Merge pull request #3731 from freqtrade/release_2020.8

Release 2020.8

.dependabot/config.yml

@@ -1,17 +0,0 @@
-version: 1
-
-update_configs:
-  - package_manager: "python"
-    directory: "/"
-    update_schedule: "weekly"
-    allowed_updates:
-    - match:
-        update_type: "all"
-    target_branch: "develop"
-
-  - package_manager: "docker"
-    directory: "/"
-    update_schedule: "daily"
-    allowed_updates:
-    - match:
-        update_type: "all"

.github/ISSUE_TEMPLATE.md

@@ -1,33 +0,0 @@
-## Step 1: Have you search for this issue before posting it?
-
-If you have discovered a bug in the bot, please [search our issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue). 
-If it hasn't been reported, please create a new issue.
-
-## Step 2: Describe your environment
-
-  * Operating system: ____
-  * Python Version: _____ (`python -V`)
-  * CCXT version: _____ (`pip freeze | grep ccxt`)
-  * Branch: Master | Develop
-  * Last Commit ID: _____ (`git log --format="%H" -n 1`)
-  
-## Step 3: Describe the problem:
-
-*Explain the problem you have encountered*
-
-### Steps to reproduce:
-
-  1. _____
-  2. _____
-  3. _____
-  
-### Observed Results:
-
-  * What happened?
-  * What did you expect to happen?
-
-### Relevant code exceptions or logs:
-
-  ```
-  // paste your log here
-  ```

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,48 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: "Triage Needed"
+assignees: ''
+
+---
+<!-- 
+Have you searched for similar issues before posting it?
+
+If you have discovered a bug in the bot, please [search our issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue). 
+If it hasn't been reported, please create a new issue.
+
+Please do not use bug reports to request new features.
+-->
+
+## Describe your environment
+
+  * Operating system: ____
+  * Python Version: _____ (`python -V`)
+  * CCXT version: _____ (`pip freeze | grep ccxt`)
+  * Freqtrade Version: ____ (`freqtrade -V` or `docker-compose run --rm freqtrade -V` for Freqtrade running in docker)
+  
+Note: All issues other than enhancement requests will be closed without further comment if the above template is deleted or not filled out.
+
+## Describe the problem:
+
+*Explain the problem you have encountered*
+
+### Steps to reproduce:
+
+  1. _____
+  2. _____
+  3. _____
+  
+### Observed Results:
+
+  * What happened?
+  * What did you expect to happen?
+
+### Relevant code exceptions or logs
+
+Note: Please copy/paste text of the messages, no screenshots of logs please.
+
+  ```
+  // paste your log here
+  ```

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,27 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+<!-- 
+Note: this section will not show up in the issue.
+Have you search for this feature before requesting it? It's highly likely that a similar request was already filed.
+-->
+
+## Describe your environment
+(if applicable)
+
+  * Operating system: ____
+  * Python Version: _____ (`python -V`)
+  * CCXT version: _____ (`pip freeze | grep ccxt`)
+  * Freqtrade Version: ____ (`freqtrade -V` or `docker-compose run --rm freqtrade -V` for Freqtrade running in docker)
+
+
+## Describe the enhancement
+
+*Explain the enhancement you would like*
+

.github/ISSUE_TEMPLATE/question.md

@@ -0,0 +1,25 @@
+---
+name: BQuestion
+about: Ask a question you could not find an answer in the docs
+title: ''
+labels: "Question"
+assignees: ''
+
+---
+<!-- 
+Have you searched for similar issues before posting it?
+Did you have a VERY good look at the [documentation](https://www.freqtrade.io/en/latest/) and are sure that the question is not explained there
+
+Please do not use the question template to report bugs or to request new features.
+-->
+
+## Describe your environment
+
+  * Operating system: ____
+  * Python Version: _____ (`python -V`)
+  * CCXT version: _____ (`pip freeze | grep ccxt`)
+  * Freqtrade Version: ____ (`freqtrade -V` or `docker-compose run --rm freqtrade -V` for Freqtrade running in docker)
+  
+## Your question
+
+*Ask the question you have not been able to find an answer in our [Documentation](https://www.freqtrade.io/en/latest/)*

.github/dependabot.yml

@@ -0,0 +1,13 @@
+version: 2
+updates:
+- package-ecosystem: docker
+  directory: "/"
+  schedule:
+    interval: daily
+  open-pull-requests-limit: 10
+- package-ecosystem: pip
+  directory: "/"
+  schedule:
+    interval: weekly
+  open-pull-requests-limit: 10
+  target-branch: develop

.github/workflows/ci.yml

@@ -7,6 +7,8 @@ on:
       - develop
       - github_actions_tests
     tags:
+    release:
+      types: [published]
   pull_request:
   schedule:
     - cron:  '0 5 * * 4'
@@ -18,10 +20,10 @@ jobs:
     strategy:
       matrix:
         os: [ ubuntu-18.04, macos-latest ]
-        python-version: [3.7]
+        python-version: [3.7, 3.8]
 
     steps:
-    - uses: actions/checkout@v1
+    - uses: actions/checkout@v2
 
     - name: Set up Python
       uses: actions/setup-python@v1
@@ -64,19 +66,17 @@ jobs:
         pip install -e .
 
     - name: Tests
-      env:
-        COVERALLS_REPO_TOKEN: ${{ secrets.COVERALLS_REPO_TOKEN }}
-        COVERALLS_SERVICE_NAME: travis-ci
-        TRAVIS: "true"
       run: |
         pytest --random-order --cov=freqtrade --cov-config=.coveragerc
+
+    - name: Coveralls
+      if: (startsWith(matrix.os, 'ubuntu') && matrix.python-version == '3.8')
+      env:
+        # Coveralls token. Not used as secret due to github not providing secrets to forked repositories
+        COVERALLS_REPO_TOKEN: 6D1m0xupS3FgutfuGao8keFf9Hc0FpIXu
+      run: |
         # Allow failure for coveralls
-        # Fake travis environment to get coveralls working correctly
-        export TRAVIS_PULL_REQUEST="https://github.com/${GITHUB_REPOSITORY}/pull/$(cat $GITHUB_EVENT_PATH | jq -r .number)"
-        export TRAVIS_BRANCH=${GITHUB_REF#"ref/heads"}
-        export CI_BRANCH=${GITHUB_REF#"ref/heads"}
-        echo "${TRAVIS_BRANCH}"
-        coveralls || true
+        coveralls -v  || true
 
     - name: Backtesting
       run: |
@@ -88,7 +88,7 @@ jobs:
       run: |
         cp config.json.example config.json
         freqtrade create-userdir --userdir user_data
-        freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt
+        freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --print-all
 
     - name: Flake8
       run: |
@@ -100,7 +100,7 @@ jobs:
 
     - name: Slack Notification
       uses: homoluctus/slatify@v1.8.0
-      if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
+      if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
       with:
         type: ${{ job.status }}
         job_name: '*Freqtrade CI ${{ matrix.os }}*'
@@ -115,10 +115,10 @@ jobs:
     strategy:
       matrix:
         os: [ windows-latest ]
-        python-version: [3.7]
+        python-version: [3.7, 3.8]
 
     steps:
-    - uses: actions/checkout@v1
+    - uses: actions/checkout@v2
 
     - name: Set up Python
       uses: actions/setup-python@v1
@@ -130,8 +130,7 @@ jobs:
       if: startsWith(runner.os, 'Windows')
       with:
         path: ~\AppData\Local\pip\Cache
-        key: ${{ runner.os }}-pip
-        restore-keys: ${{ runner.os }}-pip
+        key: ${{ matrix.os }}-${{ matrix.python-version }}-pip
 
     - name: Installation
       run: |
@@ -151,7 +150,7 @@ jobs:
       run: |
         cp config.json.example config.json
         freqtrade create-userdir --userdir user_data
-        freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt
+        freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --print-all
 
     - name: Flake8
       run: |
@@ -163,7 +162,7 @@ jobs:
 
     - name: Slack Notification
       uses: homoluctus/slatify@v1.8.0
-      if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
+      if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
       with:
         type: ${{ job.status }}
         job_name: '*Freqtrade CI windows*'
@@ -175,7 +174,7 @@ jobs:
   docs_check:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v1
+    - uses: actions/checkout@v2
 
     - name: Documentation syntax
       run: |
@@ -190,37 +189,105 @@ jobs:
         channel: '#notifications'
         url: ${{ secrets.SLACK_WEBHOOK }}
 
+  cleanup-prior-runs:
+    runs-on: ubuntu-latest
+    steps:
+    - name: Cleanup previous runs on this branch
+      uses: rokroskar/workflow-run-cleanup-action@v0.2.2
+      if: "!startsWith(github.ref, 'refs/tags/') && github.ref != 'refs/heads/master' && github.repository == 'freqtrade/freqtrade'"
+      env:
+        GITHUB_TOKEN: "${{ secrets.GITHUB_TOKEN }}"
+
+  # Notify on slack only once - when CI completes (and after deploy) in case it's successfull
+  notify-complete:
+    needs: [ build, build_windows, docs_check ]
+    runs-on: ubuntu-latest
+    steps:
+    - name: Slack Notification
+      uses: homoluctus/slatify@v1.8.0
+      if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
+      with:
+        type: ${{ job.status }}
+        job_name: '*Freqtrade CI*'
+        channel: '#notifications'
+        url: ${{ secrets.SLACK_WEBHOOK }}
+
   deploy:
     needs: [ build, build_windows, docs_check ]
     runs-on: ubuntu-18.04
-    if: (github.event_name == 'push' || github.event_name == 'schedule') && github.repository == 'freqtrade/freqtrade'
+    if: (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'release') && github.repository == 'freqtrade/freqtrade'
     steps:
-    - uses: actions/checkout@v1
+    - uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v1
+      with:
+        python-version: 3.8
 
     - name: Extract branch name
       shell: bash
       run: echo "##[set-output name=branch;]$(echo ${GITHUB_REF#refs/heads/})"
       id: extract_branch
 
+    - name: Build distribution
+      run: |
+        pip install -U setuptools wheel
+        python setup.py sdist bdist_wheel
+
+    - name: Publish to PyPI (Test)
+      uses: pypa/gh-action-pypi-publish@master
+      if: (steps.extract_branch.outputs.branch == 'master' || github.event_name == 'release')
+      with:
+        user: __token__
+        password: ${{ secrets.pypi_test_password }}
+        repository_url: https://test.pypi.org/legacy/
+
+    - name: Publish to PyPI
+      uses: pypa/gh-action-pypi-publish@master
+      if: (steps.extract_branch.outputs.branch == 'master' || github.event_name == 'release')
+      with:
+        user: __token__
+        password: ${{ secrets.pypi_password }}
+
+    - name: Dockerhub login
+      env:
+        DOCKER_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}
+        DOCKER_USERNAME: ${{ secrets.DOCKER_USERNAME }}
+      run: |
+        echo "${DOCKER_PASSWORD}" | docker login --username ${DOCKER_USERNAME} --password-stdin
+
     - name: Build and test and push docker image
       env:
         IMAGE_NAME: freqtradeorg/freqtrade
-        DOCKER_USERNAME: ${{ secrets.DOCKER_USERNAME }}
-        DOCKER_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}
         BRANCH_NAME: ${{ steps.extract_branch.outputs.branch }}
       run: |
         build_helpers/publish_docker.sh
 
-    - name: Build raspberry image for ${{ steps.extract_branch.outputs.branch }}_pi
-      uses: elgohr/Publish-Docker-Github-Action@2.7
+    # We need docker experimental to pull the ARM image.
+    - name: Switch docker to experimental
+      run: |
+          docker version -f '{{.Server.Experimental}}'
+          echo $'{\n    "experimental": true\n}' | sudo tee /etc/docker/daemon.json
+          sudo systemctl restart docker
+          docker version -f '{{.Server.Experimental}}'
+
+    - name: Set up Docker Buildx
+      id: buildx
+      uses: crazy-max/ghaction-docker-buildx@v1
       with:
-        name: freqtradeorg/freqtrade:${{ steps.extract_branch.outputs.branch }}_pi
-        username: ${{ secrets.DOCKER_USERNAME }}
-        password: ${{ secrets.DOCKER_PASSWORD }}
-        dockerfile: Dockerfile.pi
-        # cache: true
-        cache: ${{ github.event_name != 'schedule' }}
-        tag_names: true
+        buildx-version: latest
+        qemu-version: latest
+
+    - name: Available platforms
+      run: echo ${{ steps.buildx.outputs.platforms }}
+
+    - name: Build Raspberry docker image
+      env:
+        IMAGE_NAME: freqtradeorg/freqtrade
+        BRANCH_NAME: ${{ steps.extract_branch.outputs.branch }}_pi
+      run: |
+        build_helpers/publish_docker_pi.sh
+
 
     - name: Slack Notification
       uses: homoluctus/slatify@v1.8.0

.gitignore

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

.travis.yml

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

CONTRIBUTING.md

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

Dockerfile

@@ -1,7 +1,7 @@
-FROM python:3.7.5-slim-stretch
+FROM python:3.8.5-slim-buster
 
 RUN apt-get update \
-    && apt-get -y install curl build-essential libssl-dev \
+    && apt-get -y install curl build-essential libssl-dev sqlite3 \
     && apt-get clean \
     && pip install --upgrade pip
 

Dockerfile.armhf

@@ -0,0 +1,29 @@
+FROM --platform=linux/arm/v7 python:3.7.7-slim-buster
+
+RUN apt-get update \
+  && apt-get -y install curl build-essential libssl-dev libffi-dev libatlas3-base libgfortran5 sqlite3 \
+  && apt-get clean \
+  && pip install --upgrade pip \
+  && echo "[global]\nextra-index-url=https://www.piwheels.org/simple" > /etc/pip.conf
+
+# Prepare environment
+RUN mkdir /freqtrade
+WORKDIR /freqtrade
+
+# Install TA-lib
+COPY build_helpers/* /tmp/
+RUN cd /tmp && /tmp/install_ta-lib.sh && rm -r /tmp/*ta-lib*
+
+ENV LD_LIBRARY_PATH /usr/local/lib
+
+# Install dependencies
+COPY requirements.txt requirements-common.txt /freqtrade/
+RUN pip install numpy --no-cache-dir \
+  && pip install -r requirements.txt --no-cache-dir
+
+# Install and execute
+COPY . /freqtrade/
+RUN pip install -e . --no-cache-dir
+ENTRYPOINT ["freqtrade"]
+# Default to trade mode
+CMD [ "trade" ]

