],
 ```
 
+As this Filter uses past performance of the bot, it'll have some startup-period - and should only be used after the bot has a few 100 trades in the database.
+
 !!! Warning "Backtesting"
     `PerformanceFilter` does not support backtesting mode.
 
@@ -218,6 +220,9 @@ Using this parameter without `minutes` is highly discouraged, as it can lead to
 
 Filters low-value coins which would not allow setting stoplosses.
 
+!!! Warning "Backtesting"
+    `PrecisionFilter` does not support backtesting mode using multiple strategies.
+
 #### PriceFilter
 
 The `PriceFilter` allows filtering of pairs by price. Currently the following price filters are supported:
@@ -241,7 +246,7 @@ On exchanges that deduct fees from the receiving currency (e.g. FTX) - this can
 The `low_price_ratio` setting removes pairs where a raise of 1 price unit (pip) is above the `low_price_ratio` ratio.
 This option is disabled by default, and will only apply if set to > 0.
 
-For `PriceFiler` at least one of its `min_price`, `max_price` or `low_price_ratio` settings must be applied.
+For `PriceFilter` at least one of its `min_price`, `max_price` or `low_price_ratio` settings must be applied.
 
 Calculation example:
 
@@ -255,7 +260,7 @@ Min price precision for SHITCOIN/BTC is 8 decimals. If its price is 0.00000011 -
 Shuffles (randomizes) pairs in the pairlist. It can be used for preventing the bot from trading some of the pairs more frequently then others when you want all pairs be treated with the same priority.
 
 !!! Tip
-    You may set the `seed` value for this Pairlist to obtain reproducible results, which can be useful for repeated backtesting sessions. If `seed` is not set, the pairs are shuffled in the non-repeatable random order.
+    You may set the `seed` value for this Pairlist to obtain reproducible results, which can be useful for repeated backtesting sessions. If `seed` is not set, the pairs are shuffled in the non-repeatable random order. ShuffleFilter will automatically detect runmodes and apply the `seed` only for backtesting modes - if a `seed` value is set.
 
 #### SpreadFilter
 
@@ -290,7 +295,7 @@ If the trading range over the last 10 days is <1% or >99%, remove the pair from
 
 #### VolatilityFilter
 
-Volatility is the degree of historical variation of a pairs over time, is is measured by the standard deviation of logarithmic daily returns. Returns are assumed to be normally distributed, although actual distribution might be different. In a normal distribution, 68% of observations fall within one standard deviation and 95% of observations fall within two standard deviations. Assuming a volatility of 0.05 means that the expected returns for 20 out of 30 days is expected to be less than 5% (one standard deviation). Volatility is a positive ratio of the expected deviation of return and can be greater than 1.00. Please refer to the wikipedia definition of [`volatility`](https://en.wikipedia.org/wiki/Volatility_(finance)).
+Volatility is the degree of historical variation of a pairs over time, it is measured by the standard deviation of logarithmic daily returns. Returns are assumed to be normally distributed, although actual distribution might be different. In a normal distribution, 68% of observations fall within one standard deviation and 95% of observations fall within two standard deviations. Assuming a volatility of 0.05 means that the expected returns for 20 out of 30 days is expected to be less than 5% (one standard deviation). Volatility is a positive ratio of the expected deviation of return and can be greater than 1.00. Please refer to the wikipedia definition of [`volatility`](https://en.wikipedia.org/wiki/Volatility_(finance)).
 
 This filter removes pairs if the average volatility over a `lookback_days` days is below `min_volatility` or above `max_volatility`. Since this is a filter that requires additional data, the results are cached for `refresh_period`.
 
@@ -344,5 +349,5 @@ The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets,
         "refresh_period": 86400
     },
     {"method": "ShuffleFilter", "seed": 42}
-    ],
+],
 ```

docs/index.md

@@ -11,7 +11,7 @@
 
 ## Introduction
 
-Freqtrade is a crypto-currency algorithmic trading software developed in python (3.7+) and supported on Windows, macOS and Linux.
+Freqtrade is a free and open source crypto trading bot written in Python. It is designed to support all major exchanges and be controlled via Telegram or webUI. It contains backtesting, plotting and money management tools as well as strategy optimization by machine learning.
 
 !!! Danger "DISCLAIMER"
     This software is for educational purposes only. Do not risk money which you are afraid to lose. USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS AND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR TRADING RESULTS.
@@ -20,6 +20,12 @@ Freqtrade is a crypto-currency algorithmic trading software developed in python
 
     We strongly recommend you to have basic coding skills and Python knowledge. Do not hesitate to read the source code and understand the mechanisms of this bot, algorithms and techniques implemented in it.
 
+![freqtrade screenshot](assets/freqtrade-screenshot.png)
+
+## Sponsored promotion
+
+[![tokenbot-promo](assets/TokenBot-Freqtrade-banner.png)](https://tokenbot.com/?utm_source=github&utm_medium=freqtrade&utm_campaign=algodevs)
+
 ## Features
 
 - Develop your Strategy: Write your strategy in python, using [pandas](https://pandas.pydata.org/). Example strategies to inspire you are available in the [strategy repository](https://github.com/freqtrade/freqtrade-strategies).
@@ -29,18 +35,19 @@ Freqtrade is a crypto-currency algorithmic trading software developed in python
 - Select markets: Create your static list or use an automatic one based on top traded volumes and/or prices (not available during backtesting). You can also explicitly blacklist markets you don't want to trade.
 - Run: Test your strategy with simulated money (Dry-Run mode) or deploy it with real money (Live-Trade mode).
 - Run using Edge (optional module): The concept is to find the best historical [trade expectancy](edge.md#expectancy) by markets based on variation of the stop-loss and then allow/reject markets to trade. The sizing of the trade is based on a risk of a percentage of your capital.
-- Control/Monitor: Use Telegram or a REST API (start/stop the bot, show profit/loss, daily summary, current open trades results, etc.).
+- Control/Monitor: Use Telegram or a WebUI (start/stop the bot, show profit/loss, daily summary, current open trades results, etc.).
 - Analyse: Further analysis can be performed on either Backtesting data or Freqtrade trading history (SQL database), including automated standard plots, and methods to load the data into [interactive environments](data-analysis.md).
 
 ## Supported exchange marketplaces
 
 Please read the [exchange specific notes](exchanges.md) to learn about eventual, special configurations needed for each exchange.
 
-- [X] [Binance](https://www.binance.com/) ([*Note for binance users](docs/exchanges.md#binance-blacklist))
+- [X] [Binance](https://www.binance.com/) ([*Note for binance users](exchanges.md#binance-blacklist))
 - [X] [Bittrex](https://bittrex.com/)
 - [X] [FTX](https://ftx.com)
-- [X] [Kraken](https://kraken.com/)
 - [X] [Gate.io](https://www.gate.io/ref/6266643)
+- [X] [Kraken](https://kraken.com/)
+- [X] [OKX](https://www.okx.com/)
 - [ ] [potentially many others through <img alt="ccxt" width="30px" src="assets/ccxt-logo.svg" />](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
 
 ### Community tested
@@ -66,7 +73,7 @@ To run this bot we recommend you a linux cloud instance with a minimum of:
 
 Alternatively
 
-- Python 3.7+
+- Python 3.8+
 - pip (pip3)
 - git
 - TA-Lib
@@ -80,4 +87,4 @@ For any questions not covered by the documentation or for further information ab
 
 ## Ready to try?
 
-Begin by reading our installation guide [for docker](docker_quickstart.md) (recommended), or for [installation without docker](installation.md).
+Begin by reading the installation guide [for docker](docker_quickstart.md) (recommended), or for [installation without docker](installation.md).

docs/installation.md

@@ -24,7 +24,7 @@ The easiest way to install and run Freqtrade is to clone the bot Github reposito
     The `stable` branch contains the code of the last release (done usually once per month on an approximately one week old snapshot of the `develop` branch to prevent packaging bugs, so potentially it's more stable).
 
 !!! Note
-    Python3.7 or higher and the corresponding `pip` are assumed to be available. The install-script will warn you and stop if that's not the case. `git` is also needed to clone the Freqtrade repository.  
+    Python3.8 or higher and the corresponding `pip` are assumed to be available. The install-script will warn you and stop if that's not the case. `git` is also needed to clone the Freqtrade repository.  
     Also, python headers (`python<yourversion>-dev` / `python<yourversion>-devel`) must be available for the installation to complete successfully.
 
 !!! Warning "Up-to-date clock"
@@ -36,9 +36,13 @@ The easiest way to install and run Freqtrade is to clone the bot Github reposito
 
 These requirements apply to both [Script Installation](#script-installation) and [Manual Installation](#manual-installation).
 
+!!! Note "ARM64 systems"
+    If you are running an ARM64 system (like a MacOS M1 or an Oracle VM), please use [docker](docker_quickstart.md) to run freqtrade.
+    While native installation is possible with some manual effort, this is not supported at the moment.
+
 ### Install guide
 
-* [Python >= 3.7.x](http://docs.python-guide.org/en/latest/starting/installation/)
+* [Python >= 3.8.x](http://docs.python-guide.org/en/latest/starting/installation/)
 * [pip](https://pip.pypa.io/en/stable/installing/)
 * [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
 * [virtualenv](https://virtualenv.pypa.io/en/stable/installation.html) (Recommended)
@@ -50,7 +54,7 @@ We've included/collected install instructions for Ubuntu, MacOS, and Windows. Th
 OS Specific steps are listed first, the [Common](#common) section below is necessary for all systems.
 
 !!! Note
-    Python3.7 or higher and the corresponding pip are assumed to be available.
+    Python3.8 or higher and the corresponding pip are assumed to be available.
 
 === "Debian/Ubuntu"
     #### Install necessary dependencies
@@ -60,18 +64,18 @@ OS Specific steps are listed first, the [Common](#common) section below is neces
     sudo apt-get update
 
     # install packages
-    sudo apt install -y python3-pip python3-venv python3-dev python3-pandas git
+    sudo apt install -y python3-pip python3-venv python3-dev python3-pandas git curl
     ```
 
 === "RaspberryPi/Raspbian"
     The following assumes the latest [Raspbian Buster lite image](https://www.raspberrypi.org/downloads/raspbian/).
-    This image comes with python3.7 preinstalled, making it easy to get freqtrade up and running.
+    This image comes with python3.9 preinstalled, making it easy to get freqtrade up and running.
 
     Tested using a Raspberry Pi 3 with the Raspbian Buster lite image, all updates applied.
 
 
     ```bash
-    sudo apt-get install python3-venv libatlas-base-dev cmake
+    sudo apt-get install python3-venv libatlas-base-dev cmake curl
     # Use pywheels.org to speed up installation
     sudo echo "[global]\nextra-index-url=https://www.piwheels.org/simple" > tee /etc/pip.conf
 
@@ -165,7 +169,7 @@ You can as well update, configure and reset the codebase of your bot with `./scr
 ** --install **
 
 With this option, the script will install the bot and most dependencies:
-You will need to have git and python3.7+ installed beforehand for this to work.
+You will need to have git and python3.8+ installed beforehand for this to work.
 
 * Mandatory software as: `ta-lib`
 * Setup your virtualenv under `.env/`
@@ -416,16 +420,3 @@ open /Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_10
 ```
 
 If this file is inexistent, then you're probably on a different version of MacOS, so you may need to consult the internet for specific resolution details.
-
-### MacOS installation error with python 3.9
-
-When using python 3.9 on macOS, it's currently necessary to install some os-level modules to allow dependencies to compile.
-The errors you'll see happen during installation and are related to the installation of `tables` or `blosc`.
-
-You can install the necessary libraries with the following command:
-
-```bash
-brew install hdf5 c-blosc
-```
-
-After this, please run the installation (script) again.

docs/plotting.md

@@ -164,16 +164,17 @@ The resulting plot will have the following elements:
 
 An advanced plot configuration can be specified in the strategy in the `plot_config` parameter.
 
-Additional features when using plot_config include:
+Additional features when using `plot_config` include:
 
 * Specify colors per indicator
 * Specify additional subplots
-* Specify indicator pairs to fill area in between 
+* Specify indicator pairs to fill area in between
 
 The sample plot configuration below specifies fixed colors for the indicators. Otherwise, consecutive plots may produce different color schemes each time, making comparisons difficult.
 It also allows multiple subplots to display both MACD and RSI at the same time.
 
 Plot type can be configured using `type` key. Possible types are:
+
 * `scatter` corresponding to `plotly.graph_objects.Scatter` class (default).
 * `bar` corresponding to `plotly.graph_objects.Bar` class.
 
@@ -182,40 +183,89 @@ Extra parameters to `plotly.graph_objects.*` constructor can be specified in `pl
 Sample configuration with inline comments explaining the process:
 
 ``` python
-    plot_config = {
-        'main_plot': {
-            # Configuration for main plot indicators.
-            # Specifies `ema10` to be red, and `ema50` to be a shade of gray
-            'ema10': {'color': 'red'},
-            'ema50': {'color': '#CCCCCC'},
-            # By omitting color, a random color is selected.
-            'sar': {},
-	    # fill area between senkou_a and senkou_b
-	    'senkou_a': {
-	        'color': 'green', #optional
-	        'fill_to': 'senkou_b',
-	        'fill_label': 'Ichimoku Cloud', #optional
-	        'fill_color': 'rgba(255,76,46,0.2)', #optional
-	    },
-	    # plot senkou_b, too. Not only the area to it.
-	    'senkou_b': {}
+@property
+def plot_config(self):
+    """
+        There are a lot of solutions how to build the return dictionary.
+        The only important point is the return value.
+        Example:
+            plot_config = {'main_plot': {}, 'subplots': {}}
+
+    """
+    plot_config = {}
+    plot_config['main_plot'] = {
+        # Configuration for main plot indicators.
+        # Assumes 2 parameters, emashort and emalong to be specified.
+        f'ema_{self.emashort.value}': {'color': 'red'},
+        f'ema_{self.emalong.value}': {'color': '#CCCCCC'},
+        # By omitting color, a random color is selected.
+        'sar': {},
+        # fill area between senkou_a and senkou_b
+        'senkou_a': {
+            'color': 'green', #optional
+            'fill_to': 'senkou_b',
+            'fill_label': 'Ichimoku Cloud', #optional
+            'fill_color': 'rgba(255,76,46,0.2)', #optional
         },
-        'subplots': {
-            # Create subplot MACD
-            "MACD": {
-                'macd': {'color': 'blue', 'fill_to': 'macdhist'},
-                'macdsignal': {'color': 'orange'},
-                'macdhist': {'type': 'bar', 'plotly': {'opacity': 0.9}}
-            },
-            # Additional subplot RSI
-            "RSI": {
-                'rsi': {'color': 'red'}
-            }
+        # plot senkou_b, too. Not only the area to it.
+        'senkou_b': {}
+    }
+    plot_config['subplots'] = {
+         # Create subplot MACD
+        "MACD": {
+            'macd': {'color': 'blue', 'fill_to': 'macdhist'},
+            'macdsignal': {'color': 'orange'},
+            'macdhist': {'type': 'bar', 'plotly': {'opacity': 0.9}}
+        },
+        # Additional subplot RSI
+        "RSI": {
+            'rsi': {'color': 'red'}
         }
     }
 
+    return plot_config
 ```
 
+??? Note "As attribute (former method)"
+    Assigning plot_config is also possible as Attribute (this used to be the default way).
+    This has the disadvantage that strategy parameters are not available, preventing certain configurations from working.
+
+    ``` python
+        plot_config = {
+            'main_plot': {
+                # Configuration for main plot indicators.
+                # Specifies `ema10` to be red, and `ema50` to be a shade of gray
+                'ema10': {'color': 'red'},
+                'ema50': {'color': '#CCCCCC'},
+                # By omitting color, a random color is selected.
+                'sar': {},
+            # fill area between senkou_a and senkou_b
+            'senkou_a': {
+                'color': 'green', #optional
+                'fill_to': 'senkou_b',
+                'fill_label': 'Ichimoku Cloud', #optional
+                'fill_color': 'rgba(255,76,46,0.2)', #optional
+            },
+            # plot senkou_b, too. Not only the area to it.
+            'senkou_b': {}
+            },
+            'subplots': {
+                # Create subplot MACD
+                "MACD": {
+                    'macd': {'color': 'blue', 'fill_to': 'macdhist'},
+                    'macdsignal': {'color': 'orange'},
+                    'macdhist': {'type': 'bar', 'plotly': {'opacity': 0.9}}
+                },
+                # Additional subplot RSI
+                "RSI": {
+                    'rsi': {'color': 'red'}
+                }
+            }
+        }
+
+    ```
+
+
 !!! Note
     The above configuration assumes that `ema10`, `ema50`, `senkou_a`, `senkou_b`,
     `macd`, `macdsignal`, `macdhist` and `rsi` are columns in the DataFrame created by the strategy.
@@ -223,6 +273,9 @@ Sample configuration with inline comments explaining the process:
 !!! Warning
     `plotly` arguments are only supported with plotly library and will not work with freq-ui.
 
+!!! Note "Trade position adjustments"
+    If `position_adjustment_enable` / `adjust_trade_position()` is used, the trade initial buy price is averaged over multiple orders and the trade start price will most likely appear outside the candle range.
+
 ## Plot profit
 
 ![plot-profit](assets/plot-profit.png)
@@ -233,6 +286,8 @@ The `plot-profit` subcommand shows an interactive graph with three plots:
 * The summarized profit made by backtesting.
 Note that this is not the real-world profit, but more of an estimate.
 * Profit for each individual pair.
+* Parallelism of trades.
+* Underwater (Periods of drawdown).
 
 The first graph is good to get a grip of how the overall market progresses.
 
@@ -242,6 +297,8 @@ 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.
 
+The forth graph can help you analyze trade parallelism, showing how often max_open_trades have been maxed out.
+
 Possible options for the `freqtrade plot-profit` subcommand:
 
 ```
@@ -261,8 +318,8 @@ optional arguments:
                         Specify what timerange of data to use.
   --export EXPORT       Export backtest results, argument are: trades.
                         Example: `--export=trades`
-  --export-filename PATH
-                        Save backtest results to the file with this filename.
+  --export-filename PATH, --backtest-filename PATH
+                        Use backtest results from this filename.
                         Requires `--export` to be set as well. Example:
                         `--export-filename=user_data/backtest_results/backtest
                         _today.json`

docs/requirements-docs.txt

@@ -1,4 +1,4 @@
 mkdocs==1.2.3
-mkdocs-material==7.3.4
+mkdocs-material==8.2.1
 mdx_truly_sane_lists==1.2
-pymdown-extensions==9.0
+pymdown-extensions==9.2

docs/rest-api.md

@@ -38,6 +38,11 @@ Sample configuration:
 !!! Danger "Security warning"
     By default, the configuration listens on localhost only (so it's not reachable from other systems). We strongly recommend to not expose this API to the internet and choose a strong, unique password, since others will potentially be able to control your bot.
 
+??? Note "API/UI Access on a remote servers"
+    If you're running on a VPS, you should consider using either a ssh tunnel, or setup a VPN (openVPN, wireguard) to connect to your bot.
+    This will ensure that freqUI is not directly exposed to the internet, which is not recommended for security reasons (freqUI does not support https out of the box).
+    Setup of these tools is not part of this tutorial, however many good tutorials can be found on the internet.
+
 You can then access the API by going to `http://127.0.0.1:8080/api/v1/ping` in a browser to check if the API is running correctly.
 This should return the response:
 
@@ -330,12 +335,15 @@ Since the access token has a short timeout (15 min) - the `token/refresh` reques
 
 ### CORS
 
-All web-based front-ends are subject to [CORS](https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS) - Cross-Origin Resource Sharing.
-Since most of the requests to the Freqtrade API must be authenticated, a proper CORS policy is key to avoid security problems.
-Also, the standard disallows `*` CORS policies for requests with credentials, so this setting must be set appropriately.
+This whole section is only necessary in cross-origin cases (where you multiple bot API's running on `localhost:8081`, `localhost:8082`, ...), and want to combine them into one FreqUI instance.
 
-Users can configure this themselves via the `CORS_origins` configuration setting.
-It consists of a list of allowed sites that are allowed to consume resources from the bot's API.
+??? info "Technical explanation"
+    All web-based front-ends are subject to [CORS](https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS) - Cross-Origin Resource Sharing.
+    Since most of the requests to the Freqtrade API must be authenticated, a proper CORS policy is key to avoid security problems.
+    Also, the standard disallows `*` CORS policies for requests with credentials, so this setting must be set appropriately.
+
+Users can allow access from different origin URL's to the bot API via the `CORS_origins` configuration setting.
+It consists of a list of allowed URL's that are allowed to consume resources from the bot's API.
 
 Assuming your application is deployed as `https://frequi.freqtrade.io/home/` - this would mean that the following configuration becomes necessary:
 
@@ -348,5 +356,19 @@ Assuming your application is deployed as `https://frequi.freqtrade.io/home/` - t
 }
 ```
 
+In the following (pretty common) case, FreqUI is accessible on `http://localhost:8080/trade` (this is what you see in your navbar when navigating to freqUI).
+![freqUI url](assets/frequi_url.png)
+
+The correct configuration for this case is `http://localhost:8080` - the main part of the URL including the port.
+
+```jsonc
+{
+    //...
+    "jwt_secret_key": "somethingrandom",
+    "CORS_origins": ["http://localhost:8080"],
+    //...
+}
+```
+
 !!! Note
     We strongly recommend to also set `jwt_secret_key` to something random and known only to yourself to avoid unauthorized access to your bot.

docs/stoploss.md

@@ -2,6 +2,7 @@
 
 The `stoploss` configuration parameter is loss as ratio that should trigger a sale.
 For example, value `-0.10` will cause immediate sell if the profit dips below -10% for a given trade. This parameter is optional.
+Stoploss calculations do include fees, so a stoploss of -10% is placed exactly 10% below the entry point.
 
 Most of the strategy files already include the optimal `stoploss` value.
 
@@ -30,7 +31,7 @@ These modes can be configured with these values:
 ### stoploss_on_exchange and stoploss_on_exchange_limit_ratio
 
 Enable or Disable stop loss on exchange.
-If the stoploss is *on exchange* it means a stoploss limit order is placed on the exchange immediately after buy order happens successfully. This will protect you against sudden crashes in market as the order will be in the queue immediately and if market goes down then the order has more chance of being fulfilled.
+If the stoploss is *on exchange* it means a stoploss limit order is placed on the exchange immediately after buy order fills. This will protect you against sudden crashes in market, as the order execution happens purely within the exchange, and has no potential network overhead.
 
 If `stoploss_on_exchange` uses limit orders, the exchange needs 2 prices, the stoploss_price and the Limit price.  
 `stoploss` defines the stop-price where the limit order is placed - and limit should be slightly below this.  
@@ -182,7 +183,7 @@ For example, simplified math:
 * the bot buys an asset at a price of 100$
 * the stop loss is defined at -10%
 * the stop loss would get triggered once the asset drops below 90$
-* stoploss will remain at 90$ unless asset increases to or above our configured offset
+* stoploss will remain at 90$ unless asset increases to or above the configured offset
 * assuming the asset now increases to 103$ (where we have the offset configured)
 * the stop loss will now be -2% of 103$ = 100.94$
 * now the asset drops in value to 101\$, the stop loss will still be 100.94$ and would trigger at 100.94$

docs/strategy-advanced.md

@@ -77,43 +77,6 @@ class AwesomeStrategy(IStrategy):
 
 ***
 
-## Custom sell signal
-
-It is possible to define custom sell signals, indicating that specified position should be sold. This is very useful when we need to customize sell conditions for each individual trade, or if you need the trade profit to take the sell decision. 
-
-For example you could implement a 1:2 risk-reward ROI with `custom_sell()`.
-
-Using custom_sell() signals in place of stoploss though *is not recommended*. It is a inferior method to using `custom_stoploss()` in this regard - which also allows you to keep the stoploss on exchange.
-
-!!! Note
-    Returning a `string` or `True` from this method is equal to setting sell signal on a candle at specified time. This method is not called when sell signal is set already, or if sell signals are disabled (`use_sell_signal=False` or `sell_profit_only=True` while profit is below `sell_profit_offset`). `string` max length is 64 characters. Exceeding this limit will cause the message to be truncated to 64 characters.
-
-An example of how we can use different indicators depending on the current profit and also sell trades that were open longer than one day:
-
-``` python
-class AwesomeStrategy(IStrategy):
-    def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
-                    current_profit: float, **kwargs):
-        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
-        last_candle = dataframe.iloc[-1].squeeze()
-
-        # Above 20% profit, sell when rsi < 80
-        if current_profit > 0.2:
-            if last_candle['rsi'] < 80:
-                return 'rsi_below_80'
-
-        # Between 2% and 10%, sell if EMA-long above EMA-short
-        if 0.02 < current_profit < 0.1:
-            if last_candle['emalong'] > last_candle['emashort']:
-                return 'ema_long_below_80'
-
-        # Sell any positions at a loss if they are held for more than one day.
-        if current_profit < 0.0 and (current_time - trade.open_date_utc).days >= 1:
-            return 'unclog'
-```
-
-See [Dataframe access](#dataframe-access) for more information about dataframe use in strategy callbacks.
-
 ## Buy Tag
 
 When your strategy has multiple buy signals, you can name the signal that triggered.
@@ -143,506 +106,41 @@ def custom_sell(self, pair: str, trade: Trade, current_time: datetime, current_r
 !!! Note
     `buy_tag` is limited to 100 characters, remaining data will be truncated.
 
+## Exit tag
 
-## Custom stoploss
-
-The stoploss price can only ever move upwards - if the stoploss value returned from `custom_stoploss` would result in a lower stoploss price than was previously set, it will be ignored. The traditional `stoploss` value serves as an absolute lower level and will be instated as the initial stoploss.
-
-The usage of the custom stoploss method must be enabled by setting `use_custom_stoploss=True` on the strategy object.
-The method must return a stoploss value (float / number) as a percentage of the current price.
-E.g. If the `current_rate` is 200 USD, then returning `0.02` will set the stoploss price 2% lower, at 196 USD.
-
-The absolute value of the return value is used (the sign is ignored), so returning `0.05` or `-0.05` have the same result, a stoploss 5% below the current price.
-
-To simulate a regular trailing stoploss of 4% (trailing 4% behind the maximum reached price) you would use the following very simple method:
+Similar to [Buy Tagging](#buy-tag), you can also specify a sell tag.
 
