Merge pull request #6464 from freqtrade/new_release

New release 2022.2.1

.github/ISSUE_TEMPLATE/feature_request.md

@@ -24,3 +24,4 @@ Have you search for this feature before requesting it? It's highly likely that a
 ## Describe the enhancement
 
 *Explain the enhancement you would like*
+

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,9 +1,9 @@
-<!-- Thank you for sending your pull request. But first, have you included
+Thank you for sending your pull request. But first, have you included
 unit tests, and is your code PEP8 conformant? [More details](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)
--->
+
 ## Summary
 
-<!-- Explain in one sentence the goal of this PR -->
+Explain in one sentence the goal of this PR
 
 Solve the issue: #___
 
@@ -14,4 +14,4 @@ Solve the issue: #___
 
 ## What's new?
 
-<!-- Explain in details what this PR solve or improve. You can include visuals. -->
+*Explain in details what this PR solve or improve. You can include visuals.* 

.github/workflows/ci.yml

@@ -13,36 +13,32 @@ on:
   schedule:
     - cron:  '0 5 * * 4'
 
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: true
-
 jobs:
   build_linux:
 
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ ubuntu-18.04, ubuntu-20.04, ubuntu-22.04 ]
+        os: [ ubuntu-18.04, ubuntu-20.04 ]
         python-version: ["3.8", "3.9", "3.10"]
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v2
 
     - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v2
       with:
         python-version: ${{ matrix.python-version }}
 
     - name: Cache_dependencies
-      uses: actions/cache@v3
+      uses: actions/cache@v2
       id: cache
       with:
         path: ~/dependencies/
         key: ${{ runner.os }}-dependencies
 
     - name: pip cache (linux)
-      uses: actions/cache@v3
+      uses: actions/cache@v2
       if: runner.os == 'Linux'
       with:
         path: ~/.cache/pip
@@ -66,15 +62,15 @@ jobs:
     - name: Tests
       run: |
         pytest --random-order --cov=freqtrade --cov-config=.coveragerc
-      if: matrix.python-version != '3.9' || matrix.os != 'ubuntu-22.04'
+      if: matrix.python-version != '3.9'
 
     - name: Tests incl. ccxt compatibility tests
       run: |
         pytest --random-order --cov=freqtrade --cov-config=.coveragerc --longrun
-      if: matrix.python-version == '3.9' && matrix.os == 'ubuntu-22.04'
+      if: matrix.python-version == '3.9'
 
     - name: Coveralls
-      if: (runner.os == 'Linux' && matrix.python-version == '3.9')
+      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
@@ -82,13 +78,11 @@ jobs:
         # Allow failure for coveralls
         coveralls || true
 
-    - name: Backtesting (multi)
+    - name: Backtesting
       run: |
         cp config_examples/config_bittrex.example.json config.json
         freqtrade create-userdir --userdir user_data
-        freqtrade new-strategy -s AwesomeStrategy
-        freqtrade new-strategy -s AwesomeStrategyMin --template minimal
-        freqtrade backtesting --datadir tests/testdata --strategy-list AwesomeStrategy AwesomeStrategyMin -i 5m
+        freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
 
     - name: Hyperopt
       run: |
@@ -106,7 +100,7 @@ jobs:
 
     - name: Mypy
       run: |
-        mypy freqtrade scripts tests
+        mypy freqtrade scripts
 
     - name: Discord notification
       uses: rjstone/discord-webhook-notify@v1
@@ -124,22 +118,22 @@ jobs:
         python-version: ["3.8", "3.9", "3.10"]
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v2
 
     - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v2
       with:
         python-version: ${{ matrix.python-version }}
 
     - name: Cache_dependencies
-      uses: actions/cache@v3
+      uses: actions/cache@v2
       id: cache
       with:
         path: ~/dependencies/
         key: ${{ runner.os }}-dependencies
 
     - name: pip cache (macOS)
-      uses: actions/cache@v3
+      uses: actions/cache@v2
       if: runner.os == 'macOS'
       with:
         path: ~/Library/Caches/pip
@@ -164,14 +158,22 @@ jobs:
 
     - name: Tests
       run: |
-        pytest --random-order
+        pytest --random-order --cov=freqtrade --cov-config=.coveragerc
+
+    - name: Coveralls
+      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
+      run: |
+        # Allow failure for coveralls
+        coveralls -v  || true
 
     - name: Backtesting
       run: |
         cp config_examples/config_bittrex.example.json config.json
         freqtrade create-userdir --userdir user_data
-        freqtrade new-strategy -s AwesomeStrategyAdv --template advanced
-        freqtrade backtesting --datadir tests/testdata --strategy AwesomeStrategyAdv
+        freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
 
     - name: Hyperopt
       run: |
@@ -208,15 +210,15 @@ jobs:
         python-version: ["3.8", "3.9", "3.10"]
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v2
 
     - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v2
       with:
         python-version: ${{ matrix.python-version }}
 
     - name: Pip cache (Windows)
-      uses: actions/cache@v3
+      uses: actions/cache@preview
       with:
         path: ~\AppData\Local\pip\Cache
         key: ${{ matrix.os }}-${{ matrix.python-version }}-pip
@@ -227,7 +229,7 @@ jobs:
 
     - name: Tests
       run: |
-        pytest --random-order
+        pytest --random-order --cov=freqtrade --cov-config=.coveragerc
 
     - name: Backtesting
       run: |
@@ -247,7 +249,7 @@ jobs:
 
     - name: Mypy
       run: |
-        mypy freqtrade scripts tests
+        mypy freqtrade scripts
 
     - name: Discord notification
       uses: rjstone/discord-webhook-notify@v1
@@ -257,34 +259,19 @@ jobs:
           details: Test Failed
           webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
-  mypy_version_check:
-    runs-on: ubuntu-20.04
-    steps:
-    - uses: actions/checkout@v3
-
-    - name: Set up Python
-      uses: actions/setup-python@v4
-      with:
-        python-version: "3.10"
-
-    - name: pre-commit dependencies
-      run: |
-        pip install pyaml
-        python build_helpers/pre_commit_update.py
-
   docs_check:
     runs-on: ubuntu-20.04
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v2
 
     - name: Documentation syntax
       run: |
         ./tests/test_docs.sh
 
     - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v2
       with:
-        python-version: "3.10"
+        python-version: 3.8
 
     - name: Documentation build
       run: |
@@ -300,14 +287,19 @@ jobs:
           details: Freqtrade doc test failed!
           webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
+  cleanup-prior-runs:
+    runs-on: ubuntu-20.04
+    steps:
+    - name: Cleanup previous runs on this branch
+      uses: rokroskar/workflow-run-cleanup-action@v0.3.3
+      if: "!startsWith(github.ref, 'refs/tags/') && github.ref != 'refs/heads/stable' && github.repository == 'freqtrade/freqtrade'"
+      env:
+        GITHUB_TOKEN: "${{ secrets.GITHUB_TOKEN }}"
+
   # 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, mypy_version_check ]
+    needs: [ build_linux, build_macos, build_windows, docs_check ]
     runs-on: ubuntu-20.04
-    # Discord notification can't handle schedule events
-    if: (github.event_name != 'schedule')
-    permissions:
-      repository-projects: read
     steps:
 
     - name: Check user permission
@@ -327,18 +319,18 @@ jobs:
           webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
   deploy:
-    needs: [ build_linux, build_macos, build_windows, docs_check, mypy_version_check ]
+    needs: [ build_linux, build_macos, build_windows, docs_check ]
     runs-on: ubuntu-20.04
 
     if: (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'release') && github.repository == 'freqtrade/freqtrade'
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v2
 
     - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v2
       with:
-        python-version: "3.9"
+        python-version: 3.8
 
     - name: Extract branch name
       shell: bash
@@ -351,7 +343,7 @@ jobs:
         python setup.py sdist bdist_wheel
 
     - name: Publish to PyPI (Test)
-      uses: pypa/gh-action-pypi-publish@v1.5.1
+      uses: pypa/gh-action-pypi-publish@master
       if: (github.event_name == 'release')
       with:
         user: __token__
@@ -359,7 +351,7 @@ jobs:
         repository_url: https://test.pypi.org/legacy/
 
     - name: Publish to PyPI
-      uses: pypa/gh-action-pypi-publish@v1.5.1
+      uses: pypa/gh-action-pypi-publish@master
       if: (github.event_name == 'release')
       with:
         user: __token__
@@ -399,7 +391,7 @@ jobs:
 
     - name: Discord notification
       uses: rjstone/discord-webhook-notify@v1
-      if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false) && (github.event_name != 'schedule')
+      if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
       with:
           severity: info
           details: Deploy Succeeded!
@@ -413,7 +405,7 @@ jobs:
     if: (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'release') && github.repository == 'freqtrade/freqtrade'
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v2
 
     - name: Extract branch name
       shell: bash

.github/workflows/docker_update_readme.yml

@@ -8,10 +8,11 @@ jobs:
   dockerHubDescription:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v1
     - name: Docker Hub Description
-      uses: peter-evans/dockerhub-description@v3
+      uses: peter-evans/dockerhub-description@v2.4.3
       env:
         DOCKERHUB_USERNAME: ${{ secrets.DOCKER_USERNAME }}
         DOCKERHUB_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}
         DOCKERHUB_REPOSITORY: freqtradeorg/freqtrade
+

.gitignore

@@ -7,15 +7,10 @@ logfile.txt
 user_data/*
 !user_data/strategy/sample_strategy.py
 !user_data/notebooks
-!user_data/models
-!user_data/freqaimodels
-user_data/freqaimodels/*
-user_data/models/*
 user_data/notebooks/*
 freqtrade-plot.html
 freqtrade-profit-plot.html
 freqtrade/rpc/api_server/ui/*
-build_helpers/ta-lib/*
 
 # Macos related
 .DS_Store
@@ -85,8 +80,6 @@ instance/
 
 # Sphinx documentation
 docs/_build/
-# Mkdocs documentation
-site/
 
 # PyBuilder
 target/
@@ -112,4 +105,3 @@ target/
 !config_examples/config_ftx.example.json
 !config_examples/config_full.example.json
 !config_examples/config_kraken.example.json
-!config_examples/config_freqai.example.json

.pre-commit-config.yaml

@@ -1,46 +0,0 @@
-# See https://pre-commit.com for more information
-# See https://pre-commit.com/hooks.html for more hooks
-repos:
-  - repo: https://github.com/pycqa/flake8
-    rev: "4.0.1"
-    hooks:
-      - id: flake8
-        # stages: [push]
-
-  - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: "v0.942"
-    hooks:
-      - id: mypy
-        exclude: build_helpers
-        additional_dependencies:
-          - types-cachetools==5.2.1
-          - types-filelock==3.2.7
-          - types-requests==2.28.8
-          - types-tabulate==0.8.11
-          - types-python-dateutil==2.8.19
-        # stages: [push]
-
-  - repo: https://github.com/pycqa/isort
-    rev: "5.10.1"
-    hooks:
-      - id: isort
-        name: isort (python)
-        # stages: [push]
-
-  - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v2.4.0
-    hooks:
-      - id: end-of-file-fixer
-        exclude: |
-            (?x)^(
-                tests/.*|
-                .*\.svg
-            )$
-      - id: mixed-line-ending
-      - id: debug-statements
-      - id: check-ast
-      - id: trailing-whitespace
-        exclude: |
-          (?x)^(
-                .*\.md
-            )$

.pylintrc

@@ -7,3 +7,4 @@ ignore=vendor
 
 [TYPECHECK]
 ignored-modules=numpy,talib,talib.abstract
+

Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.10.6-slim-bullseye as base
+FROM python:3.9.9-slim-bullseye as base
 
 # Setup env
 ENV LANG C.UTF-8

MANIFEST.in

@@ -2,6 +2,5 @@ include LICENSE
 include README.md
 recursive-include freqtrade *.py
 recursive-include freqtrade/templates/ *.j2 *.ipynb
-include freqtrade/exchange/binance_leverage_tiers.json
 include freqtrade/rpc/api_server/ui/fallback_file.html
 include freqtrade/rpc/api_server/ui/favicon.ico

README.md

@@ -9,6 +9,10 @@ Freqtrade is a free and open source crypto trading bot written in Python. It is
 
 ![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
@@ -26,23 +30,14 @@ hesitate to read the source code and understand the mechanism of this bot.
 
 Please read the [exchange specific notes](docs/exchanges.md) to learn about eventual, special configurations needed for each exchange.
 
-- [X] [Binance](https://www.binance.com/)
+- [X] [Binance](https://www.binance.com/) ([*Note for binance users](docs/exchanges.md#binance-blacklist))
 - [X] [Bittrex](https://bittrex.com/)
-- [X] [FTX](https://ftx.com/#a=2258149)
+- [X] [FTX](https://ftx.com)
 - [X] [Gate.io](https://www.gate.io/ref/6266643)
-- [X] [Huobi](http://huobi.com/)
 - [X] [Kraken](https://kraken.com/)
-- [X] [OKX](https://okx.com/) (Former OKEX)
+- [X] [OKX](https://www.okx.com/)
 - [ ] [potentially many others](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
 
-### Supported Futures Exchanges (experimental)
-
-- [X] [Binance](https://www.binance.com/)
-- [X] [Gate.io](https://www.gate.io/ref/6266643)
-- [X] [OKX](https://okx.com/).
-
-Please make sure to read the [exchange specific notes](docs/exchanges.md), as well as the [trading with leverage](docs/leverage.md) documentation before diving in.
-
 ### Community tested
 
 Exchanges confirmed working by the community:
@@ -63,7 +58,6 @@ Please find the complete documentation on the [freqtrade website](https://www.fr
 - [x] **Dry-run**: Run the bot without paying money.
 - [x] **Backtesting**: Run a simulation of your buy/sell strategy.
 - [x] **Strategy Optimization by machine learning**: Use machine learning to optimize your buy/sell strategy parameters with real exchange data.
-- [X] **Adaptive prediction modeling**: Build a smart strategy with FreqAI that self-trains to the market via adaptive machine learning methods. [Learn more](https://www.freqtrade.io/en/stable/freqai/)
 - [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.
@@ -74,9 +68,15 @@ Please find the complete documentation on the [freqtrade website](https://www.fr
 
 ## Quick start
 
-Please refer to the [Docker Quickstart documentation](https://www.freqtrade.io/en/stable/docker_quickstart/) on how to get started quickly.
+Freqtrade provides a Linux/macOS script to install all dependencies and help you to configure the bot.
 
-For further (native) installation methods, please refer to the [Installation documentation page](https://www.freqtrade.io/en/stable/installation/).
+```bash
+git clone -b develop https://github.com/freqtrade/freqtrade.git
+cd freqtrade
+./setup.sh --install
+```
+
+For any other type of installation please refer to [Installation doc](https://www.freqtrade.io/en/stable/installation/).
 
 ## Basic Usage
 
@@ -133,8 +133,7 @@ Telegram is not mandatory. However, this is a great way to control your bot. Mor
 - `/stopbuy`: Stop entering new trades.
 - `/status <trade_id>|[table]`: Lists all or specific open trades.
 - `/profit [<n>]`: Lists cumulative profit from all finished trades, over the last n days.
-- `/forceexit <trade_id>|all`: Instantly exits the given trade (Ignoring `minimum_roi`).
-- `/fx <trade_id>|all`: Alias to `/forceexit`
+- `/forcesell <trade_id>|all`: Instantly sells the given trade (Ignoring `minimum_roi`).
 - `/performance`: Show performance of each finished trade grouped by pair
 - `/balance`: Show account balance per currency.
 - `/daily <n>`: Shows profit or loss per day, over the last n days.
@@ -194,7 +193,7 @@ Issues labeled [good first issue](https://github.com/freqtrade/freqtrade/labels/
 
 The clock must be accurate, synchronized to a NTP server very frequently to avoid problems with communication to the exchanges.
 
-### Minimum hardware required
+### Min hardware required
 
 To run this bot we recommend you a cloud instance with a minimum of:
 

build_helpers/install_ta-lib.sh

@@ -4,7 +4,7 @@ else
   INSTALL_LOC=${1}
 fi
 echo "Installing to ${INSTALL_LOC}"
-if [ -n "$2" ] || [ ! -f "${INSTALL_LOC}/lib/libta_lib.a" ]; then
+if [ ! -f "${INSTALL_LOC}/lib/libta_lib.a" ]; then
   tar zxvf ta-lib-0.4.0-src.tar.gz
   cd ta-lib \
   && sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h \
@@ -17,17 +17,11 @@ if [ -n "$2" ] || [ ! -f "${INSTALL_LOC}/lib/libta_lib.a" ]; then
     cd .. && rm -rf ./ta-lib/
     exit 1
   fi
-  if [ -z "$2" ]; then
-    which sudo && sudo make install || make install
-    if [ -x "$(command -v apt-get)" ]; then
-      echo "Updating library path using ldconfig"
-      sudo ldconfig
-    fi
-  else
-    # Don't install with sudo
-    make install
+  which sudo && sudo make install || make install
+  if [ -x "$(command -v apt-get)" ]; then
+    echo "Updating library path using ldconfig"
+    sudo ldconfig
   fi
-
   cd .. && rm -rf ./ta-lib/
 else
   echo "TA-lib already installed, skipping installation"

build_helpers/pre_commit_update.py

@@ -1,42 +0,0 @@
-# File used in CI to ensure pre-commit dependencies are kept uptodate.
-
-import sys
-from pathlib import Path
-
-import yaml
-
-
-pre_commit_file = Path('.pre-commit-config.yaml')
-require_dev = Path('requirements-dev.txt')
-
-with require_dev.open('r') as rfile:
-    requirements = rfile.readlines()
-
-# Extract types only
-type_reqs = [r.strip('\n') for r in requirements if r.startswith('types-')]
-
-with pre_commit_file.open('r') as file:
-    f = yaml.load(file, Loader=yaml.FullLoader)
-
-
-mypy_repo = [repo for repo in f['repos'] if repo['repo']
-             == 'https://github.com/pre-commit/mirrors-mypy']
-
-hooks = mypy_repo[0]['hooks'][0]['additional_dependencies']
-
-errors = []
-for hook in hooks:
-    if hook not in type_reqs:
-        errors.append(f"{hook} is missing in requirements-dev.txt.")
-
-for req in type_reqs:
-    if req not in hooks:
-        errors.append(f"{req} is missing in pre-config file.")
-
-
-if errors:
-    for e in errors:
-        print(e)
-    sys.exit(1)
-
-sys.exit(0)

build_helpers/publish_docker_arm64.sh

@@ -6,12 +6,10 @@ export DOCKER_BUILDKIT=1
 # Replace / with _ to create a valid tag
 TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g")
 TAG_PLOT=${TAG}_plot
-TAG_FREQAI=${TAG}_freqai
 TAG_PI="${TAG}_pi"
 
 TAG_ARM=${TAG}_arm
 TAG_PLOT_ARM=${TAG_PLOT}_arm
-TAG_FREQAI_ARM=${TAG_FREQAI}_arm
 CACHE_IMAGE=freqtradeorg/freqtrade_cache
 
 echo "Running for ${TAG}"
@@ -40,13 +38,11 @@ fi
 docker tag freqtrade:$TAG_ARM ${CACHE_IMAGE}:$TAG_ARM
 
 docker build --cache-from freqtrade:${TAG_ARM} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG_ARM} -t freqtrade:${TAG_PLOT_ARM} -f docker/Dockerfile.plot .
-docker build --cache-from freqtrade:${TAG_ARM} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG_ARM} -t freqtrade:${TAG_FREQAI_ARM} -f docker/Dockerfile.freqai .
 
 docker tag freqtrade:$TAG_PLOT_ARM ${CACHE_IMAGE}:$TAG_PLOT_ARM
-docker tag freqtrade:$TAG_FREQAI_ARM ${CACHE_IMAGE}:$TAG_FREQAI_ARM
 
 # Run backtest
-docker run --rm -v $(pwd)/config_examples/config_bittrex.example.json:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG_ARM} backtesting --datadir /tests/testdata --strategy-path /tests/strategy/strats/ --strategy StrategyTestV3
+docker run --rm -v $(pwd)/config_examples/config_bittrex.example.json:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG_ARM} backtesting --datadir /tests/testdata --strategy-path /tests/strategy/strats/ --strategy StrategyTestV2
 
 if [ $? -ne 0 ]; then
     echo "failed running backtest"
@@ -57,7 +53,6 @@ docker images
 
 # docker push ${IMAGE_NAME}
 docker push ${CACHE_IMAGE}:$TAG_PLOT_ARM
-docker push ${CACHE_IMAGE}:$TAG_FREQAI_ARM
 docker push ${CACHE_IMAGE}:$TAG_ARM
 
 # Create multi-arch image
@@ -71,9 +66,6 @@ docker manifest push -p ${IMAGE_NAME}:${TAG}
 docker manifest create ${IMAGE_NAME}:${TAG_PLOT} ${CACHE_IMAGE}:${TAG_PLOT_ARM} ${CACHE_IMAGE}:${TAG_PLOT}
 docker manifest push -p ${IMAGE_NAME}:${TAG_PLOT}
 
-docker manifest create ${IMAGE_NAME}:${TAG_FREQAI} ${CACHE_IMAGE}:${TAG_FREQAI_ARM} ${CACHE_IMAGE}:${TAG_FREQAI}
-docker manifest push -p ${IMAGE_NAME}:${TAG_FREQAI}
-
 # Tag as latest for develop builds
 if [ "${TAG}" = "develop" ]; then
     docker manifest create ${IMAGE_NAME}:latest ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI} ${CACHE_IMAGE}:${TAG}

build_helpers/publish_docker_multi.sh

@@ -5,7 +5,6 @@
 # Replace / with _ to create a valid tag
 TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g")
 TAG_PLOT=${TAG}_plot
-TAG_FREQAI=${TAG}_freqai
 TAG_PI="${TAG}_pi"
 
 PI_PLATFORM="linux/arm/v7"
@@ -50,13 +49,11 @@ fi
 docker tag freqtrade:$TAG ${CACHE_IMAGE}:$TAG
 
 docker build --cache-from freqtrade:${TAG} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG} -t freqtrade:${TAG_PLOT} -f docker/Dockerfile.plot .
-docker build --cache-from freqtrade:${TAG} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG} -t freqtrade:${TAG_FREQAI} -f docker/Dockerfile.freqai .
 
 docker tag freqtrade:$TAG_PLOT ${CACHE_IMAGE}:$TAG_PLOT
-docker tag freqtrade:$TAG_FREQAI ${CACHE_IMAGE}:$TAG_FREQAI
 
 # Run backtest
-docker run --rm -v $(pwd)/config_examples/config_bittrex.example.json:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG} backtesting --datadir /tests/testdata --strategy-path /tests/strategy/strats/ --strategy StrategyTestV3
+docker run --rm -v $(pwd)/config_examples/config_bittrex.example.json:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG} backtesting --datadir /tests/testdata --strategy-path /tests/strategy/strats/ --strategy StrategyTestV2
 
 if [ $? -ne 0 ]; then
     echo "failed running backtest"
@@ -67,7 +64,6 @@ docker images
 
 docker push ${CACHE_IMAGE}
 docker push ${CACHE_IMAGE}:$TAG_PLOT
-docker push ${CACHE_IMAGE}:$TAG_FREQAI
 docker push ${CACHE_IMAGE}:$TAG
 
 

config_examples/config_binance.example.json

@@ -8,23 +8,21 @@
     "dry_run": true,
     "cancel_open_orders_on_exit": false,
     "unfilledtimeout": {
-        "entry": 10,
-        "exit": 10,
+        "buy": 10,
+        "sell": 10,
         "exit_timeout_count": 0,
         "unit": "minutes"
     },
-    "entry_pricing": {
-        "price_side": "same",
+    "bid_strategy": {
+        "ask_last_balance": 0.0,
         "use_order_book": true,
         "order_book_top": 1,
-        "price_last_balance": 0.0,
         "check_depth_of_market": {
             "enabled": false,
             "bids_to_ask_delta": 1
         }
     },
-    "exit_pricing": {
-        "price_side": "same",
+    "ask_strategy": {
         "use_order_book": true,
         "order_book_top": 1
     },
@@ -90,7 +88,7 @@
     },
     "bot_name": "freqtrade",
     "initial_state": "running",
-    "force_entry_enable": false,
+    "forcebuy_enable": false,
     "internals": {
         "process_throttle_secs": 5
     }

config_examples/config_bittrex.example.json

@@ -8,23 +8,21 @@
     "dry_run": true,
     "cancel_open_orders_on_exit": false,
     "unfilledtimeout": {
-        "entry": 10,
-        "exit": 10,
+        "buy": 10,
+        "sell": 10,
         "exit_timeout_count": 0,
         "unit": "minutes"
     },
-    "entry_pricing": {
-        "price_side": "same",
+    "bid_strategy": {
         "use_order_book": true,
+        "ask_last_balance": 0.0,
         "order_book_top": 1,
-        "price_last_balance": 0.0,
         "check_depth_of_market": {
             "enabled": false,
             "bids_to_ask_delta": 1
         }
     },
-    "exit_pricing":{
-        "price_side": "same",
+    "ask_strategy":{
         "use_order_book": true,
         "order_book_top": 1
     },
@@ -87,7 +85,7 @@
     },
     "bot_name": "freqtrade",
     "initial_state": "running",
-    "force_entry_enable": false,
+    "forcebuy_enable": false,
     "internals": {
         "process_throttle_secs": 5
     }

config_examples/config_freqai.example.json

@@ -1,96 +0,0 @@
-{
-    "trading_mode": "futures",
-    "margin_mode": "isolated",
-    "max_open_trades": 5,
-    "stake_currency": "USDT",
-    "stake_amount": 200,
-    "tradable_balance_ratio": 1,
-    "fiat_display_currency": "USD",
-    "dry_run": true,
-    "timeframe": "3m",
-    "dry_run_wallet": 1000,
-    "cancel_open_orders_on_exit": true,
-    "unfilledtimeout": {
-        "entry": 10,
-        "exit": 30
-    },
-    "exchange": {
-        "name": "binance",
-        "key": "",
-        "secret": "",
-        "ccxt_config": {
-            "enableRateLimit": true
-        },
-        "ccxt_async_config": {
-            "enableRateLimit": true,
-            "rateLimit": 200
-        },
-        "pair_whitelist": [
-            "1INCH/USDT",
-            "ALGO/USDT"
-        ],
-        "pair_blacklist": []
-    },
-    "entry_pricing": {
-        "price_side": "same",
-        "use_order_book": true,
-        "order_book_top": 1,
-        "price_last_balance": 0.0,
-        "check_depth_of_market": {
-            "enabled": false,
-            "bids_to_ask_delta": 1
-        }
-    },
-    "exit_pricing": {
-        "price_side": "other",
-        "use_order_book": true,
-        "order_book_top": 1
-    },
-    "pairlists": [
-        {
-            "method": "StaticPairList"
-        }
-    ],
-    "freqai": {
-        "enabled": true,
-        "startup_candles": 10000,
-        "purge_old_models": true,
-        "train_period_days": 15,
-        "backtest_period_days": 7,
-        "live_retrain_hours": 0,
-        "identifier": "uniqe-id",
-        "feature_parameters": {
-            "include_timeframes": [
-                "3m",
-                "15m",
-                "1h"
-            ],
-            "include_corr_pairlist": [
-                "BTC/USDT",
-                "ETH/USDT"
-            ],
-            "label_period_candles": 20,
-            "include_shifted_candles": 2,
-            "DI_threshold": 0.9,
-            "weight_factor": 0.9,
-            "principal_component_analysis": false,
-            "use_SVM_to_remove_outliers": true,
-            "stratify_training_data": 0,
-            "indicator_max_period_candles": 20,
-            "indicator_periods_candles": [10, 20]
-        },
-        "data_split_parameters": {
-            "test_size": 0.33,
-            "random_state": 1
-        },
-        "model_training_parameters": {
-            "n_estimators": 1000
-        }
-    },
-    "bot_name": "",
-    "force_entry_enable": true,
-    "initial_state": "running",
-    "internals": {
-        "process_throttle_secs": 5
-    }
-}

config_examples/config_ftx.example.json

@@ -8,23 +8,21 @@
     "dry_run": true,
     "cancel_open_orders_on_exit": false,
     "unfilledtimeout": {
-        "entry": 10,
-        "exit": 10,
+        "buy": 10,
+        "sell": 10,
         "exit_timeout_count": 0,
         "unit": "minutes"
     },
-    "entry_pricing": {
-        "price_side": "same",
+    "bid_strategy": {
+        "ask_last_balance": 0.0,
         "use_order_book": true,
         "order_book_top": 1,
-        "price_last_balance": 0.0,
         "check_depth_of_market": {
             "enabled": false,
             "bids_to_ask_delta": 1
         }
     },
-    "exit_pricing": {
-        "price_side": "same",
+    "ask_strategy": {
         "use_order_book": true,
         "order_book_top": 1
     },
@@ -89,7 +87,7 @@
     },
     "bot_name": "freqtrade",
     "initial_state": "running",
-    "force_entry_enable": false,
+    "forcebuy_enable": false,
     "internals": {
         "process_throttle_secs": 5
     }

config_examples/config_full.example.json

@@ -5,7 +5,6 @@
     "tradable_balance_ratio": 0.99,
     "fiat_display_currency": "USD",
     "amount_reserve_percent": 0.05,
-    "available_capital": 1000,
     "amend_last_stake_amount": false,
     "last_stake_amount_min_ratio": 0.5,
     "dry_run": true,
@@ -16,13 +15,11 @@
     "trailing_stop_positive": 0.005,
     "trailing_stop_positive_offset": 0.0051,
     "trailing_only_offset_is_reached": false,
-    "use_exit_signal": true,
-    "exit_profit_only": false,
-    "exit_profit_offset": 0.0,
-    "ignore_roi_if_entry_signal": false,
+    "use_sell_signal": true,
+    "sell_profit_only": false,
+    "sell_profit_offset": 0.0,
+    "ignore_roi_if_buy_signal": false,
     "ignore_buying_expired_candle_after": 300,
-    "trading_mode": "spot",
-    "margin_mode": "",
     "minimal_roi": {
         "40":  0.0,
         "30":  0.01,
@@ -31,41 +28,39 @@
     },
     "stoploss": -0.10,
     "unfilledtimeout": {
-        "entry": 10,
-        "exit": 10,
+        "buy": 10,
+        "sell": 10,
         "exit_timeout_count": 0,
         "unit": "minutes"
     },
-    "entry_pricing": {
-        "price_side": "same",
+    "bid_strategy": {
+        "price_side": "bid",
         "use_order_book": true,
+        "ask_last_balance": 0.0,
         "order_book_top": 1,
-        "price_last_balance": 0.0,
         "check_depth_of_market": {
             "enabled": false,
             "bids_to_ask_delta": 1
         }
     },
-    "exit_pricing":{
-        "price_side": "same",
+    "ask_strategy":{
+        "price_side": "ask",
         "use_order_book": true,
-        "order_book_top": 1,
-        "price_last_balance": 0.0
+        "order_book_top": 1
     },
     "order_types": {
-        "entry": "limit",
-        "exit": "limit",
-        "emergency_exit": "market",
-        "force_exit": "market",
-        "force_entry": "market",
+        "buy": "limit",
+        "sell": "limit",
+        "emergencysell": "market",
+        "forcesell": "market",
+        "forcebuy": "market",
         "stoploss": "market",
         "stoploss_on_exchange": false,
-        "stoploss_on_exchange_interval": 60,
-        "stoploss_on_exchange_limit_ratio": 0.99
+        "stoploss_on_exchange_interval": 60
     },
     "order_time_in_force": {
-        "entry": "gtc",
-        "exit": "gtc"
+        "buy": "gtc",
+        "sell": "gtc"
     },
     "pairlists": [
         {"method": "StaticPairList"},
@@ -93,7 +88,6 @@
         "secret": "your_exchange_secret",
         "password": "",
         "log_responses": false,
-        // "unknown_fee_rate": 1,
         "ccxt_config": {},
         "ccxt_async_config": {},
         "pair_whitelist": [
@@ -141,24 +135,23 @@
             "status": "on",
             "warning": "on",
             "startup": "on",
-            "entry": "on",
-            "entry_fill": "on",
-            "exit": {
+            "buy": "on",
+            "buy_fill": "on",
+            "sell": {
                 "roi": "off",
-                "emergency_exit": "off",
-                "force_exit": "off",
-                "exit_signal": "off",
+                "emergency_sell": "off",
+                "force_sell": "off",
+                "sell_signal": "off",
                 "trailing_stop_loss": "off",
                 "stop_loss": "off",
                 "stoploss_on_exchange": "off",
-                "custom_exit": "off"
+                "custom_sell": "off"
             },
-            "exit_fill": "on",
-            "entry_cancel": "on",
-            "exit_cancel": "on",
+            "sell_fill": "on",
+            "buy_cancel": "on",
+            "sell_cancel": "on",
             "protection_trigger": "off",
-            "protection_trigger_global": "on",
-            "show_candle": "off"
+            "protection_trigger_global": "on"
         },
         "reload": true,
         "balance_dust_level": 0.01
@@ -177,7 +170,7 @@
     "bot_name": "freqtrade",
     "db_url": "sqlite:///tradesv3.sqlite",
     "initial_state": "running",
-    "force_entry_enable": false,
+    "forcebuy_enable": false,
     "internals": {
         "process_throttle_secs": 5,
         "heartbeat_interval": 60
@@ -185,8 +178,6 @@
     "disable_dataframe_checks": false,
     "strategy": "SampleStrategy",
     "strategy_path": "user_data/strategies/",
-    "recursive_strategy_search": false,
-    "add_config_files": [],
     "dataformat_ohlcv": "json",
     "dataformat_trades": "jsongz"
 }

config_examples/config_kraken.example.json

@@ -8,23 +8,21 @@
     "dry_run": true,
     "cancel_open_orders_on_exit": false,
     "unfilledtimeout": {
-        "entry": 10,
-        "exit": 10,
+        "buy": 10,
+        "sell": 10,
         "exit_timeout_count": 0,
         "unit": "minutes"
     },
-    "entry_pricing": {
-        "price_side": "same",
+    "bid_strategy": {
         "use_order_book": true,
+        "ask_last_balance": 0.0,
         "order_book_top": 1,
-        "price_last_balance": 0.0,
         "check_depth_of_market": {
             "enabled": false,
             "bids_to_ask_delta": 1
         }
     },
-    "exit_pricing":{
-        "price_side": "same",
+    "ask_strategy":{
         "use_order_book": true,
         "order_book_top": 1
     },
@@ -95,7 +93,7 @@
     },
     "bot_name": "freqtrade",
     "initial_state": "running",
-    "force_entry_enable": false,
+    "forcebuy_enable": false,
     "internals": {
         "process_throttle_secs": 5
     },

docker/Dockerfile.armhf

@@ -1,4 +1,4 @@
-FROM python:3.9.12-slim-bullseye as base
+FROM python:3.9.9-slim-bullseye as base
 
 # Setup env
 ENV LANG C.UTF-8

docker/Dockerfile.custom

@@ -7,5 +7,4 @@ FROM freqtradeorg/freqtrade:develop
 # The below dependency - pyti - serves as an example. Please use whatever you need!
 RUN pip install --user pyti
 
-# Switch back to user (only if you required root above)
 # USER ftuser

docker/Dockerfile.freqai

@@ -1,9 +0,0 @@
-ARG sourceimage=freqtradeorg/freqtrade
-ARG sourcetag=develop
-FROM ${sourceimage}:${sourcetag}
-
-# Install dependencies
-COPY requirements-freqai.txt /freqtrade/
-
-RUN pip install -r requirements-freqai.txt --user --no-cache-dir
-

docs/advanced-backtesting.md

@@ -1,102 +0,0 @@
-# Advanced Backtesting Analysis
-
-## Analyze the buy/entry and sell/exit tags
-
-It can be helpful to understand how a strategy behaves according to the buy/entry tags used to
-mark up different buy conditions. You might want to see more complex statistics about each buy and
-sell condition above those provided by the default backtesting output. You may also want to
-determine indicator values on the signal candle that resulted in a trade opening.
-
-!!! Note
-    The following buy reason analysis is only available for backtesting, *not hyperopt*.
-
-We need to run backtesting with the `--export` option set to `signals` to enable the exporting of
-signals **and** trades:
-
-``` bash
-freqtrade backtesting -c <config.json> --timeframe <tf> --strategy <strategy_name> --timerange=<timerange> --export=signals
-```
-
-This will tell freqtrade to output a pickled dictionary of strategy, pairs and corresponding
-DataFrame of the candles that resulted in buy signals. Depending on how many buys your strategy
-makes, this file may get quite large, so periodically check your `user_data/backtest_results`
-folder to delete old exports.
-
-Before running your next backtest, make sure you either delete your old backtest results or run
-backtesting with the `--cache none` option to make sure no cached results are used.
-
-If all goes well, you should now see a `backtest-result-{timestamp}_signals.pkl` file in the
-`user_data/backtest_results` folder.
-
-To analyze the entry/exit tags, we now need to use the `freqtrade backtesting-analysis` command
-with `--analysis-groups` option provided with space-separated arguments (default `0 1 2`):
-
-``` bash
-freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 1 2 3 4
-```
-
-This command will read from the last backtesting results. The `--analysis-groups` option is
-used to specify the various tabular outputs showing the profit fo each group or trade,
-ranging from the simplest (0) to the most detailed per pair, per buy and per sell tag (4):
-
-* 1: profit summaries grouped by enter_tag
-* 2: profit summaries grouped by enter_tag and exit_tag
-* 3: profit summaries grouped by pair and enter_tag
-* 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
-
-More options are available by running with the `-h` option.
-
-### Using export-filename
-
-Normally, `backtesting-analysis` uses the latest backtest results, but if you wanted to go
-back to a previous backtest output, you need to supply the `--export-filename` option.
-You can supply the same parameter to `backtest-analysis` with the name of the final backtest
-output file. This allows you to keep historical versions of backtest results and re-analyse
-them at a later date:
-
-``` bash
-freqtrade backtesting -c <config.json> --timeframe <tf> --strategy <strategy_name> --timerange=<timerange> --export=signals --export-filename=/tmp/mystrat_backtest.json
-```
-
-You should see some output similar to below in the logs with the name of the timestamped
-filename that was exported:
-
-```
-2022-06-14 16:28:32,698 - freqtrade.misc - INFO - dumping json to "/tmp/mystrat_backtest-2022-06-14_16-28-32.json"
-```
-
-You can then use that filename in `backtesting-analysis`:
-
-```
-freqtrade backtesting-analysis -c <config.json> --export-filename=/tmp/mystrat_backtest-2022-06-14_16-28-32.json
-```
-
-### Tuning the buy tags and sell tags to display
-
-To show only certain buy and sell tags in the displayed output, use the following two options:
-
-```
---enter-reason-list : Space-separated list of enter signals to analyse. Default: "all"
---exit-reason-list : Space-separated list of exit signals to analyse. Default: "all"
-```
-
-For example:
-
-```bash
-freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 2 --enter-reason-list enter_tag_a enter_tag_b --exit-reason-list roi custom_exit_tag_a stop_loss
-```
-
-### Outputting signal candle indicators
-
-The real power of `freqtrade backtesting-analysis` comes from the ability to print out the indicator
-values present on signal candles to allow fine-grained investigation and tuning of buy signal
-indicators. To print out a column for a given set of indicators, use the `--indicator-list`
-option:
-
-```bash
-freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 2 --enter-reason-list enter_tag_a enter_tag_b --exit-reason-list roi custom_exit_tag_a stop_loss --indicator-list rsi rsi_1h bb_lowerband ema_9 macd macdsignal
-```
-
-The indicators have to be present in your strategy's main DataFrame (either for your main
-timeframe or for informative timeframes) otherwise they will simply be ignored in the script
-output.

docs/advanced-hyperopt.md

@@ -56,7 +56,7 @@ Currently, the arguments are:
 
 * `results`: DataFrame containing the resulting trades.
     The following columns are available in results (corresponds to the output-file of backtesting when used with `--export trades`):  
-    `pair, profit_ratio, profit_abs, open_date, open_rate, fee_open, close_date, close_rate, fee_close, amount, trade_duration, is_open, exit_reason, stake_amount, min_rate, max_rate, stop_loss_ratio, stop_loss_abs`
+    `pair, profit_ratio, profit_abs, open_date, open_rate, fee_open, close_date, close_rate, fee_close, amount, trade_duration, is_open, sell_reason, stake_amount, min_rate, max_rate, stop_loss_ratio, stop_loss_abs`
 * `trade_count`: Amount of trades (identical to `len(results)`)
 * `min_date`: Start date of the timerange used
 * `min_date`: End date of the timerange used
@@ -98,23 +98,6 @@ class MyAwesomeStrategy(IStrategy):
 !!! Note
     All overrides are optional and can be mixed/matched as necessary.
 
-### Dynamic parameters
-
-Parameters can also be defined dynamically, but must be available to the instance once the * [`bot_start()` callback](strategy-callbacks.md#bot-start) has been called.
-
-``` python
-
-class MyAwesomeStrategy(IStrategy):
-
-    def bot_start(self, **kwargs) -> None:
-        self.buy_adx = IntParameter(20, 30, default=30, optimize=True)
-
-    # ...
-```
-
-!!! Warning
-    Parameters created this way will not show up in the `list-strategies` parameter count.
-
 ### Overriding Base estimator
 
 You can define your own estimator for Hyperopt by implementing `generate_estimator()` in the Hyperopt subclass.

docs/backtesting.md

@@ -20,14 +20,13 @@ usage: freqtrade backtesting [-h] [-v] [--logfile FILE] [-V] [-c PATH]
                              [--dry-run-wallet DRY_RUN_WALLET]
                              [--timeframe-detail TIMEFRAME_DETAIL]
                              [--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
-                             [--export {none,trades,signals}]
-                             [--export-filename PATH]
+                             [--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
-  -i TIMEFRAME, --timeframe TIMEFRAME
+  -i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
                         Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
   --timerange TIMERANGE
                         Specify what timerange of data to use.
@@ -64,17 +63,18 @@ optional arguments:
                         `30m`, `1h`, `1d`).
   --strategy-list STRATEGY_LIST [STRATEGY_LIST ...]
                         Provide a space-separated list of strategies to
-                        backtest. Please note that timeframe needs to be set
-                        either in config or via command line. When using this
-                        together with `--export trades`, the strategy-name is
-                        injected into the filename (so `backtest-data.json`
-                        becomes `backtest-data-SampleStrategy.json`
-  --export {none,trades,signals}
+                        backtest. Please note that ticker-interval needs to be
+                        set either in config or via command line. When using
+                        this together with `--export trades`, the strategy-
+                        name is injected into the filename (so `backtest-
+                        data.json` becomes `backtest-data-SampleStrategy.json`
+  --export {none,trades}
                         Export backtest results (default: trades).
-  --export-filename PATH, --backtest-filename PATH
-                        Use this filename for backtest results.Requires
-                        `--export` to be set as well. Example: `--export-filen
-                        ame=user_data/backtest_results/backtest_today.json`
+  --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`
   --breakdown {day,week,month} [{day,week,month} ...]
                         Show backtesting breakdown per [day, week, month].
   --cache {none,day,week,month}
@@ -274,68 +274,57 @@ A backtesting result will look like that:
 | XRP/BTC  |     35 |           0.66 |          22.96 |       0.00114897 |          11.48 | 3:49:00      |    12     0    23   34.3 |
 | ZEC/BTC  |     22 |          -0.46 |         -10.18 |      -0.00050971 |          -5.09 | 2:22:00      |     7     0    15   31.8 |
 | TOTAL    |    429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00      |   186     0   243   43.4 |
-========================================================= EXIT REASON STATS ==========================================================
-| Exit Reason        |   Sells |  Wins |  Draws |  Losses |
+========================================================= SELL REASON STATS ==========================================================
+| Sell Reason        |   Sells |  Wins |  Draws |  Losses |
 |:-------------------|--------:|------:|-------:|--------:|
 | trailing_stop_loss |     205 |   150 |      0 |      55 |
 | stop_loss          |     166 |     0 |      0 |     166 |
-| exit_signal        |      56 |    36 |      0 |      20 |
-| force_exit         |       2 |     0 |      0 |       2 |
+| sell_signal        |      56 |    36 |      0 |      20 |
+| force_sell         |       2 |     0 |      0 |       2 |
 ====================================================== LEFT OPEN TRADES REPORT ======================================================
 | Pair     |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration   |  Win Draw Loss Win% |
 |:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|--------------------:|
 | ADA/BTC  |      1 |           0.89 |           0.89 |       0.00004434 |           0.44 | 6:00:00        |    1    0    0  100 |
 | LTC/BTC  |      1 |           0.68 |           0.68 |       0.00003421 |           0.34 | 2:00:00        |    1    0    0  100 |
 | TOTAL    |      2 |           0.78 |           1.57 |       0.00007855 |           0.78 | 4:00:00        |    2    0    0  100 |
-================== SUMMARY METRICS ==================
-| Metric                      | Value               |
-|-----------------------------+---------------------|
-| Backtesting from            | 2019-01-01 00:00:00 |
-| Backtesting to              | 2019-05-01 00:00:00 |
-| Max open trades             | 3                   |
-|                             |                     |
-| Total/Daily Avg Trades      | 429 / 3.575         |
-| Starting balance            | 0.01000000 BTC      |
-| Final balance               | 0.01762792 BTC      |
-| Absolute profit             | 0.00762792 BTC      |
-| Total profit %              | 76.2%               |
-| CAGR %                      | 460.87%             |
-| Profit factor               | 1.11                |
-| Avg. stake amount           | 0.001      BTC      |
-| Total trade volume          | 0.429      BTC      |
-|                             |                     |
-| Long / Short                | 352 / 77            |
-| Total profit Long %         | 1250.58%            |
-| Total profit Short %        | -15.02%             |
-| Absolute profit Long        | 0.00838792 BTC      |
-| Absolute profit Short       | -0.00076 BTC        |
-|                             |                     |
-| Best Pair                   | LSK/BTC 26.26%      |
-| Worst Pair                  | ZEC/BTC -10.18%     |
-| Best Trade                  | LSK/BTC 4.25%       |
-| Worst Trade                 | ZEC/BTC -10.25%     |
-| Best day                    | 0.00076 BTC         |
-| Worst day                   | -0.00036 BTC        |
-| Days win/draw/lose          | 12 / 82 / 25        |
-| Avg. Duration Winners       | 4:23:00             |
-| Avg. Duration Loser         | 6:55:00             |
-| Rejected Entry signals      | 3089                |
-| Entry/Exit Timeouts         | 0 / 0               |
-| Canceled Trade Entries      | 34                  |
-| Canceled Entry Orders       | 123                 |
-| Replaced Entry Orders       | 89                  |
-|                             |                     |
-| Min balance                 | 0.00945123 BTC      |
-| Max balance                 | 0.01846651 BTC      |
-| Max % of account underwater | 25.19%              |
-| Absolute Drawdown (Account) | 13.33%              |
-| Drawdown                    | 0.0015 BTC          |
-| Drawdown high               | 0.0013 BTC          |
-| Drawdown low                | -0.0002 BTC         |
-| Drawdown Start              | 2019-02-15 14:10:00 |
-| Drawdown End                | 2019-04-11 18:15:00 |
-| Market change               | -5.88%              |
-=====================================================
+=============== SUMMARY METRICS ===============
+| Metric                | Value               |
+|-----------------------+---------------------|
+| Backtesting from      | 2019-01-01 00:00:00 |
+| Backtesting to        | 2019-05-01 00:00:00 |
+| Max open trades       | 3                   |
+|                       |                     |
+| Total/Daily Avg Trades| 429 / 3.575         |
+| Starting balance      | 0.01000000 BTC      |
+| Final balance         | 0.01762792 BTC      |
+| Absolute profit       | 0.00762792 BTC      |
+| Total profit %        | 76.2%               |
+| Trades per day        | 3.575               |
+| Avg. stake amount     | 0.001      BTC      |
+| Total trade volume    | 0.429      BTC      |
+|                       |                     |
+| Best Pair             | LSK/BTC 26.26%      |
+| Worst Pair            | ZEC/BTC -10.18%     |
+| Best Trade            | LSK/BTC 4.25%       |
+| Worst Trade           | ZEC/BTC -10.25%     |
+| Best day              | 0.00076 BTC         |
+| Worst day             | -0.00036 BTC        |
+| Days win/draw/lose    | 12 / 82 / 25        |
+| 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 (Account)    | 13.33%              |
+| Drawdown              | 0.0015 BTC          |
+| Drawdown high         | 0.0013 BTC          |
+| Drawdown low          | -0.0002 BTC         |
+| Drawdown Start        | 2019-02-15 14:10:00 |
+| Drawdown End          | 2019-04-11 18:15:00 |
+| Market change         | -5.88%              |
+===============================================
 ```
 
 ### Backtesting report table
@@ -356,9 +345,9 @@ The column `Avg Profit %` shows the average profit for all trades made while the
 The column `Tot Profit %` shows instead the total profit % in relation to the starting balance.
 In the above results, we have a starting balance of 0.01 BTC and an absolute profit of 0.00762792 BTC - so the `Tot Profit %` will be `(0.00762792 / 0.01) * 100 ~= 76.2%`.
 
-Your strategy performance is influenced by your buy strategy, your exit strategy, and also by the `minimal_roi` and `stop_loss` you have set.
+Your strategy performance is influenced by your buy strategy, your sell strategy, and also by the `minimal_roi` and `stop_loss` you have set.
 
-For example, if your `minimal_roi` is only `"0":  0.01` you cannot expect the bot to make more profit than 1% (because it will exit every time a trade reaches 1%).
+For example, if your `minimal_roi` is only `"0":  0.01` you cannot expect the bot to make more profit than 1% (because it will sell every time a trade reaches 1%).
 
 ```json
 "minimal_roi": {
@@ -370,14 +359,14 @@ On the other hand, if you set a too high `minimal_roi` like `"0":  0.55`
 (55%), there is almost no chance that the bot will ever reach this profit.
 Hence, keep in mind that your performance is an integral mix of all different elements of the strategy, your configuration, and the crypto-currency pairs you have set up.
 
-### Exit reasons table
+### Sell reasons table
 
-The 2nd table contains a recap of exit reasons.
-This table can tell you which area needs some additional work (e.g. all or many of the `exit_signal` trades are losses, so you should work on improving the exit signal, or consider disabling it).
+The 2nd table contains a recap of sell reasons.
+This table can tell you which area needs some additional work (e.g. all or many of the `sell_signal` trades are losses, so you should work on improving the sell signal, or consider disabling it).
 
 ### Left open trades table
 
-The 3rd table contains all trades the bot had to `force_exit` at the end of the backtesting period to present you the full picture.
+The 3rd table contains all trades the bot had to `forcesell` at the end of the backtesting period to present you the full picture.
 This is necessary to simulate realistic behavior, since the backtest period has to end at some point, while realistically, you could leave the bot running forever.
 These trades are also included in the first table, but are also shown separately in this table for clarity.
 
@@ -387,55 +376,43 @@ The last element of the backtest report is the summary metrics table.
 It contains some useful key metrics about performance of your strategy on backtesting data.
 
 ```
-================== SUMMARY METRICS ==================
-| Metric                      | Value               |
-|-----------------------------+---------------------|
-| Backtesting from            | 2019-01-01 00:00:00 |
-| Backtesting to              | 2019-05-01 00:00:00 |
-| Max open trades             | 3                   |
-|                             |                     |
-| Total/Daily Avg Trades      | 429 / 3.575         |
-| Starting balance            | 0.01000000 BTC      |
-| Final balance               | 0.01762792 BTC      |
-| Absolute profit             | 0.00762792 BTC      |
-| Total profit %              | 76.2%               |
-| CAGR %                      | 460.87%             |
-| Profit factor               | 1.11                |
-| Avg. stake amount           | 0.001      BTC      |
-| Total trade volume          | 0.429      BTC      |
-|                             |                     |
-| Long / Short                | 352 / 77            |
-| Total profit Long %         | 1250.58%            |
-| Total profit Short %        | -15.02%             |
-| Absolute profit Long        | 0.00838792 BTC      |
-| Absolute profit Short       | -0.00076 BTC        |
-|                             |                     |
-| Best Pair                   | LSK/BTC 26.26%      |
-| Worst Pair                  | ZEC/BTC -10.18%     |
-| Best Trade                  | LSK/BTC 4.25%       |
-| Worst Trade                 | ZEC/BTC -10.25%     |
-| Best day                    | 0.00076 BTC         |
-| Worst day                   | -0.00036 BTC        |
-| Days win/draw/lose          | 12 / 82 / 25        |
-| Avg. Duration Winners       | 4:23:00             |
-| Avg. Duration Loser         | 6:55:00             |
-| Rejected Entry signals      | 3089                |
-| Entry/Exit Timeouts         | 0 / 0               |
-| Canceled Trade Entries      | 34                  |
-| Canceled Entry Orders       | 123                 |
-| Replaced Entry Orders       | 89                  |
-|                             |                     |
-| Min balance                 | 0.00945123 BTC      |
-| Max balance                 | 0.01846651 BTC      |
-| Max % of account underwater | 25.19%              |
-| Absolute Drawdown (Account) | 13.33%              |
-| Drawdown                    | 0.0015 BTC          |
-| Drawdown high               | 0.0013 BTC          |
-| Drawdown low                | -0.0002 BTC         |
-| Drawdown Start              | 2019-02-15 14:10:00 |
-| Drawdown End                | 2019-04-11 18:15:00 |
-| Market change               | -5.88%              |
-=====================================================
+=============== SUMMARY METRICS ===============
+| Metric                | Value               |
+|-----------------------+---------------------|
+| Backtesting from      | 2019-01-01 00:00:00 |
+| Backtesting to        | 2019-05-01 00:00:00 |
+| Max open trades       | 3                   |
+|                       |                     |
+| Total/Daily Avg Trades| 429 / 3.575         |
+| Starting balance      | 0.01000000 BTC      |
+| Final balance         | 0.01762792 BTC      |
+| Absolute profit       | 0.00762792 BTC      |
+| Total profit %        | 76.2%               |
+| Avg. stake amount     | 0.001      BTC      |
+| Total trade volume    | 0.429      BTC      |
+|                       |                     |
+| Best Pair             | LSK/BTC 26.26%      |
+| Worst Pair            | ZEC/BTC -10.18%     |
+| Best Trade            | LSK/BTC 4.25%       |
+| Worst Trade           | ZEC/BTC -10.25%     |
+| Best day              | 0.00076 BTC         |
+| Worst day             | -0.00036 BTC        |
+| Days win/draw/lose    | 12 / 82 / 25        |
+| 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 (Account)    | 13.33%              |
+| Drawdown              | 0.0015 BTC          |
+| Drawdown high         | 0.0013 BTC          |
+| Drawdown low          | -0.0002 BTC         |
+| Drawdown Start        | 2019-02-15 14:10:00 |
+| Drawdown End          | 2019-04-11 18:15:00 |
+| Market change         | -5.88%              |
+===============================================
 
 ```
 
@@ -446,8 +423,6 @@ It contains some useful key metrics about performance of your strategy on backte
 - `Final balance`: Final balance - starting balance + absolute profit.
 - `Absolute profit`: Profit made in stake currency.
 - `Total profit %`: Total profit. Aligned to the `TOTAL` row's `Tot Profit %` from the first table. Calculated as `(End capital − Starting capital) / Starting capital`.
-- `CAGR %`: Compound annual growth rate.
-- `Profit factor`: profit / loss.
 - `Avg. stake amount`: Average stake amount, either `stake_amount` or the average when using dynamic stake amount.
 - `Total trade volume`: Volume generated on the exchange to reach the above profit.
 - `Best Pair` / `Worst Pair`: Best and worst performing pair, and it's corresponding `Cum Profit %`.
@@ -455,22 +430,14 @@ It contains some useful key metrics about performance of your strategy on backte
 - `Best day` / `Worst day`: Best and worst day based on daily profit.
 - `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 Entry signals`: Trade entry signals that could not be acted upon due to `max_open_trades` being reached.
+- `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).
-- `Canceled Trade Entries`: Number of trades that have been canceled by user request via `adjust_entry_price`.
-- `Canceled Entry Orders`: Number of entry orders that have been canceled by user request via `adjust_entry_price`.
-- `Replaced Entry Orders`: Number of entry orders that have been replaced by user request via `adjust_entry_price`.
 - `Min balance` / `Max balance`: Lowest and Highest Wallet balance during the backtest period.
-- `Max % of account underwater`: Maximum percentage your account has decreased from the top since the simulation started.
-Calculated as the maximum of `(Max Balance - Current Balance) / (Max Balance)`.
-- `Absolute Drawdown (Account)`: Maximum Account Drawdown experienced. Calculated as `(Absolute Drawdown) / (DrawdownHigh + startingBalance)`.
+- `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.
-- `Long / Short`: Split long/short values (Only shown when short trades were made).
-- `Total profit Long %` / `Absolute profit Long`: Profit long trades only (Only shown when short trades were made).
-- `Total profit Short %` / `Absolute profit Short`: Profit short trades only (Only shown when short trades were made).
 