Dockerfile.pi

@@ -1,41 +0,0 @@
-FROM balenalib/raspberrypi3-debian:stretch
-
-RUN [ "cross-build-start" ]
-
-RUN apt-get update \
- && apt-get -y install wget curl build-essential libssl-dev libffi-dev \
- && apt-get clean
-
-# Prepare environment
-RUN mkdir /freqtrade
-WORKDIR /freqtrade
-
-# Install TA-lib
-COPY build_helpers/ta-lib-0.4.0-src.tar.gz /freqtrade/
-RUN tar -xzf /freqtrade/ta-lib-0.4.0-src.tar.gz \
- && cd /freqtrade/ta-lib/ \
- && ./configure \
- && make \
- && make install \
- && rm /freqtrade/ta-lib-0.4.0-src.tar.gz
-
-ENV LD_LIBRARY_PATH /usr/local/lib
-
-# Install berryconda
-RUN wget -q https://github.com/jjhelmus/berryconda/releases/download/v2.0.0/Berryconda3-2.0.0-Linux-armv7l.sh \
- && bash ./Berryconda3-2.0.0-Linux-armv7l.sh -b \
- && rm Berryconda3-2.0.0-Linux-armv7l.sh
-
-# Install dependencies
-COPY requirements-common.txt /freqtrade/
-RUN ~/berryconda3/bin/conda install -y numpy pandas \
- && ~/berryconda3/bin/pip install -r requirements-common.txt --no-cache-dir
-
-# Install and execute
-COPY . /freqtrade/
-RUN ~/berryconda3/bin/pip install -e . --no-cache-dir
-
-RUN [ "cross-build-end" ]
-
-ENTRYPOINT ["/root/berryconda3/bin/python","./freqtrade/main.py"]
-CMD [ "trade" ]

MANIFEST.in

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

README.md

@@ -1,6 +1,6 @@
 # Freqtrade
 
-[![Build Status](https://travis-ci.org/freqtrade/freqtrade.svg?branch=develop)](https://travis-ci.org/freqtrade/freqtrade)
+[![Freqtrade CI](https://github.com/freqtrade/freqtrade/workflows/Freqtrade%20CI/badge.svg)](https://github.com/freqtrade/freqtrade/actions/)
 [![Coverage Status](https://coveralls.io/repos/github/freqtrade/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/freqtrade/freqtrade?branch=develop)
 [![Documentation](https://readthedocs.org/projects/freqtrade/badge/)](https://www.freqtrade.io)
 [![Maintainability](https://api.codeclimate.com/v1/badges/5737e6d668200b7518ff/maintainability)](https://codeclimate.com/github/freqtrade/freqtrade/maintainability)
@@ -25,7 +25,8 @@ hesitate to read the source code and understand the mechanism of this bot.
 ## Exchange marketplaces supported
 
 - [X] [Bittrex](https://bittrex.com/)
-- [X] [Binance](https://www.binance.com/) ([*Note for binance users](#a-note-on-binance))
+- [X] [Binance](https://www.binance.com/) ([*Note for binance users](docs/exchanges.md#blacklists))
+- [X] [Kraken](https://kraken.com/)
 - [ ] [113 others to tests](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
 
 ## Documentation
@@ -67,40 +68,43 @@ For any other type of installation please refer to [Installation doc](https://ww
 ### Bot commands
 
 ```
-usage: freqtrade [-h] [-v] [--logfile FILE] [--version] [-c PATH] [-d PATH]
-                 [-s NAME] [--strategy-path PATH] [--dynamic-whitelist [INT]]
-                 [--db-url PATH] [--sd-notify]
-                 {backtesting,edge,hyperopt} ...
+usage: freqtrade [-h] [-V]
+                 {trade,create-userdir,new-config,new-hyperopt,new-strategy,download-data,convert-data,convert-trade-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,plot-dataframe,plot-profit}
+                 ...
 
 Free, open source crypto trading bot
 
 positional arguments:
-  {backtesting,edge,hyperopt}
+  {trade,create-userdir,new-config,new-hyperopt,new-strategy,download-data,convert-data,convert-trade-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,plot-dataframe,plot-profit}
+    trade               Trade module.
+    create-userdir      Create user-data directory.
+    new-config          Create new config
+    new-hyperopt        Create new hyperopt
+    new-strategy        Create new strategy
+    download-data       Download backtesting data.
+    convert-data        Convert candle (OHLCV) data from one format to
+                        another.
+    convert-trade-data  Convert trade data from one format to another.
     backtesting         Backtesting module.
     edge                Edge module.
     hyperopt            Hyperopt module.
+    hyperopt-list       List Hyperopt results
+    hyperopt-show       Show details of Hyperopt results
+    list-exchanges      Print available exchanges.
+    list-hyperopts      Print available hyperopt classes.
+    list-markets        Print markets on exchange.
+    list-pairs          Print pairs on exchange.
+    list-strategies     Print available strategies.
+    list-timeframes     Print available timeframes for the exchange.
+    show-trades         Show trades.
+    test-pairlist       Test your pairlist configuration.
+    plot-dataframe      Plot candles with indicators.
+    plot-profit         Generate plot showing profits.
 
 optional arguments:
   -h, --help            show this help message and exit
-  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
-  --logfile FILE        Log to the file specified
-  --version             show program's version number and exit
-  -c PATH, --config PATH
-                        Specify configuration file (default: None). Multiple
-                        --config options may be used.
-  -d PATH, --datadir PATH
-                        Path to backtest data.
-  -s NAME, --strategy NAME
-                        Specify strategy class name (default:
-                        DefaultStrategy).
-  --strategy-path PATH  Specify additional strategy lookup path.
-  --dynamic-whitelist [INT]
-                        Dynamically generate and update whitelist based on 24h
-                        BaseVolume (default: 20). DEPRECATED.
-  --db-url PATH         Override trades database URL, this is useful if
-                        dry_run is enabled or in custom deployments (default:
-                        None).
-  --sd-notify           Notify systemd service manager.
+  -V, --version         show program's version number and exit
+
 ```
 
 ### Telegram RPC commands
@@ -119,7 +123,6 @@ Telegram is not mandatory. However, this is a great way to control your bot. Mor
 - `/help`: Show help message
 - `/version`: Show version
 
-
 ## Development branches
 
 The project is currently setup in two main branches:

bin/freqtrade

@@ -1,11 +0,0 @@
-#!/usr/bin/env python3
-
-import sys
-import warnings
-
-from freqtrade.main import main
-
-warnings.warn(
-    "Deprecated - To continue to run the bot like this, please run `pip install -e .` again.",
-    DeprecationWarning)
-main(sys.argv[1:])

build_helpers/install_windows.ps1

@@ -2,7 +2,16 @@
 # Downloaded from https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib
 # Invoke-WebRequest -Uri "https://download.lfd.uci.edu/pythonlibs/xxxxxxx/TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl" -OutFile "TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl"
 
-pip install build_helpers\TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl
+python -m pip install --upgrade pip
+
+$pyv = python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')"
+
+if ($pyv -eq '3.7') {
+    pip install build_helpers\TA_Lib-0.4.18-cp37-cp37m-win_amd64.whl
+}
+if ($pyv -eq '3.8') {
+    pip install build_helpers\TA_Lib-0.4.18-cp38-cp38-win_amd64.whl
+}
 
 pip install -r requirements-dev.txt
 pip install -e .

build_helpers/publish_docker.sh

@@ -23,7 +23,7 @@ if [ $? -ne 0 ]; then
 fi
 
 # Run backtest
-docker run --rm -v $(pwd)/config.json.example:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG} backtesting --datadir /tests/testdata --strategy DefaultStrategy
+docker run --rm -v $(pwd)/config.json.example:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG} backtesting --datadir /tests/testdata --strategy-path /tests/strategy/strats/ --strategy DefaultStrategy
 
 if [ $? -ne 0 ]; then
     echo "failed running backtest"
@@ -42,14 +42,6 @@ if [ "${TAG}" = "develop" ]; then
     docker tag freqtrade:$TAG ${IMAGE_NAME}:latest
 fi
 
-# Login
-docker login -u $DOCKER_USERNAME -p $DOCKER_PASSWORD
-
-if [ $? -ne 0 ]; then
-    echo "failed login"
-    return 1
-fi
-
 # Show all available images
 docker images
 

build_helpers/publish_docker_pi.sh

@@ -0,0 +1,36 @@
+#!/bin/sh
+
+# The below assumes a correctly setup docker buildx environment
+
+# Replace / with _ to create a valid tag
+TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g")
+PI_PLATFORM="linux/arm/v7"
+echo "Running for ${TAG}"
+CACHE_TAG=freqtradeorg/freqtrade_cache:${TAG}_cache
+
+# Add commit and commit_message to docker container
+echo "${GITHUB_SHA}" > freqtrade_commit
+
+if [ "${GITHUB_EVENT_NAME}" = "schedule" ]; then
+    echo "event ${GITHUB_EVENT_NAME}: full rebuild - skipping cache"
+    docker buildx build \
+        --cache-to=type=registry,ref=${CACHE_TAG} \
+        -f Dockerfile.armhf \
+        --platform ${PI_PLATFORM} \
+        -t ${IMAGE_NAME}:${TAG} --push .
+else
+    echo "event ${GITHUB_EVENT_NAME}: building with cache"
+    # Pull last build to avoid rebuilding the whole image
+    # docker pull --platform ${PI_PLATFORM} ${IMAGE_NAME}:${TAG}
+    docker buildx build \
+        --cache-from=type=registry,ref=${CACHE_TAG} \
+        --cache-to=type=registry,ref=${CACHE_TAG} \
+        -f Dockerfile.armhf \
+        --platform ${PI_PLATFORM} \
+        -t ${IMAGE_NAME}:${TAG} --push .
+fi
+
+if [ $? -ne 0 ]; then
+    echo "failed building image"
+    return 1
+fi

config.json.example

@@ -2,9 +2,11 @@
     "max_open_trades": 3,
     "stake_currency": "BTC",
     "stake_amount": 0.05,
+    "tradable_balance_ratio": 0.99,
     "fiat_display_currency": "USD",
-    "ticker_interval" : "5m",
+    "timeframe": "5m",
     "dry_run": false,
+    "cancel_open_orders_on_exit": false,
     "trailing_stop": false,
     "unfilledtimeout": {
         "buy": 10,
@@ -22,7 +24,7 @@
     "ask_strategy":{
         "use_order_book": false,
         "order_book_min": 1,
-        "order_book_max": 9,
+        "order_book_max": 1,
         "use_sell_signal": true,
         "sell_profit_only": false,
         "ignore_roi_if_buy_signal": false
@@ -43,7 +45,7 @@
             "DASH/BTC",
             "ZEC/BTC",
             "XLM/BTC",
-            "NXT/BTC",
+            "XRP/BTC",
             "TRX/BTC",
             "ADA/BTC",
             "XMR/BTC"
@@ -59,7 +61,6 @@
         "enabled": false,
         "process_throttle_secs": 3600,
         "calculate_since_number_of_days": 7,
-        "capital_available_percentage": 0.5,
         "allowed_risk": 0.01,
         "stoploss_range_min": -0.01,
         "stoploss_range_max": -0.1,
@@ -75,6 +76,16 @@
         "token": "your_telegram_token",
         "chat_id": "your_telegram_chat_id"
     },
+    "api_server": {
+        "enabled": false,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8080,
+        "verbosity": "info",
+        "jwt_secret_key": "somethingrandom",
+        "CORS_origins": [],
+        "username": "",
+        "password": ""
+    },
     "initial_state": "running",
     "forcebuy_enable": false,
     "internals": {

config_binance.json.example

@@ -2,9 +2,11 @@
     "max_open_trades": 3,
     "stake_currency": "BTC",
     "stake_amount": 0.05,
+    "tradable_balance_ratio": 0.99,
     "fiat_display_currency": "USD",
-    "ticker_interval" : "5m",
+    "timeframe": "5m",
     "dry_run": true,
+    "cancel_open_orders_on_exit": false,
     "trailing_stop": false,
     "unfilledtimeout": {
         "buy": 10,
@@ -22,7 +24,7 @@
     "ask_strategy":{
         "use_order_book": false,
         "order_book_min": 1,
-        "order_book_max": 9,
+        "order_book_max": 1,
         "use_sell_signal": true,
         "sell_profit_only": false,
         "ignore_roi_if_buy_signal": false
@@ -64,7 +66,6 @@
         "enabled": false,
         "process_throttle_secs": 3600,
         "calculate_since_number_of_days": 7,
-        "capital_available_percentage": 0.5,
         "allowed_risk": 0.01,
         "stoploss_range_min": -0.01,
         "stoploss_range_max": -0.1,
@@ -80,6 +81,16 @@
         "token": "your_telegram_token",
         "chat_id": "your_telegram_chat_id"
     },
+    "api_server": {
+        "enabled": false,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8080,
+        "verbosity": "info",
+        "jwt_secret_key": "somethingrandom",
+        "CORS_origins": [],
+        "username": "",
+        "password": ""
+    },
     "initial_state": "running",
     "forcebuy_enable": false,
     "internals": {

config_full.json.example

@@ -2,10 +2,14 @@
     "max_open_trades": 3,
     "stake_currency": "BTC",
     "stake_amount": 0.05,
+    "tradable_balance_ratio": 0.99,
     "fiat_display_currency": "USD",
-    "amount_reserve_percent" : 0.05,
+    "amount_reserve_percent": 0.05,
+    "amend_last_stake_amount": false,
+    "last_stake_amount_min_ratio": 0.5,
     "dry_run": false,
-    "ticker_interval": "5m",
+    "cancel_open_orders_on_exit": false,
+    "timeframe": "5m",
     "trailing_stop": false,
     "trailing_stop_positive": 0.005,
     "trailing_stop_positive_offset": 0.0051,
@@ -22,6 +26,7 @@
         "sell": 30
     },
     "bid_strategy": {
+        "price_side": "bid",
         "use_order_book": false,
         "ask_last_balance": 0.0,
         "order_book_top": 1,
@@ -31,9 +36,10 @@
         }
     },
     "ask_strategy":{
+        "price_side": "ask",
         "use_order_book": false,
         "order_book_min": 1,
-        "order_book_max": 9,
+        "order_book_max": 1,
         "use_sell_signal": true,
         "sell_profit_only": false,
         "ignore_roi_if_buy_signal": false
@@ -58,9 +64,10 @@
             "sort_key": "quoteVolume",
             "refresh_period": 1800
         },
+        {"method": "AgeFilter", "min_days_listed": 10},
         {"method": "PrecisionFilter"},
-        {"method": "PriceFilter", "low_price_ratio": 0.01
-        }
+        {"method": "PriceFilter", "low_price_ratio": 0.01, "min_price": 0.00000010},
+        {"method": "SpreadFilter", "max_spread_ratio": 0.005}
     ],
     "exchange": {
         "name": "bittrex",
@@ -96,7 +103,6 @@
         "enabled": false,
         "process_throttle_secs": 3600,
         "calculate_since_number_of_days": 7,
-        "capital_available_percentage": 0.5,
         "allowed_risk": 0.01,
         "stoploss_range_min": -0.01,
         "stoploss_range_max": -0.1,
@@ -116,6 +122,9 @@
         "enabled": false,
         "listen_ip_address": "127.0.0.1",
         "listen_port": 8080,
+        "verbosity": "info",
+        "jwt_secret_key": "somethingrandom",
+        "CORS_origins": [],
         "username": "freqtrader",
         "password": "SuperSecurePassword"
     },
@@ -126,6 +135,9 @@
         "process_throttle_secs": 5,
         "heartbeat_interval": 60
     },
+    "disable_dataframe_checks": false,
     "strategy": "DefaultStrategy",
-    "strategy_path": "user_data/strategies/"
+    "strategy_path": "user_data/strategies/",
+    "dataformat_ohlcv": "json",
+    "dataformat_trades": "jsongz"
 }