 ``` python
-# additional imports required
-from datetime import datetime
-from freqtrade.persistence import Trade
+def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    dataframe.loc[
+        (
+            (dataframe['rsi'] > 70) &
+            (dataframe['volume'] > 0)
+        ),
+        ['sell', 'exit_tag']] = (1, 'exit_rsi')
 
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    use_custom_stoploss = True
-
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
-                        current_rate: float, current_profit: float, **kwargs) -> float:
-        """
-        Custom stoploss logic, returning the new distance relative to current_rate (as ratio).
-        e.g. returning -0.05 would create a stoploss 5% below current_rate.
-        The custom stoploss can never be below self.stoploss, which serves as a hard maximum loss.
-
-        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
-
-        When not implemented by a strategy, returns the initial stoploss value
-        Only called when use_custom_stoploss is set to True.
-
-        :param pair: Pair that's currently analyzed
-        :param trade: trade object.
-        :param current_time: datetime object, containing the current datetime
-        :param current_rate: Rate, calculated based on pricing settings in ask_strategy.
-        :param current_profit: Current profit (as ratio), calculated based on current_rate.
-        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
-        :return float: New stoploss value, relative to the current rate
-        """
-        return -0.04
+    return dataframe
 ```
 
-Stoploss on exchange works similar to `trailing_stop`, and the stoploss on exchange is updated as configured in `stoploss_on_exchange_interval` ([More details about stoploss on exchange](stoploss.md#stop-loss-on-exchange-freqtrade)).
-
-!!! Note "Use of dates"
-    All time-based calculations should be done based on `current_time` - using `datetime.now()` or `datetime.utcnow()` is discouraged, as this will break backtesting support.
-
-!!! Tip "Trailing stoploss"
-    It's recommended to disable `trailing_stop` when using custom stoploss values. Both can work in tandem, but you might encounter the trailing stop to move the price higher while your custom function would not want this, causing conflicting behavior.
-
-### Custom stoploss examples
-
-The next section will show some examples on what's possible with the custom stoploss function.
-Of course, many more things are possible, and all examples can be combined at will.
-
-#### Time based trailing stop
-
-Use the initial stoploss for the first 60 minutes, after this change to 10% trailing stoploss, and after 2 hours (120 minutes) we use a 5% trailing stoploss.
-
-``` python
-from datetime import datetime, timedelta
-from freqtrade.persistence import Trade
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    use_custom_stoploss = True
-
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
-                        current_rate: float, current_profit: float, **kwargs) -> float:
-
-        # Make sure you have the longest interval first - these conditions are evaluated from top to bottom.
-        if current_time - timedelta(minutes=120) > trade.open_date_utc:
-            return -0.05
-        elif current_time - timedelta(minutes=60) > trade.open_date_utc:
-            return -0.10
-        return 1
-```
-
-#### Different stoploss per pair
-
-Use a different stoploss depending on the pair.
-In this example, we'll trail the highest price with 10% trailing stoploss for `ETH/BTC` and `XRP/BTC`, with 5% trailing stoploss for `LTC/BTC` and with 15% for all other pairs.
-
-``` python
-from datetime import datetime
-from freqtrade.persistence import Trade
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    use_custom_stoploss = True
-
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
-                        current_rate: float, current_profit: float, **kwargs) -> float:
-
-        if pair in ('ETH/BTC', 'XRP/BTC'):
-            return -0.10
-        elif pair in ('LTC/BTC'):
-            return -0.05
-        return -0.15
-```
-
-#### Trailing stoploss with positive offset
-
-Use the initial stoploss until the profit is above 4%, then use a trailing stoploss of 50% of the current profit with a minimum of 2.5% and a maximum of 5%.
-
-Please note that the stoploss can only increase, values lower than the current stoploss are ignored.
-
-``` python
-from datetime import datetime, timedelta
-from freqtrade.persistence import Trade
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    use_custom_stoploss = True
-
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
-                        current_rate: float, current_profit: float, **kwargs) -> float:
-
-        if current_profit < 0.04:
-            return -1 # return a value bigger than the initial stoploss to keep using the initial stoploss
-
-        # After reaching the desired offset, allow the stoploss to trail by half the profit
-        desired_stoploss = current_profit / 2
-
-        # Use a minimum of 2.5% and a maximum of 5%
-        return max(min(desired_stoploss, 0.05), 0.025)
-```
-
-#### Calculating stoploss relative to open price
-
-Stoploss values returned from `custom_stoploss()` always specify a percentage relative to `current_rate`. In order to set a stoploss relative to the *open* price, we need to use `current_profit` to calculate what percentage relative to the `current_rate` will give you the same result as if the percentage was specified from the open price.
-
-The helper function [`stoploss_from_open()`](strategy-customization.md#stoploss_from_open) can be used to convert from an open price relative stop, to a current price relative stop which can be returned from `custom_stoploss()`.
-
-### Calculating stoploss percentage from absolute price
-
-Stoploss values returned from `custom_stoploss()` always specify a percentage relative to `current_rate`. In order to set a stoploss at specified absolute price level, we need to use `stop_rate` to calculate what percentage relative to the `current_rate` will give you the same result as if the percentage was specified from the open price.
-
-The helper function [`stoploss_from_absolute()`](strategy-customization.md#stoploss_from_absolute) can be used to convert from an absolute price, to a current price relative stop which can be returned from `custom_stoploss()`.
-
-#### Stepped stoploss
-
-Instead of continuously trailing behind the current price, this example sets fixed stoploss price levels based on the current profit.
-
-* Use the regular stoploss until 20% profit is reached
-* Once profit is > 20% - set stoploss to 7% above open price.
-* Once profit is > 25% - set stoploss to 15% above open price.
-* Once profit is > 40% - set stoploss to 25% above open price.
-
-``` python
-from datetime import datetime
-from freqtrade.persistence import Trade
-from freqtrade.strategy import stoploss_from_open
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    use_custom_stoploss = True
-
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
-                        current_rate: float, current_profit: float, **kwargs) -> float:
-
-        # evaluate highest to lowest, so that highest possible stop is used
-        if current_profit > 0.40:
-            return stoploss_from_open(0.25, current_profit)
-        elif current_profit > 0.25:
-            return stoploss_from_open(0.15, current_profit)
-        elif current_profit > 0.20:
-            return stoploss_from_open(0.07, current_profit)
-
-        # return maximum stoploss value, keeping current stoploss price unchanged
-        return 1
-```
-
-#### Custom stoploss using an indicator from dataframe example
-
-Absolute stoploss value may be derived from indicators stored in dataframe. Example uses parabolic SAR below the price as stoploss.
-
-``` python
-class AwesomeStrategy(IStrategy):
-
-    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        # <...>
-        dataframe['sar'] = ta.SAR(dataframe)
-
-    use_custom_stoploss = True
-
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
-                        current_rate: float, current_profit: float, **kwargs) -> float:
-
-        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
-        last_candle = dataframe.iloc[-1].squeeze()
-
-        # Use parabolic sar as absolute stoploss price
-        stoploss_price = last_candle['sar']
-
-        # Convert absolute price to percentage relative to current_rate
-        if stoploss_price < current_rate:
-            return (stoploss_price / current_rate) - 1
-
-        # return maximum stoploss value, keeping current stoploss price unchanged
-        return 1
-```
-
-See [Dataframe access](#dataframe-access) for more information about dataframe use in strategy callbacks.
-
----
-
-## Custom order price rules
-
-By default, freqtrade use the orderbook to automatically set an order price([Relevant documentation](configuration.md#prices-used-for-orders)), you also have the option to create custom order prices based on your strategy.
-
-You can use this feature by creating a `custom_entry_price()` function in your strategy file to customize entry prices and `custom_exit_price()` for exits.
+The provided exit-tag is then used as sell-reason - and shown as such in backtest results.
 
 !!! Note
-    If your custom pricing function return None or an invalid value, price will fall back to `proposed_rate`, which is based on the regular pricing configuration.
+    `sell_reason` is limited to 100 characters, remaining data will be truncated.
 
-### Custom order entry and exit price example
+## Strategy version
+
+You can implement custom strategy versioning by using the "version" method, and returning the version you would like this strategy to have.
 
 ``` python
-from datetime import datetime, timedelta, timezone
-from freqtrade.persistence import Trade
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    def custom_entry_price(self, pair: str, current_time: datetime,
-                           proposed_rate, **kwargs) -> float:
-
-        dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
-                                                                timeframe=self.timeframe)
-        new_entryprice = dataframe['bollinger_10_lowerband'].iat[-1]
-        
-        return new_entryprice
-
-    def custom_exit_price(self, pair: str, trade: Trade,
-                          current_time: datetime, proposed_rate: float,
-                          current_profit: float, **kwargs) -> float:
-
-        dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
-                                                                timeframe=self.timeframe)
-        new_exitprice = dataframe['bollinger_10_upperband'].iat[-1]
-        
-        return new_exitprice
-
-```
-
-!!! Warning
-    Modifying entry and exit prices will only work for limit orders. Depending on the price chosen, this can result in a lot of unfilled orders. By default the maximum allowed distance between the current price and the custom price is 2%, this value can be changed in config with the `custom_price_max_distance_ratio` parameter.
-
-!!! Example
-    If the new_entryprice is 97, the proposed_rate is 100 and the `custom_price_max_distance_ratio` is set to 2%, The retained valid custom entry price will be 98.
-
-!!! Warning "No backtesting support"
-    Custom entry-prices are currently not supported during backtesting.
-
-## Custom order timeout rules
-
-Simple, time-based 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 order types, which allows you to decide based on custom criteria if an 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, timedelta, timezone
-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_utc < datetime.now(timezone.utc) - timedelta(minutes=5):
-            return True
-        elif trade.open_rate > 10 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=3):
-            return True
-        elif trade.open_rate < 1 and trade.open_date_utc < datetime.now(timezone.utc) - 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_utc < datetime.now(timezone.utc) - timedelta(minutes=5):
-            return True
-        elif trade.open_rate > 10 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=3):
-            return True
-        elif trade.open_rate < 1 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(hours=24):
-           return True
-        return False
+def version(self) -> str:
+    """
+    Returns version of the strategy.
+    """
+    return "1.1"
 ```
 
 !!! 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
-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
-```
-
----
-
-## Bot loop start callback
-
-A simple callback which is called once at the start of every bot throttling iteration.
-This can be used to perform calculations which are pair independent (apply to all pairs), loading of external data, etc.
-
-``` python
-import requests
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    def bot_loop_start(self, **kwargs) -> None:
-        """
-        Called at the start of the bot iteration (one loop).
-        Might be used to perform pair-independent tasks
-        (e.g. gather some remote resource for comparison)
-        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
-        """
-        if self.config['runmode'].value in ('live', 'dry_run'):
-            # Assign this to the class by using self.*
-            # can then be used by populate_* methods
-            self.remote_data = requests.get('https://some_remote_source.example.com')
-
-```
-
-## Bot order confirmation
-
-### Trade entry (buy order) confirmation
-
-`confirm_trade_entry()` can be used to abort a trade entry at the latest second (maybe because the price is not what we expect).
-
-``` python
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
-                            time_in_force: str, current_time: datetime, **kwargs) -> bool:
-        """
-        Called right before placing a buy order.
-        Timing for this function is critical, so avoid doing heavy computations or
-        network requests in this method.
-
-        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
-
-        When not implemented by a strategy, returns True (always confirming).
-
-        :param pair: Pair that's about to be bought.
-        :param order_type: Order type (as configured in order_types). usually limit or market.
-        :param amount: Amount in target (quote) currency that's going to be traded.
-        :param rate: Rate that's going to be used when using limit orders
-        :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
-        :param current_time: datetime object, containing the current datetime
-        :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 placed on the exchange.
-            False aborts the process
-        """
-        return True
-
-```
-
-### Trade exit (sell order) confirmation
-
-`confirm_trade_exit()` can be used to abort a trade exit (sell) at the latest second (maybe because the price is not what we expect).
-
-``` python
-from freqtrade.persistence import Trade
-
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
-                           rate: float, time_in_force: str, sell_reason: str,
-                           current_time: datetime, **kwargs) -> bool:
-        """
-        Called right before placing a regular sell order.
-        Timing for this function is critical, so avoid doing heavy computations or
-        network requests in this method.
-
-        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
-
-        When not implemented by a strategy, returns True (always confirming).
-
-        :param pair: Pair that's about to be sold.
-        :param order_type: Order type (as configured in order_types). usually limit or market.
-        :param amount: Amount in quote currency.
-        :param rate: Rate that's going to be used when using limit orders
-        :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
-        :param sell_reason: Sell reason.
-            Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
-                           'sell_signal', 'force_sell', 'emergency_sell']
-        :param current_time: datetime object, containing the current datetime
-        :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 placed on the exchange.
-            False aborts the process
-        """
-        if sell_reason == 'force_sell' and trade.calc_profit_ratio(rate) < 0:
-            # Reject force-sells with negative profit
-            # This is just a sample, please adjust to your needs
-            # (this does not necessarily make sense, assuming you know when you're force-selling)
-            return False
-        return True
-
-```
-
-### Stake size management
-
-It is possible to manage your risk by reducing or increasing stake amount when placing a new trade.
-
-```python
-class AwesomeStrategy(IStrategy):
-    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
-                            proposed_stake: float, min_stake: float, max_stake: float,
-                            **kwargs) -> float:
-
-        dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
-        current_candle = dataframe.iloc[-1].squeeze()
-
-        if current_candle['fastk_rsi_1h'] > current_candle['fastd_rsi_1h']:
-            if self.config['stake_amount'] == 'unlimited':
-                # Use entire available wallet during favorable conditions when in compounding mode.
-                return max_stake
-            else:
-                # Compound profits during favorable conditions instead of using a static stake.
-                return self.wallets.get_total_stake_amount() / self.config['max_open_trades']
-
-        # Use default stake amount.
-        return proposed_stake
-```
-
-Freqtrade will fall back to the `proposed_stake` value should your code raise an exception. The exception itself will be logged.
-
-!!! Tip
-    You do not _have_ to ensure that `min_stake <= returned_value <= max_stake`. Trades will succeed as the returned value will be clamped to supported range and this acton will be logged.
-
-!!! Tip
-    Returning `0` or `None` will prevent trades from being placed.
-
----
+    You should make sure to implement proper version control (like a git repository) alongside this, as freqtrade will not keep historic versions of your strategy, so it's up to the user to be able to eventually roll back to a prior version of the strategy.
 
 ## Derived strategies
 
@@ -724,9 +222,9 @@ should be rewritten to
 ```python
 frames = [dataframe]
 for val in self.buy_ema_short.range:
-    frames.append({
+    frames.append(DataFrame({
         f'ema_short_{val}': ta.EMA(dataframe, timeperiod=val)
-    })
+    }))
 
 # Append columns to existing dataframe
 merged_frame = pd.concat(frames, axis=1)

docs/strategy-callbacks.md

@@ -0,0 +1,684 @@
+# Strategy Callbacks
+
+While the main strategy functions (`populate_indicators()`, `populate_buy_trend()`, `populate_sell_trend()`) should be used in a vectorized way, and are only called [once during backtesting](bot-basics.md#backtesting-hyperopt-execution-logic), callbacks are called "whenever needed".
+
+As such, you should avoid doing heavy calculations in callbacks to avoid delays during operations.
+Depending on the callback used, they may be called when entering / exiting a trade, or throughout the duration of a trade.
+
+Currently available callbacks:
+
+* [`bot_loop_start()`](#bot-loop-start)
+* [`custom_stake_amount()`](#custom-stake-size)
+* [`custom_sell()`](#custom-sell-signal)
+* [`custom_stoploss()`](#custom-stoploss)
+* [`custom_entry_price()` and `custom_exit_price()`](#custom-order-price-rules)
+* [`check_buy_timeout()` and `check_sell_timeout()](#custom-order-timeout-rules)
+* [`confirm_trade_entry()`](#trade-entry-buy-order-confirmation)
+* [`confirm_trade_exit()`](#trade-exit-sell-order-confirmation)
+* [`adjust_trade_position()`](#adjust-trade-position)
+
+!!! Tip "Callback calling sequence"
+    You can find the callback calling sequence in [bot-basics](bot-basics.md#bot-execution-logic)
+
+## Bot loop start
+
+A simple callback which is called once at the start of every bot throttling iteration (roughly every 5 seconds, unless configured differently).
+This can be used to perform calculations which are pair independent (apply to all pairs), loading of external data, etc.
+
+``` python
+import requests
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    def bot_loop_start(self, **kwargs) -> None:
+        """
+        Called at the start of the bot iteration (one loop).
+        Might be used to perform pair-independent tasks
+        (e.g. gather some remote resource for comparison)
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        """
+        if self.config['runmode'].value in ('live', 'dry_run'):
+            # Assign this to the class by using self.*
+            # can then be used by populate_* methods
+            self.remote_data = requests.get('https://some_remote_source.example.com')
+
+```
+
+## Custom Stake size
+
+Called before entering a trade, makes it possible to manage your position size when placing a new trade.
+
+```python
+class AwesomeStrategy(IStrategy):
+    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
+                            proposed_stake: float, min_stake: float, max_stake: float,
+                            entry_tag: Optional[str], **kwargs) -> float:
+
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
+        current_candle = dataframe.iloc[-1].squeeze()
+
+        if current_candle['fastk_rsi_1h'] > current_candle['fastd_rsi_1h']:
+            if self.config['stake_amount'] == 'unlimited':
+                # Use entire available wallet during favorable conditions when in compounding mode.
+                return max_stake
+            else:
+                # Compound profits during favorable conditions instead of using a static stake.
+                return self.wallets.get_total_stake_amount() / self.config['max_open_trades']
+
+        # Use default stake amount.
+        return proposed_stake
+```
+
+Freqtrade will fall back to the `proposed_stake` value should your code raise an exception. The exception itself will be logged.
+
+!!! Tip
+    You do not _have_ to ensure that `min_stake <= returned_value <= max_stake`. Trades will succeed as the returned value will be clamped to supported range and this action will be logged.
+
+!!! Tip
+    Returning `0` or `None` will prevent trades from being placed.
+
+## Custom sell signal
+
+Called for open trade every throttling iteration (roughly every 5 seconds) until a trade is closed.
+
+Allows to define custom sell signals, indicating that specified position should be sold. This is very useful when we need to customize sell conditions for each individual trade, or if you need trade data to make an exit decision.
+
+For example you could implement a 1:2 risk-reward ROI with `custom_sell()`.
+
+Using custom_sell() signals in place of stoploss though *is not recommended*. It is a inferior method to using `custom_stoploss()` in this regard - which also allows you to keep the stoploss on exchange.
+
+!!! Note
+    Returning a (none-empty) `string` or `True` from this method is equal to setting sell signal on a candle at specified time. This method is not called when sell signal is set already, or if sell signals are disabled (`use_sell_signal=False` or `sell_profit_only=True` while profit is below `sell_profit_offset`). `string` max length is 64 characters. Exceeding this limit will cause the message to be truncated to 64 characters.
+
+An example of how we can use different indicators depending on the current profit and also sell trades that were open longer than one day:
+
+``` python
+class AwesomeStrategy(IStrategy):
+    def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
+                    current_profit: float, **kwargs):
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+        last_candle = dataframe.iloc[-1].squeeze()
+
+        # Above 20% profit, sell when rsi < 80
+        if current_profit > 0.2:
+            if last_candle['rsi'] < 80:
+                return 'rsi_below_80'
+
+        # Between 2% and 10%, sell if EMA-long above EMA-short
+        if 0.02 < current_profit < 0.1:
+            if last_candle['emalong'] > last_candle['emashort']:
+                return 'ema_long_below_80'
+
+        # Sell any positions at a loss if they are held for more than one day.
+        if current_profit < 0.0 and (current_time - trade.open_date_utc).days >= 1:
+            return 'unclog'
+```
+
+See [Dataframe access](strategy-advanced.md#dataframe-access) for more information about dataframe use in strategy callbacks.
+
+## Custom stoploss
+
+Called for open trade every throttling iteration (roughly every 5 seconds) until a trade is closed. 
+The usage of the custom stoploss method must be enabled by setting `use_custom_stoploss=True` on the strategy object.
+
+The stoploss price can only ever move upwards - if the stoploss value returned from `custom_stoploss` would result in a lower stoploss price than was previously set, it will be ignored. The traditional `stoploss` value serves as an absolute lower level and will be instated as the initial stoploss (before this method is called for the first time for a trade).
+
+The method must return a stoploss value (float / number) as a percentage of the current price.
+E.g. If the `current_rate` is 200 USD, then returning `0.02` will set the stoploss price 2% lower, at 196 USD.
+
+The absolute value of the return value is used (the sign is ignored), so returning `0.05` or `-0.05` have the same result, a stoploss 5% below the current price.
+
+To simulate a regular trailing stoploss of 4% (trailing 4% behind the maximum reached price) you would use the following very simple method:
+
+``` python
+# additional imports required
+from datetime import datetime
+from freqtrade.persistence import Trade
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                        current_rate: float, current_profit: float, **kwargs) -> float:
+        """
+        Custom stoploss logic, returning the new distance relative to current_rate (as ratio).
+        e.g. returning -0.05 would create a stoploss 5% below current_rate.
+        The custom stoploss can never be below self.stoploss, which serves as a hard maximum loss.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+        When not implemented by a strategy, returns the initial stoploss value
+        Only called when use_custom_stoploss is set to True.
+
+        :param pair: Pair that's currently analyzed
+        :param trade: trade object.
+        :param current_time: datetime object, containing the current datetime
+        :param current_rate: Rate, calculated based on pricing settings in ask_strategy.
+        :param current_profit: Current profit (as ratio), calculated based on current_rate.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return float: New stoploss value, relative to the current rate
+        """
+        return -0.04
+```
+
+Stoploss on exchange works similar to `trailing_stop`, and the stoploss on exchange is updated as configured in `stoploss_on_exchange_interval` ([More details about stoploss on exchange](stoploss.md#stop-loss-on-exchange-freqtrade)).
+
+!!! Note "Use of dates"
+    All time-based calculations should be done based on `current_time` - using `datetime.now()` or `datetime.utcnow()` is discouraged, as this will break backtesting support.
+
+!!! Tip "Trailing stoploss"
+    It's recommended to disable `trailing_stop` when using custom stoploss values. Both can work in tandem, but you might encounter the trailing stop to move the price higher while your custom function would not want this, causing conflicting behavior.
+
+### Custom stoploss examples
+
+The next section will show some examples on what's possible with the custom stoploss function.
+Of course, many more things are possible, and all examples can be combined at will.
+
+#### Time based trailing stop
+
+Use the initial stoploss for the first 60 minutes, after this change to 10% trailing stoploss, and after 2 hours (120 minutes) we use a 5% trailing stoploss.
+
+``` python
+from datetime import datetime, timedelta
+from freqtrade.persistence import Trade
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                        current_rate: float, current_profit: float, **kwargs) -> float:
+
+        # Make sure you have the longest interval first - these conditions are evaluated from top to bottom.
+        if current_time - timedelta(minutes=120) > trade.open_date_utc:
+            return -0.05
+        elif current_time - timedelta(minutes=60) > trade.open_date_utc:
+            return -0.10
+        return 1
+```
+
+#### Different stoploss per pair
+
+Use a different stoploss depending on the pair.
+In this example, we'll trail the highest price with 10% trailing stoploss for `ETH/BTC` and `XRP/BTC`, with 5% trailing stoploss for `LTC/BTC` and with 15% for all other pairs.
+
+``` python
+from datetime import datetime
+from freqtrade.persistence import Trade
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                        current_rate: float, current_profit: float, **kwargs) -> float:
+
+        if pair in ('ETH/BTC', 'XRP/BTC'):
+            return -0.10
+        elif pair in ('LTC/BTC'):
+            return -0.05
+        return -0.15
+```
+
+#### Trailing stoploss with positive offset
+
+Use the initial stoploss until the profit is above 4%, then use a trailing stoploss of 50% of the current profit with a minimum of 2.5% and a maximum of 5%.
+
+Please note that the stoploss can only increase, values lower than the current stoploss are ignored.
+
+``` python
+from datetime import datetime, timedelta
+from freqtrade.persistence import Trade
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                        current_rate: float, current_profit: float, **kwargs) -> float:
+
+        if current_profit < 0.04:
+            return -1 # return a value bigger than the initial stoploss to keep using the initial stoploss
+
+        # After reaching the desired offset, allow the stoploss to trail by half the profit
+        desired_stoploss = current_profit / 2
+
+        # Use a minimum of 2.5% and a maximum of 5%
+        return max(min(desired_stoploss, 0.05), 0.025)
+```
+
+#### Stepped stoploss
+
+Instead of continuously trailing behind the current price, this example sets fixed stoploss price levels based on the current profit.
+
+* Use the regular stoploss until 20% profit is reached
+* Once profit is > 20% - set stoploss to 7% above open price.
+* Once profit is > 25% - set stoploss to 15% above open price.
+* Once profit is > 40% - set stoploss to 25% above open price.
+
+``` python
+from datetime import datetime
+from freqtrade.persistence import Trade
+from freqtrade.strategy import stoploss_from_open
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                        current_rate: float, current_profit: float, **kwargs) -> float:
+
+        # evaluate highest to lowest, so that highest possible stop is used
+        if current_profit > 0.40:
+            return stoploss_from_open(0.25, current_profit)
+        elif current_profit > 0.25:
+            return stoploss_from_open(0.15, current_profit)
+        elif current_profit > 0.20:
+            return stoploss_from_open(0.07, current_profit)
+
+        # return maximum stoploss value, keeping current stoploss price unchanged
+        return 1
+```
+
+#### Custom stoploss using an indicator from dataframe example
+
+Absolute stoploss value may be derived from indicators stored in dataframe. Example uses parabolic SAR below the price as stoploss.
+
+``` python
+class AwesomeStrategy(IStrategy):
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # <...>
+        dataframe['sar'] = ta.SAR(dataframe)
+
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                        current_rate: float, current_profit: float, **kwargs) -> float:
+
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+        last_candle = dataframe.iloc[-1].squeeze()
+
+        # Use parabolic sar as absolute stoploss price
+        stoploss_price = last_candle['sar']
+
+        # Convert absolute price to percentage relative to current_rate
+        if stoploss_price < current_rate:
+            return (stoploss_price / current_rate) - 1
+
+        # return maximum stoploss value, keeping current stoploss price unchanged
+        return 1
+```
+
+See [Dataframe access](strategy-advanced.md#dataframe-access) for more information about dataframe use in strategy callbacks.
+
+### Common helpers for stoploss calculations
+
+#### Stoploss relative to open price
+
+Stoploss values returned from `custom_stoploss()` always specify a percentage relative to `current_rate`. In order to set a stoploss relative to the *open* price, we need to use `current_profit` to calculate what percentage relative to the `current_rate` will give you the same result as if the percentage was specified from the open price.
+
+The helper function [`stoploss_from_open()`](strategy-customization.md#stoploss_from_open) can be used to convert from an open price relative stop, to a current price relative stop which can be returned from `custom_stoploss()`.
+
+#### Stoploss percentage from absolute price
+
+Stoploss values returned from `custom_stoploss()` always specify a percentage relative to `current_rate`. In order to set a stoploss at specified absolute price level, we need to use `stop_rate` to calculate what percentage relative to the `current_rate` will give you the same result as if the percentage was specified from the open price.
+
+The helper function [`stoploss_from_absolute()`](strategy-customization.md#stoploss_from_absolute) can be used to convert from an absolute price, to a current price relative stop which can be returned from `custom_stoploss()`.
+
+---
+
+## Custom order price rules
+
+By default, freqtrade use the orderbook to automatically set an order price([Relevant documentation](configuration.md#prices-used-for-orders)), you also have the option to create custom order prices based on your strategy.
+
+You can use this feature by creating a `custom_entry_price()` function in your strategy file to customize entry prices and `custom_exit_price()` for exits.
+
+Each of these methods are called right before placing an order on the exchange.
+
+!!! Note
+    If your custom pricing function return None or an invalid value, price will fall back to `proposed_rate`, which is based on the regular pricing configuration.
+
+### Custom order entry and exit price example
+
+``` python
+from datetime import datetime, timedelta, timezone
+from freqtrade.persistence import Trade
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    def custom_entry_price(self, pair: str, current_time: datetime, proposed_rate: float, 
+                           entry_tag: Optional[str], **kwargs) -> float:
+
+        dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
+                                                                timeframe=self.timeframe)
+        new_entryprice = dataframe['bollinger_10_lowerband'].iat[-1]
+        
+        return new_entryprice
+
+    def custom_exit_price(self, pair: str, trade: Trade,
+                          current_time: datetime, proposed_rate: float,
+                          current_profit: float, **kwargs) -> float:
+
+        dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
+                                                                timeframe=self.timeframe)
+        new_exitprice = dataframe['bollinger_10_upperband'].iat[-1]
+        
+        return new_exitprice
+
+```
+
+!!! Warning
+    Modifying entry and exit prices will only work for limit orders. Depending on the price chosen, this can result in a lot of unfilled orders. By default the maximum allowed distance between the current price and the custom price is 2%, this value can be changed in config with the `custom_price_max_distance_ratio` parameter.  
+    **Example**:  
+    If the new_entryprice is 97, the proposed_rate is 100 and the `custom_price_max_distance_ratio` is set to 2%, The retained valid custom entry price will be 98, which is 2% below the current (proposed) rate.
+
+!!! Warning "Backtesting"
+    Custom prices are supported in backtesting (starting with 2021.12), and orders will fill if the price falls within the candle's low/high range.
+    Orders that don't fill immediately are subject to regular timeout handling, which happens once per (detail) candle.
+    `custom_exit_price()` is only called for sells of type Sell_signal and Custom sell. All other sell-types will use regular backtesting prices.
+
+## Custom order timeout rules
+
+Simple, time-based 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 order types, which allows you to decide based on custom criteria if an order did time out or not.
+
+!!! Note
+    Backtesting fills orders if their price falls within the candle's low/high range.
+    The below callbacks will be called once per (detail) candle for orders that don't fill immediately (which use custom pricing).
+
+### Custom order timeout example
+
+Called for every open order until that order is either filled or cancelled.
+`check_buy_timeout()` is called for trade entries, while `check_sell_timeout()` is called for trade exit orders.
+
+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, timedelta
+from freqtrade.persistence import Trade
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    # Set unfilledtimeout to 25 hours, since the 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, 
+                          current_time: datetime, **kwargs) -> bool:
+        if trade.open_rate > 100 and trade.open_date_utc < current_time - timedelta(minutes=5):
+            return True
+        elif trade.open_rate > 10 and trade.open_date_utc < current_time - timedelta(minutes=3):
+            return True
+        elif trade.open_rate < 1 and trade.open_date_utc < current_time - timedelta(hours=24):
+           return True
+        return False
+
+
+    def check_sell_timeout(self, pair: str, trade: Trade, order: dict,
+                           current_time: datetime, **kwargs) -> bool:
+        if trade.open_rate > 100 and trade.open_date_utc < current_time - timedelta(minutes=5):
+            return True
+        elif trade.open_rate > 10 and trade.open_date_utc < current_time - timedelta(minutes=3):
+            return True
+        elif trade.open_rate < 1 and trade.open_date_utc < current_time - 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
+from freqtrade.persistence import Trade
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    # Set unfilledtimeout to 25 hours, since the 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,
+                          current_time: datetime, **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,
+                           current_time: datetime, **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
+```
+
+---
+
+## Bot order confirmation
+
+Confirm trade entry / exits.
+This are the last methods that will be called before an order is placed.
+
+### Trade entry (buy order) confirmation
+
+`confirm_trade_entry()` can be used to abort a trade entry at the latest second (maybe because the price is not what we expect).
+
+``` python
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
+                            time_in_force: str, current_time: datetime, entry_tag: Optional[str], 
+                            **kwargs) -> bool:
+        """
+        Called right before placing a buy order.
+        Timing for this function is critical, so avoid doing heavy computations or
+        network requests in this method.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+        When not implemented by a strategy, returns True (always confirming).
+
+        :param pair: Pair that's about to be bought.
+        :param order_type: Order type (as configured in order_types). usually limit or market.
+        :param amount: Amount in target (quote) currency that's going to be traded.
+        :param rate: Rate that's going to be used when using limit orders
+        :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
+        :param current_time: datetime object, containing the current datetime
+        :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 placed on the exchange.
+            False aborts the process
+        """
+        return True
+
+```
+
+### Trade exit (sell order) confirmation
+
+`confirm_trade_exit()` can be used to abort a trade exit (sell) at the latest second (maybe because the price is not what we expect).
+
+``` python
+from freqtrade.persistence import Trade
+
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
+                           rate: float, time_in_force: str, sell_reason: str,
+                           current_time: datetime, **kwargs) -> bool:
+        """
+        Called right before placing a regular sell order.
+        Timing for this function is critical, so avoid doing heavy computations or
+        network requests in this method.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+        When not implemented by a strategy, returns True (always confirming).
+
+        :param pair: Pair that's about to be sold.
+        :param order_type: Order type (as configured in order_types). usually limit or market.
+        :param amount: Amount in quote currency.
+        :param rate: Rate that's going to be used when using limit orders
+        :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
+        :param sell_reason: Sell reason.
+            Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
+                           'sell_signal', 'force_sell', 'emergency_sell']
+        :param current_time: datetime object, containing the current datetime
+        :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 placed on the exchange.
+            False aborts the process
+        """
+        if sell_reason == 'force_sell' and trade.calc_profit_ratio(rate) < 0:
+            # Reject force-sells with negative profit
+            # This is just a sample, please adjust to your needs
+            # (this does not necessarily make sense, assuming you know when you're force-selling)
+            return False
+        return True
+
+```
+
+## Adjust trade position
+
+The `position_adjustment_enable` strategy property enables the usage of `adjust_trade_position()` callback in the strategy.
+For performance reasons, it's disabled by default and freqtrade will show a warning message on startup if enabled.
+`adjust_trade_position()` can be used to perform additional orders, for example to manage risk with DCA (Dollar Cost Averaging).
+
+`max_entry_position_adjustment` property is used to limit the number of additional buys per trade (on top of the first buy) that the bot can execute. By default, the value is -1 which means the bot have no limit on number of adjustment buys.
+
+The strategy is expected to return a stake_amount (in stake currency) between `min_stake` and `max_stake` if and when an additional buy order should be made (position is increased).
+If there are not enough funds in the wallet (the return value is above `max_stake`) then the signal will be ignored.
+Additional orders also result in additional fees and those orders don't count towards `max_open_trades`.
+
+This callback is **not** called when there is an open order (either buy or sell) waiting for execution, or when you have reached the maximum amount of extra buys that you have set on `max_entry_position_adjustment`.
+`adjust_trade_position()` is called very frequently for the duration of a trade, so you must keep your implementation as performant as possible.
+
+!!! Note "About stake size"
+    Using fixed stake size means it will be the amount used for the first order, just like without position adjustment.
+    If you wish to buy additional orders with DCA, then make sure to leave enough funds in the wallet for that.
+    Using 'unlimited' stake amount with DCA orders requires you to also implement the `custom_stake_amount()` callback to avoid allocating all funds to the initial order.
+
+!!! Warning
+    Stoploss is still calculated from the initial opening price, not averaged price.
+
+!!! Warning "/stopbuy"
+    While `/stopbuy` command stops the bot from entering new trades, the position adjustment feature will continue buying new orders on existing trades.
+
+!!! Warning "Backtesting"
+    During backtesting this callback is called for each candle in `timeframe` or `timeframe_detail`, so performance will be affected.
+
+``` python
+from freqtrade.persistence import Trade
+
+
+class DigDeeperStrategy(IStrategy):
+    
+    position_adjustment_enable = True
+    
+    # Attempts to handle large drops with DCA. High stoploss is required.
+    stoploss = -0.30
+    
+    # ... populate_* methods
+    
+    # Example specific variables
+    max_entry_position_adjustment = 3
+    # This number is explained a bit further down
+    max_dca_multiplier = 5.5
+    
+    # This is called when placing the initial order (opening trade)
+    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
+                            proposed_stake: float, min_stake: float, max_stake: float,
+                            entry_tag: Optional[str], **kwargs) -> float:
+        
+        # We need to leave most of the funds for possible further DCA orders
+        # This also applies to fixed stakes
+        return proposed_stake / self.max_dca_multiplier
+        
+    def adjust_trade_position(self, trade: Trade, current_time: datetime,
+                              current_rate: float, current_profit: float, min_stake: float,
+                              max_stake: float, **kwargs):
+        """
+        Custom trade adjustment logic, returning the stake amount that a trade should be increased.
+        This means extra buy orders with additional fees.
+ 
+        :param trade: trade object.
+        :param current_time: datetime object, containing the current datetime
+        :param current_rate: Current buy rate.
+        :param current_profit: Current profit (as ratio), calculated based on current_rate.
+        :param min_stake: Minimal stake size allowed by exchange.
+        :param max_stake: Balance available for trading.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return float: Stake amount to adjust your trade
+        """
+        
+        if current_profit > -0.05:
+            return None
+
+        # Obtain pair dataframe (just to show how to access it)
+        dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
+        # Only buy when not actively falling price.
+        last_candle = dataframe.iloc[-1].squeeze()
+        previous_candle = dataframe.iloc[-2].squeeze()
+        if last_candle['close'] < previous_candle['close']:
+            return None
+
+        filled_buys = trade.select_filled_orders('buy')
+        count_of_buys = trade.nr_of_successful_buys
+        # Allow up to 3 additional increasingly larger buys (4 in total)
+        # Initial buy is 1x
+        # If that falls to -5% profit, we buy 1.25x more, average profit should increase to roughly -2.2%
+        # If that falls down to -5% again, we buy 1.5x more
+        # If that falls once again down to -5%, we buy 1.75x more
+        # Total stake for this trade would be 1 + 1.25 + 1.5 + 1.75 = 5.5x of the initial allowed stake.
+        # That is why max_dca_multiplier is 5.5
+        # Hope you have a deep wallet!
+        try:
+            # This returns first order stake size
+            stake_amount = filled_buys[0].cost
+            # This then calculates current safety order size
+            stake_amount = stake_amount * (1 + (count_of_buys * 0.25))
+            return stake_amount
+        except Exception as exception:
+            return None
+
+        return None
+
+```