 ### Daily / Weekly / Monthly breakdown
 
@@ -479,7 +446,7 @@ You can get an overview over daily / weekly or monthly results by using the `--b
 To visualize daily and weekly breakdowns, you can use the following:
 
 ``` bash
-freqtrade backtesting --strategy MyAwesomeStrategy --breakdown day week
+freqtrade backtesting --strategy MyAwesomeStrategy --breakdown day month
 ```
 
 ``` output
@@ -495,7 +462,7 @@ freqtrade backtesting --strategy MyAwesomeStrategy --breakdown day week
 
 ```
 
-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. Below that there will be a second table for the summarized values of weeks indicated by the date of the closing Sunday. The same would apply to a monthly breakdown indicated by the last day of the 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
 
@@ -514,54 +481,35 @@ You can then load the trades to perform further analysis as shown in the [data a
 
 Since backtesting lacks some detailed information about what happens within a candle, it needs to take a few assumptions:
 
-- Exchange [trading limits](#trading-limits-in-backtesting) are respected
 - Buys happen at open-price
 - All orders are filled at the requested price (no slippage, no unfilled orders)
-- Exit-signal exits happen at open-price of the consecutive candle
-- Exit-signal is favored over Stoploss, because exit-signals are assumed to trigger on candle's open
+- Sell-signal sells happen at open-price of the consecutive candle
+- Sell-signal is favored over Stoploss, because sell-signals are assumed to trigger on candle's open
 - ROI
-  - exits are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the exit will be at 2%)
-  - exits are never "below the candle", so a ROI of 2% may result in a exit at 2.4% if low was at 2.4% profit
-  - Forceexits caused by `<N>=-1` ROI entries use low as exit value, unless N falls on the candle open (e.g. `120: -1` for 1h candles)
-- Stoploss exits happen exactly at stoploss price, even if low was lower, but the loss will be `2 * fees` higher than the stoploss price
-- Stoploss is evaluated before ROI within one candle. So you can often see more trades with the `stoploss` exit reason comparing to the results obtained with the same strategy in the Dry Run/Live Trade modes
+  - sells are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the sell will be at 2%)
+  - sells are never "below the candle", so a ROI of 2% may result in a sell at 2.4% if low was at 2.4% profit
+  - Forcesells caused by `<N>=-1` ROI entries use low as sell value, unless N falls on the candle open (e.g. `120: -1` for 1h candles)
+- Stoploss sells happen exactly at stoploss price, even if low was lower, but the loss will be `2 * fees` higher than the stoploss price
+- Stoploss is evaluated before ROI within one candle. So you can often see more trades with the `stoploss` sell reason comparing to the results obtained with the same strategy in the Dry Run/Live Trade modes
 - 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 exits with large high-low difference are backtested correctly)
+  - 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
-- Exit-reason does not explain if a trade was positive or negative, just what triggered the exit (this can look odd if negative ROI values are used)
+- 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)
-  - Exit-signal
+  - Sell-signal
+  - ROI (if not stoploss)
   - Stoploss
-  - ROI
-  - Trailing 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.
 Also, keep in mind that past results don't guarantee future success.
 
 In addition to the above assumptions, strategy authors should carefully read the [Common Mistakes](strategy-customization.md#common-mistakes-when-developing-strategies) section, to avoid using data in backtesting which is not available in real market conditions.
 
-### Trading limits in backtesting
-
-Exchanges have certain trading limits, like minimum base currency, or minimum stake (quote) currency.
-These limits are usually listed in the exchange documentation as "trading rules" or similar.
-
-Backtesting (as well as live and dry-run) does honor these limits, and will ensure that a stoploss can be placed below this value - so the value will be slightly higher than what the exchange specifies.
-Freqtrade has however no information about historic limits.
-
-This can lead to situations where trading-limits are inflated by using a historic price, resulting in minimum amounts > 50$.
-
-For example:
-
-BTC minimum tradable amount is 0.001.
-BTC trades at 22.000\$ today (0.001 BTC is related to this) - but the backtesting period includes prices as high as 50.000\$.
-Today's minimum would be `0.001 * 22_000` - or 22\$.  
-However the limit could also be 50$ - based on `0.001 * 50_000` in some historic setting.
-
-## Improved backtest accuracy
+### Improved backtest accuracy
 
 One big limitation of backtesting is it's inability to know how prices moved intra-candle (was high before close, or viceversa?).
 So assuming you run backtesting with a 1h timeframe, there will be 4 prices for that candle (Open, High, Low, Close).
@@ -576,7 +524,7 @@ freqtrade backtesting --strategy AwesomeStrategy --timeframe 1h --timeframe-deta
 ```
 
 This will load 1h data as well as 5m data for the timeframe. The strategy will be analyzed with the 1h timeframe - and for every "open trade candle" (candles where a trade is open) the 5m data will be used to simulate intra-candle movements.
-All callback functions (`custom_exit()`, `custom_stoploss()`, ... ) will be running for each 5m candle once the trade is opened (so 12 times in the above example of 1h timeframe, and 5m detailed timeframe).
+All callback functions (`custom_sell()`, `custom_stoploss()`, ... ) will be running for each 5m candle once the trade is opened (so 12 times in the above example of 1h timeframe, and 5m detailed timeframe).
 
 `--timeframe-detail` must be smaller than the original timeframe, otherwise backtesting will fail to start.
 

docs/bot-basics.md

@@ -20,34 +20,30 @@ All profit calculations of Freqtrade include fees. For Backtesting / Hyperopt /
 ## Bot execution logic
 
 Starting freqtrade in dry-run or live mode (using `freqtrade trade`) will start the bot and start the bot iteration loop.
-This will also run the `bot_start()` callback.
-
-By default, the bot loop runs every few seconds (`internals.process_throttle_secs`) and performs the following actions:
+By default, loop runs every few seconds (`internals.process_throttle_secs`) and does roughly the following in the following sequence:
 
 * Fetch open trades from persistence.
 * Calculate current list of tradable pairs.
-* Download OHLCV data for the pairlist including all [informative pairs](strategy-customization.md#get-data-for-non-tradeable-pairs)  
+* Download ohlcv data for the pairlist including all [informative pairs](strategy-customization.md#get-data-for-non-tradeable-pairs)  
   This step is only executed once per Candle to avoid unnecessary network traffic.
 * Call `bot_loop_start()` strategy callback.
 * Analyze strategy per pair.
   * Call `populate_indicators()`
-  * Call `populate_entry_trend()`
-  * Call `populate_exit_trend()`
+  * Call `populate_buy_trend()`
+  * Call `populate_sell_trend()`
 * Check timeouts for open orders.
-  * Calls `check_entry_timeout()` strategy callback for open entry orders.
-  * Calls `check_exit_timeout()` strategy callback for open exit orders.
-  * Calls `adjust_entry_price()` strategy callback for open entry orders.
-* Verifies existing positions and eventually places exit orders.
-  * Considers stoploss, ROI and exit-signal, `custom_exit()` and `custom_stoploss()`.
-  * Determine exit-price based on `exit_pricing` configuration setting or by using the `custom_exit_price()` callback.
-  * Before a exit order is placed, `confirm_trade_exit()` strategy callback is called.
+  * Calls `check_buy_timeout()` strategy callback for open buy orders.
+  * Calls `check_sell_timeout()` strategy callback for open sell orders.
+* Verifies existing positions and eventually places sell orders.
+  * 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 entry signal trying to enter new positions.
-  * Determine entry-price based on `entry_pricing` configuration setting, or by using the `custom_entry_price()` callback.
-  * In Margin and Futures mode, `leverage()` strategy callback is called to determine the desired leverage.
+* 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.
   * Determine stake size by calling the `custom_stake_amount()` callback.
-  * Before an entry order is placed, `confirm_trade_entry()` strategy callback is called.
+  * Before a buy order is placed, `confirm_trade_entry()` strategy callback is called.
 
 This loop will be repeated again and again until the bot is stopped.
 
@@ -56,21 +52,17 @@ This loop will be repeated again and again until the bot is stopped.
 [backtesting](backtesting.md) or [hyperopt](hyperopt.md) do only part of the above logic, since most of the trading operations are fully simulated.
 
 * Load historic data for configured pairlist.
-* Calls `bot_start()` once.
 * Calls `bot_loop_start()` once.
 * Calculate indicators (calls `populate_indicators()` once per pair).
-* Calculate entry / exit signals (calls `populate_entry_trend()` and `populate_exit_trend()` once per pair).
+* Calculate buy / sell signals (calls `populate_buy_trend()` and `populate_sell_trend()` once per pair).
 * Loops per candle simulating entry and exit points.
-  * Check for Order timeouts, either via the `unfilledtimeout` configuration, or via `check_entry_timeout()` / `check_exit_timeout()` strategy callbacks.
-  * Calls `adjust_entry_price()` strategy callback for open entry orders.
-  * Check for trade entry signals (`enter_long` / `enter_short` columns).
-  * Confirm trade entry / exits (calls `confirm_trade_entry()` and `confirm_trade_exit()` if implemented in the strategy).
+  * 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).
-  * In Margin and Futures mode, `leverage()` strategy callback is called to determine the desired leverage.
   * 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_exit()` to find custom exit points.
-  * For exits based on exit-signal and custom-exit: Call `custom_exit_price()` to determine exit price (Prices are moved to be within the closing candle).
+  * 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

@@ -53,72 +53,20 @@ FREQTRADE__EXCHANGE__SECRET=<yourExchangeSecret>
 
 Multiple configuration files can be specified and used by the bot or the bot can read its configuration parameters from the process standard input stream.
 
-You can specify additional configuration files in `add_config_files`. Files specified in this parameter will be loaded and merged with the initial config file. The files are resolved relative to the initial configuration file.
-This is similar to using multiple `--config` parameters, but simpler in usage as you don't have to specify all files for all commands.
-
 !!! Tip "Use multiple configuration files to keep secrets secret"
     You can use a 2nd configuration file containing your secrets. That way you can share your "primary" configuration file, while still keeping your API keys for yourself.
 
-    ``` json title="user_data/config.json"
-    "add_config_files": [
-        "config-private.json"
-    ]
-    ```
-
-    ``` bash
-    freqtrade trade --config user_data/config.json <...>
-    ```
-
-    The 2nd file should only specify what you intend to override.
-    If a key is in more than one of the configurations, then the "last specified configuration" wins (in the above example, `config-private.json`).
-
-    For one-off commands, you can also use the below syntax by specifying multiple "--config" parameters.
-
     ``` bash
     freqtrade trade --config user_data/config.json --config user_data/config-private.json <...>
     ```
-
-    This is equivalent to the example above - but `config-private.json` is specified as cli argument.
-
-??? Note "config collision handling"
-    If the same configuration setting takes place in both `config.json` and `config-import.json`, then the parent configuration wins.
-    In the below case, `max_open_trades` would be 3 after the merging - as the reusable "import" configuration has this key overwritten.
-
-    ``` json title="user_data/config.json"
-    {
-        "max_open_trades": 3,
-        "stake_currency": "USDT",
-        "add_config_files": [
-            "config-import.json"
-        ]
-    }
-    ```
-
-    ``` json title="user_data/config-import.json"
-    {
-        "max_open_trades": 10,
-        "stake_amount": "unlimited",
-    }
-    ```
-
-    Resulting combined configuration:
-
-    ``` json title="Result"
-    {
-        "max_open_trades": 3,
-        "stake_currency": "USDT",
-        "stake_amount": "unlimited"
-    }
-    ```
+    The 2nd file should only specify what you intend to override.
+    If a key is in more than one of the configurations, then the "last specified configuration" wins (in the above example, `config-private.json`).
 
 ## Configuration parameters
 
 The table below will list all configuration parameters available.
 
 Freqtrade can also load many options via command line (CLI) arguments (check out the commands `--help` output for details).
-
-### Configuration option prevalence
-
 The prevalence for all Options is as follows:
 
 - CLI arguments override any other option
@@ -126,8 +74,6 @@ The prevalence for all Options is as follows:
 - Configuration files are used in sequence (the last file wins) and override Strategy configurations.
 - Strategy configurations are only used if they are not set via configuration or command-line arguments. These options are marked with [Strategy Override](#parameters-in-the-strategy) in the below table.
 
-### Parameters table
-
 Mandatory parameters are marked as **Required**, which means that they are required to be set in one of the possible ways.
 
 |  Parameter | Description |
@@ -140,51 +86,41 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `amend_last_stake_amount` | Use reduced last stake amount if necessary. [More information below](#configuring-amount-per-trade). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
 | `last_stake_amount_min_ratio` | Defines minimum stake amount that has to be left and executed. Applies only to the last stake amount when it's amended to a reduced value (i.e. if `amend_last_stake_amount` is set to `true`). [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.5`.* <br> **Datatype:** Float (as ratio)
 | `amount_reserve_percent` | Reserve some amount in min pair stake amount. The bot will reserve `amount_reserve_percent` + stoploss value when calculating min pair stake amount in order to avoid possible trade refusals. <br>*Defaults to `0.05` (5%).* <br> **Datatype:** Positive Float as ratio.
-| `timeframe` | The timeframe to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). Usually missing in configuration, and specified in the strategy. [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
+| `timeframe` | The timeframe (former ticker interval) to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
 | `fiat_display_currency` | Fiat currency used to show your profits. [More information below](#what-values-can-be-used-for-fiat_display_currency). <br> **Datatype:** String
 | `dry_run` | **Required.** Define if the bot must be in Dry Run or production mode. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
 | `dry_run_wallet` | Define the starting amount in stake currency for the simulated wallet used by the bot running in Dry Run mode.<br>*Defaults to `1000`.* <br> **Datatype:** Float
 | `cancel_open_orders_on_exit` | Cancel open orders when the `/stop` RPC command is issued, `Ctrl+C` is pressed or the bot dies unexpectedly. When set to `true`, this allows you to use `/stop` to cancel unfilled and partially filled orders in the event of a market crash. It does not impact open positions. <br>*Defaults to `false`.* <br> **Datatype:** Boolean
-| `process_only_new_candles` | Enable processing of indicators only when new candles arrive. If false each loop populates the indicators, this will mean the same candle is processed many times creating system load but can be useful of your strategy depends on tick data not only candle. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `true`.*  <br> **Datatype:** Boolean
-| `minimal_roi` | **Required.** Set the threshold as ratio the bot will use to exit a trade. [More information below](#understand-minimal_roi). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
+| `process_only_new_candles` | Enable processing of indicators only when new candles arrive. If false each loop populates the indicators, this will mean the same candle is processed many times creating system load but can be useful of your strategy depends on tick data not only candle. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*  <br> **Datatype:** Boolean
+| `minimal_roi` | **Required.** Set the threshold as ratio the bot will use to sell a trade. [More information below](#understand-minimal_roi). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
 | `stoploss` |  **Required.** Value as ratio of the stoploss used by the bot. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy).  <br> **Datatype:** Float (as ratio)
 | `trailing_stop` | Enables trailing stoploss (based on `stoploss` in either configuration or strategy file). More details in the [stoploss documentation](stoploss.md#trailing-stop-loss). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Boolean
 | `trailing_stop_positive` | Changes stoploss once profit has been reached. More details in the [stoploss documentation](stoploss.md#trailing-stop-loss-custom-positive-loss). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Float
 | `trailing_stop_positive_offset` | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive. More details in the [stoploss documentation](stoploss.md#trailing-stop-loss-only-once-the-trade-has-reached-a-certain-offset). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0` (no offset).* <br> **Datatype:** Float
 | `trailing_only_offset_is_reached` | Only apply trailing stoploss when the offset is reached. [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*  <br> **Datatype:** Boolean
 | `fee` | Fee used during backtesting / dry-runs. Should normally not be configured, which has freqtrade fall back to the exchange default fee. Set as ratio (e.g. 0.001 = 0.1%). Fee is applied twice for each trade, once when buying, once when selling. <br> **Datatype:** Float (as ratio)
-| `futures_funding_rate` | User-specified funding rate to be used when historical funding rates are not available from the exchange. This does not overwrite real historical rates. It is recommended that this be set to 0 unless you are testing a specific coin and you understand how the funding rate will affect freqtrade's profit calculations. [More information here](leverage.md#unavailable-funding-rates) <br>*Defaults to None.*<br> **Datatype:** Float
-| `trading_mode` | Specifies if you want to trade regularly, trade with leverage, or trade contracts whose prices are derived from matching cryptocurrency prices. [leverage documentation](leverage.md). <br>*Defaults to `"spot"`.*  <br> **Datatype:** String
-| `margin_mode` | When trading with leverage, this determines if the collateral owned by the trader will be shared or isolated to each trading pair [leverage documentation](leverage.md). <br> **Datatype:** String
-| `liquidation_buffer` | A ratio specifying how large of a safety net to place between the liquidation price and the stoploss to prevent a position from reaching the liquidation price [leverage documentation](leverage.md). <br>*Defaults to `0.05`.*  <br> **Datatype:** Float
-| | **Unfilled timeout**
-| `unfilledtimeout.entry` | **Required.** How long (in minutes or seconds) the bot will wait for an unfilled entry 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.exit` | **Required.** How long (in minutes or seconds) the bot will wait for an unfilled exit 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.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 exit is triggered. 0 to disable and allow unlimited order cancels. [Strategy Override](#parameters-in-the-strategy).<br>*Defaults to `0`.* <br> **Datatype:** Integer
-| | **Pricing**
-| `entry_pricing.price_side` | Select the side of the spread the bot should look at to get the entry rate. [More information below](#buy-price-side).<br> *Defaults to `same`.* <br> **Datatype:** String (either `ask`, `bid`, `same` or `other`).
-| `entry_pricing.price_last_balance` | **Required.** Interpolate the bidding price. More information [below](#entry-price-without-orderbook-enabled).
-| `entry_pricing.use_order_book` | Enable entering using the rates in [Order Book Entry](#entry-price-with-orderbook-enabled). <br> *Defaults to `True`.*<br> **Datatype:** Boolean
-| `entry_pricing.order_book_top` | Bot will use the top N rate in Order Book "price_side" to enter a trade. I.e. a value of 2 will allow the bot to pick the 2nd entry in [Order Book Entry](#entry-price-with-orderbook-enabled). <br>*Defaults to `1`.*  <br> **Datatype:** Positive Integer
-| `entry_pricing. check_depth_of_market.enabled` | Do not enter if the difference of buy orders and sell orders is met in Order Book. [Check market depth](#check-depth-of-market). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
-| `entry_pricing. check_depth_of_market.bids_to_ask_delta` | The difference ratio of buy orders and sell orders found in Order Book. A value below 1 means sell order size is greater, while value greater than 1 means buy order size is higher. [Check market depth](#check-depth-of-market) <br> *Defaults to `0`.*  <br> **Datatype:** Float (as ratio)
-| `exit_pricing.price_side` | Select the side of the spread the bot should look at to get the exit rate. [More information below](#exit-price-side).<br> *Defaults to `same`.* <br> **Datatype:** String (either `ask`, `bid`, `same` or `other`).
-| `exit_pricing.price_last_balance` | Interpolate the exiting price. More information [below](#exit-price-without-orderbook-enabled).
-| `exit_pricing.use_order_book` | Enable exiting of open trades using [Order Book Exit](#exit-price-with-orderbook-enabled). <br> *Defaults to `True`.*<br> **Datatype:** Boolean
-| `exit_pricing.order_book_top` | Bot will use the top N rate in Order Book "price_side" to exit. I.e. a value of 2 will allow the bot to pick the 2nd ask rate in [Order Book Exit](#exit-price-with-orderbook-enabled)<br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
-| `custom_price_max_distance_ratio` | Configure maximum distance ratio between current and custom entry or exit price. <br>*Defaults to `0.02` 2%).*<br> **Datatype:** Positive float
-| | **TODO**
-| `use_exit_signal` | Use exit signals produced by the strategy in addition to the `minimal_roi`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `true`.* <br> **Datatype:** Boolean
-| `exit_profit_only` | Wait until the bot reaches `exit_profit_offset` before taking an exit decision. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
-| `exit_profit_offset` | Exit-signal is only active above this value. Only active in combination with `exit_profit_only=True`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0`.* <br> **Datatype:** Float (as ratio)
-| `ignore_roi_if_entry_signal` | Do not exit if the entry signal is still active. This setting takes preference over `minimal_roi` and `use_exit_signal`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
+| `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
+| `bid_strategy.order_book_top` | Bot will use the top N rate in Order Book "price_side" to buy. I.e. a value of 2 will allow the bot to pick the 2nd bid rate in [Order Book Bids](#buy-price-with-orderbook-enabled). <br>*Defaults to `1`.*  <br> **Datatype:** Positive Integer
+| `bid_strategy. check_depth_of_market.enabled` | Do not buy if the difference of buy orders and sell orders is met in Order Book. [Check market depth](#check-depth-of-market). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
+| `bid_strategy. check_depth_of_market.bids_to_ask_delta` | The difference ratio of buy orders and sell orders found in Order Book. A value below 1 means sell order size is greater, while value greater than 1 means buy order size is higher. [Check market depth](#check-depth-of-market) <br> *Defaults to `0`.*  <br> **Datatype:** Float (as ratio)
+| `ask_strategy.price_side` | Select the side of the spread the bot should look at to get the sell rate. [More information below](#sell-price-side).<br> *Defaults to `ask`.* <br> **Datatype:** String (either `ask` or `bid`).
+| `ask_strategy.bid_last_balance` | Interpolate the selling price. More information [below](#sell-price-without-orderbook-enabled).
+| `ask_strategy.use_order_book` | Enable selling of open trades using [Order Book Asks](#sell-price-with-orderbook-enabled). <br> **Datatype:** Boolean
+| `ask_strategy.order_book_top` | Bot will use the top N rate in Order Book "price_side" to sell. I.e. a value of 2 will allow the bot to pick the 2nd ask rate in [Order Book Asks](#sell-price-with-orderbook-enabled)<br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
+| `use_sell_signal` | Use sell signals produced by the strategy in addition to the `minimal_roi`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `true`.* <br> **Datatype:** Boolean
+| `sell_profit_only` | Wait until the bot reaches `sell_profit_offset` before taking a sell decision. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
+| `sell_profit_offset` | Sell-signal is only active above this value. Only active in combination with `sell_profit_only=True`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0`.* <br> **Datatype:** Float (as ratio)
+| `ignore_roi_if_buy_signal` | Do not sell if the buy signal is still active. This setting takes preference over `minimal_roi` and `use_sell_signal`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
 | `ignore_buying_expired_candle_after` | Specifies the number of seconds until a buy signal is no longer used. <br> **Datatype:** Integer
-| `order_types` | Configure order-types depending on the action (`"entry"`, `"exit"`, `"stoploss"`, `"stoploss_on_exchange"`). [More information below](#understand-order_types). [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Dict
-| `order_time_in_force` | Configure time in force for entry and exit orders. [More information below](#understand-order_time_in_force). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
-| `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
-| | **Exchange**
+| `order_types` | Configure order-types depending on the action (`"buy"`, `"sell"`, `"stoploss"`, `"stoploss_on_exchange"`). [More information below](#understand-order_types). [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Dict
+| `order_time_in_force` | Configure time in force for buy and sell orders. [More information below](#understand-order_time_in_force). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
+| `custom_price_max_distance_ratio` | Configure maximum distance ratio between current and custom entry or exit price. <br>*Defaults to `0.02` 2%).*<br> **Datatype:** Positive float
 | `exchange.name` | **Required.** Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename). <br> **Datatype:** String
 | `exchange.sandbox` | Use the 'sandbox' version of the exchange, where the exchange provides a sandbox for risk-free integration. See [here](sandbox-testing.md) in more details.<br> **Datatype:** Boolean
 | `exchange.key` | API key to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
@@ -201,29 +137,21 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `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
-| | **Plugins**
-| `edge.*` | Please refer to [edge configuration document](edge.md) for detailed explanation of all possible configuration options.
 | `pairlists` | Define one or more pairlists to be used. [More information](plugins.md#pairlists-and-pairlist-handlers). <br>*Defaults to `StaticPairList`.*  <br> **Datatype:** List of Dicts
 | `protections` | Define one or more protections to be used. [More information](plugins.md#protections). <br> **Datatype:** List of Dicts
-| | **Telegram**
 | `telegram.enabled` | Enable the usage of Telegram. <br> **Datatype:** Boolean
 | `telegram.token` | Your Telegram bot token. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
 | `telegram.chat_id` | Your personal Telegram account id. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
 | `telegram.balance_dust_level` | Dust-level (in stake currency) - currencies with a balance below this will not be shown by `/balance`. <br> **Datatype:** float
-| `telegram.reload` | Allow "reload" buttons on telegram messages. <br>*Defaults to `True`.<br> **Datatype:** boolean
-| `telegram.notification_settings.*` | Detailed notification settings. Refer to the [telegram documentation](telegram-usage.md) for details.<br> **Datatype:** dictionary
-| | **Webhook**
 | `webhook.enabled` | Enable usage of Webhook notifications <br> **Datatype:** Boolean
 | `webhook.url` | URL for the webhook. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `webhook.webhookentry` | Payload to send on entry. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `webhook.webhookentrycancel` | Payload to send on entry order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `webhook.webhookentryfill` | Payload to send on entry order filled. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `webhook.webhookexit` | Payload to send on exit. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `webhook.webhookexitcancel` | Payload to send on exit order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `webhook.webhookexitfill` | Payload to send on exit order filled. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhookbuy` | Payload to send on buy. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhookbuycancel` | Payload to send on buy order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhooksell` | Payload to send on sell. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhooksellcancel` | Payload to send on sell order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
 | `webhook.webhookstatus` | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| | **Rest API / FreqUI**
 | `api_server.enabled` | Enable usage of API Server. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** Boolean
 | `api_server.listen_ip_address` | Bind IP address. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** IPv4
 | `api_server.listen_port` | Bind Port. See the [API Server documentation](rest-api.md) for more details. <br>**Datatype:** Integer between 1024 and 65535
@@ -231,22 +159,21 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `api_server.username` | Username for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
 | `api_server.password` | Password for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
 | `bot_name` | Name of the bot. Passed via API to a client - can be shown to distinguish / name bots.<br> *Defaults to `freqtrade`*<br> **Datatype:** String
-| | **Other**
+| `db_url` | Declares database URL to use. NOTE: This defaults to `sqlite:///tradesv3.dryrun.sqlite` if `dry_run` is `true`, and to `sqlite:///tradesv3.sqlite` for production instances. <br> **Datatype:** String, SQLAlchemy connect string
 | `initial_state` | Defines the initial application state. If set to stopped, then the bot has to be explicitly started via `/start` RPC command. <br>*Defaults to `stopped`.* <br> **Datatype:** Enum, either `stopped` or `running`
-| `force_entry_enable` | Enables the RPC Commands to force a Trade entry. More information below. <br> **Datatype:** Boolean
+| `forcebuy_enable` | Enables the RPC Commands to force a buy. More information below. <br> **Datatype:** Boolean
 | `disable_dataframe_checks` | Disable checking the OHLCV dataframe returned from the strategy methods for correctness. Only use when intentionally changing the dataframe and understand what you are doing. [Strategy Override](#parameters-in-the-strategy).<br> *Defaults to `False`*. <br> **Datatype:** Boolean
+| `strategy` | **Required** Defines Strategy class to use. Recommended to be set via `--strategy NAME`. <br> **Datatype:** ClassName
+| `strategy_path` | Adds an additional strategy lookup path (must be a directory). <br> **Datatype:** String
 | `internals.process_throttle_secs` | Set the process throttle, or minimum loop duration for one bot iteration loop. Value in second. <br>*Defaults to `5` seconds.* <br> **Datatype:** Positive Integer
 | `internals.heartbeat_interval` | Print heartbeat message every N seconds. Set to 0 to disable heartbeat messages. <br>*Defaults to `60` seconds.* <br> **Datatype:** Positive Integer or 0
 | `internals.sd_notify` | Enables use of the sd_notify protocol to tell systemd service manager about changes in the bot state and issue keep-alive pings. See [here](installation.md#7-optional-configure-freqtrade-as-a-systemd-service) for more details. <br> **Datatype:** Boolean
-| `strategy` | **Required** Defines Strategy class to use. Recommended to be set via `--strategy NAME`. <br> **Datatype:** ClassName
-| `strategy_path` | Adds an additional strategy lookup path (must be a directory). <br> **Datatype:** String
-| `recursive_strategy_search` | Set to `true` to recursively search sub-directories inside `user_data/strategies` for a strategy. <br> **Datatype:** Boolean
-| `user_data_dir` | Directory containing user data. <br> *Defaults to `./user_data/`*. <br> **Datatype:** String
-| `db_url` | Declares database URL to use. NOTE: This defaults to `sqlite:///tradesv3.dryrun.sqlite` if `dry_run` is `true`, and to `sqlite:///tradesv3.sqlite` for production instances. <br> **Datatype:** String, SQLAlchemy connect string
 | `logfile` | Specifies logfile name. Uses a rolling strategy for log file rotation for 10 files with the 1MB limit per file. <br> **Datatype:** String
-| `add_config_files` | Additional config files. These files will be loaded and merged with the current config file. The files are resolved relative to the initial file.<br> *Defaults to `[]`*. <br> **Datatype:** List of strings
+| `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
 
@@ -266,10 +193,10 @@ Values set in the configuration file always overwrite values set in the strategy
 * `order_time_in_force`
 * `unfilledtimeout`
 * `disable_dataframe_checks`
-- `use_exit_signal`
-* `exit_profit_only`
-- `exit_profit_offset`
-- `ignore_roi_if_entry_signal`
+* `use_sell_signal`
+* `sell_profit_only`
+* `sell_profit_offset`
+* `ignore_roi_if_buy_signal`
 * `ignore_buying_expired_candle_after`
 * `position_adjustment_enable`
 * `max_entry_position_adjustment`
@@ -398,10 +325,10 @@ See the example below:
 
 ```json
 "minimal_roi": {
-    "40": 0.0,    # Exit after 40 minutes if the profit is not negative
-    "30": 0.01,   # Exit after 30 minutes if there is at least 1% profit
-    "20": 0.02,   # Exit after 20 minutes if there is at least 2% profit
-    "0":  0.04    # Exit immediately if there is at least 4% profit
+    "40": 0.0,    # Sell after 40 minutes if the profit is not negative
+    "30": 0.01,   # Sell after 30 minutes if there is at least 1% profit
+    "20": 0.02,   # Sell after 20 minutes if there is at least 2% profit
+    "0":  0.04    # Sell immediately if there is at least 4% profit
 },
 ```
 
@@ -410,14 +337,14 @@ This parameter can be set in either Strategy or Configuration file. If you use i
 `minimal_roi` value from the strategy file.
 If it is not set in either Strategy or Configuration, a default of 1000% `{"0": 10}` is used, and minimal ROI is disabled unless your trade generates 1000% profit.
 
-!!! Note "Special case to forceexit after a specific time"
-    A special case presents using `"<N>": -1` as ROI. This forces the bot to exit a trade after N Minutes, no matter if it's positive or negative, so represents a time-limited force-exit.
+!!! Note "Special case to forcesell after a specific time"
+    A special case presents using `"<N>": -1` as ROI. This forces the bot to sell a trade after N Minutes, no matter if it's positive or negative, so represents a time-limited force-sell.
 
-### Understand force_entry_enable
+### Understand forcebuy_enable
 
-The `force_entry_enable` configuration parameter enables the usage of force-enter (`/forcelong`, `/forceshort`) commands via Telegram and REST API.
+The `forcebuy_enable` configuration parameter enables the usage of forcebuy commands via Telegram and REST API.
 For security reasons, it's disabled by default, and freqtrade will show a warning message on startup if enabled.
-For example, you can send `/forceenter ETH/BTC` to the bot, which will result in freqtrade buying the pair and holds it until a regular exit-signal (ROI, stoploss, /forceexit) appears.
+For example, you can send `/forcebuy ETH/BTC` to the bot, which will result in freqtrade buying the pair and holds it until a regular sell-signal (ROI, stoploss, /forcesell) appears.
 
 This can be dangerous with some strategies, so use with care.
 
@@ -444,27 +371,29 @@ For example, if your strategy is using a 1h timeframe, and you only want to buy
 
 ### Understand order_types
 
-The `order_types` configuration parameter maps actions (`entry`, `exit`, `stoploss`, `emergency_exit`, `force_exit`, `force_entry`) to order-types (`market`, `limit`, ...) as well as configures stoploss to be on the exchange and defines stoploss on exchange update interval in seconds.
+The `order_types` configuration parameter maps actions (`buy`, `sell`, `stoploss`, `emergencysell`, `forcesell`, `forcebuy`) to order-types (`market`, `limit`, ...) as well as configures stoploss to be on the exchange and defines stoploss on exchange update interval in seconds.
 
-This allows to enter using limit orders, exit using limit-orders, and create stoplosses using market orders.
-It also allows to set the
-stoploss "on exchange" which means stoploss order would be placed immediately once the buy order is fulfilled.
+This allows to buy using limit orders, sell using
+limit-orders, and create stoplosses using market orders. It also allows to set the
+stoploss "on exchange" which means stoploss order would be placed immediately once
+the buy order is fulfilled.
     
 `order_types` set in the configuration file overwrites values set in the strategy as a whole, so you need to configure the whole `order_types` dictionary in one place.
 
-If this is configured, the following 4 values (`entry`, `exit`, `stoploss` and `stoploss_on_exchange`) need to be present, otherwise, the bot will fail to start.
+If this is configured, the following 4 values (`buy`, `sell`, `stoploss` and
+`stoploss_on_exchange`) need to be present, otherwise, the bot will fail to start.
 
-For information on (`emergency_exit`,`force_exit`, `force_entry`, `stoploss_on_exchange`,`stoploss_on_exchange_interval`,`stoploss_on_exchange_limit_ratio`) please see stop loss documentation [stop loss on exchange](stoploss.md)
+For information on (`emergencysell`,`forcesell`, `forcebuy`, `stoploss_on_exchange`,`stoploss_on_exchange_interval`,`stoploss_on_exchange_limit_ratio`) please see stop loss documentation [stop loss on exchange](stoploss.md)
 
 Syntax for Strategy:
 
 ```python
 order_types = {
-    "entry": "limit",
-    "exit": "limit",
-    "emergency_exit": "market",
-    "force_entry": "market",
-    "force_exit": "market",
+    "buy": "limit",
+    "sell": "limit",
+    "emergencysell": "market",
+    "forcebuy": "market",
+    "forcesell": "market",
     "stoploss": "market",
     "stoploss_on_exchange": False,
     "stoploss_on_exchange_interval": 60,
@@ -476,11 +405,11 @@ Configuration:
 
 ```json
 "order_types": {
-    "entry": "limit",
-    "exit": "limit",
-    "emergency_exit": "market",
-    "force_entry": "market",
-    "force_exit": "market",
+    "buy": "limit",
+    "sell": "limit",
+    "emergencysell": "market",
+    "forcebuy": "market",
+    "forcesell": "market",
     "stoploss": "market",
     "stoploss_on_exchange": false,
     "stoploss_on_exchange_interval": 60
@@ -503,7 +432,7 @@ Configuration:
     If `stoploss_on_exchange` is enabled and the stoploss is cancelled manually on the exchange, then the bot will create a new stoploss order.
 
 !!! Warning "Warning: stoploss_on_exchange failures"
-    If stoploss on exchange creation fails for some reason, then an "emergency exit" is initiated. By default, this will exit the trade using a market order. The order-type for the emergency-exit can be changed by setting the `emergency_exit` value in the `order_types` dictionary - however, this is not advised.
+    If stoploss on exchange creation fails for some reason, then an "emergency sell" is initiated. By default, this will sell the asset using a market order. The order-type for the emergency-sell can be changed by setting the `emergencysell` value in the `order_types` dictionary - however, this is not advised.
 
 ### Understand order_time_in_force
 
@@ -533,8 +462,8 @@ The possible values are: `gtc` (default), `fok` or `ioc`.
 
 ``` python
 "order_time_in_force": {
-    "entry": "gtc",
-    "exit": "gtc"
+    "buy": "gtc",
+    "sell": "gtc"
 },
 ```
 
@@ -580,10 +509,10 @@ creating trades on the exchange.
 
 ```json
 "exchange": {
-    "name": "bittrex",
-    "key": "key",
-    "secret": "secret",
-    ...
+        "name": "bittrex",
+        "key": "key",
+        "secret": "secret",
+        ...
 }
 ```
 
@@ -600,7 +529,7 @@ Once you will be happy with your bot performance running in the Dry-run mode, yo
 * Market orders fill based on orderbook volume the moment the order is placed.
 * Limit orders fill once the price reaches the defined level - or time out based on `unfilledtimeout` settings.
 * In combination with `stoploss_on_exchange`, the stop_loss price is assumed to be filled.
-* Open orders (not trades, which are stored in the database) are kept open after bot restarts, with the assumption that they were not filled while being offline.
+* Open orders (not trades, which are stored in the database) are reset on bot restart.
 
 ## Switch to production mode
 

docs/data-analysis.md

@@ -122,6 +122,5 @@ Best avoid relative paths, since this starts at the storage location of the jupy
 
 * [Strategy debugging](strategy_analysis_example.md) - also available as Jupyter notebook (`user_data/notebooks/strategy_analysis_example.ipynb`)
 * [Plotting](plotting.md)
-* [Tag Analysis](advanced-backtesting.md)
 
 Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data.

docs/data-download.md

@@ -29,8 +29,6 @@ usage: freqtrade download-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
                                [--erase]
                                [--data-format-ohlcv {json,jsongz,hdf5}]
                                [--data-format-trades {json,jsongz,hdf5}]
-                               [--trading-mode {spot,margin,futures}]
-                               [--prepend]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -61,9 +59,6 @@ optional arguments:
   --data-format-trades {json,jsongz,hdf5}
                         Storage format for downloaded trades data. (default:
                         `jsongz`).
-  --trading-mode {spot,margin,futures}
-                        Select Trading mode
-  --prepend             Allow data prepending.
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
@@ -159,21 +154,10 @@ freqtrade download-data --exchange binance --pairs .*/USDT
 - To change the exchange used to download the historical data from, please use a different configuration file (you'll probably need to adjust rate limits etc.)
 - To use `pairs.json` from some other directory, use `--pairs-file some_other_dir/pairs.json`.
 - To download historical candle (OHLCV) data for only 10 days, use `--days 10` (defaults to 30 days).
-- To download historical candle (OHLCV) data from a fixed starting point, use `--timerange 20200101-` - which will download all data from January 1st, 2020.
+- To download historical candle (OHLCV) data from a fixed starting point, use `--timerange 20200101-` - which will download all data from January 1st, 2020. Eventually set end dates are ignored.
 - Use `--timeframes` to specify what timeframe download the historical candle (OHLCV) data for. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute data.
 - To use exchange, timeframe and list of pairs as defined in your configuration file, use the `-c/--config` option. With this, the script uses the whitelist defined in the config as the list of currency pairs to download data for and does not require the pairs.json file. You can combine `-c/--config` with most other options.
 
-#### Download additional data before the current timerange
-
-Assuming you downloaded all data from 2022 (`--timerange 20220101-`) - but you'd now like to also backtest with earlier data.
-You can do so by using the `--prepend` flag, combined with `--timerange` - specifying an end-date.
-
-``` bash
-freqtrade download-data --exchange binance --pairs ETH/USDT XRP/USDT BTC/USDT --prepend --timerange 20210101-20220101
-```
-
-!!! Note
-    Freqtrade will ignore the end-date in this mode if data is available, updating the end-date to the existing data start point.
 
 ### Data format
 
@@ -209,14 +193,11 @@ usage: freqtrade convert-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
                               {json,jsongz,hdf5} --format-to
                               {json,jsongz,hdf5} [--erase]
                               [-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]]
-                              [--exchange EXCHANGE]
-                              [--trading-mode {spot,margin,futures}]
-                              [--candle-types {spot,,futures,mark,index,premiumIndex,funding_rate} [{spot,,futures,mark,index,premiumIndex,funding_rate} ...]]
 
 optional arguments:
   -h, --help            show this help message and exit
   -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
-                        Limit command to these pairs. Pairs are space-
+                        Show profits for only these pairs. Pairs are space-
                         separated.
   --format-from {json,jsongz,hdf5}
                         Source format for data conversion.
@@ -227,12 +208,6 @@ optional arguments:
   -t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]
                         Specify which tickers to download. Space-separated
                         list. Default: `1m 5m`.
-  --exchange EXCHANGE   Exchange name (default: `bittrex`). Only valid if no
-                        config is provided.
-  --trading-mode {spot,margin,futures}
-                        Select Trading mode
-  --candle-types {spot,,futures,mark,index,premiumIndex,funding_rate} [{spot,,futures,mark,index,premiumIndex,funding_rate} ...]
-                        Select candle type to use
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
@@ -249,7 +224,6 @@ Common arguments:
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
                         Path to userdata directory.
-
 ```
 
 ##### Example converting data
@@ -373,7 +347,6 @@ usage: freqtrade list-data [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
                            [--userdir PATH] [--exchange EXCHANGE]
                            [--data-format-ohlcv {json,jsongz,hdf5}]
                            [-p PAIRS [PAIRS ...]]
-                           [--trading-mode {spot,margin,futures}]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -383,10 +356,8 @@ optional arguments:
                         Storage format for downloaded candle (OHLCV) data.
                         (default: `json`).
   -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
-                        Limit command to these pairs. Pairs are space-
+                        Show profits for only these pairs. Pairs are space-
                         separated.
-  --trading-mode {spot,margin,futures}
-                        Select Trading mode
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).

docs/deprecated.md

@@ -24,10 +24,6 @@ Please refer to [pairlists](plugins.md#pairlists-and-pairlist-handlers) instead.
 Did only download the latest 500 candles, so was ineffective in getting good backtest data.
 Removed in 2019-7-dev (develop branch) and in freqtrade 2019.8.
 
-### `ticker_interval` (now `timeframe`)
-
-Support for `ticker_interval` terminology was deprecated in 2020.6 in favor of `timeframe` - and compatibility code was removed in 2022.3.
-
 ### Allow running multiple pairlists in sequence
 
 The former `"pairlist"` section in the configuration has been removed, and is replaced by `"pairlists"` - being a list to specify a sequence of pairlists.
@@ -38,7 +34,7 @@ The old section of configuration parameters (`"pairlist"`) has been deprecated i
 
 Since only quoteVolume can be compared between assets, the other options (bidVolume, askVolume) have been deprecated in 2020.4, and have been removed in 2020.9.
 
-### Using order book steps for exit price
+### Using order book steps for sell price
 
 Using `order_book_min` and `order_book_max` used to allow stepping the orderbook and trying to find the next ROI slot - trying to place sell-orders early.
 As this does however increase risk and provides no benefit, it's been removed for maintainability purposes in 2021.7.
@@ -47,30 +43,3 @@ As this does however increase risk and provides no benefit, it's been removed fo
 
 Using separate hyperopt files was deprecated in 2021.4 and was removed in 2021.9.
 Please switch to the new [Parametrized Strategies](hyperopt.md) to benefit from the new hyperopt interface.
-
-## Strategy changes between V2 and V3
-
-Isolated Futures / short trading was introduced in 2022.4. This required major changes to configuration settings, strategy interfaces, ...
-
-We have put a great effort into keeping compatibility with existing strategies, so if you just want to continue using freqtrade in spot markets, there are no changes necessary.
-While we may drop support for the current interface sometime in the future, we will announce this separately and have an appropriate transition period.
-
-Please follow the [Strategy migration](strategy_migration.md) guide to migrate your strategy to the new format to start using the new functionalities.
-
-### webhooks - changes with 2022.4
-
-#### `buy_tag` has been renamed to `enter_tag`
-
-This should apply only to your strategy and potentially to webhooks.
-We will keep a compatibility layer for 1-2 versions (so both `buy_tag` and `enter_tag` will still work), but support for this in webhooks will disappear after that.
-
-#### Naming changes
-
-Webhook terminology changed from "sell" to "exit", and from "buy" to "entry".
-
-* `webhookbuy` -> `webhookentry`
-* `webhookbuyfill` -> `webhookentryfill`
-* `webhookbuycancel` -> `webhookentrycancel`
-* `webhooksell` -> `webhookexit`
-* `webhooksellfill` -> `webhookexitfill`
-* `webhooksellcancel` -> `webhookexitcancel`

docs/developer.md

@@ -26,9 +26,6 @@ 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`.
 
-Then install the git hook scripts by running `pre-commit install`, so your changes will be verified locally before committing.
-This avoids a lot of waiting for CI already, as some basic formatting checks are done locally on your machine.
-
 Before opening a pull request, please familiarize yourself with our [Contributing Guidelines](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md).
 
 ### Devcontainer setup
@@ -68,36 +65,6 @@ def test_method_to_test(caplog):
 
 ```
 
-### Debug configuration
-
-To debug freqtrade, we recommend VSCode with the following launch configuration (located in `.vscode/launch.json`).
-Details will obviously vary between setups - but this should work to get you started.
-
-``` json
-{
-    "name": "freqtrade trade",
-    "type": "python",
-    "request": "launch",
-    "module": "freqtrade",
-    "console": "integratedTerminal",
-    "args": [
-        "trade",
-        // Optional:
-        // "--userdir", "user_data",
-        "--strategy", 
-        "MyAwesomeStrategy",
-    ]
-},
-```
-
-Command line arguments can be added in the `"args"` array.
-This method can also be used to debug a strategy, by setting the breakpoints within the strategy.
-
-A similar setup can also be taken for Pycharm - using `freqtrade` as module name, and setting the command line arguments as "parameters".
-
-!!! Note "Startup directory"
-    This assumes that you have the repository checked out, and the editor is started at the repository root level (so setup.py is at the top level of your repository).
-
 ## ErrorHandling
 
 Freqtrade Exceptions all inherit from `FreqtradeException`.
@@ -230,12 +197,11 @@ For that reason, they must implement the following methods:
 * `global_stop()`
 * `stop_per_pair()`.
 
-`global_stop()` and `stop_per_pair()` must return a ProtectionReturn object, which consists of:
+`global_stop()` and `stop_per_pair()` must return a ProtectionReturn tuple, which consists of:
 
 * lock pair - boolean
 * lock until - datetime - until when should the pair be locked (will be rounded up to the next new candle)
 * reason - string, used for logging and storage in the database
-* lock_side - long, short or '*'.
 
 The `until` portion should be calculated using the provided `calculate_lock_end()` method.
 
@@ -254,13 +220,13 @@ Protections can have 2 different ways to stop trading for a limited :
 ##### Protections - per pair
 
 Protections that implement the per pair approach must set `has_local_stop=True`.
-The method `stop_per_pair()` will be called whenever a trade closed (exit order completed).
+The method `stop_per_pair()` will be called whenever a trade closed (sell order completed).
 
 ##### Protections - global protection
 
 These Protections should do their evaluation across all pairs, and consequently will also lock all pairs from trading (called a global PairLock).
 Global protection must set `has_global_stop=True` to be evaluated for global stops.
-The method `global_stop()` will be called whenever a trade closed (exit order completed).
+The method `global_stop()` will be called whenever a trade closed (sell order completed).
 
 ##### Protections - calculating lock end time
 
@@ -298,7 +264,7 @@ Additional tests / steps to complete:
 * Check if balance shows correctly (*)
 * Create market order (*)
 * Create limit order (*)
-* Complete trade (enter + exit) (*)
+* Complete trade (buy + sell) (*)
   * Compare result calculation between exchange and bot
   * Ensure fees are applied correctly (check the database against the exchange)
 
@@ -344,32 +310,6 @@ The output will show the last entry from the Exchange as well as the current UTC
 If the day shows the same day, then the last candle can be assumed as incomplete and should be dropped (leave the setting `"ohlcv_partial_candle"` from the exchange-class untouched / True). Otherwise, set `"ohlcv_partial_candle"` to `False` to not drop Candles (shown in the example above).
 Another way is to run this command multiple times in a row and observe if the volume is changing (while the date remains the same).
 
-### Update binance cached leverage tiers
-
-Updating leveraged tiers should be done regularly - and requires an authenticated account with futures enabled.
-
-``` python
-import ccxt
-import json
-from pathlib import Path
-
-exchange = ccxt.binance({
-    'apiKey': '<apikey>',
-    'secret': '<secret>'
-    'options': {'defaultType': 'future'}
-    })
-_ = exchange.load_markets()
-
-lev_tiers = exchange.fetch_leverage_tiers()
-
-# Assumes this is running in the root of the repository.
-file = Path('freqtrade/exchange/binance_leverage_tiers.json')
-json.dump(dict(sorted(lev_tiers.items())), file.open('w'), indent=2)
-
-```
-
-This file should then be contributed upstream, so others can benefit from this, too.
-
 ## Updating example notebooks
 
 To keep the jupyter notebooks aligned with the documentation, the following should be ran after updating a example notebook.

docs/edge.md

@@ -222,7 +222,7 @@ usage: freqtrade edge [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
 
 optional arguments:
   -h, --help            show this help message and exit
-  -i TIMEFRAME, --timeframe TIMEFRAME
+  -i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
                         Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
   --timerange TIMERANGE
                         Specify what timerange of data to use.

docs/exchanges.md

@@ -1,6 +1,6 @@
 # Exchange-specific Notes
 
-This page combines common gotchas and Information which are exchange-specific and most likely don't apply to other exchanges.
+This page combines common gotchas and informations which are exchange-specific and most likely don't apply to other exchanges.
 
 ## Exchange configuration
 
@@ -57,35 +57,13 @@ This configuration enables kraken, as well as rate-limiting to avoid bans from t
 Binance supports [time_in_force](configuration.md#understand-order_time_in_force).
 
 !!! Tip "Stoploss on Exchange"
-    Binance supports `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange..
+    Binance supports `stoploss_on_exchange` and uses stop-loss-limit orders. It provides great advantages, so we recommend to benefit from it.
 
 ### Binance Blacklist
 
 For Binance, please add `"BNB/<STAKE>"` to your blacklist to avoid issues.
 Accounts having BNB accounts use this to pay for fees - if your first trade happens to be on `BNB`, further trades will consume this position and make the initial BNB trade unsellable as the expected amount is not there anymore.
 
-### Binance Futures
-
-Binance has specific (unfortunately complex) [Futures Trading Quantitative Rules](https://www.binance.com/en/support/faq/4f462ebe6ff445d4a170be7d9e897272) which need to be followed, and which prohibit a too low stake-amount (among others) for too many orders.
-Violating these rules will result in a trading restriction.
-
-When trading on Binance Futures market, orderbook must be used because there is no price ticker data for futures.
-
-``` jsonc
-  "entry_pricing": {
-      "use_order_book": true,
-      "order_book_top": 1,
-      "check_depth_of_market": {
-          "enabled": false,
-          "bids_to_ask_delta": 1
-      }
-  },
-  "exit_pricing": {
-      "use_order_book": true,
-      "order_book_top": 1
-  },
-```
-
 ### Binance sites
 
 Binance has been split into 2, and users must use the correct ccxt exchange ID for their exchange, otherwise API keys are not recognized.
@@ -199,21 +177,12 @@ Kucoin requires a passphrase for each api key, you will therefore need to add th
 
 Kucoin supports [time_in_force](configuration.md#understand-order_time_in_force).
 
-!!! Tip "Stoploss on Exchange"
-    Kucoin supports `stoploss_on_exchange` and can use both stop-loss-market and stop-loss-limit orders. It provides great advantages, so we recommend to benefit from it.
-    You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type of stoploss shall be used.
-
 ### Kucoin Blacklists
 
 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.
 
-## Huobi
-
-!!! Tip "Stoploss on Exchange"
-    Huobi supports `stoploss_on_exchange` and uses `stop-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange.
-
-## OKX (former OKEX)
+## 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:
 
@@ -230,16 +199,8 @@ OKX requires a passphrase for each api key, you will therefore need to add this
 !!! 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.
 
-!!! Warning "Futures"
-    OKX Futures has the concept of "position mode" - which can be Net or long/short (hedge mode).
-    Freqtrade supports both modes - but changing the mode mid-trading is not supported and will lead to exceptions and failures to place trades.
-    OKX also only provides MARK candles for the past ~3 months. Backtesting futures prior to that date will therefore lead to slight deviations, as funding-fees cannot be calculated correctly without this data.
-
 ## Gate.io
 
-!!! Tip "Stoploss on Exchange"
-    Gate.io supports `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange..
-
 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.
 

docs/faq.md

@@ -6,15 +6,13 @@ Freqtrade supports spot trading only.
 
 ### Can I open short positions?
 
-Freqtrade can open short positions in futures markets.
-This requires the strategy to be made for this - and `"trading_mode": "futures"` in the configuration.
-Please make sure to read the [relevant documentation page](leverage.md) first.
+No, Freqtrade does not support trading with margin / leverage, and cannot open short positions.
 
-In spot markets, you can in some cases use leveraged spot tokens, which reflect an inverted pair (eg. BTCUP/USD, BTCDOWN/USD, ETHBULL/USD, ETHBEAR/USD,...) which can be traded with Freqtrade.
+In some cases, your exchange may provide leveraged spot tokens which can be traded with Freqtrade eg. BTCUP/USD, BTCDOWN/USD, ETHBULL/USD, ETHBEAR/USD, etc...
 
 ### Can I trade options or futures?
 
-Futures trading is supported for selected exchanges.
+No, options and futures trading are not supported.
 