config_kraken.json.example

@@ -2,9 +2,11 @@
     "max_open_trades": 5,
     "stake_currency": "EUR",
     "stake_amount": 10,
+    "tradable_balance_ratio": 0.99,
     "fiat_display_currency": "EUR",
-    "ticker_interval" : "5m",
+    "timeframe": "5m",
     "dry_run": true,
+    "cancel_open_orders_on_exit": false,
     "trailing_stop": false,
     "unfilledtimeout": {
         "buy": 10,
@@ -22,7 +24,7 @@
     "ask_strategy":{
         "use_order_book": false,
         "order_book_min": 1,
-        "order_book_max": 9,
+        "order_book_max": 1,
         "use_sell_signal": true,
         "sell_profit_only": false,
         "ignore_roi_if_buy_signal": false
@@ -70,7 +72,6 @@
         "enabled": false,
         "process_throttle_secs": 3600,
         "calculate_since_number_of_days": 7,
-        "capital_available_percentage": 0.5,
         "allowed_risk": 0.01,
         "stoploss_range_min": -0.01,
         "stoploss_range_max": -0.1,
@@ -86,6 +87,16 @@
         "token": "your_telegram_token",
         "chat_id": "your_telegram_chat_id"
     },
+    "api_server": {
+        "enabled": false,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8080,
+        "verbosity": "info",
+        "jwt_secret_key": "somethingrandom",
+        "CORS_origins": [],
+        "username": "",
+        "password": ""
+    },
     "initial_state": "running",
     "forcebuy_enable": false,
     "internals": {

docker-compose.yml

@@ -3,6 +3,19 @@ version: '3'
 services:
   freqtrade:
     image: freqtradeorg/freqtrade:master
+    # image: freqtradeorg/freqtrade:develop
+    # Build step - only needed when additional dependencies are needed
+    # build:
+    #   context: .
+    #   dockerfile: "./Dockerfile.technical"
+    restart: unless-stopped
+    container_name: freqtrade
     volumes:
       - "./user_data:/freqtrade/user_data"
-      - "./config.json:/freqtrade/config.json"
+    # Default command used when running `docker compose up`
+    command: >
+      trade
+      --logfile /freqtrade/user_data/logs/freqtrade.log
+      --db-url sqlite:////freqtrade/user_data/tradesv3.sqlite
+      --config /freqtrade/user_data/config.json
+      --strategy SampleStrategy

docs/advanced-hyperopt.md

@@ -4,6 +4,34 @@ This page explains some advanced Hyperopt topics that may require higher
 coding skills and Python knowledge than creation of an ordinal hyperoptimization
 class.
 
+## Derived hyperopt classes
+
+Custom hyperop classes can be derived in the same way [it can be done for strategies](strategy-customization.md#derived-strategies).
+
+Applying to hyperoptimization, as an example, you may override how dimensions are defined in your optimization hyperspace:
+
+```python
+class MyAwesomeHyperOpt(IHyperOpt):
+    ...
+    # Uses default stoploss dimension
+
+class MyAwesomeHyperOpt2(MyAwesomeHyperOpt):
+    @staticmethod
+    def stoploss_space() -> List[Dimension]:
+        # Override boundaries for stoploss
+        return [
+            Real(-0.33, -0.01, name='stoploss'),
+        ]
+```
+
+and then quickly switch between hyperopt classes, running optimization process with hyperopt class you need in each particular case:
+
+```
+$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt ...
+or
+$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt2 ...
+```
+
 ## Creating and using a custom loss function
 
 To use a custom loss function class, make sure that the function `hyperopt_loss_function` is defined in your custom hyperopt loss class.
@@ -35,8 +63,8 @@ class SuperDuperHyperOptLoss(IHyperOptLoss):
         * 0.25: Avoiding trade loss
         * 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
         """
-        total_profit = results.profit_percent.sum()
-        trade_duration = results.trade_duration.mean()
+        total_profit = results['profit_percent'].sum()
+        trade_duration = results['trade_duration'].mean()
 
         trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
         profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)

docs/advanced-setup.md

@@ -4,6 +4,54 @@ This page explains some advanced tasks and configuration options that can be per
 
 If you do not know what things mentioned here mean, you probably do not need it.
 
+## Running multiple instances of Freqtrade
+
+This section will show you how to run multiple bots at the same time, on the same machine.
+
+### Things to consider
+
+* Use different database files.
+* Use different Telegram bots (requires multiple different configuration files; applies only when Telegram is enabled).
+* Use different ports (applies only when Freqtrade REST API webserver is enabled).
+
+### Different database files
+
+In order to keep track of your trades, profits, etc., freqtrade is using a SQLite database where it stores various types of information such as the trades you performed in the past and the current position(s) you are holding at any time. This allows you to keep track of your profits, but most importantly, keep track of ongoing activity if the bot process would be restarted or would be terminated unexpectedly.
+
+Freqtrade will, by default, use separate database files for dry-run and live bots (this assumes no database-url is given in either configuration nor via command line argument).
+For live trading mode, the default database will be `tradesv3.sqlite` and for dry-run it will be `tradesv3.dryrun.sqlite`.
+
+The optional argument to the trade command used to specify the path of these files is `--db-url`, which requires a valid SQLAlchemy url.
+So when you are starting a bot with only the config and strategy arguments in dry-run mode, the following 2 commands would have the same outcome.
+
+``` bash
+freqtrade trade -c MyConfig.json -s MyStrategy
+# is equivalent to
+freqtrade trade -c MyConfig.json -s MyStrategy --db-url sqlite:///tradesv3.dryrun.sqlite
+```
+
+It means that if you are running the trade command in two different terminals, for example to test your strategy both for trades in USDT and in another instance for trades in BTC, you will have to run them with different databases.
+
+If you specify the URL of a database which does not exist, freqtrade will create one with the name you specified. So to test your custom strategy with BTC and USDT stake currencies, you could use the following commands (in 2 separate terminals):
+
+``` bash
+# Terminal 1:
+freqtrade trade -c MyConfigBTC.json -s MyCustomStrategy --db-url sqlite:///user_data/tradesBTC.dryrun.sqlite
+# Terminal 2:
+freqtrade trade -c MyConfigUSDT.json -s MyCustomStrategy --db-url sqlite:///user_data/tradesUSDT.dryrun.sqlite
+```
+
+Conversely, if you wish to do the same thing in production mode, you will also have to create at least one new database (in addition to the default one) and specify the path to the "live" databases, for example:
+
+``` bash
+# Terminal 1:
+freqtrade trade -c MyConfigBTC.json -s MyCustomStrategy --db-url sqlite:///user_data/tradesBTC.live.sqlite
+# Terminal 2:
+freqtrade trade -c MyConfigUSDT.json -s MyCustomStrategy --db-url sqlite:///user_data/tradesUSDT.live.sqlite
+```
+
+For more information regarding usage of the sqlite databases, for example to manually enter or remove trades, please refer to the [SQL Cheatsheet](sql_cheatsheet.md).
+
 ## Configure the bot running as a systemd service
 
 Copy the `freqtrade.service` file to your systemd user directory (usually `~/.config/systemd/user`) and update `WorkingDirectory` and `ExecStart` to match your setup.
@@ -37,30 +85,30 @@ as the watchdog.
 
 ## Advanced Logging
 
-On many Linux systems the bot can be configured to send its log messages to `syslog` or `journald` system services. Logging to a remote `syslog` server is also available on Windows. The special values for the `--logfilename` command line option can be used for this.
+On many Linux systems the bot can be configured to send its log messages to `syslog` or `journald` system services. Logging to a remote `syslog` server is also available on Windows. The special values for the `--logfile` command line option can be used for this.
 
 ### Logging to syslog
 
-To send Freqtrade log messages to a local or remote `syslog` service use the `--logfilename` command line option with the value in the following format:
+To send Freqtrade log messages to a local or remote `syslog` service use the `--logfile` command line option with the value in the following format:
 
-* `--logfilename syslog:<syslog_address>` -- send log messages to `syslog` service using the `<syslog_address>` as the syslog address.
+* `--logfile syslog:<syslog_address>` -- send log messages to `syslog` service using the `<syslog_address>` as the syslog address.
 
 The syslog address can be either a Unix domain socket (socket filename) or a UDP socket specification, consisting of IP address and UDP port, separated by the `:` character.
 
 So, the following are the examples of possible usages:
 
-* `--logfilename syslog:/dev/log` -- log to syslog (rsyslog) using the `/dev/log` socket, suitable for most systems.
-* `--logfilename syslog` -- same as above, the shortcut for `/dev/log`.
-* `--logfilename syslog:/var/run/syslog` -- log to syslog (rsyslog) using the `/var/run/syslog` socket. Use this on MacOS.
-* `--logfilename syslog:localhost:514` -- log to local syslog using UDP socket, if it listens on port 514.
-* `--logfilename syslog:<ip>:514` -- log to remote syslog at IP address and port 514. This may be used on Windows for remote logging to an external syslog server.
+* `--logfile syslog:/dev/log` -- log to syslog (rsyslog) using the `/dev/log` socket, suitable for most systems.
+* `--logfile syslog` -- same as above, the shortcut for `/dev/log`.
+* `--logfile syslog:/var/run/syslog` -- log to syslog (rsyslog) using the `/var/run/syslog` socket. Use this on MacOS.
+* `--logfile syslog:localhost:514` -- log to local syslog using UDP socket, if it listens on port 514.
+* `--logfile syslog:<ip>:514` -- log to remote syslog at IP address and port 514. This may be used on Windows for remote logging to an external syslog server.
 
 Log messages are send to `syslog` with the `user` facility. So you can see them with the following commands:
 
 * `tail -f /var/log/user`, or 
 * install a comprehensive graphical viewer (for instance, 'Log File Viewer' for Ubuntu).
 
-On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfilename syslog` or `--logfilename journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.
+On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfile syslog` or `--logfile journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.
 
 For `rsyslog` the messages from the bot can be redirected into a separate dedicated log file. To achieve this, add
 ```
@@ -78,9 +126,9 @@ $RepeatedMsgReduction on
 
 This needs the `systemd` python package installed as the dependency, which is not available on Windows. Hence, the whole journald logging functionality is not available for a bot running on Windows.
 
-To send Freqtrade log messages to `journald` system service use the `--logfilename` command line option with the value in the following format:
+To send Freqtrade log messages to `journald` system service use the `--logfile` command line option with the value in the following format:
 
-* `--logfilename journald` -- send log messages to `journald`.
+* `--logfile journald` -- send log messages to `journald`.
 
 Log messages are send to `journald` with the `user` facility. So you can see them with the following commands:
 
@@ -89,4 +137,4 @@ Log messages are send to `journald` with the `user` facility. So you can see the
 
 There are many other options in the `journalctl` utility to filter the messages, see manual pages for this utility.
 
-On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfilename syslog` or `--logfilename journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.
+On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfile syslog` or `--logfile journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.

docs/backtesting.md

@@ -11,30 +11,34 @@ Now you have good Buy and Sell strategies and some historic data, you want to te
 real data. This is what we call
 [backtesting](https://en.wikipedia.org/wiki/Backtesting).
 
-Backtesting will use the crypto-currencies (pairs) from your config file and load ticker data from `user_data/data/<exchange>` by default.
-If no data is available for the exchange / pair / ticker interval combination, backtesting will ask you to download them first using `freqtrade download-data`.
+Backtesting will use the crypto-currencies (pairs) from your config file and load historical candle (OHCLV) data from `user_data/data/<exchange>` by default.
+If no data is available for the exchange / pair / timeframe combination, backtesting will ask you to download them first using `freqtrade download-data`.
 For details on downloading, please refer to the [Data Downloading](data-download.md) section in the documentation.
 
 The result of backtesting will confirm if your bot has better odds of making a profit than a loss.
 
-!!! Tip "Using dynamic pairlists for backtesting"
-    While using dynamic pairlists during backtesting is not possible, a dynamic pairlist using current data can be generated via the [`test-pairlist`](utils.md#test-pairlist) command, and needs to be specified as `"pair_whitelist"` attribute in the configuration.
+!!! Warning "Using dynamic pairlists for backtesting"
+    Using dynamic pairlists is possible, however it relies on the current market conditions - which will not reflect the historic status of the pairlist.
+    Also, when using pairlists other than StaticPairlist, reproducability of backtesting-results cannot be guaranteed.
+    Please read the [pairlists documentation](configuration.md#pairlists) for more information.
+
+    To achieve reproducible results, best generate a pairlist via the [`test-pairlist`](utils.md#test-pairlist) command and use that as static pairlist.
 
 ### Run a backtesting against the currencies listed in your config file
 
-#### With 5 min tickers (Per default)
+#### With 5 min candle (OHLCV) data (per default)
 
 ```bash
 freqtrade backtesting
 ```
 
-#### With 1 min tickers
+#### With 1 min candle (OHLCV) data
 
 ```bash
-freqtrade backtesting --ticker-interval 1m
+freqtrade backtesting --timeframe 1m
 ```
 
-#### Using a different on-disk ticker-data source
+#### Using a different on-disk historical candle (OHLCV) data source
 
 Assume you downloaded the history data from the Bittrex exchange and kept it in the `user_data/data/bittrex-20180101` directory. 
 You can then use this data for backtesting as follows:
@@ -54,7 +58,7 @@ Where `-s SampleStrategy` refers to the class name within the strategy file `sam
 #### Comparing multiple Strategies
 
 ```bash
-freqtrade backtesting --strategy-list SampleStrategy1 AwesomeStrategy --ticker-interval 5m
+freqtrade backtesting --strategy-list SampleStrategy1 AwesomeStrategy --timeframe 5m
 ```
 
 Where `SampleStrategy1` and `AwesomeStrategy` refer to class names of strategies.
@@ -62,7 +66,7 @@ Where `SampleStrategy1` and `AwesomeStrategy` refer to class names of strategies
 #### Exporting trades to file
 
 ```bash
-freqtrade backtesting --export trades
+freqtrade backtesting --export trades --config config.json --strategy SampleStrategy
 ```
 
 The exported trades can be used for [further analysis](#further-backtest-result-analysis), or can be used by the plotting script `plot_dataframe.py` in the scripts directory.
@@ -78,13 +82,17 @@ Please also read about the [strategy startup period](strategy-customization.md#s
 #### Supplying custom fee value
 
 Sometimes your account has certain fee rebates (fee reductions starting with a certain account size or monthly volume), which are not visible to ccxt.
-To account for this in backtesting, you can use `--fee 0.001` to supply this value to backtesting.
-This fee must be a percentage, and will be applied twice (once for trade entry, and once for trade exit).
+To account for this in backtesting, you can use the `--fee` command line option to supply this value to backtesting.
+This fee must be a ratio, and will be applied twice (once for trade entry, and once for trade exit).
+
+For example, if the buying and selling commission fee is 0.1% (i.e., 0.001 written as ratio), then you would run backtesting as the following:
 
 ```bash
 freqtrade backtesting --fee 0.001
 ```
 
+!!! Note
+    Only supply this option (or the corresponding configuration parameter) if you want to experiment with different fee values. By default, Backtesting fetches the default fee from the exchange pair/market info.
 
 #### Running backtest with smaller testset by using timerange
 
@@ -115,50 +123,66 @@ A backtesting result will look like that:
 