docs/strategy-customization.md

@@ -4,33 +4,23 @@ This page explains how to customize your strategies, add new indicators and set
 
 Please familiarize yourself with [Freqtrade basics](bot-basics.md) first, which provides overall info on how the bot operates.
 
-## Install a custom strategy file
-
-This is very simple. Copy paste your strategy file into the directory `user_data/strategies`.
-
-Let assume you have a class called `AwesomeStrategy` in the file `AwesomeStrategy.py`:
-
-1. Move your file into `user_data/strategies` (you should have `user_data/strategies/AwesomeStrategy.py`
-2. Start the bot with the param `--strategy AwesomeStrategy` (the parameter is the class name)
-
-```bash
-freqtrade trade --strategy AwesomeStrategy
-```
-
 ## Develop your own strategy
 
 The bot includes a default strategy file.
 Also, several other strategies are available in the [strategy repository](https://github.com/freqtrade/freqtrade-strategies).
 
 You will however most likely have your own idea for a strategy.
-This document intends to help you develop one for yourself.
+This document intends to help you convert your strategy idea into your own strategy.
 
-To get started, use `freqtrade new-strategy --strategy AwesomeStrategy`.
+To get started, use `freqtrade new-strategy --strategy AwesomeStrategy` (you can obviously use your own naming for your strategy).
 This will create a new strategy file from a template, which will be located under `user_data/strategies/AwesomeStrategy.py`.
 
 !!! Note
     This is just a template file, which will most likely not be profitable out of the box.
 
+??? Hint "Different template levels"
+    `freqtrade new-strategy` has an additional parameter, `--template`, which controls the amount of pre-build information you get in the created strategy. Use `--template minimal` to get an empty strategy without any indicator examples, or `--template advanced` to get a template with most callbacks defined.
+
 ### Anatomy of a strategy
 
 A strategy file contains all the information needed to build a good strategy:
@@ -67,6 +57,46 @@ file as reference.**
     needs to take care to avoid having the strategy utilize data from the future.
     Some common patterns for this are listed in the [Common Mistakes](#common-mistakes-when-developing-strategies) section of this document.
 
+### Dataframe
+
+Freqtrade uses [pandas](https://pandas.pydata.org/) to store/provide the candlestick (OHLCV) data.
+Pandas is a great library developed for processing large amounts of data.
+
+Each row in a dataframe corresponds to one candle on a chart, with the latest candle always being the last in the dataframe (sorted by date).
+
+``` output
+> dataframe.head()
+                       date      open      high       low     close     volume
+0 2021-11-09 23:25:00+00:00  67279.67  67321.84  67255.01  67300.97   44.62253
+1 2021-11-09 23:30:00+00:00  67300.97  67301.34  67183.03  67187.01   61.38076
+2 2021-11-09 23:35:00+00:00  67187.02  67187.02  67031.93  67123.81  113.42728
+3 2021-11-09 23:40:00+00:00  67123.80  67222.40  67080.33  67160.48   78.96008
+4 2021-11-09 23:45:00+00:00  67160.48  67160.48  66901.26  66943.37  111.39292
+```
+
+Pandas provides fast ways to calculate metrics. To benefit from this speed, it's advised to not use loops, but use vectorized methods instead.
+
+Vectorized operations perform calculations across the whole range of data and are therefore, compared to looping through each row, a lot faster when calculating indicators.
+
+As a dataframe is a table, simple python comparisons like the following will not work
+
+``` python
+    if dataframe['rsi'] > 30:
+        dataframe['buy'] = 1
+```
+
+The above section will fail with `The truth value of a Series is ambiguous. [...]`.
+
+This must instead be written in a pandas-compatible way, so the operation is performed across the whole dataframe.
+
+``` python
+    dataframe.loc[
+        (dataframe['rsi'] > 30)
+    , 'buy'] = 1
+```
+
+With this section, you have a new column in your dataframe, which has `1` assigned whenever RSI is above 30.
+
 ### Customize Indicators
 
 Buy and sell strategies need indicators. You can add more indicators by extending the list contained in the method `populate_indicators()` from your strategy file.
@@ -134,7 +164,7 @@ Additional technical libraries can be installed as necessary, or custom indicato
 
 ### Strategy startup period
 
-Most indicators have an instable startup period, in which they are either not available, or the calculation is incorrect. This can lead to inconsistencies, since Freqtrade does not know how long this instable period should be.
+Most indicators have an instable startup period, in which they are either not available (NaN), or the calculation is incorrect. This can lead to inconsistencies, since Freqtrade does not know how long this instable period should be.
 To account for this, the strategy can be assigned the `startup_candle_count` attribute.
 This should be set to the maximum number of candles that the strategy requires to calculate stable indicators.
 
@@ -146,8 +176,14 @@ In this example strategy, this should be set to 100 (`startup_candle_count = 100
 
 By letting the bot know how much history is needed, backtest trades can start at the specified timerange during backtesting and hyperopt.
 
+!!! Warning "Using x calls to get OHLCV"
+    If you receive a warning like `WARNING - Using 3 calls to get OHLCV. This can result in slower operations for the bot. Please check if you really need 1500 candles for your strategy` - you should consider if you really need this much historic data for your signals.
+    Having this will cause Freqtrade to make multiple calls for the same pair, which will obviously be slower than one network request.
+    As a consequence, Freqtrade will take longer to refresh candles - and should therefore be avoided if possible.
+    This is capped to 5 total calls to avoid overloading the exchange, or make freqtrade too slow.
+
 !!! Warning
-    `startup_candle_count` should be below `ohlcv_candle_limit` (which is 500 for most exchanges) - since only this amount of candles will be available during Dry-Run/Live Trade operations.
+    `startup_candle_count` should be below `ohlcv_candle_limit * 5` (which is 500 * 5 for most exchanges) - since only this amount of candles will be available during Dry-Run/Live Trade operations.
 
 #### Example
 
@@ -281,20 +317,14 @@ class AwesomeStrategy(IStrategy):
 
 Setting a stoploss is highly recommended to protect your capital from strong moves against you.
 
-Sample:
+Sample of setting a 10% stoploss:
 
 ``` python
 stoploss = -0.10
 ```
 
-This would signify a stoploss of -10%.
-
 For the full documentation on stoploss features, look at the dedicated [stoploss page](stoploss.md).
 
-If your exchange supports it, it's recommended to also set `"stoploss_on_exchange"` in the order_types dictionary, so your stoploss is on the exchange and cannot be missed due to network problems, high load or other reasons.
-
-For more information on order_types please look [here](configuration.md#understand-order_types).
-
 ### Timeframe (formerly ticker interval)
 
 This is the set of candles the bot should download and use for the analysis.
@@ -310,7 +340,20 @@ The metadata-dict (available for `populate_buy_trend`, `populate_sell_trend`, `p
 Currently this is `pair`, which can be accessed using `metadata['pair']` - and will return a pair in the format `XRP/BTC`.
 
 The Metadata-dict should not be modified and does not persist information across multiple calls.
-Instead, have a look at the section [Storing information](strategy-advanced.md#Storing-information)
+Instead, have a look at the [Storing information](strategy-advanced.md#Storing-information) section.
+
+## Strategy file loading
+
+By default, freqtrade will attempt to load strategies from all `.py` files within `user_data/strategies`.
+
+Assuming your strategy is called `AwesomeStrategy`, stored in the file `user_data/strategies/AwesomeStrategy.py`, then you can start freqtrade with `freqtrade trade --strategy AwesomeStrategy`.
+Note that we're using the class-name, not the file name.
+
+You can use `freqtrade list-strategies` to see a list of all strategies Freqtrade is able to load (all strategies in the correct folder).
+It will also include a "status" field, highlighting potential problems.
+
+??? Hint "Customize strategy directory"
+    You can use a different directory by using `--strategy-path user_data/otherPath`. This parameter is available to all commands that require a strategy.
 
 ## Informative Pairs
 
@@ -511,9 +554,9 @@ The strategy might look something like this:
 
 *Scan through the top 10 pairs by volume using the `VolumePairList` every 5 minutes and use a 14 day RSI to buy and sell.*
 
-Due to the limited available data, it's very difficult to resample our `5m` candles into daily candles for use in a 14 day RSI. Most exchanges limit us to just 500 candles which effectively gives us around 1.74 daily candles. We need 14 days at least!
+Due to the limited available data, it's very difficult to resample `5m` candles into daily candles for use in a 14 day RSI. Most exchanges limit us to just 500 candles which effectively gives us around 1.74 daily candles. We need 14 days at least!
 
-Since we can't resample our data we will have to use an informative pair; and since our whitelist will be dynamic we don't know which pair(s) to use.
+Since we can't resample the data we will have to use an informative pair; and since the whitelist will be dynamic we don't know which pair(s) to use.
 
 This is where calling `self.dp.current_whitelist()` comes in handy.
 
@@ -795,7 +838,7 @@ In some situations it may be confusing to deal with stops relative to current ra
 
     from datetime import datetime
     from freqtrade.persistence import Trade
-    from freqtrade.strategy import IStrategy, stoploss_from_open
+    from freqtrade.strategy import IStrategy, stoploss_from_absolute
 
     class AwesomeStrategy(IStrategy):
 
@@ -896,7 +939,8 @@ Sometimes it may be desired to lock a pair after certain events happen (e.g. mul
 Freqtrade has an easy method to do this from within the strategy, by calling `self.lock_pair(pair, until, [reason])`.
 `until` must be a datetime object in the future, after which trading will be re-enabled for that pair, while `reason` is an optional string detailing why the pair was locked.
 
-Locks can also be lifted manually, by calling `self.unlock_pair(pair)`.
+Locks can also be lifted manually, by calling `self.unlock_pair(pair)` or `self.unlock_reason(<reason>)` - providing reason the pair was locked with.
+`self.unlock_reason(<reason>)` will unlock all pairs currently locked with the provided reason.
 
 To verify if a pair is currently locked, use `self.is_pair_locked(pair)`.
 
@@ -966,9 +1010,13 @@ The following lists some common patterns which should be avoided to prevent frus
 - don't use `dataframe['volume'].mean()`. This uses the full DataFrame for backtesting, including data from the future. Use `dataframe['volume'].rolling(<window>).mean()` instead
 - don't use `.resample('1h')`. This uses the left border of the interval, so moves data from an hour to the start of the hour. Use `.resample('1h', label='right')` instead.
 
+### Colliding signals
+
+When buy and sell signals collide (both `'buy'` and `'sell'` are 1), freqtrade will do nothing and ignore the entry (buy) signal. This will avoid trades that buy, and sell immediately. Obviously, this can potentially lead to missed entries.
+
 ## Further strategy ideas
 
-To get additional Ideas for strategies, head over to our [strategy repository](https://github.com/freqtrade/freqtrade-strategies). Feel free to use them as they are - but results will depend on the current market situation, pairs used etc. - therefore please backtest the strategy for your exchange/desired pairs first, evaluate carefully, use at your own risk.
+To get additional Ideas for strategies, head over to the [strategy repository](https://github.com/freqtrade/freqtrade-strategies). Feel free to use them as they are - but results will depend on the current market situation, pairs used etc. - therefore please backtest the strategy for your exchange/desired pairs first, evaluate carefully, use at your own risk.
 Feel free to use any of them as inspiration for your own strategies.
 We're happy to accept Pull Requests containing new Strategies to that repo.
 

docs/strategy_analysis_example.md

@@ -50,7 +50,9 @@ candles.head()
 ```python
 # Load strategy using values set above
 from freqtrade.resolvers import StrategyResolver
+from freqtrade.data.dataprovider import DataProvider
 strategy = StrategyResolver.load_strategy(config)
+strategy.dp = DataProvider(config, None, None)
 
 # Generate buy/sell signals using strategy
 df = strategy.analyze_ticker(candles, {'pair': pair})
@@ -228,7 +230,7 @@ graph = generate_candlestick_graph(pair=pair,
 # Show graph inline
 # graph.show()
 
-# Render graph in a separate window
+# Render graph in a seperate window
 graph.show(renderer="browser")
 
 ```

docs/telegram-usage.md

@@ -58,6 +58,8 @@ For the Freqtrade configuration, you can then use the the full value (including
 ```json
    "chat_id": "-1001332619709"
 ```
+!!! Warning "Using telegram groups"
+    When using telegram groups, you're giving every member of the telegram group access to your freqtrade bot and to all commands possible via telegram. Please make sure that you can trust everyone in the telegram group to avoid unpleasent surprises.
 
 ## Control telegram noise
 
@@ -175,6 +177,8 @@ official commands. You can ask at any moment for help with `/help`.
 | `/performance` | Show performance of each finished trade grouped by pair
 | `/balance` | Show account balance per currency
 | `/daily <n>` | Shows profit or loss per day, over the last n days (n defaults to 7)
+| `/weekly <n>` | Shows profit or loss per week, over the last n weeks (n defaults to 8)
+| `/monthly <n>` | Shows profit or loss per month, over the last n months (n defaults to 6)
 | `/stats` | Shows Wins / losses by Sell reason as well as Avg. holding durations for buys and sells
 | `/whitelist` | Show the current whitelist
 | `/blacklist [pair]` | Show the current blacklist, or adds a pair to the blacklist.
@@ -307,8 +311,7 @@ Return the balance of all crypto-currency your have on the exchange.
 
 ### /daily <n>
 
-Per default `/daily` will return the 7 last days.
-The example below if for `/daily 3`:
+Per default `/daily` will return the 7 last days. The example below if for `/daily 3`:
 
 > **Daily Profit over the last 3 days:**
 ```
@@ -319,6 +322,34 @@ Day         Profit BTC      Profit USD
 2018-01-01  0.00269130 BTC  34.986 USD
 ```
 
+### /weekly <n>
+
+Per default `/weekly` will return the 8 last weeks, including the current week. Each week starts
+from Monday. The example below if for `/weekly 3`:
+
+> **Weekly Profit over the last 3 weeks (starting from Monday):**
+```
+Monday         Profit BTC      Profit USD
+----------  --------------  ------------
+2018-01-03  0.00224175 BTC  29,142 USD
+2017-12-27  0.00033131 BTC   4,307 USD
+2017-12-20  0.00269130 BTC  34.986 USD
+```
+
+### /monthly <n>
+
+Per default `/monthly` will return the 6 last months, including the current month. The example below
+if for `/monthly 3`:
+
+> **Monthly Profit over the last 3 months:**
+```
+Month         Profit BTC      Profit USD
+----------  --------------  ------------
+2018-01     0.00224175 BTC  29,142 USD
+2017-12     0.00033131 BTC   4,307 USD
+2017-11     0.00269130 BTC  34.986 USD
+```
+
 ### /whitelist
 
 Shows the current whitelist

docs/utils.md

@@ -59,7 +59,7 @@ $ freqtrade new-config --config config_binance.json
 ? Do you want to enable Dry-run (simulated trades)?  Yes
 ? Please insert your stake currency: BTC
 ? Please insert your stake amount: 0.05
-? Please insert max_open_trades (Integer or 'unlimited'): 3
+? Please insert max_open_trades (Integer or -1 for unlimited open trades): 3
 ? Please insert your desired timeframe (e.g. 5m): 5m
 ? Please insert your display Currency (for reporting): USD
 ? Select exchange  binance
@@ -577,6 +577,46 @@ Common arguments:
 
 ```
 
+## Show previous Backtest results
+
+Allows you to show previous backtest results.
+Adding `--show-pair-list` outputs a sorted pair list you can easily copy/paste into your configuration (omitting bad pairs).
+
+??? Warning "Strategy overfitting"
+    Only using winning pairs can lead to an overfitted strategy, which will not work well on future data. Make sure to extensively test your strategy in dry-run before risking real money.
+
+```
+usage: freqtrade backtesting-show [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                                  [-d PATH] [--userdir PATH]
+                                  [--export-filename PATH] [--show-pair-list]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --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`
+  --show-pair-list      Show backtesting pairlist sorted by profit.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
+
 ## List Hyperopt results
 
 You can list the hyperoptimization epochs the Hyperopt module evaluated previously with the `hyperopt-list` sub-command.

docs/webhook-config.md

@@ -48,9 +48,9 @@ Sample configuration (tested using IFTTT).
     },
 ```
 
-The url in `webhook.url` should point to the correct url for your webhook. If you're using [IFTTT](https://ifttt.com) (as shown in the sample above) please insert our event and key to the url.
+The url in `webhook.url` should point to the correct url for your webhook. If you're using [IFTTT](https://ifttt.com) (as shown in the sample above) please insert your event and key to the url.
 
-You can set the POST body format to Form-Encoded (default) or JSON-Encoded. Use `"format": "form"` or `"format": "json"` respectively. Example configuration for Mattermost Cloud integration:
+You can set the POST body format to Form-Encoded (default), JSON-Encoded, or raw data. Use `"format": "form"`, `"format": "json"`, or `"format": "raw"` respectively. Example configuration for Mattermost Cloud integration:
 
 ```json
   "webhook": {
@@ -63,7 +63,36 @@ You can set the POST body format to Form-Encoded (default) or JSON-Encoded. Use
     },
 ```
 
-The result would be POST request with e.g. `{"text":"Status: running"}` body and `Content-Type: application/json` header which results `Status: running` message in the Mattermost channel.
+The result would be a POST request with e.g. `{"text":"Status: running"}` body and `Content-Type: application/json` header which results `Status: running` message in the Mattermost channel.
+
+When using the Form-Encoded or JSON-Encoded configuration you can configure any number of payload values, and both the key and value will be ouput in the POST request. However, when using the raw data format you can only configure one value and it **must** be named `"data"`. In this instance the data key will not be output in the POST request, only the value. For example:
+
+```json
+  "webhook": {
+        "enabled": true,
+        "url": "https://<YOURHOOKURL>",
+        "format": "raw",
+        "webhookstatus": {
+            "data": "Status: {status}"
+        }
+    },
+```
+
+The result would be a POST request with e.g. `Status: running` body and `Content-Type: text/plain` header.
+
+Optional parameters are available to enable automatic retries for webhook messages. The `webhook.retries` parameter can be set for the maximum number of retries the webhook request should attempt if it is unsuccessful (i.e. HTTP response status is not 200). By default this is set to `0` which is disabled. An additional `webhook.retry_delay` parameter can be set to specify the time in seconds between retry attempts. By default this is set to `0.1` (i.e. 100ms). Note that increasing the number of retries or retry delay may slow down the trader if there are connectivity issues with the webhook. Example configuration for retries:
+
+```json
+  "webhook": {
+        "enabled": true,
+        "url": "https://<YOURHOOKURL>",
+        "retries": 3,
+        "retry_delay": 0.2,
+        "webhookstatus": {
+            "status": "Status: {status}"
+        }
+    },
+```
 
 Different payloads can be configured for different events. Not all fields are necessary, but you should configure at least one of the dicts, otherwise the webhook will never be called.
 
@@ -75,11 +104,13 @@ Possible parameters are:
 * `trade_id`
 * `exchange`
 * `pair`
-* `limit`
+* ~~`limit` # Deprecated - should no longer be used.~~
+* `open_rate`
 * `amount`
 * `open_date`
 * `stake_amount`
 * `stake_currency`
+* `base_currency`
 * `fiat_currency`
 * `order_type`
 * `current_rate`
@@ -98,6 +129,7 @@ Possible parameters are:
 * `open_date`
 * `stake_amount`
 * `stake_currency`
+* `base_currency`
 * `fiat_currency`
 * `order_type`
 * `current_rate`
@@ -116,7 +148,10 @@ Possible parameters are:
 * `open_date`
 * `stake_amount`
 * `stake_currency`
+* `base_currency`
 * `fiat_currency`
+* `order_type`
+* `current_rate`
 * `buy_tag`
 
 ### Webhooksell
@@ -134,6 +169,7 @@ Possible parameters are:
 * `profit_amount`
 * `profit_ratio`
 * `stake_currency`
+* `base_currency`
 * `fiat_currency`
 * `sell_reason`
 * `order_type`
@@ -156,6 +192,7 @@ Possible parameters are:
 * `profit_amount`
 * `profit_ratio`
 * `stake_currency`
+* `base_currency`
 * `fiat_currency`
 * `sell_reason`
 * `order_type`
@@ -178,6 +215,7 @@ Possible parameters are:
 * `profit_amount`
 * `profit_ratio`
 * `stake_currency`
+* `base_currency`
 * `fiat_currency`
 * `sell_reason`
 * `order_type`

docs/windows_installation.md

@@ -23,9 +23,9 @@ git clone https://github.com/freqtrade/freqtrade.git
 
 Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7/ta-lib#windows).
 
-As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial pre-compiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which need to be downloaded and installed using `pip install TA_Lib-0.4.21-cp38-cp38-win_amd64.whl` (make sure to use the version matching your python version).
+As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial pre-compiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which need to be downloaded and installed using `pip install TA_Lib-0.4.24-cp38-cp38-win_amd64.whl` (make sure to use the version matching your python version).
 