 ## Beginner Tips & Tricks
 
@@ -79,7 +77,7 @@ You can use "current" market data by using the [dataprovider](strategy-customiza
 
 ### Is there a setting to only SELL the coins being held and not perform anymore BUYS?
 
-You can use the `/stopbuy` command in Telegram to prevent future buys, followed by `/forceexit all` (sell all open trades).
+You can use the `/stopbuy` command in Telegram to prevent future buys, followed by `/forcesell all` (sell all open trades).
 
 ### I want to run multiple bots on the same machine
 
@@ -119,10 +117,10 @@ As the message says, your exchange does not support market orders and you have o
 
 To fix this, redefine order types in the strategy to use "limit" instead of "market":
 
-``` python
+```
     order_types = {
         ...
-        "stoploss": "limit",
+        'stoploss': 'limit',
         ...
     }
 ```

docs/freqai.md

@@ -1,769 +0,0 @@
-![freqai-logo](assets/freqai_doc_logo.svg)
-
-# FreqAI
-
-FreqAI is a module designed to automate a variety of tasks associated with training a predictive model to generate market forecasts given a set of input features.
-
-Among the the features included:
-
-* **Self-adaptive retraining**: retrain models during live deployments to self-adapt to the market in an unsupervised manner.
-* **Rapid feature engineering**: create large rich feature sets (10k+ features) based on simple user created strategies.
-* **High performance**: adaptive retraining occurs on separate thread (or on GPU if available) from inferencing and bot trade operations. Keep newest models and data in memory for rapid inferencing.
-* **Realistic backtesting**: emulate self-adaptive retraining with backtesting module that automates past retraining.
-* **Modifiable**: use the generalized and robust architecture for incorporating any machine learning library/method available in Python. Seven examples available.
-* **Smart outlier removal**: remove outliers from training and prediction sets using a variety of outlier detection techniques.
-* **Crash resilience**: model storage to disk to make reloading from a crash fast and easy (and purge obsolete files for sustained dry/live runs).
-* **Automated data normalization**: normalize the data in a smart and statistically safe way.
-* **Automatic data download**: compute the data download timerange and update historic data (in live deployments).
-* **Clean incoming data** safe NaN handling before training and prediction.
-* **Dimensionality reduction**: reduce the size of the training data via Principal Component Analysis.
-* **Deploy bot fleets**: set one bot to train models while a fleet of other bots inference into the models and handle trades.
-
-## Quick start
-
-The easiest way to quickly test FreqAI is to run it in dry run with the following command
-
-```bash
-freqtrade trade --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates
-```
-
-where the user will see the boot-up process of auto-data downloading, followed by simultaneous training and trading.
-
-The example strategy, example prediction model, and example config can all be found in
-`freqtrade/templates/FreqaiExampleStrategy.py`, `freqtrade/freqai/prediction_models/LightGBMRegressor.py`,
-`config_examples/config_freqai.example.json`, respectively.
-
-## General approach
-
-The user provides FreqAI with a set of custom *base* indicators (created inside the strategy the same way
-a typical Freqtrade strategy is created) as well as target values which look into the future.
-FreqAI trains a model to predict the target value based on the input of custom indicators for each pair in the whitelist. These models are consistently retrained to adapt to market conditions. FreqAI offers the ability to both backtest strategies (emulating reality with periodic retraining) and deploy dry/live. In dry/live conditions, FreqAI can be set to constant retraining in a background thread in an effort to keep models as young as possible.
-
-An overview of the algorithm is shown here to help users understand the data processing pipeline and the model usage. 
-
-![freqai-algo](assets/freqai_algo.png)
-
-## Background and vocabulary
-
-**Features** are the quantities with which a model is trained. $X_i$ represents the
-vector of all features for a single candle. In FreqAI, the user
-builds the features from anything they can construct in the strategy.
-
-**Labels** are the target values with which the weights inside a model are trained
-toward. Each set of features is associated with a single label, which is also
-defined within the strategy by the user. These labels intentionally look into the
-future, and are not available to the model during dryrun/live/backtesting.
-
-**Training** refers to the process of feeding individual feature sets into the
-model with associated labels with the goal of matching input feature sets to associated labels.
-
-**Train data** is a subset of the historic data which is fed to the model during
-training to adjust weights. This data directly influences weight connections in the model.
-
-**Test data** is a subset of the historic data which is used to evaluate the
-intermediate performance of the model during training. This data does not
-directly influence nodal weights within the model.
-
-## Install prerequisites
-
-The normal Freqtrade install process will ask the user if they wish to install FreqAI dependencies. The user should reply "yes" to this question if they wish to use FreqAI. If the user did not reply yes, they can manually install these dependencies after the install with:
-
-``` bash
-pip install -r requirements-freqai.txt
-```
-
-!!! Note
-    Catboost will not be installed on arm devices (raspberry, Mac M1, ARM based VPS, ...), since Catboost does not provide wheels for this platform.
-
-### Usage with docker
-
-For docker users, a dedicated tag with freqAI dependencies is available as `:freqai`.
-As such - you can replace the image line in your docker-compose file with `image: freqtradeorg/freqtrade:develop_freqai`.
-This image contains the regular freqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices.
-
-## Configuring FreqAI
-
-### Parameter table
-
-The table below will list all configuration parameters available for FreqAI.
-
-Mandatory parameters are marked as **Required**, which means that they are required to be set in one of the possible ways.
-
-|  Parameter | Description |
-|------------|-------------|
-| `freqai` | **Required.** The parent dictionary containing all the parameters below for controlling FreqAI. <br> **Datatype:** dictionary.
-| `identifier` | **Required.** A unique name for the current model. This can be reused to reload pre-trained models/data. <br> **Datatype:** string.
-| `train_period_days` | **Required.** Number of days to use for the training data (width of the sliding window). <br> **Datatype:** positive integer.
-| `backtest_period_days` | **Required.** Number of days to inference into the trained model before sliding the window and retraining. This can be fractional days, but beware that the user provided `timerange` will be divided by this number to yield the number of trainings necessary to complete the backtest. <br> **Datatype:** Float.
-| `live_retrain_hours` | Frequency of retraining during dry/live runs. Default set to 0, which means it will retrain as often as possible. <br> **Datatype:** Float > 0.
-| `follow_mode` | If true, this instance of FreqAI will look for models associated with `identifier` and load those for inferencing. A `follower` will **not** train new models. `False` by default. <br> **Datatype:** boolean.
-| `startup_candles` | Number of candles needed for *backtesting only* to ensure all indicators are non NaNs at the start of the first train period. <br> **Datatype:** positive integer.
-| `fit_live_predictions_candles` | Computes target (label) statistics from prediction data, instead of from the training data set. Number of candles is the number of historical candles it uses to generate the statistics. <br> **Datatype:** positive integer.
-| `purge_old_models` | Tell FreqAI to delete obsolete models. Otherwise, all historic models will remain on disk. Defaults to `False`. <br> **Datatype:** boolean.
-| `expiration_hours` | Ask FreqAI to avoid making predictions if a model is more than `expiration_hours` old. Defaults to 0 which means models never expire. <br> **Datatype:** positive integer.
-|  |  **Feature Parameters**
-| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples shown [here](#feature-engineering) <br> **Datatype:** dictionary.
-| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` will be created for each coin in this list, and that set of features is added to the base asset feature set. <br> **Datatype:** list of assets (strings).
-| `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for and added as features to the base asset feature set. <br> **Datatype:** list of timeframes (strings).
-| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators`, refer to `templates/FreqaiExampleStrategy.py` for detailed usage. The user can create custom labels, making use of this parameter not. <br> **Datatype:** positive integer.
-| `include_shifted_candles` | Parameter used to add a sense of temporal recency to flattened regression type input data. `include_shifted_candles` takes all features, duplicates and shifts them by the number indicated by user. <br> **Datatype:** positive integer.
-| `DI_threshold` | Activates the Dissimilarity Index for outlier detection when above 0, explained in detail [here](#removing-outliers-with-the-dissimilarity-index). <br> **Datatype:** positive float (typically below 1).
-| `weight_factor` | Used to set weights for training data points according to their recency, see details and a figure of how it works [here](#controlling-the-model-learning-process). <br> **Datatype:** positive float (typically below 1).
-| `principal_component_analysis` | Ask FreqAI to automatically reduce the dimensionality of the data set using PCA. <br> **Datatype:** boolean.
-| `use_SVM_to_remove_outliers` | Ask FreqAI to train a support vector machine to detect and remove outliers from the training data set as well as from incoming data points. <br> **Datatype:** boolean.
-| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. E.g. `nu` *Very* broadly, is the percentage of data points that should be considered outliers. `shuffle` is by default false to maintain reproducibility. But these and all others can be added/changed in this dictionary. <br> **Datatype:** dictionary.
-| `stratify_training_data` | This value is used to indicate the stratification of the data. e.g. 2 would set every 2nd data point into a separate dataset to be pulled from during training/testing. <br> **Datatype:** positive integer.
-| `indicator_max_period_candles` | The maximum *period* used in `populate_any_indicators()` for indicator creation. FreqAI uses this information in combination with the maximum timeframe to calculate how many data points it should download so that the first data point does not have a NaN <br> **Datatype:** positive integer.
-| `indicator_periods_candles` | A list of integers used to duplicate all indicators according to a set of periods and add them to the feature set. <br> **Datatype:** list of positive integers.
-| `use_DBSCAN_to_remove_outliers` | Inactive by default. If true, FreqAI clusters data using DBSCAN to identify and remove outliers from training and prediction data. <br> **Datatype:** float (fraction of 1).
-|  |  **Data split parameters**
-| `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) <br> **Datatype:** dictionary.
-| `test_size` | Fraction of data that should be used for testing instead of training. <br> **Datatype:** positive float below 1.
-| `shuffle` | Shuffle the training data points during training. Typically for time-series forecasting, this is set to False. <br> **Datatype:** boolean.
-|  |  **Model training parameters**
-| `model_training_parameters` | A flexible dictionary that includes all parameters available by the user selected library. For example, if the user uses `LightGBMRegressor`, then this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html). If the user selects a different model, then this dictionary can contain any parameter from that different model.  <br> **Datatype:** dictionary.
-| `n_estimators` | A common parameter among regressors which sets the number of boosted trees to fit <br> **Datatype:** integer.
-| `learning_rate` | A common parameter among regressors which sets the boosting learning rate. <br> **Datatype:** float.
-| `n_jobs`, `thread_count`, `task_type` | Different libraries use different parameter names to control the number of threads used for parallel processing or whether or not it is a `task_type` of `gpu` or `cpu`. <br> **Datatype:** float.
-|  |  **Extraneous parameters**
-| `keras` | If your model makes use of keras (typical of Tensorflow based prediction models), activate this flag so that the model save/loading follows keras standards. Default value `false`  <br> **Datatype:** boolean.
-| `conv_width` | The width of a convolutional neural network input tensor. This replaces the need for `shift` by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. Default value, 2 <br> **Datatype:** integer.
-
-### Important FreqAI dataframe key patterns
-
-Here are the values the user can expect to include/use inside the typical strategy dataframe (`df[]`):
-
-|  DataFrame Key | Description |
-|------------|-------------|
-| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target inside FreqAI (typically following the naming convention `&-s*`). These same dataframe columns names are fed back to the user as the predictions. For example, the user wishes to predict the price change in the next 40 candles (similar to `templates/FreqaiExampleStrategy.py`) by setting `df['&-s_close']`. FreqAI makes the predictions and gives them back to the user under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`. <br> **Datatype:** depends on the output of the model.
-| `df['&*_std/mean']` | The standard deviation and mean values of the user defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand rarity of prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` to evaluate how often a particular prediction was observed during training (or historically with `fit_live_predictions_candles`)<br> **Datatype:** float.
-| `df['do_predict']` | An indication of an outlier, this return value is integer between -1 and 2 which lets the user understand if the prediction is trustworthy or not. `do_predict==1` means the prediction is trustworthy. If the [Dissimilarity Index](#removing-outliers-with-the-dissimilarity-index) is above the user defined threshold, it will subtract 1 from `do_predict`. If `use_SVM_to_remove_outliers()` is active, then the Support Vector Machine (SVM) may also detect outliers in training and prediction data. In this case, the SVM will also subtract one from `do_predict`.  A particular case is when `do_predict == 2`, it means that the model has expired due to `expired_hours`. <br> **Datatype:** integer between -1 and 2.
-| `df['DI_values']` | The raw Dissimilarity Index values to give the user a sense of confidence in the prediction. Lower DI means the data point is closer to the trained parameter space. <br> **Datatype:** float.
-| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature inside FreqAI. For example, the user can include the rsi in the training feature set (similar to `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](#building-the-feature-set). <br>**Note**: since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table.) these features are removed from the dataframe upon return from FreqAI. If the user wishes to keep a particular type of feature for plotting purposes, you can prepend it with `%%`. <br> **Datatype:** depends on the output of the model.
-
-### Example config file
-
-The user interface is isolated to the typical config file. A typical FreqAI config setup could include:
-
-```json
-    "freqai": {
-        "startup_candles": 10000,
-        "purge_old_models": true,
-        "train_period_days": 30,
-        "backtest_period_days": 7,
-        "identifier" : "unique-id",
-        "feature_parameters" : {
-            "include_timeframes": ["5m","15m","4h"],
-            "include_corr_pairlist": [
-                "ETH/USD",
-                "LINK/USD",
-                "BNB/USD"
-            ],
-            "label_period_candles": 24,
-            "include_shifted_candles": 2,
-            "weight_factor":  0,
-            "indicator_max_period_candles": 20,
-            "indicator_periods_candles": [10, 20]
-        },
-        "data_split_parameters" : {
-            "test_size": 0.25,
-            "random_state": 42
-        },
-        "model_training_parameters" : {
-            "n_estimators": 100,
-            "random_state": 42,
-            "learning_rate": 0.02,
-            "task_type": "CPU",
-        },
-    }
-```
-
-### Feature engineering
-
-Features are added by the user inside the `populate_any_indicators()` method of the strategy
-by prepending indicators with `%` and labels are added by prepending `&`.  
-There are some important components/structures that the user *must* include when building their feature set.
-Another structure to consider is the  location of the labels at the bottom of the example function (below `if set_generalized_indicators:`).
-This is where the user will add single features and labels to their feature set to avoid duplication from
-various configuration parameters which multiply the feature set such as `include_timeframes`.
-
-```python
-    def populate_any_indicators(
-        self, pair, df, tf, informative=None, set_generalized_indicators=False
-    ):
-        """
-        Function designed to automatically generate, name and merge features
-        from user indicated timeframes in the configuration file. User controls the indicators
-        passed to the training/prediction by prepending indicators with `'%-' + coin `
-        (see convention below). I.e. user should not prepend any supporting metrics
-        (e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
-        model.
-        :param pair: pair to be used as informative
-        :param df: strategy dataframe which will receive merges from informatives
-        :param tf: timeframe of the dataframe which will modify the feature names
-        :param informative: the dataframe associated with the informative pair
-        :param coin: the name of the coin which will modify the feature names.
-        """
-
-        coint = pair.split('/')[0]
-
-        if informative is None:
-            informative = self.dp.get_pair_dataframe(pair, tf)
-
-        # first loop is automatically duplicating indicators for time periods
-        for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
-            t = int(t)
-            informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
-            informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
-            informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
-
-            bollinger = qtpylib.bollinger_bands(
-                qtpylib.typical_price(informative), window=t, stds=2.2
-            )
-            informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"]
-            informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"]
-            informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"]
-
-            informative[f"%-{coin}bb_width-period_{t}"] = (
-                informative[f"{coin}bb_upperband-period_{t}"]
-                - informative[f"{coin}bb_lowerband-period_{t}"]
-            ) / informative[f"{coin}bb_middleband-period_{t}"]
-            informative[f"%-{coin}close-bb_lower-period_{t}"] = (
-                informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"]
-            )
-
-            informative[f"%-{coin}relative_volume-period_{t}"] = (
-                informative["volume"] / informative["volume"].rolling(t).mean()
-            )
-
-        indicators = [col for col in informative if col.startswith("%")]
-        # This loop duplicates and shifts all indicators to add a sense of recency to data
-        for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
-            if n == 0:
-                continue
-            informative_shift = informative[indicators].shift(n)
-            informative_shift = informative_shift.add_suffix("_shift-" + str(n))
-            informative = pd.concat((informative, informative_shift), axis=1)
-
-        df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
-        skip_columns = [
-            (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
-        ]
-        df = df.drop(columns=skip_columns)
-
-        # Add generalized indicators here (because in live, it will call this
-        # function to populate indicators during training). Notice how we ensure not to
-        # add them multiple times
-        if set_generalized_indicators:
-            df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
-            df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
-
-            # user adds targets here by prepending them with &- (see convention below)
-            # If user wishes to use multiple targets, a multioutput prediction model
-            # needs to be used such as templates/CatboostPredictionMultiModel.py
-            df["&-s_close"] = (
-                df["close"]
-                .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
-                .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
-                .mean()
-                / df["close"]
-                - 1
-            )
-
-        return df
-```
-
-The user of the present example does not wish to pass the `bb_lowerband` as a feature to the model,
-and has therefore not prepended it with `%`. The user does, however, wish to pass `bb_width` to the
-model for training/prediction and has therefore prepended it with `%`.
-
-The `include_timeframes` from the example config above are the timeframes (`tf`) of each call to `populate_any_indicators()`
-included metric for inclusion in the feature set. In the present case, the user is asking for the
-`5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set.
-
-In addition, the user can ask for each of these features to be included from
-informative pairs using the `include_corr_pairlist`. This means that the present feature
-set will include all the features from `populate_any_indicators` on all the `include_timeframes` for each of
-`ETH/USD`, `LINK/USD`, and `BNB/USD`.
-
-`include_shifted_candles` is another user controlled parameter which indicates the number of previous
-candles to include in the present feature set. In other words, `include_shifted_candles: 2`, tells
-FreqAI to include the the past 2 candles for each of the features included in the dataset.
-
-In total, the number of features the present user has created is:
-
-length of `include_timeframes` * no. features in `populate_any_indicators()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles`  
-$3 * 3 * 3 * 2 * 2 = 108$.
-
-!!! Note
-    Features **must** be defined in `populate_any_indicators()`. Making features in `populate_indicators()`
-    will fail in live/dry mode. If the user wishes to add generalized features that are not associated with 
-    a specific pair or timeframe, they should use the following structure inside `populate_any_indicators()`
-    (as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`:
-
-    ```python
-        def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False):
-
-            ...
-
-            # Add generalized indicators here (because in live, it will call only this function to populate 
-            # indicators for retraining). Notice how we ensure not to add them multiple times by associating
-            # these generalized indicators to the basepair/timeframe
-            if set_generalized_indicators:
-                df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7
-                df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25
-
-                # user adds targets here by prepending them with &- (see convention below)
-                # If user wishes to use multiple targets, a multioutput prediction model
-                # needs to be used such as templates/CatboostPredictionMultiModel.py
-                df["&-s_close"] = (
-                    df["close"]
-                    .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
-                    .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
-                    .mean()
-                    / df["close"]
-                    - 1
-                    )
-    ```
-
-    (Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`)
-
-### Deciding the sliding training window and backtesting duration
-
-Users define the backtesting timerange with the typical `--timerange` parameter in the user
-configuration file. `train_period_days` is the duration of the sliding training window, while
-`backtest_period_days` is the sliding backtesting window, both in number of days (`backtest_period_days` can be
-a float to indicate sub daily retraining in live/dry mode). In the present example,
-the user is asking FreqAI to use a training period of 30 days and backtest the subsequent 7 days.
-This means that if the user sets `--timerange 20210501-20210701`,
-FreqAI will train 8 separate models (because the full range comprises 8 weeks),
-and then backtest the subsequent week associated with each of the 8 training
-data set timerange months. Users can think of this as a "sliding window" which
-emulates FreqAI retraining itself once per week in live using the previous
-month of data.
-
-In live, the required training data is automatically computed and downloaded. However, in backtesting
-the user must manually enter the required number of `startup_candles` in the config. This value
-is used to increase the available data to FreqAI and should be sufficient to enable all indicators
-to be NaN free at the beginning of the first training timerange. This boils down to identifying the
-highest timeframe (`4h` in present example)  and the longest indicator period (25 in present example)
-and adding this to the `train_period_days`. The units need to be in the base candle time frame:
-
-`startup_candles` = ( 4 hours * 25 max period * 60 minutes/hour + 30 day train_period_days * 1440 minutes per day ) / 5 min (base time frame) = 1488.
-
-!!! Note
-    In dry/live, this is all precomputed and handled automatically. Thus, `startup_candle` has no influence on dry/live.
-
-!!! Note
-    Although fractional `backtest_period_days` is allowed, the user should be ware that the `--timerange` is divided by this value to determine the number of models that FreqAI will need to train in order to backtest the full range. For example, if the user wants to set a `--timerange` of 10 days, and asks for a `backtest_period_days` of 0.1, FreqAI will need to train 100 models per pair to complete the full backtest. This is why it is physically impossible to truly backtest FreqAI adaptive training. The best way to fully test a model is to run it dry and let it constantly train. In this case, backtesting would take the exact same amount of time as a dry run.
-
-## Running FreqAI
-
-### Backtesting
-
-The FreqAI backtesting module can be executed with the following command:
-
-```bash
-freqtrade backtesting --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor --timerange 20210501-20210701
-```
-
-Backtesting mode requires the user to have the data pre-downloaded (unlike dry/live, where FreqAI automatically downloads the necessary data). The user should be careful to consider that the range of the downloaded data is more than the backtesting range. This is because FreqAI needs data prior to the desired backtesting range in order to train a model to be ready to make predictions on the first candle of the user set backtesting range. More details on how to calculate the data download timerange can be found [here](#deciding-the-sliding-training-window-and-backtesting-duration).
-
-If this command has never been executed with the existing config file, then it will train a new model
-for each pair, for each backtesting window within the bigger `--timerange`.
-
-!!! Note "Model reuse"
-    Once the training is completed, the user can execute this again with the same config file and
-    FreqAI will find the trained models and load them instead of spending time training. This is useful
-    if the user wants to tweak (or even hyperopt) buy and sell criteria inside the strategy. IF the user
-    *wants* to retrain a new model with the same config file, then he/she should simply change the `identifier`.
-    This way, the user can return to using any model they wish by simply changing the `identifier`.
-
----
-
-### Building a freqai strategy
-
-The FreqAI strategy requires the user to include the following lines of code in the strategy:
-
-```python
-
-    def informative_pairs(self):
-        whitelist_pairs = self.dp.current_whitelist()
-        corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
-        informative_pairs = []
-        for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
-            for pair in whitelist_pairs:
-                informative_pairs.append((pair, tf))
-            for pair in corr_pairs:
-                if pair in whitelist_pairs:
-                    continue  # avoid duplication
-                informative_pairs.append((pair, tf))
-        return informative_pairs
-
-    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-
-        # All indicators must be populated by populate_any_indicators() for live functionality
-        # to work correctly.
-
-        # the model will return all labels created by user in `populate_any_indicators` 
-        # (& appended targets), an indication of whether or not the prediction should be accepted, 
-        # the target mean/std values for each of the labels created by user in 
-        # `populate_any_indicators()` for each training period.
-
-        dataframe = self.freqai.start(dataframe, metadata, self)
-
-        return dataframe
-
-    def populate_any_indicators(
-        self, pair, df, tf, informative=None, set_generalized_indicators=False
-    ):
-        """
-        Function designed to automatically generate, name and merge features
-        from user indicated timeframes in the configuration file. User controls the indicators
-        passed to the training/prediction by prepending indicators with `'%-' + coin `
-        (see convention below). I.e. user should not prepend any supporting metrics
-        (e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
-        model.
-        :param pair: pair to be used as informative
-        :param df: strategy dataframe which will receive merges from informatives
-        :param tf: timeframe of the dataframe which will modify the feature names
-        :param informative: the dataframe associated with the informative pair
-        :param coin: the name of the coin which will modify the feature names.
-        """
-
-        coin = pair.split('/')[0]
-
-        if informative is None:
-            informative = self.dp.get_pair_dataframe(pair, tf)
-
-        # first loop is automatically duplicating indicators for time periods
-        for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
-            t = int(t)
-            informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
-            informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
-            informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
-
-        indicators = [col for col in informative if col.startswith("%")]
-        # This loop duplicates and shifts all indicators to add a sense of recency to data
-        for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
-            if n == 0:
-                continue
-            informative_shift = informative[indicators].shift(n)
-            informative_shift = informative_shift.add_suffix("_shift-" + str(n))
-            informative = pd.concat((informative, informative_shift), axis=1)
-
-        df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
-        skip_columns = [
-            (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
-        ]
-        df = df.drop(columns=skip_columns)
-
-        # Add generalized indicators here (because in live, it will call this
-        # function to populate indicators during training). Notice how we ensure not to
-        # add them multiple times
-        if set_generalized_indicators:
-
-            # user adds targets here by prepending them with &- (see convention below)
-            # If user wishes to use multiple targets, a multioutput prediction model
-            # needs to be used such as templates/CatboostPredictionMultiModel.py
-            df["&-s_close"] = (
-                df["close"]
-                .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
-                .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
-                .mean()
-                / df["close"]
-                - 1
-            )
-
-        return df
-
-
-```
-
-Notice how the `populate_any_indicators()` is where the user adds their own features and labels ([more information](#feature-engineering)). See a full example at `templates/FreqaiExampleStrategy.py`.
-
-### Setting classifier targets
-
-FreqAI includes a the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. Typically, the user would set the targets using strings:
-
-```python
-df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')
-```
-
-### Running the model live
-
-FreqAI can be run dry/live using the following command
-
-```bash
-freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor
-```
-
-By default, FreqAI will not find any existing models and will start by training a new one
-given the user configuration settings. Following training, it will use that model to make predictions on incoming candles until a new model is available. New models are typically generated as often as possible, with FreqAI managing an internal queue of the pairs to try and keep all models equally "young." FreqAI will always use the newest trained model to make predictions on incoming live data. If users do not want FreqAI to retrain new models as often as possible, they can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before retraining a new model. Additionally, users can set `expired_hours` to tell FreqAI to avoid making predictions on models aged over this number of hours.
-
-If the user wishes to start dry/live from a backtested saved model, the user only needs to reuse
-the same `identifier` parameter
-
-```json
-    "freqai": {
-        "identifier": "example",
-        "live_retrain_hours": 1
-    }
-```
-
-In this case, although FreqAI will initiate with a
-pre-trained model, it will still check to see how much time has elapsed since the model was trained,
-and if a full `live_retrain_hours` has elapsed since the end of the loaded model, FreqAI will self retrain.
-
-## Data analysis techniques
-
-### Controlling the model learning process
-
-Model training parameters are unique to the ML library used by the user. FreqAI allows users to set any parameter for any library using the `model_training_parameters` dictionary in the user configuration file. The example configuration files show some of the example parameters associated with `Catboost` and `LightGBM`, but users can add any parameters available in those libraries.
-
-Data split parameters are defined in `data_split_parameters` which can be any parameters associated with `Sklearn`'s `train_test_split()` function. FreqAI includes some additional parameters such `weight_factor` which allows the user to weight more recent data more strongly
-than past data via an exponential function:
-
-$$ W_i = \exp(\frac{-i}{\alpha*n}) $$
-
-where $W_i$ is the weight of data point $i$ in a total set of $n$ data points.
-
-![weight-factor](assets/weights_factor.png)
-
-`train_test_split()` has a parameters called `shuffle`, which users also have access to in FreqAI, that allows them to keep the data unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data.
-
-Finally, `label_period_candles` defines the offset used for the `labels`. In the present example,
-the user is asking for `labels` that are 24 candles in the future.
-
-### Removing outliers with the Dissimilarity Index
-
-The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each
-prediction by the model. To do so, FreqAI measures the distance between each training
-data point and all other training data points:
-
-$$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$
-
-where $d_{ab}$ is the distance between the normalized points $a$ and $b$. $p$
-is the number of features i.e. the length of the vector $X$. 
-The characteristic distance, $\overline{d}$ for a set of training data points is simply the mean
-of the average distances:
-
-$$ \overline{d} = \sum_{a=1}^n(\sum_{b=1}^n(d_{ab}/n)/n) $$
-
-$\overline{d}$ quantifies the spread of the training data, which is compared to
-the distance between the new prediction feature vectors, $X_k$ and all the training
-data:
-
-$$ d_k = \arg \min d_{k,i} $$
-
-which enables the estimation of a Dissimilarity Index:
-
-$$ DI_k = d_k/\overline{d} $$
-
-Equity and crypto markets suffer from a high level of non-patterned noise in the
-form of outlier data points. The dissimilarity index allows predictions which
-are outliers and not existent in the model feature space, to be thrown out due
-to low levels of certainty. Activating the Dissimilarity Index can be achieved with:
-
-```json
-    "freqai": {
-        "feature_parameters" : {
-            "DI_threshold": 1
-        }
-    }
-```
-
-The user can tweak the DI with `DI_threshold` to increase or decrease the extrapolation of the trained model.
-
-### Reducing data dimensionality with Principal Component Analysis
-
-Users can reduce the dimensionality of their features by activating the `principal_component_analysis`:
-
-```json
-    "freqai": {
-        "feature_parameters" : {
-            "principal_component_analysis": true
-        }
-    }
-```
-
-Which will perform PCA on the features and reduce the dimensionality of the data so that the explained
-variance of the data set is >= 0.999.
-
-### Removing outliers using a Support Vector Machine (SVM)
-
-The user can tell FreqAI to remove outlier data points from the training/test data sets by setting:
-
-```json
-    "freqai": {
-        "feature_parameters" : {
-            "use_SVM_to_remove_outliers": true
-        }
-    }
-```
-
-FreqAI will train an SVM on the training data (or components if the user activated
-`principal_component_analysis`) and remove any data point that it deems to be sitting beyond the feature space.
-
-### Clustering the training data and removing outliers with DBSCAN
-
-The user can configure FreqAI to use DBSCAN to cluster training data and remove outliers from the training data set. The user activates `use_DBSCAN_to_remove_outliers` to cluster training data for identification of outliers. Also used to detect incoming outliers for prediction data points.
-
-```json
-    "freqai": {
-        "feature_parameters" : {
-            "use_DBSCAN_to_remove_outliers": true
-        }
-    }
-```
-
-### Stratifying the data
-
-The user can stratify the training/testing data using:
-
-```json
-    "freqai": {
-        "feature_parameters" : {
-            "stratify_training_data": 3
-        }
-    }
-```
-
-which will split the data chronologically so that every Xth data points is a testing data point. In the
-present example, the user is asking for every third data point in the dataframe to be used for
-testing, the other points are used for training.
-
-## Setting up a follower
-
-The user can define:
-
-```json
-    "freqai": {
-        "follow_mode": true,
-        "identifier": "example"
-    }
-```
-
-to indicate to the bot that it should not train models, but instead should look for models trained
-by a leader with the same `identifier`. In this example, the user has a leader bot with the
-`identifier: "example"` already running or launching simultaneously as the present follower.
-The follower will load models created by the leader and inference them to obtain predictions.
-
-## Purging old model data
-
-FreqAI stores new model files each time it retrains. These files become obsolete as new models
-are trained and FreqAI adapts to the new market conditions. Users planning to leave FreqAI running
-for extended periods of time with high frequency retraining should set `purge_old_models` in their
-config:
-
-```json
-    "freqai": {
-        "purge_old_models": true,
-    }
-```
-
-which will automatically purge all models older than the two most recently trained ones.
-
-## Defining model expirations
-
-During dry/live, FreqAI trains each pair sequentially (on separate threads/GPU from the main
-Freqtrade bot). This means there is always an age discrepancy between models. If a user is training
-on 50 pairs, and each pair requires 5 minutes to train, the oldest model will be over 4 hours old. 
-This may be undesirable if the characteristic time scale (read trade duration target) for a strategy 
-is much less than 4 hours. The user can decide to only make trade entries if the model is less than 
-a certain number of hours in age by setting the `expiration_hours` in the config file:
-
-```json
-    "freqai": {
-        "expiration_hours": 0.5,
-    }
-```
-
-In the present example, the user will only allow predictions on models that are less than 1/2 hours
-old.
-
-## Choosing the calculation of the `target_roi`
-
-As shown in `templates/FreqaiExampleStrategy.py`, the `target_roi` is based on two metrics computed
-by FreqAI: `label_mean` and `label_std`. These are the statistics associated with the labels used
-*during the most recent training*.  
-This allows the model to know what magnitude of a target to be expecting since it is directly stemming from the training data.
-By default, FreqAI computes this based on training data and it assumes the labels are Gaussian distributed.
-These are big assumptions that the user should consider when creating their labels. If the user wants to consider the population
-of *historical predictions* for creating the dynamic target instead of the trained labels, the user
-can do so by setting `fit_live_prediction_candles` to the number of historical prediction candles
-the user wishes to use to generate target statistics.
-
-```json
-    "freqai": {
-        "fit_live_prediction_candles": 300,
-    }
-```
-
-If the user sets this value, FreqAI will initially use the predictions from the training data set
-and then subsequently begin introducing real prediction data as it is generated. FreqAI will save
-this historical data to be reloaded if the user stops and restarts with the same `identifier`.
-
-## Extra returns per train
-
-Users may find that there are some important metrics that they'd like to return to the strategy at the end of each retrain.
-Users can include these metrics by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside their custom prediction
-model class. FreqAI takes the `my_new_value` assigned in this dictionary and expands it to fit the return dataframe to the strategy.
-The user can then use the value in the strategy with `dataframe['my_new_value']`. An example of how this is already used in FreqAI is
-the `&*_mean` and `&*_std` values, which indicate the mean and standard deviation of that particular label during the most recent training.
-Another example is shown below if the user wants to use live metrics from the trade database.
-
-The user needs to set the standard dictionary in the config so FreqAI can return proper dataframe shapes:
-
-```json
-    "freqai": {
-        "extra_returns_per_train": {"total_profit": 4}
-    }
-```
-
-These values will likely be overridden by the user prediction model, but in the case where the user model has yet to set them, or needs
-a default initial value - this is the value that will be returned.
-
-## Building an IFreqaiModel
-
-FreqAI has multiple example prediction model based libraries such as `Catboost` regression (`freqai/prediction_models/CatboostRegressor.py`) and `LightGBM` regression.
-However, users can customize and create their own prediction models using the `IFreqaiModel` class.
-Users are encouraged to inherit `train()` and `predict()` to let them customize various aspects of their training procedures.
-
-## Additional information
-
-### Common pitfalls
-
-FreqAI cannot be combined with `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically).
-This is for performance reasons - FreqAI relies on making quick predictions/retrains. To do this effectively,
-it needs to download all the training data at the beginning of a dry/live instance. FreqAI stores and appends
-new candles automatically for future retrains. But this means that if new pairs arrive later in the dry run due
-to a volume pairlist, it will not have the data ready. FreqAI does work, however, with the `ShufflePairlist`.
-
-### Feature normalization
-
-The feature set created by the user is automatically normalized to the training data only.
-This includes all test data and unseen prediction data (dry/live/backtest).
-
-### File structure
-
-`user_data_dir/models/` contains all the data associated with the trainings and backtests.
-This file structure is heavily controlled and read by the `FreqaiDataKitchen()`
-and should therefore not be modified.
-
-## Credits
-
-FreqAI was developed by a group of individuals who all contributed specific skillsets to the project.
-
-Conception and software development:
-Robert Caulk @robcaulk
-
-Theoretical brainstorming:
-Elin Törnquist @thorntwig
-
-Code review, software architecture brainstorming:
-@xmatthias
-
-Beta testing and bug reporting:
-@bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm
-Juha Nykänen @suikula, Wagner Costa @wagnercosta

docs/hyperopt.md

@@ -40,15 +40,13 @@ pip install -r requirements-hyperopt.txt
 ```
 usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
                           [--userdir PATH] [-s NAME] [--strategy-path PATH]
-                          [--recursive-strategy-search] [-i TIMEFRAME]
-                          [--timerange TIMERANGE]
+                          [-i TIMEFRAME] [--timerange TIMERANGE]
                           [--data-format-ohlcv {json,jsongz,hdf5}]
                           [--max-open-trades INT]
                           [--stake-amount STAKE_AMOUNT] [--fee FLOAT]
                           [-p PAIRS [PAIRS ...]] [--hyperopt-path PATH]
                           [--eps] [--dmmp] [--enable-protections]
-                          [--dry-run-wallet DRY_RUN_WALLET]
-                          [--timeframe-detail TIMEFRAME_DETAIL] [-e INT]
+                          [--dry-run-wallet DRY_RUN_WALLET] [-e INT]
                           [--spaces {all,buy,sell,roi,stoploss,trailing,protection,default} [{all,buy,sell,roi,stoploss,trailing,protection,default} ...]]
                           [--print-all] [--no-color] [--print-json] [-j JOBS]
                           [--random-state INT] [--min-trades INT]
@@ -57,7 +55,7 @@ usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
 
 optional arguments:
   -h, --help            show this help message and exit
-  -i TIMEFRAME, --timeframe TIMEFRAME
+  -i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
                         Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
   --timerange TIMERANGE
                         Specify what timerange of data to use.
@@ -91,9 +89,6 @@ optional arguments:
   --dry-run-wallet DRY_RUN_WALLET, --starting-balance DRY_RUN_WALLET
                         Starting balance, used for backtesting / hyperopt and
                         dry-runs.
-  --timeframe-detail TIMEFRAME_DETAIL
-                        Specify detail timeframe for backtesting (`1m`, `5m`,
-                        `30m`, `1h`, `1d`).
   -e INT, --epochs INT  Specify number of epochs (default: 100).
   --spaces {all,buy,sell,roi,stoploss,trailing,protection,default} [{all,buy,sell,roi,stoploss,trailing,protection,default} ...]
                         Specify which parameters to hyperopt. Space-separated
@@ -121,9 +116,7 @@ optional arguments:
                         ShortTradeDurHyperOptLoss, OnlyProfitHyperOptLoss,
                         SharpeHyperOptLoss, SharpeHyperOptLossDaily,
                         SortinoHyperOptLoss, SortinoHyperOptLossDaily,
-                        CalmarHyperOptLoss, MaxDrawDownHyperOptLoss,
-                        MaxDrawDownRelativeHyperOptLoss,
-                        ProfitDrawDownHyperOptLoss
+                        CalmarHyperOptLoss, MaxDrawDownHyperOptLoss, ProfitDrawDownHyperOptLoss
   --disable-param-export
                         Disable automatic hyperopt parameter export.
   --ignore-missing-spaces, --ignore-unparameterized-spaces
@@ -151,9 +144,7 @@ Strategy arguments:
                         Specify strategy class name which will be used by the
                         bot.
   --strategy-path PATH  Specify additional strategy lookup path.
-  --recursive-strategy-search
-                        Recursively search for a strategy in the strategies
-                        folder.
+
 ```
 
 ### Hyperopt checklist
@@ -162,8 +153,8 @@ Checklist on all tasks / possibilities in hyperopt
 
 Depending on the space you want to optimize, only some of the below are required:
 
-* define parameters with `space='buy'` - for entry signal optimization
-* define parameters with `space='sell'` - for exit signal optimization
+* define parameters with `space='buy'` - for buy signal optimization
+* define parameters with `space='sell'` - for sell signal optimization
 
 !!! Note
     `populate_indicators` needs to create all indicators any of the spaces may use, otherwise hyperopt will not work.
@@ -189,7 +180,7 @@ Hyperopt will first load your data into memory and will then run `populate_indic
 
 Hyperopt will then spawn into different processes (number of processors, or `-j <n>`), and run backtesting over and over again, changing the parameters that are part of the `--spaces` defined.
 
-For every new set of parameters, freqtrade will run first `populate_entry_trend()` followed by `populate_exit_trend()`, and then run the regular backtesting process to simulate trades.
+For every new set of parameters, freqtrade will run first `populate_buy_trend()` followed by `populate_sell_trend()`, and then run the regular backtesting process to simulate trades.
 
 After backtesting, the results are passed into the [loss function](#loss-functions), which will evaluate if this result was better or worse than previous results.  
 Based on the loss function result, hyperopt will determine the next set of parameters to try in the next round of backtesting.
@@ -199,7 +190,7 @@ Based on the loss function result, hyperopt will determine the next set of param
 There are two places you need to change in your strategy file to add a new buy hyperopt for testing:
 
 * Define the parameters at the class level hyperopt shall be optimizing.
-* Within `populate_entry_trend()` - use defined parameter values instead of raw constants.
+* Within `populate_buy_trend()` - use defined parameter values instead of raw constants.
 
 There you have two different types of indicators: 1. `guards` and 2. `triggers`.
 
@@ -209,24 +200,24 @@ There you have two different types of indicators: 1. `guards` and 2. `triggers`.
 !!! Hint "Guards and Triggers"
     Technically, there is no difference between Guards and Triggers.  
     However, this guide will make this distinction to make it clear that signals should not be "sticking".
-    Sticking signals are signals that are active for multiple candles. This can lead into entering a signal late (right before the signal disappears - which means that the chance of success is a lot lower than right at the beginning).
+    Sticking signals are signals that are active for multiple candles. This can lead into buying a signal late (right before the signal disappears - which means that the chance of success is a lot lower than right at the beginning).
 
 Hyper-optimization will, for each epoch round, pick one trigger and possibly multiple guards.
 
-#### Exit signal optimization
+#### Sell optimization
 
-Similar to the entry-signal above, exit-signals can also be optimized.
+Similar to the buy-signal above, sell-signals can also be optimized.
 Place the corresponding settings into the following methods
 
 * Define the parameters at the class level hyperopt shall be optimizing, either naming them `sell_*`, or by explicitly defining `space='sell'`.
-* Within `populate_exit_trend()` - use defined parameter values instead of raw constants.
+* Within `populate_sell_trend()` - use defined parameter values instead of raw constants.
 
 The configuration and rules are the same than for buy signals.
 
 ## Solving a Mystery
 
-Let's say you are curious: should you use MACD crossings or lower Bollinger Bands to trigger your long entries.
-And you also wonder should you use RSI or ADX to help with those decisions.
+Let's say you are curious: should you use MACD crossings or lower Bollinger Bands to trigger your buys. 
+And you also wonder should you use RSI or ADX to help with those buy decisions. 
 If you decide to use RSI or ADX, which values should I use for them? 
 
 So let's use hyperparameter optimization to solve this mystery.
@@ -279,12 +270,11 @@ The last one we call `trigger` and use it to decide which buy trigger we want to
 !!! Note "Parameter space assignment"
     Parameters must either be assigned to a variable named `buy_*` or `sell_*` - or contain `space='buy'` | `space='sell'` to be assigned to a space correctly.
     If no parameter is available for a space, you'll receive the error that no space was found when running hyperopt.
-    Parameters with unclear space (e.g. `adx_period = IntParameter(4, 24, default=14)` - no explicit nor implicit space) will not be detected and will therefore be ignored.
 
 So let's write the buy strategy using these values:
 
 ```python
-    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         conditions = []
         # GUARDS AND TRENDS
         if self.buy_adx_enabled.value:
@@ -306,12 +296,12 @@ So let's write the buy strategy using these values:
         if conditions:
             dataframe.loc[
                 reduce(lambda x, y: x & y, conditions),
-                'enter_long'] = 1
+                'buy'] = 1
 
         return dataframe
 ```
 
-Hyperopt will now call `populate_entry_trend()` many times (`epochs`) with different value combinations.  
+Hyperopt will now call `populate_buy_trend()` many times (`epochs`) with different value combinations.  
 It will use the given historical data and simulate buys based on the buy signals generated with the above function.  
 Based on the results, hyperopt will tell you which parameter combination produced the best results (based on the configured [loss function](#loss-functions)).
 
@@ -342,7 +332,6 @@ There are four parameter types each suited for different purposes.
 ## Optimizing an indicator parameter
 
 Assuming you have a simple strategy in mind - a EMA cross strategy (2 Moving averages crossing) - and you'd like to find the ideal parameters for this strategy.
-By default, we assume a stoploss of 5% - and a take-profit (`minimal_roi`) of 10% - which means freqtrade will sell the trade once 10% profit has been reached.
 
 ``` python
 from pandas import DataFrame
@@ -357,9 +346,6 @@ import freqtrade.vendor.qtpylib.indicators as qtpylib
 class MyAwesomeStrategy(IStrategy):
     stoploss = -0.05
     timeframe = '15m'
-    minimal_roi = {
-        "0":  0.10
-    },
     # Define the parameter spaces
     buy_ema_short = IntParameter(3, 50, default=5)
     buy_ema_long = IntParameter(15, 200, default=50)
@@ -378,7 +364,7 @@ class MyAwesomeStrategy(IStrategy):
         
         return dataframe
 
-    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         conditions = []
         conditions.append(qtpylib.crossed_above(
                 dataframe[f'ema_short_{self.buy_ema_short.value}'], dataframe[f'ema_long_{self.buy_ema_long.value}']
@@ -390,11 +376,11 @@ class MyAwesomeStrategy(IStrategy):
         if conditions:
             dataframe.loc[
                 reduce(lambda x, y: x & y, conditions),
-                'enter_long'] = 1
+                'buy'] = 1
         return dataframe
 
-    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-          conditions = []
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        conditions = []
         conditions.append(qtpylib.crossed_above(
                 dataframe[f'ema_long_{self.buy_ema_long.value}'], dataframe[f'ema_short_{self.buy_ema_short.value}']
             ))
@@ -405,7 +391,7 @@ class MyAwesomeStrategy(IStrategy):
         if conditions:
             dataframe.loc[
                 reduce(lambda x, y: x & y, conditions),
-                'exit_long'] = 1
+                'sell'] = 1
         return dataframe
 ```
 
@@ -415,7 +401,7 @@ Using `self.buy_ema_short.range` will return a range object containing all entri
 In this case (`IntParameter(3, 50, default=5)`), the loop would run for all numbers between 3 and 50 (`[3, 4, 5, ... 49, 50]`).
 By using this in a loop, hyperopt will generate 48 new columns (`['buy_ema_3', 'buy_ema_4', ... , 'buy_ema_50']`).
 
-Hyperopt itself will then use the selected value to create the buy and sell signals.
+Hyperopt itself will then use the selected value to create the buy and sell signals
 
 While this strategy is most likely too simple to provide consistent profit, it should serve as an example how optimize indicator parameters.
 
@@ -577,8 +563,7 @@ Currently, the following loss functions are builtin:
 * `SharpeHyperOptLossDaily` - optimizes Sharpe Ratio calculated on **daily** trade returns relative to standard deviation.
 * `SortinoHyperOptLoss` - optimizes Sortino Ratio calculated on trade returns relative to **downside** standard deviation.
 * `SortinoHyperOptLossDaily` - optimizes Sortino Ratio calculated on **daily** trade returns relative to **downside** standard deviation.
-* `MaxDrawDownHyperOptLoss` - Optimizes Maximum absolute drawdown.
-* `MaxDrawDownRelativeHyperOptLoss` -  Optimizes both maximum absolute drawdown while also adjusting for maximum relative drawdown.
+* `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.
 
@@ -692,7 +677,7 @@ class MyAwesomeStrategy(IStrategy):
 
 !!! Note
     Values in the configuration file will overwrite Parameter-file level parameters - and both will overwrite parameters within the strategy.
-    The prevalence is therefore: config > parameter file > strategy `*_params` > parameter default
+    The prevalence is therefore: config > parameter file > strategy
 
 ### Understand Hyperopt ROI results
 
@@ -874,28 +859,10 @@ You can also enable position stacking in the configuration file by explicitly se
 As hyperopt consumes a lot of memory (the complete data needs to be in memory once per parallel backtesting process), it's likely that you run into "out of memory" errors.
 To combat these, you have multiple options:
 
-* Reduce the amount of pairs.
-* Reduce the timerange used (`--timerange <timerange>`).
-* Avoid using `--timeframe-detail` (this loads a lot of additional data into memory).
-* Reduce the number of parallel processes (`-j <n>`).
-* Increase the memory of your machine.
-
-
-## The objective has been evaluated at this point before.
-
-If you see `The objective has been evaluated at this point before.` - then this is a sign that your space has been exhausted, or is close to that.
-Basically all points in your space have been hit (or a local minima has been hit) - and hyperopt does no longer find points in the multi-dimensional space it did not try yet.
-Freqtrade tries to counter the "local minima" problem by using new, randomized points in this case.
-
-Example:
-
-``` python
-buy_ema_short = IntParameter(5, 20, default=10, space="buy", optimize=True)
-# This is the only parameter in the buy space
-```
-
-The `buy_ema_short` space has 15 possible values (`5, 6, ... 19, 20`). If you now run hyperopt for the buy space, hyperopt will only have 15 values to try before running out of options.
-Your epochs should therefore be aligned to the possible values - or you should be ready to interrupt a run if you norice a lot of `The objective has been evaluated at this point before.` warnings.
+* reduce the amount of pairs
+* reduce the timerange used (`--timerange <timerange>`)
+* reduce the number of parallel processes (`-j <n>`)
+* Increase the memory of your machine
 
 ## Show details of Hyperopt results
 

docs/includes/pairlists.md

@@ -44,7 +44,7 @@ It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklis
 ```json
 "pairlists": [
     {"method": "StaticPairList"}
-],
+    ],
 ```
 
 By default, only currently enabled pairs are allowed.
@@ -160,17 +160,17 @@ This filter allows freqtrade to ignore pairs until they have been listed for at
 
 Offsets an incoming pairlist by a given `offset` value.
 
-As an example it can be used in conjunction with `VolumeFilter` to remove the top X volume pairs. Or to split a larger pairlist on two bot instances.
+As an example it can be used in conjunction with `VolumeFilter` to remove the top X volume pairs. Or to split
+a larger pairlist on two bot instances.
 
-Example to remove the first 10 pairs from the pairlist, and takes the next 20 (taking items 10-30 of the initial list):
+Example to remove the first 10 pairs from the pairlist:
 
 ```json
 "pairlists": [
     // ...
     {
         "method": "OffsetFilter",
-        "offset": 10,
-        "number_assets": 20
+        "offset": 10
     }
 ],
 ```
@@ -181,7 +181,7 @@ Example to remove the first 10 pairs from the pairlist, and takes the next 20 (t
     `VolumeFilter`.
 
 !!! Note
-    An offset larger than the total length of the incoming pairlist will result in an empty pairlist.
+    An offset larger then the total length of the incoming pairlist will result in an empty pairlist.
 
 #### PerformanceFilter
 

docs/includes/pricing.md

@@ -1,6 +1,6 @@
 ## Prices used for orders
 
-Prices for regular orders can be controlled via the parameter structures `entry_pricing` for trade entries and `exit_pricing` for trade exits.
+Prices for regular orders can be controlled via the parameter structures `bid_strategy` for buying and `ask_strategy` for selling.
 Prices are always retrieved right before an order is placed, either by querying the exchange tickers or by using the orderbook data.
 
 !!! Note
@@ -9,11 +9,20 @@ Prices are always retrieved right before an order is placed, either by querying
 !!! Warning "Using market orders"
     Please read the section [Market order pricing](#market-order-pricing) section when using market orders.
 
-### Entry price
+### Buy price
 
-#### Enter price side
+#### Check depth of market
 
-The configuration setting `entry_pricing.price_side` defines the side of the orderbook the bot looks for when buying.
+When check depth of market is enabled (`bid_strategy.check_depth_of_market.enabled=True`), the buy signals are filtered based on the orderbook depth (sum of all amounts) for each orderbook side.
+
+Orderbook `bid` (buy) side depth is then divided by the orderbook `ask` (sell) side depth and the resulting delta is compared to the value of the `bid_strategy.check_depth_of_market.bids_to_ask_delta` parameter. The buy order is only executed if the orderbook delta is greater than or equal to the configured delta value.
+
+!!! Note
+    A delta value below 1 means that `ask` (sell) orderbook side depth is greater than the depth of the `bid` (buy) orderbook side, while a value greater than 1 means opposite (depth of the buy side is higher than the depth of the sell side).
+
+#### Buy price side
+
+The configuration setting `bid_strategy.price_side` defines the side of the spread the bot looks for when buying.
 
 The following displays an orderbook.
 
@@ -29,53 +38,30 @@ The following displays an orderbook.
 ...
 ```
 
-If `entry_pricing.price_side` is set to `"bid"`, then the bot will use 99 as entry price.  
-In line with that, if `entry_pricing.price_side` is set to `"ask"`, then the bot will use 101 as entry price.
+If `bid_strategy.price_side` is set to `"bid"`, then the bot will use 99 as buying price.  
+In line with that, if `bid_strategy.price_side` is set to `"ask"`, then the bot will use 101 as buying price.
 
-Depending on the order direction (_long_/_short_), this will lead to different results. Therefore we recommend to use `"same"` or `"other"` for this configuration instead.
-This would result in the following pricing matrix:
-
-| direction | Order | setting | price | crosses spread |
-|------ |--------|-----|-----|-----|
-| long  | buy  | ask   | 101 | yes |
-| long  | buy  | bid   | 99  | no  |
-| long  | buy  | same  | 99  | no  |
-| long  | buy  | other | 101 | yes |
-| short | sell | ask   | 101 | no  |
-| short | sell | bid   | 99  | yes |
-| short | sell | same  | 101 | no  |
-| short | sell | other | 99  | yes |
-
-Using the other side of the orderbook often guarantees quicker filled orders, but the bot can also end up paying more than what would have been necessary.
+Using `ask` price often guarantees quicker filled orders, but the bot can also end up paying more than what would have been necessary.
 Taker fees instead of maker fees will most likely apply even when using limit buy orders.
-Also, prices at the "other" side of the spread are higher than prices at the "bid" side in the orderbook, so the order behaves similar to a market order (however with a maximum price).
+Also, prices at the "ask" side of the spread are higher than prices at the "bid" side in the orderbook, so the order behaves similar to a market order (however with a maximum price).
 
-#### Entry price with Orderbook enabled
+#### Buy price with Orderbook enabled
 
-When entering a trade with the orderbook enabled (`entry_pricing.use_order_book=True`), Freqtrade fetches the `entry_pricing.order_book_top` entries from the orderbook and uses the entry specified as `entry_pricing.order_book_top` on the configured side (`entry_pricing.price_side`) of the orderbook. 1 specifies the topmost entry in the orderbook, while 2 would use the 2nd entry in the orderbook, and so on.
+When buying with the orderbook enabled (`bid_strategy.use_order_book=True`), Freqtrade fetches the `bid_strategy.order_book_top` entries from the orderbook and uses the entry specified as `bid_strategy.order_book_top` on the configured side (`bid_strategy.price_side`) of the orderbook. 1 specifies the topmost entry in the orderbook, while 2 would use the 2nd entry in the orderbook, and so on.
 
-#### Entry price without Orderbook enabled
+#### Buy price without Orderbook enabled
 
-The following section uses `side` as the configured `entry_pricing.price_side` (defaults to `"same"`).
+The following section uses `side` as the configured `bid_strategy.price_side`.
 
-When not using orderbook (`entry_pricing.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `side` price is above the `last` price), it calculates a rate between `side` and `last` price based on `entry_pricing.price_last_balance`.
+When not using orderbook (`bid_strategy.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `side` price is above the `last` price), it calculates a rate between `side` and `last` price.
 
-The `entry_pricing.price_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the `last` price and values between those interpolate between ask and last price.
+The `bid_strategy.ask_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the `last` price and values between those interpolate between ask and last price.
 