 ```
 ========================================================= BACKTESTING REPORT ========================================================
-| pair     |   buy count |   avg profit % |   cum profit % |   tot profit BTC |   tot profit % | avg duration   |   profit |   loss |
-|:---------|------------:|---------------:|---------------:|-----------------:|---------------:|:---------------|---------:|-------:|
-| ADA/BTC  |          35 |          -0.11 |          -3.88 |      -0.00019428 |          -1.94 | 4:35:00        |       14 |     21 |
-| ARK/BTC  |          11 |          -0.41 |          -4.52 |      -0.00022647 |          -2.26 | 2:03:00        |        3 |      8 |
-| BTS/BTC  |          32 |           0.31 |           9.78 |       0.00048938 |           4.89 | 5:05:00        |       18 |     14 |
-| DASH/BTC |          13 |          -0.08 |          -1.07 |      -0.00005343 |          -0.53 | 4:39:00        |        6 |      7 |
-| ENG/BTC  |          18 |           1.36 |          24.54 |       0.00122807 |          12.27 | 2:50:00        |        8 |     10 |
-| EOS/BTC  |          36 |           0.08 |           3.06 |       0.00015304 |           1.53 | 3:34:00        |       16 |     20 |
-| ETC/BTC  |          26 |           0.37 |           9.51 |       0.00047576 |           4.75 | 6:14:00        |       11 |     15 |
-| ETH/BTC  |          33 |           0.30 |           9.96 |       0.00049856 |           4.98 | 7:31:00        |       16 |     17 |
-| IOTA/BTC |          32 |           0.03 |           1.09 |       0.00005444 |           0.54 | 3:12:00        |       14 |     18 |
-| LSK/BTC  |          15 |           1.75 |          26.26 |       0.00131413 |          13.13 | 2:58:00        |        6 |      9 |
-| LTC/BTC  |          32 |          -0.04 |          -1.38 |      -0.00006886 |          -0.69 | 4:49:00        |       11 |     21 |
-| NANO/BTC |          17 |           1.26 |          21.39 |       0.00107058 |          10.70 | 1:55:00        |       10 |      7 |
-| NEO/BTC  |          23 |           0.82 |          18.97 |       0.00094936 |           9.48 | 2:59:00        |       10 |     13 |
-| REQ/BTC  |           9 |           1.17 |          10.54 |       0.00052734 |           5.27 | 3:47:00        |        4 |      5 |
-| XLM/BTC  |          16 |           1.22 |          19.54 |       0.00097800 |           9.77 | 3:15:00        |        7 |      9 |
-| XMR/BTC  |          23 |          -0.18 |          -4.13 |      -0.00020696 |          -2.07 | 5:30:00        |       12 |     11 |
-| XRP/BTC  |          35 |           0.66 |          22.96 |       0.00114897 |          11.48 | 3:49:00        |       12 |     23 |
-| ZEC/BTC  |          22 |          -0.46 |         -10.18 |      -0.00050971 |          -5.09 | 2:22:00        |        7 |     15 |
-| TOTAL    |         429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00        |      186 |    243 |
+| Pair     |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration   |  Wins |  Draws |  Losses |
+|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|--------:|
+| ADA/BTC  |     35 |          -0.11 |          -3.88 |      -0.00019428 |          -1.94 | 4:35:00        |    14 |      0 |      21 |
+| ARK/BTC  |     11 |          -0.41 |          -4.52 |      -0.00022647 |          -2.26 | 2:03:00        |     3 |      0 |       8 |
+| BTS/BTC  |     32 |           0.31 |           9.78 |       0.00048938 |           4.89 | 5:05:00        |    18 |      0 |      14 |
+| DASH/BTC |     13 |          -0.08 |          -1.07 |      -0.00005343 |          -0.53 | 4:39:00        |     6 |      0 |       7 |
+| ENG/BTC  |     18 |           1.36 |          24.54 |       0.00122807 |          12.27 | 2:50:00        |     8 |      0 |      10 |
+| EOS/BTC  |     36 |           0.08 |           3.06 |       0.00015304 |           1.53 | 3:34:00        |    16 |      0 |      20 |
+| ETC/BTC  |     26 |           0.37 |           9.51 |       0.00047576 |           4.75 | 6:14:00        |    11 |      0 |      15 |
+| ETH/BTC  |     33 |           0.30 |           9.96 |       0.00049856 |           4.98 | 7:31:00        |    16 |      0 |      17 |
+| IOTA/BTC |     32 |           0.03 |           1.09 |       0.00005444 |           0.54 | 3:12:00        |    14 |      0 |      18 |
+| LSK/BTC  |     15 |           1.75 |          26.26 |       0.00131413 |          13.13 | 2:58:00        |     6 |      0 |       9 |
+| LTC/BTC  |     32 |          -0.04 |          -1.38 |      -0.00006886 |          -0.69 | 4:49:00        |    11 |      0 |      21 |
+| NANO/BTC |     17 |           1.26 |          21.39 |       0.00107058 |          10.70 | 1:55:00        |    10 |      0 |       7 |
+| NEO/BTC  |     23 |           0.82 |          18.97 |       0.00094936 |           9.48 | 2:59:00        |    10 |      0 |      13 |
+| REQ/BTC  |      9 |           1.17 |          10.54 |       0.00052734 |           5.27 | 3:47:00        |     4 |      0 |       5 |
+| XLM/BTC  |     16 |           1.22 |          19.54 |       0.00097800 |           9.77 | 3:15:00        |     7 |      0 |       9 |
+| XMR/BTC  |     23 |          -0.18 |          -4.13 |      -0.00020696 |          -2.07 | 5:30:00        |    12 |      0 |      11 |
+| XRP/BTC  |     35 |           0.66 |          22.96 |       0.00114897 |          11.48 | 3:49:00        |    12 |      0 |      23 |
+| ZEC/BTC  |     22 |          -0.46 |         -10.18 |      -0.00050971 |          -5.09 | 2:22:00        |     7 |      0 |      15 |
+| TOTAL    |    429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00        |   186 |      0 |     243 |
 ========================================================= SELL REASON STATS =========================================================
-| Sell Reason        |   Count |
-|:-------------------|--------:|
-| trailing_stop_loss |     205 |
-| stop_loss          |     166 |
-| sell_signal        |      56 |
-| force_sell         |       2 |
+| Sell Reason        |   Sells |  Wins |  Draws |  Losses |
+|:-------------------|--------:|------:|-------:|--------:|
+| trailing_stop_loss |     205 |   150 |      0 |      55 |
+| stop_loss          |     166 |     0 |      0 |     166 |
+| sell_signal        |      56 |    36 |      0 |      20 |
+| force_sell         |       2 |     0 |      0 |       2 |
 ====================================================== LEFT OPEN TRADES REPORT ======================================================
-| pair     |   buy count |   avg profit % |   cum profit % |   tot profit BTC |   tot profit % | avg duration   |   profit |   loss |
-|:---------|------------:|---------------:|---------------:|-----------------:|---------------:|:---------------|---------:|-------:|
-| ADA/BTC  |           1 |           0.89 |           0.89 |       0.00004434 |           0.44 | 6:00:00        |        1 |      0 |
-| LTC/BTC  |           1 |           0.68 |           0.68 |       0.00003421 |           0.34 | 2:00:00        |        1 |      0 |
-| TOTAL    |           2 |           0.78 |           1.57 |       0.00007855 |           0.78 | 4:00:00        |        2 |      0 |
+| Pair     |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration   |  Wins |  Draws |  Losses |
+|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|--------:|
+| ADA/BTC  |      1 |           0.89 |           0.89 |       0.00004434 |           0.44 | 6:00:00        |     1 |      0 |       0 |
+| LTC/BTC  |      1 |           0.68 |           0.68 |       0.00003421 |           0.34 | 2:00:00        |     1 |      0 |       0 |
+| TOTAL    |      2 |           0.78 |           1.57 |       0.00007855 |           0.78 | 4:00:00        |     2 |      0 |       0 |
+=============== SUMMARY METRICS ===============
+| Metric                | Value               |
+|-----------------------+---------------------|
+| Backtesting from      | 2019-01-01 00:00:00 |
+| Backtesting to        | 2019-05-01 00:00:00 |
+| Total trades          | 429                 |
+| First trade           | 2019-01-01 18:30:00 |
+| First trade Pair      | EOS/USDT            |
+| Total Profit %        | 152.41%             |
+| Trades per day        | 3.575               |
+| Best day              | 25.27%              |
+| Worst day             | -30.67%             |
+| Avg. Duration Winners | 4:23:00             |
+| Avg. Duration Loser   | 6:55:00             |
+|                       |                     |
+| Max Drawdown          | 50.63%              |
+| Drawdown Start        | 2019-02-15 14:10:00 |
+| Drawdown End          | 2019-04-11 18:15:00 |
+| Market change         | -5.88%              |
+===============================================
 ```
 
+### Backtesting report table
+
 The 1st table contains all trades the bot made, including "left open trades".
 
-The 2nd table contains a recap of sell reasons.
-
-The 3rd table contains all trades the bot had to `forcesell` at the end of the backtest period to present a full picture.
-This is necessary to simulate realistic behaviour, 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 extracted separately for clarity.
-
 The last line will give you the overall performance of your strategy,
 here:
 
@@ -187,19 +211,75 @@ 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.
 
+### Sell reasons table
+
+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 `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.
+
+### Summary metrics
+
+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 |
+| Total trades          | 429                 |
+| First trade           | 2019-01-01 18:30:00 |
+| First trade Pair      | EOS/USDT            |
+| Total Profit %        | 152.41%             |
+| Trades per day        | 3.575               |
+| Best day              | 25.27%              |
+| Worst day             | -30.67%             |
+| Avg. Duration Winners | 4:23:00             |
+| Avg. Duration Loser   | 6:55:00             |
+|                       |                     |
+| Max Drawdown          | 50.63%              |
+| Drawdown Start        | 2019-02-15 14:10:00 |
+| Drawdown End          | 2019-04-11 18:15:00 |
+| Market change         | -5.88%              |
+===============================================
+
+```
+
+- `Total trades`: Identical to the total trades of the backtest output table.
+- `First trade`: First trade entered.
+- `First trade pair`: Which pair was part of the first trade.
+- `Backtesting from` / `Backtesting to`: Backtesting range (usually defined with the `--timerange` option).
+- `Total Profit %`: Total profit per stake amount. Aligned to the TOTAL column of the first table.
+- `Trades per day`: Total trades divided by the backtesting duration in days (this will give you information about how many trades to expect from the strategy).
+- `Best day` / `Worst day`: Best and worst day based on daily profit.
+- `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.
+- `Max Drawdown`: Maximum drawdown experienced. For example, the value of 50% means that from highest to subsequent lowest point, a 50% drop was experienced).
+- `Drawdown Start` / `Drawdown End`: Start and end datetimes 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.
+
 ### Assumptions made by backtesting
 
 Since backtesting lacks some detailed information about what happens within a candle, it needs to take a few assumptions:
 
 - Buys happen at open-price
 - Sell signal sells happen at open-price of the following candle
-- Low happens before high for stoploss, protecting capital first.
-- ROI sells are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the sell will be at 2%)
+- Low happens before high for stoploss, protecting capital first
+- ROI
+  - 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
 - Trailing stoploss
   - High happens first - adjusting stoploss
   - Low uses the adjusted stoploss (so sells with large high-low difference are backtested correctly)
 - 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)
+- Stoploss (and trailing stoploss) is evaluated before ROI within one candle. So you can often see more trades with the `stoploss` and/or `trailing_stop` sell reason comparing to the results obtained with the same strategy in the Dry Run/Live Trade modes.
 
 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.
@@ -215,13 +295,13 @@ You can then load the trades to perform further analysis as shown in our [data a
 
 To compare multiple strategies, a list of Strategies can be provided to backtesting.
 
-This is limited to 1 ticker-interval per run, however, data is only loaded once from disk so if you have multiple
+This is limited to 1 timeframe value per run. However, data is only loaded once from disk so if you have multiple
 strategies you'd like to compare, this will give a nice runtime boost.
 
 All listed Strategies need to be in the same directory.
 
 ``` bash
-freqtrade backtesting --timerange 20180401-20180410 --ticker-interval 5m --strategy-list Strategy001 Strategy002 --export trades
+freqtrade backtesting --timerange 20180401-20180410 --timeframe 5m --strategy-list Strategy001 Strategy002 --export trades
 ```
 
 This will save the results to `user_data/backtest_results/backtest-result-<strategy>.json`, injecting the strategy-name into the target filename.
@@ -229,11 +309,11 @@ There will be an additional table comparing win/losses of the different strategi
 Detailed output for all strategies one after the other will be available, so make sure to scroll up to see the details per strategy.
 
 ```
-=========================================================== Strategy Summary ===========================================================
-| Strategy    |   buy count |   avg profit % |   cum profit % |   tot profit BTC |   tot profit % | avg duration   |   profit |   loss |
-|:------------|------------:|---------------:|---------------:|-----------------:|---------------:|:---------------|---------:|-------:|
-| Strategy1   |         429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00        |      186 |    243 |
-| Strategy2   |        1487 |          -0.13 |        -197.58 |      -0.00988917 |         -98.79 | 4:43:00        |      662 |    825 |
+=========================================================== STRATEGY SUMMARY ===========================================================
+| Strategy    |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration   |  Wins |  Draws | Losses |
+|:------------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|-------:|
+| Strategy1   |    429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00        |   186 |      0 |    243 |
+| Strategy2   |   1487 |          -0.13 |        -197.58 |      -0.00988917 |         -98.79 | 4:43:00        |   662 |      0 |    825 |
 ```
 
 ## Next step

docs/bot-basics.md

@@ -0,0 +1,58 @@
+# Freqtrade basics
+
+This page provides you some basic concepts on how Freqtrade works and operates.
+
+## Freqtrade terminology
+
+* Trade: Open position.
+* Open Order: Order which is currently placed on the exchange, and is not yet complete.
+* Pair: Tradable pair, usually in the format of Quote/Base (e.g. XRP/USDT).
+* Timeframe: Candle length to use (e.g. `"5m"`, `"1h"`, ...).
+* Indicators: Technical indicators (SMA, EMA, RSI, ...).
+* Limit order: Limit orders which execute at the defined limit price or better.
+* Market order: Guaranteed to fill, may move price depending on the order size.
+
+## Fee handling
+
+All profit calculations of Freqtrade include fees. For Backtesting / Hyperopt / Dry-run modes, the exchange default fee is used (lowest tier on the exchange). For live operations, fees are used as applied by the exchange (this includes BNB rebates etc.).
+
+## Bot execution logic
+
+Starting freqtrade in dry-run or live mode (using `freqtrade trade`) will start the bot and start the bot iteration loop.
+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)  
+  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_buy_trend()`
+  * Call `populate_sell_trend()`
+* Check timeouts for open orders.
+  * 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.
+  * Determine sell-price based on `ask_strategy` configuration setting.
+  * Before a sell order is placed, `confirm_trade_exit()` strategy callback is called.
+* Check if trade-slots are still available (if `max_open_trades` is reached).
+* Verifies buy signal trying to enter new positions.
+  * Determine buy-price based on `bid_strategy` configuration setting.
+  * 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.
+
+## Backtesting / Hyperopt execution logic
+
+[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.
+* Calculate indicators (calls `populate_indicators()`).
+* Calls `populate_buy_trend()` and `populate_sell_trend()`
+* Loops per candle simulating entry and exit points.
+* Generate backtest report output
+
+!!! Note
+    Both Backtesting and Hyperopt include exchange default Fees in the calculation. Custom fees can be passed to backtesting / hyperopt by specifying the `--fee` argument.

docs/bot-usage.md

@@ -9,22 +9,35 @@ This page explains the different parameters of the bot and how to run it.
 