-Freqtrade provides these dependencies for the latest 2 Python versions (3.7 and 3.8) and for 64bit Windows.
+Freqtrade provides these dependencies for the latest 3 Python versions (3.8, 3.9 and 3.10) and for 64bit Windows.
 Other versions must be downloaded from the above link.
 
 ``` powershell
@@ -54,6 +54,8 @@ error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual C++
 
 Unfortunately, many packages requiring compilation don't provide a pre-built wheel. It is therefore mandatory to have a C/C++ compiler installed and available for your python environment to use.
 
-The easiest way is to download install Microsoft Visual Studio Community [here](https://visualstudio.microsoft.com/downloads/) and make sure to install "Common Tools for Visual C++" to enable building C code on Windows. Unfortunately, this is a heavy download / dependency (~4Gb) so you might want to consider WSL or [docker compose](docker_quickstart.md) first.
+You can download the Visual C++ build tools from [here](https://visualstudio.microsoft.com/visual-cpp-build-tools/) and install "Desktop development with C++" in it's default configuration. Unfortunately, this is a heavy download / dependency so you might want to consider WSL2 or [docker compose](docker_quickstart.md) first.
+
+![Windows installation](assets/windows_install.png)
 
 ---

environment.yml

@@ -4,7 +4,7 @@ channels:
 # - defaults
 dependencies:
 # 1/4 req main
-    - python>=3.7,<3.9
+    - python>=3.8,<=3.10
     - numpy
     - pandas
     - pip
@@ -25,9 +25,12 @@ dependencies:
     - fastapi
     - uvicorn
     - pyjwt
+    - aiofiles
+    - psutil
     - colorama
     - questionary
     - prompt-toolkit
+    - python-dateutil
 
 
     # ============================

freqtrade/__init__.py

@@ -1,5 +1,5 @@
 """ Freqtrade bot """
-__version__ = '2021.10'
+__version__ = '2022.2.2'
 
 if __version__ == 'develop':
 

freqtrade/__main__.py

@@ -3,7 +3,7 @@
 __main__.py for Freqtrade
 To launch Freqtrade as a module
 
-> python -m freqtrade (with Python >= 3.7)
+> python -m freqtrade (with Python >= 3.8)
 """
 
 from freqtrade import main

freqtrade/commands/__init__.py

@@ -16,7 +16,8 @@ from freqtrade.commands.hyperopt_commands import start_hyperopt_list, start_hype
 from freqtrade.commands.list_commands import (start_list_exchanges, start_list_markets,
                                               start_list_strategies, start_list_timeframes,
                                               start_show_trades)
-from freqtrade.commands.optimize_commands import start_backtesting, start_edge, start_hyperopt
+from freqtrade.commands.optimize_commands import (start_backtesting, start_backtesting_show,
+                                                  start_edge, start_hyperopt)
 from freqtrade.commands.pairlist_commands import start_test_pairlist
 from freqtrade.commands.plot_commands import start_plot_dataframe, start_plot_profit
 from freqtrade.commands.trade_commands import start_trading

freqtrade/commands/arguments.py

@@ -24,7 +24,7 @@ ARGS_COMMON_OPTIMIZE = ["timeframe", "timerange", "dataformat_ohlcv",
 ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + ["position_stacking", "use_max_market_positions",
                                         "enable_protections", "dry_run_wallet", "timeframe_detail",
                                         "strategy_list", "export", "exportfilename",
-                                        "backtest_breakdown"]
+                                        "backtest_breakdown", "backtest_cache"]
 
 ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
                                         "position_stacking", "use_max_market_positions",
@@ -41,6 +41,8 @@ ARGS_LIST_STRATEGIES = ["strategy_path", "print_one_column", "print_colorized"]
 
 ARGS_LIST_HYPEROPTS = ["hyperopt_path", "print_one_column", "print_colorized"]
 
+ARGS_BACKTEST_SHOW = ["exportfilename", "backtest_show_pair_list"]
+
 ARGS_LIST_EXCHANGES = ["print_one_column", "list_exchanges_all"]
 
 ARGS_LIST_TIMEFRAMES = ["exchange", "print_one_column"]
@@ -73,7 +75,7 @@ ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",
                        "timerange", "timeframe", "no_trades"]
 
 ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
-                    "trade_source", "timeframe", "plot_auto_open"]
+                    "trade_source", "timeframe", "plot_auto_open", ]
 
 ARGS_INSTALL_UI = ["erase_ui_only", 'ui_version']
 
@@ -94,7 +96,7 @@ ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperop
 
 NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
                     "list-markets", "list-pairs", "list-strategies", "list-data",
-                    "hyperopt-list", "hyperopt-show",
+                    "hyperopt-list", "hyperopt-show", "backtest-filter",
                     "plot-dataframe", "plot-profit", "show-trades", "trades-to-ohlcv"]
 
 NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-strategy"]
@@ -173,7 +175,8 @@ class Arguments:
         self.parser = argparse.ArgumentParser(description='Free, open source crypto trading bot')
         self._build_args(optionlist=['version'], parser=self.parser)
 
-        from freqtrade.commands import (start_backtesting, start_convert_data, start_convert_trades,
+        from freqtrade.commands import (start_backtesting, start_backtesting_show,
+                                        start_convert_data, start_convert_trades,
                                         start_create_userdir, start_download_data, start_edge,
                                         start_hyperopt, start_hyperopt_list, start_hyperopt_show,
                                         start_install_ui, start_list_data, start_list_exchanges,
@@ -264,6 +267,15 @@ class Arguments:
         backtesting_cmd.set_defaults(func=start_backtesting)
         self._build_args(optionlist=ARGS_BACKTEST, parser=backtesting_cmd)
 
+        # Add backtesting-show subcommand
+        backtesting_show_cmd = subparsers.add_parser(
+            'backtesting-show',
+            help='Show past Backtest results',
+            parents=[_common_parser],
+        )
+        backtesting_show_cmd.set_defaults(func=start_backtesting_show)
+        self._build_args(optionlist=ARGS_BACKTEST_SHOW, parser=backtesting_show_cmd)
+
         # Add edge subcommand
         edge_cmd = subparsers.add_parser('edge', help='Edge module.',
                                          parents=[_common_parser, _strategy_parser])

freqtrade/commands/build_config_commands.py

@@ -76,18 +76,23 @@ def ask_user_config() -> Dict[str, Any]:
         {
             "type": "text",
             "name": "max_open_trades",
-            "message": f"Please insert max_open_trades (Integer or '{UNLIMITED_STAKE_AMOUNT}'):",
+            "message": "Please insert max_open_trades (Integer or -1 for unlimited open trades):",
             "default": "3",
-            "validate": lambda val: val == UNLIMITED_STAKE_AMOUNT or validate_is_int(val),
-            "filter": lambda val: '"' + UNLIMITED_STAKE_AMOUNT + '"'
-            if val == UNLIMITED_STAKE_AMOUNT
-            else val
+            "validate": lambda val: validate_is_int(val)
+        },
+        {
+            "type": "select",
+            "name": "timeframe_in_config",
+            "message": "Time",
+            "choices": ["Have the strategy define timeframe.", "Override in configuration."]
         },
         {
             "type": "text",
             "name": "timeframe",
             "message": "Please insert your desired timeframe (e.g. 5m):",
             "default": "5m",
+            "when": lambda x: x["timeframe_in_config"] == 'Override in configuration.'
+
         },
         {
             "type": "text",
@@ -107,6 +112,7 @@ def ask_user_config() -> Dict[str, Any]:
                 "ftx",
                 "kucoin",
                 "gateio",
+                "okx",
                 Separator(),
                 "other",
             ],
@@ -134,7 +140,7 @@ def ask_user_config() -> Dict[str, Any]:
             "type": "password",
             "name": "exchange_key_password",
             "message": "Insert Exchange API Key password",
-            "when": lambda x: not x['dry_run'] and x['exchange_name'] == 'kucoin'
+            "when": lambda x: not x['dry_run'] and x['exchange_name'] in ('kucoin', 'okx')
         },
         {
             "type": "confirm",

freqtrade/commands/cli_options.py

@@ -152,6 +152,12 @@ AVAILABLE_CLI_OPTIONS = {
         action='store_false',
         default=True,
     ),
+    "backtest_show_pair_list": Arg(
+        '--show-pair-list',
+        help='Show backtesting pairlist sorted by profit.',
+        action='store_true',
+        default=False,
+    ),
     "enable_protections": Arg(
         '--enable-protections', '--enableprotections',
         help='Enable protections for backtesting.'
@@ -176,11 +182,12 @@ AVAILABLE_CLI_OPTIONS = {
 
     ),
     "exportfilename": Arg(
-        '--export-filename',
-        help='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`',
-        metavar='PATH',
+        "--export-filename",
+        "--backtest-filename",
+        help="Use this filename for backtest results."
+        "Requires `--export` to be set as well. "
+        "Example: `--export-filename=user_data/backtest_results/backtest_today.json`",
+        metavar="PATH",
     ),
     "disableparamexport": Arg(
         '--disable-param-export',
@@ -199,6 +206,12 @@ AVAILABLE_CLI_OPTIONS = {
         nargs='+',
         choices=constants.BACKTEST_BREAKDOWNS
     ),
+    "backtest_cache": Arg(
+        '--cache',
+        help='Load a cached backtest result no older than specified age (default: %(default)s).',
+        default=constants.BACKTEST_CACHE_DEFAULT,
+        choices=constants.BACKTEST_CACHE_AGE,
+    ),
     # Edge
     "stoploss_range": Arg(
         '--stoplosses',

freqtrade/commands/optimize_commands.py

@@ -54,6 +54,22 @@ def start_backtesting(args: Dict[str, Any]) -> None:
     backtesting.start()
 
 
+def start_backtesting_show(args: Dict[str, Any]) -> None:
+    """
+    Show previous backtest result
+    """
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    from freqtrade.data.btanalysis import load_backtest_stats
+    from freqtrade.optimize.optimize_reports import show_backtest_results, show_sorted_pairlist
+
+    results = load_backtest_stats(config['exportfilename'])
+
+    show_backtest_results(config, results)
+    show_sorted_pairlist(config, results)
+
+
 def start_hyperopt(args: Dict[str, Any]) -> None:
     """
     Start hyperopt script

freqtrade/configuration/PeriodicCache.py

@@ -1,6 +1,6 @@
 from datetime import datetime, timezone
 
-from cachetools.ttl import TTLCache
+from cachetools import TTLCache
 
 
 class PeriodicCache(TTLCache):

freqtrade/configuration/configuration.py

@@ -245,6 +245,10 @@ class Configuration:
         self._args_to_config(config, argname='timeframe_detail',
                              logstring='Parameter --timeframe-detail detected, '
                              'using {} for intra-candle backtesting ...')
+
+        self._args_to_config(config, argname='backtest_show_pair_list',
+                             logstring='Parameter --show-pair-list detected.')
+
         self._args_to_config(config, argname='stake_amount',
                              logstring='Parameter --stake-amount detected, '
                              'overriding stake_amount to: {} ...')
@@ -272,6 +276,9 @@ class Configuration:
         self._args_to_config(config, argname='backtest_breakdown',
                              logstring='Parameter --breakdown detected ...')
 
+        self._args_to_config(config, argname='backtest_cache',
+                             logstring='Parameter --cache={} detected ...')
+
         self._args_to_config(config, argname='disableparamexport',
                              logstring='Parameter --disableparamexport detected: {} ...')
 
@@ -424,7 +431,6 @@ class Configuration:
                              logstring='Using "{}" to store trades data.')
 
     def _process_data_options(self, config: Dict[str, Any]) -> None:
-
         self._args_to_config(config, argname='new_pairs_days',
                              logstring='Detected --new-pairs-days: {}')
 

freqtrade/configuration/environment_vars.py

@@ -32,6 +32,7 @@ def flat_vars_to_nested_dict(env_dict: Dict[str, Any], prefix: str) -> Dict[str,
     :param prefix: Prefix to consider (usually FREQTRADE__)
     :return: Nested dict based on available and relevant variables.
     """
+    no_convert = ['CHAT_ID']
     relevant_vars: Dict[str, Any] = {}
 
     for env_var, val in sorted(env_dict.items()):
@@ -39,9 +40,9 @@ def flat_vars_to_nested_dict(env_dict: Dict[str, Any], prefix: str) -> Dict[str,
             logger.info(f"Loading variable '{env_var}'")
             key = env_var.replace(prefix, '')
             for k in reversed(key.split('__')):
-                val = {k.lower(): get_var_typed(val) if type(val) != dict else val}
+                val = {k.lower(): get_var_typed(val)
+                       if type(val) != dict and k not in no_convert else val}
             relevant_vars = deep_merge_dicts(val, relevant_vars)
-
     return relevant_vars
 
 

freqtrade/constants.py

@@ -25,7 +25,8 @@ ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
 HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss',
                          'SharpeHyperOptLoss', 'SharpeHyperOptLossDaily',
                          'SortinoHyperOptLoss', 'SortinoHyperOptLossDaily',
-                         'MaxDrawDownHyperOptLoss']
+                         'CalmarHyperOptLoss',
+                         'MaxDrawDownHyperOptLoss', 'ProfitDrawDownHyperOptLoss']
 AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
                        'AgeFilter', 'OffsetFilter', 'PerformanceFilter',
                        'PrecisionFilter', 'PriceFilter', 'RangeStabilityFilter',
@@ -33,6 +34,8 @@ AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
 AVAILABLE_PROTECTIONS = ['CooldownPeriod', 'LowProfitPairs', 'MaxDrawdown', 'StoplossGuard']
 AVAILABLE_DATAHANDLERS = ['json', 'jsongz', 'hdf5']
 BACKTEST_BREAKDOWNS = ['day', 'week', 'month']
+BACKTEST_CACHE_AGE = ['none', 'day', 'week', 'month']
+BACKTEST_CACHE_DEFAULT = 'day'
 DRY_RUN_WALLET = 1000
 DATETIME_PRINT_FORMAT = '%Y-%m-%d %H:%M:%S'
 MATH_CLOSE_PREC = 1e-14  # Precision used for float comparisons
@@ -49,11 +52,12 @@ USERPATH_STRATEGIES = 'strategies'
 USERPATH_NOTEBOOKS = 'notebooks'
 
 TELEGRAM_SETTING_OPTIONS = ['on', 'off', 'silent']
+WEBHOOK_FORMAT_OPTIONS = ['form', 'json', 'raw']
+
 ENV_VAR_PREFIX = 'FREQTRADE__'
 
 NON_OPEN_EXCHANGE_STATES = ('cancelled', 'canceled', 'closed', 'expired')
 
-
 # Define decimals per coin for outputs
 # Only used for outputs.
 DECIMAL_PER_COIN_FALLBACK = 3  # Should be low to avoid listing all possible FIAT's
@@ -67,7 +71,6 @@ DUST_PER_COIN = {
     'ETH': 0.01
 }
 
-
 # Source files with destination directories within user-directory
 USER_DATA_FILES = {
     'sample_strategy.py': USERPATH_STRATEGIES,
@@ -157,6 +160,7 @@ CONF_SCHEMA = {
             'properties': {
                 'buy': {'type': 'number', 'minimum': 1},
                 'sell': {'type': 'number', 'minimum': 1},
+                'exit_timeout_count': {'type': 'number', 'minimum': 0, 'default': 0},
                 'unit': {'type': 'string', 'enum': TIMEOUT_UNITS, 'default': 'minutes'}
             }
         },
@@ -198,7 +202,7 @@ CONF_SCHEMA = {
             'required': ['price_side']
         },
         'custom_price_max_distance_ratio': {
-           'type': 'number', 'minimum': 0.0
+            'type': 'number', 'minimum': 0.0
         },
         'order_types': {
             'type': 'object',
@@ -207,7 +211,10 @@ CONF_SCHEMA = {
                 'sell': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
                 'forcesell': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
                 'forcebuy': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
-                'emergencysell': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
+                'emergencysell': {
+                    'type': 'string',
+                    'enum': ORDERTYPE_POSSIBILITIES,
+                    'default': 'market'},
                 'stoploss': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
                 'stoploss_on_exchange': {'type': 'boolean'},
                 'stoploss_on_exchange_interval': {'type': 'number'},
@@ -309,10 +316,16 @@ CONF_SCHEMA = {
             'type': 'object',
             'properties': {
                 'enabled': {'type': 'boolean'},
+                'url': {'type': 'string'},
+                'format': {'type': 'string', 'enum': WEBHOOK_FORMAT_OPTIONS, 'default': 'form'},
+                'retries': {'type': 'integer', 'minimum': 0},
+                'retry_delay': {'type': 'number', 'minimum': 0},
                 'webhookbuy': {'type': 'object'},
                 'webhookbuycancel': {'type': 'object'},
+                'webhookbuyfill': {'type': 'object'},
                 'webhooksell': {'type': 'object'},
                 'webhooksellcancel': {'type': 'object'},
+                'webhooksellfill': {'type': 'object'},
                 'webhookstatus': {'type': 'object'},
             },
         },
@@ -351,14 +364,16 @@ CONF_SCHEMA = {
         },
         'dataformat_ohlcv': {
             'type': 'string',
-                    'enum': AVAILABLE_DATAHANDLERS,
-                    'default': 'json'
+            'enum': AVAILABLE_DATAHANDLERS,
+            'default': 'json'
         },
         'dataformat_trades': {
             'type': 'string',
-                    'enum': AVAILABLE_DATAHANDLERS,
-                    'default': 'jsongz'
-        }
+            'enum': AVAILABLE_DATAHANDLERS,
+            'default': 'jsongz'
+        },
+        'position_adjustment_enable': {'type': 'boolean'},
+        'max_entry_position_adjustment': {'type': ['integer', 'number'], 'minimum': -1},
     },
     'definitions': {
         'exchange': {
@@ -384,6 +399,7 @@ CONF_SCHEMA = {
                     },
                     'uniqueItems': True
                 },
+                'unknown_fee_rate': {'type': 'number'},
                 'outdated_offset': {'type': 'integer', 'minimum': 1},
                 'markets_refresh_interval': {'type': 'integer'},
                 'ccxt_config': {'type': 'object'},
@@ -440,6 +456,7 @@ SCHEMA_BACKTEST_REQUIRED = [
     'dry_run_wallet',
     'dataformat_ohlcv',
     'dataformat_trades',
+    'unfilledtimeout',
 ]
 
 SCHEMA_MINIMAL_REQUIRED = [

freqtrade/data/btanalysis.py

@@ -2,6 +2,8 @@
 Helpers when analyzing backtest data
 """
 import logging
+from copy import copy
+from datetime import datetime, timezone
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple, Union
 
@@ -9,21 +11,13 @@ import numpy as np
 import pandas as pd
 
 from freqtrade.constants import LAST_BT_RESULT_FN
-from freqtrade.misc import json_load
+from freqtrade.exceptions import OperationalException
+from freqtrade.misc import get_backtest_metadata_filename, json_load
 from freqtrade.persistence import LocalTrade, Trade, init_db
 
 
 logger = logging.getLogger(__name__)
 
-# Old format - maybe remove?
-BT_DATA_COLUMNS_OLD = ["pair", "profit_percent", "open_date", "close_date", "index",
-                       "trade_duration", "open_rate", "close_rate", "open_at_end", "sell_reason"]
-
-# Mid-term format, created by BacktestResult Named Tuple
-BT_DATA_COLUMNS_MID = ['pair', 'profit_percent', 'open_date', 'close_date', 'trade_duration',
-                       'open_rate', 'close_rate', 'open_at_end', 'sell_reason', 'fee_open',
-                       'fee_close', 'amount', 'profit_abs', 'profit_ratio']
-
 # Newest format
 BT_DATA_COLUMNS = ['pair', 'stake_amount', 'amount', 'open_date', 'close_date',
                    'open_rate', 'close_rate',
@@ -106,10 +100,30 @@ def get_latest_hyperopt_file(directory: Union[Path, str], predef_filename: str =
     if isinstance(directory, str):
         directory = Path(directory)
     if predef_filename:
+        if Path(predef_filename).is_absolute():
+            raise OperationalException(
+                "--hyperopt-filename expects only the filename, not an absolute path.")
         return directory / predef_filename
     return directory / get_latest_hyperopt_filename(directory)
 
 
+def load_backtest_metadata(filename: Union[Path, str]) -> Dict[str, Any]:
+    """
+    Read metadata dictionary from backtest results file without reading and deserializing entire
+    file.
+    :param filename: path to backtest results file.
+    :return: metadata dict or None if metadata is not present.
+    """
+    filename = get_backtest_metadata_filename(filename)
+    try:
+        with filename.open() as fp:
+            return json_load(fp)
+    except FileNotFoundError:
+        return {}
+    except Exception as e:
+        raise OperationalException('Unexpected error while loading backtest metadata.') from e
+
+
 def load_backtest_stats(filename: Union[Path, str]) -> Dict[str, Any]:
     """
     Load backtest statistics file.
@@ -126,9 +140,80 @@ def load_backtest_stats(filename: Union[Path, str]) -> Dict[str, Any]:
     with filename.open() as file:
         data = json_load(file)
 
+    # Legacy list format does not contain metadata.
+    if isinstance(data, dict):
+        data['metadata'] = load_backtest_metadata(filename)
+
     return data
 
 
+def _load_and_merge_backtest_result(strategy_name: str, filename: Path, results: Dict[str, Any]):
+    bt_data = load_backtest_stats(filename)
+    for k in ('metadata', 'strategy'):
+        results[k][strategy_name] = bt_data[k][strategy_name]
+    comparison = bt_data['strategy_comparison']
+    for i in range(len(comparison)):
+        if comparison[i]['key'] == strategy_name:
+            results['strategy_comparison'].append(comparison[i])
+            break
+
+
+def find_existing_backtest_stats(dirname: Union[Path, str], run_ids: Dict[str, str],
+                                 min_backtest_date: datetime = None) -> Dict[str, Any]:
+    """
+    Find existing backtest stats that match specified run IDs and load them.
+    :param dirname: pathlib.Path object, or string pointing to the file.
+    :param run_ids: {strategy_name: id_string} dictionary.
+    :param min_backtest_date: do not load a backtest older than specified date.
+    :return: results dict.
+    """
+    # Copy so we can modify this dict without affecting parent scope.
+    run_ids = copy(run_ids)
+    dirname = Path(dirname)
+    results: Dict[str, Any] = {
+        'metadata': {},
+        'strategy': {},
+        'strategy_comparison': [],
+    }
+
+    # Weird glob expression here avoids including .meta.json files.
+    for filename in reversed(sorted(dirname.glob('backtest-result-*-[0-9][0-9].json'))):
+        metadata = load_backtest_metadata(filename)
+        if not metadata:
+            # Files are sorted from newest to oldest. When file without metadata is encountered it
+            # is safe to assume older files will also not have any metadata.
+            break
+
+        for strategy_name, run_id in list(run_ids.items()):
+            strategy_metadata = metadata.get(strategy_name, None)
+            if not strategy_metadata:
+                # This strategy is not present in analyzed backtest.
+                continue
+
+            if min_backtest_date is not None:
+                try:
+                    backtest_date = strategy_metadata['backtest_start_time']
+                except KeyError:
+                    # TODO: this can be removed starting from feb 2022
+                    # The metadata-file without start_time was only available in develop
+                    # and was never included in an official release.
+                    # Older metadata format without backtest time, too old to consider.
+                    return results
+                backtest_date = datetime.fromtimestamp(backtest_date, tz=timezone.utc)
+                if backtest_date < min_backtest_date:
+                    # Do not use a cached result for this strategy as first result is too old.
+                    del run_ids[strategy_name]
+                    continue
+
+            if strategy_metadata['run_id'] == run_id:
+                del run_ids[strategy_name]
+                _load_and_merge_backtest_result(strategy_name, filename, results)
+
+        if len(run_ids) == 0:
+            break
+    return results
+
+
 def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = None) -> pd.DataFrame:
     """
     Load backtest data file.
@@ -167,23 +252,9 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
                                               )
     else:
         # old format - only with lists.
-        df = pd.DataFrame(data, columns=BT_DATA_COLUMNS_OLD)
-        if not df.empty:
-            df['open_date'] = pd.to_datetime(df['open_date'],
-                                             unit='s',
-                                             utc=True,
-                                             infer_datetime_format=True
-                                             )
-            df['close_date'] = pd.to_datetime(df['close_date'],
-                                              unit='s',
-                                              utc=True,
-                                              infer_datetime_format=True
-                                              )
-            # Create compatibility with new format
-            df['profit_abs'] = df['close_rate'] - df['open_rate']
+        raise OperationalException(
+            "Backtest-results with only trades data are no longer supported.")
     if not df.empty:
-        if 'profit_ratio' not in df.columns:
-            df['profit_ratio'] = df['profit_percent']
         df = df.sort_values("open_date").reset_index(drop=True)
     return df
 
@@ -325,6 +396,7 @@ def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
     :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.
+    :raise: ValueError if no data is provided.
     """
     df_comb = pd.concat([data[pair].set_index('date').rename(
         {column: pair}, axis=1)[pair] for pair in data], axis=1)
@@ -360,9 +432,19 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
     return df
 
 
-def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
-                           value_col: str = 'profit_ratio'
-                           ) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float]:
+def _calc_drawdown_series(profit_results: pd.DataFrame, *, date_col: str, value_col: str
+                          ) -> pd.DataFrame:
+    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']
+    max_drawdown_df['date'] = profit_results.loc[:, date_col]
+    return max_drawdown_df
+
+
+def calculate_underwater(trades: pd.DataFrame, *, date_col: str = 'close_date',
+                         value_col: str = 'profit_ratio'
+                         ):
     """
     Calculate max drawdown and the corresponding close dates
     :param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
@@ -375,10 +457,29 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date'
     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']
+    max_drawdown_df = _calc_drawdown_series(profit_results, date_col=date_col, value_col=value_col)
+
+    return max_drawdown_df
+
+
+def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
+                           value_col: str = 'profit_abs', starting_balance: float = 0
+                           ) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float, float]:
+    """
+    Calculate max drawdown and the corresponding close dates
+    :param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
+    :param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
+    :param value_col: Column in DataFrame to use for values (defaults to 'profit_abs')
+    :param starting_balance: Portfolio starting balance - properly calculate relative drawdown.
+    :return: Tuple (float, highdate, lowdate, highvalue, lowvalue, relative_drawdown)
+             with absolute max drawdown, high and low time and high and low value,
+             and the relative account drawdown
+    :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 = _calc_drawdown_series(profit_results, date_col=date_col, value_col=value_col)
 
     idxmin = max_drawdown_df['drawdown'].idxmin()
     if idxmin == 0:
@@ -388,7 +489,18 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date'
     high_val = max_drawdown_df.loc[max_drawdown_df.iloc[:idxmin]
                                    ['high_value'].idxmax(), 'cumulative']
     low_val = max_drawdown_df.loc[idxmin, 'cumulative']
-    return abs(min(max_drawdown_df['drawdown'])), high_date, low_date, high_val, low_val
+    max_drawdown_rel = 0.0
+    if high_val + starting_balance != 0:
+        max_drawdown_rel = (high_val - low_val) / (high_val + starting_balance)
+
+    return (
+        abs(min(max_drawdown_df['drawdown'])),
+        high_date,
+        low_date,
+        high_val,
+        low_val,
+        max_drawdown_rel
+    )
 
 
 def calculate_csum(trades: pd.DataFrame, starting_balance: float = 0) -> Tuple[float, float]:

freqtrade/data/converter.py

@@ -113,7 +113,7 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str)
     pct_missing = (len_after - len_before) / len_before if len_before > 0 else 0
     if len_before != len_after:
         message = (f"Missing data fillup for {pair}: before: {len_before} - after: {len_after}"
-                   f" - {round(pct_missing * 100, 2)}%")
+                   f" - {pct_missing:.2%}")
         if pct_missing > 0.01:
             logger.info(message)
         else:

freqtrade/data/history/hdf5datahandler.py

@@ -6,7 +6,6 @@ from typing import List, Optional
 import numpy as np
 import pandas as pd
 
-from freqtrade import misc
 from freqtrade.configuration import TimeRange
 from freqtrade.constants import (DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS,
                                  ListPairsWithTimeframes, TradeList)
@@ -61,10 +60,10 @@ class HDF5DataHandler(IDataHandler):
 
         filename = self._pair_data_filename(self._datadir, pair, timeframe)
 
-        ds = pd.HDFStore(filename, mode='a', complevel=9, complib='blosc')
-        ds.put(key, _data.loc[:, self._columns], format='table', data_columns=['date'])
-
-        ds.close()
+        _data.loc[:, self._columns].to_hdf(
+            filename, key, mode='a', complevel=9, complib='blosc',
+            format='table', data_columns=['date']
+        )
 
     def _ohlcv_load(self, pair: str, timeframe: str,
                     timerange: Optional[TimeRange] = None) -> pd.DataFrame:
@@ -99,19 +98,6 @@ class HDF5DataHandler(IDataHandler):
                                           'low': 'float', 'close': 'float', 'volume': 'float'})
         return pairdata
 
-    def ohlcv_purge(self, pair: str, timeframe: str) -> bool:
-        """
-        Remove data for this pair
-        :param pair: Delete data for this pair.
-        :param timeframe: Timeframe (e.g. "5m")
-        :return: True when deleted, false if file did not exist.
-        """
-        filename = self._pair_data_filename(self._datadir, pair, timeframe)
-        if filename.exists():
-            filename.unlink()
-            return True
-        return False
-
     def ohlcv_append(self, pair: str, timeframe: str, data: pd.DataFrame) -> None:
         """
         Append data to existing data structures
@@ -142,11 +128,11 @@ class HDF5DataHandler(IDataHandler):
         """
         key = self._pair_trades_key(pair)
 
-        ds = pd.HDFStore(self._pair_trades_filename(self._datadir, pair),
-                         mode='a', complevel=9, complib='blosc')
-        ds.put(key, pd.DataFrame(data, columns=DEFAULT_TRADES_COLUMNS),
-               format='table', data_columns=['timestamp'])
-        ds.close()
+        pd.DataFrame(data, columns=DEFAULT_TRADES_COLUMNS).to_hdf(
+            self._pair_trades_filename(self._datadir, pair), key,
+            mode='a', complevel=9, complib='blosc',
+            format='table', data_columns=['timestamp']
+        )
 
     def trades_append(self, pair: str, data: TradeList):
         """
@@ -180,17 +166,9 @@ class HDF5DataHandler(IDataHandler):
         trades[['id', 'type']] = trades[['id', 'type']].replace({np.nan: None})
         return trades.values.tolist()
 
-    def trades_purge(self, pair: str) -> bool:
-        """
-        Remove data for this pair
-        :param pair: Delete data for this pair.
-        :return: True when deleted, false if file did not exist.
-        """
-        filename = self._pair_trades_filename(self._datadir, pair)
-        if filename.exists():
-            filename.unlink()
-            return True
-        return False
+    @classmethod
+    def _get_file_extension(cls):
+        return "h5"
 
     @classmethod
     def _pair_ohlcv_key(cls, pair: str, timeframe: str) -> str:
@@ -199,15 +177,3 @@ class HDF5DataHandler(IDataHandler):
     @classmethod
     def _pair_trades_key(cls, pair: str) -> str:
         return f"{pair}/trades"
-
-    @classmethod