-#### Check depth of market
+### Sell price
 
-When check depth of market is enabled (`entry_pricing.check_depth_of_market.enabled=True`), the entry signals are filtered based on the orderbook depth (sum of all amounts) for each orderbook side.
+#### Sell price side
 
-Orderbook `bid` (buy) side depth is then divided by the orderbook `ask` (sell) side depth and the resulting delta is compared to the value of the `entry_pricing.check_depth_of_market.bids_to_ask_delta` parameter. The entry order is only executed if the orderbook delta is greater than or equal to the configured delta value.
-
-!!! Note
-    A delta value below 1 means that `ask` (sell) orderbook side depth is greater than the depth of the `bid` (buy) orderbook side, while a value greater than 1 means opposite (depth of the buy side is higher than the depth of the sell side).
-
-### Exit price
-
-#### Exit price side
-
-The configuration setting `exit_pricing.price_side` defines the side of the spread the bot looks for when exiting a trade.
+The configuration setting `ask_strategy.price_side` defines the side of the spread the bot looks for when selling.
 
 The following displays an orderbook:
 
@@ -91,54 +77,40 @@ The following displays an orderbook:
 ...
 ```
 
-If `exit_pricing.price_side` is set to `"ask"`, then the bot will use 101 as exiting price.  
-In line with that, if `exit_pricing.price_side` is set to `"bid"`, then the bot will use 99 as exiting price.
+If `ask_strategy.price_side` is set to `"ask"`, then the bot will use 101 as selling price.  
+In line with that, if `ask_strategy.price_side` is set to `"bid"`, then the bot will use 99 as selling price.
 
-Depending on the order direction (_long_/_short_), this will lead to different results. Therefore we recommend to use `"same"` or `"other"` for this configuration instead.
-This would result in the following pricing matrix:
+#### Sell price with Orderbook enabled
 
-| Direction | Order | setting | price | crosses spread |
-|------ |--------|-----|-----|-----|
-| long  | sell | ask   | 101 | no  |
-| long  | sell | bid   | 99  | yes |
-| long  | sell | same  | 101 | no  |
-| long  | sell | other | 99  | yes |
-| short | buy  | ask   | 101 | yes |
-| short | buy  | bid   | 99  | no  |
-| short | buy  | same  | 99  | no  |
-| short | buy  | other | 101 | yes |
-
-#### Exit price with Orderbook enabled
-
-When exiting with the orderbook enabled (`exit_pricing.use_order_book=True`), Freqtrade fetches the `exit_pricing.order_book_top` entries in the orderbook and uses the entry specified as `exit_pricing.order_book_top` from the configured side (`exit_pricing.price_side`) as trade exit price.
+When selling with the orderbook enabled (`ask_strategy.use_order_book=True`), Freqtrade fetches the `ask_strategy.order_book_top` entries in the orderbook and uses the entry specified as `ask_strategy.order_book_top` from the configured side (`ask_strategy.price_side`) as selling price.
 
 1 specifies the topmost entry in the orderbook, while 2 would use the 2nd entry in the orderbook, and so on.
 
-#### Exit price without Orderbook enabled
+#### Sell price without Orderbook enabled
 
-The following section uses `side` as the configured `exit_pricing.price_side` (defaults to `"ask"`).
+When not using orderbook (`ask_strategy.use_order_book=False`), the price at the `ask_strategy.price_side` side (defaults to `"ask"`) from the ticker will be used as the sell price.
 
-When not using orderbook (`exit_pricing.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's above the `last` traded price from the ticker. Otherwise (when the `side` price is below the `last` price), it calculates a rate between `side` and `last` price based on `exit_pricing.price_last_balance`.
+When not using orderbook (`ask_strategy.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `side` price is above the `last` price), it calculates a rate between `side` and `last` price.
 
-The `exit_pricing.price_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the last price and values between those interpolate between `side` and last price.
+The `ask_strategy.bid_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the last price and values between those interpolate between `side` and last price.
 
 ### Market order pricing
 
 When using market orders, prices should be configured to use the "correct" side of the orderbook to allow realistic pricing detection.
-Assuming both entry and exits are using market orders, a configuration similar to the following must be used
+Assuming both buy and sell are using market orders, a configuration similar to the following might be used
 
 ``` jsonc
   "order_types": {
-    "entry": "market",
-    "exit": "market"
+    "buy": "market",
+    "sell": "market"
     // ...
   },
-  "entry_pricing": {
-    "price_side": "other",
+  "bid_strategy": {
+    "price_side": "ask",
     // ...
   },
-  "exit_pricing":{
-    "price_side": "other",
+  "ask_strategy":{
+    "price_side": "bid",
     // ...
   },
 ```

docs/includes/protections.md

@@ -48,10 +48,6 @@ If `trade_limit` or more trades resulted in stoploss, trading will stop for `sto
 
 This applies across all pairs, unless `only_per_pair` is set to true, which will then only look at one pair at a time.
 
-Similarly, this protection will by default look at all trades (long and short). For futures bots, setting `only_per_side` will make the bot only consider one side, and will then only lock this one side, allowing for example shorts to continue after a series of long stoplosses.
-
-`required_profit` will determine the required relative profit (or loss) for stoplosses to consider. This should normally not be set and defaults to 0.0 - which means all losing stoplosses will be triggering a block.
-
 The below example stops trading for all pairs for 4 candles after the last trade if the bot hit stoploss 4 times within the last 24 candles.
 
 ``` python
@@ -63,9 +59,7 @@ def protections(self):
             "lookback_period_candles": 24,
             "trade_limit": 4,
             "stop_duration_candles": 4,
-            "required_profit": 0.0,
-            "only_per_pair": False,
-            "only_per_side": False
+            "only_per_pair": False
         }
     ]
 ```
@@ -99,8 +93,6 @@ def protections(self):
 `LowProfitPairs` uses all trades for a pair within `lookback_period` in minutes (or in candles when using `lookback_period_candles`) to determine the overall profit ratio.
 If that ratio is below `required_profit`, that pair will be locked for `stop_duration` in minutes (or in candles when using `stop_duration_candles`).
 
-For futures bots, setting `only_per_side` will make the bot only consider one side, and will then only lock this one side, allowing for example shorts to continue after a series of long losses.
-
 The below example will stop trading a pair for 60 minutes if the pair does not have a required profit of 2% (and a minimum of 2 trades) within the last 6 candles.
 
 ``` python
@@ -112,8 +104,7 @@ def protections(self):
             "lookback_period_candles": 6,
             "trade_limit": 2,
             "stop_duration": 60,
-            "required_profit": 0.02,
-            "only_per_pair": False,
+            "required_profit": 0.02
         }
     ]
 ```

docs/index.md

@@ -22,6 +22,10 @@ Freqtrade is a free and open source crypto trading bot written in Python. It is
 
 ![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).
@@ -38,23 +42,14 @@ Freqtrade is a free and open source crypto trading bot written in Python. It is
 
 Please read the [exchange specific notes](exchanges.md) to learn about eventual, special configurations needed for each exchange.
 
-- [X] [Binance](https://www.binance.com/)
+- [X] [Binance](https://www.binance.com/) ([*Note for binance users](exchanges.md#binance-blacklist))
 - [X] [Bittrex](https://bittrex.com/)
-- [X] [FTX](https://ftx.com/#a=2258149)
+- [X] [FTX](https://ftx.com)
 - [X] [Gate.io](https://www.gate.io/ref/6266643)
-- [X] [Huobi](http://huobi.com/)
 - [X] [Kraken](https://kraken.com/)
-- [X] [OKX](https://okx.com/) (Former OKEX)
+- [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)_
 
-### Supported Futures Exchanges (experimental)
-
-- [X] [Binance](https://www.binance.com/)
-- [X] [Gate.io](https://www.gate.io/ref/6266643)
-- [X] [OKX](https://okx.com/).
-
-Please make sure to read the [exchange specific notes](exchanges.md), as well as the [trading with leverage](leverage.md) documentation before diving in.
-
 ### Community tested
 
 Exchanges confirmed working by the community:

docs/installation.md

@@ -326,16 +326,6 @@ python3 -m pip install --upgrade pip
 python3 -m pip install -e .
 ```
 
-Patch conda libta-lib (Linux only)
-
-```bash
-# Ensure that the environment is active!
-conda activate freqtrade-conda
-
-cd build_helpers
-bash install_ta-lib.sh ${CONDA_PREFIX} nosudo
-```
-
 ### Congratulations
 
 [You are ready](#you-are-ready), and run the bot

docs/leverage.md

@@ -1,136 +0,0 @@
-# Trading with Leverage
-
-!!! Warning "Beta feature"
-    This feature is still in it's testing phase. Should you notice something you think is wrong please let us know via Discord or via Github Issue.
-
-!!! Note "Multiple bots on one account"
-    You can't run 2 bots on the same account with leverage. For leveraged / margin trading, freqtrade assumes it's the only user of the account, and all liquidation levels are calculated based on this assumption.
-
-!!! Danger "Trading with leverage is very risky"
-    Do not trade with a leverage > 1 using a strategy that hasn't shown positive results in a live run using the spot market. Check the stoploss of your strategy. With a leverage of 2, a stoploss of 0.5 (50%) would be too low, and these trades would be liquidated before reaching that stoploss.
-    We do not assume any responsibility for eventual losses that occur from using this software or this mode.
-
-    Please only use advanced trading modes when you know how freqtrade (and your strategy) works.
-    Also, never risk more than what you can afford to lose.
-
-Please read the [strategy migration guide](strategy_migration.md#strategy-migration-between-v2-and-v3) to migrate your strategy from a freqtrade v2 strategy, to v3 strategy that can short and trade futures.
-
-## Shorting
-
-Shorting is not possible when trading with [`trading_mode`](#understand-tradingmode) set to `spot`. To short trade, `trading_mode` must be set to `margin`(currently unavailable) or [`futures`](#futures), with [`margin_mode`](#margin-mode) set to `cross`(currently unavailable) or [`isolated`](#isolated-margin-mode)
-
-For a strategy to short, the strategy class must set the class variable `can_short = True`
-
-Please read [strategy customization](strategy-customization.md#entry-signal-rules) for instructions on how to set signals to enter and exit short trades.
-
-## Understand `trading_mode`
-
-The possible values are: `spot` (default), `margin`(*Currently unavailable*) or `futures`.
-
-### Spot
-
-Regular trading mode (low risk)
-
-- Long trades only (No short trades).
-- No leverage.
-- No Liquidation.
-- Profits gained/lost are equal to the change in value of the assets (minus trading fees).
-
-### Leverage trading modes
-
-With leverage, a trader borrows capital from the exchange. The capital must be re-payed fully to the exchange (potentially with interest), and the trader keeps any profits, or pays any losses, from any trades made using the borrowed capital.
-
-Because the capital must always be re-payed, exchanges will **liquidate** (forcefully sell the traders assets) a trade made using borrowed capital when the total value of assets in the leverage account drops to a certain point (a point where the total value of losses is less than the value of the collateral that the trader actually owns in the leverage account), in order to ensure that the trader has enough capital to pay the borrowed assets back to the exchange. The exchange will also charge a **liquidation fee**, adding to the traders losses.
-
-For this reason, **DO NOT TRADE WITH LEVERAGE IF YOU DON'T KNOW EXACTLY WHAT YOUR DOING. LEVERAGE TRADING IS HIGH RISK, AND CAN RESULT IN THE VALUE OF YOUR ASSETS DROPPING TO 0 VERY QUICKLY, WITH NO CHANCE OF INCREASING IN VALUE AGAIN.**
-
-#### Margin (currently unavailable)
-
-Trading occurs on the spot market, but the exchange lends currency to you in an amount equal to the chosen leverage. You pay the amount lent to you back to the exchange with interest, and your profits/losses are multiplied by the leverage specified.
-
-#### Futures
-
-Perpetual swaps (also known as Perpetual Futures) are contracts traded at a price that is closely tied to the underlying asset they are based off of (ex.). You are not trading the actual asset but instead are trading a derivative contract. Perpetual swap contracts can last indefinitely, in contrast to futures or option contracts.
-
-In addition to the gains/losses from the change in price of the futures contract, traders also exchange _funding fees_, which are gains/losses worth an amount that is derived from the difference in price between the futures contract and the underlying asset. The difference in price between a futures contract and the underlying asset varies between exchanges.
-
-To trade in futures markets, you'll have to set `trading_mode` to "futures".
-You will also have to pick a "margin mode" (explanation below) - with freqtrade currently only supporting isolated margin.
-
-``` json
-"trading_mode": "futures",
-"margin_mode": "isolated"
-```
-
-### Margin mode
-
-On top of `trading_mode` - you will also have to configure your `margin_mode`.
-While freqtrade currently only supports one margin mode, this will change, and by configuring it now you're all set for future updates.
-
-The possible values are: `isolated`, or `cross`(*currently unavailable*).
-
-#### Isolated margin mode
-
-Each market(trading pair), keeps collateral in a separate account
-
-``` json
-"margin_mode": "isolated"
-```
-
-#### Cross margin mode (currently unavailable)
-
-One account is used to share collateral between markets (trading pairs). Margin is taken from total account balance to avoid liquidation when needed.
-
-``` json
-"margin_mode": "cross"
-```
-
-## Set leverage to use
-
-Different strategies and risk profiles will require different levels of leverage.
-While you could configure one static leverage value - freqtrade offers you the flexibility to adjust this via [strategy leverage callback](strategy-callbacks.md#leverage-callback) - which allows you to use different leverages by pair, or based on some other factor benefitting your strategy result.
-
-If not implemented, leverage defaults to 1x (no leverage).
-
-!!! Warning
-    Higher leverage also equals higher risk - be sure you fully understand the implications of using leverage!
-
-## Understand `liquidation_buffer`
-
-*Defaults to `0.05`*
-
-A ratio specifying how large of a safety net to place between the liquidation price and the stoploss to prevent a position from reaching the liquidation price.
-This artificial liquidation price is calculated as:
-
-`freqtrade_liquidation_price = liquidation_price ± (abs(open_rate - liquidation_price) * liquidation_buffer)`
-
-- `±` = `+` for long trades
-- `±` = `-` for short trades
-
-Possible values are any floats between 0.0 and 0.99
-
-**ex:** If a trade is entered at a price of 10 coin/USDT, and the liquidation price of this trade is 8 coin/USDT, then with `liquidation_buffer` set to `0.05` the minimum stoploss for this trade would be $8 + ((10 - 8) * 0.05) = 8 + 0.1 = 8.1$
-
-!!! Danger "A `liquidation_buffer` of 0.0, or a low `liquidation_buffer` is likely to result in liquidations, and liquidation fees"
-    Currently Freqtrade is able to calculate liquidation prices, but does not calculate liquidation fees. Setting your `liquidation_buffer` to 0.0, or using a low `liquidation_buffer` could result in your positions being liquidated. Freqtrade does not track liquidation fees, so liquidations will result in inaccurate profit/loss results for your bot. If you use a low `liquidation_buffer`, it is recommended to use `stoploss_on_exchange` if your exchange supports this.
-
-## Unavailable funding rates
-
-For futures data, exchanges commonly provide the futures candles, the marks, and the funding rates. However, it is common that whilst candles and marks might be available, the funding rates are not. This can affect backtesting timeranges, i.e. you may only be able to test recent timeranges and not earlier, experiencing the `No data found. Terminating.` error. To get around this, add the `futures_funding_rate` config option as listed in [configuration.md](configuration.md), and it is recommended that you set this to `0`, unless you know a given specific funding rate for your pair, exchange and timerange. Setting this to anything other than `0` can have drastic effects on your profit calculations within strategy, e.g. within the `custom_exit`, `custom_stoploss`, etc functions.
-
-!!! Warning "This will mean your backtests are inaccurate."
-    This will not overwrite funding rates that are available from the exchange, but bear in mind that setting a false funding rate will mean backtesting results will be inaccurate for historical timeranges where funding rates are not available.
-
-### Developer
-
-#### Margin mode
-
-For shorts, the currency which pays the interest fee for the `borrowed` currency is purchased at the same time of the closing trade (This means that the amount purchased in short closing trades is greater than the amount sold in short opening trades).
-
-For longs, the currency which pays the interest fee for the `borrowed` will already be owned by the user and does not need to be purchased. The interest is subtracted from the `close_value` of the trade.
-
-All Fees are included in `current_profit` calculations during the trade.
-
-#### Futures mode
-
-Funding fees are either added or subtracted from the total amount of a trade

docs/plotting.md

@@ -14,7 +14,7 @@ pip install -U -r requirements-plot.txt
 
 The `freqtrade plot-dataframe` subcommand shows an interactive graph with three subplots:
 
-* Main plot with candlesticks and indicators following price (sma/ema)
+* Main plot with candlestics and indicators following price (sma/ema)
 * Volume bars
 * Additional indicators as specified by `--indicators2`
 
@@ -65,7 +65,7 @@ optional arguments:
                         _today.json`
   --timerange TIMERANGE
                         Specify what timerange of data to use.
-  -i TIMEFRAME, --timeframe TIMEFRAME
+  -i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
                         Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
   --no-trades           Skip using trades from backtesting file and DB.
 
@@ -96,7 +96,7 @@ Strategy arguments:
 Example:
 
 ``` bash
-freqtrade plot-dataframe -p BTC/ETH --strategy AwesomeStrategy
+freqtrade plot-dataframe -p BTC/ETH
 ```
 
 The `-p/--pairs` argument can be used to specify pairs you would like to plot.
@@ -107,6 +107,9 @@ The `-p/--pairs` argument can be used to specify pairs you would like to plot.
 Specify custom indicators.
 Use `--indicators1` for the main plot and `--indicators2` for the subplot below (if values are in a different range than prices).
 
+!!! Tip
+    You will almost certainly want to specify a custom strategy! This can be done by adding `-s Classname` / `--strategy ClassName` to the command.
+
 ``` bash
 freqtrade plot-dataframe --strategy AwesomeStrategy -p BTC/ETH --indicators1 sma ema --indicators2 macd
 ```
@@ -327,7 +330,7 @@ optional arguments:
   --trade-source {DB,file}
                         Specify the source for trades (Can be DB or file
                         (backtest file)) Default: file
-  -i TIMEFRAME, --timeframe TIMEFRAME
+  -i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
                         Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
   --auto-open           Automatically open generated plot.
 

docs/requirements-docs.txt

@@ -1,6 +1,4 @@
-markdown==3.3.7
-mkdocs==1.3.1
-mkdocs-material==8.4.0
-mdx_truly_sane_lists==1.3
-pymdown-extensions==9.5
-jinja2==3.1.2
+mkdocs==1.2.3
+mkdocs-material==8.2.1
+mdx_truly_sane_lists==1.2
+pymdown-extensions==9.2

docs/rest-api.md

@@ -145,10 +145,9 @@ python3 scripts/rest_client.py --config rest_config.json <command> [optional par
 | `locks` | Displays currently locked pairs.
 | `delete_lock <lock_id>` | Deletes (disables) the lock by id.
 | `profit` | Display a summary of your profit/loss from close trades and some stats about your performance.
-| `forceexit <trade_id>` | Instantly exits the given trade  (Ignoring `minimum_roi`).
-| `forceexit all` | Instantly exits all open trades (Ignoring `minimum_roi`).
-| `forceenter <pair> [rate]` | Instantly enters the given pair. Rate is optional. (`force_entry_enable` must be set to True)
-| `forceenter <pair> <side> [rate]` | Instantly longs or shorts the given pair. Rate is optional. (`force_entry_enable` must be set to True)
+| `forcesell <trade_id>` | Instantly sells the given trade  (Ignoring `minimum_roi`).
+| `forcesell all` | Instantly sells all open trades (Ignoring `minimum_roi`).
+| `forcebuy <pair> [rate]` | Instantly buys the given pair. Rate is optional. (`forcebuy_enable` must be set to True)
 | `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).
@@ -216,15 +215,8 @@ forcebuy
         :param pair: Pair to buy (ETH/BTC)
         :param price: Optional - price to buy
 
-forceenter
-	Force entering a trade
-
-        :param pair: Pair to buy (ETH/BTC)
-        :param side: 'long' or 'short'
-        :param price: Optional - price to buy
-
-forceexit
-	Force-exit a trade.
+forcesell
+	Force-sell a trade.
 
         :param tradeid: Id of the trade (can be received via status command)
 
@@ -293,9 +285,6 @@ strategy
 
         :param strategy: Strategy class name
 
-sysinfo
-	Provides system information (CPU, RAM usage)
-
 trade
 	Return specific trade
 

docs/sandbox-testing.md

@@ -104,16 +104,16 @@ To mitigate this, you can try to match the first order on the opposite orderbook
 
 ``` jsonc
   "order_types": {
-    "entry": "limit",
-    "exit": "limit"
+    "buy": "limit",
+    "sell": "limit"
     // ...
   },
-  "entry_pricing": {
-    "price_side": "other",
+  "bid_strategy": {
+    "price_side": "ask",
     // ...
   },
-  "exit_pricing":{
-    "price_side": "other",
+  "ask_strategy":{
+    "price_side": "bid",
     // ...
   },
   ```

docs/sql_cheatsheet.md

@@ -49,14 +49,14 @@ sqlite3
 SELECT * FROM trades;
 ```
 
-## Fix trade still open after a manual exit on the exchange
+## Fix trade still open after a manual sell on the exchange
 
 !!! Warning
-    Manually selling a pair on the exchange will not be detected by the bot and it will try to sell anyway. Whenever possible, /forceexit <tradeid> should be used to accomplish the same thing.  
+    Manually selling a pair on the exchange will not be detected by the bot and it will try to sell anyway. Whenever possible, forcesell <tradeid> should be used to accomplish the same thing.  
     It is strongly advised to backup your database file before making any manual changes.
 
 !!! Note
-    This should not be necessary after /forceexit, as force_exit orders are closed automatically by the bot on the next iteration.
+    This should not be necessary after /forcesell, as forcesell orders are closed automatically by the bot on the next iteration.
 
 ```sql
 UPDATE trades
@@ -65,7 +65,7 @@ SET is_open=0,
   close_rate=<close_rate>,
   close_profit = close_rate / open_rate - 1,
   close_profit_abs = (amount * <close_rate> * (1 - fee_close) - (amount * (open_rate * (1 - fee_open)))),
-  exit_reason=<exit_reason>
+  sell_reason=<sell_reason>
 WHERE id=<trade_ID_to_update>;
 ```
 
@@ -78,7 +78,7 @@ SET is_open=0,
   close_rate=0.19638016,
   close_profit=0.0496,
   close_profit_abs = (amount * 0.19638016 * (1 - fee_close) - (amount * (open_rate * (1 - fee_open)))),
-  exit_reason='force_exit'  
+  sell_reason='force_sell'  
 WHERE id=31;
 ```
 
@@ -89,12 +89,11 @@ WHERE id=31;
 
 If you'd still like to remove a trade from the database directly, you can use the below query.
 
-!!! Danger
-    Some systems (Ubuntu) disable foreign keys in their sqlite3 packaging. When using sqlite - please ensure that foreign keys are on by running `PRAGMA foreign_keys = ON` before the above query.
-
 ```sql
 DELETE FROM trades WHERE id = <tradeid>;
+```
 
+```sql
 DELETE FROM trades WHERE id = 31;
 ```
 
@@ -103,20 +102,13 @@ DELETE FROM trades WHERE id = 31;
 
 ## Use a different database system
 
-Freqtrade is using SQLAlchemy, which supports multiple different database systems. As such, a multitude of database systems should be supported.
-Freqtrade does not depend or install any additional database driver. Please refer to the [SQLAlchemy docs](https://docs.sqlalchemy.org/en/14/core/engines.html#database-urls) on installation instructions for the respective database systems.
-
-The following systems have been tested and are known to work with freqtrade:
-
-* sqlite (default)
-* PostgreSQL)
-* MariaDB
-
 !!! Warning
-    By using one of the below database systems, you acknowledge that you know how to manage such a system. The freqtrade team will not provide any support with setup or maintenance (or backups) of the below database systems.
+    By using one of the below database systems, you acknowledge that you know how to manage such a system. Freqtrade will not provide any support with setup or maintenance (or backups) of the below database systems.
 
 ### PostgreSQL
 
+Freqtrade supports PostgreSQL by using SQLAlchemy, which supports multiple different database systems.
+
 Installation:
 `pip install psycopg2-binary`
 

docs/stoploss.md

@@ -17,14 +17,14 @@ Those stoploss modes can be *on exchange* or *off exchange*.
 These modes can be configured with these values:
 
 ``` python
-    'emergency_exit': 'market',
+    'emergencysell': 'market',
     'stoploss_on_exchange': False
     'stoploss_on_exchange_interval': 60,
     'stoploss_on_exchange_limit_ratio': 0.99
 ```
 
 !!! Note
-    Stoploss on exchange is only supported for Binance (stop-loss-limit), Huobi (stop-limit), Kraken (stop-loss-market, stop-loss-limit), FTX (stop limit and stop-market) Gateio (stop-limit), and Kucoin (stop-limit and stop-market) as of now.  
+    Stoploss on exchange is only supported for Binance (stop-loss-limit), Kraken (stop-loss-market, stop-loss-limit) and FTX (stop limit and stop-market) as of now.  
     <ins>Do not set too low/tight stoploss value if using stop loss on exchange!</ins>  
     If set to low/tight then you have greater risk of missing fill on the order and stoploss will not work.
 
@@ -52,30 +52,30 @@ The bot cannot do these every 5 seconds (at each iteration), otherwise it would
 So this parameter will tell the bot how often it should update the stoploss order. The default value is 60 (1 minute).
 This same logic will reapply a stoploss order on the exchange should you cancel it accidentally.
 
-### force_exit
+### forcesell
 
-`force_exit` is an optional value, which defaults to the same value as `exit` and is used when sending a `/forceexit` command from Telegram or from the Rest API.
+`forcesell` is an optional value, which defaults to the same value as `sell` and is used when sending a `/forcesell` command from Telegram or from the Rest API.
 
-### force_entry
+### forcebuy
 
-`force_entry` is an optional value, which defaults to the same value as `entry` and is used when sending a `/forceentry` command from Telegram or from the Rest API.
+`forcebuy` is an optional value, which defaults to the same value as `buy` and is used when sending a `/forcebuy` command from Telegram or from the Rest API.
 
-### emergency_exit
+### emergencysell
 
-`emergency_exit` is an optional value, which defaults to `market` and is used when creating stop loss on exchange orders fails.
+`emergencysell` is an optional value, which defaults to `market` and is used when creating stop loss on exchange orders fails.
 The below is the default which is used if not changed in strategy or configuration file.
 
 Example from strategy file:
 
 ``` python
 order_types = {
-    "entry": "limit",
-    "exit": "limit",
-    "emergency_exit": "market",
-    "stoploss": "market",
-    "stoploss_on_exchange": True,
-    "stoploss_on_exchange_interval": 60,
-    "stoploss_on_exchange_limit_ratio": 0.99
+    'buy': 'limit',
+    'sell': 'limit',
+    'emergencysell': 'market',
+    'stoploss': 'market',
+    'stoploss_on_exchange': True,
+    'stoploss_on_exchange_interval': 60,
+    'stoploss_on_exchange_limit_ratio': 0.99
 }
 ```
 
@@ -130,7 +130,7 @@ In summary: The stoploss will be adjusted to be always be -10% of the highest ob
 
 ### Trailing stop loss, custom positive loss
 
-You could also have a default stop loss when you are in the red with your buy (buy - fee), but once you hit a positive result (or an offset you define) the system will utilize a new stop loss, which can have a different value.
+It is also possible to have a default stop loss, when you are in the red with your buy (buy - fee), but once you hit positive result the system will utilize a new stop loss, which can have a different value.
 For example, your default stop loss is -10%, but once you have more than 0% profit (example 0.1%) a different trailing stoploss will be used.
 
 !!! Note
@@ -142,8 +142,6 @@ Both values require `trailing_stop` to be set to true and `trailing_stop_positiv
     stoploss = -0.10
     trailing_stop = True
     trailing_stop_positive = 0.02
-    trailing_stop_positive_offset = 0.0
-    trailing_only_offset_is_reached = False  # Default - not necessary for this example
 ```
 
 For example, simplified math:
@@ -158,31 +156,11 @@ For example, simplified math:
 The 0.02 would translate to a -2% stop loss.
 Before this, `stoploss` is used for the trailing stoploss.
 
-!!! Tip "Use an offset to change your stoploss"
-    Use `trailing_stop_positive_offset` to ensure that your new trailing stoploss will be in profit by setting `trailing_stop_positive_offset` higher than `trailing_stop_positive`. Your first new stoploss value will then already have locked in profits.
-
-    Example with simplified math:
-
-    ``` python
-        stoploss = -0.10
-        trailing_stop = True
-        trailing_stop_positive = 0.02
-        trailing_stop_positive_offset = 0.03
-    ```
-
-    * the bot buys an asset at a price of 100$
-    * the stop loss is defined at -10%, so the stop loss would get triggered once the asset drops below 90$
-    * assuming the asset now increases to 102$
-    * the stoploss will now be at 91.8$ - 10% below the highest observed rate
-    * assuming the asset now increases to 103.5$ (above the offset configured)
-    * the stop loss will now be -2% of 103.5$ = 101.43$
-    * now the asset drops in value to 102\$, the stop loss will still be 101.43$ and would trigger once price breaks below 101.43$
-
 ### Trailing stop loss only once the trade has reached a certain offset
 
-You can also keep a static stoploss until the offset is reached, and then trail the trade to take profits once the market turns.
+It is also possible to use a static stoploss until the offset is reached, and then trail the trade to take profits once the market turns.
 
-If `trailing_only_offset_is_reached = True` then the trailing stoploss is only activated once the offset is reached. Until then, the stoploss remains at the configured `stoploss`.
+If `"trailing_only_offset_is_reached": true` then the trailing stoploss is only activated once the offset is reached. Until then, the stoploss remains at the configured `stoploss`.
 This option can be used with or without `trailing_stop_positive`, but uses `trailing_stop_positive_offset` as offset.
 
 ``` python
@@ -213,18 +191,6 @@ For example, simplified math:
 !!! Tip
     Make sure to have this value (`trailing_stop_positive_offset`) lower than minimal ROI, otherwise minimal ROI will apply first and sell the trade.
 
-## Stoploss and Leverage
-
-Stoploss should be thought of as "risk on this trade" - so a stoploss of 10% on a 100$ trade means you are willing to lose 10$ (10%) on this trade - which would trigger if the price moves 10% to the downside.
-
-When using leverage, the same principle is applied - with stoploss defining the risk on the trade (the amount you are willing to lose).
-
-Therefore, a stoploss of 10% on a 10x trade would trigger on a 1% price move.
-If your stake amount (own capital) was 100$ - this trade would be 1000$ at 10x (after leverage).
-If price moves 1% - you've lost 10$ of your own capital - therfore stoploss will trigger in this case.
-
-Make sure to be aware of this, and avoid using too tight stoploss (at 10x leverage, 10% risk may be too little to allow the trade to "breath" a little).
-
 ## Changing stoploss on open trades
 
 A stoploss on an open trade can be changed by changing the value in the configuration or strategy and use the `/reload_config` command (alternatively, completely stopping and restarting the bot also works).

docs/strategy-advanced.md

@@ -49,7 +49,7 @@ from freqtrade.exchange import timeframe_to_prev_date
 
 class AwesomeStrategy(IStrategy):
     def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount: float,
-                           rate: float, time_in_force: str, exit_reason: str,
+                           rate: float, time_in_force: str, sell_reason: str,
                            current_time: 'datetime', **kwargs) -> bool:
         # Obtain pair dataframe.
         dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
@@ -77,47 +77,47 @@ class AwesomeStrategy(IStrategy):
 
 ***
 
-## Enter Tag
+## Buy Tag
 
 When your strategy has multiple buy signals, you can name the signal that triggered.
-Then you can access you buy signal on `custom_exit`
+Then you can access you buy signal on `custom_sell`
 
 ```python
-def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
     dataframe.loc[
         (
             (dataframe['rsi'] < 35) &
             (dataframe['volume'] > 0)
         ),
-        ['enter_long', 'enter_tag']] = (1, 'buy_signal_rsi')
+        ['buy', 'buy_tag']] = (1, 'buy_signal_rsi')
 
     return dataframe
 
-def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
-                current_profit: float, **kwargs):
+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()
-    if trade.enter_tag == 'buy_signal_rsi' and last_candle['rsi'] > 80:
+    if trade.buy_tag == 'buy_signal_rsi' and last_candle['rsi'] > 80:
         return 'sell_signal_rsi'
     return None
 
 ```
 
 !!! Note
-    `enter_tag` is limited to 100 characters, remaining data will be truncated.
+    `buy_tag` is limited to 100 characters, remaining data will be truncated.
 
 ## Exit tag
 
 Similar to [Buy Tagging](#buy-tag), you can also specify a sell tag.
 
 ``` python
-def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
     dataframe.loc[
         (
             (dataframe['rsi'] > 70) &
             (dataframe['volume'] > 0)
         ),
-        ['exit_long', 'exit_tag']] = (1, 'exit_rsi')
+        ['sell', 'exit_tag']] = (1, 'exit_rsi')
 
     return dataframe
 ```
@@ -125,7 +125,7 @@ def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame
 The provided exit-tag is then used as sell-reason - and shown as such in backtest results.
 
 !!! Note
-    `exit_reason` is limited to 100 characters, remaining data will be truncated.
+    `sell_reason` is limited to 100 characters, remaining data will be truncated.
 
 ## Strategy version
 
@@ -146,7 +146,7 @@ def version(self) -> str:
 
 The strategies can be derived from other strategies. This avoids duplication of your custom strategy code. You can use this technique to override small parts of your main strategy, leaving the rest untouched:
 
-``` python title="user_data/strategies/myawesomestrategy.py"
+``` python
 class MyAwesomeStrategy(IStrategy):
     ...
     stoploss = 0.13
@@ -155,10 +155,6 @@ class MyAwesomeStrategy(IStrategy):
     # should be in any custom strategy...
     ...
 
-```
-
-``` python title="user_data/strategies/MyAwesomeStrategy2.py"
-from myawesomestrategy import MyAwesomeStrategy
 class MyAwesomeStrategy2(MyAwesomeStrategy):
     # Override something
     stoploss = 0.08
@@ -167,7 +163,16 @@ class MyAwesomeStrategy2(MyAwesomeStrategy):
 
 Both attributes and methods may be overridden, altering behavior of the original strategy in a way you need.
 
-While keeping the subclass in the same file is technically possible, it can lead to some problems with hyperopt parameter files, we therefore recommend to use separate strategy files, and import the parent strategy as shown above.
+!!! Note "Parent-strategy in different files"
+    If you have the parent-strategy in a different file, you'll need to add the following to the top of your "child"-file to ensure proper loading, otherwise freqtrade may not be able to load the parent strategy correctly.
+
+    ``` python
+    import sys
+    from pathlib import Path
+    sys.path.append(str(Path(__file__).parent))
+
+    from myawesomestrategy import MyAwesomeStrategy
+    ```
 
 ## Embedding Strategies
 
@@ -224,5 +229,3 @@ for val in self.buy_ema_short.range:
 # Append columns to existing dataframe
 merged_frame = pd.concat(frames, axis=1)
 ```
-
-Freqtrade does however also counter this by running `dataframe.copy()` on the dataframe right after the `populate_indicators()` method - so performance implications of this should be low to non-existant.

docs/strategy-callbacks.md

@@ -1,54 +1,25 @@
 # Strategy Callbacks
 
-While the main strategy functions (`populate_indicators()`, `populate_entry_trend()`, `populate_exit_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".
+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_start()`](#bot-start)
 * [`bot_loop_start()`](#bot-loop-start)
-* [`custom_stake_amount()`](#stake-size-management)
-* [`custom_exit()`](#custom-exit-signal)
+* [`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_entry_timeout()` and `check_exit_timeout()`](#custom-order-timeout-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)
-* [`adjust_entry_price()`](#adjust-entry-price)
-* [`leverage()`](#leverage-callback)
 
 !!! Tip "Callback calling sequence"
     You can find the callback calling sequence in [bot-basics](bot-basics.md#bot-execution-logic)
 
-## Bot start
-
-A simple callback which is called once when the strategy is loaded.
-This can be used to perform actions that must only be performed once and runs after dataprovider and wallet are set
-
-``` python
-import requests
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    def bot_start(self, **kwargs) -> None:
-        """
-        Called only once after bot instantiation.
-        :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.cust_remote_data = requests.get('https://some_remote_source.example.com')
-
-```
-
-During hyperopt, this runs only once at startup.
-
 ## 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).
@@ -75,16 +46,15 @@ class AwesomeStrategy(IStrategy):
 
 ```
 
-### Stake size management
+## 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: Optional[float], max_stake: float,
-                            leverage: float, entry_tag: Optional[str], side: str,
-                            **kwargs) -> 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()
@@ -109,25 +79,24 @@ Freqtrade will fall back to the `proposed_stake` value should your code raise an
 !!! Tip
     Returning `0` or `None` will prevent trades from being placed.
 
-## Custom exit signal
+## Custom sell signal
 
 Called for open trade every throttling iteration (roughly every 5 seconds) until a trade is closed.
 
-Allows to define custom exit signals, indicating that specified position should be sold. This is very useful when we need to customize exit conditions for each individual trade, or if you need trade data to make an exit decision.
+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_exit()`.
+For example you could implement a 1:2 risk-reward ROI with `custom_sell()`.
 
-Using `custom_exit()` 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.
+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 exit signal on a candle at specified time. This method is not called when exit signal is set already, or if exit signals are disabled (`use_exit_signal=False`). `string` max length is 64 characters. Exceeding this limit will cause the message to be truncated to 64 characters.
-    `custom_exit()` will ignore `exit_profit_only`, and will always be called unless `use_exit_signal=False`, even if there is a new enter signal.
+    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 exit trades that were open longer than one day:
+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_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
+    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()
@@ -151,11 +120,10 @@ See [Dataframe access](strategy-advanced.md#dataframe-access) for more informati
 
 ## Custom stoploss
 
-Called for open trade every iteration (roughly every 5 seconds) until a trade is closed.
-
+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), and is still mandatory.
+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.
@@ -190,7 +158,7 @@ class AwesomeStrategy(IStrategy):
         :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 exit_pricing.
+        :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
@@ -315,11 +283,11 @@ class AwesomeStrategy(IStrategy):
 
         # evaluate highest to lowest, so that highest possible stop is used
         if current_profit > 0.40:
-            return stoploss_from_open(0.25, current_profit, is_short=trade.is_short)
+            return stoploss_from_open(0.25, current_profit)
         elif current_profit > 0.25:
-            return stoploss_from_open(0.15, current_profit, is_short=trade.is_short)
+            return stoploss_from_open(0.15, current_profit)
         elif current_profit > 0.20:
-            return stoploss_from_open(0.07, current_profit, is_short=trade.is_short)
+            return stoploss_from_open(0.07, current_profit)
 
         # return maximum stoploss value, keeping current stoploss price unchanged
         return 1
@@ -395,7 +363,7 @@ class AwesomeStrategy(IStrategy):
     # ... populate_* methods
 
     def custom_entry_price(self, pair: str, current_time: datetime, proposed_rate: float, 
-                           entry_tag: Optional[str], side: str, **kwargs) -> float:
+                           entry_tag: Optional[str], **kwargs) -> float:
 
         dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
                                                                 timeframe=self.timeframe)
@@ -405,7 +373,7 @@ class AwesomeStrategy(IStrategy):
 
     def custom_exit_price(self, pair: str, trade: Trade,
                           current_time: datetime, proposed_rate: float,
-                          current_profit: float, exit_tag: Optional[str], **kwargs) -> float:
+                          current_profit: float, **kwargs) -> float:
 
         dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
                                                                 timeframe=self.timeframe)
@@ -423,7 +391,7 @@ class AwesomeStrategy(IStrategy):
 !!! 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 exit_signal and Custom exit. All other exit-types will use regular backtesting prices.
+    `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
 
@@ -438,7 +406,7 @@ However, freqtrade also offers a custom callback for both order types, which all
 ### Custom order timeout example
 
 Called for every open order until that order is either filled or cancelled.
-`check_entry_timeout()` is called for trade entries, while `check_exit_timeout()` is called for trade exit orders.
+`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.
@@ -447,7 +415,7 @@ The function must return either `True` (cancel order) or `False` (keep order ali
 
 ``` python
 from datetime import datetime, timedelta
-from freqtrade.persistence import Trade, Order
+from freqtrade.persistence import Trade
 
 class AwesomeStrategy(IStrategy):
 
@@ -455,12 +423,12 @@ class AwesomeStrategy(IStrategy):
 
     # Set unfilledtimeout to 25 hours, since the maximum timeout from below is 24 hours.
     unfilledtimeout = {
-        'entry': 60 * 25,
-        'exit': 60 * 25
+        'buy': 60 * 25,
+        'sell': 60 * 25
     }
 
-    def check_entry_timeout(self, pair: str, trade: 'Trade', order: 'Order',
-                            current_time: datetime, **kwargs) -> bool:
+    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):
@@ -470,7 +438,7 @@ class AwesomeStrategy(IStrategy):
         return False
 
 
-    def check_exit_timeout(self, pair: str, trade: Trade, order: 'Order',
+    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
@@ -488,7 +456,7 @@ class AwesomeStrategy(IStrategy):
 
 ``` python
 from datetime import datetime
-from freqtrade.persistence import Trade, Order
+from freqtrade.persistence import Trade
 
 class AwesomeStrategy(IStrategy):
 
@@ -496,26 +464,26 @@ class AwesomeStrategy(IStrategy):
 
     # Set unfilledtimeout to 25 hours, since the maximum timeout from below is 24 hours.
     unfilledtimeout = {
-        'entry': 60 * 25,
-        'exit': 60 * 25
+        'buy': 60 * 25,
+        'sell': 60 * 25
     }
 
-    def check_entry_timeout(self, pair: str, trade: 'Trade', order: 'Order',
-                            current_time: datetime, **kwargs) -> bool:
+    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:
+        if current_price > order['price'] * 1.02:
             return True
         return False
 
 
-    def check_exit_timeout(self, pair: str, trade: 'Trade', order: 'Order',
+    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:
+        if current_price < order['price'] * 0.98:
             return True
         return False
 ```
@@ -538,9 +506,9 @@ class AwesomeStrategy(IStrategy):
 
     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], 
-                            side: str, **kwargs) -> bool:
+                            **kwargs) -> bool:
         """
-        Called right before placing a entry order.
+        Called right before placing a buy order.
         Timing for this function is critical, so avoid doing heavy computations or
         network requests in this method.
 
@@ -548,15 +516,12 @@ class AwesomeStrategy(IStrategy):
 
         When not implemented by a strategy, returns True (always confirming).
 
-        :param pair: Pair that's about to be bought/shorted.
+        :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 (base) currency that's going to be traded.
+        :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
-                     or current rate for market orders.
         :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
         :param current_time: datetime object, containing the current datetime
-        :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
-        :param side: 'long' or 'short' - indicating the direction of the proposed trade
         :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
@@ -569,14 +534,6 @@ class AwesomeStrategy(IStrategy):
 
 `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).
 
-`confirm_trade_exit()` may be called multiple times within one iteration for the same trade if different exit-reasons apply.
-The exit-reasons (if applicable) will be in the following sequence:
-
-* `exit_signal` / `custom_exit`
-* `stop_loss`
-* `roi`
-* `trailing_stop_loss`
-
 ``` python
 from freqtrade.persistence import Trade
 
@@ -586,10 +543,10 @@ 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, exit_reason: str,
+                           rate: float, time_in_force: str, sell_reason: str,
                            current_time: datetime, **kwargs) -> bool:
         """
-        Called right before placing a regular exit order.
+        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.
 
@@ -597,22 +554,20 @@ class AwesomeStrategy(IStrategy):
 
         When not implemented by a strategy, returns True (always confirming).
 
-        :param pair: Pair for trade that's about to be exited.
-        :param trade: trade object.
+        :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 base currency.
+        :param amount: Amount in quote currency.
         :param rate: Rate that's going to be used when using limit orders
-                     or current rate for market orders.
         :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
-        :param exit_reason: Exit reason.
+        :param sell_reason: Sell reason.
             Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
-                           'exit_signal', 'force_exit', 'emergency_exit']
+                           '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, then the exit-order is placed on the exchange.
+        :return bool: When True is returned, then the sell-order is placed on the exchange.
             False aborts the process
         """
-        if exit_reason == 'force_exit' and trade.calc_profit_ratio(rate) < 0:
+        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)
@@ -621,15 +576,11 @@ class AwesomeStrategy(IStrategy):
 
 ```
 
-!!! Warning
-    `confirm_trade_exit()` can prevent stoploss exits, causing significant losses as this would ignore stoploss exits.
-    `confirm_trade_exit()` will not be called for Liquidations - as liquidations are forced by the exchange, and therefore cannot be rejected.
-
 ## 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) or to increase or decrease positions.
+`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.
 
@@ -637,14 +588,9 @@ The strategy is expected to return a stake_amount (in stake currency) between `m
 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.
-
+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.
 
-Additional Buys are ignored once you have reached the maximum amount of extra buys that you have set on `max_entry_position_adjustment`, but the callback is called anyway looking for partial exits.
-
-Position adjustments will always be applied in the direction of the trade, so a positive value will always increase your position (negative values will decrease your position), no matter if it's a long or short trade. Modifications to leverage are not 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.
@@ -652,12 +598,12 @@ Position adjustments will always be applied in the direction of the trade, so a
 
 !!! Warning
     Stoploss is still calculated from the initial opening price, not averaged price.
-    Regular stoploss rules still apply (cannot move down).
 
+!!! 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 run-time performance will be affected.
+    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
@@ -679,50 +625,30 @@ class DigDeeperStrategy(IStrategy):
     
     # 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: Optional[float], max_stake: float,
-                            leverage: float, entry_tag: Optional[str], side: str,
-                            **kwargs) -> 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: Optional[float], max_stake: float,
-                              current_entry_rate: float, current_exit_rate: float,
-                              current_entry_profit: float, current_exit_profit: float,
-                              **kwargs) -> Optional[float]:
+                              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 or decreased.
-        This means extra buy or sell orders with additional fees.
-        Only called when `position_adjustment_enable` is set to True.
-
-        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
-
-        When not implemented by a strategy, returns None
+        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 (for both entries and exits)
-        :param max_stake: Maximum stake allowed (either through balance, or by exchange limits).
-        :param current_entry_rate: Current rate using entry pricing.
-        :param current_exit_rate: Current rate using exit pricing.
-        :param current_entry_profit: Current profit using entry pricing.
-        :param current_exit_profit: Current profit using exit pricing.
+        :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,
-                       Positive values to increase position, Negative values to decrease position.
-                       Return None for no action.
+        :return float: Stake amount to adjust your trade
         """
         
-        if current_profit > 0.05 and trade.nr_of_successful_exits == 0:
-            # Take half of the profit at +5%
-            return -(trade.stake_amount / 2)
-
         if current_profit > -0.05:
             return None
 
@@ -734,8 +660,8 @@ class DigDeeperStrategy(IStrategy):
         if last_candle['close'] < previous_candle['close']:
             return None
 
-        filled_entries = trade.select_filled_orders(trade.entry_side)
-        count_of_entries = trade.nr_of_successful_entries
+        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%
@@ -746,9 +672,9 @@ class DigDeeperStrategy(IStrategy):
         # Hope you have a deep wallet!
         try:
             # This returns first order stake size
-            stake_amount = filled_entries[0].cost
+            stake_amount = filled_buys[0].cost
             # This then calculates current safety order size
-            stake_amount = stake_amount * (1 + (count_of_entries * 0.25))
+            stake_amount = stake_amount * (1 + (count_of_buys * 0.25))
             return stake_amount
         except Exception as exception:
             return None
@@ -756,117 +682,3 @@ class DigDeeperStrategy(IStrategy):
         return None
 
 ```
-
-### Position adjust calculations
-
-* Entry rates are calculated using weighted averages.
-* Exits will not influence the average entry rate.
-* Partial exit relative profit is relative to the average entry price at this point.
-* Final exit relative profit is calculated based on the total invested capital. (See example below)
-
-??? example "Calculation example"
-    *This example assumes 0 fees for simplicity, and a long position on an imaginary coin.*  
-    
-    * Buy 100@8\$ 
-    * Buy 100@9\$ -> Avg price: 8.5\$
-    * Sell 100@10\$ -> Avg price: 8.5\$, realized profit 150\$, 17.65%
-    * Buy 150@11\$ -> Avg price: 10\$, realized profit 150\$, 17.65%
-    * Sell 100@12\$ -> Avg price: 10\$, total realized profit 350\$, 20%
-    * Sell 150@14\$ -> Avg price: 10\$, total realized profit 950\$, 40%
-
-    The total profit for this trade was 950$ on a 3350$ investment (`100@8$ + 100@9$ + 150@11$`). As such - the final relative profit is 28.35% (`950 / 3350`).
-
-## Adjust Entry Price
-
-The `adjust_entry_price()` callback may be used by strategy developer to refresh/replace limit orders upon arrival of new candles.
-Be aware that `custom_entry_price()` is still the one dictating initial entry limit order price target at the time of entry trigger.
-
-Orders can be cancelled out of this callback by returning `None`.
-
-Returning `current_order_rate` will keep the order on the exchange "as is".
-Returning any other price will cancel the existing order, and replace it with a new order.
-
-The trade open-date (`trade.open_date_utc`) will remain at the time of the very first order placed.
-Please make sure to be aware of this - and eventually adjust your logic in other callbacks to account for this, and use the date of the first filled order instead.
-
-!!! Warning "Regular timeout"
-    Entry `unfilledtimeout` mechanism (as well as `check_entry_timeout()`) takes precedence over this.
-    Entry Orders that are cancelled via the above methods will not have this callback called. Be sure to update timeout values to match your expectations.
-
-```python
-from freqtrade.persistence import Trade
-from datetime import timedelta
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    def adjust_entry_price(self, trade: Trade, order: Optional[Order], pair: str,
-                           current_time: datetime, proposed_rate: float, current_order_rate: float,
-                           entry_tag: Optional[str], side: str, **kwargs) -> float:
-        """
-        Entry price re-adjustment logic, returning the user desired limit price.
-        This only executes when a order was already placed, still open (unfilled fully or partially)
-        and not timed out on subsequent candles after entry trigger.
-
-        When not implemented by a strategy, returns current_order_rate as default.
-        If current_order_rate is returned then the existing order is maintained.
-        If None is returned then order gets canceled but not replaced by a new one.
-
-        :param pair: Pair that's currently analyzed
-        :param trade: Trade object.
-        :param order: Order object
-        :param current_time: datetime object, containing the current datetime
-        :param proposed_rate: Rate, calculated based on pricing settings in entry_pricing.
-        :param current_order_rate: Rate of the existing order in place.
-        :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
-        :param side: 'long' or 'short' - indicating the direction of the proposed trade
-        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
-        :return float: New entry price value if provided
-
-        """
-        # Limit orders to use and follow SMA200 as price target for the first 10 minutes since entry trigger for BTC/USDT pair.
-        if pair == 'BTC/USDT' and entry_tag == 'long_sma200' and side == 'long' and (current_time - timedelta(minutes=10) > trade.open_date_utc:
-            # just cancel the order if it has been filled more than half of the amount
-            if order.filled > order.remaining:
-                return None
-            else:
-                dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
-                current_candle = dataframe.iloc[-1].squeeze()
-                # desired price
-                return current_candle['sma_200']
-        # default: maintain existing order
-        return current_order_rate
-```
-
-## Leverage Callback
-
-When trading in markets that allow leverage, this method must return the desired Leverage (Defaults to 1 -> No leverage).
-
-Assuming a capital of 500USDT, a trade with leverage=3 would result in a position with 500 x 3 = 1500 USDT.
-
-Values that are above `max_leverage` will be adjusted to `max_leverage`.
-For markets / exchanges that don't support leverage, this method is ignored.
-
-``` python
-class AwesomeStrategy(IStrategy):
-    def leverage(self, pair: str, current_time: datetime, current_rate: float,
-                 proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
-                 **kwargs) -> float:
-        """
-        Customize leverage for each new trade. This method is only called in futures mode.
-
-        :param pair: Pair that's currently analyzed
-        :param current_time: datetime object, containing the current datetime
-        :param current_rate: Rate, calculated based on pricing settings in exit_pricing.
-        :param proposed_leverage: A leverage proposed by the bot.
-        :param max_leverage: Max leverage allowed on this pair
-        :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
-        :param side: 'long' or 'short' - indicating the direction of the proposed trade
-        :return: A leverage amount, which is between 1.0 and max_leverage.
-        """
-        return 1.0
-```
-
-All profit calculations include leverage. Stoploss / ROI also include leverage in their calculation.
-Defining a stoploss of 10% at 10x leverage would trigger the stoploss with a 1% move to the downside.

docs/strategy-customization.md

@@ -26,8 +26,8 @@ This will create a new strategy file from a template, which will be located unde
 A strategy file contains all the information needed to build a good strategy:
 
 - Indicators
-- Entry strategy rules
-- Exit strategy rules
+- Buy strategy rules
+- Sell strategy rules
 - Minimal ROI recommended
 - Stoploss strongly recommended
 
@@ -35,7 +35,7 @@ The bot also include a sample strategy called `SampleStrategy` you can update: `
 You can test it with the parameter: `--strategy SampleStrategy`
 
 Additionally, there is an attribute called `INTERFACE_VERSION`, which defines the version of the strategy interface the bot should use.
-The current version is 3 - which is also the default when it's not set explicitly in the strategy.
+The current version is 2 - which is also the default when it's not set explicitly in the strategy.
 
 Future versions will require this to be set.
 