 ```
 usage: freqtrade [-h] [-V]
-                 {trade,backtesting,edge,hyperopt,create-userdir,list-exchanges,list-timeframes,download-data,plot-dataframe,plot-profit}
+                 {trade,create-userdir,new-config,new-hyperopt,new-strategy,download-data,convert-data,convert-trade-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,plot-dataframe,plot-profit}
                  ...
 
 Free, open source crypto trading bot
 
 positional arguments:
-  {trade,backtesting,edge,hyperopt,create-userdir,list-exchanges,list-timeframes,download-data,plot-dataframe,plot-profit}
+  {trade,create-userdir,new-config,new-hyperopt,new-strategy,download-data,convert-data,convert-trade-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,plot-dataframe,plot-profit}
     trade               Trade module.
+    create-userdir      Create user-data directory.
+    new-config          Create new config
+    new-hyperopt        Create new hyperopt
+    new-strategy        Create new strategy
+    download-data       Download backtesting data.
+    convert-data        Convert candle (OHLCV) data from one format to
+                        another.
+    convert-trade-data  Convert trade data from one format to another.
     backtesting         Backtesting module.
     edge                Edge module.
     hyperopt            Hyperopt module.
-    create-userdir      Create user-data directory.
+    hyperopt-list       List Hyperopt results
+    hyperopt-show       Show details of Hyperopt results
     list-exchanges      Print available exchanges.
-    list-timeframes     Print available ticker intervals (timeframes) for the
-                        exchange.
-    download-data       Download backtesting data.
+    list-hyperopts      Print available hyperopt classes.
+    list-markets        Print markets on exchange.
+    list-pairs          Print pairs on exchange.
+    list-strategies     Print available strategies.
+    list-timeframes     Print available timeframes for the exchange.
+    show-trades         Show trades.
+    test-pairlist       Test your pairlist configuration.
     plot-dataframe      Plot candles with indicators.
     plot-profit         Generate plot showing profits.
 
@@ -45,19 +58,23 @@ 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://` for Dry Run).
+                        Live Run mode, `sqlite:///tradesv3.dryrun.sqlite` for
+                        Dry Run).
   --sd-notify           Notify systemd service manager.
   --dry-run             Enforce dry-run for trading (removes Exchange secrets
                         and simulates trades).
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
-  --logfile FILE        Log to the file specified.
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
   -V, --version         show program's version number and exit
   -c PATH, --config PATH
-                        Specify configuration file (default: `config.json`).
-                        Multiple --config options may be used. Can be set to
-                        `-` to read config from stdin.
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
   -d PATH, --datadir PATH
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
@@ -68,6 +85,7 @@ Strategy arguments:
                         Specify strategy class name which will be used by the
                         bot.
   --strategy-path PATH  Specify additional strategy lookup path.
+
 ```
 
 ### How to specify which configuration file be used?
@@ -138,10 +156,10 @@ It is recommended to use version control to keep track of changes to your strate
 ### How to use **--strategy**?
 
 This parameter will allow you to load your custom strategy class.
-Per default without `--strategy` or `-s` the bot will load the
-`DefaultStrategy` included with the bot (`freqtrade/strategy/default_strategy.py`).
+To test the bot installation, you can use the `SampleStrategy` installed by the `create-userdir` subcommand (usually `user_data/strategy/sample_strategy.py`).
 
-The bot will search your strategy file within `user_data/strategies` and `freqtrade/strategy`.
+The bot will search your strategy file within `user_data/strategies`.
+To use other directories, please read the next section about `--strategy-path`.
 
 To load a strategy, simply pass the class name (e.g.: `CustomStrategy`) in this parameter.
 
@@ -191,24 +209,26 @@ Backtesting also uses the config specified via `-c/--config`.
 ```
 usage: freqtrade backtesting [-h] [-v] [--logfile FILE] [-V] [-c PATH]
                              [-d PATH] [--userdir PATH] [-s NAME]
-                             [--strategy-path PATH] [-i TICKER_INTERVAL]
-                             [--timerange TIMERANGE] [--max_open_trades INT]
-                             [--stake_amount STAKE_AMOUNT] [--fee FLOAT]
+                             [--strategy-path PATH] [-i TIMEFRAME]
+                             [--timerange TIMERANGE] [--max-open-trades INT]
+                             [--stake-amount STAKE_AMOUNT] [--fee FLOAT]
                              [--eps] [--dmmp]
                              [--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
                              [--export EXPORT] [--export-filename PATH]
 
 optional arguments:
   -h, --help            show this help message and exit
-  -i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
+  -i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
                         Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
                         `1d`).
   --timerange TIMERANGE
                         Specify what timerange of data to use.
-  --max_open_trades INT
-                        Specify max_open_trades to use.
-  --stake_amount STAKE_AMOUNT
-                        Specify stake_amount.
+  --max-open-trades INT
+                        Override the value of the `max_open_trades`
+                        configuration setting.
+  --stake-amount STAKE_AMOUNT
+                        Override the value of the `stake_amount` configuration
+                        setting.
   --fee FLOAT           Specify fee ratio. Will be applied twice (on trade
                         entry and exit).
   --eps, --enable-position-stacking
@@ -236,12 +256,15 @@ optional arguments:
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
-  --logfile FILE        Log to the file specified.
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
   -V, --version         show program's version number and exit
   -c PATH, --config PATH
-                        Specify configuration file (default: `config.json`).
-                        Multiple --config options may be used. Can be set to
-                        `-` to read config from stdin.
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
   -d PATH, --datadir PATH
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
@@ -264,32 +287,34 @@ Check the corresponding [Data Downloading](data-download.md) section for more de
 ## Hyperopt commands
 
 To optimize your strategy, you can use hyperopt parameter hyperoptimization
-to find optimal parameter values for your stategy.
+to find optimal parameter values for your strategy.
 
 ```
 usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
                           [--userdir PATH] [-s NAME] [--strategy-path PATH]
-                          [-i TICKER_INTERVAL] [--timerange TIMERANGE]
-                          [--max_open_trades INT]
-                          [--stake_amount STAKE_AMOUNT] [--fee FLOAT]
+                          [-i TIMEFRAME] [--timerange TIMERANGE]
+                          [--max-open-trades INT]
+                          [--stake-amount STAKE_AMOUNT] [--fee FLOAT]
                           [--hyperopt NAME] [--hyperopt-path PATH] [--eps]
                           [-e INT]
-                          [--spaces {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]]
+                          [--spaces {all,buy,sell,roi,stoploss,trailing,default} [{all,buy,sell,roi,stoploss,trailing,default} ...]]
                           [--dmmp] [--print-all] [--no-color] [--print-json]
                           [-j JOBS] [--random-state INT] [--min-trades INT]
                           [--continue] [--hyperopt-loss NAME]
 
 optional arguments:
   -h, --help            show this help message and exit
-  -i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
+  -i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
                         Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
                         `1d`).
   --timerange TIMERANGE
                         Specify what timerange of data to use.
-  --max_open_trades INT
-                        Specify max_open_trades to use.
-  --stake_amount STAKE_AMOUNT
-                        Specify stake_amount.
+  --max-open-trades INT
+                        Override the value of the `max_open_trades`
+                        configuration setting.
+  --stake-amount STAKE_AMOUNT
+                        Override the value of the `stake_amount` configuration
+                        setting.
   --fee FLOAT           Specify fee ratio. Will be applied twice (on trade
                         entry and exit).
   --hyperopt NAME       Specify hyperopt class name which will be used by the
@@ -300,9 +325,9 @@ optional arguments:
                         Allow buying the same pair multiple times (position
                         stacking).
   -e INT, --epochs INT  Specify number of epochs (default: 100).
-  --spaces {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]
+  --spaces {all,buy,sell,roi,stoploss,trailing,default} [{all,buy,sell,roi,stoploss,trailing,default} ...]
                         Specify which parameters to hyperopt. Space-separated
-                        list. Default: `all`.
+                        list.
   --dmmp, --disable-max-market-positions
                         Disable applying `max_open_trades` during backtest
                         (same as setting `max_open_trades` to a very high
@@ -310,7 +335,7 @@ optional arguments:
   --print-all           Print all results, not only the best ones.
   --no-color            Disable colorization of hyperopt results. May be
                         useful if you are redirecting output to a file.
-  --print-json          Print best result detailization in JSON format.
+  --print-json          Print output in JSON format.
   -j JOBS, --job-workers JOBS
                         The number of concurrently running jobs for
                         hyperoptimization (hyperopt worker processes). If -1
@@ -329,17 +354,22 @@ optional arguments:
                         generate completely different results, since the
                         target for optimization is different. Built-in
                         Hyperopt-loss-functions are: DefaultHyperOptLoss,
-                        OnlyProfitHyperOptLoss, SharpeHyperOptLoss (default:
+                        OnlyProfitHyperOptLoss, SharpeHyperOptLoss,
+                        SharpeHyperOptLossDaily, SortinoHyperOptLoss,
+                        SortinoHyperOptLossDaily.(default:
                         `DefaultHyperOptLoss`).
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
-  --logfile FILE        Log to the file specified.
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
   -V, --version         show program's version number and exit
   -c PATH, --config PATH
-                        Specify configuration file (default: `config.json`).
-                        Multiple --config options may be used. Can be set to
-                        `-` to read config from stdin.
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
   -d PATH, --datadir PATH
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
@@ -350,6 +380,7 @@ Strategy arguments:
                         Specify strategy class name which will be used by the
                         bot.
   --strategy-path PATH  Specify additional strategy lookup path.
+
 ```
 
 ## Edge commands
@@ -359,21 +390,23 @@ To know your trade expectancy and winrate against historical data, you can use E
 ```
 usage: freqtrade edge [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
                       [--userdir PATH] [-s NAME] [--strategy-path PATH]
-                      [-i TICKER_INTERVAL] [--timerange TIMERANGE]
-                      [--max_open_trades INT] [--stake_amount STAKE_AMOUNT]
+                      [-i TIMEFRAME] [--timerange TIMERANGE]
+                      [--max-open-trades INT] [--stake-amount STAKE_AMOUNT]
                       [--fee FLOAT] [--stoplosses STOPLOSS_RANGE]
 
 optional arguments:
   -h, --help            show this help message and exit
-  -i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
+  -i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
                         Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
                         `1d`).
   --timerange TIMERANGE
                         Specify what timerange of data to use.
-  --max_open_trades INT
-                        Specify max_open_trades to use.
-  --stake_amount STAKE_AMOUNT
-                        Specify stake_amount.
+  --max-open-trades INT
+                        Override the value of the `max_open_trades`
+                        configuration setting.
+  --stake-amount STAKE_AMOUNT
+                        Override the value of the `stake_amount` configuration
+                        setting.
   --fee FLOAT           Specify fee ratio. Will be applied twice (on trade
                         entry and exit).
   --stoplosses STOPLOSS_RANGE
@@ -384,12 +417,15 @@ optional arguments:
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
-  --logfile FILE        Log to the file specified.
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
   -V, --version         show program's version number and exit
   -c PATH, --config PATH
-                        Specify configuration file (default: `config.json`).
-                        Multiple --config options may be used. Can be set to
-                        `-` to read config from stdin.
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
   -d PATH, --datadir PATH
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
@@ -400,6 +436,7 @@ Strategy arguments:
                         Specify strategy class name which will be used by the
                         bot.
   --strategy-path PATH  Specify additional strategy lookup path.
+
 ```
 
 To understand edge and how to read the results, please read the [edge documentation](edge.md).

docs/configuration.md

@@ -34,78 +34,91 @@ The prevelance for all Options is as follows:
 
 - CLI arguments override any other option
 - Configuration files are used in sequence (last file wins), and override Strategy configurations.
-- Strategy configurations are only used if they are not set via configuration or via command line arguments. These options are market with [Strategy Override](#parameters-in-the-strategy) in the below table.
+- Strategy configurations are only used if they are not set via configuration or via command line arguments. These options are marked with [Strategy Override](#parameters-in-the-strategy) in the below table.
 
 Mandatory parameters are marked as **Required**, which means that they are required to be set in one of the possible ways.
 