-    def _pair_data_filename(cls, datadir: Path, pair: str, timeframe: str) -> Path:
-        pair_s = misc.pair_to_filename(pair)
-        filename = datadir.joinpath(f'{pair_s}-{timeframe}.h5')
-        return filename
-
-    @classmethod
-    def _pair_trades_filename(cls, datadir: Path, pair: str) -> Path:
-        pair_s = misc.pair_to_filename(pair)
-        filename = datadir.joinpath(f'{pair_s}-trades.h5')
-        return filename

freqtrade/data/history/history_utils.py

@@ -5,7 +5,7 @@ from pathlib import Path
 from typing import Dict, List, Optional, Tuple
 
 import arrow
-from pandas import DataFrame
+from pandas import DataFrame, concat
 
 from freqtrade.configuration import TimeRange
 from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
@@ -208,7 +208,7 @@ def _download_pair_history(pair: str, *,
         else:
             # Run cleaning again to ensure there were no duplicate candles
             # Especially between existing and new data.
-            data = clean_ohlcv_dataframe(data.append(new_dataframe), timeframe, pair,
+            data = clean_ohlcv_dataframe(concat([data, new_dataframe], axis=0), timeframe, pair,
                                          fill_missing=False, drop_incomplete=False)
 
         logger.debug("New  Start: %s",

freqtrade/data/history/idatahandler.py

@@ -12,6 +12,7 @@ from typing import List, Optional, Type
 
 from pandas import DataFrame
 
+from freqtrade import misc
 from freqtrade.configuration import TimeRange
 from freqtrade.constants import ListPairsWithTimeframes, TradeList
 from freqtrade.data.converter import clean_ohlcv_dataframe, trades_remove_duplicates, trim_dataframe
@@ -26,6 +27,13 @@ class IDataHandler(ABC):
     def __init__(self, datadir: Path) -> None:
         self._datadir = datadir
 
+    @classmethod
+    def _get_file_extension(cls) -> str:
+        """
+        Get file extension for this particular datahandler
+        """
+        raise NotImplementedError()
+
     @abstractclassmethod
     def ohlcv_get_available_data(cls, datadir: Path) -> ListPairsWithTimeframes:
         """
@@ -70,7 +78,6 @@ class IDataHandler(ABC):
         :return: DataFrame with ohlcv data, or empty DataFrame
         """
 
-    @abstractmethod
     def ohlcv_purge(self, pair: str, timeframe: str) -> bool:
         """
         Remove data for this pair
@@ -78,6 +85,11 @@ class IDataHandler(ABC):
         :param timeframe: Timeframe (e.g. "5m")
         :return: True when deleted, false if file did not exist.
         """
+        filename = self._pair_data_filename(self._datadir, pair, timeframe)
+        if filename.exists():
+            filename.unlink()
+            return True
+        return False
 
     @abstractmethod
     def ohlcv_append(self, pair: str, timeframe: str, data: DataFrame) -> None:
@@ -123,13 +135,17 @@ class IDataHandler(ABC):
         :return: List of trades
         """
 
-    @abstractmethod
     def trades_purge(self, pair: str) -> bool:
         """
         Remove data for this pair
         :param pair: Delete data for this pair.
         :return: True when deleted, false if file did not exist.
         """
+        filename = self._pair_trades_filename(self._datadir, pair)
+        if filename.exists():
+            filename.unlink()
+            return True
+        return False
 
     def trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
         """
@@ -141,6 +157,18 @@ class IDataHandler(ABC):
         """
         return trades_remove_duplicates(self._trades_load(pair, timerange=timerange))
 
+    @classmethod
+    def _pair_data_filename(cls, datadir: Path, pair: str, timeframe: str) -> Path:
+        pair_s = misc.pair_to_filename(pair)
+        filename = datadir.joinpath(f'{pair_s}-{timeframe}.{cls._get_file_extension()}')
+        return filename
+
+    @classmethod
+    def _pair_trades_filename(cls, datadir: Path, pair: str) -> Path:
+        pair_s = misc.pair_to_filename(pair)
+        filename = datadir.joinpath(f'{pair_s}-trades.{cls._get_file_extension()}')
+        return filename
+
     def ohlcv_load(self, pair, timeframe: str,
                    timerange: Optional[TimeRange] = None,
                    fill_missing: bool = True,
@@ -173,7 +201,7 @@ class IDataHandler(ABC):
             enddate = pairdf.iloc[-1]['date']
 
             if timerange_startup:
-                self._validate_pairdata(pair, pairdf, timerange_startup)
+                self._validate_pairdata(pair, pairdf, timeframe, timerange_startup)
                 pairdf = trim_dataframe(pairdf, timerange_startup)
                 if self._check_empty_df(pairdf, pair, timeframe, warn_no_data):
                     return pairdf
@@ -200,7 +228,7 @@ class IDataHandler(ABC):
             return True
         return False
 
-    def _validate_pairdata(self, pair, pairdata: DataFrame, timerange: TimeRange):
+    def _validate_pairdata(self, pair, pairdata: DataFrame, timeframe: str, timerange: TimeRange):
         """
         Validates pairdata for missing data at start end end and logs warnings.
         :param pairdata: Dataframe to validate
@@ -210,12 +238,12 @@ class IDataHandler(ABC):
         if timerange.starttype == 'date':
             start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
             if pairdata.iloc[0]['date'] > start:
-                logger.warning(f"Missing data at start for pair {pair}, "
+                logger.warning(f"Missing data at start for pair {pair} at {timeframe}, "
                                f"data starts at {pairdata.iloc[0]['date']:%Y-%m-%d %H:%M:%S}")
         if timerange.stoptype == 'date':
             stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
             if pairdata.iloc[-1]['date'] < stop:
-                logger.warning(f"Missing data at end for pair {pair}, "
+                logger.warning(f"Missing data at end for pair {pair} at {timeframe}, "
                                f"data ends at {pairdata.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}")
 
 

freqtrade/data/history/jsondatahandler.py

@@ -174,34 +174,10 @@ class JsonDataHandler(IDataHandler):
             pass
         return tradesdata
 
-    def trades_purge(self, pair: str) -> bool:
-        """
-        Remove data for this pair
-        :param pair: Delete data for this pair.
-        :return: True when deleted, false if file did not exist.
-        """
-        filename = self._pair_trades_filename(self._datadir, pair)
-        if filename.exists():
-            filename.unlink()
-            return True
-        return False
-
-    @classmethod
-    def _pair_data_filename(cls, datadir: Path, pair: str, timeframe: str) -> Path:
-        pair_s = misc.pair_to_filename(pair)
-        filename = datadir.joinpath(f'{pair_s}-{timeframe}.{cls._get_file_extension()}')
-        return filename
-
     @classmethod
     def _get_file_extension(cls):
         return "json.gz" if cls._use_zip else "json"
 
-    @classmethod
-    def _pair_trades_filename(cls, datadir: Path, pair: str) -> Path:
-        pair_s = misc.pair_to_filename(pair)
-        filename = datadir.joinpath(f'{pair_s}-trades.{cls._get_file_extension()}')
-        return filename
-
 
 class JsonGzDataHandler(JsonDataHandler):
 

freqtrade/enums/__init__.py

@@ -1,5 +1,6 @@
 # flake8: noqa: F401
 from freqtrade.enums.backteststate import BacktestState
+from freqtrade.enums.ordertypevalue import OrderTypeValues
 from freqtrade.enums.rpcmessagetype import RPCMessageType
 from freqtrade.enums.runmode import NON_UTIL_MODES, OPTIMIZE_MODES, TRADING_MODES, RunMode
 from freqtrade.enums.selltype import SellType

freqtrade/enums/ordertypevalue.py

@@ -0,0 +1,6 @@
+from enum import Enum
+
+
+class OrderTypeValues(str, Enum):
+    limit = 'limit'
+    market = 'market'

freqtrade/enums/signaltype.py

@@ -14,3 +14,4 @@ class SignalTagType(Enum):
     Enum for signal columns
     """
     BUY_TAG = "buy_tag"
+    EXIT_TAG = "exit_tag"

freqtrade/exceptions.py

@@ -1,5 +1,3 @@
-
-
 class FreqtradeException(Exception):
     """
     Freqtrade base exception. Handled at the outermost level.

freqtrade/exchange/__init__.py

@@ -5,6 +5,7 @@ from freqtrade.exchange.exchange import Exchange
 # isort: on
 from freqtrade.exchange.bibox import Bibox
 from freqtrade.exchange.binance import Binance
+from freqtrade.exchange.bitpanda import Bitpanda
 from freqtrade.exchange.bittrex import Bittrex
 from freqtrade.exchange.bybit import Bybit
 from freqtrade.exchange.coinbasepro import Coinbasepro
@@ -19,3 +20,4 @@ from freqtrade.exchange.gateio import Gateio
 from freqtrade.exchange.hitbtc import Hitbtc
 from freqtrade.exchange.kraken import Kraken
 from freqtrade.exchange.kucoin import Kucoin
+from freqtrade.exchange.okx import Okx

freqtrade/exchange/binance.py

@@ -1,6 +1,6 @@
 """ Binance exchange subclass """
 import logging
-from typing import Dict, List
+from typing import Dict, List, Tuple
 
 import arrow
 import ccxt
@@ -93,8 +93,9 @@ class Binance(Exchange):
             raise OperationalException(e) from e
 
     async def _async_get_historic_ohlcv(self, pair: str, timeframe: str,
-                                        since_ms: int, is_new_pair: bool
-                                        ) -> List:
+                                        since_ms: int, is_new_pair: bool = False,
+                                        raise_: bool = False
+                                        ) -> Tuple[str, str, List]:
         """
         Overwrite to introduce "fast new pair" functionality by detecting the pair's listing date
         Does not work for other exchanges, which don't return the earliest data when called with "0"
@@ -107,4 +108,5 @@ class Binance(Exchange):
                 logger.info(f"Candle-data for {pair} available starting with "
                             f"{arrow.get(since_ms // 1000).isoformat()}.")
         return await super()._async_get_historic_ohlcv(
-            pair=pair, timeframe=timeframe, since_ms=since_ms, is_new_pair=is_new_pair)
+            pair=pair, timeframe=timeframe, since_ms=since_ms, is_new_pair=is_new_pair,
+            raise_=raise_)

freqtrade/exchange/bitpanda.py

@@ -0,0 +1,37 @@
+""" Bitpanda exchange subclass """
+import logging
+from datetime import datetime, timezone
+from typing import Dict, List, Optional
+
+from freqtrade.exchange import Exchange
+
+
+logger = logging.getLogger(__name__)
+
+
+class Bitpanda(Exchange):
+    """
+    Bitpanda exchange class. Contains adjustments needed for Freqtrade to work
+    with this exchange.
+    """
+
+    def get_trades_for_order(self, order_id: str, pair: str, since: datetime,
+                             params: Optional[Dict] = None) -> List:
+        """
+        Fetch Orders using the "fetch_my_trades" endpoint and filter them by order-id.
+        The "since" argument passed in is coming from the database and is in UTC,
+        as timezone-native datetime object.
+        From the python documentation:
+            > Naive datetime instances are assumed to represent local time
+        Therefore, calling "since.timestamp()" will get the UTC timestamp, after applying the
+        transformation from local timezone to UTC.
+        This works for timezones UTC+ since then the result will contain trades from a few hours
+        instead of from the last 5 seconds, however fails for UTC- timezones,
+        since we're then asking for trades with a "since" argument in the future.
+
+        :param order_id order_id: Order-id as given when creating the order
+        :param pair: Pair the order is for
+        :param since: datetime object of the order creation time. Assumes object is in UTC.
+        """
+        params = {'to': int(datetime.now(timezone.utc).timestamp() * 1000)}
+        return super().get_trades_for_order(order_id, pair, since, params)

freqtrade/exchange/common.py

@@ -4,9 +4,20 @@ import time
 from functools import wraps
 
 from freqtrade.exceptions import DDosProtection, RetryableOrderError, TemporaryError
+from freqtrade.mixins import LoggingMixin
 
 
 logger = logging.getLogger(__name__)
+__logging_mixin = None
+
+
+def _get_logging_mixin():
+    # Logging-mixin to cache kucoin responses
+    # Only to be used in retrier
+    global __logging_mixin
+    if not __logging_mixin:
+        __logging_mixin = LoggingMixin(logger)
+    return __logging_mixin
 
 
 # Maximum default retry count.
@@ -16,13 +27,15 @@ API_FETCH_ORDER_RETRY_COUNT = 5
 
 BAD_EXCHANGES = {
     "bitmex": "Various reasons.",
-    "phemex": "Does not provide history. ",
+    "phemex": "Does not provide history.",
+    "probit": "Requires additional, regular calls to `signIn()`.",
     "poloniex": "Does not provide fetch_order endpoint to fetch both open and closed orders.",
 }
 
 MAP_EXCHANGE_CHILDCLASS = {
     'binanceus': 'binance',
     'binanceje': 'binance',
+    'okex': 'okx',
 }
 
 
@@ -72,21 +85,33 @@ def calculate_backoff(retrycount, max_retries):
 def retrier_async(f):
     async def wrapper(*args, **kwargs):
         count = kwargs.pop('count', API_RETRY_COUNT)
+        kucoin = args[0].name == "Kucoin"  # Check if the exchange is KuCoin.
         try:
             return await f(*args, **kwargs)
         except TemporaryError as ex:
-            logger.warning('%s() returned exception: "%s"', f.__name__, ex)
+            msg = f'{f.__name__}() returned exception: "{ex}". '
             if count > 0:
-                logger.warning('retrying %s() still for %s times', f.__name__, count)
+                msg += f'Retrying still for {count} times.'
                 count -= 1
-                kwargs.update({'count': count})
+                kwargs['count'] = count
                 if isinstance(ex, DDosProtection):
-                    backoff_delay = calculate_backoff(count + 1, API_RETRY_COUNT)
-                    logger.info(f"Applying DDosProtection backoff delay: {backoff_delay}")
-                    await asyncio.sleep(backoff_delay)
+                    if kucoin and "429000" in str(ex):
+                        # Temporary fix for 429000 error on kucoin
+                        # see https://github.com/freqtrade/freqtrade/issues/5700 for details.
+                        _get_logging_mixin().log_once(
+                            f"Kucoin 429 error, avoid triggering DDosProtection backoff delay. "
+                            f"{count} tries left before giving up", logmethod=logger.warning)
+                        # Reset msg to avoid logging too many times.
+                        msg = ''
+                    else:
+                        backoff_delay = calculate_backoff(count + 1, API_RETRY_COUNT)
+                        logger.info(f"Applying DDosProtection backoff delay: {backoff_delay}")
+                        await asyncio.sleep(backoff_delay)
+                if msg:
+                    logger.warning(msg)
                 return await wrapper(*args, **kwargs)
             else:
-                logger.warning('Giving up retrying: %s()', f.__name__)
+                logger.warning(msg + 'Giving up.')
                 raise ex
     return wrapper
 
@@ -99,9 +124,9 @@ def retrier(_func=None, retries=API_RETRY_COUNT):
             try:
                 return f(*args, **kwargs)
             except (TemporaryError, RetryableOrderError) as ex:
-                logger.warning('%s() returned exception: "%s"', f.__name__, ex)
+                msg = f'{f.__name__}() returned exception: "{ex}". '
                 if count > 0:
-                    logger.warning('retrying %s() still for %s times', f.__name__, count)
+                    logger.warning(msg + f'Retrying still for {count} times.')
                     count -= 1
                     kwargs.update({'count': count})
                     if isinstance(ex, (DDosProtection, RetryableOrderError)):
@@ -111,7 +136,7 @@ def retrier(_func=None, retries=API_RETRY_COUNT):
                         time.sleep(backoff_delay)
                     return wrapper(*args, **kwargs)
                 else:
-                    logger.warning('Giving up retrying: %s()', f.__name__)
+                    logger.warning(msg + 'Giving up.')
                     raise ex
         return wrapper
     # Support both @retrier and @retrier(retries=2) syntax

freqtrade/exchange/exchange.py

@@ -7,7 +7,7 @@ import http
 import inspect
 import logging
 from copy import deepcopy
-from datetime import datetime, timezone
+from datetime import datetime, timedelta, timezone
 from math import ceil
 from typing import Any, Dict, List, Optional, Tuple
 
@@ -67,6 +67,8 @@ class Exchange:
         "ohlcv_params": {},
         "ohlcv_candle_limit": 500,
         "ohlcv_partial_candle": True,
+        # Check https://github.com/ccxt/ccxt/issues/10767 for removal of ohlcv_volume_currency
+        "ohlcv_volume_currency": "base",  # "base" or "quote"
         "trades_pagination": "time",  # Possible are "time" or "id"
         "trades_pagination_arg": "since",
         "l2_limit_range": None,
@@ -83,6 +85,8 @@ class Exchange:
         self._api: ccxt.Exchange = None
         self._api_async: ccxt_async.Exchange = None
         self._markets: Dict = {}
+        self.loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(self.loop)
 
         self._config.update(config)
 
@@ -155,8 +159,8 @@ class Exchange:
                 self.validate_pairs(config['exchange']['pair_whitelist'])
             self.validate_ordertypes(config.get('order_types', {}))
             self.validate_order_time_in_force(config.get('order_time_in_force', {}))
-            self.validate_required_startup_candles(config.get('startup_candle_count', 0),
-                                                   config.get('timeframe', ''))
+            self.required_candle_call_count = self.validate_required_startup_candles(
+                config.get('startup_candle_count', 0), config.get('timeframe', ''))
 
         # Converts the interval provided in minutes in config to seconds
         self.markets_refresh_interval: int = exchange_config.get(
@@ -170,8 +174,10 @@ class Exchange:
 
     def close(self):
         logger.debug("Exchange object destroyed, closing async loop")
-        if self._api_async and inspect.iscoroutinefunction(self._api_async.close):
-            asyncio.get_event_loop().run_until_complete(self._api_async.close())
+        if (self._api_async and inspect.iscoroutinefunction(self._api_async.close)
+                and self._api_async.session):
+            logger.info("Closing async ccxt session.")
+            self.loop.run_until_complete(self._api_async.close())
 
     def _init_ccxt(self, exchange_config: Dict[str, Any], ccxt_module: CcxtModuleType = ccxt,
                    ccxt_kwargs: Dict = {}) -> ccxt.Exchange:
@@ -326,7 +332,7 @@ class Exchange:
     def _load_async_markets(self, reload: bool = False) -> None:
         try:
             if self._api_async:
-                asyncio.get_event_loop().run_until_complete(
+                self.loop.run_until_complete(
                     self._api_async.load_markets(reload=reload))
 
         except (asyncio.TimeoutError, ccxt.BaseError) as e:
@@ -471,16 +477,29 @@ class Exchange:
             raise OperationalException(
                 f'Time in force policies are not supported for {self.name} yet.')
 
-    def validate_required_startup_candles(self, startup_candles: int, timeframe: str) -> None:
+    def validate_required_startup_candles(self, startup_candles: int, timeframe: str) -> int:
         """
         Checks if required startup_candles is more than ohlcv_candle_limit().
         Requires a grace-period of 5 candles - so a startup-period up to 494 is allowed by default.
         """
         candle_limit = self.ohlcv_candle_limit(timeframe)
-        if startup_candles + 5 > candle_limit:
+        # Require one more candle - to account for the still open candle.
+        candle_count = startup_candles + 1
+        # Allow 5 calls to the exchange per pair
+        required_candle_call_count = int(
+            (candle_count / candle_limit) + (0 if candle_count % candle_limit == 0 else 1))
+
+        if required_candle_call_count > 5:
+            # Only allow 5 calls per pair to somewhat limit the impact
             raise OperationalException(
-                f"This strategy requires {startup_candles} candles to start. "
-                f"{self.name} only provides {candle_limit - 5} for {timeframe}.")
+                f"This strategy requires {startup_candles} candles to start, which is more than 5x "
+                f"the amount of candles {self.name} provides for {timeframe}.")
+
+        if required_candle_call_count > 1:
+            logger.warning(f"Using {required_candle_call_count} calls to get OHLCV. "
+                           f"This can result in slower operations for the bot. Please check "
+                           f"if you really need {startup_candles} candles for your strategy")
+        return required_candle_call_count
 
     def exchange_has(self, endpoint: str) -> bool:
         """
@@ -593,8 +612,9 @@ class Exchange:
             'cost': _amount * rate,
             'type': ordertype,
             'side': side,
+            'filled': 0,
             'remaining': _amount,
-            'datetime': arrow.utcnow().isoformat(),
+            'datetime': arrow.utcnow().strftime('%Y-%m-%dT%H:%M:%S.%fZ'),
             'timestamp': arrow.utcnow().int_timestamp * 1000,
             'status': "closed" if ordertype == "market" else "open",
             'fee': None,
@@ -608,6 +628,7 @@ class Exchange:
             average = self.get_dry_market_fill_price(pair, side, amount, rate)
             dry_order.update({
                 'average': average,
+                'filled': _amount,
                 'cost': dry_order['amount'] * average,
             })
             dry_order = self.add_dry_order_fee(pair, dry_order)
@@ -639,7 +660,8 @@ class Exchange:
             max_slippage_val = rate * ((1 + slippage) if side == 'buy' else (1 - slippage))
 
             remaining_amount = amount
-            filled_amount = 0
+            filled_amount = 0.0
+            book_entry_price = 0.0
             for book_entry in ob[ob_type]:
                 book_entry_price = book_entry[0]
                 book_entry_coin_volume = book_entry[1]
@@ -672,16 +694,20 @@ class Exchange:
         if not self.exchange_has('fetchL2OrderBook'):
             return True
         ob = self.fetch_l2_order_book(pair, 1)
-        if side == 'buy':
-            price = ob['asks'][0][0]
-            logger.debug(f"{pair} checking dry buy-order: price={price}, limit={limit}")
-            if limit >= price:
-                return True
-        else:
-            price = ob['bids'][0][0]
-            logger.debug(f"{pair} checking dry sell-order: price={price}, limit={limit}")
-            if limit <= price:
-                return True
+        try:
+            if side == 'buy':
+                price = ob['asks'][0][0]
+                logger.debug(f"{pair} checking dry buy-order: price={price}, limit={limit}")
+                if limit >= price:
+                    return True
+            else:
+                price = ob['bids'][0][0]
+                logger.debug(f"{pair} checking dry sell-order: price={price}, limit={limit}")
+                if limit <= price:
+                    return True
+        except IndexError:
+            # Ignore empty orderbooks when filling - can be filled with the next iteration.
+            pass
         return False
 
     def check_dry_limit_order_filled(self, order: Dict[str, Any]) -> Dict[str, Any]:
@@ -927,7 +953,7 @@ class Exchange:
             raise OperationalException(e) from e
 
     @retrier
-    def get_tickers(self, cached: bool = False) -> Dict:
+    def get_tickers(self, symbols: List[str] = None, cached: bool = False) -> Dict:
         """
         :param cached: Allow cached result
         :return: fetch_tickers result
@@ -937,7 +963,7 @@ class Exchange:
             if tickers:
                 return tickers
         try:
-            tickers = self._api.fetch_tickers()
+            tickers = self._api.fetch_tickers(symbols)
             self._fetch_tickers_cache['fetch_tickers'] = tickers
             return tickers
         except ccxt.NotSupported as e:
@@ -1074,7 +1100,8 @@ class Exchange:
     # Fee handling
 
     @retrier
-    def get_trades_for_order(self, order_id: str, pair: str, since: datetime) -> List:
+    def get_trades_for_order(self, order_id: str, pair: str, since: datetime,
+                             params: Optional[Dict] = None) -> List:
         """
         Fetch Orders using the "fetch_my_trades" endpoint and filter them by order-id.
         The "since" argument passed in is coming from the database and is in UTC,
@@ -1098,8 +1125,10 @@ class Exchange:
         try:
             # Allow 5s offset to catch slight time offsets (discovered in #1185)
             # since needs to be int in milliseconds
+            _params = params if params else {}
             my_trades = self._api.fetch_my_trades(
-                pair, int((since.replace(tzinfo=timezone.utc).timestamp() - 5) * 1000))
+                pair, int((since.replace(tzinfo=timezone.utc).timestamp() - 5) * 1000),
+                params=_params)
             matched_trades = [trade for trade in my_trades if trade['order'] == order_id]
 
             self._log_exchange_response('get_trades_for_order', matched_trades)
@@ -1177,9 +1206,11 @@ class Exchange:
                 tick = self.fetch_ticker(comb)
 
                 fee_to_quote_rate = safe_value_fallback2(tick, tick, 'last', 'ask')
-                return round((order['fee']['cost'] * fee_to_quote_rate) / order['cost'], 8)
             except ExchangeError:
-                return None
+                fee_to_quote_rate = self._config['exchange'].get('unknown_fee_rate', None)
+                if not fee_to_quote_rate:
+                    return None
+            return round((order['fee']['cost'] * fee_to_quote_rate) / order['cost'], 8)
 
     def extract_cost_curr_rate(self, order: Dict) -> Tuple[float, str, Optional[float]]:
         """
@@ -1205,9 +1236,11 @@ class Exchange:
         :param since_ms: Timestamp in milliseconds to get history from
         :return: List with candle (OHLCV) data
         """
-        return asyncio.get_event_loop().run_until_complete(
+        pair, timeframe, data = self.loop.run_until_complete(
             self._async_get_historic_ohlcv(pair=pair, timeframe=timeframe,
                                            since_ms=since_ms, is_new_pair=is_new_pair))
+        logger.info(f"Downloaded data for {pair} with length {len(data)}.")
+        return data
 
     def get_historic_ohlcv_as_df(self, pair: str, timeframe: str,
                                  since_ms: int) -> DataFrame:
@@ -1223,8 +1256,9 @@ class Exchange:
                                   drop_incomplete=self._ohlcv_partial_candle)
 
     async def _async_get_historic_ohlcv(self, pair: str, timeframe: str,
-                                        since_ms: int, is_new_pair: bool
-                                        ) -> List:
+                                        since_ms: int, is_new_pair: bool = False,
+                                        raise_: bool = False
+                                        ) -> Tuple[str, str, List]:
         """
         Download historic ohlcv
         :param is_new_pair: used by binance subclass to allow "fast" new pair downloading
@@ -1247,16 +1281,18 @@ class Exchange:
             results = await asyncio.gather(*input_coro, return_exceptions=True)
             for res in results:
                 if isinstance(res, Exception):
-                    logger.warning("Async code raised an exception: %s", res.__class__.__name__)
+                    logger.warning(f"Async code raised an exception: {repr(res)}")
+                    if raise_:
+                        raise
                     continue
-                # Deconstruct tuple if it's not an exception
-                p, _, new_data = res
-                if p == pair:
-                    data.extend(new_data)
+                else:
+                    # Deconstruct tuple if it's not an exception
+                    p, _, new_data = res
+                    if p == pair:
+                        data.extend(new_data)
         # Sort data again after extending the result - above calls return in "async order"
         data = sorted(data, key=lambda x: x[0])
-        logger.info(f"Downloaded data for {pair} with length {len(data)}.")
-        return data
+        return pair, timeframe, data
 
     def refresh_latest_ohlcv(self, pair_list: ListPairsWithTimeframes, *,
                              since_ms: Optional[int] = None, cache: bool = True
@@ -1276,10 +1312,22 @@ class Exchange:
         cached_pairs = []
         # Gather coroutines to run
         for pair, timeframe in set(pair_list):
-            if (((pair, timeframe) not in self._klines)
+            if ((pair, timeframe) not in self._klines or not cache
                     or self._now_is_time_to_refresh(pair, timeframe)):
-                input_coroutines.append(self._async_get_candle_history(pair, timeframe,
-                                                                       since_ms=since_ms))
+                if not since_ms and self.required_candle_call_count > 1:
+                    # Multiple calls for one pair - to get more history
+                    one_call = timeframe_to_msecs(timeframe) * self.ohlcv_candle_limit(timeframe)
+                    move_to = one_call * self.required_candle_call_count
+                    now = timeframe_to_next_date(timeframe)
+                    since_ms = int((now - timedelta(seconds=move_to // 1000)).timestamp() * 1000)
+
+                if since_ms:
+                    input_coroutines.append(self._async_get_historic_ohlcv(
+                        pair, timeframe, since_ms=since_ms, raise_=True))
+                else:
+                    # One call ... "regular" refresh
+                    input_coroutines.append(self._async_get_candle_history(
+                        pair, timeframe, since_ms=since_ms))
             else:
                 logger.debug(
                     "Using cached candle (OHLCV) data for pair %s, timeframe %s ...",
@@ -1287,27 +1335,32 @@ class Exchange:
                 )
                 cached_pairs.append((pair, timeframe))
 
-        results = asyncio.get_event_loop().run_until_complete(
-            asyncio.gather(*input_coroutines, return_exceptions=True))
-
         results_df = {}
-        # handle caching
-        for res in results:
-            if isinstance(res, Exception):
-                logger.warning("Async code raised an exception: %s", res.__class__.__name__)
-                continue
-            # Deconstruct tuple (has 3 elements)
-            pair, timeframe, ticks = res
-            # keeping last candle time as last refreshed time of the pair
-            if ticks:
-                self._pairs_last_refresh_time[(pair, timeframe)] = ticks[-1][0] // 1000
-            # keeping parsed dataframe in cache
-            ohlcv_df = ohlcv_to_dataframe(
-                ticks, timeframe, pair=pair, fill_missing=True,
-                drop_incomplete=self._ohlcv_partial_candle)
-            results_df[(pair, timeframe)] = ohlcv_df
-            if cache:
-                self._klines[(pair, timeframe)] = ohlcv_df
+        # Chunk requests into batches of 100 to avoid overwelming ccxt Throttling
+        for input_coro in chunks(input_coroutines, 100):
+            async def gather_stuff():
+                return await asyncio.gather(*input_coro, return_exceptions=True)
+
+            results = self.loop.run_until_complete(gather_stuff())
+
+            # handle caching
+            for res in results:
+                if isinstance(res, Exception):
+                    logger.warning(f"Async code raised an exception: {repr(res)}")
+                    continue
+                # Deconstruct tuple (has 3 elements)
+                pair, timeframe, ticks = res
+                # keeping last candle time as last refreshed time of the pair
+                if ticks:
+                    self._pairs_last_refresh_time[(pair, timeframe)] = ticks[-1][0] // 1000
+                # keeping parsed dataframe in cache
+                ohlcv_df = ohlcv_to_dataframe(
+                    ticks, timeframe, pair=pair, fill_missing=True,
+                    drop_incomplete=self._ohlcv_partial_candle)
+                results_df[(pair, timeframe)] = ohlcv_df
+                if cache:
+                    self._klines[(pair, timeframe)] = ohlcv_df
+
         # Return cached klines
         for pair, timeframe in cached_pairs:
             results_df[(pair, timeframe)] = self.klines((pair, timeframe), copy=False)
@@ -1524,7 +1577,7 @@ class Exchange:
         if not self.exchange_has("fetchTrades"):
             raise OperationalException("This exchange does not support downloading Trades.")
 
-        return asyncio.get_event_loop().run_until_complete(
+        return self.loop.run_until_complete(
             self._async_get_trade_history(pair=pair, since=since,
                                           until=until, from_id=from_id))
 