@@ -82,7 +82,7 @@ As a dataframe is a table, simple python comparisons like the following will not
 
 ``` python
     if dataframe['rsi'] > 30:
-        dataframe['enter_long'] = 1
+        dataframe['buy'] = 1
 ```
 
 The above section will fail with `The truth value of a Series is ambiguous. [...]`.
@@ -92,16 +92,16 @@ This must instead be written in a pandas-compatible way, so the operation is per
 ``` python
     dataframe.loc[
         (dataframe['rsi'] > 30)
-    , 'enter_long'] = 1
+    , '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 signals need indicators. You can add more indicators by extending the list contained in the method `populate_indicators()` from your strategy file.
+Buy and sell strategies need indicators. You can add more indicators by extending the list contained in the method `populate_indicators()` from your strategy file.
 
-You should only add the indicators used in either `populate_entry_trend()`, `populate_exit_trend()`, or to populate another indicator, otherwise performance may suffer.
+You should only add the indicators used in either `populate_buy_trend()`, `populate_sell_trend()`, or to populate another indicator, otherwise performance may suffer.
 
 It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
 
@@ -199,18 +199,18 @@ If this data is available, indicators will be calculated with this extended time
 !!! Note
     If data for the startup period is not available, then the timerange will be adjusted to account for this startup period - so Backtesting would start at 2019-01-01 08:30:00.
 
-### Entry signal rules
+### Buy signal rules
 
-Edit the method `populate_entry_trend()` in your strategy file to update your entry strategy.
+Edit the method `populate_buy_trend()` in your strategy file to update your buy strategy.
 
 It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
 
-This method will also define a new column, `"enter_long"` (`"enter_short"` for shorts), which needs to contain 1 for entries, and 0 for "no action". `enter_long` is a mandatory column that must be set even if the strategy is shorting only.
+This method will also define a new column, `"buy"`, which needs to contain 1 for buys, and 0 for "no action".
 
 Sample from `user_data/strategies/sample_strategy.py`:
 
 ```python
-def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
     """
     Based on TA indicators, populates the buy signal for the given dataframe
     :param dataframe: DataFrame populated with indicators
@@ -224,58 +224,29 @@ def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFram
             (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard
             (dataframe['volume'] > 0)  # Make sure Volume is not 0
         ),
-        ['enter_long', 'enter_tag']] = (1, 'rsi_cross')
+        'buy'] = 1
 
     return dataframe
 ```
 
-??? Note "Enter short trades"
-    Short-entries can be created by setting `enter_short` (corresponds to `enter_long` for long trades).
-    The `enter_tag` column remains identical.
-    Short-trades need to be supported by your exchange and market configuration!
-    Please make sure to set [`can_short`]() appropriately on your strategy if you intend to short.
-
-    ```python
-    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        dataframe.loc[
-            (
-                (qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
-                (dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard
-                (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard
-                (dataframe['volume'] > 0)  # Make sure Volume is not 0
-            ),
-            ['enter_long', 'enter_tag']] = (1, 'rsi_cross')
-
-        dataframe.loc[
-            (
-                (qtpylib.crossed_below(dataframe['rsi'], 70)) &  # Signal: RSI crosses below 70
-                (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard
-                (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard
-                (dataframe['volume'] > 0)  # Make sure Volume is not 0
-            ),
-            ['enter_short', 'enter_tag']] = (1, 'rsi_cross')
-
-        return dataframe
-    ```
-
 !!! Note
     Buying requires sellers to buy from - therefore volume needs to be > 0 (`dataframe['volume'] > 0`) to make sure that the bot does not buy/sell in no-activity periods.
 
-### Exit signal rules
+### Sell signal rules
 
-Edit the method `populate_exit_trend()` into your strategy file to update your exit strategy.
-Please note that the exit-signal is only used if `use_exit_signal` is set to true in the configuration.
+Edit the method `populate_sell_trend()` into your strategy file to update your sell strategy.
+Please note that the sell-signal is only used if `use_sell_signal` is set to true in the configuration.
 
 It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
 
-This method will also define a new column, `"exit_long"` (`"exit_short"` for shorts), which needs to contain 1 for exits, and 0 for "no action".
+This method will also define a new column, `"sell"`, which needs to contain 1 for sells, and 0 for "no action".
 
 Sample from `user_data/strategies/sample_strategy.py`:
 
 ```python
-def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
     """
-    Based on TA indicators, populates the exit signal for the given dataframe
+    Based on TA indicators, populates the sell signal for the given dataframe
     :param dataframe: DataFrame populated with indicators
     :param metadata: Additional information, like the currently traded pair
     :return: DataFrame with buy column
@@ -287,39 +258,13 @@ def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame
             (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard
             (dataframe['volume'] > 0)  # Make sure Volume is not 0
         ),
-        ['exit_long', 'exit_tag']] = (1, 'rsi_too_high')
+        'sell'] = 1
     return dataframe
 ```
 
-??? Note "Exit short trades"
-    Short-exits can be created by setting `exit_short` (corresponds to `exit_long`).
-    The `exit_tag` column remains identical.
-    Short-trades need to be supported by your exchange and market configuration!
-
-    ```python
-    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        dataframe.loc[
-            (
-                (qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70
-                (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard
-                (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard
-                (dataframe['volume'] > 0)  # Make sure Volume is not 0
-            ),
-            ['exit_long', 'exit_tag']] = (1, 'rsi_too_high')
-        dataframe.loc[
-            (
-                (qtpylib.crossed_below(dataframe['rsi'], 30)) &  # Signal: RSI crosses below 30
-                (dataframe['tema'] < dataframe['bb_middleband']) &  # Guard
-                (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard
-                (dataframe['volume'] > 0)  # Make sure Volume is not 0
-            ),
-            ['exit_short', 'exit_tag']] = (1, 'rsi_too_low')
-        return dataframe
-    ```
-
 ### Minimal ROI
 
-This dict defines the minimal Return On Investment (ROI) a trade should reach before exiting, independent from the exit signal.
+This dict defines the minimal Return On Investment (ROI) a trade should reach before selling, independent from the sell signal.
 
 It is of the following format, with the dict key (left side of the colon) being the minutes passed since the trade opened, and the value (right side of the colon) being the percentage.
 
@@ -334,10 +279,10 @@ minimal_roi = {
 
 The above configuration would therefore mean:
 
-- Exit whenever 4% profit was reached
-- Exit when 2% profit was reached (in effect after 20 minutes)
-- Exit when 1% profit was reached (in effect after 30 minutes)
-- Exit when trade is non-loosing (in effect after 40 minutes)
+- Sell whenever 4% profit was reached
+- Sell when 2% profit was reached (in effect after 20 minutes)
+- Sell when 1% profit was reached (in effect after 30 minutes)
+- Sell when trade is non-loosing (in effect after 40 minutes)
 
 The calculation does include fees.
 
@@ -349,7 +294,7 @@ minimal_roi = {
 }
 ```
 
-While technically not completely disabled, this would exit once the trade reaches 10000% Profit.
+While technically not completely disabled, this would sell once the trade reaches 10000% Profit.
 
 To use times based on candle duration (timeframe), the following snippet can be handy.
 This will allow you to change the timeframe for the strategy, and ROI times will still be set as candles (e.g. after 3 candles ...)
@@ -380,24 +325,18 @@ stoploss = -0.10
 
 For the full documentation on stoploss features, look at the dedicated [stoploss page](stoploss.md).
 
-### Timeframe
+### Timeframe (formerly ticker interval)
 
 This is the set of candles the bot should download and use for the analysis.
 Common values are `"1m"`, `"5m"`, `"15m"`, `"1h"`, however all values supported by your exchange should work.
 
-Please note that the same entry/exit signals may work well with one timeframe, but not with the others.
+Please note that the same buy/sell signals may work well with one timeframe, but not with the others.
 
 This setting is accessible within the strategy methods as the `self.timeframe` attribute.
 
-### Can short
-
-To use short signals in futures markets, you will have to let us know to do so by setting `can_short=True`.
-Strategies which enable this will fail to load on spot markets.
-Disabling of this will have short signals ignored (also in futures markets).
-
 ### Metadata dict
 
-The metadata-dict (available for `populate_entry_trend`, `populate_exit_trend`, `populate_indicators`) contains additional information.
+The metadata-dict (available for `populate_buy_trend`, `populate_sell_trend`, `populate_indicators`) contains additional information.
 Currently this is `pair`, which can be accessed using `metadata['pair']` - and will return a pair in the format `XRP/BTC`.
 
 The Metadata-dict should not be modified and does not persist information across multiple calls.
@@ -443,19 +382,6 @@ A full sample can be found [in the DataProvider section](#complete-data-provider
     It is however better to use resampling to longer timeframes whenever possible
     to avoid hammering the exchange with too many requests and risk being blocked.
 
-??? Note "Alternative candle types"
-    Informative_pairs can also provide a 3rd tuple element defining the candle type explicitly.
-    Availability of alternative candle-types will depend on the trading-mode and the exchange. Details about this can be found in the exchange documentation.
-
-    ``` python
-    def informative_pairs(self):
-        return [
-            ("ETH/USDT", "5m", ""),   # Uses default candletype, depends on trading_mode 
-            ("ETH/USDT", "5m", "spot"),   # Forces usage of spot candles
-            ("BTC/TUSD", "15m", "futures"),  # Uses futures candles
-            ("BTC/TUSD", "15m", "mark"),  # Uses mark candles
-        ]
-    ```
 ***
 
 ### Informative pairs decorator (`@informative()`)
@@ -469,8 +395,6 @@ for more information.
     ``` python
     def informative(timeframe: str, asset: str = '',
                     fmt: Optional[Union[str, Callable[[KwArg(str)], str]]] = None,
-                    *,
-                    candle_type: Optional[CandleType] = None,
                     ffill: bool = True) -> Callable[[PopulateIndicators], PopulateIndicators]:
         """
         A decorator for populate_indicators_Nn(self, dataframe, metadata), allowing these functions to
@@ -499,7 +423,6 @@ for more information.
         * {column} - name of dataframe column.
         * {timeframe} - timeframe of informative dataframe.
         :param ffill: ffill dataframe after merging informative pair.
-        :param candle_type: '', mark, index, premiumIndex, or funding_rate
         """
     ```
 
@@ -528,7 +451,7 @@ for more information.
 
         # Define BTC/STAKE informative pair. Available in populate_indicators and other methods as
         # 'btc_rsi_1h'. Current stake currency should be specified as {stake} format variable 
-        # instead of hard-coding actual stake currency. Available in populate_indicators and other 
+        # instead of hardcoding actual stake currency. Available in populate_indicators and other 
         # methods as 'btc_usdt_rsi_1h' (when stake currency is USDT).
         @informative('1h', 'BTC/{stake}')
         def populate_indicators_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -567,7 +490,7 @@ for more information.
     Use string formatting when accessing informative dataframes of other pairs. This will allow easily changing stake currency in config without having to adjust strategy code.
 
     ``` python
-    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         stake = self.config['stake_currency']
         dataframe.loc[
             (
@@ -575,7 +498,7 @@ for more information.
                 &
                 (dataframe['volume'] > 0)
             ),
-            ['enter_long', 'enter_tag']] = (1, 'buy_signal_rsi')
+            ['buy', 'buy_tag']] = (1, 'buy_signal_rsi')
     
         return dataframe
     ```
@@ -587,6 +510,7 @@ for more information.
     will overwrite previously defined method and not produce any errors due to limitations of Python programming language. In such cases you will find that indicators
     created in earlier-defined methods are not available in the dataframe. Carefully review method names and make sure they are unique!
 
+
 ## Additional data (DataProvider)
 
 The strategy provides access to the `DataProvider`. This allows you to get additional data to use in your strategy.
@@ -617,8 +541,9 @@ Please always check the mode of operation to select the correct method to get da
 ### *available_pairs*
 
 ``` python
-for pair, timeframe in self.dp.available_pairs:
-    print(f"available {pair}, {timeframe}")
+if self.dp:
+    for pair, timeframe in self.dp.available_pairs:
+        print(f"available {pair}, {timeframe}")
 ```
 
 ### *current_whitelist()*
@@ -629,7 +554,7 @@ 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 `5m` candles into daily candles for use in a 14 day RSI. Most exchanges limit us to just 500-1000 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 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.
 
@@ -645,16 +570,14 @@ This is where calling `self.dp.current_whitelist()` comes in handy.
         return informative_pairs
 ```
 
-??? Note "Plotting with current_whitelist"
-    Current whitelist is not supported for `plot-dataframe`, as this command is usually used by providing an explicit pairlist - and would therefore make the return values of this method misleading.
-
 ### *get_pair_dataframe(pair, timeframe)*
 
 ``` python
 # fetch live / historical candle (OHLCV) data for the first informative pair
-inf_pair, inf_timeframe = self.informative_pairs()[0]
-informative = self.dp.get_pair_dataframe(pair=inf_pair,
-                                            timeframe=inf_timeframe)
+if self.dp:
+    inf_pair, inf_timeframe = self.informative_pairs()[0]
+    informative = self.dp.get_pair_dataframe(pair=inf_pair,
+                                             timeframe=inf_timeframe)
 ```
 
 !!! Warning "Warning about backtesting"
@@ -669,9 +592,10 @@ It can also be used in specific callbacks to get the signal that caused the acti
 
 ``` python
 # fetch current dataframe
-if self.dp.runmode.value in ('live', 'dry_run'):
-    dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=metadata['pair'],
-                                                                timeframe=self.timeframe)
+if self.dp:
+    if self.dp.runmode.value in ('live', 'dry_run'):
+        dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=metadata['pair'],
+                                                                 timeframe=self.timeframe)
 ```
 
 !!! Note "No data available"
@@ -681,10 +605,11 @@ if self.dp.runmode.value in ('live', 'dry_run'):
 ### *orderbook(pair, maximum)*
 
 ``` python
-if self.dp.runmode.value in ('live', 'dry_run'):
-    ob = self.dp.orderbook(metadata['pair'], 1)
-    dataframe['best_bid'] = ob['bids'][0][0]
-    dataframe['best_ask'] = ob['asks'][0][0]
+if self.dp:
+    if self.dp.runmode.value in ('live', 'dry_run'):
+        ob = self.dp.orderbook(metadata['pair'], 1)
+        dataframe['best_bid'] = ob['bids'][0][0]
+        dataframe['best_ask'] = ob['asks'][0][0]
 ```
 
 The orderbook structure is aligned with the order structure from [ccxt](https://github.com/ccxt/ccxt/wiki/Manual#order-book-structure), so the result will look as follows:
@@ -713,11 +638,12 @@ Therefore, using `ob['bids'][0][0]` as demonstrated above will result in using t
 ### *ticker(pair)*
 
 ``` python
-if self.dp.runmode.value in ('live', 'dry_run'):
-    ticker = self.dp.ticker(metadata['pair'])
-    dataframe['last_price'] = ticker['last']
-    dataframe['volume24h'] = ticker['quoteVolume']
-    dataframe['vwap'] = ticker['vwap']
+if self.dp:
+    if self.dp.runmode.value in ('live', 'dry_run'):
+        ticker = self.dp.ticker(metadata['pair'])
+        dataframe['last_price'] = ticker['last']
+        dataframe['volume24h'] = ticker['quoteVolume']
+        dataframe['vwap'] = ticker['vwap']
 ```
 
 !!! Warning
@@ -727,24 +653,7 @@ if self.dp.runmode.value in ('live', 'dry_run'):
     data returned from the exchange and add appropriate error handling / defaults.
 
 !!! Warning "Warning about backtesting"
-    This method will always return up-to-date values - so usage during backtesting / hyperopt without runmode checks will lead to wrong results.
-
-### Send Notification
-
-The dataprovider `.send_msg()` function allows you to send custom notifications from your strategy.
-Identical notifications will only be sent once per candle, unless the 2nd argument (`always_send`) is set to True.
-
-``` python
-    self.dp.send_msg(f"{metadata['pair']} just got hot!")
-
-    # Force send this notification, avoid caching (Please read warning below!)
-    self.dp.send_msg(f"{metadata['pair']} just got hot!", always_send=True)
-```
-
-Notifications will only be sent in trading modes (Live/Dry-run) - so this method can be called without conditions for backtesting.
-
-!!! Warning "Spamming"
-    You can spam yourself pretty good by setting `always_send=True` in this method. Use this with great care and only in conditions you know will not happen throughout a candle to avoid a message every 5 seconds.
+    This method will always return up-to-date values - so usage during backtesting / hyperopt will lead to wrong results.
 
 ### Complete Data-provider sample
 
@@ -797,7 +706,7 @@ class SampleStrategy(IStrategy):
 
         return dataframe
 
-    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
 
         dataframe.loc[
             (
@@ -805,7 +714,7 @@ class SampleStrategy(IStrategy):
                 (dataframe['rsi_1d'] < 30) &                     # Ensure daily RSI is < 30
                 (dataframe['volume'] > 0)                        # Ensure this candle had volume (important for backtesting)
             ),
-            ['enter_long', 'enter_tag']] = (1, 'rsi_cross')
+            'buy'] = 1
 
 ```
 
@@ -882,7 +791,7 @@ Stoploss values returned from `custom_stoploss` must specify a percentage relati
 
     Say the open price was $100, and `current_price` is $121 (`current_profit` will be `0.21`).  
 
-    If we want a stop price at 7% above the open price we can call `stoploss_from_open(0.07, current_profit, False)` which will return `0.1157024793`.  11.57% below $121 is $107, which is the same as 7% above $100.
+    If we want a stop price at 7% above the open price we can call `stoploss_from_open(0.07, current_profit)` which will return `0.1157024793`.  11.57% below $121 is $107, which is the same as 7% above $100.
 
 
     ``` python
@@ -902,7 +811,7 @@ Stoploss values returned from `custom_stoploss` must specify a percentage relati
 
             # once the profit has risen above 10%, keep the stoploss at 7% above the open price
             if current_profit > 0.10:
-                return stoploss_from_open(0.07, current_profit, is_short=trade.is_short)
+                return stoploss_from_open(0.07, current_profit)
 
             return 1
 
@@ -913,7 +822,7 @@ Stoploss values returned from `custom_stoploss` must specify a percentage relati
 !!! Note
     Providing invalid input to `stoploss_from_open()` may produce "CustomStoploss function did not return valid stoploss" warnings.
     This may happen if `current_profit` parameter is below specified `open_relative_stop`. Such situations may arise when closing trade
-    is blocked by `confirm_trade_exit()` method. Warnings can be solved by never blocking stop loss sells by checking `exit_reason` in
+    is blocked by `confirm_trade_exit()` method. Warnings can be solved by never blocking stop loss sells by checking `sell_reason` in
     `confirm_trade_exit()`, or by using `return stoploss_from_open(...) or 1` idiom, which will request to not change stop loss when
     `current_profit < open_relative_stop`.
 
@@ -923,7 +832,7 @@ In some situations it may be confusing to deal with stops relative to current ra
 
 ??? Example "Returning a stoploss using absolute price from the custom stoploss function"
 
-    If we want to trail a stop price at 2xATR below current price we can call `stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate, is_short=trade.is_short)`.
+    If we want to trail a stop price at 2xATR below current proce we can call `stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate)`.
 
     ``` python
 
@@ -943,7 +852,7 @@ In some situations it may be confusing to deal with stops relative to current ra
                             current_rate: float, current_profit: float, **kwargs) -> float:
             dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
             candle = dataframe.iloc[-1].squeeze()
-            return stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate, is_short=trade.is_short)
+            return stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate)
 
     ```
 
@@ -1011,7 +920,7 @@ if self.config['runmode'].value in ('live', 'dry_run'):
 Sample return value: ETH/BTC had 5 trades, with a total profit of 1.5% (ratio of 0.015).
 
 ``` json
-{"pair": "ETH/BTC", "profit": 0.015, "count": 5}
+{'pair': "ETH/BTC", 'profit': 0.015, 'count': 5}
 ```
 
 !!! Warning
@@ -1065,16 +974,16 @@ if self.config['runmode'].value in ('live', 'dry_run'):
 
 ## Print created dataframe
 
-To inspect the created dataframe, you can issue a print-statement in either `populate_entry_trend()` or `populate_exit_trend()`.
+To inspect the created dataframe, you can issue a print-statement in either `populate_buy_trend()` or `populate_sell_trend()`.
 You may also want to print the pair so it's clear what data is currently shown.
 
 ``` python
-def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
     dataframe.loc[
         (
             #>> whatever condition<<<
         ),
-        ['enter_long', 'enter_tag']] = (1, 'somestring')
+        'buy'] = 1
 
     # Print the Analyzed pair
     print(f"result for {metadata['pair']}")
@@ -1103,12 +1012,7 @@ The following lists some common patterns which should be avoided to prevent frus
 
 ### Colliding signals
 
-When conflicting signals collide (e.g. both `'enter_long'` and `'exit_long'` are 1), freqtrade will do nothing and ignore the entry signal. This will avoid trades that enter, and exit immediately. Obviously, this can potentially lead to missed entries.
-
-The following rules apply, and entry signals will be ignored if more than one of the 3 signals is set:
-
-- `enter_long` -> `exit_long`, `enter_short`
-- `enter_short` -> `exit_short`, `enter_long`
+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
 

docs/strategy_analysis_example.md

@@ -14,7 +14,7 @@ from freqtrade.configuration import Configuration
 
 # Initialize empty configuration object
 config = Configuration.from_files([])
-# Optionally (recommended), use existing configuration file
+# Optionally, use existing configuration file
 # config = Configuration.from_files(["config.json"])
 
 # Define some constants
@@ -22,7 +22,7 @@ config["timeframe"] = "5m"
 # Name of the strategy class
 config["strategy"] = "SampleStrategy"
 # Location of the data
-data_location = config['datadir']
+data_location = Path(config['user_data_dir'], 'data', 'binance')
 # Pair to analyze - Only use one pair here
 pair = "BTC/USDT"
 ```
@@ -31,13 +31,11 @@ pair = "BTC/USDT"
 ```python
 # Load data using values set above
 from freqtrade.data.history import load_pair_history
-from freqtrade.enums import CandleType
 
 candles = load_pair_history(datadir=data_location,
                             timeframe=config["timeframe"],
                             pair=pair,
                             data_format = "hdf5",
-                            candle_type=CandleType.SPOT,
                             )
 
 # Confirm success
@@ -75,7 +73,7 @@ df.tail()
 
 ```python
 # Report results
-print(f"Generated {df['enter_long'].sum()} entry signals")
+print(f"Generated {df['buy'].sum()} buy signals")
 data = df.set_index('date', drop=False)
 data.tail()
 ```
@@ -131,7 +129,7 @@ print(stats['strategy_comparison'])
 trades = load_backtest_data(backtest_dir)
 
 # Show value-counts per pair
-trades.groupby("pair")["exit_reason"].value_counts()
+trades.groupby("pair")["sell_reason"].value_counts()
 ```
 
 ## Plotting daily profit / equity line
@@ -184,7 +182,7 @@ from freqtrade.data.btanalysis import load_trades_from_db
 trades = load_trades_from_db("sqlite:///tradesv3.sqlite")
 
 # Display results
-trades.groupby("pair")["exit_reason"].value_counts()
+trades.groupby("pair")["sell_reason"].value_counts()
 ```
 
 ## Analyze the loaded trades for trade parallelism
@@ -246,7 +244,7 @@ import plotly.figure_factory as ff
 hist_data = [trades.profit_ratio]
 group_labels = ['profit_ratio']  # name of the dataset
 
-fig = ff.create_distplot(hist_data, group_labels, bin_size=0.01)
+fig = ff.create_distplot(hist_data, group_labels,bin_size=0.01)
 fig.show()
 
 ```

docs/strategy_migration.md

@@ -1,471 +0,0 @@
-# Strategy Migration between V2 and V3
-
-To support new markets and trade-types (namely short trades / trades with leverage), some things had to change in the interface.
-If you intend on using markets other than spot markets, please migrate your strategy to the new format.
-
-We have put a great effort into keeping compatibility with existing strategies, so if you just want to continue using freqtrade in __spot markets__, there should be no changes necessary for now.
-
-You can use the quick summary as checklist. Please refer to the detailed sections below for full migration details.
-
-## Quick summary / migration checklist
-
-Note : `forcesell`, `forcebuy`, `emergencysell` are changed to `force_exit`, `force_enter`, `emergency_exit` respectively.
-
-* Strategy methods:
-  * [`populate_buy_trend()` -> `populate_entry_trend()`](#populate_buy_trend)
-  * [`populate_sell_trend()` -> `populate_exit_trend()`](#populate_sell_trend)
-  * [`custom_sell()` -> `custom_exit()`](#custom_sell)
-  * [`check_buy_timeout()` -> `check_entry_timeout()`](#custom_entry_timeout)
-  * [`check_sell_timeout()` -> `check_exit_timeout()`](#custom_entry_timeout)
-  * New `side` argument to callbacks without trade object
-    * [`custom_stake_amount`](#custom_stake_amount)
-    * [`confirm_trade_entry`](#confirm_trade_entry)
-    * [`custom_entry_price`](#custom_entry_price)
-  * [Changed argument name in `confirm_trade_exit`](#confirm_trade_exit)
-* Dataframe columns:
-  * [`buy` -> `enter_long`](#populate_buy_trend)
-  * [`sell` -> `exit_long`](#populate_sell_trend)
-  * [`buy_tag` -> `enter_tag` (used for both long and short trades)](#populate_buy_trend)
-  * [New column `enter_short` and corresponding new column `exit_short`](#populate_sell_trend)
-* trade-object now has the following new properties:
-  * `is_short`
-  * `entry_side`
-  * `exit_side`
-  * `trade_direction`
-  * renamed: `sell_reason` -> `exit_reason`
-* [Renamed `trade.nr_of_successful_buys` to `trade.nr_of_successful_entries` (mostly relevant for `adjust_trade_position()`)](#adjust-trade-position-changes)
-* Introduced new [`leverage` callback](strategy-callbacks.md#leverage-callback).
-* Informative pairs can now pass a 3rd element in the Tuple, defining the candle type.
-* `@informative` decorator now takes an optional `candle_type` argument.
-* [helper methods](#helper-methods) `stoploss_from_open` and `stoploss_from_absolute` now take `is_short` as additional argument.
-* `INTERFACE_VERSION` should be set to 3.
-* [Strategy/Configuration settings](#strategyconfiguration-settings).
-  * `order_time_in_force` buy -> entry, sell -> exit.
-  * `order_types` buy -> entry, sell -> exit.
-  * `unfilledtimeout` buy -> entry, sell -> exit.
-* Terminology changes
-  * Sell reasons changed to reflect the new naming of "exit" instead of sells. Be careful in your strategy if you're using `exit_reason` checks and eventually update your strategy.
-    * `sell_signal` -> `exit_signal`
-    * `custom_sell` -> `custom_exit`
-    * `force_sell` -> `force_exit`
-    * `emergency_sell` -> `emergency_exit`
-  * Webhook terminology changed from "sell" to "exit", and from "buy" to entry
-    * `webhookbuy` -> `webhookentry`
-    * `webhookbuyfill` -> `webhookentryfill`
-    * `webhookbuycancel` -> `webhookentrycancel`
-    * `webhooksell` -> `webhookexit`
-    * `webhooksellfill` -> `webhookexitfill`
-    * `webhooksellcancel` -> `webhookexitcancel`
-  * Telegram notification settings
-    * `buy` -> `entry`
-    * `buy_fill` -> `entry_fill`
-    * `buy_cancel` -> `entry_cancel`
-    * `sell` -> `exit`
-    * `sell_fill` -> `exit_fill`
-    * `sell_cancel` -> `exit_cancel`
-  * Strategy/config settings:
-    * `use_sell_signal` -> `use_exit_signal`
-    * `sell_profit_only` -> `exit_profit_only`
-    * `sell_profit_offset` -> `exit_profit_offset`
-    * `ignore_roi_if_buy_signal` -> `ignore_roi_if_entry_signal`
-    * `forcebuy_enable` -> `force_entry_enable`
-
-## Extensive explanation
-
-### `populate_buy_trend`
-
-In `populate_buy_trend()` - you will want to change the columns you assign from `'buy`' to `'enter_long'`, as well as the method name from `populate_buy_trend` to `populate_entry_trend`.
-
-```python hl_lines="1 9"
-def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-    dataframe.loc[
-        (
-            (qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
-            (dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard
-            (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard
-            (dataframe['volume'] > 0)  # Make sure Volume is not 0
-        ),
-        ['buy', 'buy_tag']] = (1, 'rsi_cross')
-
-    return dataframe
-```
-
-After:
-
-```python hl_lines="1 9"
-def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-    dataframe.loc[
-        (
-            (qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
-            (dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard
-            (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard
-            (dataframe['volume'] > 0)  # Make sure Volume is not 0
-        ),
-        ['enter_long', 'enter_tag']] = (1, 'rsi_cross')
-
-    return dataframe
-```
-
-Please refer to the [Strategy documentation](strategy-customization.md#entry-signal-rules) on how to enter and exit short trades.
-
-### `populate_sell_trend`
-
-Similar to `populate_buy_trend`, `populate_sell_trend()` will be renamed to `populate_exit_trend()`.
-We'll also change the column from `'sell'` to `'exit_long'`.
-
-``` python hl_lines="1 9"
-def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-    dataframe.loc[
-        (
-            (qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70
-            (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard
-            (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard
-            (dataframe['volume'] > 0)  # Make sure Volume is not 0
-        ),
-        ['sell', 'exit_tag']] = (1, 'some_exit_tag')
-    return dataframe
-```
-
-After
-
-``` python hl_lines="1 9"
-def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-    dataframe.loc[
-        (
-            (qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70
-            (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard
-            (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard
-            (dataframe['volume'] > 0)  # Make sure Volume is not 0
-        ),
-        ['exit_long', 'exit_tag']] = (1, 'some_exit_tag')
-    return dataframe
-```
-
-Please refer to the [Strategy documentation](strategy-customization.md#exit-signal-rules) on how to enter and exit short trades.
-
-### `custom_sell`
-
-`custom_sell` has been renamed to `custom_exit`.
-It's now also being called for every iteration, independent of current profit and `exit_profit_only` settings.
-
-``` python hl_lines="2"
-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()
-        # ...
-```
-
-``` python hl_lines="2"
-class AwesomeStrategy(IStrategy):
-    def custom_exit(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()
-        # ...
-```
-
-### `custom_entry_timeout`
-
-`check_buy_timeout()` has been renamed to `check_entry_timeout()`, and `check_sell_timeout()` has been renamed to `check_exit_timeout()`.
-
-``` python hl_lines="2 6"
-class AwesomeStrategy(IStrategy):
-    def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict, 
-                            current_time: datetime, **kwargs) -> bool:
-        return False
-
-    def check_sell_timeout(self, pair: str, trade: 'Trade', order: dict, 
-                            current_time: datetime, **kwargs) -> bool:
-        return False 
-```
-
-``` python hl_lines="2 6"
-class AwesomeStrategy(IStrategy):
-    def check_entry_timeout(self, pair: str, trade: 'Trade', order: 'Order', 
-                            current_time: datetime, **kwargs) -> bool:
-        return False
-
-    def check_exit_timeout(self, pair: str, trade: 'Trade', order: 'Order', 
-                            current_time: datetime, **kwargs) -> bool:
-        return False 
-```
-
-### `custom_stake_amount`
-
-New string argument `side` - which can be either `"long"` or `"short"`.
-
-``` python hl_lines="4"
-class AwesomeStrategy(IStrategy):
-    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
-                            proposed_stake: float, min_stake: Optional[float], max_stake: float,
-                            entry_tag: Optional[str], **kwargs) -> float:
-        # ... 
-        return proposed_stake
-```
-
-``` python hl_lines="4"
-class AwesomeStrategy(IStrategy):
-    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
-                            proposed_stake: float, min_stake: Optional[float], max_stake: float,
-                            entry_tag: Optional[str], side: str, **kwargs) -> float:
-        # ... 
-        return proposed_stake
-```
-
-### `confirm_trade_entry`
-
-New string argument `side` - which can be either `"long"` or `"short"`.
-
-``` python hl_lines="4"
-class AwesomeStrategy(IStrategy):
-    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:
-      return True
-```
-
-After: 
-
-``` python hl_lines="4"
-class AwesomeStrategy(IStrategy):
-    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], 
-                            side: str, **kwargs) -> bool:
-      return True
-```
-
-### `confirm_trade_exit`
-
-Changed argument `sell_reason` to `exit_reason`.
-For compatibility, `sell_reason` will still be provided for a limited time.
-
-``` python hl_lines="3"
-class AwesomeStrategy(IStrategy):
-    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:
-    return True
-```
-
-After:
-
-``` python hl_lines="3"
-class AwesomeStrategy(IStrategy):
-    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
-                           rate: float, time_in_force: str, exit_reason: str,
-                           current_time: datetime, **kwargs) -> bool:
-    return True
-```
-
-### `custom_entry_price`
-
-New string argument `side` - which can be either `"long"` or `"short"`.
-
-``` python hl_lines="3"
-class AwesomeStrategy(IStrategy):
-    def custom_entry_price(self, pair: str, current_time: datetime, proposed_rate: float,
-                           entry_tag: Optional[str], **kwargs) -> float:
-      return proposed_rate
-```
-
-After:
-
-``` python hl_lines="3"
-class AwesomeStrategy(IStrategy):
-    def custom_entry_price(self, pair: str, current_time: datetime, proposed_rate: float,
-                           entry_tag: Optional[str], side: str, **kwargs) -> float:
-      return proposed_rate
-```
-
-### Adjust trade position changes
-
-While adjust-trade-position itself did not change, you should no longer use `trade.nr_of_successful_buys` - and instead use `trade.nr_of_successful_entries`, which will also include short entries.
-
-### Helper methods
-
-Added argument "is_short" to `stoploss_from_open` and `stoploss_from_absolute`.
-This should be given the value of `trade.is_short`.
-
-``` python hl_lines="5 7"
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
-                        current_rate: float, current_profit: float, **kwargs) -> float:
-        # once the profit has risen above 10%, keep the stoploss at 7% above the open price
-        if current_profit > 0.10:
-            return stoploss_from_open(0.07, current_profit)
-
-        return stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate)
-
-        return 1
-
-```
-
-After:
-
-``` python hl_lines="5 7"
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
-                        current_rate: float, current_profit: float, **kwargs) -> float:
-        # once the profit has risen above 10%, keep the stoploss at 7% above the open price
-        if current_profit > 0.10:
-            return stoploss_from_open(0.07, current_profit, is_short=trade.is_short)
-
-        return stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate, is_short=trade.is_short)
-
-
-```
-
-### Strategy/Configuration settings
-
-#### `order_time_in_force`
-
-`order_time_in_force` attributes changed from `"buy"` to `"entry"` and `"sell"` to `"exit"`.
-
-``` python
-    order_time_in_force: Dict = {
-        "buy": "gtc",
-        "sell": "gtc",
-    }
-```
-
-After:
-
-``` python hl_lines="2 3"
-    order_time_in_force: Dict = {
-        "entry": "gtc",
-        "exit": "gtc",
-    }
-```
-
-#### `order_types`
-
-`order_types` have changed all wordings from `buy` to `entry` - and `sell` to `exit`.
-And two words are joined with `_`. 
-
-``` python hl_lines="2-6"
-    order_types = {
-        "buy": "limit",
-        "sell": "limit",
-        "emergencysell": "market",
-        "forcesell": "market",
-        "forcebuy": "market",
-        "stoploss": "market",
-        "stoploss_on_exchange": false,
-        "stoploss_on_exchange_interval": 60
-    }
-```
-
-After:
-
-``` python hl_lines="2-6"
-    order_types = {
-        "entry": "limit",
-        "exit": "limit",
-        "emergency_exit": "market",
-        "force_exit": "market",
-        "force_entry": "market",
-        "stoploss": "market",
-        "stoploss_on_exchange": false,
-        "stoploss_on_exchange_interval": 60
-    }
-```
-
-#### Strategy level settings
-
-* `use_sell_signal` -> `use_exit_signal`
-* `sell_profit_only` -> `exit_profit_only`
-* `sell_profit_offset` -> `exit_profit_offset`
-* `ignore_roi_if_buy_signal` -> `ignore_roi_if_entry_signal`
-
-``` python hl_lines="2-5"
-    # These values can be overridden in the config.
-    use_sell_signal = True
-    sell_profit_only = True
-    sell_profit_offset: 0.01
-    ignore_roi_if_buy_signal = False
-```
-
-After:
-
-``` python hl_lines="2-5"
-    # These values can be overridden in the config.
-    use_exit_signal = True
-    exit_profit_only = True
-    exit_profit_offset: 0.01
-    ignore_roi_if_entry_signal = False
-```
-
-#### `unfilledtimeout`
-
-`unfilledtimeout` have changed all wordings from `buy` to `entry` - and `sell` to `exit`.
-
-``` python hl_lines="2-3"
-unfilledtimeout = {
-        "buy": 10,
-        "sell": 10,
-        "exit_timeout_count": 0,
-        "unit": "minutes"
-    }
-```
-
-After:
-
-``` python hl_lines="2-3"
-unfilledtimeout = {
-        "entry": 10,
-        "exit": 10,
-        "exit_timeout_count": 0,
-        "unit": "minutes"
-    }
-```
-
-#### `order pricing`
-
-Order pricing changed in 2 ways. `bid_strategy` was renamed to `entry_pricing` and `ask_strategy` was renamed to `exit_pricing`.
-The attributes `ask_last_balance` -> `price_last_balance` and `bid_last_balance` -> `price_last_balance` were renamed as well.
-Also, price-side can now be defined as `ask`, `bid`, `same` or `other`.
-Please refer to the [pricing documentation](configuration.md#prices-used-for-orders) for more information.
-
-``` json hl_lines="2-3 6 12-13 16"
-{
-    "bid_strategy": {
-        "price_side": "bid",
-        "use_order_book": true,
-        "order_book_top": 1,
-        "ask_last_balance": 0.0,
-        "check_depth_of_market": {
-            "enabled": false,
-            "bids_to_ask_delta": 1
-        }
-    },
-    "ask_strategy":{
-        "price_side": "ask",
-        "use_order_book": true,
-        "order_book_top": 1,
-        "bid_last_balance": 0.0
-    }
-}
-```
-
-after:
-
-``` json  hl_lines="2-3 6 12-13 16"
-{
-    "entry_pricing": {
-        "price_side": "same",
-        "use_order_book": true,
-        "order_book_top": 1,
-        "price_last_balance": 0.0,
-        "check_depth_of_market": {
-            "enabled": false,
-            "bids_to_ask_delta": 1
-        }
-    },
-    "exit_pricing":{
-        "price_side": "same",
-        "use_order_book": true,
-        "order_book_top": 1,
-        "price_last_balance": 0.0
-    }
-}
-```

docs/telegram-usage.md

@@ -81,37 +81,34 @@ Example configuration showing the different settings:
         "status": "silent",
         "warning": "on",
         "startup": "off",
-        "entry": "silent",
-        "exit": {
+        "buy": "silent",
+        "sell": {
             "roi": "silent",
-            "emergency_exit": "on",
-            "force_exit": "on",
-            "exit_signal": "silent",
+            "emergency_sell": "on",
+            "force_sell": "on",
+            "sell_signal": "silent",
             "trailing_stop_loss": "on",
             "stop_loss": "on",
             "stoploss_on_exchange": "on",
-            "custom_exit": "silent"
+            "custom_sell": "silent"
         },
-        "entry_cancel": "silent",
-        "exit_cancel": "on",
-        "entry_fill": "off",
-        "exit_fill": "off",
+        "buy_cancel": "silent",
+        "sell_cancel": "on",
+        "buy_fill": "off",
+        "sell_fill": "off",
         "protection_trigger": "off",
-        "protection_trigger_global": "on",
-        "strategy_msg": "off",
-        "show_candle": "off"
+        "protection_trigger_global": "on"
     },
     "reload": true,
     "balance_dust_level": 0.01
 },
 ```
 
-`entry` notifications are sent when the order is placed, while `entry_fill` notifications are sent when the order is filled on the exchange.
-`exit` notifications are sent when the order is placed, while `exit_fill` notifications are sent when the order is filled on the exchange.
+`buy` notifications are sent when the order is placed, while `buy_fill` notifications are sent when the order is filled on the exchange.
+`sell` notifications are sent when the order is placed, while `sell_fill` notifications are sent when the order is filled on the exchange.
 `*_fill` notifications are off by default and must be explicitly enabled.
 `protection_trigger` notifications are sent when a protection triggers and `protection_trigger_global` notifications trigger when global protections are triggered.
-`strategy_msg` - Receive notifications from the strategy, sent via `self.dp.send_msg()` from the strategy [more details](strategy-customization.md#send-notification).
-`show_candle` - show candle values as part of entry/exit messages. Only possible values are `"ohlc"` or `"off"`.
+
 
 `balance_dust_level` will define what the `/balance` command takes as "dust" - Currencies with a balance below this will be shown.
 `reload` allows you to disable reload-buttons on selected messages.
@@ -174,20 +171,16 @@ official commands. You can ask at any moment for help with `/help`.
 | `/locks` | Show currently locked pairs.
 | `/unlock <pair or lock_id>` | Remove the lock for this pair (or for this lock id).
 | `/profit [<n>]` | Display a summary of your profit/loss from close trades and some stats about your performance, over the last n days (all trades by default)
-| `/forceexit <trade_id> | /fx <tradeid>` | Instantly exits the given trade  (Ignoring `minimum_roi`).
-| `/forceexit all | /fx all` | Instantly exits all open trades (Ignoring `minimum_roi`).
-| `/fx` | alias for `/forceexit`
-| `/forcelong <pair> [rate]` | Instantly buys the given pair. Rate is optional and only applies to limit orders. (`force_entry_enable` must be set to True)
-| `/forceshort <pair> [rate]` | Instantly shorts the given pair. Rate is optional and only applies to limit orders. This will only work on non-spot markets. (`force_entry_enable` must be set to True)
+| `/forcesell <trade_id>` | Instantly sells the given trade  (Ignoring `minimum_roi`).
+| `/forcesell all` | Instantly sells all open trades (Ignoring `minimum_roi`).
+| `/forcebuy <pair> [rate]` | Instantly buys the given pair. Rate is optional and only applies to limit orders. (`forcebuy_enable` must be set to True)
 | `/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 Exit reason as well as Avg. holding durations for buys and sells
-| `/exits` | Shows Wins / losses by Exit reason as well as Avg. holding durations for buys and sells
-| `/entries` | Shows Wins / losses by Exit reason as well as Avg. holding durations for buys and sells
-| `/whitelist [sorted] [baseonly]` | Show the current whitelist. Optionally display in alphabetical order and/or with just the base currency of each pairing.
+| `/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.
 | `/edge` | Show validated pairs by Edge if it is enabled.
 | `/help` | Show help message
@@ -223,14 +216,11 @@ Once all positions are sold, run `/stop` to completely stop the bot.
 ### /status
 
 For each open trade, the bot will send you the following message.
-Enter Tag is configurable via Strategy.
 
 > **Trade ID:** `123` `(since 1 days ago)`  
 > **Current Pair:** CVC/BTC  
-> **Direction:** Long
-> **Leverage:** 1.0
+> **Open Since:** `1 days ago`  
 > **Amount:** `26.64180098`  
-> **Enter Tag:** Awesome Long Signal
 > **Open Rate:** `0.00007489`  
 > **Current Rate:** `0.00007489`  
 > **Current Profit:** `12.95%`  
@@ -241,10 +231,10 @@ Enter Tag is configurable via Strategy.
 Return the status of all open trades in a table format.
 
 ```
-ID L/S    Pair     Since   Profit
-----    --------  -------  --------
-  67 L   SC/BTC    1 d      13.33%
- 123 S   CVC/BTC   1 h      12.95%
+   ID  Pair      Since    Profit
+----  --------  -------  --------
+  67  SC/BTC    1 d      13.33%
+ 123  CVC/BTC   1 h      12.95%
 ```
 
 ### /count
@@ -273,38 +263,26 @@ Return a summary of your profit/loss and performance.
 > **Latest Trade opened:** `2 minutes ago`  
 > **Avg. Duration:** `2:33:45`  
 > **Best Performing:** `PAY/BTC: 50.23%`  
-> **Trading volume:** `0.5 BTC`  
-> **Profit factor:** `1.04`  
-> **Max Drawdown:** `9.23% (0.01255 BTC)`  
 
 The relative profit of `1.2%` is the average profit per trade.  
 The relative profit of `15.2 Σ%` is be based on the starting capital - so in this case, the starting capital was `0.00485701 * 1.152 = 0.00738 BTC`.
 Starting capital is either taken from the `available_capital` setting, or calculated by using current wallet size - profits.
-Profit Factor is calculated as gross profits / gross losses - and should serve as an overall metric for the strategy.  
-Max drawdown corresponds to the backtesting metric `Absolute Drawdown (Account)` - calculated as `(Absolute Drawdown) / (DrawdownHigh + startingBalance)`.
 
-### /forceexit <trade_id>
+### /forcesell <trade_id>
 
-> **BINANCE:** Exiting BTC/LTC with limit `0.01650000 (profit: ~-4.07%, -0.00008168)`
+> **BITTREX:** Selling BTC/LTC with limit `0.01650000 (profit: ~-4.07%, -0.00008168)`
 
-!!! Tip
-    You can get a list of all open trades by calling `/forceexit` without parameter, which will show a list of buttons to simply exit a trade.
-    This command has an alias in `/fx` - which has the same capabilities, but is faster to type in "emergency" situations.
+### /forcebuy <pair> [rate]
 
-### /forcelong <pair> [rate] | /forceshort <pair> [rate]
+> **BITTREX:** Buying ETH/BTC with limit `0.03400000` (`1.000000 ETH`, `225.290 USD`)
 
-`/forcebuy <pair> [rate]` is also supported for longs but should be considered deprecated.
-
-> **BINANCE:** Long ETH/BTC with limit `0.03400000` (`1.000000 ETH`, `225.290 USD`)
-
-Omitting the pair will open a query asking for the pair to trade (based on the current whitelist).
-Trades created through `/forcelong` will have the buy-tag of `force_entry`.
+Omitting the pair will open a query asking for the pair to buy (based on the current whitelist).
 
 ![Telegram force-buy screenshot](assets/telegram_forcebuy.png)
 
-Note that for this to work, `force_entry_enable` needs to be set to true.
+Note that for this to work, `forcebuy_enable` needs to be set to true.
 
-[More details](configuration.md#understand-force_entry_enable)
+[More details](configuration.md#understand-forcebuy_enable)
 
 ### /performance
 
@@ -337,11 +315,11 @@ Per default `/daily` will return the 7 last days. The example below if for `/dai
 
 > **Daily Profit over the last 3 days:**
 ```
-Day (count)     USDT          USD         Profit %
---------------  ------------  ----------  ----------
-2022-06-11 (1)  -0.746 USDT   -0.75 USD   -0.08%
-2022-06-10 (0)  0 USDT        0.00 USD    0.00%
-2022-06-09 (5)  20 USDT       20.10 USD   5.00%
+Day         Profit BTC      Profit USD
+----------  --------------  ------------
+2018-01-03  0.00224175 BTC  29,142 USD
+2018-01-02  0.00033131 BTC   4,307 USD
+2018-01-01  0.00269130 BTC  34.986 USD
 ```
 
 ### /weekly <n>
@@ -351,11 +329,11 @@ from Monday. The example below if for `/weekly 3`:
 
 > **Weekly Profit over the last 3 weeks (starting from Monday):**
 ```
-Monday (count)  Profit BTC      Profit USD   Profit %
--------------  --------------  ------------    ----------
-2018-01-03 (5)  0.00224175 BTC  29,142 USD   4.98%
-2017-12-27 (1)  0.00033131 BTC   4,307 USD   0.00%
-2017-12-20 (4)  0.00269130 BTC  34.986 USD   5.12%
+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>
@@ -365,11 +343,11 @@ if for `/monthly 3`:
 
 > **Monthly Profit over the last 3 months:**
 ```
-Month (count)  Profit BTC      Profit USD    Profit %
--------------  --------------  ------------    ----------
-2018-01 (20)    0.00224175 BTC  29,142 USD  4.98%
-2017-12 (5)    0.00033131 BTC   4,307 USD   0.00%
-2017-11 (10)    0.00269130 BTC  34.986 USD  5.10%
+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

docs/updating.md

@@ -2,10 +2,6 @@
 
 To update your freqtrade installation, please use one of the below methods, corresponding to your installation method.
 
-!!! Note "Tracking changes"
-    Breaking changes / changed behavior will be documented in the changelog that is posted alongside every release.
-    For the develop branch, please follow PR's to avoid being surprised by changes.
-
 ## docker-compose
 
 !!! Note "Legacy installations using the `master` image"
@@ -32,8 +28,4 @@ Please ensure that you're also updating dependencies - otherwise things might br
 ``` bash
 git pull
 pip install -U -r requirements.txt
-pip install -e .
-
-# Ensure freqUI is at the latest version
-freqtrade install-ui 
 ```

docs/utils.md

@@ -119,7 +119,6 @@ This subcommand is useful for finding problems in your environment with loading
 usage: freqtrade list-strategies [-h] [-v] [--logfile FILE] [-V] [-c PATH]
                                  [-d PATH] [--userdir PATH]
                                  [--strategy-path PATH] [-1] [--no-color]
-                                 [--recursive-strategy-search]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -127,9 +126,6 @@ optional arguments:
   -1, --one-column      Print output in one column.
   --no-color            Disable colorization of hyperopt results. May be
                         useful if you are redirecting output to a file.
-  --recursive-strategy-search
-                        Recursively search for a strategy in the strategies
-                        folder.
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
@@ -138,10 +134,9 @@ Common arguments:
                         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.
+                        Specify configuration file (default: `config.json`).
+                        Multiple --config options may be used. Can be set to
+                        `-` to read config from stdin.
   -d PATH, --datadir PATH
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
@@ -444,15 +439,14 @@ usage: freqtrade list-markets [-h] [-v] [--logfile FILE] [-V] [-c PATH]
                               [-d PATH] [--userdir PATH] [--exchange EXCHANGE]
                               [--print-list] [--print-json] [-1] [--print-csv]
                               [--base BASE_CURRENCY [BASE_CURRENCY ...]]
-                              [--quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]] [-a]
-                              [--trading-mode {spot,margin,futures}]
+                              [--quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]]
+                              [-a]
 
 usage: freqtrade list-pairs [-h] [-v] [--logfile FILE] [-V] [-c PATH]
                             [-d PATH] [--userdir PATH] [--exchange EXCHANGE]
                             [--print-list] [--print-json] [-1] [--print-csv]
                             [--base BASE_CURRENCY [BASE_CURRENCY ...]]
                             [--quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]] [-a]
-                            [--trading-mode {spot,margin,futures}]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -469,8 +463,6 @@ optional arguments:
                         Specify quote currency(-ies). Space-separated list.
   -a, --all             Print all pairs or market symbols. By default only
                         active ones are shown.
-  --trading-mode {spot,margin,futures}
-                        Select Trading mode
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
@@ -525,25 +517,20 @@ Requires a configuration with specified `pairlists` attribute.
 Can be used to generate static pairlists to be used during backtesting / hyperopt.
 
 ```
-usage: freqtrade test-pairlist [-h] [-v] [-c PATH]
+usage: freqtrade test-pairlist [-h] [-c PATH]
                                [--quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]]
-                               [-1] [--print-json] [--exchange EXCHANGE]
+                               [-1] [--print-json]
 
 optional arguments:
   -h, --help            show this help message and exit
-  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
   -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.
+                        Specify configuration file (default: `config.json`).
+                        Multiple --config options may be used. Can be set to
+                        `-` to read config from stdin.
   --quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]
                         Specify quote currency(-ies). Space-separated list.
   -1, --one-column      Print output in one column.
   --print-json          Print list of pairs or market symbols in JSON format.
-  --exchange EXCHANGE   Exchange name (default: `bittrex`). Only valid if no
-                        config is provided.
-
 ```
 
 ### Examples
@@ -554,27 +541,6 @@ Show whitelist when using a [dynamic pairlist](plugins.md#pairlists).
 freqtrade test-pairlist --config config.json --quote USDT BTC
 ```
 
-## Convert database
-
-`freqtrade convert-db` can be used to convert your database from one system to another (sqlite -> postgres, postgres -> other postgres), migrating all trades, orders and Pairlocks.
-
-Please refer to the [SQL cheatsheet](sql_cheatsheet.md#use-a-different-database-system) to learn about requirements for different database systems.
-
-```
-usage: freqtrade convert-db [-h] [--db-url PATH] [--db-url-from PATH]
-
-optional arguments:
-  -h, --help          show this help message and exit
-  --db-url PATH       Override trades database URL, this is useful in custom
-                      deployments (default: `sqlite:///tradesv3.sqlite` for
-                      Live Run mode, `sqlite:///tradesv3.dryrun.sqlite` for
-                      Dry Run).
-  --db-url-from PATH  Source db url to use when migrating a database.
-```
-
-!!! Warning
-    Please ensure to only use this on an empty target database. Freqtrade will perform a regular migration, but may fail if entries already existed.
-
 ## Webserver mode
 
 !!! Warning "Experimental"
@@ -611,26 +577,6 @@ Common arguments:
 
 ```
 
-### Webserver mode - docker
-
-You can also use webserver mode via docker.
-Starting a one-off container requires the configuration of the port explicitly, as ports are not exposed by default.
-You can use `docker-compose run --rm -p 127.0.0.1:8080:8080 freqtrade webserver` to start a one-off container that'll be removed once you stop it. This assumes that port 8080 is still available and no other bot is running on that port.
-
-Alternatively, you can reconfigure the docker-compose file to have the command updated:
-
-``` yml
-    command: >
-      webserver
-      --config /freqtrade/user_data/config.json
-```
-
-You can now use `docker-compose up` to start the webserver.
-This assumes that the configuration has a webserver enabled and configured for docker (listening port = `0.0.0.0`).
-
-!!! Tip
-    Don't forget to reset the command back to the trade command if you want to start a live or dry-run bot. 
-
 ## Show previous Backtest results
 
 Allows you to show previous backtest results.
@@ -671,61 +617,6 @@ Common arguments:
 
 ```
 
-## Detailed backtest analysis
-
-Advanced backtest result analysis.
-
-More details in the [Backtesting analysis](advanced-backtesting.md#analyze-the-buyentry-and-sellexit-tags) Section.
-
-```
-usage: freqtrade backtesting-analysis [-h] [-v] [--logfile FILE] [-V]
-                                      [-c PATH] [-d PATH] [--userdir PATH]
-                                      [--export-filename PATH]
-                                      [--analysis-groups {0,1,2,3,4} [{0,1,2,3,4} ...]]
-                                      [--enter-reason-list ENTER_REASON_LIST [ENTER_REASON_LIST ...]]
-                                      [--exit-reason-list EXIT_REASON_LIST [EXIT_REASON_LIST ...]]
-                                      [--indicator-list INDICATOR_LIST [INDICATOR_LIST ...]]
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --export-filename PATH, --backtest-filename PATH
-                        Use this filename for backtest results.Requires
-                        `--export` to be set as well. Example: `--export-filen
-                        ame=user_data/backtest_results/backtest_today.json`
-  --analysis-groups {0,1,2,3,4} [{0,1,2,3,4} ...]
-                        grouping output - 0: simple wins/losses by enter tag,
-                        1: by enter_tag, 2: by enter_tag and exit_tag, 3: by
-                        pair and enter_tag, 4: by pair, enter_ and exit_tag
-                        (this can get quite large)
-  --enter-reason-list ENTER_REASON_LIST [ENTER_REASON_LIST ...]
-                        Comma separated list of entry signals to analyse.
-                        Default: all. e.g. 'entry_tag_a,entry_tag_b'
-  --exit-reason-list EXIT_REASON_LIST [EXIT_REASON_LIST ...]
-                        Comma separated list of exit signals to analyse.
-                        Default: all. e.g.
-                        'exit_tag_a,roi,stop_loss,trailing_stop_loss'
-  --indicator-list INDICATOR_LIST [INDICATOR_LIST ...]
-                        Comma separated list of indicators to analyse. e.g.
-                        'close,rsi,bb_lowerband,profit_abs'
-
-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

@@ -10,33 +10,33 @@ Sample configuration (tested using IFTTT).
   "webhook": {
         "enabled": true,
         "url": "https://maker.ifttt.com/trigger/<YOUREVENT>/with/key/<YOURKEY>/",
-        "webhookentry": {
+        "webhookbuy": {
             "value1": "Buying {pair}",
             "value2": "limit {limit:8f}",
             "value3": "{stake_amount:8f} {stake_currency}"
         },
-        "webhookentrycancel": {
+        "webhookbuycancel": {
             "value1": "Cancelling Open Buy Order for {pair}",
             "value2": "limit {limit:8f}",
             "value3": "{stake_amount:8f} {stake_currency}"
         },
-         "webhookentryfill": {
+         "webhookbuyfill": {
             "value1": "Buy Order for {pair} filled",
             "value2": "at {open_rate:8f}",
             "value3": ""
         },
-        "webhookexit": {
-            "value1": "Exiting {pair}",
+        "webhooksell": {
+            "value1": "Selling {pair}",
             "value2": "limit {limit:8f}",
             "value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
         },
-        "webhookexitcancel": {
-            "value1": "Cancelling Open Exit Order for {pair}",
+        "webhooksellcancel": {
+            "value1": "Cancelling Open Sell Order for {pair}",
             "value2": "limit {limit:8f}",
             "value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
         },
-        "webhookexitfill": {
-            "value1": "Exit Order for {pair} filled",
+        "webhooksellfill": {
+            "value1": "Sell Order for {pair} filled",
             "value2": "at {close_rate:8f}.",
             "value3": ""
         },
@@ -96,16 +96,14 @@ Optional parameters are available to enable automatic retries for webhook messag
 
 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.
 