-|  Command | Description |
-|----------|-------------|
-| `max_open_trades` | **Required.** Number of trades open your bot will have. If -1 then it is ignored (i.e. potentially unlimited open trades).<br> ***Datatype:*** *Positive integer or -1.*
-| `stake_currency` | **Required.** Crypto-currency used for trading. [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *String*
-| `stake_amount` | **Required.** Amount of crypto-currency your bot will use for each trade. Set it to `"unlimited"` to allow the bot to use all available balance. [More information below](#understand-stake_amount). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *Positive float or `"unlimited"`.*
-| `amount_reserve_percent` | Reserve some amount in min pair stake amount. The bot will reserve `amount_reserve_percent` + stoploss value when calculating min pair stake amount in order to avoid possible trade refusals. <br>*Defaults to `0.05` (5%).* <br> ***Datatype:*** *Positive Float as ratio.*
-| `ticker_interval` | The ticker interval to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *String*
-| `fiat_display_currency` | Fiat currency used to show your profits. [More information below](#what-values-can-be-used-for-fiat_display_currency). <br> ***Datatype:*** *String*
-| `dry_run` | **Required.** Define if the bot must be in Dry Run or production mode. <br>*Defaults to `true`.* <br> ***Datatype:*** *Boolean*
-| `dry_run_wallet` | Overrides the default amount of 999.9 stake currency units in the wallet used by the bot running in the Dry Run mode if you need it for any reason. <br> ***Datatype:*** *Float*
-| `process_only_new_candles` | Enable processing of indicators only when new candles arrive. If false each loop populates the indicators, this will mean the same candle is processed many times creating system load but can be useful of your strategy depends on tick data not only candle. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*  <br> ***Datatype:*** *Boolean*
-| `minimal_roi` | **Required.** Set the threshold in percent the bot will use to sell a trade. [More information below](#understand-minimal_roi). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *Dict*
-| `stoploss` |  **Required.** Value of the stoploss in percent used by the bot. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy).  <br> ***Datatype:*** *Float (as ratio)*
-| `trailing_stop` | Enables trailing stoploss (based on `stoploss` in either configuration or strategy file). More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *Boolean*
-| `trailing_stop_positive` | Changes stoploss once profit has been reached. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *Float*
-| `trailing_stop_positive_offset` | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0` (no offset).* <br> ***Datatype:*** *Float*
-| `trailing_only_offset_is_reached` | Only apply trailing stoploss when the offset is reached. [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*  <br> ***Datatype:*** *Boolean*
-| `unfilledtimeout.buy` | **Required.** How long (in minutes) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled.  <br> ***Datatype:*** *Integer*
-| `unfilledtimeout.sell` | **Required.** How long (in minutes) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled. <br> ***Datatype:*** *Integer*
-| `bid_strategy.ask_last_balance` | **Required.** Set the bidding price. More information [below](#understand-ask_last_balance). 
-| `bid_strategy.use_order_book` | Enable buying using the rates in Order Book Bids. <br> ***Datatype:*** *Boolean*
-| `bid_strategy.order_book_top` | Bot will use the top N rate in Order Book Bids. I.e. a value of 2 will allow the bot to pick the 2nd bid rate in Order Book Bids.  *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. <br>*Defaults to `false`.* <br> ***Datatype:*** *Boolean*
-| `bid_strategy. check_depth_of_market.bids_to_ask_delta` | The % difference of buy orders and sell orders found in Order Book. A value lesser than 1 means sell orders is greater, while value greater than 1 means buy orders is higher.  *Defaults to `0`.*  <br> ***Datatype:*** *Float (as ratio)*
-| `ask_strategy.use_order_book` | Enable selling of open trades using Order Book Asks. <br> ***Datatype:*** *Boolean*
-| `ask_strategy.order_book_min` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> ***Datatype:*** *Positive Integer*
-| `ask_strategy.order_book_max` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> ***Datatype:*** *Positive Integer*
-| `ask_strategy.use_sell_signal` | Use sell signals produced by the strategy in addition to the `minimal_roi`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `true`.* <br> ***Datatype:*** *Boolean*
-| `ask_strategy.sell_profit_only` | Wait until the bot makes a positive profit before taking a sell decision. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> ***Datatype:*** *Boolean*
-| `ask_strategy.ignore_roi_if_buy_signal` | Do not sell if the buy signal is still active. This setting takes preference over `minimal_roi` and `use_sell_signal`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> ***Datatype:*** *Boolean*
-| `order_types` | Configure order-types depending on the action (`"buy"`, `"sell"`, `"stoploss"`, `"stoploss_on_exchange"`). [More information below](#understand-order_types). [Strategy Override](#parameters-in-the-strategy).<br> ***Datatype:*** *Dict*
-| `order_time_in_force` | Configure time in force for buy and sell orders. [More information below](#understand-order_time_in_force). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *Dict*
-| `exchange.name` | **Required.** Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename). <br> ***Datatype:*** *String*
-| `exchange.sandbox` | Use the 'sandbox' version of the exchange, where the exchange provides a sandbox for risk-free integration. See [here](sandbox-testing.md) in more details.<br> ***Datatype:*** *Boolean*
-| `exchange.key` | API key to use for the exchange. Only required when you are in production mode. **Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
-| `exchange.secret` | API secret to use for the exchange. Only required when you are in production mode. **Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
-| `exchange.password` | API password to use for the exchange. Only required when you are in production mode and for exchanges that use password for API requests. **Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
-| `exchange.pair_whitelist` | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Not used by VolumePairList (see [below](#dynamic-pairlists)). <br> ***Datatype:*** *List*
-| `exchange.pair_blacklist` | List of pairs the bot must absolutely avoid for trading and backtesting (see [below](#dynamic-pairlists)). <br> ***Datatype:*** *List*
-| `exchange.ccxt_config` | Additional CCXT parameters passed to the regular ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> ***Datatype:*** *Dict*
-| `exchange.ccxt_async_config` | Additional CCXT parameters passed to the async ccxt instance. Parameters may differ from exchange to exchange  and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> ***Datatype:*** *Dict*
-| `exchange.markets_refresh_interval` | The interval in minutes in which markets are reloaded. <br>*Defaults to `60` minutes.* <br> ***Datatype:*** *Positive Integer*
+|  Parameter | Description |
+|------------|-------------|
+| `max_open_trades` | **Required.** Number of open trades your bot is allowed to have. Only one open trade per pair is possible, so the length of your pairlist is another limitation which can apply. If -1 then it is ignored (i.e. potentially unlimited open trades, limited by the pairlist). [More information below](#configuring-amount-per-trade).<br> **Datatype:** Positive integer or -1.
+| `stake_currency` | **Required.** Crypto-currency used for trading. [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
+| `stake_amount` | **Required.** Amount of crypto-currency your bot will use for each trade. Set it to `"unlimited"` to allow the bot to use all available balance. [More information below](#configuring-amount-per-trade). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Positive float or `"unlimited"`.
+| `tradable_balance_ratio` | Ratio of the total account balance the bot is allowed to trade. [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.99` 99%).*<br> **Datatype:** Positive float between `0.1` and `1.0`.
+| `amend_last_stake_amount` | Use reduced last stake amount if necessary. [More information below](#configuring-amount-per-trade). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
+| `last_stake_amount_min_ratio` | Defines minimum stake amount that has to be left and executed. Applies only to the last stake amount when it's amended to a reduced value (i.e. if `amend_last_stake_amount` is set to `true`). [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.5`.* <br> **Datatype:** Float (as ratio)
+| `amount_reserve_percent` | Reserve some amount in min pair stake amount. The bot will reserve `amount_reserve_percent` + stoploss value when calculating min pair stake amount in order to avoid possible trade refusals. <br>*Defaults to `0.05` (5%).* <br> **Datatype:** Positive Float as ratio.
+| `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 the 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 `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
+| `unfilledtimeout.buy` | **Required.** How long (in minutes) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
+| `unfilledtimeout.sell` | **Required.** How long (in minutes) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
+| `bid_strategy.price_side` | Select the side of the spread the bot should look at to get the buy rate. [More information below](#buy-price-side).<br> *Defaults to `bid`.* <br> **Datatype:** String (either `ask` or `bid`).
+| `bid_strategy.ask_last_balance` | **Required.** Set the bidding price. More information [below](#buy-price-without-orderbook-enabled).
+| `bid_strategy.use_order_book` | Enable buying using the rates in [Order Book Bids](#buy-price-with-orderbook-enabled). <br> **Datatype:** Boolean
+| `bid_strategy.order_book_top` | Bot will use the top N rate in Order Book Bids to buy. I.e. a value of 2 will allow the bot to pick the 2nd bid rate in [Order Book Bids](#buy-price-with-orderbook-enabled). <br>*Defaults to `1`.*  <br> **Datatype:** Positive Integer
+| `bid_strategy. check_depth_of_market.enabled` | Do not buy if the difference of buy orders and sell orders is met in Order Book. [Check market depth](#check-depth-of-market). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
+| `bid_strategy. check_depth_of_market.bids_to_ask_delta` | The difference ratio of buy orders and sell orders found in Order Book. A value below 1 means sell order size is greater, while value greater than 1 means buy order size is higher. [Check market depth](#check-depth-of-market) <br> *Defaults to `0`.*  <br> **Datatype:** Float (as ratio)
+| `ask_strategy.price_side` | Select the side of the spread the bot should look at to get the sell rate. [More information below](#sell-price-side).<br> *Defaults to `ask`.* <br> **Datatype:** String (either `ask` or `bid`).
+| `ask_strategy.use_order_book` | Enable selling of open trades using [Order Book Asks](#sell-price-with-orderbook-enabled). <br> **Datatype:** Boolean
+| `ask_strategy.order_book_min` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
+| `ask_strategy.order_book_max` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
+| `ask_strategy.use_sell_signal` | Use sell signals produced by the strategy in addition to the `minimal_roi`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `true`.* <br> **Datatype:** Boolean
+| `ask_strategy.sell_profit_only` | Wait until the bot makes a positive profit before taking a sell decision. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
+| `ask_strategy.ignore_roi_if_buy_signal` | Do not sell if the buy signal is still active. This setting takes preference over `minimal_roi` and `use_sell_signal`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
+| `order_types` | Configure order-types depending on the action (`"buy"`, `"sell"`, `"stoploss"`, `"stoploss_on_exchange"`). [More information below](#understand-order_types). [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Dict
+| `order_time_in_force` | Configure time in force for buy and sell orders. [More information below](#understand-order_time_in_force). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
+| `exchange.name` | **Required.** Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename). <br> **Datatype:** String
+| `exchange.sandbox` | Use the 'sandbox' version of the exchange, where the exchange provides a sandbox for risk-free integration. See [here](sandbox-testing.md) in more details.<br> **Datatype:** Boolean
+| `exchange.key` | API key to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
+| `exchange.secret` | API secret to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
+| `exchange.password` | API password to use for the exchange. Only required when you are in production mode and for exchanges that use password for API requests.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
+| `exchange.pair_whitelist` | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Not used by VolumePairList (see [below](#pairlists-and-pairlist-handlers)). <br> **Datatype:** List
+| `exchange.pair_blacklist` | List of pairs the bot must absolutely avoid for trading and backtesting (see [below](#pairlists-and-pairlist-handlers)). <br> **Datatype:** List
+| `exchange.ccxt_config` | Additional CCXT parameters passed to both ccxt instances (sync and async). This is usually the correct place for ccxt configurations. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
+| `exchange.ccxt_sync_config` | Additional CCXT parameters passed to the regular (sync) ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
+| `exchange.ccxt_async_config` | Additional CCXT parameters passed to the async ccxt instance. Parameters may differ from exchange to exchange  and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
+| `exchange.markets_refresh_interval` | The interval in minutes in which markets are reloaded. <br>*Defaults to `60` minutes.* <br> **Datatype:** Positive Integer
 | `edge.*` | Please refer to [edge configuration document](edge.md) for detailed explanation.
-| `experimental.block_bad_exchanges` | Block exchanges known to not work with freqtrade. Leave on default unless you want to test if that exchange works now. <br>*Defaults to `true`.* <br> ***Datatype:*** *Boolean*
-| `pairlists` | Define one or more pairlists to be used. [More information below](#dynamic-pairlists). <br>*Defaults to `StaticPairList`.*  <br> ***Datatype:*** *List of Dicts*
-| `telegram.enabled` | Enable the usage of Telegram. <br> ***Datatype:*** *Boolean*
-| `telegram.token` | Your Telegram bot token. Only required if `telegram.enabled` is `true`. **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`. **Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
-| `webhook.enabled` | Enable usage of Webhook notifications <br> ***Datatype:*** *Boolean*
-| `webhook.url` | URL for the webhook. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> ***Datatype:*** *String*
-| `webhook.webhookbuy` | Payload to send on buy. Only required if `webhook.enabled` is `true`. See the [webhook documentationV](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 documentationV](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 documentationV](webhook-config.md) for more details. <br> ***Datatype:*** *String*
-| `api_server.enabled` | Enable usage of API Server. See the [API Server documentation](rest-api.md) for more details. <br> ***Datatype:*** *Boolean*
-| `api_server.listen_ip_address` | Bind IP address. See the [API Server documentation](rest-api.md) for more details. <br> ***Datatype:*** *IPv4*
-| `api_server.listen_port` | Bind Port. See the [API Server documentation](rest-api.md) for more details. <br> ***Datatype:*** *Integer between 1024 and 65535*
-| `api_server.username` | Username for API server. See the [API Server documentation](rest-api.md) for more details. **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. **Keep it in secret, do not disclose publicly.**<br> ***Datatype:*** *String*
-| `db_url` | Declares database URL to use. NOTE: This defaults to `sqlite://` if `dry_run` is `true`, and to `sqlite:///tradesv3.sqlite` for production instances. <br> ***Datatype:*** *String, SQLAlchemy connect string*
-| `initial_state` | Defines the initial application state. More information below. <br>*Defaults to `stopped`.* <br> ***Datatype:*** *Enum, either `stopped` or `running`*
-| `forcebuy_enable` | Enables the RPC Commands to force a buy. More information below. <br> ***Datatype:*** *Boolean*
-| `strategy` | **Required** Defines Strategy class to use. Recommended to be set via `--strategy NAME`. <br> ***Datatype:*** *ClassName*
-| `strategy_path` | Adds an additional strategy lookup path (must be a directory). <br> ***Datatype:*** *String*
-| `internals.process_throttle_secs` | Set the process throttle. Value in second. <br>*Defaults to `5` seconds.* <br> ***Datatype:*** *Positive Integer*
-| `internals.heartbeat_interval` | Print heartbeat message every N seconds. Set to 0 to disable heartbeat messages. <br>*Defaults to `60` seconds.* <br> ***Datatype:*** *Positive Integer or 0*
-| `internals.sd_notify` | Enables use of the sd_notify protocol to tell systemd service manager about changes in the bot state and issue keep-alive pings. See [here](installation.md#7-optional-configure-freqtrade-as-a-systemd-service) for more details. <br> ***Datatype:*** *Boolean*
-| `logfile` | Specifies logfile name. Uses a rolling strategy for log file rotation for 10 files with the 1MB limit per file. <br> ***Datatype:*** *String*
-| `user_data_dir` | Directory containing user data. <br> *Defaults to `./user_data/`*. <br> ***Datatype:*** *String*
+| `experimental.block_bad_exchanges` | Block exchanges known to not work with freqtrade. Leave on default unless you want to test if that exchange works now. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
+| `pairlists` | Define one or more pairlists to be used. [More information below](#pairlists-and-pairlist-handlers). <br>*Defaults to `StaticPairList`.*  <br> **Datatype:** List of Dicts
+| `telegram.enabled` | Enable the usage of Telegram. <br> **Datatype:** Boolean
+| `telegram.token` | Your Telegram bot token. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
+| `telegram.chat_id` | Your personal Telegram account id. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
+| `webhook.enabled` | Enable usage of Webhook notifications <br> **Datatype:** Boolean
+| `webhook.url` | URL for the webhook. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhookbuy` | Payload to send on buy. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhookbuycancel` | Payload to send on buy order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhooksell` | Payload to send on sell. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhooksellcancel` | Payload to send on sell order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhookstatus` | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `api_server.enabled` | Enable usage of API Server. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** Boolean
+| `api_server.listen_ip_address` | Bind IP address. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** IPv4
+| `api_server.listen_port` | Bind Port. See the [API Server documentation](rest-api.md) for more details. <br>**Datatype:** Integer between 1024 and 65535
+| `api_server.verbosity` | Logging verbosity. `info` will print all RPC Calls, while "error" will only display errors. <br>**Datatype:** Enum, either `info` or `error`. Defaults to `info`.
+| `api_server.username` | Username for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
+| `api_server.password` | Password for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
+| `db_url` | Declares database URL to use. NOTE: This defaults to `sqlite:///tradesv3.dryrun.sqlite` if `dry_run` is `true`, and to `sqlite:///tradesv3.sqlite` for production instances. <br> **Datatype:** String, SQLAlchemy connect string
+| `initial_state` | Defines the initial application state. More information below. <br>*Defaults to `stopped`.* <br> **Datatype:** Enum, either `stopped` or `running`
+| `forcebuy_enable` | Enables the RPC Commands to force a buy. More information below. <br> **Datatype:** Boolean
+| `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. Value in second. <br>*Defaults to `5` seconds.* <br> **Datatype:** Positive Integer
+| `internals.heartbeat_interval` | Print heartbeat message every N seconds. Set to 0 to disable heartbeat messages. <br>*Defaults to `60` seconds.* <br> **Datatype:** Positive Integer or 0
+| `internals.sd_notify` | Enables use of the sd_notify protocol to tell systemd service manager about changes in the bot state and issue keep-alive pings. See [here](installation.md#7-optional-configure-freqtrade-as-a-systemd-service) for more details. <br> **Datatype:** Boolean
+| `logfile` | Specifies logfile name. Uses a rolling strategy for log file rotation for 10 files with the 1MB limit per file. <br> **Datatype:** String
+| `user_data_dir` | Directory containing user data. <br> *Defaults to `./user_data/`*. <br> **Datatype:** String
+| `dataformat_ohlcv` | Data format to use to store historical candle (OHLCV) data. <br> *Defaults to `json`*. <br> **Datatype:** String
+| `dataformat_trades` | Data format to use to store historical trades data. <br> *Defaults to `jsongz`*. <br> **Datatype:** String
 
 ### Parameters in the strategy
 
@@ -113,7 +126,7 @@ The following parameters can be set in either configuration file or strategy.
 Values set in the configuration file always overwrite values set in the strategy.
 