@@ -1534,7 +1587,7 @@ 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', 'kraken']
+    return exchange_name in ['bittrex', 'binance', 'kraken', 'ftx', 'gateio', 'okx']
 
 
 def ccxt_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:

freqtrade/exchange/ftx.py

@@ -19,6 +19,7 @@ class Ftx(Exchange):
     _ft_has: Dict = {
         "stoploss_on_exchange": True,
         "ohlcv_candle_limit": 1500,
+        "ohlcv_volume_currency": "quote",
     }
 
     def market_is_tradable(self, market: Dict[str, Any]) -> bool:
@@ -105,15 +106,18 @@ class Ftx(Exchange):
                 if order[0].get('status') == 'closed':
                     # Trigger order was triggered ...
                     real_order_id = order[0].get('info', {}).get('orderId')
+                    # OrderId may be None for stoploss-market orders
+                    # But contains "average" in these cases.
+                    if real_order_id:
+                        order1 = self._api.fetch_order(real_order_id, pair)
+                        self._log_exchange_response('fetch_stoploss_order1', order1)
+                        # Fake type to stop - as this was really a stop order.
+                        order1['id_stop'] = order1['id']
+                        order1['id'] = order_id
+                        order1['type'] = 'stop'
+                        order1['status_stop'] = 'triggered'
+                        return order1
 
-                    order1 = self._api.fetch_order(real_order_id, pair)
-                    self._log_exchange_response('fetch_stoploss_order1', order1)
-                    # Fake type to stop - as this was really a stop order.
-                    order1['id_stop'] = order1['id']
-                    order1['id'] = order_id
-                    order1['type'] = 'stop'
-                    order1['status_stop'] = 'triggered'
-                    return order1
                 return order[0]
             else:
                 raise InvalidOrderException(f"Could not get stoploss order for id {order_id}")

freqtrade/exchange/gateio.py

@@ -21,6 +21,7 @@ class Gateio(Exchange):
 
     _ft_has: Dict = {
         "ohlcv_candle_limit": 1000,
+        "ohlcv_volume_currency": "quote",
     }
 
     _headers = {'X-Gate-Channel-Id': 'freqtrade'}

freqtrade/exchange/kraken.py

@@ -1,6 +1,6 @@
 """ Kraken exchange subclass """
 import logging
-from typing import Any, Dict
+from typing import Any, Dict, List
 
 import ccxt
 
@@ -33,6 +33,12 @@ class Kraken(Exchange):
         return (parent_check and
                 market.get('darkpool', False) is False)
 
+    def get_tickers(self, symbols: List[str] = None, cached: bool = False) -> Dict:
+        # Only fetch tickers for current stake currency
+        # Otherwise the request for kraken becomes too large.
+        symbols = list(self.get_markets(quote_currencies=[self._config['stake_currency']]))
+        return super().get_tickers(symbols=symbols, cached=cached)
+
     @retrier
     def get_balances(self) -> dict:
         if self._config['dry_run']:

freqtrade/exchange/kucoin.py

@@ -1,4 +1,4 @@
-""" Kucoin exchange subclass """
+"""Kucoin exchange subclass."""
 import logging
 from typing import Dict
 
@@ -9,9 +9,9 @@ logger = logging.getLogger(__name__)
 
 
 class Kucoin(Exchange):
-    """
-    Kucoin exchange class. Contains adjustments needed for Freqtrade to work
-    with this exchange.
+    """Kucoin exchange class.
+
+    Contains adjustments needed for Freqtrade to work with this exchange.
 
     Please note that this exchange is not included in the list of exchanges
     officially supported by the Freqtrade development team. So some features

freqtrade/exchange/okx.py

@@ -0,0 +1,18 @@
+import logging
+from typing import Dict
+
+from freqtrade.exchange import Exchange
+
+
+logger = logging.getLogger(__name__)
+
+
+class Okx(Exchange):
+    """Okx exchange class.
+
+    Contains adjustments needed for Freqtrade to work with this exchange.
+    """
+
+    _ft_has: Dict = {
+        "ohlcv_candle_limit": 300,
+    }

freqtrade/freqtradebot.py

@@ -7,16 +7,14 @@ import traceback
 from datetime import datetime, timezone
 from math import isclose
 from threading import Lock
-from typing import Any, Dict, List, Optional
-
-import arrow
+from typing import Any, Dict, List, Optional, Tuple
 
 from freqtrade import __version__, constants
 from freqtrade.configuration import validate_config_consistency
 from freqtrade.data.converter import order_book_to_dataframe
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.edge import Edge
-from freqtrade.enums import RPCMessageType, SellType, State
+from freqtrade.enums import RPCMessageType, RunMode, SellType, State
 from freqtrade.exceptions import (DependencyException, ExchangeError, InsufficientFundsError,
                                   InvalidOrderException, PricingError)
 from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
@@ -102,6 +100,8 @@ class FreqtradeBot(LoggingMixin):
         self._exit_lock = Lock()
         LoggingMixin.__init__(self, logger, timeframe_to_seconds(self.strategy.timeframe))
 
+        self.last_process = datetime(1970, 1, 1, tzinfo=timezone.utc)
+
     def notify_status(self, msg: str) -> None:
         """
         Public method for users of this class (worker, etc.) to send notifications
@@ -126,6 +126,7 @@ class FreqtradeBot(LoggingMixin):
 
         self.rpc.cleanup()
         cleanup_db()
+        self.exchange.close()
 
     def startup(self) -> None:
         """
@@ -178,11 +179,17 @@ class FreqtradeBot(LoggingMixin):
             # First process current opened trades (positions)
             self.exit_positions(trades)
 
+        # Check if we need to adjust our current positions before attempting to buy new trades.
+        if self.strategy.position_adjustment_enable:
+            with self._exit_lock:
+                self.process_open_trade_positions()
+
         # Then looking for buy opportunities
         if self.get_free_open_trades():
             self.enter_positions()
 
         Trade.commit()
+        self.last_process = datetime.now(timezone.utc)
 
     def process_stopped(self) -> None:
         """
@@ -193,19 +200,20 @@ class FreqtradeBot(LoggingMixin):
 
     def check_for_open_trades(self):
         """
-        Notify the user when the bot is stopped
+        Notify the user when the bot is stopped (not reloaded)
         and there are still open trades active.
         """
         open_trades = Trade.get_trades([Trade.is_open.is_(True)]).all()
 
-        if len(open_trades) != 0:
+        if len(open_trades) != 0 and self.state != State.RELOAD_CONFIG:
             msg = {
                 'type': RPCMessageType.WARNING,
-                'status':  f"{len(open_trades)} open trades active.\n\n"
-                           f"Handle these trades manually on {self.exchange.name}, "
-                           f"or '/start' the bot again and use '/stopbuy' "
-                           f"to handle open trades gracefully. \n"
-                           f"{'Trades are simulated.' if self.config['dry_run'] else ''}",
+                'status':
+                    f"{len(open_trades)} open trades active.\n\n"
+                    f"Handle these trades manually on {self.exchange.name}, "
+                    f"or '/start' the bot again and use '/stopbuy' "
+                    f"to handle open trades gracefully. \n"
+                    f"{'Note: Trades are simulated (dry run).' if self.config['dry_run'] else ''}",
             }
             self.rpc.send_msg(msg)
 
@@ -277,39 +285,19 @@ class FreqtradeBot(LoggingMixin):
                 if order:
                     logger.info(f"Updating sell-fee on trade {trade} for order {order.order_id}.")
                     self.update_trade_state(trade, order.order_id,
-                                            stoploss_order=order.ft_order_side == 'stoploss')
+                                            stoploss_order=order.ft_order_side == 'stoploss',
+                                            send_msg=False)
 
         trades: List[Trade] = Trade.get_open_trades_without_assigned_fees()
         for trade in trades:
             if trade.is_open and not trade.fee_updated('buy'):
                 order = trade.select_order('buy', False)
-                if order:
+                open_order = trade.select_order('buy', True)
+                if order and open_order is None:
                     logger.info(f"Updating buy-fee on trade {trade} for order {order.order_id}.")
-                    self.update_trade_state(trade, order.order_id)
+                    self.update_trade_state(trade, order.order_id, send_msg=False)
 
     def handle_insufficient_funds(self, trade: Trade):
-        """
-        Determine if we ever opened a sell order for this trade.
-        If not, try update buy fees - otherwise "refind" the open order we obviously lost.
-        """
-        sell_order = trade.select_order('sell', None)
-        if sell_order:
-            self.refind_lost_order(trade)
-        else:
-            self.reupdate_enter_order_fees(trade)
-
-    def reupdate_enter_order_fees(self, trade: Trade):
-        """
-        Get buy order from database, and try to reupdate.
-        Handles trades where the initial fee-update did not work.
-        """
-        logger.info(f"Trying to reupdate buy fees for {trade}")
-        order = trade.select_order('buy', False)
-        if order:
-            logger.info(f"Updating buy-fee on trade {trade} for order {order.order_id}.")
-            self.update_trade_state(trade, order.order_id)
-
-    def refind_lost_order(self, trade):
         """
         Try refinding a lost trade.
         Only used when InsufficientFunds appears on sell orders (stoploss or sell).
@@ -322,9 +310,6 @@ class FreqtradeBot(LoggingMixin):
             if not order.ft_is_open:
                 logger.debug(f"Order {order} is no longer open.")
                 continue
-            if order.ft_order_side == 'buy':
-                # Skip buy side - this is handled by reupdate_buy_order_fees
-                continue
             try:
                 fo = self.exchange.fetch_order_or_stoploss_order(order.order_id, order.ft_pair,
                                                                  order.ft_order_side == 'stoploss')
@@ -336,6 +321,9 @@ class FreqtradeBot(LoggingMixin):
                     if fo and fo['status'] == 'open':
                         # Assume this as the open order
                         trade.open_order_id = order.order_id
+                elif order.ft_order_side == 'buy':
+                    if fo and fo['status'] == 'open':
+                        trade.open_order_id = order.order_id
                 if fo:
                     logger.info(f"Found {order} for trade {trade}.")
                     self.update_trade_state(trade, order.order_id, fo,
@@ -420,7 +408,7 @@ class FreqtradeBot(LoggingMixin):
             return False
 
         # running get_signal on historical data fetched
-        (buy, sell, buy_tag) = self.strategy.get_signal(
+        (buy, sell, buy_tag, _) = self.strategy.get_signal(
             pair,
             self.strategy.timeframe,
             analyzed_df
@@ -441,6 +429,59 @@ class FreqtradeBot(LoggingMixin):
         else:
             return False
 
+#
+# BUY / increase positions / DCA logic and methods
+#
+    def process_open_trade_positions(self):
+        """
+        Tries to execute additional buy or sell orders for open trades (positions)
+        """
+        # Walk through each pair and check if it needs changes
+        for trade in Trade.get_open_trades():
+            # If there is any open orders, wait for them to finish.
+            if trade.open_order_id is None:
+                try:
+                    self.check_and_call_adjust_trade_position(trade)
+                except DependencyException as exception:
+                    logger.warning(
+                        f"Unable to adjust position of trade for {trade.pair}: {exception}")
+
+    def check_and_call_adjust_trade_position(self, trade: Trade):
+        """
+        Check the implemented trading strategy for adjustment command.
+        If the strategy triggers the adjustment, a new order gets issued.
+        Once that completes, the existing trade is modified to match new data.
+        """
+        if self.strategy.max_entry_position_adjustment > -1:
+            count_of_buys = trade.nr_of_successful_buys
+            if count_of_buys > self.strategy.max_entry_position_adjustment:
+                logger.debug(f"Max adjustment entries for {trade.pair} has been reached.")
+                return
+        else:
+            logger.debug("Max adjustment entries is set to unlimited.")
+        current_rate = self.exchange.get_rate(trade.pair, refresh=True, side="buy")
+        current_profit = trade.calc_profit_ratio(current_rate)
+
+        min_stake_amount = self.exchange.get_min_pair_stake_amount(trade.pair,
+                                                                   current_rate,
+                                                                   self.strategy.stoploss)
+        max_stake_amount = self.wallets.get_available_stake_amount()
+        logger.debug(f"Calling adjust_trade_position for pair {trade.pair}")
+        stake_amount = strategy_safe_wrapper(self.strategy.adjust_trade_position,
+                                             default_retval=None)(
+            trade=trade, current_time=datetime.now(timezone.utc), current_rate=current_rate,
+            current_profit=current_profit, min_stake=min_stake_amount, max_stake=max_stake_amount)
+
+        if stake_amount is not None and stake_amount > 0.0:
+            # We should increase our position
+            self.execute_entry(trade.pair, stake_amount, trade=trade)
+
+        if stake_amount is not None and stake_amount < 0.0:
+            # We should decrease our position
+            # TODO: Selling part of the trade not implemented yet.
+            logger.error(f"Unable to decrease trade position / sell partially"
+                         f" for pair {trade.pair}, feature not implemented.")
+
     def _check_depth_of_market_buy(self, pair: str, conf: Dict) -> bool:
         """
         Checks depth of market before executing a buy
@@ -465,58 +506,40 @@ class FreqtradeBot(LoggingMixin):
             logger.info(f"Bids to asks delta for {pair} does not satisfy condition.")
             return False
 
-    def execute_entry(self, pair: str, stake_amount: float, price: Optional[float] = None,
-                      forcebuy: bool = False, buy_tag: Optional[str] = None) -> bool:
+    def execute_entry(self, pair: str, stake_amount: float, price: Optional[float] = None, *,
+                      ordertype: Optional[str] = None, buy_tag: Optional[str] = None,
+                      trade: Optional[Trade] = None) -> bool:
         """
         Executes a limit buy for the given pair
         :param pair: pair for which we want to create a LIMIT_BUY
         :param stake_amount: amount of stake-currency for the pair
         :return: True if a buy order is created, false if it fails.
         """
-        time_in_force = self.strategy.order_time_in_force['buy']
 
-        if price:
-            enter_limit_requested = price
-        else:
-            # Calculate price
-            proposed_enter_rate = self.exchange.get_rate(pair, refresh=True, side="buy")
-            custom_entry_price = strategy_safe_wrapper(self.strategy.custom_entry_price,
-                                                       default_retval=proposed_enter_rate)(
-                pair=pair, current_time=datetime.now(timezone.utc),
-                proposed_rate=proposed_enter_rate)
+        pos_adjust = trade is not None
 
-            enter_limit_requested = self.get_valid_price(custom_entry_price, proposed_enter_rate)
-
-        if not enter_limit_requested:
-            raise PricingError('Could not determine buy price.')
-
-        min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, enter_limit_requested,
-                                                                   self.strategy.stoploss)
-
-        if not self.edge:
-            max_stake_amount = self.wallets.get_available_stake_amount()
-            stake_amount = strategy_safe_wrapper(self.strategy.custom_stake_amount,
-                                                 default_retval=stake_amount)(
-                pair=pair, current_time=datetime.now(timezone.utc),
-                current_rate=enter_limit_requested, proposed_stake=stake_amount,
-                min_stake=min_stake_amount, max_stake=max_stake_amount)
-        stake_amount = self.wallets._validate_stake_amount(pair, stake_amount, min_stake_amount)
+        enter_limit_requested, stake_amount = self.get_valid_enter_price_and_stake(
+            pair, price, stake_amount, buy_tag, trade)
 
         if not stake_amount:
             return False
 
-        logger.info(f"Buy signal found: about create a new trade for {pair} with stake_amount: "
-                    f"{stake_amount} ...")
+        if pos_adjust:
+            logger.info(f"Position adjust: about to create a new order for {pair} with stake: "
+                        f"{stake_amount} for {trade}")
+        else:
+            logger.info(f"Buy signal found: about create a new trade for {pair} with stake_amount: "
+                        f"{stake_amount} ...")
 
         amount = stake_amount / enter_limit_requested
-        order_type = self.strategy.order_types['buy']
-        if forcebuy:
-            # Forcebuy can define a different ordertype
-            order_type = self.strategy.order_types.get('forcebuy', order_type)
+        order_type = ordertype or self.strategy.order_types['buy']
+        time_in_force = self.strategy.order_time_in_force['buy']
 
-        if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
+        if not pos_adjust and not strategy_safe_wrapper(
+                self.strategy.confirm_trade_entry, default_retval=True)(
                 pair=pair, order_type=order_type, amount=amount, rate=enter_limit_requested,
-                time_in_force=time_in_force, current_time=datetime.now(timezone.utc)):
+                time_in_force=time_in_force, current_time=datetime.now(timezone.utc),
+                entry_tag=buy_tag):
             logger.info(f"User requested abortion of buying {pair}")
             return False
         amount = self.exchange.amount_to_precision(pair, amount)
@@ -526,6 +549,7 @@ class FreqtradeBot(LoggingMixin):
         order_obj = Order.parse_from_ccxt_object(order, pair, 'buy')
         order_id = order['id']
         order_status = order.get('status', None)
+        logger.info(f"Order #{order_id} was created for {pair} and status is {order_status}.")
 
         # we assume the order is executed at the price requested
         enter_limit_filled_price = enter_limit_requested
@@ -561,57 +585,125 @@ class FreqtradeBot(LoggingMixin):
 
         # Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
         fee = self.exchange.get_fee(symbol=pair, taker_or_maker='maker')
-        trade = Trade(
-            pair=pair,
-            stake_amount=stake_amount,
-            amount=amount,
-            is_open=True,
-            amount_requested=amount_requested,
-            fee_open=fee,
-            fee_close=fee,
-            open_rate=enter_limit_filled_price,
-            open_rate_requested=enter_limit_requested,
-            open_date=datetime.utcnow(),
-            exchange=self.exchange.id,
-            open_order_id=order_id,
-            strategy=self.strategy.get_strategy_name(),
-            buy_tag=buy_tag,
-            timeframe=timeframe_to_minutes(self.config['timeframe'])
-        )
+        # This is a new trade
+        if trade is None:
+            trade = Trade(
+                pair=pair,
+                stake_amount=stake_amount,
+                amount=amount,
+                is_open=True,
+                amount_requested=amount_requested,
+                fee_open=fee,
+                fee_close=fee,
+                open_rate=enter_limit_filled_price,
+                open_rate_requested=enter_limit_requested,
+                open_date=datetime.utcnow(),
+                exchange=self.exchange.id,
+                open_order_id=order_id,
+                fee_open_currency=None,
+                strategy=self.strategy.get_strategy_name(),
+                buy_tag=buy_tag,
+                timeframe=timeframe_to_minutes(self.config['timeframe'])
+            )
+        else:
+            # This is additional buy, we reset fee_open_currency so timeout checking can work
+            trade.is_open = True
+            trade.fee_open_currency = None
+            trade.open_rate_requested = enter_limit_requested
+            trade.open_order_id = order_id
+
         trade.orders.append(order_obj)
-
-        # Update fees if order is closed
-        if order_status == 'closed':
-            self.update_trade_state(trade, order_id, order)
-
+        trade.recalc_trade_from_orders()
         Trade.query.session.add(trade)
         Trade.commit()
 
         # Updating wallets
         self.wallets.update()
 
-        self._notify_enter(trade, order_type)
+        self._notify_enter(trade, order, order_type)
+
+        if pos_adjust:
+            if order_status == 'closed':
+                logger.info(f"DCA order closed, trade should be up to date: {trade}")
+                trade = self.cancel_stoploss_on_exchange(trade)
+            else:
+                logger.info(f"DCA order {order_status}, will wait for resolution: {trade}")
+
+        # Update fees if order is closed
+        if order_status == 'closed':
+            self.update_trade_state(trade, order_id, order)
 
         return True
 
-    def _notify_enter(self, trade: Trade, order_type: str) -> None:
+    def cancel_stoploss_on_exchange(self, trade: Trade) -> Trade:
+        # First cancelling stoploss on exchange ...
+        if self.strategy.order_types.get('stoploss_on_exchange') and trade.stoploss_order_id:
+            try:
+                logger.info(f"Canceling stoploss on exchange for {trade}")
+                co = self.exchange.cancel_stoploss_order_with_result(
+                    trade.stoploss_order_id, trade.pair, trade.amount)
+                trade.update_order(co)
+            except InvalidOrderException:
+                logger.exception(f"Could not cancel stoploss order {trade.stoploss_order_id}")
+        return trade
+
+    def get_valid_enter_price_and_stake(
+            self, pair: str, price: Optional[float], stake_amount: float,
+            entry_tag: Optional[str],
+            trade: Optional[Trade]) -> Tuple[float, float]:
+        if price:
+            enter_limit_requested = price
+        else:
+            # Calculate price
+            proposed_enter_rate = self.exchange.get_rate(pair, refresh=True, side="buy")
+            custom_entry_price = strategy_safe_wrapper(self.strategy.custom_entry_price,
+                                                       default_retval=proposed_enter_rate)(
+                pair=pair, current_time=datetime.now(timezone.utc),
+                proposed_rate=proposed_enter_rate, entry_tag=entry_tag)
+
+            enter_limit_requested = self.get_valid_price(custom_entry_price, proposed_enter_rate)
+        if not enter_limit_requested:
+            raise PricingError('Could not determine buy price.')
+        min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, enter_limit_requested,
+                                                                   self.strategy.stoploss)
+        if not self.edge and trade is None:
+            max_stake_amount = self.wallets.get_available_stake_amount()
+            stake_amount = strategy_safe_wrapper(self.strategy.custom_stake_amount,
+                                                 default_retval=stake_amount)(
+                pair=pair, current_time=datetime.now(timezone.utc),
+                current_rate=enter_limit_requested, proposed_stake=stake_amount,
+                min_stake=min_stake_amount, max_stake=max_stake_amount, entry_tag=entry_tag)
+        stake_amount = self.wallets.validate_stake_amount(pair, stake_amount, min_stake_amount)
+        return enter_limit_requested, stake_amount
+
+    def _notify_enter(self, trade: Trade, order: Dict, order_type: Optional[str] = None,
+                      fill: bool = False) -> None:
         """
         Sends rpc notification when a buy occurred.
         """
+        open_rate = safe_value_fallback(order, 'average', 'price')
+        if open_rate is None:
+            open_rate = trade.open_rate
+
+        current_rate = trade.open_rate_requested
+        if self.dataprovider.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
+            current_rate = self.exchange.get_rate(trade.pair, refresh=False, side="buy")
+
         msg = {
             'trade_id': trade.id,
-            'type': RPCMessageType.BUY,
+            'type': RPCMessageType.BUY_FILL if fill else RPCMessageType.BUY,
             'buy_tag': trade.buy_tag,
             'exchange': self.exchange.name.capitalize(),
             'pair': trade.pair,
-            'limit': trade.open_rate,
+            'limit': open_rate,  # Deprecated (?)
+            'open_rate': open_rate,
             'order_type': order_type,
             'stake_amount': trade.stake_amount,
             'stake_currency': self.config['stake_currency'],
             'fiat_currency': self.config.get('fiat_display_currency', None),
-            'amount': trade.amount,
+            'amount': safe_value_fallback(order, 'filled', 'amount') or trade.amount,
             'open_date': trade.open_date or datetime.utcnow(),
-            'current_rate': trade.open_rate_requested,
+            'current_rate': current_rate,
         }
 
         # Send the message
@@ -643,22 +735,6 @@ class FreqtradeBot(LoggingMixin):
         # Send the message
         self.rpc.send_msg(msg)
 
-    def _notify_enter_fill(self, trade: Trade) -> None:
-        msg = {
-            'trade_id': trade.id,
-            'type': RPCMessageType.BUY_FILL,
-            'buy_tag': trade.buy_tag,
-            'exchange': self.exchange.name.capitalize(),
-            'pair': trade.pair,
-            'open_rate': trade.open_rate,
-            'stake_amount': trade.stake_amount,
-            'stake_currency': self.config['stake_currency'],
-            'fiat_currency': self.config.get('fiat_display_currency', None),
-            'amount': trade.amount,
-            'open_date': trade.open_date,
-        }
-        self.rpc.send_msg(msg)
-
 #
 # SELL / exit positions / close trades logic and methods
 #
@@ -681,7 +757,7 @@ class FreqtradeBot(LoggingMixin):
                     trades_closed += 1
 
             except DependencyException as exception:
-                logger.warning('Unable to sell trade %s: %s', trade.pair, exception)
+                logger.warning(f'Unable to sell trade {trade.pair}: {exception}')
 
         # Updating wallets if any trade occurred
         if trades_closed:
@@ -700,21 +776,22 @@ class FreqtradeBot(LoggingMixin):
         logger.debug('Handling %s ...', trade)
 
         (buy, sell) = (False, False)
+        exit_tag = None
 
         if (self.config.get('use_sell_signal', True) or
                 self.config.get('ignore_roi_if_buy_signal', False)):
             analyzed_df, _ = self.dataprovider.get_analyzed_dataframe(trade.pair,
                                                                       self.strategy.timeframe)
 
-            (buy, sell, _) = self.strategy.get_signal(
+            (buy, sell, _, exit_tag) = self.strategy.get_signal(
                 trade.pair,
                 self.strategy.timeframe,
                 analyzed_df
             )
 
         logger.debug('checking sell')
-        exit_rate = self.exchange.get_rate(trade.pair, refresh=True, side="sell")
-        if self._check_and_execute_exit(trade, exit_rate, buy, sell):
+        sell_rate = self.exchange.get_rate(trade.pair, refresh=True, side="sell")
+        if self._check_and_execute_exit(trade, sell_rate, buy, sell, exit_tag):
             return True
 
         logger.debug('Found no sell signal for %s.', trade)
@@ -852,35 +929,24 @@ class FreqtradeBot(LoggingMixin):
                                    f"for pair {trade.pair}.")
 
     def _check_and_execute_exit(self, trade: Trade, exit_rate: float,
-                                buy: bool, sell: bool) -> bool:
+                                buy: bool, sell: bool, exit_tag: Optional[str]) -> bool:
         """
         Check and execute exit
         """
+
         should_sell = self.strategy.should_sell(
             trade, exit_rate, datetime.now(timezone.utc), buy, sell,
             force_stoploss=self.edge.stoploss(trade.pair) if self.edge else 0
         )
 
         if should_sell.sell_flag:
-            logger.info(f'Executing Sell for {trade.pair}. Reason: {should_sell.sell_type}')
-            self.execute_trade_exit(trade, exit_rate, should_sell)
+            logger.info(
+                f'Executing Sell for {trade.pair}. Reason: {should_sell.sell_type}. '
+                f'Tag: {exit_tag if exit_tag is not None else "None"}')
+            self.execute_trade_exit(trade, exit_rate, should_sell, exit_tag=exit_tag)
             return True
         return False
 
-    def _check_timed_out(self, side: str, order: dict) -> bool:
-        """
-        Check if timeout is active, and if the order is still open and timed out
-        """
-        timeout = self.config.get('unfilledtimeout', {}).get(side)
-        ordertime = arrow.get(order['datetime']).datetime
-        if timeout is not None:
-            timeout_unit = self.config.get('unfilledtimeout', {}).get('unit', 'minutes')
-            timeout_kwargs = {timeout_unit: -timeout}
-            timeout_threshold = arrow.utcnow().shift(**timeout_kwargs).datetime
-            return (order['status'] == 'open' and order['side'] == side
-                    and ordertime < timeout_threshold)
-        return False
-
     def check_handle_timedout(self) -> None:
         """
         Check if any orders are timed out and cancel if necessary
@@ -899,23 +965,32 @@ class FreqtradeBot(LoggingMixin):
 
             fully_cancelled = self.update_trade_state(trade, trade.open_order_id, order)
 
+            order_obj = trade.select_order_by_order_id(trade.open_order_id)
+
             if (order['side'] == 'buy' and (order['status'] == 'open' or fully_cancelled) and (
                     fully_cancelled
-                    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))):
+                    or (order_obj and self.strategy.ft_check_timed_out(
+                        'buy', trade, order_obj, datetime.now(timezone.utc))
+                        ))):
                 self.handle_cancel_enter(trade, order, constants.CANCEL_REASON['TIMEOUT'])
 
             elif (order['side'] == 'sell' and (order['status'] == 'open' or fully_cancelled) and (
                   fully_cancelled
-                  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))):
-                self.handle_cancel_exit(trade, order, constants.CANCEL_REASON['TIMEOUT'])
+                  or (order_obj and self.strategy.ft_check_timed_out(
+                      'sell', trade, order_obj, datetime.now(timezone.utc))
+                      ))):
+                canceled = self.handle_cancel_exit(trade, order, constants.CANCEL_REASON['TIMEOUT'])
+                canceled_count = trade.get_exit_order_count()
+                max_timeouts = self.config.get('unfilledtimeout', {}).get('exit_timeout_count', 0)
+                if canceled and max_timeouts > 0 and canceled_count >= max_timeouts:
+                    logger.warning(f'Emergencyselling trade {trade}, as the sell order '
+                                   f'timed out {max_timeouts} times.')
+                    try:
+                        self.execute_trade_exit(
+                            trade, order.get('price'),
+                            sell_reason=SellCheckTuple(sell_type=SellType.EMERGENCY_SELL))
+                    except DependencyException as exception:
+                        logger.warning(f'Unable to emergency sell trade {trade.pair}: {exception}')
 
     def cancel_all_open_orders(self) -> None:
         """
@@ -946,12 +1021,12 @@ class FreqtradeBot(LoggingMixin):
 
         # Cancelled orders may have the status of 'canceled' or 'closed'
         if order['status'] not in constants.NON_OPEN_EXCHANGE_STATES:
-            filled_val = order.get('filled', 0.0) or 0.0
+            filled_val: float = order.get('filled', 0.0) or 0.0
             filled_stake = filled_val * trade.open_rate
             minstake = self.exchange.get_min_pair_stake_amount(
                 trade.pair, trade.open_rate, self.strategy.stoploss)
 
-            if filled_val > 0 and filled_stake < minstake:
+            if filled_val > 0 and minstake and filled_stake < minstake:
                 logger.warning(
                     f"Order {trade.open_order_id} for {trade.pair} not cancelled, "
                     f"as the filled amount of {filled_val} would result in an unsellable trade.")