-### Webhookentry
+### Webhookbuy
 
-The fields in `webhook.webhookentry` are filled when the bot executes a long/short. Parameters are filled using string.format.
+The fields in `webhook.webhookbuy` are filled when the bot executes a buy. Parameters are filled using string.format.
 Possible parameters are:
 
 * `trade_id`
 * `exchange`
 * `pair`
-* `direction`
-* `leverage`
 * ~~`limit` # Deprecated - should no longer be used.~~
 * `open_rate`
 * `amount`
@@ -116,18 +114,16 @@ Possible parameters are:
 * `fiat_currency`
 * `order_type`
 * `current_rate`
-* `enter_tag`
+* `buy_tag`
 
-### Webhookentrycancel
+### Webhookbuycancel
 
-The fields in `webhook.webhookentrycancel` are filled when the bot cancels a long/short order. Parameters are filled using string.format.
+The fields in `webhook.webhookbuycancel` are filled when the bot cancels a buy order. Parameters are filled using string.format.
 Possible parameters are:
 
 * `trade_id`
 * `exchange`
 * `pair`
-* `direction`
-* `leverage`
 * `limit`
 * `amount`
 * `open_date`
@@ -137,18 +133,16 @@ Possible parameters are:
 * `fiat_currency`
 * `order_type`
 * `current_rate`
-* `enter_tag`
+* `buy_tag`
 
-### Webhookentryfill
+### Webhookbuyfill
 
-The fields in `webhook.webhookentryfill` are filled when the bot filled a long/short order. Parameters are filled using string.format.
+The fields in `webhook.webhookbuyfill` are filled when the bot filled a buy order. Parameters are filled using string.format.
 Possible parameters are:
 
 * `trade_id`
 * `exchange`
 * `pair`
-* `direction`
-* `leverage`
 * `open_rate`
 * `amount`
 * `open_date`
@@ -158,18 +152,16 @@ Possible parameters are:
 * `fiat_currency`
 * `order_type`
 * `current_rate`
-* `enter_tag`
+* `buy_tag`
 
-### Webhookexit
+### Webhooksell
 
-The fields in `webhook.webhookexit` are filled when the bot exits a trade. Parameters are filled using string.format.
+The fields in `webhook.webhooksell` are filled when the bot sells a trade. Parameters are filled using string.format.
 Possible parameters are:
 
 * `trade_id`
 * `exchange`
 * `pair`
-* `direction`
-* `leverage`
 * `gain`
 * `limit`
 * `amount`
@@ -179,21 +171,19 @@ Possible parameters are:
 * `stake_currency`
 * `base_currency`
 * `fiat_currency`
-* `exit_reason`
+* `sell_reason`
 * `order_type`
 * `open_date`
 * `close_date`
 
-### Webhookexitfill
+### Webhooksellfill
 
-The fields in `webhook.webhookexitfill` are filled when the bot fills a exit order (closes a Trade). Parameters are filled using string.format.
+The fields in `webhook.webhooksellfill` are filled when the bot fills a sell order (closes a Trae). Parameters are filled using string.format.
 Possible parameters are:
 
 * `trade_id`
 * `exchange`
 * `pair`
-* `direction`
-* `leverage`
 * `gain`
 * `close_rate`
 * `amount`
@@ -204,21 +194,19 @@ Possible parameters are:
 * `stake_currency`
 * `base_currency`
 * `fiat_currency`
-* `exit_reason`
+* `sell_reason`
 * `order_type`
 * `open_date`
 * `close_date`
 
-### Webhookexitcancel
+### Webhooksellcancel
 
-The fields in `webhook.webhookexitcancel` are filled when the bot cancels a exit order. Parameters are filled using string.format.
+The fields in `webhook.webhooksellcancel` are filled when the bot cancels a sell order. Parameters are filled using string.format.
 Possible parameters are:
 
 * `trade_id`
 * `exchange`
 * `pair`
-* `direction`
-* `leverage`
 * `gain`
 * `limit`
 * `amount`
@@ -229,7 +217,7 @@ Possible parameters are:
 * `stake_currency`
 * `base_currency`
 * `fiat_currency`
-* `exit_reason`
+* `sell_reason`
 * `order_type`
 * `open_date`
 * `close_date`
@@ -239,52 +227,3 @@ Possible parameters are:
 The fields in `webhook.webhookstatus` are used for regular status messages (Started / Stopped / ...). Parameters are filled using string.format.
 
 The only possible value here is `{status}`.
-
-## Discord
-
-A special form of webhooks is available for discord.
-You can configure this as follows:
-
-```json
-"discord": {
-    "enabled": true,
-    "webhook_url": "https://discord.com/api/webhooks/<Your webhook URL ...>",
-    "exit_fill": [
-        {"Trade ID": "{trade_id}"},
-        {"Exchange": "{exchange}"},
-        {"Pair": "{pair}"},
-        {"Direction": "{direction}"},
-        {"Open rate": "{open_rate}"},
-        {"Close rate": "{close_rate}"},
-        {"Amount": "{amount}"},
-        {"Open date": "{open_date:%Y-%m-%d %H:%M:%S}"},
-        {"Close date": "{close_date:%Y-%m-%d %H:%M:%S}"},
-        {"Profit": "{profit_amount} {stake_currency}"},
-        {"Profitability": "{profit_ratio:.2%}"},
-        {"Enter tag": "{enter_tag}"},
-        {"Exit Reason": "{exit_reason}"},
-        {"Strategy": "{strategy}"},
-        {"Timeframe": "{timeframe}"},
-    ],
-    "entry_fill": [
-        {"Trade ID": "{trade_id}"},
-        {"Exchange": "{exchange}"},
-        {"Pair": "{pair}"},
-        {"Direction": "{direction}"},
-        {"Open rate": "{open_rate}"},
-        {"Amount": "{amount}"},
-        {"Open date": "{open_date:%Y-%m-%d %H:%M:%S}"},
-        {"Enter tag": "{enter_tag}"},
-        {"Strategy": "{strategy} {timeframe}"},
-    ]
-}
-```
-
-
-The above represents the default (`exit_fill` and `entry_fill` are optional and will default to the above configuration) - modifications are obviously possible.
-
-Available fields correspond to the fields for webhooks and are documented in the corresponding webhook sections.
-
-The notifications will look as follows by default.
-
-![discord-notification](assets/discord_notification.png)

environment.yml

@@ -9,7 +9,6 @@ dependencies:
     - pandas
     - pip
 
-    - py-find-1st
     - aiohttp
     - SQLAlchemy
     - python-telegram-bot
@@ -31,9 +30,7 @@ dependencies:
     - colorama
     - questionary
     - prompt-toolkit
-    - schedule
     - python-dateutil
-    - joblib
 
 
     # ============================
@@ -56,6 +53,7 @@ dependencies:
     - scikit-learn
     - filelock
     - scikit-optimize
+    - joblib
     - progressbar2
     # ============================
     # 4/4 req plot
@@ -65,7 +63,7 @@ dependencies:
 
     - pip:
         - pycoingecko
-        # - py_find_1st
+        - py_find_1st
         - tables
         - pytest-random-order
         - ccxt

freqtrade.service

@@ -11,3 +11,4 @@ Restart=on-failure
 
 [Install]
 WantedBy=default.target
+

freqtrade.service.watchdog

@@ -27,3 +27,4 @@ WatchdogSec=20
 
 [Install]
 WantedBy=default.target
+

freqtrade/__init__.py

@@ -1,14 +1,27 @@
 """ Freqtrade bot """
-__version__ = '2022.8.dev'
+__version__ = '2022.2.1'
+
+if __version__ == 'develop':
 
-if 'dev' in __version__:
     try:
         import subprocess
 
-        __version__ = __version__ + '-' + subprocess.check_output(
+        __version__ = 'develop-' + subprocess.check_output(
             ['git', 'log', '--format="%h"', '-n 1'],
             stderr=subprocess.DEVNULL).decode("utf-8").rstrip().strip('"')
 
+        # from datetime import datetime
+        # last_release = subprocess.check_output(
+        #     ['git', 'tag']
+        # ).decode('utf-8').split()[-1].split(".")
+        # # Releases are in the format "2020.1" - we increment the latest version for dev.
+        # prefix = f"{last_release[0]}.{int(last_release[1]) + 1}"
+        # dev_version = int(datetime.now().timestamp() // 1000)
+        # __version__ = f"{prefix}.dev{dev_version}"
+
+        #  subprocess.check_output(
+        #     ['git', 'log', '--format="%h"', '-n 1'],
+        #     stderr=subprocess.DEVNULL).decode("utf-8").rstrip().strip('"')
     except Exception:  # pragma: no cover
         # git not available, ignore
         try:

freqtrade/commands/__init__.py

@@ -6,12 +6,10 @@ Contains all start-commands, subcommands and CLI Interface creation.
 Note: Be careful with file-scoped imports in these subfiles.
     as they are parsed on startup, nothing containing optional modules should be loaded.
 """
-from freqtrade.commands.analyze_commands import start_analysis_entries_exits
 from freqtrade.commands.arguments import Arguments
 from freqtrade.commands.build_config_commands import start_new_config
 from freqtrade.commands.data_commands import (start_convert_data, start_convert_trades,
                                               start_download_data, start_list_data)
-from freqtrade.commands.db_commands import start_convert_db
 from freqtrade.commands.deploy_commands import (start_create_userdir, start_install_ui,
                                                 start_new_strategy)
 from freqtrade.commands.hyperopt_commands import start_hyperopt_list, start_hyperopt_show

freqtrade/commands/analyze_commands.py

@@ -1,69 +0,0 @@
-import logging
-from pathlib import Path
-from typing import Any, Dict
-
-from freqtrade.configuration import setup_utils_configuration
-from freqtrade.enums import RunMode
-from freqtrade.exceptions import OperationalException
-
-
-logger = logging.getLogger(__name__)
-
-
-def setup_analyze_configuration(args: Dict[str, Any], method: RunMode) -> Dict[str, Any]:
-    """
-    Prepare the configuration for the entry/exit reason analysis module
-    :param args: Cli args from Arguments()
-    :param method: Bot running mode
-    :return: Configuration
-    """
-    config = setup_utils_configuration(args, method)
-
-    no_unlimited_runmodes = {
-        RunMode.BACKTEST: 'backtesting',
-    }
-    if method in no_unlimited_runmodes.keys():
-        from freqtrade.data.btanalysis import get_latest_backtest_filename
-
-        if 'exportfilename' in config:
-            if config['exportfilename'].is_dir():
-                btfile = Path(get_latest_backtest_filename(config['exportfilename']))
-                signals_file = f"{config['exportfilename']}/{btfile.stem}_signals.pkl"
-            else:
-                if config['exportfilename'].exists():
-                    btfile = Path(config['exportfilename'])
-                    signals_file = f"{btfile.parent}/{btfile.stem}_signals.pkl"
-                else:
-                    raise OperationalException(f"{config['exportfilename']} does not exist.")
-        else:
-            raise OperationalException('exportfilename not in config.')
-
-        if (not Path(signals_file).exists()):
-            raise OperationalException(
-                (f"Cannot find latest backtest signals file: {signals_file}."
-                  "Run backtesting with `--export signals`.")
-            )
-
-    return config
-
-
-def start_analysis_entries_exits(args: Dict[str, Any]) -> None:
-    """
-    Start analysis script
-    :param args: Cli args from Arguments()
-    :return: None
-    """
-    from freqtrade.data.entryexitanalysis import process_entry_exit_reasons
-
-    # Initialize configuration
-    config = setup_analyze_configuration(args, RunMode.BACKTEST)
-
-    logger.info('Starting freqtrade in analysis mode')
-
-    process_entry_exit_reasons(config['exportfilename'],
-                               config['exchange']['pair_whitelist'],
-                               config['analysis_groups'],
-                               config['enter_reason_list'],
-                               config['exit_reason_list'],
-                               config['indicator_list']
-                               )

freqtrade/commands/arguments.py

@@ -12,8 +12,7 @@ from freqtrade.constants import DEFAULT_CONFIG
 
 ARGS_COMMON = ["verbosity", "logfile", "version", "config", "datadir", "user_data_dir"]
 
-ARGS_STRATEGY = ["strategy", "strategy_path", "recursive_strategy_search", "freqaimodel",
-                 "freqaimodel_path"]
+ARGS_STRATEGY = ["strategy", "strategy_path"]
 
 ARGS_TRADE = ["db_url", "sd_notify", "dry_run", "dry_run_wallet", "fee"]
 
@@ -29,7 +28,7 @@ ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + ["position_stacking", "use_max_market_pos
 
 ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
                                         "position_stacking", "use_max_market_positions",
-                                        "enable_protections", "dry_run_wallet", "timeframe_detail",
+                                        "enable_protections", "dry_run_wallet",
                                         "epochs", "spaces", "print_all",
                                         "print_colorized", "print_json", "hyperopt_jobs",
                                         "hyperopt_random_state", "hyperopt_min_trades",
@@ -38,8 +37,7 @@ ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
 
 ARGS_EDGE = ARGS_COMMON_OPTIMIZE + ["stoploss_range"]
 
-ARGS_LIST_STRATEGIES = ["strategy_path", "print_one_column", "print_colorized",
-                        "recursive_strategy_search"]
+ARGS_LIST_STRATEGIES = ["strategy_path", "print_one_column", "print_colorized"]
 
 ARGS_LIST_HYPEROPTS = ["hyperopt_path", "print_one_column", "print_colorized"]
 
@@ -50,11 +48,10 @@ ARGS_LIST_EXCHANGES = ["print_one_column", "list_exchanges_all"]
 ARGS_LIST_TIMEFRAMES = ["exchange", "print_one_column"]
 
 ARGS_LIST_PAIRS = ["exchange", "print_list", "list_pairs_print_json", "print_one_column",
-                   "print_csv", "base_currencies", "quote_currencies", "list_pairs_all",
-                   "trading_mode"]
+                   "print_csv", "base_currencies", "quote_currencies", "list_pairs_all"]
 
 ARGS_TEST_PAIRLIST = ["verbosity", "config", "quote_currencies", "print_one_column",
-                      "list_pairs_print_json", "exchange"]
+                      "list_pairs_print_json"]
 
 ARGS_CREATE_USERDIR = ["user_data_dir", "reset"]
 
@@ -63,18 +60,15 @@ ARGS_BUILD_CONFIG = ["config"]
 ARGS_BUILD_STRATEGY = ["user_data_dir", "strategy", "template"]
 
 ARGS_CONVERT_DATA = ["pairs", "format_from", "format_to", "erase"]
-
-ARGS_CONVERT_DATA_OHLCV = ARGS_CONVERT_DATA + ["timeframes", "exchange", "trading_mode",
-                                               "candle_types"]
+ARGS_CONVERT_DATA_OHLCV = ARGS_CONVERT_DATA + ["timeframes"]
 
 ARGS_CONVERT_TRADES = ["pairs", "timeframes", "exchange", "dataformat_ohlcv", "dataformat_trades"]
 
-ARGS_LIST_DATA = ["exchange", "dataformat_ohlcv", "pairs", "trading_mode"]
+ARGS_LIST_DATA = ["exchange", "dataformat_ohlcv", "pairs"]
 
 ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "new_pairs_days", "include_inactive",
                       "timerange", "download_trades", "exchange", "timeframes",
-                      "erase", "dataformat_ohlcv", "dataformat_trades", "trading_mode",
-                      "prepend_data"]
+                      "erase", "dataformat_ohlcv", "dataformat_trades"]
 
 ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",
                        "db_url", "trade_source", "export", "exportfilename",
@@ -83,9 +77,7 @@ ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",
 ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
                     "trade_source", "timeframe", "plot_auto_open", ]
 
-ARGS_CONVERT_DB = ["db_url", "db_url_from"]
-
-ARGS_INSTALL_UI = ["erase_ui_only", "ui_version"]
+ARGS_INSTALL_UI = ["erase_ui_only", 'ui_version']
 
 ARGS_SHOW_TRADES = ["db_url", "trade_ids", "print_json"]
 
@@ -102,9 +94,6 @@ ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperop
                       "print_json", "hyperoptexportfilename", "hyperopt_show_no_header",
                       "disableparamexport", "backtest_breakdown"]
 
-ARGS_ANALYZE_ENTRIES_EXITS = ["exportfilename", "analysis_groups", "enter_reason_list",
-                              "exit_reason_list", "indicator_list"]
-
 NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
                     "list-markets", "list-pairs", "list-strategies", "list-data",
                     "hyperopt-list", "hyperopt-show", "backtest-filter",
@@ -186,9 +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_analysis_entries_exits, start_backtesting,
-                                        start_backtesting_show, start_convert_data,
-                                        start_convert_db, 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,
@@ -288,13 +276,6 @@ class Arguments:
         backtesting_show_cmd.set_defaults(func=start_backtesting_show)
         self._build_args(optionlist=ARGS_BACKTEST_SHOW, parser=backtesting_show_cmd)
 
-        # Add backtesting analysis subcommand
-        analysis_cmd = subparsers.add_parser('backtesting-analysis',
-                                             help='Backtest Analysis module.',
-                                             parents=[_common_parser])
-        analysis_cmd.set_defaults(func=start_analysis_entries_exits)
-        self._build_args(optionlist=ARGS_ANALYZE_ENTRIES_EXITS, parser=analysis_cmd)
-
         # Add edge subcommand
         edge_cmd = subparsers.add_parser('edge', help='Edge module.',
                                          parents=[_common_parser, _strategy_parser])
@@ -388,14 +369,6 @@ class Arguments:
         test_pairlist_cmd.set_defaults(func=start_test_pairlist)
         self._build_args(optionlist=ARGS_TEST_PAIRLIST, parser=test_pairlist_cmd)
 
-        # Add db-convert subcommand
-        convert_db = subparsers.add_parser(
-            "convert-db",
-            help="Migrate database to different system",
-        )
-        convert_db.set_defaults(func=start_convert_db)
-        self._build_args(optionlist=ARGS_CONVERT_DB, parser=convert_db)
-
         # Add install-ui subcommand
         install_ui_cmd = subparsers.add_parser(
             'install-ui',

freqtrade/commands/build_config_commands.py

@@ -67,7 +67,7 @@ def ask_user_config() -> Dict[str, Any]:
             "type": "text",
             "name": "stake_amount",
             "message": f"Please insert your stake amount (Number or '{UNLIMITED_STAKE_AMOUNT}'):",
-            "default": "unlimited",
+            "default": "100",
             "validate": lambda val: val == UNLIMITED_STAKE_AMOUNT or validate_is_float(val),
             "filter": lambda val: '"' + UNLIMITED_STAKE_AMOUNT + '"'
             if val == UNLIMITED_STAKE_AMOUNT
@@ -104,28 +104,19 @@ def ask_user_config() -> Dict[str, Any]:
             "type": "select",
             "name": "exchange_name",
             "message": "Select exchange",
-            "choices": lambda x: [
+            "choices": [
                 "binance",
                 "binanceus",
                 "bittrex",
-                "ftx",
-                "gateio",
-                "huobi",
                 "kraken",
+                "ftx",
                 "kucoin",
+                "gateio",
                 "okx",
-                Separator("------------------"),
+                Separator(),
                 "other",
             ],
         },
-        {
-            "type": "confirm",
-            "name": "trading_mode",
-            "message": "Do you want to trade Perpetual Swaps (perpetual futures)?",
-            "default": False,
-            "filter": lambda val: 'futures' if val else 'spot',
-            "when": lambda x: x["exchange_name"] in ['binance', 'gateio', 'okx'],
-        },
         {
             "type": "autocomplete",
             "name": "exchange_name",
@@ -164,7 +155,7 @@ def ask_user_config() -> Dict[str, Any]:
             "when": lambda x: x['telegram']
         },
         {
-            "type": "password",
+            "type": "text",
             "name": "telegram_chat_id",
             "message": "Insert Telegram chat id",
             "when": lambda x: x['telegram']
@@ -191,7 +182,7 @@ def ask_user_config() -> Dict[str, Any]:
             "when": lambda x: x['api_server']
         },
         {
-            "type": "password",
+            "type": "text",
             "name": "api_server_password",
             "message": "Insert api-server password",
             "when": lambda x: x['api_server']
@@ -202,13 +193,7 @@ def ask_user_config() -> Dict[str, Any]:
     if not answers:
         # Interrupted questionary sessions return an empty dict.
         raise OperationalException("User interrupted interactive questions.")
-    # Ensure default is set for non-futures exchanges
-    answers['trading_mode'] = answers.get('trading_mode', "spot")
-    answers['margin_mode'] = (
-        'isolated'
-        if answers.get('trading_mode') == 'futures'
-        else ''
-    )
+
     # Force JWT token to be a random string
     answers['api_server_jwt_key'] = secrets.token_hex()
 

freqtrade/commands/cli_options.py

@@ -5,7 +5,6 @@ from argparse import SUPPRESS, ArgumentTypeError
 
 from freqtrade import __version__, constants
 from freqtrade.constants import HYPEROPT_LOSS_BUILTIN
-from freqtrade.enums import CandleType
 
 
 def check_int_positive(value: str) -> int:
@@ -83,11 +82,6 @@ AVAILABLE_CLI_OPTIONS = {
         help='Reset sample files to their original state.',
         action='store_true',
     ),
-    "recursive_strategy_search": Arg(
-        '--recursive-strategy-search',
-        help='Recursively search for a strategy in the strategies folder.',
-        action='store_true',
-    ),
     # Main options
     "strategy": Arg(
         '-s', '--strategy',
@@ -106,11 +100,6 @@ AVAILABLE_CLI_OPTIONS = {
         f'`{constants.DEFAULT_DB_DRYRUN_URL}` for Dry Run).',
         metavar='PATH',
     ),
-    "db_url_from": Arg(
-        '--db-url-from',
-        help='Source db url to use when migrating a database.',
-        metavar='PATH',
-    ),
     "sd_notify": Arg(
         '--sd-notify',
         help='Notify systemd service manager.',
@@ -128,7 +117,7 @@ AVAILABLE_CLI_OPTIONS = {
     ),
     # Optimize common
     "timeframe": Arg(
-        '-i', '--timeframe',
+        '-i', '--timeframe', '--ticker-interval',
         help='Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).',
     ),
     "timerange": Arg(
@@ -180,7 +169,7 @@ AVAILABLE_CLI_OPTIONS = {
     "strategy_list": Arg(
         '--strategy-list',
         help='Provide a space-separated list of strategies to backtest. '
-        'Please note that timeframe needs to be set either in config '
+        'Please note that ticker-interval needs to be set either in config '
         'or via command line. When using this together with `--export trades`, '
         'the strategy-name is injected into the filename '
         '(so `backtest-data.json` becomes `backtest-data-SampleStrategy.json`',
@@ -190,6 +179,7 @@ AVAILABLE_CLI_OPTIONS = {
         '--export',
         help='Export backtest results (default: trades).',
         choices=constants.EXPORT_OPTIONS,
+
     ),
     "exportfilename": Arg(
         "--export-filename",
@@ -366,17 +356,6 @@ AVAILABLE_CLI_OPTIONS = {
         nargs='+',
         metavar='BASE_CURRENCY',
     ),
-    "trading_mode": Arg(
-        '--trading-mode',
-        help='Select Trading mode',
-        choices=constants.TRADING_MODES,
-    ),
-    "candle_types": Arg(
-        '--candle-types',
-        help='Select candle type to use',
-        choices=[c.value for c in CandleType],
-        nargs='+',
-    ),
     # Script options
     "pairs": Arg(
         '-p', '--pairs',
@@ -448,11 +427,6 @@ AVAILABLE_CLI_OPTIONS = {
         default=['1m', '5m'],
         nargs='+',
     ),
-    "prepend_data": Arg(
-        '--prepend',
-        help='Allow data prepending.',
-        action='store_true',
-    ),
     "erase": Arg(
         '--erase',
         help='Clean all existing data for the selected exchange/pairs/timeframes.',
@@ -614,47 +588,4 @@ AVAILABLE_CLI_OPTIONS = {
               "that do not contain any parameters."),
         action="store_true",
     ),
-    "analysis_groups": Arg(
-        "--analysis-groups",
-        help=("grouping output - "
-              "0: simple wins/losses by enter tag, "
-              "1: by enter_tag, "
-              "2: by enter_tag and exit_tag, "
-              "3: by pair and enter_tag, "
-              "4: by pair, enter_ and exit_tag (this can get quite large)"),
-        nargs='+',
-        default=['0', '1', '2'],
-        choices=['0', '1', '2', '3', '4'],
-    ),
-    "enter_reason_list": Arg(
-        "--enter-reason-list",
-        help=("Comma separated list of entry signals to analyse. Default: all. "
-              "e.g. 'entry_tag_a,entry_tag_b'"),
-        nargs='+',
-        default=['all'],
-    ),
-    "exit_reason_list": Arg(
-        "--exit-reason-list",
-        help=("Comma separated list of exit signals to analyse. Default: all. "
-              "e.g. 'exit_tag_a,roi,stop_loss,trailing_stop_loss'"),
-        nargs='+',
-        default=['all'],
-    ),
-    "indicator_list": Arg(
-        "--indicator-list",
-        help=("Comma separated list of indicators to analyse. "
-              "e.g. 'close,rsi,bb_lowerband,profit_abs'"),
-        nargs='+',
-        default=[],
-    ),
-    "freqaimodel": Arg(
-        '--freqaimodel',
-        help='Specify a custom freqaimodels.',
-        metavar='NAME',
-    ),
-    "freqaimodel_path": Arg(
-        '--freqaimodel-path',
-        help='Specify additional lookup path for freqaimodels.',
-        metavar='PATH',
-    ),
 }

freqtrade/commands/data_commands.py

@@ -8,11 +8,11 @@ from freqtrade.configuration import TimeRange, setup_utils_configuration
 from freqtrade.data.converter import convert_ohlcv_format, convert_trades_format
 from freqtrade.data.history import (convert_trades_to_ohlcv, refresh_backtest_ohlcv_data,
                                     refresh_backtest_trades_data)
-from freqtrade.enums import CandleType, RunMode, TradingMode
+from freqtrade.enums import RunMode
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_minutes
 from freqtrade.exchange.exchange import market_is_active
-from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist, expand_pairlist
+from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
 from freqtrade.resolvers import ExchangeResolver
 
 
@@ -50,8 +50,7 @@ def start_download_data(args: Dict[str, Any]) -> None:
     exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
     markets = [p for p, m in exchange.markets.items() if market_is_active(m)
                or config.get('include_inactive')]
-
-    expanded_pairs = dynamic_expand_pairlist(config, markets)
+    expanded_pairs = expand_pairlist(config['pairs'], markets)
 
     # Manual validations of relevant settings
     if not config['exchange'].get('skip_pair_validation', False):
@@ -65,8 +64,6 @@ def start_download_data(args: Dict[str, Any]) -> None:
     try:
 
         if config.get('download_trades'):
-            if config.get('trading_mode') == 'futures':
-                raise OperationalException("Trade download not supported for futures.")
             pairs_not_available = refresh_backtest_trades_data(
                 exchange, pairs=expanded_pairs, datadir=config['datadir'],
                 timerange=timerange, new_pairs_days=config['new_pairs_days'],
@@ -80,20 +77,11 @@ def start_download_data(args: Dict[str, Any]) -> None:
                 data_format_trades=config['dataformat_trades'],
             )
         else:
-            if not exchange._ft_has.get('ohlcv_has_history', True):
-                raise OperationalException(
-                    f"Historic klines not available for {exchange.name}. "
-                    "Please use `--dl-trades` instead for this exchange "
-                    "(will unfortunately take a long time)."
-                    )
             pairs_not_available = refresh_backtest_ohlcv_data(
                 exchange, pairs=expanded_pairs, timeframes=config['timeframes'],
                 datadir=config['datadir'], timerange=timerange,
                 new_pairs_days=config['new_pairs_days'],
-                erase=bool(config.get('erase')), data_format=config['dataformat_ohlcv'],
-                trading_mode=config.get('trading_mode', 'spot'),
-                prepend=config.get('prepend_data', False)
-            )
+                erase=bool(config.get('erase')), data_format=config['dataformat_ohlcv'])
 
     except KeyboardInterrupt:
         sys.exit("SIGINT received, aborting ...")
@@ -145,11 +133,9 @@ def start_convert_data(args: Dict[str, Any], ohlcv: bool = True) -> None:
     """
     config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
     if ohlcv:
-        candle_types = [CandleType.from_string(ct) for ct in config.get('candle_types', ['spot'])]
-        for candle_type in candle_types:
-            convert_ohlcv_format(config,
-                                 convert_from=args['format_from'], convert_to=args['format_to'],
-                                 erase=args['erase'], candle_type=candle_type)
+        convert_ohlcv_format(config,
+                             convert_from=args['format_from'], convert_to=args['format_to'],
+                             erase=args['erase'])
     else:
         convert_trades_format(config,
                               convert_from=args['format_from'], convert_to=args['format_to'],
@@ -168,26 +154,17 @@ def start_list_data(args: Dict[str, Any]) -> None:
     from freqtrade.data.history.idatahandler import get_datahandler
     dhc = get_datahandler(config['datadir'], config['dataformat_ohlcv'])
 
-    paircombs = dhc.ohlcv_get_available_data(
-        config['datadir'],
-        config.get('trading_mode', TradingMode.SPOT)
-        )
+    paircombs = dhc.ohlcv_get_available_data(config['datadir'])
 
     if args['pairs']:
         paircombs = [comb for comb in paircombs if comb[0] in args['pairs']]
 
     print(f"Found {len(paircombs)} pair / timeframe combinations.")
     groupedpair = defaultdict(list)
-    for pair, timeframe, candle_type in sorted(
-        paircombs,
-        key=lambda x: (x[0], timeframe_to_minutes(x[1]), x[2])
-    ):
-        groupedpair[(pair, candle_type)].append(timeframe)
+    for pair, timeframe in sorted(paircombs, key=lambda x: (x[0], timeframe_to_minutes(x[1]))):
+        groupedpair[pair].append(timeframe)
 
     if groupedpair:
-        print(tabulate([
-            (pair, ', '.join(timeframes), candle_type)
-            for (pair, candle_type), timeframes in groupedpair.items()
-        ],
-            headers=("Pair", "Timeframe", "Type"),
-            tablefmt='psql', stralign='right'))
+        print(tabulate([(pair, ', '.join(timeframes)) for pair, timeframes in groupedpair.items()],
+                       headers=("Pair", "Timeframe"),
+                       tablefmt='psql', stralign='right'))

freqtrade/commands/db_commands.py

@@ -1,55 +0,0 @@
-import logging
-from typing import Any, Dict
-
-from sqlalchemy import func
-
-from freqtrade.configuration.config_setup import setup_utils_configuration
-from freqtrade.enums.runmode import RunMode
-
-
-logger = logging.getLogger(__name__)
-
-
-def start_convert_db(args: Dict[str, Any]) -> None:
-    from sqlalchemy.orm import make_transient
-
-    from freqtrade.persistence import Order, Trade, init_db
-    from freqtrade.persistence.migrations import set_sequence_ids
-    from freqtrade.persistence.pairlock import PairLock
-
-    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
-
-    init_db(config['db_url'])
-    session_target = Trade._session
-    init_db(config['db_url_from'])
-    logger.info("Starting db migration.")
-
-    trade_count = 0
-    pairlock_count = 0
-    for trade in Trade.get_trades():
-        trade_count += 1
-        make_transient(trade)
-        for o in trade.orders:
-            make_transient(o)
-
-        session_target.add(trade)
-
-    session_target.commit()
-
-    for pairlock in PairLock.query:
-        pairlock_count += 1
-        make_transient(pairlock)
-        session_target.add(pairlock)
-    session_target.commit()
-
-    # Update sequences
-    max_trade_id = session_target.query(func.max(Trade.id)).scalar()
-    max_order_id = session_target.query(func.max(Order.id)).scalar()
-    max_pairlock_id = session_target.query(func.max(PairLock.id)).scalar()
-
-    set_sequence_ids(session_target.get_bind(),
-                     trade_id=max_trade_id,
-                     order_id=max_order_id,
-                     pairlock_id=max_pairlock_id)
-
-    logger.info(f"Migrated {trade_count} Trades, and {pairlock_count} Pairlocks.")

freqtrade/commands/hyperopt_commands.py

@@ -24,7 +24,7 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
 
     print_colorized = config.get('print_colorized', False)
     print_json = config.get('print_json', False)
-    export_csv = config.get('export_csv')
+    export_csv = config.get('export_csv', None)
     no_details = config.get('hyperopt_list_no_details', False)
     no_header = False
 

freqtrade/commands/list_commands.py

@@ -41,7 +41,7 @@ def start_list_exchanges(args: Dict[str, Any]) -> None:
         print(tabulate(exchanges, headers=['Exchange name', 'Valid', 'reason']))
 
 
-def _print_objs_tabular(objs: List, print_colorized: bool, base_dir: Path) -> None:
+def _print_objs_tabular(objs: List, print_colorized: bool) -> None:
     if print_colorized:
         colorama_init(autoreset=True)
         red = Fore.RED
@@ -55,7 +55,7 @@ def _print_objs_tabular(objs: List, print_colorized: bool, base_dir: Path) -> No
     names = [s['name'] for s in objs]
     objs_to_print = [{
         'name': s['name'] if s['name'] else "--",
-        'location': s['location'].relative_to(base_dir),
+        'location': s['location'].name,
         'status': (red + "LOAD FAILED" + reset if s['class'] is None
                    else "OK" if names.count(s['name']) == 1
                    else yellow + "DUPLICATE NAME" + reset)
@@ -77,8 +77,7 @@ def start_list_strategies(args: Dict[str, Any]) -> None:
     config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
 
     directory = Path(config.get('strategy_path', config['user_data_dir'] / USERPATH_STRATEGIES))
-    strategy_objs = StrategyResolver.search_all_objects(
-        directory, not args['print_one_column'], config.get('recursive_strategy_search', False))
+    strategy_objs = StrategyResolver.search_all_objects(directory, not args['print_one_column'])
     # Sort alphabetically
     strategy_objs = sorted(strategy_objs, key=lambda x: x['name'])
     for obj in strategy_objs:
@@ -90,7 +89,7 @@ def start_list_strategies(args: Dict[str, Any]) -> None:
     if args['print_one_column']:
         print('\n'.join([s['name'] for s in strategy_objs]))
     else:
-        _print_objs_tabular(strategy_objs, config.get('print_colorized', False), directory)
+        _print_objs_tabular(strategy_objs, config.get('print_colorized', False))
 
 
 def start_list_timeframes(args: Dict[str, Any]) -> None:
@@ -132,7 +131,7 @@ def start_list_markets(args: Dict[str, Any], pairs_only: bool = False) -> None:
     try:
         pairs = exchange.get_markets(base_currencies=base_currencies,
                                      quote_currencies=quote_currencies,
-                                     tradable_only=pairs_only,
+                                     pairs_only=pairs_only,
                                      active_only=active_only)
         # Sort the pairs/markets by symbol
         pairs = dict(sorted(pairs.items()))
@@ -152,19 +151,15 @@ def start_list_markets(args: Dict[str, Any], pairs_only: bool = False) -> None:
                         if quote_currencies else ""))
 
         headers = ["Id", "Symbol", "Base", "Quote", "Active",
-                   "Spot", "Margin", "Future", "Leverage"]
+                   *(['Is pair'] if not pairs_only else [])]
 
-        tabular_data = [{
-                'Id': v['id'],
-                'Symbol': v['symbol'],
-                'Base': v['base'],
-                'Quote': v['quote'],
-                'Active': market_is_active(v),
-                'Spot': 'Spot' if exchange.market_is_spot(v) else '',
-                'Margin': 'Margin' if exchange.market_is_margin(v) else '',
-                'Future': 'Future' if exchange.market_is_future(v) else '',
-                'Leverage': exchange.get_max_leverage(v['symbol'], 20)
-            } for _, v in pairs.items()]
+        tabular_data = []
+        for _, v in pairs.items():
+            tabular_data.append({'Id': v['id'], 'Symbol': v['symbol'],
+                                 'Base': v['base'], 'Quote': v['quote'],
+                                 'Active': market_is_active(v),
+                                 **({'Is pair': exchange.market_is_tradable(v)}
+                                    if not pairs_only else {})})
 
         if (args.get('print_one_column', False) or
                 args.get('list_pairs_print_json', False) or
@@ -212,7 +207,7 @@ def start_show_trades(args: Dict[str, Any]) -> None:
         raise OperationalException("--db-url is required for this command.")
 
     logger.info(f'Using DB: "{parse_db_uri_for_logging(config["db_url"])}"')
-    init_db(config['db_url'])
+    init_db(config['db_url'], clean_open_orders=False)
     tfilter = []
 
     if config.get('trade_ids'):

freqtrade/commands/optimize_commands.py

@@ -25,16 +25,12 @@ def setup_optimize_configuration(args: Dict[str, Any], method: RunMode) -> Dict[
         RunMode.HYPEROPT: 'hyperoptimization',
     }
     if method in no_unlimited_runmodes.keys():
-        wallet_size = config['dry_run_wallet'] * config['tradable_balance_ratio']
-        # tradable_balance_ratio
         if (config['stake_amount'] != constants.UNLIMITED_STAKE_AMOUNT
-                and config['stake_amount'] > wallet_size):
-            wallet = round_coin_value(wallet_size, config['stake_currency'])
+                and config['stake_amount'] > config['dry_run_wallet']):
+            wallet = round_coin_value(config['dry_run_wallet'], config['stake_currency'])
             stake = round_coin_value(config['stake_amount'], config['stake_currency'])
-            raise OperationalException(
-                f"Starting balance ({wallet}) is smaller than stake_amount {stake}. "
-                f"Wallet is calculated as `dry_run_wallet * tradable_balance_ratio`."
-                )
+            raise OperationalException(f"Starting balance ({wallet}) "
+                                       f"is smaller than stake_amount {stake}.")
 
     return config
 

freqtrade/configuration/PeriodicCache.py

@@ -16,4 +16,4 @@ class PeriodicCache(TTLCache):
             return ts - offset
 
         # Init with smlight offset
-        super().__init__(maxsize=maxsize, ttl=ttl - 1e-5, timer=local_timer, getsizeof=getsizeof)
+        super().__init__(maxsize=maxsize, ttl=ttl-1e-5, timer=local_timer, getsizeof=getsizeof)

freqtrade/configuration/__init__.py

@@ -4,4 +4,5 @@ from freqtrade.configuration.check_exchange import check_exchange
 from freqtrade.configuration.config_setup import setup_utils_configuration
 from freqtrade.configuration.config_validation import validate_config_consistency
 from freqtrade.configuration.configuration import Configuration
+from freqtrade.configuration.PeriodicCache import PeriodicCache
 from freqtrade.configuration.timerange import TimeRange

freqtrade/configuration/check_exchange.py

@@ -27,7 +27,7 @@ def check_exchange(config: Dict[str, Any], check_for_bad: bool = True) -> bool:
         return True
     logger.info("Checking exchange...")
 
-    exchange = config.get('exchange', {}).get('name', '').lower()
+    exchange = config.get('exchange', {}).get('name').lower()
     if not exchange:
         raise OperationalException(
             f'This command requires a configured exchange. You should either use '

freqtrade/configuration/config_setup.py

@@ -22,6 +22,6 @@ def setup_utils_configuration(args: Dict[str, Any], method: RunMode) -> Dict[str
 
     # Ensure these modes are using Dry-run
     config['dry_run'] = True
-    validate_config_consistency(config, preliminary=True)
+    validate_config_consistency(config)
 
     return config

freqtrade/configuration/config_validation.py

@@ -6,8 +6,7 @@ from jsonschema import Draft4Validator, validators
 from jsonschema.exceptions import ValidationError, best_match
 
 from freqtrade import constants
-from freqtrade.configuration.deprecated_settings import process_deprecated_setting
-from freqtrade.enums import RunMode, TradingMode
+from freqtrade.enums import RunMode
 from freqtrade.exceptions import OperationalException
 
 
@@ -39,7 +38,7 @@ def _extend_validator(validator_class):
 FreqtradeValidator = _extend_validator(Draft4Validator)
 
 
-def validate_config_schema(conf: Dict[str, Any], preliminary: bool = False) -> Dict[str, Any]:
+def validate_config_schema(conf: Dict[str, Any]) -> Dict[str, Any]:
     """
     Validate the configuration follow the Config Schema
     :param conf: Config in JSON format
@@ -49,10 +48,7 @@ def validate_config_schema(conf: Dict[str, Any], preliminary: bool = False) -> D
     if conf.get('runmode', RunMode.OTHER) in (RunMode.DRY_RUN, RunMode.LIVE):
         conf_schema['required'] = constants.SCHEMA_TRADE_REQUIRED
     elif conf.get('runmode', RunMode.OTHER) in (RunMode.BACKTEST, RunMode.HYPEROPT):
-        if preliminary:
-            conf_schema['required'] = constants.SCHEMA_BACKTEST_REQUIRED
-        else:
-            conf_schema['required'] = constants.SCHEMA_BACKTEST_REQUIRED_FINAL
+        conf_schema['required'] = constants.SCHEMA_BACKTEST_REQUIRED
     else:
         conf_schema['required'] = constants.SCHEMA_MINIMAL_REQUIRED
     try:
@@ -67,7 +63,7 @@ def validate_config_schema(conf: Dict[str, Any], preliminary: bool = False) -> D
         )
 
 
-def validate_config_consistency(conf: Dict[str, Any], preliminary: bool = False) -> None:
+def validate_config_consistency(conf: Dict[str, Any]) -> None:
     """
     Validate the configuration consistency.
     Should be ran after loading both configuration and strategy,
@@ -84,11 +80,10 @@ def validate_config_consistency(conf: Dict[str, Any], preliminary: bool = False)
     _validate_protections(conf)
     _validate_unlimited_amount(conf)
     _validate_ask_orderbook(conf)
-    validate_migrated_strategy_settings(conf)
 
     # validate configuration before returning
     logger.info('Validating configuration ...')
-    validate_config_schema(conf, preliminary=preliminary)
+    validate_config_schema(conf)
 
 
 def _validate_unlimited_amount(conf: Dict[str, Any]) -> None:
@@ -97,8 +92,8 @@ def _validate_unlimited_amount(conf: Dict[str, Any]) -> None:
     :raise: OperationalException if config validation failed
     """
     if (not conf.get('edge', {}).get('enabled')
-        and conf.get('max_open_trades') == float('inf')
-            and conf.get('stake_amount') == constants.UNLIMITED_STAKE_AMOUNT):
+       and conf.get('max_open_trades') == float('inf')
+       and conf.get('stake_amount') == constants.UNLIMITED_STAKE_AMOUNT):
         raise OperationalException("`max_open_trades` and `stake_amount` cannot both be unlimited.")
 
 
@@ -106,15 +101,13 @@ def _validate_price_config(conf: Dict[str, Any]) -> None:
     """
     When using market orders, price sides must be using the "other" side of the price
     """
-    # TODO: The below could be an enforced setting when using market orders
-    if (conf.get('order_types', {}).get('entry') == 'market'
-            and conf.get('entry_pricing', {}).get('price_side') not in ('ask', 'other')):
-        raise OperationalException(
-            'Market entry orders require entry_pricing.price_side = "other".')
+    if (conf.get('order_types', {}).get('buy') == 'market'
+            and conf.get('bid_strategy', {}).get('price_side') != 'ask'):
+        raise OperationalException('Market buy orders require bid_strategy.price_side = "ask".')
 
-    if (conf.get('order_types', {}).get('exit') == 'market'
-            and conf.get('exit_pricing', {}).get('price_side') not in ('bid', 'other')):
-        raise OperationalException('Market exit orders require exit_pricing.price_side = "other".')
+    if (conf.get('order_types', {}).get('sell') == 'market'
+            and conf.get('ask_strategy', {}).get('price_side') != 'bid'):
+        raise OperationalException('Market sell orders require ask_strategy.price_side = "bid".')
 
 
 def _validate_trailing_stoploss(conf: Dict[str, Any]) -> None:
@@ -157,9 +150,9 @@ def _validate_edge(conf: Dict[str, Any]) -> None:
     if not conf.get('edge', {}).get('enabled'):
         return
 
-    if not conf.get('use_exit_signal', True):
+    if not conf.get('use_sell_signal', True):
         raise OperationalException(
-            "Edge requires `use_exit_signal` to be True, otherwise no sells will happen."
+            "Edge requires `use_sell_signal` to be True, otherwise no sells will happen."
         )
 
 
@@ -197,13 +190,13 @@ def _validate_protections(conf: Dict[str, Any]) -> None:
 
 
 def _validate_ask_orderbook(conf: Dict[str, Any]) -> None:
-    ask_strategy = conf.get('exit_pricing', {})
+    ask_strategy = conf.get('ask_strategy', {})
     ob_min = ask_strategy.get('order_book_min')
     ob_max = ask_strategy.get('order_book_max')
     if ob_min is not None and ob_max is not None and ask_strategy.get('use_order_book'):
         if ob_min != ob_max:
             raise OperationalException(
-                "Using order_book_max != order_book_min in exit_pricing is no longer supported."
+                "Using order_book_max != order_book_min in ask_strategy is no longer supported."
                 "Please pick one value and use `order_book_top` in the future."
             )
         else:
@@ -212,121 +205,5 @@ def _validate_ask_orderbook(conf: Dict[str, Any]) -> None:
             logger.warning(
                 "DEPRECATED: "
                 "Please use `order_book_top` instead of `order_book_min` and `order_book_max` "
-                "for your `exit_pricing` configuration."
+                "for your `ask_strategy` configuration."
             )
-
-
-def validate_migrated_strategy_settings(conf: Dict[str, Any]) -> None:
-
-    _validate_time_in_force(conf)
-    _validate_order_types(conf)
-    _validate_unfilledtimeout(conf)
-    _validate_pricing_rules(conf)
-    _strategy_settings(conf)
-
-
-def _validate_time_in_force(conf: Dict[str, Any]) -> None:
-
-    time_in_force = conf.get('order_time_in_force', {})
-    if 'buy' in time_in_force or 'sell' in time_in_force:
-        if conf.get('trading_mode', TradingMode.SPOT) != TradingMode.SPOT:
-            raise OperationalException(
-                "Please migrate your time_in_force settings to use 'entry' and 'exit'.")
-        else:
-            logger.warning(
-                "DEPRECATED: Using 'buy' and 'sell' for time_in_force is deprecated."
-                "Please migrate your time_in_force settings to use 'entry' and 'exit'."
-            )
-            process_deprecated_setting(
-                conf, 'order_time_in_force', 'buy', 'order_time_in_force', 'entry')
-
-            process_deprecated_setting(
-                conf, 'order_time_in_force', 'sell', 'order_time_in_force', 'exit')
-
-
-def _validate_order_types(conf: Dict[str, Any]) -> None:
-
-    order_types = conf.get('order_types', {})
-    old_order_types = ['buy', 'sell', 'emergencysell', 'forcebuy',
-                       'forcesell', 'emergencyexit', 'forceexit', 'forceentry']
-    if any(x in order_types for x in old_order_types):
-        if conf.get('trading_mode', TradingMode.SPOT) != TradingMode.SPOT:
-            raise OperationalException(
-                "Please migrate your order_types settings to use the new wording.")
-        else:
-            logger.warning(
-                "DEPRECATED: Using 'buy' and 'sell' for order_types is deprecated."
-                "Please migrate your order_types settings to use 'entry' and 'exit' wording."
-            )
-            for o, n in [
-                ('buy', 'entry'),
-                ('sell', 'exit'),
-                ('emergencysell', 'emergency_exit'),
-                ('forcesell', 'force_exit'),
-                ('forcebuy', 'force_entry'),
-                ('emergencyexit', 'emergency_exit'),
-                ('forceexit', 'force_exit'),
-                ('forceentry', 'force_entry'),
-            ]:
-
-                process_deprecated_setting(conf, 'order_types', o, 'order_types', n)
-
-
-def _validate_unfilledtimeout(conf: Dict[str, Any]) -> None:
-    unfilledtimeout = conf.get('unfilledtimeout', {})
-    if any(x in unfilledtimeout for x in ['buy', 'sell']):
-        if conf.get('trading_mode', TradingMode.SPOT) != TradingMode.SPOT:
-            raise OperationalException(
-                "Please migrate your unfilledtimeout settings to use the new wording.")
-        else:
-
-            logger.warning(
-                "DEPRECATED: Using 'buy' and 'sell' for unfilledtimeout is deprecated."
-                "Please migrate your unfilledtimeout settings to use 'entry' and 'exit' wording."
-            )
-            for o, n in [
-                ('buy', 'entry'),
-                ('sell', 'exit'),
-            ]:
-
-                process_deprecated_setting(conf, 'unfilledtimeout', o, 'unfilledtimeout', n)
-
-
-def _validate_pricing_rules(conf: Dict[str, Any]) -> None:
-
-    if conf.get('ask_strategy') or conf.get('bid_strategy'):
-        if conf.get('trading_mode', TradingMode.SPOT) != TradingMode.SPOT:
-            raise OperationalException(
-                "Please migrate your pricing settings to use the new wording.")
-        else:
-
-            logger.warning(
-                "DEPRECATED: Using 'ask_strategy' and 'bid_strategy' is deprecated."
-                "Please migrate your settings to use 'entry_pricing' and 'exit_pricing'."
-            )
-            conf['entry_pricing'] = {}
-            for obj in list(conf.get('bid_strategy', {}).keys()):
-                if obj == 'ask_last_balance':
-                    process_deprecated_setting(conf, 'bid_strategy', obj,
-                                               'entry_pricing', 'price_last_balance')
-                else:
-                    process_deprecated_setting(conf, 'bid_strategy', obj, 'entry_pricing', obj)
-            del conf['bid_strategy']
-
-            conf['exit_pricing'] = {}
-            for obj in list(conf.get('ask_strategy', {}).keys()):
-                if obj == 'bid_last_balance':
-                    process_deprecated_setting(conf, 'ask_strategy', obj,
-                                               'exit_pricing', 'price_last_balance')
-                else:
-                    process_deprecated_setting(conf, 'ask_strategy', obj, 'exit_pricing', obj)
-            del conf['ask_strategy']
-
-
-def _strategy_settings(conf: Dict[str, Any]) -> None:
-
-    process_deprecated_setting(conf, None, 'use_sell_signal', None, 'use_exit_signal')
-    process_deprecated_setting(conf, None, 'sell_profit_only', None, 'exit_profit_only')
-    process_deprecated_setting(conf, None, 'sell_profit_offset', None, 'exit_profit_offset')
-    process_deprecated_setting(conf, None, 'ignore_roi_if_buy_signal',
-                               None, 'ignore_roi_if_entry_signal')

freqtrade/configuration/configuration.py

@@ -12,8 +12,8 @@ from freqtrade.configuration.check_exchange import check_exchange
 from freqtrade.configuration.deprecated_settings import process_temporary_deprecated_settings
 from freqtrade.configuration.directory_operations import create_datadir, create_userdata_dir
 from freqtrade.configuration.environment_vars import enironment_vars_to_dict
-from freqtrade.configuration.load_config import load_file, load_from_files
-from freqtrade.enums import NON_UTIL_MODES, TRADING_MODES, CandleType, RunMode, TradingMode
+from freqtrade.configuration.load_config import load_config_file, load_file
+from freqtrade.enums import NON_UTIL_MODES, TRADING_MODES, RunMode
 from freqtrade.exceptions import OperationalException
 from freqtrade.loggers import setup_logging
 from freqtrade.misc import deep_merge_dicts, parse_db_uri_for_logging
@@ -55,28 +55,47 @@ class Configuration:
         :param files: List of file paths
         :return: configuration dictionary
         """
-        # Keep this method as staticmethod, so it can be used from interactive environments
         c = Configuration({'config': files}, RunMode.OTHER)
         return c.get_config()
 
+    def load_from_files(self, files: List[str]) -> Dict[str, Any]:
+
+        # Keep this method as staticmethod, so it can be used from interactive environments
+        config: Dict[str, Any] = {}
+
+        if not files:
+            return deepcopy(constants.MINIMAL_CONFIG)
+
+        # We expect here a list of config filenames
+        for path in files:
+            logger.info(f'Using config: {path} ...')
+
+            # Merge config options, overwriting old values
+            config = deep_merge_dicts(load_config_file(path), config)
+
+        # Load environment variables
+        env_data = enironment_vars_to_dict()
+        config = deep_merge_dicts(env_data, config)
+
+        config['config_files'] = files
+        # Normalize config
+        if 'internals' not in config:
+            config['internals'] = {}
+        if 'ask_strategy' not in config:
+            config['ask_strategy'] = {}
+
+        if 'pairlists' not in config:
+            config['pairlists'] = []
+
+        return config
+
     def load_config(self) -> Dict[str, Any]:
         """
         Extract information for sys.argv and load the bot configuration
         :return: Configuration dictionary
         """
         # Load all configs
-        config: Dict[str, Any] = load_from_files(self.args.get("config", []))
-
-        # Load environment variables
-        env_data = enironment_vars_to_dict()
-        config = deep_merge_dicts(env_data, config)
-
-        # Normalize config
-        if 'internals' not in config:
-            config['internals'] = {}
-
-        if 'pairlists' not in config:
-            config['pairlists'] = []
+        config: Dict[str, Any] = self.load_from_files(self.args.get("config", []))
 
         # Keep a copy of the original configuration file
         config['original_config'] = deepcopy(config)
@@ -95,10 +114,6 @@ class Configuration:
 
         self._process_data_options(config)
 
-        self._process_analyze_options(config)
-
-        self._process_freqai_options(config)
-
         # Check if the exchange set by the user is supported
         check_exchange(config, config.get('experimental', {}).get('block_bad_exchanges', True))
 
@@ -131,7 +146,7 @@ class Configuration:
                 # Default to in-memory db for dry_run if not specified
                 config['db_url'] = constants.DEFAULT_DB_DRYRUN_URL
         else:
-            if not config.get('db_url'):
+            if not config.get('db_url', None):
                 config['db_url'] = constants.DEFAULT_DB_PROD_URL
             logger.info('Dry run is disabled')
 
@@ -151,11 +166,8 @@ class Configuration:
             config.update({'db_url': self.args['db_url']})
             logger.info('Parameter --db-url detected ...')
 