 * `minimal_roi`
-* `ticker_interval`
+* `timeframe`
 * `stoploss`
 * `trailing_stop`
 * `trailing_stop_positive`
@@ -124,24 +137,64 @@ Values set in the configuration file always overwrite values set in the strategy
 * `order_time_in_force`
 * `stake_currency`
 * `stake_amount`
+* `unfilledtimeout`
+* `disable_dataframe_checks`
 * `use_sell_signal` (ask_strategy)
 * `sell_profit_only` (ask_strategy)
 * `ignore_roi_if_buy_signal` (ask_strategy)
 
-### Understand stake_amount
+### Configuring amount per trade
 
-The `stake_amount` configuration parameter is an amount of crypto-currency your bot will use for each trade.
+There are several methods to configure how much of the stake currency the bot will use to enter a trade. All methods respect the [available balance configuration](#available-balance) as explained below.
 
-The minimal configuration value is 0.0001. Please check your exchange's trading minimums to avoid problems.
+#### Available balance
+
+By default, the bot assumes that the `complete amount - 1%` is at it's disposal, and when using [dynamic stake amount](#dynamic-stake-amount), it will split the complete balance into `max_open_trades` buckets per trade.
+Freqtrade will reserve 1% for eventual fees when entering a trade and will therefore not touch that by default.
+
+You can configure the "untouched" amount by using the `tradable_balance_ratio` setting.
+
+For example, if you have 10 ETH available in your wallet on the exchange and `tradable_balance_ratio=0.5` (which is 50%), then the bot will use a maximum amount of 5 ETH for trading and considers this as available balance. The rest of the wallet is untouched by the trades.
+
+!!! Warning
+    The `tradable_balance_ratio` setting applies to the current balance (free balance + tied up in trades). Therefore, assuming the starting balance of 1000, a configuration with `tradable_balance_ratio=0.99` will not guarantee that 10 currency units will always remain available on the exchange. For example, the free amount may reduce to 5 units if the total balance is reduced to 500 (either by a losing streak, or by withdrawing balance).
+
+#### Amend last stake amount
+
+Assuming we have the tradable balance of 1000 USDT, `stake_amount=400`, and `max_open_trades=3`.
+The bot would open 2 trades, and will be unable to fill the last trading slot, since the requested 400 USDT are no longer available, since 800 USDT are already tied in other trades.
+
+To overcome this, the option `amend_last_stake_amount` can be set to `True`, which will enable the bot to reduce stake_amount to the available balance in order to fill the last trade slot.
+
+In the example above this would mean:
+
+- Trade1: 400 USDT
+- Trade2: 400 USDT
+- Trade3: 200 USDT
+
+!!! Note
+    This option only applies with [Static stake amount](#static-stake-amount) - since [Dynamic stake amount](#dynamic-stake-amount) divides the balances evenly.
+
+!!! Note
+    The minimum last stake amount can be configured using `amend_last_stake_amount` - which defaults to 0.5 (50%). This means that the minimum stake amount that's ever used is `stake_amount * 0.5`. This avoids very low stake amounts, that are close to the minimum tradable amount for the pair and can be refused by the exchange.
+
+#### Static stake amount
+
+The `stake_amount` configuration statically configures the amount of stake-currency your bot will use for each trade.
+
+The minimal configuration value is 0.0001, however, please check your exchange's trading minimums for the stake currency you're using to avoid problems.
 
 This setting works in combination with `max_open_trades`. The maximum capital engaged in trades is `stake_amount * max_open_trades`.
 For example, the bot will at most use (0.05 BTC x 3) = 0.15 BTC, assuming a configuration of `max_open_trades=3` and `stake_amount=0.05`.
 
-To allow the bot to trade all the available `stake_currency` in your account set
+!!! Note
+    This setting respects the [available balance configuration](#available-balance).
 
-```json
-"stake_amount" : "unlimited",
-```
+#### Dynamic stake amount
+
+Alternatively, you can use a dynamic stake amount, which will use the available balance on the exchange, and divide that equally by the amount of allowed trades (`max_open_trades`).
+
+To configure this, set `stake_amount="unlimited"`. We also recommend to set `tradable_balance_ratio=0.99` (99%) - to keep a minimum balance for eventual fees.
 
 In this case a trade amount is calculated as:
 
@@ -149,10 +202,23 @@ In this case a trade amount is calculated as:
 currency_balance / (max_open_trades - current_open_trades)
 ```
 
+To allow the bot to trade all the available `stake_currency` in your account (minus `tradable_balance_ratio`) set
+
+```json
+"stake_amount" : "unlimited",
+"tradable_balance_ratio": 0.99,
+```
+
+!!! Note
+    This configuration will allow increasing / decreasing stakes depending on the performance of the bot (lower stake if bot is loosing, higher stakes if the bot has a winning record, since higher balances are available).
+
+!!! Note "When using Dry-Run Mode"
+    When using `"stake_amount" : "unlimited",` in combination with Dry-Run, the balance will be simulated starting with a stake of `dry_run_wallet` which will evolve over time. It is therefore important to set `dry_run_wallet` to a sensible value (like 0.05 or 0.01 for BTC and 1000 or 100 for USDT, for example), otherwise it may simulate trades with 100 BTC (or more) or 0.05 USDT (or less) at once - which may not correspond to your real available balance or is less than the exchange minimal limit for the order amount for the stake currency.
+
 ### Understand minimal_roi
 
 The `minimal_roi` configuration parameter is a JSON object where the key is a duration
-in minutes and the value is the minimum ROI in percent.
+in minutes and the value is the minimum ROI as ratio.
 See the example below:
 
 ```json
@@ -169,6 +235,9 @@ 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 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 stoploss
 
 Go to the [stoploss documentation](stoploss.md) for more details.
@@ -201,35 +270,21 @@ before asking the strategy if we should buy or a sell an asset. After each wait
 every opened trade wether or not we should sell, and for all the remaining pairs (either the dynamic list of pairs or
 the static list of pairs) if we should buy.
 
-### Understand ask_last_balance
-
-The `ask_last_balance` configuration parameter sets the bidding price. Value `0.0` will use `ask` price, `1.0` will
-use the `last` price and values between those interpolate between ask and last
-price. Using `ask` price will guarantee quick success in bid, but bot will also
-end up paying more then would probably have been necessary.
-
 ### Understand order_types
 
-The `order_types` configuration parameter maps actions (`buy`, `sell`, `stoploss`) 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`) 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 buy using limit orders, sell using
-limit-orders, and create stoplosses using using market orders. It also allows to set the
+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.
-If `stoploss_on_exchange` and `trailing_stop` are both set, then the bot will use `stoploss_on_exchange_interval` to check and update the stoploss on exchange periodically.
-`order_types` can be set in the configuration file or in the strategy.
+    
 `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 (`buy`, `sell`, `stoploss` and
 `stoploss_on_exchange`) need to be present, otherwise the bot will fail to start.
 
-`emergencysell` is an optional value, which defaults to `market` and is used when creating stoploss on exchange orders fails.
-The below is the default which is used if this is not configured in either strategy or configuration file.
-
-Since `stoploss_on_exchange` uses limit orders, the exchange needs 2 prices, the stoploss_price and the Limit price.
-`stoploss` defines the stop-price - and limit should be slightly below this. This defaults to 0.99 / 1%.
-Calculation example: we bought the asset at 100$.
-Stop-price is 95$, then limit would be `95 * 0.99 = 94.05$` - so the stoploss will happen between 95$ and 94.05$.
+For information on (`emergencysell`,`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:
 
@@ -269,7 +324,10 @@ Configuration:
     refer to [the stoploss documentation](stoploss.md).
 
 !!! Note
-    If `stoploss_on_exchange` is enabled and the stoploss is cancelled manually on the exchange, then the bot will create a new order.
+    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 "Using market orders"
+    Please read the section [Market order pricing](#market-order-pricing) section when using market orders.
 
 !!! Warning "Warning: stoploss_on_exchange failures"
     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.
@@ -285,7 +343,7 @@ This is most of the time the default time in force. It means the order will rema
 on exchange till it is canceled by user. It can be fully or partially fulfilled.
 If partially fulfilled, the remaining will stay on the exchange till cancelled.
 
-**FOK (Full Or Kill):**
+**FOK (Fill Or Kill):**
 
 It means if the order is not executed immediately AND fully then it is canceled by the exchange.
 
@@ -315,16 +373,18 @@ The possible values are: `gtc` (default), `fok` or `ioc`.
 
 Freqtrade is based on [CCXT library](https://github.com/ccxt/ccxt) that supports over 100 cryptocurrency
 exchange markets and trading APIs. The complete up-to-date list can be found in the
-[CCXT repo homepage](https://github.com/ccxt/ccxt/tree/master/python). However, the bot was tested
-with only Bittrex and Binance.
-
-The bot was tested with the following exchanges:
+[CCXT repo homepage](https://github.com/ccxt/ccxt/tree/master/python).
+ However, the bot was tested by the development team with only Bittrex, Binance and Kraken,
+ so the these are the only officially supported exhanges:
 
 - [Bittrex](https://bittrex.com/): "bittrex"
 - [Binance](https://www.binance.com/): "binance"
+- [Kraken](https://kraken.com/): "kraken"
 
 Feel free to test other exchanges and submit your PR to improve the bot.
 
+Some exchanges require special configuration, which can be found on the [Exchange-specific Notes](exchanges.md) documentation page.
+
 #### Sample exchange configuration
 
 A exchange configuration for "binance" would look as follows:
@@ -354,7 +414,7 @@ Advanced options can be configured using the `_ft_has_params` setting, which wil
 
 Available options are listed in the exchange-class as `_ft_has_default`.
 
-For example, to test the order type `FOK` with Kraken, and modify candle_limit to 200 (so you only get 200 candles per call):
+For example, to test the order type `FOK` with Kraken, and modify candle limit to 200 (so you only get 200 candles per API call):
 
 ```json
 "exchange": {
@@ -387,26 +447,158 @@ The valid values are:
 "BTC", "ETH", "XRP", "LTC", "BCH", "USDT"
 ```
 
-## Pairlists
+## Prices used for orders
 
-Pairlists define the list of pairs that the bot should trade.
-There are [`StaticPairList`](#static-pair-list) and dynamic Whitelists available.
+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.
 
-[`PrecisionFilter`](#precision-filter) and [`PriceFilter`](#price-pair-filter) act as filters, removing low-value pairs.
+!!! Note
+    Orderbook data used by Freqtrade are the data retrieved from exchange by the ccxt's function `fetch_order_book()`, i.e. are usually data from the L2-aggregated orderbook, while the ticker data are the structures returned by the ccxt's `fetch_ticker()`/`fetch_tickers()` functions. Refer to the ccxt library [documentation](https://github.com/ccxt/ccxt/wiki/Manual#market-data) for more details.
 
-All pairlists can be chained, and a combination of all pairlists will become your new whitelist. Pairlists are executed in the sequence they are configured. You should always configure either `StaticPairList` or `DynamicPairList` as starting pairlists.
+!!! Warning "Using market orders"
+    Please read the section [Market order pricing](#market-order-pricing) section when using market orders.
 
-Inactive markets and blacklisted pairs are always removed from the resulting `pair_whitelist`.
+### Buy price
 
-### Available Pairlists
+#### Check depth of market
+
+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.
+
+``` explanation
+...
+103
+102
+101  # ask
+-------------Current spread
+99   # bid
+98
+97
+...
+```
+
+If `bid_strategy.price_side` is set to `"bid"`, then the bot will use 99 as buying price.  
+In line with that, if `bid_strategy.price_side` is set to `"ask"`, then the bot will use 101 as buying price.
+
+Using `ask` price often guarantees quicker filled orders, but the bot can also end up paying more than what would have been necessary.
+Taker fees instead of maker fees will most likely apply even when using limit buy orders.
+Also, prices at the "ask" side of the spread are higher than prices at the "bid" side in the orderbook, so the order behaves similar to a market order (however with a maximum price).
+
+#### Buy price with Orderbook enabled
+
+When buying with the orderbook enabled (`bid_strategy.use_order_book=True`), Freqtrade fetches the `bid_strategy.order_book_top` entries from the orderbook and then uses the entry specified as `bid_strategy.order_book_top` on the configured side (`bid_strategy.price_side`) of the orderbook. 1 specifies the topmost entry in the orderbook, while 2 would use the 2nd entry in the orderbook, and so on.
+
+#### Buy price without Orderbook enabled
+
+The following section uses `side` as the configured `bid_strategy.price_side`.
+
+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 `bid_strategy.ask_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the `last` price and values between those interpolate between ask and last price.
+
+### Sell price
+
+#### Sell price side
+
+The configuration setting `ask_strategy.price_side` defines the side of the spread the bot looks for when selling.
+
+The following displays an orderbook:
+
+``` explanation
+...
+103
+102
+101  # ask
+-------------Current spread
+99   # bid
+98
+97
+...
+```
+
+If `ask_strategy.price_side` is set to `"ask"`, then the bot will use 101 as selling price.  
+In line with that, if `ask_strategy.price_side` is set to `"bid"`, then the bot will use 99 as selling price.
+
+#### Sell price with Orderbook enabled
+
+When selling with the orderbook enabled (`ask_strategy.use_order_book=True`), Freqtrade fetches the `ask_strategy.order_book_max` entries in the orderbook. Then each of the orderbook steps between `ask_strategy.order_book_min` and `ask_strategy.order_book_max` on the configured orderbook side are validated for a profitable sell-possibility based on the strategy configuration (`minimal_roi` conditions) and the sell order is placed at the first profitable spot.
+
+!!! Note
+    Using `order_book_max` higher than `order_book_min` only makes sense when ask_strategy.price_side is set to `"ask"`.
+
+The idea here is to place the sell order early, to be ahead in the queue.
+
+A fixed slot (mirroring `bid_strategy.order_book_top`) can be defined by setting `ask_strategy.order_book_min` and `ask_strategy.order_book_max` to the same number.
+
+!!! Warning "Order_book_max > 1 - increased risks for stoplosses!"
+    Using `ask_strategy.order_book_max` higher than 1 will increase the risk the stoploss on exchange is cancelled too early, since an eventual [stoploss on exchange](#understand-order_types) will be cancelled as soon as the order is placed.
+    Also, the sell order will remain on the exchange for `unfilledtimeout.sell` (or until it's filled) - which can lead to missed stoplosses (with or without using stoploss on exchange).
+
+!!! Warning "Order_book_max > 1 in dry-run"
+    Using `ask_strategy.order_book_max` higher than 1 will result in improper dry-run results (significantly better than real orders executed on exchange), since dry-run assumes orders to be filled almost instantly.
+    It is therefore advised to not use this setting for dry-runs.
+
+#### Sell price without Orderbook enabled
+
+When not using orderbook (`ask_strategy.use_order_book=False`), the price at the `ask_strategy.price_side` side (defaults to `"ask"`) from the ticker will be used as the sell 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 buy and sell are using market orders, a configuration similar to the following might be used
+
+``` jsonc
+  "order_types": {
+    "buy": "market",
+    "sell": "market"
+    // ...
+  },
+  "bid_strategy": {
+    "price_side": "ask",
+    // ...
+  },
+  "ask_strategy":{
+    "price_side": "bid",
+    // ...
+  },
+```
+
+Obviously, if only one side is using limit orders, different pricing combinations can be used.
+
+## Pairlists and Pairlist Handlers
+
+Pairlist Handlers define the list of pairs (pairlist) that the bot should trade. They are configured in the `pairlists` section of the configuration settings.
+
+In your configuration, you can use Static Pairlist (defined by the [`StaticPairList`](#static-pair-list) Pairlist Handler) and Dynamic Pairlist (defined by the [`VolumePairList`](#volume-pair-list) Pairlist Handler).
+
+Additionaly, [`AgeFilter`](#agefilter), [`PrecisionFilter`](#precisionfilter), [`PriceFilter`](#pricefilter), [`ShuffleFilter`](#shufflefilter) and [`SpreadFilter`](#spreadfilter) act as Pairlist Filters, removing certain pairs and/or moving their positions in the pairlist.
+
+If multiple Pairlist Handlers are used, they are chained and a combination of all Pairlist Handlers forms the resulting pairlist the bot uses for trading and backtesting. Pairlist Handlers are executed in the sequence they are configured. You should always configure either `StaticPairList` or `VolumePairList` as the starting Pairlist Handler.
+
+Inactive markets are always removed from the resulting pairlist. Explicitly blacklisted pairs (those in the `pair_blacklist` configuration setting) are also always removed from the resulting pairlist.
+
+### Available Pairlist Handlers
 