@@ -975,10 +1050,16 @@ class FreqtradeBot(LoggingMixin):
         filled_amount = safe_value_fallback2(corder, order, 'filled', 'filled')
         if isclose(filled_amount, 0.0, abs_tol=constants.MATH_CLOSE_PREC):
             logger.info('Buy order fully cancelled. Removing %s from database.', trade)
-            # if trade is not partially completed, just delete the trade
-            trade.delete()
-            was_trade_fully_canceled = True
-            reason += f", {constants.CANCEL_REASON['FULLY_CANCELLED']}"
+            # if trade is not partially completed and it's the only order, just delete the trade
+            if len(trade.orders) <= 1:
+                trade.delete()
+                was_trade_fully_canceled = True
+                reason += f", {constants.CANCEL_REASON['FULLY_CANCELLED']}"
+            else:
+                # FIXME TODO: This could possibly reworked to not duplicate the code 15 lines below.
+                self.update_trade_state(trade, trade.open_order_id, corder)
+                trade.open_order_id = None
+                logger.info('Partial buy order timeout for %s.', trade)
         else:
             # if trade is partially complete, edit the stake details for the trade
             # and close the order
@@ -998,11 +1079,12 @@ class FreqtradeBot(LoggingMixin):
                                   reason=reason)
         return was_trade_fully_canceled
 
-    def handle_cancel_exit(self, trade: Trade, order: Dict, reason: str) -> str:
+    def handle_cancel_exit(self, trade: Trade, order: Dict, reason: str) -> bool:
         """
         Sell cancel - cancel order and update trade
-        :return: Reason for cancel
+        :return: True if exit order was cancelled, false otherwise
         """
+        cancelled = False
         # if trade is not partially completed, just cancel the order
         if order['remaining'] == order['amount'] or order.get('filled') == 0.0:
             if not self.exchange.check_order_canceled_empty(order):
@@ -1013,7 +1095,7 @@ class FreqtradeBot(LoggingMixin):
                     trade.update_order(co)
                 except InvalidOrderException:
                     logger.exception(f"Could not cancel sell order {trade.open_order_id}")
-                    return 'error cancelling order'
+                    return False
                 logger.info('Sell order %s for %s.', reason, trade)
             else:
                 reason = constants.CANCEL_REASON['CANCELLED_ON_EXCHANGE']
@@ -1027,9 +1109,11 @@ class FreqtradeBot(LoggingMixin):
             trade.close_date = None
             trade.is_open = True
             trade.open_order_id = None
+            cancelled = True
         else:
             # TODO: figure out how to handle partially complete sell orders
             reason = constants.CANCEL_REASON['PARTIALLY_FILLED_KEEP_OPEN']
+            cancelled = False
 
         self.wallets.update()
         self._notify_exit_cancel(
@@ -1037,7 +1121,7 @@ class FreqtradeBot(LoggingMixin):
             order_type=self.strategy.order_types['sell'],
             reason=reason
         )
-        return reason
+        return cancelled
 
     def _safe_exit_amount(self, pair: str, amount: float) -> float:
         """
@@ -1064,7 +1148,15 @@ class FreqtradeBot(LoggingMixin):
             raise DependencyException(
                 f"Not enough amount to sell. Trade-amount: {amount}, Wallet: {wallet_amount}")
 
-    def execute_trade_exit(self, trade: Trade, limit: float, sell_reason: SellCheckTuple) -> bool:
+    def execute_trade_exit(
+            self,
+            trade: Trade,
+            limit: float,
+            sell_reason: SellCheckTuple,
+            *,
+            exit_tag: Optional[str] = None,
+            ordertype: Optional[str] = None,
+            ) -> bool:
         """
         Executes a trade exit for the given trade and limit
         :param trade: Trade instance
@@ -1094,22 +1186,12 @@ class FreqtradeBot(LoggingMixin):
         limit = self.get_valid_price(custom_exit_price, proposed_limit_rate)
 
         # First cancelling stoploss on exchange ...
-        if self.strategy.order_types.get('stoploss_on_exchange') and trade.stoploss_order_id:
-            try:
-                co = self.exchange.cancel_stoploss_order_with_result(trade.stoploss_order_id,
-                                                                     trade.pair, trade.amount)
-                trade.update_order(co)
-            except InvalidOrderException:
-                logger.exception(f"Could not cancel stoploss order {trade.stoploss_order_id}")
+        trade = self.cancel_stoploss_on_exchange(trade)
 
-        order_type = self.strategy.order_types[sell_type]
+        order_type = ordertype or self.strategy.order_types[sell_type]
         if sell_reason.sell_type == SellType.EMERGENCY_SELL:
             # Emergency sells (default to market!)
             order_type = self.strategy.order_types.get("emergencysell", "market")
-        if sell_reason.sell_type == SellType.FORCE_SELL:
-            # Force sells (default to the sell_type defined in the strategy,
-            # but we allow this value to be changed)
-            order_type = self.strategy.order_types.get("forcesell", order_type)
 
         amount = self._safe_exit_amount(trade.pair, trade.amount)
         time_in_force = self.strategy.order_time_in_force['sell']
@@ -1140,17 +1222,17 @@ class FreqtradeBot(LoggingMixin):
         trade.open_order_id = order['id']
         trade.sell_order_status = ''
         trade.close_rate_requested = limit
-        trade.sell_reason = sell_reason.sell_reason
-        # In case of market sell orders the order can be closed immediately
-        if order.get('status', 'unknown') in ('closed', 'expired'):
-            self.update_trade_state(trade, trade.open_order_id, order)
-        Trade.commit()
+        trade.sell_reason = exit_tag or sell_reason.sell_reason
 
         # Lock pair for one candle to prevent immediate re-buys
         self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc),
                                 reason='Auto lock')
 
         self._notify_exit(trade, order_type)
+        # In case of market sell orders the order can be closed immediately
+        if order.get('status', 'unknown') in ('closed', 'expired'):
+            self.update_trade_state(trade, trade.open_order_id, order)
+        Trade.commit()
 
         return True
 
@@ -1181,6 +1263,7 @@ class FreqtradeBot(LoggingMixin):
             'current_rate': current_rate,
             'profit_amount': profit_trade,
             'profit_ratio': profit_ratio,
+            'buy_tag': trade.buy_tag,
             'sell_reason': trade.sell_reason,
             'open_date': trade.open_date,
             'close_date': trade.close_date or datetime.utcnow(),
@@ -1224,6 +1307,7 @@ class FreqtradeBot(LoggingMixin):
             'current_rate': current_rate,
             'profit_amount': profit_trade,
             'profit_ratio': profit_ratio,
+            'buy_tag': trade.buy_tag,
             'sell_reason': trade.sell_reason,
             'open_date': trade.open_date,
             'close_date': trade.close_date or datetime.now(timezone.utc),
@@ -1245,13 +1329,14 @@ class FreqtradeBot(LoggingMixin):
 #
 
     def update_trade_state(self, trade: Trade, order_id: str, action_order: Dict[str, Any] = None,
-                           stoploss_order: bool = False) -> bool:
+                           stoploss_order: bool = False, send_msg: bool = True) -> bool:
         """
         Checks trades with open orders and updates the amount if necessary
         Handles closing both buy and sell orders.
         :param trade: Trade object of the trade we're analyzing
         :param order_id: Order-id of the order we're analyzing
         :param action_order: Already acquired order object
+        :param send_msg: Send notification - should always be True except in "recovery" methods
         :return: True if order has been cancelled without being filled partially, False otherwise
         """
         if not order_id:
@@ -1259,7 +1344,7 @@ class FreqtradeBot(LoggingMixin):
             return False
 
         # Update trade with order values
-        logger.info('Found open order for %s', trade)
+        logger.info(f'Found open order for {trade}')
         try:
             order = action_order or self.exchange.fetch_order_or_stoploss_order(order_id,
                                                                                 trade.pair,
@@ -1270,33 +1355,36 @@ class FreqtradeBot(LoggingMixin):
 
         trade.update_order(order)
 
-        # Try update amount (binance-fix)
-        try:
-            new_amount = self.get_real_amount(trade, order)
-            if not isclose(safe_value_fallback(order, 'filled', 'amount'), new_amount,
-                           abs_tol=constants.MATH_CLOSE_PREC):
-                order['amount'] = new_amount
-                order.pop('filled', None)
-                trade.recalc_open_trade_value()
-        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.
+            # Handling of this will happen in check_handle_timedout.
             return True
-        trade.update(order)
+
+        order_obj = trade.select_order_by_order_id(order_id)
+        if not order_obj:
+            raise DependencyException(
+                f"Order_obj not found for {order_id}. This should not have happened.")
+        self.handle_order_fee(trade, order_obj, order)
+
+        trade.update_trade(order_obj)
+        # TODO: is the below necessary? it's already done in update_trade for filled buys
+        trade.recalc_trade_from_orders()
         Trade.commit()
 
-        # Updating wallets when order is closed
+        if order['status'] in constants.NON_OPEN_EXCHANGE_STATES:
+            # If a buy order was closed, force update on stoploss on exchange
+            if order.get('side', None) == 'buy':
+                trade = self.cancel_stoploss_on_exchange(trade)
+            # Updating wallets when order is closed
+            self.wallets.update()
+
         if not trade.is_open:
-            if not stoploss_order and not trade.open_order_id:
+            if send_msg and not stoploss_order and not trade.open_order_id:
                 self._notify_exit(trade, '', True)
             self.handle_protections(trade.pair)
-            self.wallets.update()
-        elif not trade.open_order_id:
+        elif send_msg and not trade.open_order_id:
             # Buy fill
-            self._notify_enter_fill(trade)
+            self._notify_enter(trade, order, fill=True)
 
         return False
 
@@ -1331,6 +1419,16 @@ class FreqtradeBot(LoggingMixin):
             return real_amount
         return amount
 
+    def handle_order_fee(self, trade: Trade, order_obj: Order, order: Dict[str, Any]) -> None:
+        # Try update amount (binance-fix)
+        try:
+            new_amount = self.get_real_amount(trade, order)
+            if not isclose(safe_value_fallback(order, 'filled', 'amount'), new_amount,
+                           abs_tol=constants.MATH_CLOSE_PREC):
+                order_obj.ft_fee_base = trade.amount - new_amount
+        except DependencyException as exception:
+            logger.warning("Could not update trade amount: %s", exception)
+
     def get_real_amount(self, trade: Trade, order: Dict) -> float:
         """
         Detect and update trade fee.
@@ -1361,14 +1459,17 @@ class FreqtradeBot(LoggingMixin):
                     return self.apply_fee_conditional(trade, trade_base_currency,
                                                       amount=order_amount, fee_abs=fee_cost)
                 return order_amount
-        return self.fee_detection_from_trades(trade, order, order_amount)
+        return self.fee_detection_from_trades(trade, order, order_amount, order.get('trades', []))
 
-    def fee_detection_from_trades(self, trade: Trade, order: Dict, order_amount: float) -> float:
+    def fee_detection_from_trades(self, trade: Trade, order: Dict, order_amount: float,
+                                  trades: List) -> float:
         """
-        fee-detection fallback to Trades. Parses result of fetch_my_trades to get correct fee.
+        fee-detection fallback to Trades.
+        Either uses provided trades list or the result of fetch_my_trades to get correct fee.
         """
-        trades = self.exchange.get_trades_for_order(self.exchange.get_order_id_conditional(order),
-                                                    trade.pair, trade.open_date)
+        if not trades:
+            trades = self.exchange.get_trades_for_order(
+                self.exchange.get_order_id_conditional(order), trade.pair, trade.open_date)
 
         if len(trades) == 0:
             logger.info("Applying fee on amount for %s failed: myTrade-Dict empty found", trade)

freqtrade/loggers.py

@@ -7,11 +7,25 @@ from typing import Any, Dict
 from freqtrade.exceptions import OperationalException
 
 
+class FTBufferingHandler(BufferingHandler):
+    def flush(self):
+        """
+        Override Flush behaviour - we keep half of the configured capacity
+        otherwise, we have moments with "empty" logs.
+        """
+        self.acquire()
+        try:
+            # Keep half of the records in buffer.
+            self.buffer = self.buffer[-int(self.capacity / 2):]
+        finally:
+            self.release()
+
+
 logger = logging.getLogger(__name__)
 LOGFORMAT = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
 
 # Initialize bufferhandler - will be used for /log endpoints
-bufferHandler = BufferingHandler(1000)
+bufferHandler = FTBufferingHandler(1000)
 bufferHandler.setFormatter(Formatter(LOGFORMAT))
 
 

freqtrade/main.py

@@ -9,8 +9,8 @@ from typing import Any, List
 
 
 # check min. python version
-if sys.version_info < (3, 7):  # pragma: no cover
-    sys.exit("Freqtrade requires Python version >= 3.7")
+if sys.version_info < (3, 8):  # pragma: no cover
+    sys.exit("Freqtrade requires Python version >= 3.8")
 
 from freqtrade.commands import Arguments
 from freqtrade.exceptions import FreqtradeException, OperationalException

freqtrade/misc.py

@@ -2,11 +2,13 @@
 Various tool function for Freqtrade and scripts
 """
 import gzip
+import hashlib
 import logging
 import re
+from copy import deepcopy
 from datetime import datetime
 from pathlib import Path
-from typing import Any, Iterator, List
+from typing import Any, Iterator, List, Union
 from typing.io import IO
 from urllib.parse import urlparse
 
@@ -27,18 +29,23 @@ def decimals_per_coin(coin: str):
     return DECIMALS_PER_COIN.get(coin, DECIMAL_PER_COIN_FALLBACK)
 
 
-def round_coin_value(value: float, coin: str, show_coin_name=True) -> str:
+def round_coin_value(
+        value: float, coin: str, show_coin_name=True, keep_trailing_zeros=False) -> str:
     """
     Get price value for this coin
     :param value: Value to be printed
     :param coin: Which coin are we printing the price / value for
     :param show_coin_name: Return string in format: "222.22 USDT" or "222.22"
+    :param keep_trailing_zeros: Keep trailing zeros "222.200" vs. "222.2"
     :return: Formatted / rounded value (with or without coin name)
     """
+    val = f"{value:.{decimals_per_coin(coin)}f}"
+    if not keep_trailing_zeros:
+        val = val.rstrip('0').rstrip('.')
     if show_coin_name:
-        return f"{value:.{decimals_per_coin(coin)}f} {coin}"
-    else:
-        return f"{value:.{decimals_per_coin(coin)}f}"
+        val = f"{val} {coin}"
+
+    return val
 
 
 def shorten_date(_date: str) -> str:
@@ -228,3 +235,34 @@ def parse_db_uri_for_logging(uri: str):
         return uri
     pwd = parsed_db_uri.netloc.split(':')[1].split('@')[0]
     return parsed_db_uri.geturl().replace(f':{pwd}@', ':*****@')
+
+
+def get_strategy_run_id(strategy) -> str:
+    """
+    Generate unique identification hash for a backtest run. Identical config and strategy file will
+    always return an identical hash.
+    :param strategy: strategy object.
+    :return: hex string id.
+    """
+    digest = hashlib.sha1()
+    config = deepcopy(strategy.config)
+
+    # Options that have no impact on results of individual backtest.
+    not_important_keys = ('strategy_list', 'original_config', 'telegram', 'api_server')
+    for k in not_important_keys:
+        if k in config:
+            del config[k]
+
+    # Explicitly allow NaN values (e.g. max_open_trades).
+    # as it does not matter for getting the hash.
+    digest.update(rapidjson.dumps(config, default=str,
+                                  number_mode=rapidjson.NM_NAN).encode('utf-8'))
+    with open(strategy.__file__, 'rb') as fp:
+        digest.update(fp.read())
+    return digest.hexdigest().lower()
+
+
+def get_backtest_metadata_filename(filename: Union[Path, str]) -> Path:
+    """Return metadata filename for specified backtest results file."""
+    filename = Path(filename)
+    return filename.parent / Path(f'{filename.stem}.meta{filename.suffix}')

freqtrade/optimize/backtesting.py

@@ -11,20 +11,22 @@ from typing import Any, Dict, List, Optional, Tuple
 
 from pandas import DataFrame
 
+from freqtrade import constants
 from freqtrade.configuration import TimeRange, validate_config_consistency
 from freqtrade.constants import DATETIME_PRINT_FORMAT
 from freqtrade.data import history
-from freqtrade.data.btanalysis import trade_list_to_dataframe
+from freqtrade.data.btanalysis import find_existing_backtest_stats, trade_list_to_dataframe
 from freqtrade.data.converter import trim_dataframe, trim_dataframes
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.enums import BacktestState, SellType
 from freqtrade.exceptions import DependencyException, OperationalException
 from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
+from freqtrade.misc import get_strategy_run_id
 from freqtrade.mixins import LoggingMixin
 from freqtrade.optimize.bt_progress import BTProgress
 from freqtrade.optimize.optimize_reports import (generate_backtest_stats, show_backtest_results,
                                                  store_backtest_stats)
-from freqtrade.persistence import LocalTrade, PairLocks, Trade
+from freqtrade.persistence import LocalTrade, Order, PairLocks, Trade
 from freqtrade.plugins.pairlistmanager import PairListManager
 from freqtrade.plugins.protectionmanager import ProtectionManager
 from freqtrade.resolvers import ExchangeResolver, StrategyResolver
@@ -44,6 +46,7 @@ SELL_IDX = 4
 LOW_IDX = 5
 HIGH_IDX = 6
 BUY_TAG_IDX = 7
+EXIT_TAG_IDX = 8
 
 
 class Backtesting:
@@ -59,14 +62,17 @@ class Backtesting:
 
         LoggingMixin.show_output = False
         self.config = config
-        self.results: Optional[Dict[str, Any]] = None
+        self.results: Dict[str, Any] = {}
+        self.trade_id_counter: int = 0
+        self.order_id_counter: int = 0
 
         config['dry_run'] = True
+        self.run_ids: Dict[str, str] = {}
         self.strategylist: List[IStrategy] = []
         self.all_results: Dict[str, Dict] = {}
 
         self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
-        self.dataprovider = DataProvider(self.config, None)
+        self.dataprovider = DataProvider(self.config, self.exchange)
 
         if self.config.get('strategy_list', None):
             for strat in list(self.config['strategy_list']):
@@ -88,7 +94,8 @@ class Backtesting:
         self.init_backtest_detail()
         self.pairlists = PairListManager(self.exchange, self.config)
         if 'VolumePairList' in self.pairlists.name_list:
-            raise OperationalException("VolumePairList not allowed for backtesting.")
+            raise OperationalException("VolumePairList not allowed for backtesting. "
+                                       "Please use StaticPairlist instead.")
         if 'PerformanceFilter' in self.pairlists.name_list:
             raise OperationalException("PerformanceFilter not allowed for backtesting.")
 
@@ -121,7 +128,8 @@ class Backtesting:
     def __del__(self):
         self.cleanup()
 
-    def cleanup(self):
+    @staticmethod
+    def cleanup():
         LoggingMixin.show_output = True
         PairLocks.use_db = True
         Trade.use_db = True
@@ -226,6 +234,8 @@ class Backtesting:
         PairLocks.reset_locks()
         Trade.reset_trades()
         self.rejected_trades = 0
+        self.timedout_entry_orders = 0
+        self.timedout_exit_orders = 0
         self.dataprovider.clear_cache()
         if enable_protections:
             self._load_protections(self.strategy)
@@ -244,35 +254,45 @@ class Backtesting:
         Helper function to convert a processed dataframes into lists for performance reasons.
 
         Used by backtest() - so keep this optimized for performance.
+
+        :param processed: a processed dictionary with format {pair, data}, which gets cleared to
+        optimize memory usage!
         """
         # Every change to this headers list must evaluate further usages of the resulting tuple
         # and eventually change the constants for indexes at the top
-        headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high', 'buy_tag']
+        headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high', 'buy_tag', 'exit_tag']
         data: Dict = {}
         self.progress.init_step(BacktestState.CONVERT, len(processed))
 
         # Create dict with data
-        for pair, pair_data in processed.items():
+        for pair in processed.keys():
+            pair_data = processed[pair]
             self.check_abort()
             self.progress.increment()
             if not pair_data.empty:
                 pair_data.loc[:, 'buy'] = 0  # cleanup if buy_signal is exist
                 pair_data.loc[:, 'sell'] = 0  # cleanup if sell_signal is exist
                 pair_data.loc[:, 'buy_tag'] = None  # cleanup if buy_tag is exist
+                pair_data.loc[:, 'exit_tag'] = None  # cleanup if exit_tag is exist
 
             df_analyzed = self.strategy.advise_sell(
                 self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair}).copy()
             # Trim startup period from analyzed dataframe
-            df_analyzed = trim_dataframe(df_analyzed, self.timerange,
-                                         startup_candles=self.required_startup)
+            df_analyzed = processed[pair] = pair_data = trim_dataframe(
+                df_analyzed, self.timerange, startup_candles=self.required_startup)
+            # Update dataprovider cache
+            self.dataprovider._set_cached_df(pair, self.timeframe, df_analyzed)
+
+            # Create a copy of the dataframe before shifting, that way the buy signal/tag
+            # remains on the correct candle for callbacks.
+            df_analyzed = df_analyzed.copy()
+
             # 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)
             df_analyzed.loc[:, 'buy_tag'] = df_analyzed.loc[:, 'buy_tag'].shift(1)
-
-            # Update dataprovider cache
-            self.dataprovider._set_cached_df(pair, self.timeframe, df_analyzed)
+            df_analyzed.loc[:, 'exit_tag'] = df_analyzed.loc[:, 'exit_tag'].shift(1)
 
             df_analyzed = df_analyzed.drop(df_analyzed.head(1).index)
 
@@ -312,7 +332,9 @@ class Backtesting:
                     # Worst case: price ticks tiny bit above open and dives down.
                     stop_rate = sell_row[OPEN_IDX] * (1 - abs(trade.stop_loss_pct))
                     assert stop_rate < sell_row[HIGH_IDX]
-                return stop_rate
+                # Limit lower-end to candle low to avoid sells below the low.
+                # This still remains "worst case" - but "worst realistic case".
+                return max(sell_row[LOW_IDX], stop_rate)
 
             # Set close_rate to stoploss
             return trade.stop_loss
@@ -336,6 +358,18 @@ class Backtesting:
                     # use Open rate if open_rate > calculated sell rate
                     return sell_row[OPEN_IDX]
 
+                if (
+                    trade_dur == 0
+                    # Red candle (for longs), TODO: green candle (for shorts)
+                    and sell_row[OPEN_IDX] > sell_row[CLOSE_IDX]  # Red candle
+                    and trade.open_rate < sell_row[OPEN_IDX]  # trade-open below open_rate
+                    and close_rate > sell_row[CLOSE_IDX]
+                ):
+                    # ROI on opening candles with custom pricing can only
+                    # trigger if the entry was at Open or lower.
+                    # details: https: // github.com/freqtrade/freqtrade/issues/6261
+                    # If open_rate is < open, only allow sells below the close on red candles.
+                    raise ValueError("Opening candle ROI on red candles.")
                 # Use the maximum between close_rate and low as we
                 # cannot sell outside of a candle.
                 # Applies when a new ROI setting comes in place and the whole candle is above that.
@@ -347,8 +381,42 @@ class Backtesting:
         else:
             return sell_row[OPEN_IDX]
 
+    def _get_adjust_trade_entry_for_candle(self, trade: LocalTrade, row: Tuple
+                                           ) -> LocalTrade:
+
+        current_profit = trade.calc_profit_ratio(row[OPEN_IDX])
+        min_stake = self.exchange.get_min_pair_stake_amount(trade.pair, row[OPEN_IDX], -0.1)
+        max_stake = self.wallets.get_available_stake_amount()
+        stake_amount = strategy_safe_wrapper(self.strategy.adjust_trade_position,
+                                             default_retval=None)(
+            trade=trade, current_time=row[DATE_IDX].to_pydatetime(), current_rate=row[OPEN_IDX],
+            current_profit=current_profit, min_stake=min_stake, max_stake=max_stake)
+
+        # Check if we should increase our position
+        if stake_amount is not None and stake_amount > 0.0:
+            pos_trade = self._enter_trade(trade.pair, row, stake_amount, trade)
+            if pos_trade is not None:
+                self.wallets.update()
+                return pos_trade
+
+        return trade
+
+    def _get_order_filled(self, rate: float, row: Tuple) -> bool:
+        """ Rate is within candle, therefore filled"""
+        return row[LOW_IDX] <= rate <= row[HIGH_IDX]
+
     def _get_sell_trade_entry_for_candle(self, trade: LocalTrade,
                                          sell_row: Tuple) -> Optional[LocalTrade]:
+
+        # Check if we need to adjust our current positions
+        if self.strategy.position_adjustment_enable:
+            check_adjust_buy = True
+            if self.strategy.max_entry_position_adjustment > -1:
+                count_of_buys = trade.nr_of_successful_buys
+                check_adjust_buy = (count_of_buys <= self.strategy.max_entry_position_adjustment)
+            if check_adjust_buy:
+                trade = self._get_adjust_trade_entry_for_candle(trade, sell_row)
+
         sell_candle_time = sell_row[DATE_IDX].to_pydatetime()
         sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX],  # type: ignore
                                          sell_candle_time, sell_row[BUY_IDX],
@@ -357,12 +425,29 @@ class Backtesting:
 
         if sell.sell_flag:
             trade.close_date = sell_candle_time
-            trade.sell_reason = sell.sell_reason
-            trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
-            closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
 
+            trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
+            try:
+                closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
+            except ValueError:
+                return None
+            # call the custom exit price,with default value as previous closerate
+            current_profit = trade.calc_profit_ratio(closerate)
+            order_type = self.strategy.order_types['sell']
+            if sell.sell_type in (SellType.SELL_SIGNAL, SellType.CUSTOM_SELL):
+                # Custom exit pricing only for sell-signals
+                if order_type == 'limit':
+                    closerate = strategy_safe_wrapper(self.strategy.custom_exit_price,
+                                                      default_retval=closerate)(
+                        pair=trade.pair, trade=trade,
+                        current_time=sell_candle_time,
+                        proposed_rate=closerate, current_profit=current_profit)
+                    # We can't place orders lower than current low.
+                    # freqtrade does not support this in live, and the order would fill immediately
+                    closerate = max(closerate, sell_row[LOW_IDX])
             # Confirm trade exit:
             time_in_force = self.strategy.order_time_in_force['sell']
+
             if not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=True)(
                     pair=trade.pair, trade=trade, order_type='limit', amount=trade.amount,
                     rate=closerate,
@@ -371,7 +456,39 @@ class Backtesting:
                     current_time=sell_candle_time):
                 return None
 
-            trade.close(closerate, show_msg=False)
+            trade.sell_reason = sell.sell_reason
+
+            # Checks and adds an exit tag, after checking that the length of the
+            # sell_row has the length for an exit tag column
+            if(
+                len(sell_row) > EXIT_TAG_IDX
+                and sell_row[EXIT_TAG_IDX] is not None
+                and len(sell_row[EXIT_TAG_IDX]) > 0
+            ):
+                trade.sell_reason = sell_row[EXIT_TAG_IDX]
+
+            self.order_id_counter += 1
+            order = Order(
+                id=self.order_id_counter,
+                ft_trade_id=trade.id,
+                order_date=sell_candle_time,
+                order_update_date=sell_candle_time,
+                ft_is_open=True,
+                ft_pair=trade.pair,
+                order_id=str(self.order_id_counter),
+                symbol=trade.pair,
+                ft_order_side="sell",
+                side="sell",
+                order_type=order_type,
+                status="open",
+                price=closerate,
+                average=closerate,
+                amount=trade.amount,
+                filled=0,
+                remaining=trade.amount,
+                cost=trade.amount * closerate,
+            )
+            trade.orders.append(order)
             return trade
 
         return None
@@ -385,13 +502,15 @@ class Backtesting:
             detail_data = detail_data.loc[
                 (detail_data['date'] >= sell_candle_time) &
                 (detail_data['date'] < sell_candle_end)
-             ].copy()
+            ].copy()
             if len(detail_data) == 0:
                 # Fall back to "regular" data if no detail data was found for this candle
                 return self._get_sell_trade_entry_for_candle(trade, sell_row)
             detail_data.loc[:, 'buy'] = sell_row[BUY_IDX]
             detail_data.loc[:, 'sell'] = sell_row[SELL_IDX]
-            headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
+            detail_data.loc[:, 'buy_tag'] = sell_row[BUY_TAG_IDX]
+            detail_data.loc[:, 'exit_tag'] = sell_row[EXIT_TAG_IDX]
+            headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high', 'buy_tag', 'exit_tag']
             for det_row in detail_data[headers].values.tolist():
                 res = self._get_sell_trade_entry_for_candle(trade, det_row)
                 if res:
@@ -402,49 +521,110 @@ class Backtesting:
         else:
             return self._get_sell_trade_entry_for_candle(trade, sell_row)
 
-    def _enter_trade(self, pair: str, row: List) -> Optional[LocalTrade]:
-        try:
-            stake_amount = self.wallets.get_trade_stake_amount(pair, None)
-        except DependencyException:
-            return None
+    def _enter_trade(self, pair: str, row: Tuple, stake_amount: Optional[float] = None,
+                     trade: Optional[LocalTrade] = None) -> Optional[LocalTrade]:
 
-        min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, row[OPEN_IDX], -0.05) or 0
+        current_time = row[DATE_IDX].to_pydatetime()
+        entry_tag = row[BUY_TAG_IDX] if len(row) >= BUY_TAG_IDX + 1 else None
+        # let's call the custom entry price, using the open price as default price
+        order_type = self.strategy.order_types['buy']
+        propose_rate = row[OPEN_IDX]
+        if order_type == 'limit':
+            propose_rate = strategy_safe_wrapper(self.strategy.custom_entry_price,
+                                                 default_retval=row[OPEN_IDX])(
+                pair=pair, current_time=current_time,
+                proposed_rate=propose_rate, entry_tag=entry_tag)  # default value is the open rate
+            # We can't place orders higher than current high (otherwise it'd be a stop limit buy)
+            # which freqtrade does not support in live.
+            propose_rate = min(propose_rate, row[HIGH_IDX])
+
+        min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, propose_rate, -0.05) or 0
         max_stake_amount = self.wallets.get_available_stake_amount()
 