-        self._args_to_config(config, argname='db_url_from',
-                             logstring='Parameter --db-url-from detected ...')
-
-        if config.get('force_entry_enable', False):
-            logger.warning('`force_entry_enable` RPC message enabled.')
+        if config.get('forcebuy_enable', False):
+            logger.warning('`forcebuy` RPC message enabled.')
 
         # Support for sd_notify
         if 'sd_notify' in self.args and self.args['sd_notify']:
@@ -184,7 +196,7 @@ class Configuration:
         config['user_data_dir'] = create_userdata_dir(config['user_data_dir'], create_dir=False)
         logger.info('Using user-data directory: %s ...', config['user_data_dir'])
 
-        config.update({'datadir': create_datadir(config, self.args.get('datadir'))})
+        config.update({'datadir': create_datadir(config, self.args.get('datadir', None))})
         logger.info('Using data directory: %s ...', config.get('datadir'))
 
         if self.args.get('exportfilename'):
@@ -223,7 +235,7 @@ class Configuration:
         if config.get('max_open_trades') == -1:
             config['max_open_trades'] = float('inf')
 
-        if self.args.get('stake_amount'):
+        if self.args.get('stake_amount', None):
             # Convert explicitly to float to support CLI argument for both unlimited and value
             try:
                 self.args['stake_amount'] = float(self.args['stake_amount'])
@@ -255,12 +267,6 @@ class Configuration:
         self._args_to_config(config, argname='strategy_list',
                              logstring='Using strategy list of {} strategies', logfun=len)
 
-        self._args_to_config(
-            config,
-            argname='recursive_strategy_search',
-            logstring='Recursively searching for a strategy in the strategies folder.',
-        )
-
         self._args_to_config(config, argname='timeframe',
                              logstring='Overriding timeframe with Command line argument')
 
@@ -400,8 +406,6 @@ class Configuration:
         self._args_to_config(config, argname='trade_source',
                              logstring='Using trades from: {}')
 
-        self._args_to_config(config, argname='prepend_data',
-                             logstring='Prepend detected. Allowing data prepending.')
         self._args_to_config(config, argname='erase',
                              logstring='Erase detected. Deleting existing data.')
 
@@ -429,26 +433,6 @@ class Configuration:
     def _process_data_options(self, config: Dict[str, Any]) -> None:
         self._args_to_config(config, argname='new_pairs_days',
                              logstring='Detected --new-pairs-days: {}')
-        self._args_to_config(config, argname='trading_mode',
-                             logstring='Detected --trading-mode: {}')
-        config['candle_type_def'] = CandleType.get_default(
-            config.get('trading_mode', 'spot') or 'spot')
-        config['trading_mode'] = TradingMode(config.get('trading_mode', 'spot') or 'spot')
-        self._args_to_config(config, argname='candle_types',
-                             logstring='Detected --candle-types: {}')
-
-    def _process_analyze_options(self, config: Dict[str, Any]) -> None:
-        self._args_to_config(config, argname='analysis_groups',
-                             logstring='Analysis reason groups: {}')
-
-        self._args_to_config(config, argname='enter_reason_list',
-                             logstring='Analysis enter tag list: {}')
-
-        self._args_to_config(config, argname='exit_reason_list',
-                             logstring='Analysis exit tag list: {}')
-
-        self._args_to_config(config, argname='indicator_list',
-                             logstring='Analysis indicator list: {}')
 
     def _process_runmode(self, config: Dict[str, Any]) -> None:
 
@@ -463,16 +447,6 @@ class Configuration:
 
         config.update({'runmode': self.runmode})
 
-    def _process_freqai_options(self, config: Dict[str, Any]) -> None:
-
-        self._args_to_config(config, argname='freqaimodel',
-                             logstring='Using freqaimodel class name: {}')
-
-        self._args_to_config(config, argname='freqaimodel_path',
-                             logstring='Using freqaimodel path: {}')
-
-        return
-
     def _args_to_config(self, config: Dict[str, Any], argname: str,
                         logstring: str, logfun: Optional[Callable] = None,
                         deprecated_msg: Optional[str] = None) -> None:
@@ -486,7 +460,7 @@ class Configuration:
                         configuration instead of the content)
         """
         if (argname in self.args and self.args[argname] is not None
-                and self.args[argname] is not False):
+           and self.args[argname] is not False):
 
             config.update({argname: self.args[argname]})
             if logfun:
@@ -517,8 +491,7 @@ class Configuration:
             if not pairs_file.exists():
                 raise OperationalException(f'No pairs file found with path "{pairs_file}".')
             config['pairs'] = load_file(pairs_file)
-            if isinstance(config['pairs'], list):
-                config['pairs'].sort()
+            config['pairs'].sort()
             return
 
         if 'config' in self.args and self.args['config']:
@@ -529,5 +502,5 @@ class Configuration:
             pairs_file = config['datadir'] / 'pairs.json'
             if pairs_file.exists():
                 config['pairs'] = load_file(pairs_file)
-                if 'pairs' in config and isinstance(config['pairs'], list):
+                if 'pairs' in config:
                     config['pairs'].sort()

freqtrade/configuration/deprecated_settings.py

@@ -12,15 +12,14 @@ logger = logging.getLogger(__name__)
 
 
 def check_conflicting_settings(config: Dict[str, Any],
-                               section_old: Optional[str], name_old: str,
+                               section_old: str, name_old: str,
                                section_new: Optional[str], name_new: str) -> None:
     section_new_config = config.get(section_new, {}) if section_new else config
-    section_old_config = config.get(section_old, {}) if section_old else config
+    section_old_config = config.get(section_old, {})
     if name_new in section_new_config and name_old in section_old_config:
         new_name = f"{section_new}.{name_new}" if section_new else f"{name_new}"
-        old_name = f"{section_old}.{name_old}" if section_old else f"{name_old}"
         raise OperationalException(
-            f"Conflicting settings `{new_name}` and `{old_name}` "
+            f"Conflicting settings `{new_name}` and `{section_old}.{name_old}` "
             "(DEPRECATED) detected in the configuration file. "
             "This deprecated setting will be removed in the next versions of Freqtrade. "
             f"Please delete it from your configuration and use the `{new_name}` "
@@ -48,25 +47,23 @@ def process_removed_setting(config: Dict[str, Any],
 
 
 def process_deprecated_setting(config: Dict[str, Any],
-                               section_old: Optional[str], name_old: str,
+                               section_old: str, name_old: str,
                                section_new: Optional[str], name_new: str
                                ) -> None:
     check_conflicting_settings(config, section_old, name_old, section_new, name_new)
-    section_old_config = config.get(section_old, {}) if section_old else config
+    section_old_config = config.get(section_old, {})
 
     if name_old in section_old_config:
-        section_1 = f"{section_old}.{name_old}" if section_old else f"{name_old}"
         section_2 = f"{section_new}.{name_new}" if section_new else f"{name_new}"
         logger.warning(
             "DEPRECATED: "
-            f"The `{section_1}` setting is deprecated and "
+            f"The `{section_old}.{name_old}` setting is deprecated and "
             "will be removed in the next versions of Freqtrade. "
             f"Please use the `{section_2}` setting in your configuration instead."
         )
 
         section_new_config = config.get(section_new, {}) if section_new else config
         section_new_config[name_new] = section_old_config[name_old]
-        del section_old_config[name_old]
 
 
 def process_temporary_deprecated_settings(config: Dict[str, Any]) -> None:
@@ -74,51 +71,25 @@ def process_temporary_deprecated_settings(config: Dict[str, Any]) -> None:
     # Kept for future deprecated / moved settings
     # check_conflicting_settings(config, 'ask_strategy', 'use_sell_signal',
     #                            'experimental', 'use_sell_signal')
-
+    process_deprecated_setting(config, 'ask_strategy', 'use_sell_signal',
+                               None, 'use_sell_signal')
+    process_deprecated_setting(config, 'ask_strategy', 'sell_profit_only',
+                               None, 'sell_profit_only')
+    process_deprecated_setting(config, 'ask_strategy', 'sell_profit_offset',
+                               None, 'sell_profit_offset')
+    process_deprecated_setting(config, 'ask_strategy', 'ignore_roi_if_buy_signal',
+                               None, 'ignore_roi_if_buy_signal')
     process_deprecated_setting(config, 'ask_strategy', 'ignore_buying_expired_candle_after',
                                None, 'ignore_buying_expired_candle_after')
 
-    process_deprecated_setting(config, None, 'forcebuy_enable', None, 'force_entry_enable')
-
-    # New settings
-    if config.get('telegram'):
-        process_deprecated_setting(config['telegram'], 'notification_settings', 'sell',
-                                   'notification_settings', 'exit')
-        process_deprecated_setting(config['telegram'], 'notification_settings', 'sell_fill',
-                                   'notification_settings', 'exit_fill')
-        process_deprecated_setting(config['telegram'], 'notification_settings', 'sell_cancel',
-                                   'notification_settings', 'exit_cancel')
-        process_deprecated_setting(config['telegram'], 'notification_settings', 'buy',
-                                   'notification_settings', 'entry')
-        process_deprecated_setting(config['telegram'], 'notification_settings', 'buy_fill',
-                                   'notification_settings', 'entry_fill')
-        process_deprecated_setting(config['telegram'], 'notification_settings', 'buy_cancel',
-                                   'notification_settings', 'entry_cancel')
-    if config.get('webhook'):
-        process_deprecated_setting(config, 'webhook', 'webhookbuy', 'webhook', 'webhookentry')
-        process_deprecated_setting(config, 'webhook', 'webhookbuycancel',
-                                   'webhook', 'webhookentrycancel')
-        process_deprecated_setting(config, 'webhook', 'webhookbuyfill',
-                                   'webhook', 'webhookentryfill')
-        process_deprecated_setting(config, 'webhook', 'webhooksell', 'webhook', 'webhookexit')
-        process_deprecated_setting(config, 'webhook', 'webhooksellcancel',
-                                   'webhook', 'webhookexitcancel')
-        process_deprecated_setting(config, 'webhook', 'webhooksellfill',
-                                   'webhook', 'webhookexitfill')
-
     # Legacy way - having them in experimental ...
-
-    process_removed_setting(config, 'experimental', 'use_sell_signal', None, 'use_exit_signal')
-    process_removed_setting(config, 'experimental', 'sell_profit_only', None, 'exit_profit_only')
+    process_removed_setting(config, 'experimental', 'use_sell_signal',
+                            None, 'use_sell_signal')
+    process_removed_setting(config, 'experimental', 'sell_profit_only',
+                            None, 'sell_profit_only')
     process_removed_setting(config, 'experimental', 'ignore_roi_if_buy_signal',
-                            None, 'ignore_roi_if_entry_signal')
+                            None, 'ignore_roi_if_buy_signal')
 
-    process_removed_setting(config, 'ask_strategy', 'use_sell_signal', None, 'use_exit_signal')
-    process_removed_setting(config, 'ask_strategy', 'sell_profit_only', None, 'exit_profit_only')
-    process_removed_setting(config, 'ask_strategy', 'sell_profit_offset',
-                            None, 'exit_profit_offset')
-    process_removed_setting(config, 'ask_strategy', 'ignore_roi_if_buy_signal',
-                            None, 'ignore_roi_if_entry_signal')
     if (config.get('edge', {}).get('enabled', False)
        and 'capital_available_percentage' in config.get('edge', {})):
         raise OperationalException(
@@ -129,11 +100,16 @@ def process_temporary_deprecated_settings(config: Dict[str, Any]) -> None:
             "from the edge configuration."
         )
     if 'ticker_interval' in config:
-
-        raise OperationalException(
-            "DEPRECATED: 'ticker_interval' detected. "
+        logger.warning(
+            "DEPRECATED: "
             "Please use 'timeframe' instead of 'ticker_interval."
         )
+        if 'timeframe' in config:
+            raise OperationalException(
+                "Both 'timeframe' and 'ticker_interval' detected."
+                "Please remove 'ticker_interval' from your configuration to continue operating."
+            )
+        config['timeframe'] = config['ticker_interval']
 
     if 'protections' in config:
         logger.warning("DEPRECATED: Setting 'protections' in the configuration is deprecated.")

freqtrade/configuration/directory_operations.py

@@ -15,7 +15,7 @@ def create_datadir(config: Dict[str, Any], datadir: Optional[str] = None) -> Pat
     folder = Path(datadir) if datadir else Path(f"{config['user_data_dir']}/data")
     if not datadir:
         # set datadir
-        exchange_name = config.get('exchange', {}).get('name', '').lower()
+        exchange_name = config.get('exchange', {}).get('name').lower()
         folder = folder.joinpath(exchange_name)
 
     if not folder.is_dir():

freqtrade/configuration/load_config.py

@@ -4,15 +4,12 @@ This module contain functions to load the configuration file
 import logging
 import re
 import sys
-from copy import deepcopy
 from pathlib import Path
-from typing import Any, Dict, List
+from typing import Any, Dict
 
 import rapidjson
 
-from freqtrade.constants import MINIMAL_CONFIG
 from freqtrade.exceptions import OperationalException
-from freqtrade.misc import deep_merge_dicts
 
 
 logger = logging.getLogger(__name__)
@@ -31,7 +28,7 @@ def log_config_error_range(path: str, errmsg: str) -> str:
             offset = int(offsetlist[0])
             text = Path(path).read_text()
             # Fetch an offset of 80 characters around the error line
-            subtext = text[offset - min(80, offset):offset + 80]
+            subtext = text[offset-min(80, offset):offset+80]
             segments = subtext.split('\n')
             if len(segments) > 3:
                 # Remove first and last lines, to avoid odd truncations
@@ -73,43 +70,3 @@ def load_config_file(path: str) -> Dict[str, Any]:
         )
 
     return config
-
-
-def load_from_files(files: List[str], base_path: Path = None, level: int = 0) -> Dict[str, Any]:
-    """
-    Recursively load configuration files if specified.
-    Sub-files are assumed to be relative to the initial config.
-    """
-    config: Dict[str, Any] = {}
-    if level > 5:
-        raise OperationalException("Config loop detected.")
-
-    if not files:
-        return deepcopy(MINIMAL_CONFIG)
-    files_loaded = []
-    # We expect here a list of config filenames
-    for filename in files:
-        logger.info(f'Using config: {filename} ...')
-        if filename == '-':
-            # Immediately load stdin and return
-            return load_config_file(filename)
-        file = Path(filename)
-        if base_path:
-            # Prepend basepath to allow for relative assignments
-            file = base_path / file
-
-        config_tmp = load_config_file(str(file))
-        if 'add_config_files' in config_tmp:
-            config_sub = load_from_files(
-                config_tmp['add_config_files'], file.resolve().parent, level + 1)
-            files_loaded.extend(config_sub.get('config_files', []))
-            config_tmp = deep_merge_dicts(config_tmp, config_sub)
-
-        files_loaded.insert(0, str(file))
-
-        # Merge config options, overwriting prior values
-        config = deep_merge_dicts(config_tmp, config)
-
-    config['config_files'] = files_loaded
-
-    return config

freqtrade/constants.py

@@ -3,9 +3,7 @@
 """
 bot constants
 """
-from typing import List, Literal, Tuple
-
-from freqtrade.enums import CandleType
+from typing import List, Tuple
 
 
 DEFAULT_CONFIG = 'config.json'
@@ -14,22 +12,21 @@ PROCESS_THROTTLE_SECS = 5  # sec
 HYPEROPT_EPOCH = 100  # epochs
 RETRY_TIMEOUT = 30  # sec
 TIMEOUT_UNITS = ['minutes', 'seconds']
-EXPORT_OPTIONS = ['none', 'trades', 'signals']
+EXPORT_OPTIONS = ['none', 'trades']
 DEFAULT_DB_PROD_URL = 'sqlite:///tradesv3.sqlite'
 DEFAULT_DB_DRYRUN_URL = 'sqlite:///tradesv3.dryrun.sqlite'
 UNLIMITED_STAKE_AMOUNT = 'unlimited'
 DEFAULT_AMOUNT_RESERVE_PERCENT = 0.05
-REQUIRED_ORDERTIF = ['entry', 'exit']
-REQUIRED_ORDERTYPES = ['entry', 'exit', 'stoploss', 'stoploss_on_exchange']
-PRICING_SIDES = ['ask', 'bid', 'same', 'other']
+REQUIRED_ORDERTIF = ['buy', 'sell']
+REQUIRED_ORDERTYPES = ['buy', 'sell', 'stoploss', 'stoploss_on_exchange']
+ORDERBOOK_SIDES = ['ask', 'bid']
 ORDERTYPE_POSSIBILITIES = ['limit', 'market']
 ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
 HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss',
                          'SharpeHyperOptLoss', 'SharpeHyperOptLossDaily',
                          'SortinoHyperOptLoss', 'SortinoHyperOptLossDaily',
                          'CalmarHyperOptLoss',
-                         'MaxDrawDownHyperOptLoss', 'MaxDrawDownRelativeHyperOptLoss',
-                         'ProfitDrawDownHyperOptLoss']
+                         'MaxDrawDownHyperOptLoss', 'ProfitDrawDownHyperOptLoss']
 AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
                        'AgeFilter', 'OffsetFilter', 'PerformanceFilter',
                        'PrecisionFilter', 'PriceFilter', 'RangeStabilityFilter',
@@ -46,8 +43,6 @@ DEFAULT_DATAFRAME_COLUMNS = ['date', 'open', 'high', 'low', 'close', 'volume']
 # Don't modify sequence of DEFAULT_TRADES_COLUMNS
 # it has wide consequences for stored trades files
 DEFAULT_TRADES_COLUMNS = ['timestamp', 'id', 'type', 'side', 'price', 'amount', 'cost']
-TRADING_MODES = ['spot', 'margin', 'futures']
-MARGIN_MODES = ['cross', 'isolated', '']
 
 LAST_BT_RESULT_FN = '.last_result.json'
 FTHYPT_FILEVERSION = 'fthypt_fileversion'
@@ -55,7 +50,6 @@ FTHYPT_FILEVERSION = 'fthypt_fileversion'
 USERPATH_HYPEROPTS = 'hyperopts'
 USERPATH_STRATEGIES = 'strategies'
 USERPATH_NOTEBOOKS = 'notebooks'
-USERPATH_FREQAIMODELS = 'freqaimodels'
 
 TELEGRAM_SETTING_OPTIONS = ['on', 'off', 'silent']
 WEBHOOK_FORMAT_OPTIONS = ['form', 'json', 'raw']
@@ -88,19 +82,20 @@ SUPPORTED_FIAT = [
     "AUD", "BRL", "CAD", "CHF", "CLP", "CNY", "CZK", "DKK",
     "EUR", "GBP", "HKD", "HUF", "IDR", "ILS", "INR", "JPY",
     "KRW", "MXN", "MYR", "NOK", "NZD", "PHP", "PKR", "PLN",
-    "RUB", "UAH", "SEK", "SGD", "THB", "TRY", "TWD", "ZAR",
-    "USD", "BTC", "ETH", "XRP", "LTC", "BCH"
+    "RUB", "SEK", "SGD", "THB", "TRY", "TWD", "ZAR", "USD",
+    "BTC", "ETH", "XRP", "LTC", "BCH"
 ]
 
 MINIMAL_CONFIG = {
-    "stake_currency": "",
-    "dry_run": True,
-    "exchange": {
-        "name": "",
-        "key": "",
-        "secret": "",
-        "pair_whitelist": [],
-        "ccxt_async_config": {
+    'stake_currency': '',
+    'dry_run': True,
+    'exchange': {
+        'name': '',
+        'key': '',
+        'secret': '',
+        'pair_whitelist': [],
+        'ccxt_async_config': {
+            'enableRateLimit': True,
         }
     }
 }
@@ -145,19 +140,16 @@ CONF_SCHEMA = {
             'minProperties': 1
         },
         'amount_reserve_percent': {'type': 'number', 'minimum': 0.0, 'maximum': 0.5},
-        'stoploss': {'type': 'number', 'maximum': 0, 'exclusiveMaximum': True, 'minimum': -1},
+        'stoploss': {'type': 'number', 'maximum': 0, 'exclusiveMaximum': True},
         'trailing_stop': {'type': 'boolean'},
         'trailing_stop_positive': {'type': 'number', 'minimum': 0, 'maximum': 1},
         'trailing_stop_positive_offset': {'type': 'number', 'minimum': 0, 'maximum': 1},
         'trailing_only_offset_is_reached': {'type': 'boolean'},
-        'use_exit_signal': {'type': 'boolean'},
-        'exit_profit_only': {'type': 'boolean'},
-        'exit_profit_offset': {'type': 'number'},
-        'ignore_roi_if_entry_signal': {'type': 'boolean'},
+        'use_sell_signal': {'type': 'boolean'},
+        'sell_profit_only': {'type': 'boolean'},
+        'sell_profit_offset': {'type': 'number'},
+        'ignore_roi_if_buy_signal': {'type': 'boolean'},
         'ignore_buying_expired_candle_after': {'type': 'number'},
-        'trading_mode': {'type': 'string', 'enum': TRADING_MODES},
-        'margin_mode': {'type': 'string', 'enum': MARGIN_MODES},
-        'liquidation_buffer': {'type': 'number', 'minimum': 0.0, 'maximum': 0.99},
         'backtest_breakdown': {
             'type': 'array',
             'items': {'type': 'string', 'enum': BACKTEST_BREAKDOWNS}
@@ -166,22 +158,22 @@ CONF_SCHEMA = {
         'unfilledtimeout': {
             'type': 'object',
             'properties': {
-                'entry': {'type': 'number', 'minimum': 1},
-                'exit': {'type': 'number', 'minimum': 1},
+                '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'}
             }
         },
-        'entry_pricing': {
+        'bid_strategy': {
             'type': 'object',
             'properties': {
-                'price_last_balance': {
+                'ask_last_balance': {
                     'type': 'number',
                     'minimum': 0,
                     'maximum': 1,
                     'exclusiveMaximum': False,
                 },
-                'price_side': {'type': 'string', 'enum': PRICING_SIDES, 'default': 'same'},
+                'price_side': {'type': 'string', 'enum': ORDERBOOK_SIDES, 'default': 'bid'},
                 'use_order_book': {'type': 'boolean'},
                 'order_book_top': {'type': 'integer', 'minimum': 1, 'maximum': 50, },
                 'check_depth_of_market': {
@@ -194,11 +186,11 @@ CONF_SCHEMA = {
             },
             'required': ['price_side']
         },
-        'exit_pricing': {
+        'ask_strategy': {
             'type': 'object',
             'properties': {
-                'price_side': {'type': 'string', 'enum': PRICING_SIDES, 'default': 'same'},
-                'price_last_balance': {
+                'price_side': {'type': 'string', 'enum': ORDERBOOK_SIDES, 'default': 'ask'},
+                'bid_last_balance': {
                     'type': 'number',
                     'minimum': 0,
                     'maximum': 1,
@@ -215,11 +207,11 @@ CONF_SCHEMA = {
         'order_types': {
             'type': 'object',
             'properties': {
-                'entry': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
-                'exit': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
-                'force_exit': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
-                'force_entry': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
-                'emergency_exit': {
+                'buy': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
+                'sell': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
+                'forcesell': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
+                'forcebuy': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
+                'emergencysell': {
                     'type': 'string',
                     'enum': ORDERTYPE_POSSIBILITIES,
                     'default': 'market'},
@@ -229,19 +221,18 @@ CONF_SCHEMA = {
                 'stoploss_on_exchange_limit_ratio': {'type': 'number', 'minimum': 0.0,
                                                      'maximum': 1.0}
             },
-            'required': ['entry', 'exit', 'stoploss', 'stoploss_on_exchange']
+            'required': ['buy', 'sell', 'stoploss', 'stoploss_on_exchange']
         },
         'order_time_in_force': {
             'type': 'object',
             'properties': {
-                'entry': {'type': 'string', 'enum': ORDERTIF_POSSIBILITIES},
-                'exit': {'type': 'string', 'enum': ORDERTIF_POSSIBILITIES}
+                'buy': {'type': 'string', 'enum': ORDERTIF_POSSIBILITIES},
+                'sell': {'type': 'string', 'enum': ORDERTIF_POSSIBILITIES}
             },
-            'required': REQUIRED_ORDERTIF
+            'required': ['buy', 'sell']
         },
         'exchange': {'$ref': '#/definitions/exchange'},
         'edge': {'$ref': '#/definitions/edge'},
-        'freqai': {'$ref': '#/definitions/freqai'},
         'experimental': {
             'type': 'object',
             'properties': {
@@ -287,42 +278,34 @@ CONF_SCHEMA = {
                         'status': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
                         'warning': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
                         'startup': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
-                        'entry': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
-                        'entry_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
-                        'entry_fill': {'type': 'string',
-                                       'enum': TELEGRAM_SETTING_OPTIONS,
-                                       'default': 'off'
-                                       },
-                        'exit': {
+                        'buy': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
+                        'buy_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
+                        'buy_fill': {'type': 'string',
+                                     'enum': TELEGRAM_SETTING_OPTIONS,
+                                     'default': 'off'
+                                     },
+                        'sell': {
                             'type': ['string', 'object'],
                             'additionalProperties': {
                                 'type': 'string',
                                 'enum': TELEGRAM_SETTING_OPTIONS
                             }
                         },
-                        'exit_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
-                        'exit_fill': {
+                        'sell_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
+                        'sell_fill': {
                             'type': 'string',
                             'enum': TELEGRAM_SETTING_OPTIONS,
-                            'default': 'on'
+                            'default': 'off'
                         },
                         'protection_trigger': {
                             'type': 'string',
                             'enum': TELEGRAM_SETTING_OPTIONS,
-                            'default': 'on'
+                            'default': 'off'
                         },
                         'protection_trigger_global': {
                             'type': 'string',
                             'enum': TELEGRAM_SETTING_OPTIONS,
                         },
-                        'show_candle': {
-                            'type': 'string',
-                            'enum': ['off', 'ohlc'],
-                        },
-                        'strategy_msg': {
-                            'type': 'string',
-                            'enum': TELEGRAM_SETTING_OPTIONS,
-                        },
                     }
                 },
                 'reload': {'type': 'boolean'},
@@ -337,56 +320,15 @@ CONF_SCHEMA = {
                 'format': {'type': 'string', 'enum': WEBHOOK_FORMAT_OPTIONS, 'default': 'form'},
                 'retries': {'type': 'integer', 'minimum': 0},
                 'retry_delay': {'type': 'number', 'minimum': 0},
-                'webhookentry': {'type': 'object'},
-                'webhookentrycancel': {'type': 'object'},
-                'webhookentryfill': {'type': 'object'},
-                'webhookexit': {'type': 'object'},
-                'webhookexitcancel': {'type': 'object'},
-                'webhookexitfill': {'type': 'object'},
+                'webhookbuy': {'type': 'object'},
+                'webhookbuycancel': {'type': 'object'},
+                'webhookbuyfill': {'type': 'object'},
+                'webhooksell': {'type': 'object'},
+                'webhooksellcancel': {'type': 'object'},
+                'webhooksellfill': {'type': 'object'},
                 'webhookstatus': {'type': 'object'},
             },
         },
-        'discord': {
-            'type': 'object',
-            'properties': {
-                'enabled': {'type': 'boolean'},
-                'webhook_url': {'type': 'string'},
-                "exit_fill": {
-                    'type': 'array', 'items': {'type': 'object'},
-                    'default': [
-                        {"Trade ID": "{trade_id}"},
-                        {"Exchange": "{exchange}"},
-                        {"Pair": "{pair}"},
-                        {"Direction": "{direction}"},
-                        {"Open rate": "{open_rate}"},
-                        {"Close rate": "{close_rate}"},
-                        {"Amount": "{amount}"},
-                        {"Open date": "{open_date:%Y-%m-%d %H:%M:%S}"},
-                        {"Close date": "{close_date:%Y-%m-%d %H:%M:%S}"},
-                        {"Profit": "{profit_amount} {stake_currency}"},
-                        {"Profitability": "{profit_ratio:.2%}"},
-                        {"Enter tag": "{enter_tag}"},
-                        {"Exit Reason": "{exit_reason}"},
-                        {"Strategy": "{strategy}"},
-                        {"Timeframe": "{timeframe}"},
-                    ]
-                },
-                "entry_fill": {
-                    'type': 'array', 'items': {'type': 'object'},
-                    'default': [
-                        {"Trade ID": "{trade_id}"},
-                        {"Exchange": "{exchange}"},
-                        {"Pair": "{pair}"},
-                        {"Direction": "{direction}"},
-                        {"Open rate": "{open_rate}"},
-                        {"Amount": "{amount}"},
-                        {"Open date": "{open_date:%Y-%m-%d %H:%M:%S}"},
-                        {"Enter tag": "{enter_tag}"},
-                        {"Strategy": "{strategy} {timeframe}"},
-                    ]
-                },
-            }
-        },
         'api_server': {
             'type': 'object',
             'properties': {
@@ -409,7 +351,7 @@ CONF_SCHEMA = {
         'export': {'type': 'string', 'enum': EXPORT_OPTIONS, 'default': 'trades'},
         'disableparamexport': {'type': 'boolean'},
         'initial_state': {'type': 'string', 'enum': ['running', 'stopped']},
-        'force_entry_enable': {'type': 'boolean'},
+        'forcebuy_enable': {'type': 'boolean'},
         'disable_dataframe_checks': {'type': 'boolean'},
         'internals': {
             'type': 'object',
@@ -482,60 +424,7 @@ CONF_SCHEMA = {
                 'remove_pumps': {'type': 'boolean'}
             },
             'required': ['process_throttle_secs', 'allowed_risk']
-        },
-        "freqai": {
-            "type": "object",
-            "properties": {
-                "enabled": {"type": "boolean", "default": False},
-                "keras": {"type": "boolean", "default": False},
-                "conv_width": {"type": "integer", "default": 2},
-                "train_period_days": {"type": "integer", "default": 0},
-                "backtest_period_days": {"type": "number", "default": 7},
-                "identifier": {"type": "string", "default": "example"},
-                "feature_parameters": {
-                    "type": "object",
-                    "properties": {
-                        "include_corr_pairlist": {"type": "array"},
-                        "include_timeframes": {"type": "array"},
-                        "label_period_candles": {"type": "integer"},
-                        "include_shifted_candles": {"type": "integer", "default": 0},
-                        "DI_threshold": {"type": "number", "default": 0},
-                        "weight_factor": {"type": "number", "default": 0},
-                        "principal_component_analysis": {"type": "boolean", "default": False},
-                        "use_SVM_to_remove_outliers": {"type": "boolean", "default": False},
-                        "svm_params": {"type": "object",
-                                       "properties": {
-                                           "shuffle": {"type": "boolean", "default": False},
-                                           "nu": {"type": "number", "default": 0.1}
-                                           },
-                                       }
-                    },
-                    "required": ["include_timeframes", "include_corr_pairlist", ]
-                },
-                "data_split_parameters": {
-                    "type": "object",
-                    "properties": {
-                        "test_size": {"type": "number"},
-                        "random_state": {"type": "integer"},
-                    },
-                },
-                "model_training_parameters": {
-                    "type": "object",
-                    "properties": {
-                        "n_estimators": {"type": "integer", "default": 1000}
-                    },
-                },
-            },
-            "required": [
-                "enabled",
-                "train_period_days",
-                "backtest_period_days",
-                "identifier",
-                "feature_parameters",
-                "data_split_parameters",
-                "model_training_parameters"
-                ]
-        },
+        }
     },
 }
 
@@ -549,8 +438,9 @@ SCHEMA_TRADE_REQUIRED = [
     'last_stake_amount_min_ratio',
     'dry_run',
     'dry_run_wallet',
-    'exit_pricing',
-    'entry_pricing',
+    'ask_strategy',
+    'bid_strategy',
+    'unfilledtimeout',
     'stoploss',
     'minimal_roi',
     'internals',
@@ -566,10 +456,7 @@ SCHEMA_BACKTEST_REQUIRED = [
     'dry_run_wallet',
     'dataformat_ohlcv',
     'dataformat_trades',
-]
-SCHEMA_BACKTEST_REQUIRED_FINAL = SCHEMA_BACKTEST_REQUIRED + [
-    'stoploss',
-    'minimal_roi',
+    'unfilledtimeout',
 ]
 
 SCHEMA_MINIMAL_REQUIRED = [
@@ -586,19 +473,12 @@ CANCEL_REASON = {
     "FULLY_CANCELLED": "fully cancelled",
     "ALL_CANCELLED": "cancelled (all unfilled and partially filled open orders cancelled)",
     "CANCELLED_ON_EXCHANGE": "cancelled on exchange",
-    "FORCE_EXIT": "forcesold",
-    "REPLACE": "cancelled to be replaced by new limit order",
-    "USER_CANCEL": "user requested order cancel"
+    "FORCE_SELL": "forcesold",
 }
 
 # List of pairs with their timeframes
-PairWithTimeframe = Tuple[str, str, CandleType]
+PairWithTimeframe = Tuple[str, str]
 ListPairsWithTimeframes = List[PairWithTimeframe]
 
 # Type for trades list
 TradeList = List[List]
-
-LongShort = Literal['long', 'short']
-EntryExit = Literal['entry', 'exit']
-BuySell = Literal['buy', 'sell']
-MakerTaker = Literal['maker', 'taker']

freqtrade/data/btanalysis.py

@@ -5,15 +5,14 @@ import logging
 from copy import copy
 from datetime import datetime, timezone
 from pathlib import Path
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import numpy as np
 import pandas as pd
 
 from freqtrade.constants import LAST_BT_RESULT_FN
 from freqtrade.exceptions import OperationalException
-from freqtrade.misc import json_load
-from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename
+from freqtrade.misc import get_backtest_metadata_filename, json_load
 from freqtrade.persistence import LocalTrade, Trade, init_db
 
 
@@ -23,11 +22,9 @@ logger = logging.getLogger(__name__)
 BT_DATA_COLUMNS = ['pair', 'stake_amount', 'amount', 'open_date', 'close_date',
                    'open_rate', 'close_rate',
                    'fee_open', 'fee_close', 'trade_duration',
-                   'profit_ratio', 'profit_abs', 'exit_reason',
+                   'profit_ratio', 'profit_abs', 'sell_reason',
                    'initial_stop_loss_abs', 'initial_stop_loss_ratio', 'stop_loss_abs',
-                   'stop_loss_ratio', 'min_rate', 'max_rate', 'is_open', 'enter_tag',
-                   'is_short', 'open_timestamp', 'close_timestamp', 'orders'
-                   ]
+                   'stop_loss_ratio', 'min_rate', 'max_rate', 'is_open', 'buy_tag']
 
 
 def get_latest_optimize_filename(directory: Union[Path, str], variant: str) -> str:
@@ -150,14 +147,7 @@ def load_backtest_stats(filename: Union[Path, str]) -> Dict[str, Any]:
     return data
 
 
-def load_and_merge_backtest_result(strategy_name: str, filename: Path, results: Dict[str, Any]):
-    """
-    Load one strategy from multi-strategy result
-    and merge it with results
-    :param strategy_name: Name of the strategy contained in the result
-    :param filename: Backtest-result-filename to load
-    :param results: dict to merge the result to.
-    """
+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]
@@ -168,30 +158,6 @@ def load_and_merge_backtest_result(strategy_name: str, filename: Path, results:
             break
 
 
-def _get_backtest_files(dirname: Path) -> List[Path]:
-    return list(reversed(sorted(dirname.glob('backtest-result-*-[0-9][0-9].json'))))
-
-
-def get_backtest_resultlist(dirname: Path):
-    """
-    Get list of backtest results read from metadata files
-    """
-    results = []
-    for filename in _get_backtest_files(dirname):
-        metadata = load_backtest_metadata(filename)
-        if not metadata:
-            continue
-        for s, v in metadata.items():
-            results.append({
-                'filename': filename.name,
-                'strategy': s,
-                'run_id': v['run_id'],
-                'backtest_start_time': v['backtest_start_time'],
-
-            })
-    return results
-
-
 def find_existing_backtest_stats(dirname: Union[Path, str], run_ids: Dict[str, str],
                                  min_backtest_date: datetime = None) -> Dict[str, Any]:
     """
@@ -211,7 +177,7 @@ def find_existing_backtest_stats(dirname: Union[Path, str], run_ids: Dict[str, s
     }
 
     # Weird glob expression here avoids including .meta.json files.
-    for filename in _get_backtest_files(dirname):
+    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
@@ -225,7 +191,14 @@ def find_existing_backtest_stats(dirname: Union[Path, str], run_ids: Dict[str, s
                 continue
 
             if min_backtest_date is not None:
-                backtest_date = strategy_metadata['backtest_start_time']
+                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.
@@ -234,7 +207,7 @@ def find_existing_backtest_stats(dirname: Union[Path, str], run_ids: Dict[str, s
 
             if strategy_metadata['run_id'] == run_id:
                 del run_ids[strategy_name]
-                load_and_merge_backtest_result(strategy_name, filename, results)
+                _load_and_merge_backtest_result(strategy_name, filename, results)
 
         if len(run_ids) == 0:
             break
@@ -277,15 +250,6 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
                                               utc=True,
                                               infer_datetime_format=True
                                               )
-            # Compatibility support for pre short Columns
-            if 'is_short' not in df.columns:
-                df['is_short'] = 0
-            if 'enter_tag' not in df.columns:
-                df['enter_tag'] = df['buy_tag']
-                df = df.drop(['buy_tag'], axis=1)
-            if 'orders' not in df.columns:
-                df.loc[:, 'orders'] = None
-
     else:
         # old format - only with lists.
         raise OperationalException(
@@ -339,7 +303,7 @@ def trade_list_to_dataframe(trades: List[LocalTrade]) -> pd.DataFrame:
     :param trades: List of trade objects
     :return: Dataframe with BT_DATA_COLUMNS
     """
-    df = pd.DataFrame.from_records([t.to_json(True) for t in trades], columns=BT_DATA_COLUMNS)
+    df = pd.DataFrame.from_records([t.to_json() for t in trades], columns=BT_DATA_COLUMNS)
     if len(df) > 0:
         df.loc[:, 'close_date'] = pd.to_datetime(df['close_date'], utc=True)
         df.loc[:, 'open_date'] = pd.to_datetime(df['open_date'], utc=True)
@@ -355,7 +319,7 @@ def load_trades_from_db(db_url: str, strategy: Optional[str] = None) -> pd.DataF
                      Can also serve as protection to load the correct result.
     :return: Dataframe containing Trades
     """
-    init_db(db_url)
+    init_db(db_url, clean_open_orders=False)
 
     filters = []
     if strategy:
@@ -402,3 +366,157 @@ def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame,
     trades = trades.loc[(trades['open_date'] >= trades_start) &
                         (trades['close_date'] <= trades_stop)]
     return trades
+
+
+def calculate_market_change(data: Dict[str, pd.DataFrame], column: str = "close") -> float:
+    """
+    Calculate market change based on "column".
+    Calculation is done by taking the first non-null and the last non-null element of each column
+    and calculating the pctchange as "(last - first) / first".
+    Then the results per pair are combined as mean.
+
+    :param data: Dict of Dataframes, dict key should be pair.
+    :param column: Column in the original dataframes to use
+    :return:
+    """
+    tmp_means = []
+    for pair, df in data.items():
+        start = df[column].dropna().iloc[0]
+        end = df[column].dropna().iloc[-1]
+        tmp_means.append((end - start) / start)
+
+    return float(np.mean(tmp_means))
+
+
+def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
+                                 column: str = "close") -> pd.DataFrame:
+    """
+    Combine multiple dataframes "column"
+    :param data: Dict of Dataframes, dict key should be pair.
+    :param column: Column in the original dataframes to use
+    :return: DataFrame with the column renamed to the dict key, and a column
+        named mean, containing the mean of all pairs.
+    :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)
+
+    df_comb['mean'] = df_comb.mean(axis=1)
+
+    return df_comb
+
+
+def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
+                      timeframe: str) -> pd.DataFrame:
+    """
+    Adds a column `col_name` with the cumulative profit for the given trades array.
+    :param df: DataFrame with date index
+    :param trades: DataFrame containing trades (requires columns close_date and profit_abs)
+    :param col_name: Column name that will be assigned the results
+    :param timeframe: Timeframe used during the operations
+    :return: Returns df with one additional column, col_name, containing the cumulative profit.
+    :raise: ValueError if trade-dataframe was found empty.
+    """
+    if len(trades) == 0:
+        raise ValueError("Trade dataframe empty.")
+    from freqtrade.exchange import timeframe_to_minutes
+    timeframe_minutes = timeframe_to_minutes(timeframe)
+    # Resample to timeframe to make sure trades match candles
+    _trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_date'
+                                  )[['profit_abs']].sum()
+    df.loc[:, col_name] = _trades_sum['profit_abs'].cumsum()
+    # Set first value to 0
+    df.loc[df.iloc[0].name, col_name] = 0
+    # FFill to get continuous
+    df[col_name] = df[col_name].ffill()
+    return df
+
+
+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)
+    :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_ratio')
+    :return: Tuple (float, highdate, lowdate, highvalue, lowvalue) with absolute max drawdown,
+             high and low time and high and low value.
+    :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)
+
+    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:
+        raise ValueError("No losing trade, therefore no drawdown.")
+    high_date = profit_results.loc[max_drawdown_df.iloc[:idxmin]['high_value'].idxmax(), date_col]
+    low_date = profit_results.loc[idxmin, date_col]
+    high_val = max_drawdown_df.loc[max_drawdown_df.iloc[:idxmin]
+                                   ['high_value'].idxmax(), 'cumulative']
+    low_val = max_drawdown_df.loc[idxmin, 'cumulative']
+    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]:
+    """
+    Calculate min/max cumsum of trades, to show if the wallet/stake amount ratio is sane
+    :param trades: DataFrame containing trades (requires columns close_date and profit_percent)
+    :param starting_balance: Add starting balance to results, to show the wallets high / low points
+    :return: Tuple (float, float) with cumsum of profit_abs
+    :raise: ValueError if trade-dataframe was found empty.
+    """
+    if len(trades) == 0:
+        raise ValueError("Trade dataframe empty.")
+
+    csum_df = pd.DataFrame()
+    csum_df['sum'] = trades['profit_abs'].cumsum()
+    csum_min = csum_df['sum'].min() + starting_balance
+    csum_max = csum_df['sum'].max() + starting_balance
+
+    return csum_min, csum_max

freqtrade/data/converter.py

@@ -11,7 +11,6 @@ import pandas as pd
 from pandas import DataFrame, to_datetime
 
 from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, TradeList
-from freqtrade.enums import CandleType
 
 
 logger = logging.getLogger(__name__)
@@ -262,20 +261,13 @@ def convert_trades_format(config: Dict[str, Any], convert_from: str, convert_to:
             src.trades_purge(pair=pair)
 
 
-def convert_ohlcv_format(
-    config: Dict[str, Any],
-    convert_from: str,
-    convert_to: str,
-    erase: bool,
-    candle_type: CandleType
-):
+def convert_ohlcv_format(config: Dict[str, Any], convert_from: str, convert_to: str, erase: bool):
     """
     Convert OHLCV from one format to another
     :param config: Config dictionary
     :param convert_from: Source format
     :param convert_to: Target format
     :param erase: Erase source data (does not apply if source and target format are identical)
-    :param candle_type: Any of the enum CandleType (must match trading mode!)
     """
     from freqtrade.data.history.idatahandler import get_datahandler
     src = get_datahandler(config['datadir'], convert_from)
@@ -287,11 +279,8 @@ def convert_ohlcv_format(
         config['pairs'] = []
         # Check timeframes or fall back to timeframe.
         for timeframe in timeframes:
-            config['pairs'].extend(src.ohlcv_get_pairs(
-                config['datadir'],
-                timeframe,
-                candle_type=candle_type
-            ))
+            config['pairs'].extend(src.ohlcv_get_pairs(config['datadir'],
+                                                       timeframe))
     logger.info(f"Converting candle (OHLCV) data for {config['pairs']}")
 
     for timeframe in timeframes:
@@ -300,16 +289,10 @@ def convert_ohlcv_format(
                                   timerange=None,
                                   fill_missing=False,
                                   drop_incomplete=False,
-                                  startup_candles=0,
-                                  candle_type=candle_type)
-            logger.info(f"Converting {len(data)} {candle_type} candles for {pair}")
+                                  startup_candles=0)
+            logger.info(f"Converting {len(data)} candles for {pair}")
             if len(data) > 0:
-                trg.ohlcv_store(
-                    pair=pair,
-                    timeframe=timeframe,
-                    data=data,
-                    candle_type=candle_type
-                )
+                trg.ohlcv_store(pair=pair, timeframe=timeframe, data=data)
                 if erase and convert_from != convert_to:
                     logger.info(f"Deleting source data for {pair} / {timeframe}")
-                    src.ohlcv_purge(pair=pair, timeframe=timeframe, candle_type=candle_type)
+                    src.ohlcv_purge(pair=pair, timeframe=timeframe)

freqtrade/data/dataprovider.py

@@ -5,7 +5,6 @@ including ticker and orderbook data, live and historical candle (OHLCV) data
 Common Interface for bot and strategy to access data.
 """
 import logging
-from collections import deque
 from datetime import datetime, timezone
 from typing import Any, Dict, List, Optional, Tuple
 
@@ -14,10 +13,9 @@ from pandas import DataFrame
 from freqtrade.configuration import TimeRange
 from freqtrade.constants import ListPairsWithTimeframes, PairWithTimeframe
 from freqtrade.data.history import load_pair_history
-from freqtrade.enums import CandleType, RunMode
+from freqtrade.enums import RunMode
 from freqtrade.exceptions import ExchangeError, OperationalException
 from freqtrade.exchange import Exchange, timeframe_to_seconds
-from freqtrade.util import PeriodicCache
 
 
 logger = logging.getLogger(__name__)
@@ -35,10 +33,6 @@ class DataProvider:
         self.__cached_pairs: Dict[PairWithTimeframe, Tuple[DataFrame, datetime]] = {}
         self.__slice_index: Optional[int] = None
         self.__cached_pairs_backtesting: Dict[PairWithTimeframe, DataFrame] = {}
-        self._msg_queue: deque = deque()
-
-        self.__msg_cache = PeriodicCache(
-            maxsize=1000, ttl=timeframe_to_seconds(self._config.get('timeframe', '1h')))
 
     def _set_dataframe_max_index(self, limit_index: int):
         """
@@ -47,13 +41,7 @@ class DataProvider:
         """
         self.__slice_index = limit_index
 
-    def _set_cached_df(
-        self,
-        pair: str,
-        timeframe: str,
-        dataframe: DataFrame,
-        candle_type: CandleType
-    ) -> None:
+    def _set_cached_df(self, pair: str, timeframe: str, dataframe: DataFrame) -> None:
         """
         Store cached Dataframe.
         Using private method as this should never be used by a user
@@ -61,10 +49,8 @@ class DataProvider:
         :param pair: pair to get the data for
         :param timeframe: Timeframe to get data for
         :param dataframe: analyzed dataframe
-        :param candle_type: Any of the enum CandleType (must match trading mode!)
         """
-        self.__cached_pairs[(pair, timeframe, candle_type)] = (
-            dataframe, datetime.now(timezone.utc))
+        self.__cached_pairs[(pair, timeframe)] = (dataframe, datetime.now(timezone.utc))
 
     def add_pairlisthandler(self, pairlists) -> None:
         """
@@ -72,21 +58,13 @@ class DataProvider:
         """
         self._pairlists = pairlists
 
-    def historic_ohlcv(
-        self,
-        pair: str,
-        timeframe: str = None,
-        candle_type: str = ''
-    ) -> DataFrame:
+    def historic_ohlcv(self, pair: str, timeframe: str = None) -> DataFrame:
         """
         Get stored historical candle (OHLCV) data
         :param pair: pair to get the data for
         :param timeframe: timeframe to get data for
-        :param candle_type: '', mark, index, premiumIndex, or funding_rate
         """
-        _candle_type = CandleType.from_string(
-            candle_type) if candle_type != '' else self._config['candle_type_def']
-        saved_pair = (pair, str(timeframe), _candle_type)
+        saved_pair = (pair, str(timeframe))
         if saved_pair not in self.__cached_pairs_backtesting:
             timerange = TimeRange.parse_timerange(None if self._config.get(
                 'timerange') is None else str(self._config.get('timerange')))
@@ -99,36 +77,26 @@ class DataProvider:
                 timeframe=timeframe or self._config['timeframe'],
                 datadir=self._config['datadir'],
                 timerange=timerange,
-                data_format=self._config.get('dataformat_ohlcv', 'json'),
-                candle_type=_candle_type,
-
+                data_format=self._config.get('dataformat_ohlcv', 'json')
             )
         return self.__cached_pairs_backtesting[saved_pair].copy()
 
-    def get_pair_dataframe(
-        self,
-        pair: str,
-        timeframe: str = None,
-        candle_type: str = ''
-    ) -> DataFrame:
+    def get_pair_dataframe(self, pair: str, timeframe: str = None) -> DataFrame:
         """
         Return pair candle (OHLCV) data, either live or cached historical -- depending
         on the runmode.
-        Only combinations in the pairlist or which have been specified as informative pairs
-        will be available.
         :param pair: pair to get the data for
         :param timeframe: timeframe to get data for
         :return: Dataframe for this pair
-        :param candle_type: '', mark, index, premiumIndex, or funding_rate
         """
         if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
             # Get live OHLCV data.
-            data = self.ohlcv(pair=pair, timeframe=timeframe, candle_type=candle_type)
+            data = self.ohlcv(pair=pair, timeframe=timeframe)
         else:
             # Get historical OHLCV data (cached on disk).
-            data = self.historic_ohlcv(pair=pair, timeframe=timeframe, candle_type=candle_type)
+            data = self.historic_ohlcv(pair=pair, timeframe=timeframe)
         if len(data) == 0:
-            logger.warning(f"No data found for ({pair}, {timeframe}, {candle_type}).")
+            logger.warning(f"No data found for ({pair}, {timeframe}).")
         return data
 
     def get_analyzed_dataframe(self, pair: str, timeframe: str) -> Tuple[DataFrame, datetime]:
@@ -141,7 +109,7 @@ class DataProvider:
             combination.
             Returns empty dataframe and Epoch 0 (1970-01-01) if no dataframe was cached.
         """
-        pair_key = (pair, timeframe, self._config.get('candle_type_def', CandleType.SPOT))
+        pair_key = (pair, timeframe)
         if pair_key in self.__cached_pairs:
             if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
                 df, date = self.__cached_pairs[pair_key]
@@ -209,31 +177,20 @@ class DataProvider:
             raise OperationalException(NO_EXCHANGE_EXCEPTION)
         return list(self._exchange._klines.keys())
 
-    def ohlcv(
-        self,
-        pair: str,
-        timeframe: str = None,
-        copy: bool = True,
-        candle_type: str = ''
-    ) -> DataFrame:
+    def ohlcv(self, pair: str, timeframe: str = None, copy: bool = True) -> DataFrame:
         """
         Get candle (OHLCV) data for the given pair as DataFrame
         Please use the `available_pairs` method to verify which pairs are currently cached.
         :param pair: pair to get the data for
         :param timeframe: Timeframe to get data for
-        :param candle_type: '', mark, index, premiumIndex, or funding_rate
         :param copy: copy dataframe before returning if True.
                      Use False only for read-only operations (where the dataframe is not modified)
         """
         if self._exchange is None:
             raise OperationalException(NO_EXCHANGE_EXCEPTION)
         if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
-            _candle_type = CandleType.from_string(
-                candle_type) if candle_type != '' else self._config['candle_type_def']
-            return self._exchange.klines(
-                (pair, timeframe or self._config['timeframe'], _candle_type),
-                copy=copy
-            )
+            return self._exchange.klines((pair, timeframe or self._config['timeframe']),
+                                         copy=copy)
         else:
             return DataFrame()
 