-        stake_amount = strategy_safe_wrapper(self.strategy.custom_stake_amount,
-                                             default_retval=stake_amount)(
-            pair=pair, current_time=row[DATE_IDX].to_pydatetime(), current_rate=row[OPEN_IDX],
-            proposed_stake=stake_amount, min_stake=min_stake_amount, max_stake=max_stake_amount)
-        stake_amount = self.wallets._validate_stake_amount(pair, stake_amount, min_stake_amount)
+        pos_adjust = trade is not None
+        if not pos_adjust:
+            try:
+                stake_amount = self.wallets.get_trade_stake_amount(pair, None, update=False)
+            except DependencyException:
+                return None
+
+            stake_amount = strategy_safe_wrapper(self.strategy.custom_stake_amount,
+                                                 default_retval=stake_amount)(
+                pair=pair, current_time=current_time, current_rate=propose_rate,
+                proposed_stake=stake_amount, min_stake=min_stake_amount, max_stake=max_stake_amount,
+                entry_tag=entry_tag)
+
+        stake_amount = self.wallets.validate_stake_amount(pair, stake_amount, min_stake_amount)
 
         if not stake_amount:
-            return None
+            # In case of pos adjust, still return the original trade
+            # If not pos adjust, trade is None
+            return trade
 
-        order_type = self.strategy.order_types['buy']
-        time_in_force = self.strategy.order_time_in_force['sell']
+        time_in_force = self.strategy.order_time_in_force['buy']
         # Confirm trade entry:
-        if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
-                pair=pair, order_type=order_type, amount=stake_amount, rate=row[OPEN_IDX],
-                time_in_force=time_in_force, current_time=row[DATE_IDX].to_pydatetime()):
-            return None
+        if not pos_adjust:
+            if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
+                    pair=pair, order_type=order_type, amount=stake_amount, rate=propose_rate,
+                    time_in_force=time_in_force, current_time=current_time,
+                    entry_tag=entry_tag):
+                return None
 
         if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
-            # Enter trade
-            has_buy_tag = len(row) >= BUY_TAG_IDX + 1
-            trade = LocalTrade(
-                pair=pair,
-                open_rate=row[OPEN_IDX],
-                open_date=row[DATE_IDX].to_pydatetime(),
-                stake_amount=stake_amount,
-                amount=round(stake_amount / row[OPEN_IDX], 8),
-                fee_open=self.fee,
-                fee_close=self.fee,
-                is_open=True,
-                buy_tag=row[BUY_TAG_IDX] if has_buy_tag else None,
-                exchange='backtesting',
+            self.order_id_counter += 1
+            amount = round(stake_amount / propose_rate, 8)
+            if trade is None:
+                # Enter trade
+                self.trade_id_counter += 1
+                trade = LocalTrade(
+                    id=self.trade_id_counter,
+                    open_order_id=self.order_id_counter,
+                    pair=pair,
+                    open_rate=propose_rate,
+                    open_rate_requested=propose_rate,
+                    open_date=current_time,
+                    stake_amount=stake_amount,
+                    amount=amount,
+                    amount_requested=amount,
+                    fee_open=self.fee,
+                    fee_close=self.fee,
+                    is_open=True,
+                    buy_tag=entry_tag,
+                    exchange='backtesting',
+                    orders=[]
+                )
+
+            trade.adjust_stop_loss(trade.open_rate, self.strategy.stoploss, initial=True)
+
+            order = Order(
+                id=self.order_id_counter,
+                ft_trade_id=trade.id,
+                ft_is_open=True,
+                ft_pair=trade.pair,
+                order_id=str(self.order_id_counter),
+                symbol=trade.pair,
+                ft_order_side="buy",
+                side="buy",
+                order_type=order_type,
+                status="open",
+                order_date=current_time,
+                order_filled_date=current_time,
+                order_update_date=current_time,
+                price=propose_rate,
+                average=propose_rate,
+                amount=amount,
+                filled=0,
+                remaining=amount,
+                cost=stake_amount + trade.fee_open,
             )
-            return trade
-        return None
+            if pos_adjust and self._get_order_filled(order.price, row):
+                order.close_bt_order(current_time)
+            else:
+                trade.open_order_id = str(self.order_id_counter)
+            trade.orders.append(order)
+            trade.recalc_trade_from_orders()
+
+        return trade
 
     def handle_left_open(self, open_trades: Dict[str, List[LocalTrade]],
                          data: Dict[str, List[Tuple]]) -> List[LocalTrade]:
@@ -455,6 +635,9 @@ class Backtesting:
         for pair in open_trades.keys():
             if len(open_trades[pair]) > 0:
                 for trade in open_trades[pair]:
+                    if trade.open_order_id and trade.nr_of_successful_buys == 0:
+                        # Ignore trade if buy-order did not fill yet
+                        continue
                     sell_row = data[pair][-1]
 
                     trade.close_date = sell_row[DATE_IDX].to_pydatetime()
@@ -475,6 +658,51 @@ class Backtesting:
         self.rejected_trades += 1
         return False
 
+    def run_protections(self, enable_protections, pair: str, current_time: datetime):
+        if enable_protections:
+            self.protections.stop_per_pair(pair, current_time)
+            self.protections.global_stop(current_time)
+
+    def check_order_cancel(self, trade: LocalTrade, current_time) -> bool:
+        """
+        Check if an order has been canceled.
+        Returns True if the trade should be Deleted (initial order was canceled).
+        """
+        for order in [o for o in trade.orders if o.ft_is_open]:
+
+            timedout = self.strategy.ft_check_timed_out(order.side, trade, order, current_time)
+            if timedout:
+                if order.side == 'buy':
+                    self.timedout_entry_orders += 1
+                    if trade.nr_of_successful_buys == 0:
+                        # Remove trade due to buy timeout expiration.
+                        return True
+                    else:
+                        # Close additional buy order
+                        del trade.orders[trade.orders.index(order)]
+                if order.side == 'sell':
+                    self.timedout_exit_orders += 1
+                    # Close sell order and retry selling on next signal.
+                    del trade.orders[trade.orders.index(order)]
+
+        return False
+
+    def validate_row(
+            self, data: Dict, pair: str, row_index: int, current_time: datetime) -> Optional[Tuple]:
+        try:
+            # Row is treated as "current incomplete candle".
+            # Buy / sell signals are shifted by 1 to compensate for this.
+            row = data[pair][row_index]
+        except IndexError:
+            # missing Data for one pair at the end.
+            # Warnings for this are shown during data loading
+            return None
+
+        # Waits until the time-counter reaches the start of the data for this pair.
+        if row[DATE_IDX] > current_time:
+            return None
+        return row
+
     def backtest(self, processed: Dict,
                  start_date: datetime, end_date: datetime,
                  max_open_trades: int = 0, position_stacking: bool = False,
@@ -486,7 +714,8 @@ class Backtesting:
         Of course try to not have ugly code. By some accessor are sometime slower than functions.
         Avoid extensive logging in this method and functions it calls.
 
-        :param processed: a processed dictionary with format {pair, data}
+        :param processed: a processed dictionary with format {pair, data}, which gets cleared to
+        optimize memory usage!
         :param start_date: backtesting timerange start datetime
         :param end_date: backtesting timerange end datetime
         :param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited
@@ -496,14 +725,15 @@ class Backtesting:
         """
         trades: List[LocalTrade] = []
         self.prepare_backtest(enable_protections)
-
+        # Ensure wallets are uptodate (important for --strategy-list)
+        self.wallets.update()
         # 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)
 
         # Indexes per pair, so some pairs are allowed to have a missing start.
         indexes: Dict = defaultdict(int)
-        tmp = start_date + timedelta(minutes=self.timeframe_min)
+        current_time = start_date + timedelta(minutes=self.timeframe_min)
 
         open_trades: Dict[str, List[LocalTrade]] = defaultdict(list)
         open_trade_count = 0
@@ -512,35 +742,27 @@ class Backtesting:
             (end_date - start_date) / timedelta(minutes=self.timeframe_min)))
 
         # Loop timerange and get candle for each pair at that point in time
-        while tmp <= end_date:
+        while current_time <= end_date:
             open_trade_count_start = open_trade_count
             self.check_abort()
             for i, pair in enumerate(data):
                 row_index = indexes[pair]
-                try:
-                    # Row is treated as "current incomplete candle".
-                    # Buy / sell signals are shifted by 1 to compensate for this.
-                    row = data[pair][row_index]
-                except IndexError:
-                    # missing Data for one pair at the end.
-                    # Warnings for this are shown during data loading
-                    continue
-
-                # Waits until the time-counter reaches the start of the data for this pair.
-                if row[DATE_IDX] > tmp:
+                row = self.validate_row(data, pair, row_index, current_time)
+                if not row:
                     continue
 
                 row_index += 1
                 indexes[pair] = row_index
                 self.dataprovider._set_dataframe_max_index(row_index)
 
+                # 1. Process buys.
                 # without positionstacking, we can only have one open trade per pair.
                 # max_open_trades must be respected
                 # don't open on the last row
                 if (
                     (position_stacking or len(open_trades[pair]) == 0)
                     and self.trade_slot_available(max_open_trades, open_trade_count_start)
-                    and tmp != end_date
+                    and current_time != end_date
                     and row[BUY_IDX] == 1
                     and row[SELL_IDX] != 1
                     and not PairLocks.is_pair_locked(pair, row[DATE_IDX])
@@ -548,32 +770,51 @@ class Backtesting:
                     trade = self._enter_trade(pair, row)
                     if trade:
                         # TODO: hacky workaround to avoid opening > max_open_trades
-                        # This emulates previous behaviour - not sure if this is correct
+                        # This emulates previous behavior - not sure if this is correct
                         # Prevents buying if the trade-slot was freed in this candle
                         open_trade_count_start += 1
                         open_trade_count += 1
                         # logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
                         open_trades[pair].append(trade)
-                        LocalTrade.add_bt_trade(trade)
 
                 for trade in list(open_trades[pair]):
-                    # also check the buying candle for sell conditions.
-                    trade_entry = self._get_sell_trade_entry(trade, row)
-                    # Sell occurred
-                    if trade_entry:
+                    # 2. Process buy orders.
+                    order = trade.select_order('buy', is_open=True)
+                    if order and self._get_order_filled(order.price, row):
+                        order.close_bt_order(current_time)
+                        trade.open_order_id = None
+                        LocalTrade.add_bt_trade(trade)
+                        self.wallets.update()
+
+                    # 3. Create sell orders (if any)
+                    if not trade.open_order_id:
+                        self._get_sell_trade_entry(trade, row)  # Place sell order if necessary
+
+                    # 4. Process sell orders.
+                    order = trade.select_order('sell', is_open=True)
+                    if order and self._get_order_filled(order.price, row):
+                        trade.open_order_id = None
+                        trade.close_date = current_time
+                        trade.close(order.price, show_msg=False)
+
                         # logger.debug(f"{pair} - Backtesting sell {trade}")
                         open_trade_count -= 1
                         open_trades[pair].remove(trade)
-
                         LocalTrade.close_bt_trade(trade)
-                        trades.append(trade_entry)
-                        if enable_protections:
-                            self.protections.stop_per_pair(pair, row[DATE_IDX])
-                            self.protections.global_stop(tmp)
+                        trades.append(trade)
+                        self.wallets.update()
+                        self.run_protections(enable_protections, pair, current_time)
+
+                    # 5. Cancel expired buy/sell orders.
+                    if self.check_order_cancel(trade, current_time):
+                        # Close trade due to buy timeout expiration.
+                        open_trade_count -= 1
+                        open_trades[pair].remove(trade)
+                        self.wallets.update()
 
             # Move time one configured time_interval ahead.
             self.progress.increment()
-            tmp += timedelta(minutes=self.timeframe_min)
+            current_time += timedelta(minutes=self.timeframe_min)
 
         trades += self.handle_left_open(open_trades, data=data)
         self.wallets.update()
@@ -584,6 +825,8 @@ class Backtesting:
             'config': self.strategy.config,
             'locks': PairLocks.get_all_locks(),
             'rejected_signals': self.rejected_trades,
+            'timedout_entry_orders': self.timedout_entry_orders,
+            'timedout_exit_orders': self.timedout_exit_orders,
             'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
         }
 
@@ -633,6 +876,7 @@ class Backtesting:
         )
         backtest_end_time = datetime.now(timezone.utc)
         results.update({
+            'run_id': self.run_ids.get(strat.get_strategy_name(), ''),
             'backtest_start_time': int(backtest_start_time.timestamp()),
             'backtest_end_time': int(backtest_end_time.timestamp()),
         })
@@ -640,6 +884,33 @@ class Backtesting:
 
         return min_date, max_date
 
+    def _get_min_cached_backtest_date(self):
+        min_backtest_date = None
+        backtest_cache_age = self.config.get('backtest_cache', constants.BACKTEST_CACHE_DEFAULT)
+        if self.timerange.stopts == 0 or datetime.fromtimestamp(
+           self.timerange.stopts, tz=timezone.utc) > datetime.now(tz=timezone.utc):
+            logger.warning('Backtest result caching disabled due to use of open-ended timerange.')
+        elif backtest_cache_age == 'day':
+            min_backtest_date = datetime.now(tz=timezone.utc) - timedelta(days=1)
+        elif backtest_cache_age == 'week':
+            min_backtest_date = datetime.now(tz=timezone.utc) - timedelta(weeks=1)
+        elif backtest_cache_age == 'month':
+            min_backtest_date = datetime.now(tz=timezone.utc) - timedelta(weeks=4)
+        return min_backtest_date
+
+    def load_prior_backtest(self):
+        self.run_ids = {
+            strategy.get_strategy_name(): get_strategy_run_id(strategy)
+            for strategy in self.strategylist
+        }
+
+        # Load previous result that will be updated incrementally.
+        # This can be circumvented in certain instances in combination with downloading more data
+        min_backtest_date = self._get_min_cached_backtest_date()
+        if min_backtest_date is not None:
+            self.results = find_existing_backtest_stats(
+                self.config['user_data_dir'] / 'backtest_results', self.run_ids, min_backtest_date)
+
     def start(self) -> None:
         """
         Run backtesting end-to-end
@@ -651,15 +922,38 @@ class Backtesting:
         self.load_bt_data_detail()
         logger.info("Dataload complete. Calculating indicators")
 
-        for strat in self.strategylist:
-            min_date, max_date = self.backtest_one_strategy(strat, data, timerange)
-        if len(self.strategylist) > 0:
+        self.load_prior_backtest()
 
-            self.results = generate_backtest_stats(data, self.all_results,
-                                                   min_date=min_date, max_date=max_date)
+        for strat in self.strategylist:
+            if self.results and strat.get_strategy_name() in self.results['strategy']:
+                # When previous result hash matches - reuse that result and skip backtesting.
+                logger.info(f'Reusing result of previous backtest for {strat.get_strategy_name()}')
+                continue
+            min_date, max_date = self.backtest_one_strategy(strat, data, timerange)
+
+        # Update old results with new ones.
+        if len(self.all_results) > 0:
+            results = generate_backtest_stats(
+                data, self.all_results, min_date=min_date, max_date=max_date)
+            if self.results:
+                self.results['metadata'].update(results['metadata'])
+                self.results['strategy'].update(results['strategy'])
+                self.results['strategy_comparison'].extend(results['strategy_comparison'])
+            else:
+                self.results = results
 
             if self.config.get('export', 'none') == 'trades':
                 store_backtest_stats(self.config['exportfilename'], self.results)
 
+        # Results may be mixed up now. Sort them so they follow --strategy-list order.
+        if 'strategy_list' in self.config and len(self.results) > 0:
+            self.results['strategy_comparison'] = sorted(
+                self.results['strategy_comparison'],
+                key=lambda c: self.config['strategy_list'].index(c['key']))
+            self.results['strategy'] = dict(
+                sorted(self.results['strategy'].items(),
+                       key=lambda kv: self.config['strategy_list'].index(kv[0])))
+
+        if len(self.strategylist) > 0:
             # Show backtest results
             show_backtest_results(self.config, self.results)

freqtrade/optimize/bt_progress.py

@@ -12,7 +12,7 @@ class BTProgress:
     def init_step(self, action: BacktestState, max_steps: float):
         self._action = action
         self._max_steps = max_steps
-        self._proress = 0
+        self._progress = 0
 
     def set_new_value(self, new_value: float):
         self._progress = new_value

freqtrade/optimize/edge_cli.py

@@ -34,7 +34,7 @@ class EdgeCli:
         self.config['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
         self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
         self.strategy = StrategyResolver.load_strategy(self.config)
-        self.strategy.dp = DataProvider(config, None)
+        self.strategy.dp = DataProvider(config, self.exchange)
 
         validate_config_consistency(self.config)
 

freqtrade/optimize/hyperopt.py

@@ -76,6 +76,7 @@ class Hyperopt:
         self.config = config
 
         self.backtesting = Backtesting(self.config)
+        self.pairlist = self.backtesting.pairlists.whitelist
 
         if not self.config.get('hyperopt'):
             self.custom_hyperopt = HyperOptAuto(self.config)
@@ -332,7 +333,7 @@ class Hyperopt:
         params_details = self._get_params_details(params_dict)
 
         strat_stats = generate_strategy_stats(
-            processed, self.backtesting.strategy.get_strategy_name(),
+            self.pairlist, self.backtesting.strategy.get_strategy_name(),
             backtesting_results, min_date, max_date, market_change=0
         )
         results_explanation = HyperoptTools.format_results_explanation_string(
@@ -366,7 +367,7 @@ class Hyperopt:
         }
 
     def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
-        estimator = self.custom_hyperopt.generate_estimator()
+        estimator = self.custom_hyperopt.generate_estimator(dimensions=dimensions)
 
         acq_optimizer = "sampling"
         if isinstance(estimator, str):
@@ -422,6 +423,7 @@ class Hyperopt:
         self.backtesting.exchange.close()
         self.backtesting.exchange._api = None  # type: ignore
         self.backtesting.exchange._api_async = None  # type: ignore
+        self.backtesting.exchange.loop = None  # type: ignore
         # self.backtesting.exchange = None  # type: ignore
         self.backtesting.pairlists = None  # type: ignore
 

freqtrade/optimize/hyperopt_auto.py

@@ -91,5 +91,5 @@ class HyperOptAuto(IHyperOpt):
     def trailing_space(self) -> List['Dimension']:
         return self._get_func('trailing_space')()
 
-    def generate_estimator(self) -> EstimatorType:
-        return self._get_func('generate_estimator')()
+    def generate_estimator(self, dimensions: List['Dimension'], **kwargs) -> EstimatorType:
+        return self._get_func('generate_estimator')(dimensions=dimensions, **kwargs)

freqtrade/optimize/hyperopt_interface.py

@@ -40,7 +40,7 @@ class IHyperOpt(ABC):
         IHyperOpt.ticker_interval = str(config['timeframe'])  # DEPRECATED
         IHyperOpt.timeframe = str(config['timeframe'])
 
-    def generate_estimator(self) -> EstimatorType:
+    def generate_estimator(self, dimensions: List[Dimension], **kwargs) -> EstimatorType:
         """
         Return base_estimator.
         Can be any of "GP", "RF", "ET", "GBRT" or an instance of a class

freqtrade/optimize/hyperopt_loss_calmar.py

@@ -0,0 +1,63 @@
+"""
+CalmarHyperOptLoss
+
+This module defines the alternative HyperOptLoss class which can be used for
+Hyperoptimization.
+"""
+from datetime import datetime
+from math import sqrt as msqrt
+from typing import Any, Dict
+
+from pandas import DataFrame
+
+from freqtrade.data.btanalysis import calculate_max_drawdown
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+class CalmarHyperOptLoss(IHyperOptLoss):
+    """
+    Defines the loss function for hyperopt.
+
+    This implementation uses the Calmar Ratio calculation.
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(
+        results: DataFrame,
+        trade_count: int,
+        min_date: datetime,
+        max_date: datetime,
+        config: Dict,
+        processed: Dict[str, DataFrame],
+        backtest_stats: Dict[str, Any],
+        *args,
+        **kwargs
+    ) -> float:
+        """
+        Objective function, returns smaller number for more optimal results.
+
+        Uses Calmar Ratio calculation.
+        """
+        total_profit = backtest_stats["profit_total"]
+        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 * 100
+
+        # calculate max drawdown
+        try:
+            _, _, _, _, _, max_drawdown = calculate_max_drawdown(
+                results, value_col="profit_abs"
+            )
+        except ValueError:
+            max_drawdown = 0
+
+        if max_drawdown != 0:
+            calmar_ratio = expected_returns_mean / max_drawdown * msqrt(365)
+        else:
+            # Define high (negative) calmar ratio to be clear that this is NOT optimal.
+            calmar_ratio = -20.0
+
+        # print(expected_returns_mean, max_drawdown, calmar_ratio)
+        return -calmar_ratio

freqtrade/optimize/hyperopt_loss_profit_drawdown.py

@@ -0,0 +1,30 @@
+"""
+ProfitDrawDownHyperOptLoss
+
+This module defines the alternative HyperOptLoss class based on Profit &
+Drawdown objective which can be used for Hyperoptimization.
+
+Possible to change `DRAWDOWN_MULT` to penalize drawdown objective for
+individual needs.
+"""
+from pandas import DataFrame
+
+from freqtrade.data.btanalysis import calculate_max_drawdown
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+# higher numbers penalize drawdowns more severely
+DRAWDOWN_MULT = 0.075
+
+
+class ProfitDrawDownHyperOptLoss(IHyperOptLoss):
+    @staticmethod
+    def hyperopt_loss_function(results: DataFrame, trade_count: int, *args, **kwargs) -> float:
+        total_profit = results["profit_abs"].sum()
+
+        try:
+            max_drawdown_abs = calculate_max_drawdown(results, value_col="profit_abs")[5]
+        except ValueError:
+            max_drawdown_abs = 0
+
+        return -1 * (total_profit * (1 - max_drawdown_abs * DRAWDOWN_MULT))

freqtrade/optimize/hyperopt_tools.py

@@ -137,6 +137,7 @@ class HyperoptTools():
         }
         if not HyperoptTools._test_hyperopt_results_exist(results_file):
             # No file found.
+            logger.warning(f"Hyperopt file {results_file} not found.")
             return [], 0
 
         epochs = []
@@ -284,10 +285,10 @@ class HyperoptTools():
         return (f"{results_metrics['total_trades']:6d} trades. "
                 f"{results_metrics['wins']}/{results_metrics['draws']}"
                 f"/{results_metrics['losses']} Wins/Draws/Losses. "
-                f"Avg profit {results_metrics['profit_mean'] * 100: 6.2f}%. "
-                f"Median profit {results_metrics['profit_median'] * 100: 6.2f}%. "
-                f"Total profit {results_metrics['profit_total_abs']: 11.8f} {stake_currency} "
-                f"({results_metrics['profit_total'] * 100: 7.2f}%). "
+                f"Avg profit {results_metrics['profit_mean']:7.2%}. "
+                f"Median profit {results_metrics['profit_median']:7.2%}. "
+                f"Total profit {results_metrics['profit_total_abs']:11.8f} {stake_currency} "
+                f"({results_metrics['profit_total']:8.2%}). "
                 f"Avg duration {results_metrics['holding_avg']} min."
                 )
 
@@ -299,8 +300,7 @@ class HyperoptTools():
                 f"Objective: {results['loss']:.5f}")
 
     @staticmethod
-    def prepare_trials_columns(trials: pd.DataFrame, legacy_mode: bool,
-                               has_drawdown: bool) -> pd.DataFrame:
+    def prepare_trials_columns(trials: pd.DataFrame, has_drawdown: bool) -> pd.DataFrame:
         trials['Best'] = ''
 
         if 'results_metrics.winsdrawslosses' not in trials.columns:
@@ -309,33 +309,26 @@ class HyperoptTools():
 
         if not has_drawdown:
             # Ensure compatibility with older versions of hyperopt results
-            trials['results_metrics.max_drawdown_abs'] = None
-            trials['results_metrics.max_drawdown'] = None
+            trials['results_metrics.max_drawdown_account'] = None
 
-        if not legacy_mode:
-            # New mode, using backtest result for metrics
-            trials['results_metrics.winsdrawslosses'] = trials.apply(
-                lambda x: f"{x['results_metrics.wins']} {x['results_metrics.draws']:>4} "
-                          f"{x['results_metrics.losses']:>4}", axis=1)
-            trials = trials[['Best', 'current_epoch', 'results_metrics.total_trades',
-                             'results_metrics.winsdrawslosses',
-                             'results_metrics.profit_mean', 'results_metrics.profit_total_abs',
-                             'results_metrics.profit_total', 'results_metrics.holding_avg',
-                             'results_metrics.max_drawdown', 'results_metrics.max_drawdown_abs',
-                             'loss', 'is_initial_point', 'is_best']]
+        # New mode, using backtest result for metrics
+        trials['results_metrics.winsdrawslosses'] = trials.apply(
+            lambda x: f"{x['results_metrics.wins']} {x['results_metrics.draws']:>4} "
+                      f"{x['results_metrics.losses']:>4}", axis=1)
 
-        else:
-            # Legacy mode
-            trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
-                             'results_metrics.winsdrawslosses', 'results_metrics.avg_profit',
-                             'results_metrics.total_profit', 'results_metrics.profit',
-                             'results_metrics.duration', 'results_metrics.max_drawdown',
-                             'results_metrics.max_drawdown_abs', 'loss', 'is_initial_point',
-                             'is_best']]
+        trials = trials[['Best', 'current_epoch', 'results_metrics.total_trades',
+                         'results_metrics.winsdrawslosses',
+                         'results_metrics.profit_mean', 'results_metrics.profit_total_abs',
+                         'results_metrics.profit_total', 'results_metrics.holding_avg',
+                         'results_metrics.max_drawdown',
+                         'results_metrics.max_drawdown_account', 'results_metrics.max_drawdown_abs',
+                         'loss', 'is_initial_point', 'is_best']]
 
-        trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
-                          'Total profit', 'Profit', 'Avg duration', 'Max Drawdown',
-                          'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_best']
+        trials.columns = [
+            'Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
+            'Total profit', 'Profit', 'Avg duration', 'max_drawdown', 'max_drawdown_account',
+            'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_best'
+            ]
 
         return trials
 
@@ -351,10 +344,9 @@ class HyperoptTools():
         tabulate.PRESERVE_WHITESPACE = True
         trials = json_normalize(results, max_level=1)
 
-        legacy_mode = 'results_metrics.total_trades' not in trials
-        has_drawdown = 'results_metrics.max_drawdown_abs' in trials.columns
+        has_account_drawdown = 'results_metrics.max_drawdown_account' in trials.columns
 
-        trials = HyperoptTools.prepare_trials_columns(trials, legacy_mode, has_drawdown)
+        trials = HyperoptTools.prepare_trials_columns(trials, has_account_drawdown)
 
         trials['is_profit'] = False
         trials.loc[trials['is_initial_point'], 'Best'] = '*     '
@@ -362,12 +354,12 @@ class HyperoptTools():
         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)
-        perc_multi = 1 if legacy_mode else 100
+        # perc_multi = 1 if legacy_mode else 100
         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: f'{x * perc_multi:,.2f}%'.rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
+            lambda x: f'{x:,.2%}'.rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
         )
         trials['Avg duration'] = trials['Avg duration'].apply(
             lambda x: f'{x:,.1f} m'.rjust(7, ' ') if isinstance(x, float) else f"{x}"
@@ -379,24 +371,25 @@ class HyperoptTools():
 
         stake_currency = config['stake_currency']
 
-        if has_drawdown:
-            trials['Max Drawdown'] = trials.apply(
-                lambda x: '{} {}'.format(
-                    round_coin_value(x['max_drawdown_abs'], stake_currency),
-                    '({:,.2f}%)'.format(x['Max Drawdown'] * perc_multi).rjust(10, ' ')
-                ).rjust(25 + len(stake_currency))
-                if x['Max Drawdown'] != 0.0 else '--'.rjust(25 + len(stake_currency)),
-                axis=1
-            )
-        else:
-            trials = trials.drop(columns=['Max Drawdown'])
+        trials[f"Max Drawdown{' (Acct)' if has_account_drawdown else ''}"] = trials.apply(
+            lambda x: "{} {}".format(
+                round_coin_value(x['max_drawdown_abs'], stake_currency, keep_trailing_zeros=True),
+                (f"({x['max_drawdown_account']:,.2%})"
+                    if has_account_drawdown
+                    else f"({x['max_drawdown']:,.2%})"
+                 ).rjust(10, ' ')
+            ).rjust(25 + len(stake_currency))
+            if x['max_drawdown'] != 0.0 or x['max_drawdown_account'] != 0.0
+            else '--'.rjust(25 + len(stake_currency)),
+            axis=1
+        )
 
-        trials = trials.drop(columns=['max_drawdown_abs'])
+        trials = trials.drop(columns=['max_drawdown_abs', 'max_drawdown', 'max_drawdown_account'])
 
         trials['Profit'] = trials.apply(
             lambda x: '{} {}'.format(
-                round_coin_value(x['Total profit'], stake_currency),
-                '({:,.2f}%)'.format(x['Profit'] * perc_multi).rjust(10, ' ')
+                round_coin_value(x['Total profit'], stake_currency, keep_trailing_zeros=True),
+                f"({x['Profit']:,.2%})".rjust(10, ' ')
             ).rjust(25+len(stake_currency))
             if x['Total profit'] != 0.0 else '--'.rjust(25+len(stake_currency)),
             axis=1