@@ -271,20 +228,3 @@ class DataProvider:
         if self._exchange is None:
             raise OperationalException(NO_EXCHANGE_EXCEPTION)
         return self._exchange.fetch_l2_order_book(pair, maximum)
-
-    def send_msg(self, message: str, *, always_send: bool = False) -> None:
-        """
-        Send custom RPC Notifications from your bot.
-        Will not send any bot in modes other than Dry-run or Live.
-        :param message: Message to be sent. Must be below 4096.
-        :param always_send: If False, will send the message only once per candle, and surpress
-                            identical messages.
-                            Careful as this can end up spaming your chat.
-                            Defaults to False
-        """
-        if self.runmode not in (RunMode.DRY_RUN, RunMode.LIVE):
-            return
-
-        if always_send or message not in self.__msg_cache:
-            self._msg_queue.append(message)
-        self.__msg_cache[message] = True

freqtrade/data/entryexitanalysis.py

@@ -1,227 +0,0 @@
-import logging
-from pathlib import Path
-from typing import List, Optional
-
-import joblib
-import pandas as pd
-from tabulate import tabulate
-
-from freqtrade.data.btanalysis import (get_latest_backtest_filename, load_backtest_data,
-                                       load_backtest_stats)
-from freqtrade.exceptions import OperationalException
-
-
-logger = logging.getLogger(__name__)
-
-
-def _load_signal_candles(backtest_dir: Path):
-    if backtest_dir.is_dir():
-        scpf = Path(backtest_dir,
-                    Path(get_latest_backtest_filename(backtest_dir)).stem + "_signals.pkl"
-                    )
-    else:
-        scpf = Path(backtest_dir.parent / f"{backtest_dir.stem}_signals.pkl")
-
-    try:
-        scp = open(scpf, "rb")
-        signal_candles = joblib.load(scp)
-        logger.info(f"Loaded signal candles: {str(scpf)}")
-    except Exception as e:
-        logger.error("Cannot load signal candles from pickled results: ", e)
-
-    return signal_candles
-
-
-def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_candles):
-    analysed_trades_dict = {}
-    analysed_trades_dict[strategy_name] = {}
-
-    try:
-        logger.info(f"Processing {strategy_name} : {len(pairlist)} pairs")
-
-        for pair in pairlist:
-            if pair in signal_candles[strategy_name]:
-                analysed_trades_dict[strategy_name][pair] = _analyze_candles_and_indicators(
-                                                              pair,
-                                                              trades,
-                                                              signal_candles[strategy_name][pair])
-    except Exception as e:
-        print(f"Cannot process entry/exit reasons for {strategy_name}: ", e)
-
-    return analysed_trades_dict
-
-
-def _analyze_candles_and_indicators(pair, trades, signal_candles):
-    buyf = signal_candles
-
-    if len(buyf) > 0:
-        buyf = buyf.set_index('date', drop=False)
-        trades_red = trades.loc[trades['pair'] == pair].copy()
-
-        trades_inds = pd.DataFrame()
-
-        if trades_red.shape[0] > 0 and buyf.shape[0] > 0:
-            for t, v in trades_red.open_date.items():
-                allinds = buyf.loc[(buyf['date'] < v)]
-                if allinds.shape[0] > 0:
-                    tmp_inds = allinds.iloc[[-1]]
-
-                    trades_red.loc[t, 'signal_date'] = tmp_inds['date'].values[0]
-                    trades_red.loc[t, 'enter_reason'] = trades_red.loc[t, 'enter_tag']
-                    tmp_inds.index.rename('signal_date', inplace=True)
-                    trades_inds = pd.concat([trades_inds, tmp_inds])
-
-            if 'signal_date' in trades_red:
-                trades_red['signal_date'] = pd.to_datetime(trades_red['signal_date'], utc=True)
-                trades_red.set_index('signal_date', inplace=True)
-
-                try:
-                    trades_red = pd.merge(trades_red, trades_inds, on='signal_date', how='outer')
-                except Exception as e:
-                    raise e
-        return trades_red
-    else:
-        return pd.DataFrame()
-
-
-def _do_group_table_output(bigdf, glist):
-    for g in glist:
-        # 0: summary wins/losses grouped by enter tag
-        if g == "0":
-            group_mask = ['enter_reason']
-            wins = bigdf.loc[bigdf['profit_abs'] >= 0] \
-                        .groupby(group_mask) \
-                        .agg({'profit_abs': ['sum']})
-
-            wins.columns = ['profit_abs_wins']
-            loss = bigdf.loc[bigdf['profit_abs'] < 0] \
-                        .groupby(group_mask) \
-                        .agg({'profit_abs': ['sum']})
-            loss.columns = ['profit_abs_loss']
-
-            new = bigdf.groupby(group_mask).agg({'profit_abs': [
-                                                    'count',
-                                                    lambda x: sum(x > 0),
-                                                    lambda x: sum(x <= 0)]})
-            new = pd.concat([new, wins, loss], axis=1).fillna(0)
-
-            new['profit_tot'] = new['profit_abs_wins'] - abs(new['profit_abs_loss'])
-            new['wl_ratio_pct'] = (new.iloc[:, 1] / new.iloc[:, 0] * 100).fillna(0)
-            new['avg_win'] = (new['profit_abs_wins'] / new.iloc[:, 1]).fillna(0)
-            new['avg_loss'] = (new['profit_abs_loss'] / new.iloc[:, 2]).fillna(0)
-
-            new.columns = ['total_num_buys', 'wins', 'losses', 'profit_abs_wins', 'profit_abs_loss',
-                           'profit_tot', 'wl_ratio_pct', 'avg_win', 'avg_loss']
-
-            sortcols = ['total_num_buys']
-
-            _print_table(new, sortcols, show_index=True)
-
-        else:
-            agg_mask = {'profit_abs': ['count', 'sum', 'median', 'mean'],
-                        'profit_ratio': ['sum', 'median', 'mean']}
-            agg_cols = ['num_buys', 'profit_abs_sum', 'profit_abs_median',
-                        'profit_abs_mean', 'median_profit_pct', 'mean_profit_pct',
-                        'total_profit_pct']
-            sortcols = ['profit_abs_sum', 'enter_reason']
-
-            # 1: profit summaries grouped by enter_tag
-            if g == "1":
-                group_mask = ['enter_reason']
-
-            # 2: profit summaries grouped by enter_tag and exit_tag
-            if g == "2":
-                group_mask = ['enter_reason', 'exit_reason']
-
-            # 3: profit summaries grouped by pair and enter_tag
-            if g == "3":
-                group_mask = ['pair', 'enter_reason']
-
-            # 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
-            if g == "4":
-                group_mask = ['pair', 'enter_reason', 'exit_reason']
-            if group_mask:
-                new = bigdf.groupby(group_mask).agg(agg_mask).reset_index()
-                new.columns = group_mask + agg_cols
-                new['median_profit_pct'] = new['median_profit_pct'] * 100
-                new['mean_profit_pct'] = new['mean_profit_pct'] * 100
-                new['total_profit_pct'] = new['total_profit_pct'] * 100
-
-                _print_table(new, sortcols)
-            else:
-                logger.warning("Invalid group mask specified.")
-
-
-def _print_results(analysed_trades, stratname, analysis_groups,
-                   enter_reason_list, exit_reason_list,
-                   indicator_list, columns=None):
-    if columns is None:
-        columns = ['pair', 'open_date', 'close_date', 'profit_abs', 'enter_reason', 'exit_reason']
-
-    bigdf = pd.DataFrame()
-    for pair, trades in analysed_trades[stratname].items():
-        bigdf = pd.concat([bigdf, trades], ignore_index=True)
-
-    if bigdf.shape[0] > 0 and ('enter_reason' in bigdf.columns):
-        if analysis_groups:
-            _do_group_table_output(bigdf, analysis_groups)
-
-        if enter_reason_list and "all" not in enter_reason_list:
-            bigdf = bigdf.loc[(bigdf['enter_reason'].isin(enter_reason_list))]
-
-        if exit_reason_list and "all" not in exit_reason_list:
-            bigdf = bigdf.loc[(bigdf['exit_reason'].isin(exit_reason_list))]
-
-        if "all" in indicator_list:
-            print(bigdf)
-        elif indicator_list is not None:
-            available_inds = []
-            for ind in indicator_list:
-                if ind in bigdf:
-                    available_inds.append(ind)
-            ilist = ["pair", "enter_reason", "exit_reason"] + available_inds
-            _print_table(bigdf[ilist], sortcols=['exit_reason'], show_index=False)
-    else:
-        print("\\_ No trades to show")
-
-
-def _print_table(df, sortcols=None, show_index=False):
-    if (sortcols is not None):
-        data = df.sort_values(sortcols)
-    else:
-        data = df
-
-    print(
-        tabulate(
-            data,
-            headers='keys',
-            tablefmt='psql',
-            showindex=show_index
-        )
-    )
-
-
-def process_entry_exit_reasons(backtest_dir: Path,
-                               pairlist: List[str],
-                               analysis_groups: Optional[List[str]] = ["0", "1", "2"],
-                               enter_reason_list: Optional[List[str]] = ["all"],
-                               exit_reason_list: Optional[List[str]] = ["all"],
-                               indicator_list: Optional[List[str]] = []):
-    try:
-        backtest_stats = load_backtest_stats(backtest_dir)
-        for strategy_name, results in backtest_stats['strategy'].items():
-            trades = load_backtest_data(backtest_dir, strategy_name)
-
-            if not trades.empty:
-                signal_candles = _load_signal_candles(backtest_dir)
-                analysed_trades_dict = _process_candles_and_indicators(pairlist, strategy_name,
-                                                                       trades, signal_candles)
-                _print_results(analysed_trades_dict,
-                               strategy_name,
-                               analysis_groups,
-                               enter_reason_list,
-                               exit_reason_list,
-                               indicator_list)
-
-    except ValueError as e:
-        raise OperationalException(e) from e

freqtrade/data/history/hdf5datahandler.py

@@ -7,8 +7,8 @@ import numpy as np
 import pandas as pd
 
 from freqtrade.configuration import TimeRange
-from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, TradeList
-from freqtrade.enums import CandleType
+from freqtrade.constants import (DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS,
+                                 ListPairsWithTimeframes, TradeList)
 
 from .idatahandler import IDataHandler
 
@@ -21,40 +21,44 @@ class HDF5DataHandler(IDataHandler):
     _columns = DEFAULT_DATAFRAME_COLUMNS
 
     @classmethod
-    def ohlcv_get_pairs(cls, datadir: Path, timeframe: str, candle_type: CandleType) -> List[str]:
+    def ohlcv_get_available_data(cls, datadir: Path) -> ListPairsWithTimeframes:
+        """
+        Returns a list of all pairs with ohlcv data available in this datadir
+        :param datadir: Directory to search for ohlcv files
+        :return: List of Tuples of (pair, timeframe)
+        """
+        _tmp = [re.search(r'^([a-zA-Z_]+)\-(\d+\S+)(?=.h5)', p.name)
+                for p in datadir.glob("*.h5")]
+        return [(match[1].replace('_', '/'), match[2]) for match in _tmp
+                if match and len(match.groups()) > 1]
+
+    @classmethod
+    def ohlcv_get_pairs(cls, datadir: Path, timeframe: str) -> List[str]:
         """
         Returns a list of all pairs with ohlcv data available in this datadir
         for the specified timeframe
         :param datadir: Directory to search for ohlcv files
         :param timeframe: Timeframe to search pairs for
-        :param candle_type: Any of the enum CandleType (must match trading mode!)
         :return: List of Pairs
         """
-        candle = ""
-        if candle_type != CandleType.SPOT:
-            datadir = datadir.joinpath('futures')
-            candle = f"-{candle_type}"
 
-        _tmp = [re.search(r'^(\S+)(?=\-' + timeframe + candle + '.h5)', p.name)
-                for p in datadir.glob(f"*{timeframe}{candle}.h5")]
+        _tmp = [re.search(r'^(\S+)(?=\-' + timeframe + '.h5)', p.name)
+                for p in datadir.glob(f"*{timeframe}.h5")]
         # Check if regex found something and only return these results
-        return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
+        return [match[0].replace('_', '/') for match in _tmp if match]
 
-    def ohlcv_store(
-            self, pair: str, timeframe: str, data: pd.DataFrame, candle_type: CandleType) -> None:
+    def ohlcv_store(self, pair: str, timeframe: str, data: pd.DataFrame) -> None:
         """
         Store data in hdf5 file.
         :param pair: Pair - used to generate filename
         :param timeframe: Timeframe - used to generate filename
         :param data: Dataframe containing OHLCV data
-        :param candle_type: Any of the enum CandleType (must match trading mode!)
         :return: None
         """
         key = self._pair_ohlcv_key(pair, timeframe)
         _data = data.copy()
 
-        filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
-        self.create_dir_if_needed(filename)
+        filename = self._pair_data_filename(self._datadir, pair, timeframe)
 
         _data.loc[:, self._columns].to_hdf(
             filename, key, mode='a', complevel=9, complib='blosc',
@@ -62,8 +66,7 @@ class HDF5DataHandler(IDataHandler):
         )
 
     def _ohlcv_load(self, pair: str, timeframe: str,
-                    timerange: Optional[TimeRange], candle_type: CandleType
-                    ) -> pd.DataFrame:
+                    timerange: Optional[TimeRange] = None) -> pd.DataFrame:
         """
         Internal method used to load data for one pair from disk.
         Implements the loading and conversion to a Pandas dataframe.
@@ -73,23 +76,13 @@ class HDF5DataHandler(IDataHandler):
         :param timerange: Limit data to be loaded to this timerange.
                         Optionally implemented by subclasses to avoid loading
                         all data where possible.
-        :param candle_type: Any of the enum CandleType (must match trading mode!)
         :return: DataFrame with ohlcv data, or empty DataFrame
         """
         key = self._pair_ohlcv_key(pair, timeframe)
-        filename = self._pair_data_filename(
-            self._datadir,
-            pair,
-            timeframe,
-            candle_type=candle_type
-        )
+        filename = self._pair_data_filename(self._datadir, pair, timeframe)
 
         if not filename.exists():
-            # Fallback mode for 1M files
-            filename = self._pair_data_filename(
-                self._datadir, pair, timeframe, candle_type=candle_type, no_timeframe_modify=True)
-            if not filename.exists():
-                return pd.DataFrame(columns=self._columns)
+            return pd.DataFrame(columns=self._columns)
         where = []
         if timerange:
             if timerange.starttype == 'date':
@@ -105,19 +98,12 @@ class HDF5DataHandler(IDataHandler):
                                           'low': 'float', 'close': 'float', 'volume': 'float'})
         return pairdata
 
-    def ohlcv_append(
-        self,
-        pair: str,
-        timeframe: str,
-        data: pd.DataFrame,
-        candle_type: CandleType
-    ) -> None:
+    def ohlcv_append(self, pair: str, timeframe: str, data: pd.DataFrame) -> None:
         """
         Append data to existing data structures
         :param pair: Pair
         :param timeframe: Timeframe this ohlcv data is for
         :param data: Data to append.
-        :param candle_type: Any of the enum CandleType (must match trading mode!)
         """
         raise NotImplementedError()
 
@@ -131,7 +117,7 @@ class HDF5DataHandler(IDataHandler):
         _tmp = [re.search(r'^(\S+)(?=\-trades.h5)', p.name)
                 for p in datadir.glob("*trades.h5")]
         # Check if regex found something and only return these results to avoid exceptions.
-        return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
+        return [match[0].replace('_', '/') for match in _tmp if match]
 
     def trades_store(self, pair: str, data: TradeList) -> None:
         """
@@ -186,9 +172,7 @@ class HDF5DataHandler(IDataHandler):
 
     @classmethod
     def _pair_ohlcv_key(cls, pair: str, timeframe: str) -> str:
-        # Escape futures pairs to avoid warnings
-        pair_esc = pair.replace(':', '_')
-        return f"{pair_esc}/ohlcv/tf_{timeframe}"
+        return f"{pair}/ohlcv/tf_{timeframe}"
 
     @classmethod
     def _pair_trades_key(cls, pair: str) -> str:

freqtrade/data/history/history_utils.py

@@ -12,7 +12,6 @@ from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
 from freqtrade.data.converter import (clean_ohlcv_dataframe, ohlcv_to_dataframe,
                                       trades_remove_duplicates, trades_to_ohlcv)
 from freqtrade.data.history.idatahandler import IDataHandler, get_datahandler
-from freqtrade.enums import CandleType
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import Exchange
 from freqtrade.misc import format_ms_time
@@ -30,7 +29,6 @@ def load_pair_history(pair: str,
                       startup_candles: int = 0,
                       data_format: str = None,
                       data_handler: IDataHandler = None,
-                      candle_type: CandleType = CandleType.SPOT
                       ) -> DataFrame:
     """
     Load cached ohlcv history for the given pair.
@@ -45,7 +43,6 @@ def load_pair_history(pair: str,
     :param startup_candles: Additional candles to load at the start of the period
     :param data_handler: Initialized data-handler to use.
                          Will be initialized from data_format if not set
-    :param candle_type: Any of the enum CandleType (must match trading mode!)
     :return: DataFrame with ohlcv data, or empty DataFrame
     """
     data_handler = get_datahandler(datadir, data_format, data_handler)
@@ -56,7 +53,6 @@ def load_pair_history(pair: str,
                                    fill_missing=fill_up_missing,
                                    drop_incomplete=drop_incomplete,
                                    startup_candles=startup_candles,
-                                   candle_type=candle_type,
                                    )
 
 
@@ -68,8 +64,6 @@ def load_data(datadir: Path,
               startup_candles: int = 0,
               fail_without_data: bool = False,
               data_format: str = 'json',
-              candle_type: CandleType = CandleType.SPOT,
-              user_futures_funding_rate: int = None,
               ) -> Dict[str, DataFrame]:
     """
     Load ohlcv history data for a list of pairs.
@@ -82,7 +76,6 @@ def load_data(datadir: Path,
     :param startup_candles: Additional candles to load at the start of the period
     :param fail_without_data: Raise OperationalException if no data is found.
     :param data_format: Data format which should be used. Defaults to json
-    :param candle_type: Any of the enum CandleType (must match trading mode!)
     :return: dict(<pair>:<Dataframe>)
     """
     result: Dict[str, DataFrame] = {}
@@ -96,29 +89,22 @@ def load_data(datadir: Path,
                                  datadir=datadir, timerange=timerange,
                                  fill_up_missing=fill_up_missing,
                                  startup_candles=startup_candles,
-                                 data_handler=data_handler,
-                                 candle_type=candle_type,
+                                 data_handler=data_handler
                                  )
         if not hist.empty:
             result[pair] = hist
-        else:
-            if candle_type is CandleType.FUNDING_RATE and user_futures_funding_rate is not None:
-                logger.warn(f"{pair} using user specified [{user_futures_funding_rate}]")
-            elif candle_type not in (CandleType.SPOT, CandleType.FUTURES):
-                result[pair] = DataFrame(columns=["date", "open", "close", "high", "low", "volume"])
 
     if fail_without_data and not result:
         raise OperationalException("No data found. Terminating.")
     return result
 
 
-def refresh_data(*, datadir: Path,
+def refresh_data(datadir: Path,
                  timeframe: str,
                  pairs: List[str],
                  exchange: Exchange,
                  data_format: str = None,
                  timerange: Optional[TimeRange] = None,
-                 candle_type: CandleType,
                  ) -> None:
     """
     Refresh ohlcv history data for a list of pairs.
@@ -129,25 +115,17 @@ def refresh_data(*, datadir: Path,
     :param exchange: Exchange object
     :param data_format: dataformat to use
     :param timerange: Limit data to be loaded to this timerange
-    :param candle_type: Any of the enum CandleType (must match trading mode!)
     """
     data_handler = get_datahandler(datadir, data_format)
     for idx, pair in enumerate(pairs):
         process = f'{idx}/{len(pairs)}'
         _download_pair_history(pair=pair, process=process,
                                timeframe=timeframe, datadir=datadir,
-                               timerange=timerange, exchange=exchange, data_handler=data_handler,
-                               candle_type=candle_type)
+                               timerange=timerange, exchange=exchange, data_handler=data_handler)
 
 
-def _load_cached_data_for_updating(
-    pair: str,
-    timeframe: str,
-    timerange: Optional[TimeRange],
-    data_handler: IDataHandler,
-    candle_type: CandleType,
-    prepend: bool = False,
-) -> Tuple[DataFrame, Optional[int], Optional[int]]:
+def _load_cached_data_for_updating(pair: str, timeframe: str, timerange: Optional[TimeRange],
+                                   data_handler: IDataHandler) -> Tuple[DataFrame, Optional[int]]:
     """
     Load cached data to download more data.
     If timerange is passed in, checks whether data from an before the stored data will be
@@ -157,30 +135,23 @@ def _load_cached_data_for_updating(
     Note: Only used by download_pair_history().
     """
     start = None
-    end = None
     if timerange:
         if timerange.starttype == 'date':
             start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
-        if timerange.stoptype == 'date':
-            end = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
 
     # Intentionally don't pass timerange in - since we need to load the full dataset.
     data = data_handler.ohlcv_load(pair, timeframe=timeframe,
                                    timerange=None, fill_missing=False,
-                                   drop_incomplete=True, warn_no_data=False,
-                                   candle_type=candle_type)
+                                   drop_incomplete=True, warn_no_data=False)
     if not data.empty:
-        if not prepend and start and start < data.iloc[0]['date']:
+        if start and start < data.iloc[0]['date']:
             # Earlier data than existing data requested, redownload all
             data = DataFrame(columns=DEFAULT_DATAFRAME_COLUMNS)
         else:
-            if prepend:
-                end = data.iloc[0]['date']
-            else:
-                start = data.iloc[-1]['date']
+            start = data.iloc[-1]['date']
+
     start_ms = int(start.timestamp() * 1000) if start else None
-    end_ms = int(end.timestamp() * 1000) if end else None
-    return data, start_ms, end_ms
+    return data, start_ms
 
 
 def _download_pair_history(pair: str, *,
@@ -190,42 +161,31 @@ def _download_pair_history(pair: str, *,
                            process: str = '',
                            new_pairs_days: int = 30,
                            data_handler: IDataHandler = None,
-                           timerange: Optional[TimeRange] = None,
-                           candle_type: CandleType,
-                           erase: bool = False,
-                           prepend: bool = False,
-                           ) -> bool:
+                           timerange: Optional[TimeRange] = None) -> bool:
     """
     Download latest candles from the exchange for the pair and timeframe passed in parameters
     The data is downloaded starting from the last correct data that
     exists in a cache. If timerange starts earlier than the data in the cache,
     the full data will be redownloaded
 
+    Based on @Rybolov work: https://github.com/rybolov/freqtrade-data
+
     :param pair: pair to download
     :param timeframe: Timeframe (e.g "5m")
     :param timerange: range of time to download
-    :param candle_type: Any of the enum CandleType (must match trading mode!)
-    :param erase: Erase existing data
     :return: bool with success state
     """
     data_handler = get_datahandler(datadir, data_handler=data_handler)
 
     try:
-        if erase:
-            if data_handler.ohlcv_purge(pair, timeframe, candle_type=candle_type):
-                logger.info(f'Deleting existing data for pair {pair}, {timeframe}, {candle_type}.')
+        logger.info(
+            f'Download history data for pair: "{pair}" ({process}), timeframe: {timeframe} '
+            f'and store in {datadir}.'
+        )
 
-        data, since_ms, until_ms = _load_cached_data_for_updating(
-            pair, timeframe, timerange,
-            data_handler=data_handler,
-            candle_type=candle_type,
-            prepend=prepend)
-
-        logger.info(f'({process}) - Download history data for "{pair}", {timeframe}, '
-                    f'{candle_type} and store in {datadir}. '
-                    f'From {format_ms_time(since_ms) if since_ms else "start"} to '
-                    f'{format_ms_time(until_ms) if until_ms else "now"}'
-                    )
+        # data, since_ms = _load_cached_data_for_updating_old(datadir, pair, timeframe, timerange)
+        data, since_ms = _load_cached_data_for_updating(pair, timeframe, timerange,
+                                                        data_handler=data_handler)
 
         logger.debug("Current Start: %s",
                      f"{data.iloc[0]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')
@@ -238,9 +198,7 @@ def _download_pair_history(pair: str, *,
                                                since_ms=since_ms if since_ms else
                                                arrow.utcnow().shift(
                                                    days=-new_pairs_days).int_timestamp * 1000,
-                                               is_new_pair=data.empty,
-                                               candle_type=candle_type,
-                                               until_ms=until_ms if until_ms else None
+                                               is_new_pair=data.empty
                                                )
         # TODO: Maybe move parsing to exchange class (?)
         new_dataframe = ohlcv_to_dataframe(new_data, timeframe, pair,
@@ -258,7 +216,7 @@ def _download_pair_history(pair: str, *,
         logger.debug("New End: %s",
                      f"{data.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')
 
-        data_handler.ohlcv_store(pair, timeframe, data=data, candle_type=candle_type)
+        data_handler.ohlcv_store(pair, timeframe, data=data)
         return True
 
     except Exception:
@@ -269,12 +227,9 @@ def _download_pair_history(pair: str, *,
 
 
 def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes: List[str],
-                                datadir: Path, trading_mode: str,
-                                timerange: Optional[TimeRange] = None,
+                                datadir: Path, timerange: Optional[TimeRange] = None,
                                 new_pairs_days: int = 30, erase: bool = False,
-                                data_format: str = None,
-                                prepend: bool = False,
-                                ) -> List[str]:
+                                data_format: str = None) -> List[str]:
     """
     Refresh stored ohlcv data for backtesting and hyperopt operations.
     Used by freqtrade download-data subcommand.
@@ -282,8 +237,6 @@ def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes
     """
     pairs_not_available = []
     data_handler = get_datahandler(datadir, data_format)
-    candle_type = CandleType.get_default(trading_mode)
-    process = ''
     for idx, pair in enumerate(pairs, start=1):
         if pair not in exchange.markets:
             pairs_not_available.append(pair)
@@ -291,29 +244,17 @@ def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes
             continue
         for timeframe in timeframes:
 
+            if erase:
+                if data_handler.ohlcv_purge(pair, timeframe):
+                    logger.info(
+                        f'Deleting existing data for pair {pair}, interval {timeframe}.')
+
             logger.info(f'Downloading pair {pair}, interval {timeframe}.')
             process = f'{idx}/{len(pairs)}'
             _download_pair_history(pair=pair, process=process,
                                    datadir=datadir, exchange=exchange,
                                    timerange=timerange, data_handler=data_handler,
-                                   timeframe=str(timeframe), new_pairs_days=new_pairs_days,
-                                   candle_type=candle_type,
-                                   erase=erase, prepend=prepend)
-        if trading_mode == 'futures':
-            # Predefined candletype (and timeframe) depending on exchange
-            # Downloads what is necessary to backtest based on futures data.
-            tf_mark = exchange._ft_has['mark_ohlcv_timeframe']
-            fr_candle_type = CandleType.from_string(exchange._ft_has['mark_ohlcv_price'])
-            # All exchanges need FundingRate for futures trading.
-            # The timeframe is aligned to the mark-price timeframe.
-            for funding_candle_type in (CandleType.FUNDING_RATE, fr_candle_type):
-                _download_pair_history(pair=pair, process=process,
-                                       datadir=datadir, exchange=exchange,
-                                       timerange=timerange, data_handler=data_handler,
-                                       timeframe=str(tf_mark), new_pairs_days=new_pairs_days,
-                                       candle_type=funding_candle_type,
-                                       erase=erase, prepend=prepend)
-
+                                   timeframe=str(timeframe), new_pairs_days=new_pairs_days)
     return pairs_not_available
 
 
@@ -330,9 +271,8 @@ def _download_trades_history(exchange: Exchange,
     try:
 
         until = None
-        if timerange:
-            if timerange.starttype == 'date':
-                since = timerange.startts * 1000
+        if (timerange and timerange.starttype == 'date'):
+            since = timerange.startts * 1000
             if timerange.stoptype == 'date':
                 until = timerange.stopts * 1000
         else:
@@ -413,16 +353,10 @@ def refresh_backtest_trades_data(exchange: Exchange, pairs: List[str], datadir:
     return pairs_not_available
 
 
-def convert_trades_to_ohlcv(
-    pairs: List[str],
-    timeframes: List[str],
-    datadir: Path,
-    timerange: TimeRange,
-    erase: bool = False,
-    data_format_ohlcv: str = 'json',
-    data_format_trades: str = 'jsongz',
-    candle_type: CandleType = CandleType.SPOT
-) -> None:
+def convert_trades_to_ohlcv(pairs: List[str], timeframes: List[str],
+                            datadir: Path, timerange: TimeRange, erase: bool = False,
+                            data_format_ohlcv: str = 'json',
+                            data_format_trades: str = 'jsongz') -> None:
     """
     Convert stored trades data to ohlcv data
     """
@@ -433,12 +367,12 @@ def convert_trades_to_ohlcv(
         trades = data_handler_trades.trades_load(pair)
         for timeframe in timeframes:
             if erase:
-                if data_handler_ohlcv.ohlcv_purge(pair, timeframe, candle_type=candle_type):
+                if data_handler_ohlcv.ohlcv_purge(pair, timeframe):
                     logger.info(f'Deleting existing data for pair {pair}, interval {timeframe}.')
             try:
                 ohlcv = trades_to_ohlcv(trades, timeframe)
                 # Store ohlcv
-                data_handler_ohlcv.ohlcv_store(pair, timeframe, data=ohlcv, candle_type=candle_type)
+                data_handler_ohlcv.ohlcv_store(pair, timeframe, data=ohlcv)
             except ValueError:
                 logger.exception(f'Could not convert {pair} to OHLCV.')
 

freqtrade/data/history/idatahandler.py

@@ -4,8 +4,7 @@ It's subclasses handle and storing data from disk.
 
 """
 import logging
-import re
-from abc import ABC, abstractmethod
+from abc import ABC, abstractclassmethod, abstractmethod
 from copy import deepcopy
 from datetime import datetime, timezone
 from pathlib import Path
@@ -17,7 +16,6 @@ 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
-from freqtrade.enums import CandleType, TradingMode
 from freqtrade.exchange import timeframe_to_seconds
 
 
@@ -26,8 +24,6 @@ logger = logging.getLogger(__name__)
 
 class IDataHandler(ABC):
 
-    _OHLCV_REGEX = r'^([a-zA-Z_-]+)\-(\d+[a-zA-Z]{1,2})\-?([a-zA-Z_]*)?(?=\.)'
-
     def __init__(self, datadir: Path) -> None:
         self._datadir = datadir
 
@@ -38,55 +34,37 @@ class IDataHandler(ABC):
         """
         raise NotImplementedError()
 
-    @classmethod
-    def ohlcv_get_available_data(
-            cls, datadir: Path, trading_mode: TradingMode) -> ListPairsWithTimeframes:
+    @abstractclassmethod
+    def ohlcv_get_available_data(cls, datadir: Path) -> ListPairsWithTimeframes:
         """
         Returns a list of all pairs with ohlcv data available in this datadir
         :param datadir: Directory to search for ohlcv files
-        :param trading_mode: trading-mode to be used
-        :return: List of Tuples of (pair, timeframe, CandleType)
+        :return: List of Tuples of (pair, timeframe)
         """
-        if trading_mode == TradingMode.FUTURES:
-            datadir = datadir.joinpath('futures')
-        _tmp = [
-            re.search(
-                cls._OHLCV_REGEX, p.name
-            ) for p in datadir.glob(f"*.{cls._get_file_extension()}")]
-        return [
-            (
-                cls.rebuild_pair_from_filename(match[1]),
-                cls.rebuild_timeframe_from_filename(match[2]),
-                CandleType.from_string(match[3])
-            ) for match in _tmp if match and len(match.groups()) > 1]
 
-    @classmethod
-    @abstractmethod
-    def ohlcv_get_pairs(cls, datadir: Path, timeframe: str, candle_type: CandleType) -> List[str]:
+    @abstractclassmethod
+    def ohlcv_get_pairs(cls, datadir: Path, timeframe: str) -> List[str]:
         """
         Returns a list of all pairs with ohlcv data available in this datadir
         for the specified timeframe
         :param datadir: Directory to search for ohlcv files
         :param timeframe: Timeframe to search pairs for
-        :param candle_type: Any of the enum CandleType (must match trading mode!)
         :return: List of Pairs
         """
 
     @abstractmethod
-    def ohlcv_store(
-            self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType) -> None:
+    def ohlcv_store(self, pair: str, timeframe: str, data: DataFrame) -> None:
         """
         Store ohlcv data.
         :param pair: Pair - used to generate filename
         :param timeframe: Timeframe - used to generate filename
         :param data: Dataframe containing OHLCV data
-        :param candle_type: Any of the enum CandleType (must match trading mode!)
         :return: None
         """
 
     @abstractmethod
-    def _ohlcv_load(self, pair: str, timeframe: str, timerange: Optional[TimeRange],
-                    candle_type: CandleType
+    def _ohlcv_load(self, pair: str, timeframe: str,
+                    timerange: Optional[TimeRange] = None,
                     ) -> DataFrame:
         """
         Internal method used to load data for one pair from disk.
@@ -97,42 +75,32 @@ class IDataHandler(ABC):
         :param timerange: Limit data to be loaded to this timerange.
                         Optionally implemented by subclasses to avoid loading
                         all data where possible.
-        :param candle_type: Any of the enum CandleType (must match trading mode!)
         :return: DataFrame with ohlcv data, or empty DataFrame
         """
 
-    def ohlcv_purge(self, pair: str, timeframe: str, candle_type: CandleType) -> bool:
+    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")
-        :param candle_type: Any of the enum CandleType (must match trading mode!)
         :return: True when deleted, false if file did not exist.
         """
-        filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
+        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,
-        candle_type: CandleType
-    ) -> None:
+    def ohlcv_append(self, pair: str, timeframe: str, data: DataFrame) -> None:
         """
         Append data to existing data structures
         :param pair: Pair
         :param timeframe: Timeframe this ohlcv data is for
         :param data: Data to append.
-        :param candle_type: Any of the enum CandleType (must match trading mode!)
         """
 
-    @classmethod
-    @abstractmethod
+    @abstractclassmethod
     def trades_get_pairs(cls, datadir: Path) -> List[str]:
         """
         Returns a list of all pairs for which trade data is available in this
@@ -190,33 +158,9 @@ class IDataHandler(ABC):
         return trades_remove_duplicates(self._trades_load(pair, timerange=timerange))
 
     @classmethod
-    def create_dir_if_needed(cls, datadir: Path):
-        """
-        Creates datadir if necessary
-        should only create directories for "futures" mode at the moment.
-        """
-        if not datadir.parent.is_dir():
-            datadir.parent.mkdir()
-
-    @classmethod
-    def _pair_data_filename(
-        cls,
-        datadir: Path,
-        pair: str,
-        timeframe: str,
-        candle_type: CandleType,
-        no_timeframe_modify: bool = False
-    ) -> Path:
+    def _pair_data_filename(cls, datadir: Path, pair: str, timeframe: str) -> Path:
         pair_s = misc.pair_to_filename(pair)
-        candle = ""
-        if not no_timeframe_modify:
-            timeframe = cls.timeframe_to_file(timeframe)
-
-        if candle_type != CandleType.SPOT:
-            datadir = datadir.joinpath('futures')
-            candle = f"-{candle_type}"
-        filename = datadir.joinpath(
-            f'{pair_s}-{timeframe}{candle}.{cls._get_file_extension()}')
+        filename = datadir.joinpath(f'{pair_s}-{timeframe}.{cls._get_file_extension()}')
         return filename
 
     @classmethod
@@ -225,35 +169,12 @@ class IDataHandler(ABC):
         filename = datadir.joinpath(f'{pair_s}-trades.{cls._get_file_extension()}')
         return filename
 
-    @staticmethod
-    def timeframe_to_file(timeframe: str):
-        return timeframe.replace('M', 'Mo')
-
-    @staticmethod
-    def rebuild_timeframe_from_filename(timeframe: str) -> str:
-        """
-        converts timeframe from disk to file
-        Replaces mo with M (to avoid problems on case-insensitive filesystems)
-        """
-        return re.sub('1mo', '1M', timeframe, flags=re.IGNORECASE)
-
-    @staticmethod
-    def rebuild_pair_from_filename(pair: str) -> str:
-        """
-        Rebuild pair name from filename
-        Assumes a asset name of max. 7 length to also support BTC-PERP and BTC-PERP:USD names.
-        """
-        res = re.sub(r'^(([A-Za-z]{1,10})|^([A-Za-z\-]{1,6}))(_)', r'\g<1>/', pair, 1)
-        res = re.sub('_', ':', res, 1)
-        return res
-
     def ohlcv_load(self, pair, timeframe: str,
-                   candle_type: CandleType,
                    timerange: Optional[TimeRange] = None,
                    fill_missing: bool = True,
                    drop_incomplete: bool = True,
                    startup_candles: int = 0,
-                   warn_no_data: bool = True,
+                   warn_no_data: bool = True
                    ) -> DataFrame:
         """
         Load cached candle (OHLCV) data for the given pair.
@@ -265,7 +186,6 @@ class IDataHandler(ABC):
         :param drop_incomplete: Drop last candle assuming it may be incomplete.
         :param startup_candles: Additional candles to load at the start of the period
         :param warn_no_data: Log a warning message when no data is found
-        :param candle_type: Any of the enum CandleType (must match trading mode!)
         :return: DataFrame with ohlcv data, or empty DataFrame
         """
         # Fix startup period
@@ -273,21 +193,17 @@ class IDataHandler(ABC):
         if startup_candles > 0 and timerange_startup:
             timerange_startup.subtract_start(timeframe_to_seconds(timeframe) * startup_candles)
 
-        pairdf = self._ohlcv_load(
-            pair,
-            timeframe,
-            timerange=timerange_startup,
-            candle_type=candle_type
-        )
-        if self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data):
+        pairdf = self._ohlcv_load(pair, timeframe,
+                                  timerange=timerange_startup)
+        if self._check_empty_df(pairdf, pair, timeframe, warn_no_data):
             return pairdf
         else:
             enddate = pairdf.iloc[-1]['date']
 
             if timerange_startup:
-                self._validate_pairdata(pair, pairdf, timeframe, candle_type, timerange_startup)
+                self._validate_pairdata(pair, pairdf, timeframe, timerange_startup)
                 pairdf = trim_dataframe(pairdf, timerange_startup)
-                if self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data):
+                if self._check_empty_df(pairdf, pair, timeframe, warn_no_data):
                     return pairdf
 
             # incomplete candles should only be dropped if we didn't trim the end beforehand.
@@ -296,25 +212,23 @@ class IDataHandler(ABC):
                                            fill_missing=fill_missing,
                                            drop_incomplete=(drop_incomplete and
                                                             enddate == pairdf.iloc[-1]['date']))
-            self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data)
+            self._check_empty_df(pairdf, pair, timeframe, warn_no_data)
             return pairdf
 
-    def _check_empty_df(self, pairdf: DataFrame, pair: str, timeframe: str,
-                        candle_type: CandleType, warn_no_data: bool):
+    def _check_empty_df(self, pairdf: DataFrame, pair: str, timeframe: str, warn_no_data: bool):
         """
         Warn on empty dataframe
         """
         if pairdf.empty:
             if warn_no_data:
                 logger.warning(
-                    f"No history for {pair}, {candle_type}, {timeframe} found. "
-                    "Use `freqtrade download-data` to download the data"
+                    f'No history data for pair: "{pair}", timeframe: {timeframe}. '
+                    'Use `freqtrade download-data` to download the data'
                 )
             return True
         return False
 
-    def _validate_pairdata(self, pair, pairdata: DataFrame, timeframe: str,
-                           candle_type: CandleType, 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
@@ -324,12 +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"{pair}, {candle_type}, {timeframe}, "
+                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"{pair}, {candle_type}, {timeframe}, "
+                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

@@ -8,9 +8,8 @@ from pandas import DataFrame, read_json, to_datetime
 
 from freqtrade import misc
 from freqtrade.configuration import TimeRange
-from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, TradeList
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, ListPairsWithTimeframes, TradeList
 from freqtrade.data.converter import trades_dict_to_list
-from freqtrade.enums import CandleType
 
 from .idatahandler import IDataHandler
 
@@ -24,27 +23,33 @@ class JsonDataHandler(IDataHandler):
     _columns = DEFAULT_DATAFRAME_COLUMNS
 
     @classmethod
-    def ohlcv_get_pairs(cls, datadir: Path, timeframe: str, candle_type: CandleType) -> List[str]:
+    def ohlcv_get_available_data(cls, datadir: Path) -> ListPairsWithTimeframes:
+        """
+        Returns a list of all pairs with ohlcv data available in this datadir
+        :param datadir: Directory to search for ohlcv files
+        :return: List of Tuples of (pair, timeframe)
+        """
+        _tmp = [re.search(r'^([a-zA-Z_]+)\-(\d+\S+)(?=.json)', p.name)
+                for p in datadir.glob(f"*.{cls._get_file_extension()}")]
+        return [(match[1].replace('_', '/'), match[2]) for match in _tmp
+                if match and len(match.groups()) > 1]
+
+    @classmethod
+    def ohlcv_get_pairs(cls, datadir: Path, timeframe: str) -> List[str]:
         """
         Returns a list of all pairs with ohlcv data available in this datadir
         for the specified timeframe
         :param datadir: Directory to search for ohlcv files
         :param timeframe: Timeframe to search pairs for
-        :param candle_type: Any of the enum CandleType (must match trading mode!)
         :return: List of Pairs
         """
-        candle = ""
-        if candle_type != CandleType.SPOT:
-            datadir = datadir.joinpath('futures')
-            candle = f"-{candle_type}"
 
-        _tmp = [re.search(r'^(\S+)(?=\-' + timeframe + candle + '.json)', p.name)
-                for p in datadir.glob(f"*{timeframe}{candle}.{cls._get_file_extension()}")]
+        _tmp = [re.search(r'^(\S+)(?=\-' + timeframe + '.json)', p.name)
+                for p in datadir.glob(f"*{timeframe}.{cls._get_file_extension()}")]
         # Check if regex found something and only return these results
-        return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
+        return [match[0].replace('_', '/') for match in _tmp if match]
 
-    def ohlcv_store(
-            self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType) -> None:
+    def ohlcv_store(self, pair: str, timeframe: str, data: DataFrame) -> None:
         """
         Store data in json format "values".
             format looks as follows:
@@ -52,11 +57,9 @@ class JsonDataHandler(IDataHandler):
         :param pair: Pair - used to generate filename
         :param timeframe: Timeframe - used to generate filename
         :param data: Dataframe containing OHLCV data
-        :param candle_type: Any of the enum CandleType (must match trading mode!)
         :return: None
         """
-        filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
-        self.create_dir_if_needed(filename)
+        filename = self._pair_data_filename(self._datadir, pair, timeframe)
         _data = data.copy()
         # Convert date to int
         _data['date'] = _data['date'].view(np.int64) // 1000 // 1000
@@ -67,7 +70,7 @@ class JsonDataHandler(IDataHandler):
             compression='gzip' if self._use_zip else None)
 
     def _ohlcv_load(self, pair: str, timeframe: str,
-                    timerange: Optional[TimeRange], candle_type: CandleType
+                    timerange: Optional[TimeRange] = None,
                     ) -> DataFrame:
         """
         Internal method used to load data for one pair from disk.
@@ -78,17 +81,11 @@ class JsonDataHandler(IDataHandler):
         :param timerange: Limit data to be loaded to this timerange.
                         Optionally implemented by subclasses to avoid loading
                         all data where possible.
-        :param candle_type: Any of the enum CandleType (must match trading mode!)
         :return: DataFrame with ohlcv data, or empty DataFrame
         """
-        filename = self._pair_data_filename(
-            self._datadir, pair, timeframe, candle_type=candle_type)
+        filename = self._pair_data_filename(self._datadir, pair, timeframe)
         if not filename.exists():
-            # Fallback mode for 1M files
-            filename = self._pair_data_filename(
-                self._datadir, pair, timeframe, candle_type=candle_type, no_timeframe_modify=True)
-            if not filename.exists():
-                return DataFrame(columns=self._columns)
+            return DataFrame(columns=self._columns)
         try:
             pairdata = read_json(filename, orient='values')
             pairdata.columns = self._columns
@@ -103,19 +100,25 @@ class JsonDataHandler(IDataHandler):
                                        infer_datetime_format=True)
         return pairdata
 
-    def ohlcv_append(
-        self,
-        pair: str,
-        timeframe: str,
-        data: DataFrame,
-        candle_type: CandleType
-    ) -> None:
+    def ohlcv_purge(self, pair: str, timeframe: str) -> bool:
+        """
+        Remove data for this pair
+        :param pair: Delete data for this pair.
+        :param timeframe: Timeframe (e.g. "5m")
+        :return: True when deleted, false if file did not exist.
+        """
+        filename = self._pair_data_filename(self._datadir, pair, timeframe)
+        if filename.exists():
+            filename.unlink()
+            return True
+        return False
+
+    def ohlcv_append(self, pair: str, timeframe: str, data: DataFrame) -> None:
         """
         Append data to existing data structures
         :param pair: Pair
         :param timeframe: Timeframe this ohlcv data is for
         :param data: Data to append.
-        :param candle_type: Any of the enum CandleType (must match trading mode!)
         """
         raise NotImplementedError()
 
@@ -129,7 +132,7 @@ class JsonDataHandler(IDataHandler):
         _tmp = [re.search(r'^(\S+)(?=\-trades.json)', p.name)
                 for p in datadir.glob(f"*trades.{cls._get_file_extension()}")]
         # Check if regex found something and only return these results to avoid exceptions.
-        return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
+        return [match[0].replace('_', '/') for match in _tmp if match]
 
     def trades_store(self, pair: str, data: TradeList) -> None:
         """

freqtrade/data/metrics.py

@@ -1,192 +0,0 @@
-import logging
-from typing import Dict, Tuple
-
-import numpy as np
-import pandas as pd
-
-
-logger = logging.getLogger(__name__)
-
-
-def calculate_market_change(data: Dict[str, pd.DataFrame], column: str = "close") -> float:
-    """
-    Calculate market change based on "column".
-    Calculation is done by taking the first non-null and the last non-null element of each column
-    and calculating the pctchange as "(last - first) / first".
-    Then the results per pair are combined as mean.
-
-    :param data: Dict of Dataframes, dict key should be pair.
-    :param column: Column in the original dataframes to use
-    :return:
-    """
-    tmp_means = []
-    for pair, df in data.items():
-        start = df[column].dropna().iloc[0]
-        end = df[column].dropna().iloc[-1]
-        tmp_means.append((end - start) / start)
-
-    return float(np.mean(tmp_means))
-
-
-def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
-                                 column: str = "close") -> pd.DataFrame:
-    """
-    Combine multiple dataframes "column"
-    :param data: Dict of Dataframes, dict key should be pair.
-    :param column: Column in the original dataframes to use
-    :return: DataFrame with the column renamed to the dict key, and a column
-        named mean, containing the mean of all pairs.
-    :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)
-
-    df_comb['mean'] = df_comb.mean(axis=1)
-
-    return df_comb
-
-
-def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
-                      timeframe: str) -> pd.DataFrame:
-    """
-    Adds a column `col_name` with the cumulative profit for the given trades array.
-    :param df: DataFrame with date index
-    :param trades: DataFrame containing trades (requires columns close_date and profit_abs)
-    :param col_name: Column name that will be assigned the results
-    :param timeframe: Timeframe used during the operations
-    :return: Returns df with one additional column, col_name, containing the cumulative profit.
-    :raise: ValueError if trade-dataframe was found empty.
-    """
-    if len(trades) == 0:
-        raise ValueError("Trade dataframe empty.")
-    from freqtrade.exchange import timeframe_to_minutes
-    timeframe_minutes = timeframe_to_minutes(timeframe)
-    # Resample to timeframe to make sure trades match candles
-    _trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_date'
-                                  )[['profit_abs']].sum()
-    df.loc[:, col_name] = _trades_sum['profit_abs'].cumsum()
-    # Set first value to 0
-    df.loc[df.iloc[0].name, col_name] = 0
-    # FFill to get continuous
-    df[col_name] = df[col_name].ffill()
-    return df
-
-
-def _calc_drawdown_series(profit_results: pd.DataFrame, *, date_col: str, value_col: str,
-                          starting_balance: float) -> 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]
-    if starting_balance:
-        cumulative_balance = starting_balance + max_drawdown_df['cumulative']
-        max_balance = starting_balance + max_drawdown_df['high_value']
-        max_drawdown_df['drawdown_relative'] = ((max_balance - cumulative_balance) / max_balance)
-    else:
-        # NOTE: This is not completely accurate,
-        # but might good enough if starting_balance is not available
-        max_drawdown_df['drawdown_relative'] = (
-            (max_drawdown_df['high_value'] - max_drawdown_df['cumulative'])
-            / max_drawdown_df['high_value'])
-    return max_drawdown_df
-
-
-def calculate_underwater(trades: pd.DataFrame, *, date_col: str = 'close_date',
-                         value_col: str = 'profit_ratio', starting_balance: float = 0.0
-                         ):
-    """
-    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_ratio')
-    :return: Tuple (float, highdate, lowdate, highvalue, lowvalue) with absolute max drawdown,
-             high and low time and high and low value.
-    :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,
-        starting_balance=starting_balance)
-
-    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,
-                           relative: bool = False
-                           ) -> 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,
-        starting_balance=starting_balance
-    )
-
-    idxmin = max_drawdown_df['drawdown_relative'].idxmax() if relative \
-        else max_drawdown_df['drawdown'].idxmin()
-    if idxmin == 0:
-        raise ValueError("No losing trade, therefore no drawdown.")
-    high_date = profit_results.loc[max_drawdown_df.iloc[:idxmin]['high_value'].idxmax(), date_col]
-    low_date = profit_results.loc[idxmin, date_col]
-    high_val = max_drawdown_df.loc[max_drawdown_df.iloc[:idxmin]
-                                   ['high_value'].idxmax(), 'cumulative']
-    low_val = max_drawdown_df.loc[idxmin, 'cumulative']
-    max_drawdown_rel = max_drawdown_df.loc[idxmin, 'drawdown_relative']
-
-    return (
-        abs(max_drawdown_df.loc[idxmin, '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]:
-    """
-    Calculate min/max cumsum of trades, to show if the wallet/stake amount ratio is sane
-    :param trades: DataFrame containing trades (requires columns close_date and profit_percent)
-    :param starting_balance: Add starting balance to results, to show the wallets high / low points
-    :return: Tuple (float, float) with cumsum of profit_abs
-    :raise: ValueError if trade-dataframe was found empty.
-    """
-    if len(trades) == 0:
-        raise ValueError("Trade dataframe empty.")
-
-    csum_df = pd.DataFrame()
-    csum_df['sum'] = trades['profit_abs'].cumsum()
-    csum_min = csum_df['sum'].min() + starting_balance
-    csum_max = csum_df['sum'].max() + starting_balance
-
-    return csum_min, csum_max
-
-
-def calculate_cagr(days_passed: int, starting_balance: float, final_balance: float) -> float:
-    """
-    Calculate CAGR
-    :param days_passed: Days passed between start and ending balance
-    :param starting_balance: Starting balance
-    :param final_balance: Final balance to calculate CAGR against
-    :return: CAGR
-    """
-    return (final_balance / starting_balance) ** (1 / (days_passed / 365)) - 1

freqtrade/edge/edge_positioning.py

@@ -13,7 +13,7 @@ from pandas import DataFrame
 from freqtrade.configuration import TimeRange
 from freqtrade.constants import DATETIME_PRINT_FORMAT, UNLIMITED_STAKE_AMOUNT
 from freqtrade.data.history import get_timerange, load_data, refresh_data
-from freqtrade.enums import CandleType, ExitType, RunMode
+from freqtrade.enums import RunMode, SellType
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange.exchange import timeframe_to_seconds
 from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
@@ -116,12 +116,11 @@ class Edge:
                 timeframe=self.strategy.timeframe,
                 timerange=timerange_startup,
                 data_format=self.config.get('dataformat_ohlcv', 'json'),
-                candle_type=self.config.get('candle_type_def', CandleType.SPOT),
             )
             # Download informative pairs too
             res = defaultdict(list)
-            for pair, timeframe, _ in self.strategy.gather_informative_pairs():
-                res[timeframe].append(pair)
+            for p, t in self.strategy.gather_informative_pairs():
+                res[t].append(p)
             for timeframe, inf_pairs in res.items():
                 timerange_startup = deepcopy(self._timerange)
                 timerange_startup.subtract_start(timeframe_to_seconds(
@@ -133,7 +132,6 @@ class Edge:
                     timeframe=timeframe,
                     timerange=timerange_startup,
                     data_format=self.config.get('dataformat_ohlcv', 'json'),
-                    candle_type=self.config.get('candle_type_def', CandleType.SPOT),
                 )
 
         data = load_data(
@@ -143,7 +141,6 @@ class Edge:
             timerange=self._timerange,
             startup_candles=self.strategy.startup_candle_count,
             data_format=self.config.get('dataformat_ohlcv', 'json'),
-            candle_type=self.config.get('candle_type_def', CandleType.SPOT),
         )
 
         if not data:
@@ -162,9 +159,7 @@ class Edge:
         logger.info(f'Measuring data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
                     f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
                     f'({(max_date - min_date).days} days)..')
-        # TODO: Should edge support shorts? needs to be investigated further
-        # * (add enter_short exit_short)
-        headers = ['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long']
+        headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low']
 
         trades: list = []
         for pair, pair_data in preprocessed.items():
@@ -172,13 +167,8 @@ class Edge:
             pair_data = pair_data.sort_values(by=['date'])
             pair_data = pair_data.reset_index(drop=True)
 
-            df_analyzed = self.strategy.advise_exit(
-                dataframe=self.strategy.advise_entry(
-                    dataframe=pair_data,
-                    metadata={'pair': pair}
-                ),
-                metadata={'pair': pair}
-            )[headers].copy()
+            df_analyzed = self.strategy.advise_sell(
+                self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
 
             trades += self._find_trades_for_stoploss_range(df_analyzed, pair, self._stoploss_range)
 
@@ -229,11 +219,9 @@ class Edge:
         """
         final = []
         for pair, info in self._cached_pairs.items():
-            if (
-                info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2))
-                and info.winrate > float(self.edge_config.get('minimum_winrate', 0.60))
-                and pair in pairs
-            ):
+            if info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2)) and \
+                info.winrate > float(self.edge_config.get('minimum_winrate', 0.60)) and \
+                    pair in pairs:
                 final.append(pair)
 
         if self._final_pairs != final:
@@ -258,8 +246,8 @@ class Edge:
         """
         final = []
         for pair, info in self._cached_pairs.items():
-            if (info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2)) and
-                    info.winrate > float(self.edge_config.get('minimum_winrate', 0.60))):
+            if info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2)) and \
+                    info.winrate > float(self.edge_config.get('minimum_winrate', 0.60)):
                 final.append({
                     'Pair': pair,
                     'Winrate': info.winrate,
@@ -394,8 +382,8 @@ class Edge:
         return final
 
     def _find_trades_for_stoploss_range(self, df, pair, stoploss_range):
-        buy_column = df['enter_long'].values
-        sell_column = df['exit_long'].values
+        buy_column = df['buy'].values
+        sell_column = df['sell'].values
         date_column = df['date'].values
         ohlc_columns = df[['open', 'high', 'low', 'close']].values
 
@@ -460,7 +448,7 @@ class Edge:
 
             if stop_index <= sell_index:
                 exit_index = open_trade_index + stop_index
-                exit_type = ExitType.STOP_LOSS
+                exit_type = SellType.STOP_LOSS
                 exit_price = stop_price
             elif stop_index > sell_index:
                 # If exit is SELL then we exit at the next candle
@@ -470,7 +458,7 @@ class Edge:
                 if len(ohlc_columns) - 1 < exit_index:
                     break
 
-                exit_type = ExitType.EXIT_SIGNAL
+                exit_type = SellType.SELL_SIGNAL
                 exit_price = ohlc_columns[exit_index, 0]
 
             trade = {'pair': pair,

freqtrade/enums/__init__.py

@@ -1,12 +1,8 @@
 # flake8: noqa: F401
 from freqtrade.enums.backteststate import BacktestState
-from freqtrade.enums.candletype import CandleType
-from freqtrade.enums.exitchecktuple import ExitCheckTuple
-from freqtrade.enums.exittype import ExitType
-from freqtrade.enums.marginmode import MarginMode
 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.signaltype import SignalDirection, SignalTagType, SignalType
+from freqtrade.enums.selltype import SellType
+from freqtrade.enums.signaltype import SignalTagType, SignalType
 from freqtrade.enums.state import State
-from freqtrade.enums.tradingmode import TradingMode

freqtrade/enums/candletype.py

@@ -1,27 +0,0 @@
-from enum import Enum
-
-
-class CandleType(str, Enum):
-    """Enum to distinguish candle types"""
-    SPOT = "spot"
-    FUTURES = "futures"
-    MARK = "mark"
-    INDEX = "index"
-    PREMIUMINDEX = "premiumIndex"
-
-    # TODO: Could take up less memory if these weren't a CandleType
-    FUNDING_RATE = "funding_rate"
-    # BORROW_RATE = "borrow_rate"  # * unimplemented
-
-    @staticmethod
-    def from_string(value: str) -> 'CandleType':
-        if not value:
-            # Default to spot
-            return CandleType.SPOT
-        return CandleType(value)
-
-    @staticmethod
-    def get_default(trading_mode: str) -> 'CandleType':
-        if trading_mode == 'futures':
-            return CandleType.FUTURES
-        return CandleType.SPOT

freqtrade/enums/exitchecktuple.py

@@ -1,23 +0,0 @@
-from freqtrade.enums.exittype import ExitType
-
-
-class ExitCheckTuple:
-    """
-    NamedTuple for Exit type + reason
-    """
-    exit_type: ExitType
-    exit_reason: str = ''
-
-    def __init__(self, exit_type: ExitType, exit_reason: str = ''):
-        self.exit_type = exit_type
-        self.exit_reason = exit_reason or exit_type.value
-
-    @property
-    def exit_flag(self):
-        return self.exit_type != ExitType.NONE
-
-    def __eq__(self, other):
-        return self.exit_type == other.exit_type and self.exit_reason == other.exit_reason
-
-    def __repr__(self):
-        return f"ExitCheckTuple({self.exit_type}, {self.exit_reason})"

freqtrade/enums/marginmode.py

@@ -1,12 +0,0 @@
-from enum import Enum
-
-
-class MarginMode(Enum):
-    """
-    Enum to distinguish between
-    cross margin/futures margin_mode and
-    isolated margin/futures margin_mode
-    """
-    CROSS = "cross"
-    ISOLATED = "isolated"
-    NONE = ''