 * [`StaticPairList`](#static-pair-list) (default, if not configured differently)
 * [`VolumePairList`](#volume-pair-list)
-* [`PrecisionFilter`](#precision-filter)
-* [`PriceFilter`](#price-pair-filter)
+* [`AgeFilter`](#agefilter)
+* [`PrecisionFilter`](#precisionfilter)
+* [`PriceFilter`](#pricefilter)
+* [`ShuffleFilter`](#shufflefilter)
+* [`SpreadFilter`](#spreadfilter)
 
 !!! Tip "Testing pairlists"
-    Pairlist configurations can be quite tricky to get right. Best use the [`test-pairlist`](utils.md#test-pairlist) subcommand to test your configuration quickly.
+    Pairlist configurations can be quite tricky to get right. Best use the [`test-pairlist`](utils.md#test-pairlist) utility subcommand to test your configuration quickly.
 
 #### Static Pair List
 
@@ -422,11 +614,17 @@ It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklis
 
 #### Volume Pair List
 
-`VolumePairList` selects `number_assets` top pairs based on `sort_key`, which can be one of `askVolume`, `bidVolume` and `quoteVolume` and defaults to `quoteVolume`.
+`VolumePairList` employs sorting/filtering of pairs by their trading volume. It selects `number_assets` top pairs with sorting based on the `sort_key` (which can only be `quoteVolume`).
 
-`VolumePairList` considers outputs of previous pairlists unless  it's the first configured pairlist, it does not consider `pair_whitelist`, but selects the top assets from all available markets (with matching stake-currency) on the exchange.
+When used in the chain of Pairlist Handlers in a non-leading position (after StaticPairList and other Pairlist Filters), `VolumePairList` considers outputs of previous Pairlist Handlers, adding its sorting/selection of the pairs by the trading volume.
 
-`refresh_period` allows setting the period (in seconds), at which the pairlist will be refreshed. Defaults to 1800s (30 minutes).
+When used on the leading position of the chain of Pairlist Handlers, it does not consider `pair_whitelist` configuration setting, but selects the top assets from all available markets (with matching stake-currency) on the exchange.
+
+The `refresh_period` setting allows to define the period (in seconds), at which the pairlist will be refreshed. Defaults to 1800s (30 minutes).
+
+`VolumePairList` is based on the ticker data from exchange, as reported by the ccxt library:
+
+* The `quoteVolume` is the amount of quote (stake) currency traded (bought or sold) in last 24 hours.
 
 ```json
 "pairlists": [{
@@ -434,27 +632,67 @@ It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklis
         "number_assets": 20,
         "sort_key": "quoteVolume",
         "refresh_period": 1800,
-],
+}],
 ```
 
-#### Precision Filter
+#### AgeFilter
 
-Filters low-value coins which would not allow setting a stoploss.
+Removes pairs that have been listed on the exchange for less than `min_days_listed` days (defaults to `10`).
 
-#### Price Pair Filter
+When pairs are first listed on an exchange they can suffer huge price drops and volatility
+in the first few days while the pair goes through its price-discovery period. Bots can often
+be caught out buying before the pair has finished dropping in price.
 
-The `PriceFilter` allows filtering of pairs by price.
-Currently, only `low_price_ratio` is implemented, where a raise of 1 price unit (pip) is below the `low_price_ratio` ratio.
-This option is disabled by default, and will only apply if set to <> 0.
+This filter allows freqtrade to ignore pairs until they have been listed for at least `min_days_listed` days.
+
+#### PrecisionFilter
+
+Filters low-value coins which would not allow setting stoplosses.
+
+#### PriceFilter
+
+The `PriceFilter` allows filtering of pairs by price. Currently the following price filters are supported:
+
+* `min_price`
+* `max_price`
+* `low_price_ratio`
+
+The `min_price` setting removes pairs where the price is below the specified price. This is useful if you wish to avoid trading very low-priced pairs.
+This option is disabled by default, and will only apply if set to > 0.
+
+The `max_price` setting removes pairs where the price is above the specified price. This is useful if you wish to trade only low-priced pairs.
+This option is disabled by default, and will only apply if set to > 0.
+
+The `low_price_ratio` setting removes pairs where a raise of 1 price unit (pip) is above the `low_price_ratio` ratio.
+This option is disabled by default, and will only apply if set to > 0.
+
+For `PriceFiler` at least one of its `min_price`, `max_price` or `low_price_ratio` settings must be applied.
 
 Calculation example:
-Min price precision is 8 decimals. If price is 0.00000011 - one step would be 0.00000012 - which is almost 10% higher than the previous value. 
 
-These pairs are dangerous since it may be impossible to place the desired stoploss - and often result in high losses.
+Min price precision for SHITCOIN/BTC is 8 decimals. If its price is 0.00000011 - one price step above would be 0.00000012, which is ~9% higher than the previous price value. You may filter out this pair by using PriceFilter with `low_price_ratio` set to 0.09 (9%) or with `min_price` set to 0.00000011, correspondingly.
 
-### Full Pairlist example
+!!! Warning "Low priced pairs"
+    Low priced pairs with high "1 pip movements" are dangerous since they are often illiquid and it may also be impossible to place the desired stoploss, which can often result in high losses since price needs to be rounded to the next tradable price - so instead of having a stoploss of -5%, you could end up with a stoploss of -9% simply due to price rounding.
 
-The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets, sorting by `quoteVolume` and applies both [`PrecisionFilter`](#precision-filter) and [`PriceFilter`](#price-pair-filter), filtering all assets where 1 priceunit is > 1%.
+#### ShuffleFilter
+
+Shuffles (randomizes) pairs in the pairlist. It can be used for preventing the bot from trading some of the pairs more frequently then others when you want all pairs be treated with the same priority.
+
+!!! Tip
+    You may set the `seed` value for this Pairlist to obtain reproducible results, which can be useful for repeated backtesting sessions. If `seed` is not set, the pairs are shuffled in the non-repeatable random order.
+
+#### SpreadFilter
+
+Removes pairs that have a difference between asks and bids above the specified ratio, `max_spread_ratio` (defaults to `0.005`).
+
+Example:
+
+If `DOGE/BTC` maximum bid is 0.00000026 and minimum ask is 0.00000027, the ratio is calculated as: `1 - bid/ask ~= 0.037` which is `> 0.005` and this pair will be filtered out.
+
+### Full example of Pairlist Handlers
+
+The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets, sorting pairs by `quoteVolume` and applies both [`PrecisionFilter`](#precisionfilter) and [`PriceFilter`](#price-filter), filtering all assets where 1 priceunit is > 1%. Then the `SpreadFilter` is applied and pairs are finally shuffled with the random seed set to some predefined value.
 
 ```json
 "exchange": {
@@ -467,8 +705,11 @@ The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets,
         "number_assets": 20,
         "sort_key": "quoteVolume",
     },
+    {"method": "AgeFilter", "min_days_listed": 10},
     {"method": "PrecisionFilter"},
-    {"method": "PriceFilter", "low_price_ratio": 0.01}
+    {"method": "PriceFilter", "low_price_ratio": 0.01},
+    {"method": "SpreadFilter", "max_spread_ratio": 0.005},
+    {"method": "ShuffleFilter", "seed": 42}
     ],
 ```
 
@@ -498,8 +739,18 @@ creating trades on the exchange.
 }
 ```
 
-Once you will be happy with your bot performance running in the Dry-run mode,
-you can switch it to production mode.
+Once you will be happy with your bot performance running in the Dry-run mode, you can switch it to production mode.
+
+!!! Note
+    A simulated wallet is available during dry-run mode, and will assume a starting capital of `dry_run_wallet` (defaults to 1000).
+
+### Considerations for dry-run
+
+* API-keys may or may not be provided. Only Read-Only operations (i.e. operations that do not alter account state) on the exchange are performed in the dry-run mode.
+* Wallets (`/balance`) are simulated.
+* Orders are simulated, and will not be posted to the exchange.
+* In combination with `stoploss_on_exchange`, the stop_loss price is assumed to be filled.
+* Open orders (not trades, which are stored in the database) are reset on bot restart.
 
 ## Switch to production mode
 
@@ -507,6 +758,11 @@ In production mode, the bot will engage your money. Be careful, since a wrong
 strategy can lose all your money. Be aware of what you are doing when
 you run it in production mode.
 
+### Setup your exchange account
+
+You will need to create API Keys (usually you get `key` and `secret`, some exchanges require an additional `password`) from the Exchange website and you'll need to insert this into the appropriate fields in the configuration or when asked by the `freqtrade new-config` command.
+API Keys are usually only required for live trading (trading for real money, bot running in "production mode", executing real orders on the exchange) and are not required for the bot running in dry-run (trade simulation) mode. When you setup the bot in dry-run mode, you may fill these fields with empty values.
+
 ### To switch your bot in production mode
 
 **Edit your `config.json`  file.**
@@ -528,9 +784,6 @@ you run it in production mode.
 }
 ```
 
-!!! Note
-    If you have an exchange API key yet, [see our tutorial](/pre-requisite).
-
 You should also make sure to read the [Exchanges](exchanges.md) section of the documentation to be aware of potential configuration details specific to your exchange.
 
 ### Using proxy with Freqtrade
@@ -555,7 +808,7 @@ freqtrade
 
 ## Embedding Strategies
 
-FreqTrade provides you with with an easy way to embed the strategy into your configuration file.
+Freqtrade provides you with with an easy way to embed the strategy into your configuration file.
 This is done by utilizing BASE64 encoding and providing this string at the strategy configuration field,
 in your chosen config file.
 

docs/data-analysis.md

@@ -8,6 +8,27 @@ You can analyze the results of backtests and trading history easily using Jupyte
 * Don't forget to start a Jupyter notebook server from within your conda or venv environment or use [nb_conda_kernels](https://github.com/Anaconda-Platform/nb_conda_kernels)*
 * Copy the example notebook before use so your changes don't get clobbered with the next freqtrade update.
 
+### Using virtual environment with system-wide Jupyter installation
+
+Sometimes it can be desired to use a system-wide installation of Jupyter notebook, and use a jupyter kernel from the virtual environment.
+This prevents you from installing the full jupyter suite multiple times per system, and provides an easy way to switch between tasks (freqtrade / other analytics tasks).
+
+For this to work, first activate your virtual environment and run the following commands:
+
+``` bash
+# Activate virtual environment
+source .env/bin/activate
+
+pip install ipykernel
+ipython kernel install --user --name=freqtrade
+# Restart jupyter (lab / notebook)
+# select kernel "freqtrade" in the notebook
+```
+
+!!! Note
+    This section is provided for completeness, the Freqtrade Team won't provide full support for problems with this setup and will recommend to install Jupyter in the virtual environment directly, as that is the easiest way to get jupyter notebooks up and running. For help with this setup please refer to the [Project Jupyter](https://jupyter.org/) [documentation](https://jupyter.org/documentation) or [help channels](https://jupyter.org/community).
+
+    
 ## Fine print  
 
 Some tasks don't work especially well in notebooks. For example, anything using asynchronous execution is a problem for Jupyter. Also, freqtrade's primary entry point is the shell cli, so using pure python in a notebook bypasses arguments that provide required objects and parameters to helper functions. You may need to set those values or create expected objects manually.

docs/data-download.md

@@ -12,6 +12,204 @@ Otherwise `--exchange` becomes mandatory.
     If you already have backtesting data available in your data-directory and would like to refresh this data up to today, use `--days xx` with a number slightly higher than the missing number of days. Freqtrade will keep the available data and only download the missing data.
     Be carefull though: If the number is too small (which would result in a few missing days), the whole dataset will be removed and only xx days will be downloaded.
 
+### Usage
+
+```
+usage: freqtrade download-data [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [-p PAIRS [PAIRS ...]]
+                               [--pairs-file FILE] [--days INT] [--dl-trades] [--exchange EXCHANGE]
+                               [-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]]
+                               [--erase] [--data-format-ohlcv {json,jsongz}] [--data-format-trades {json,jsongz}]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Show profits for only these pairs. Pairs are space-separated.
+  --pairs-file FILE     File containing a list of pairs to download.
+  --days INT            Download data for given number of days.
+  --dl-trades           Download trades instead of OHLCV data. The bot will resample trades to the desired timeframe as specified as
+                        --timeframes/-t.
+  --exchange EXCHANGE   Exchange name (default: `bittrex`). Only valid if no config is provided.
+  -t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]
+                        Specify which tickers to download. Space-separated list. Default: `1m 5m`.
+  --erase               Clean all existing data for the selected exchange/pairs/timeframes.
+  --data-format-ohlcv {json,jsongz}
+                        Storage format for downloaded candle (OHLCV) data. (default: `json`).
+  --data-format-trades {json,jsongz}
+                        Storage format for downloaded trades data. (default: `jsongz`).
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are: 'syslog', 'journald'. See the documentation for more details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`). Multiple --config options may be used. Can be set to `-`
+                        to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+```
+
+### Data format
+
+Freqtrade currently supports 2 dataformats, `json` (plain "text" json files) and `jsongz` (a gzipped version of json files).
+By default, OHLCV data is stored as `json` data, while trades data is stored as `jsongz` data.
+
+This can be changed via the `--data-format-ohlcv` and `--data-format-trades` parameters respectivly.
+
+If the default dataformat has been changed during download, then the keys `dataformat_ohlcv` and `dataformat_trades` in the configuration file need to be adjusted to the selected dataformat as well.
+
+!!! Note
+    You can convert between data-formats using the [convert-data](#subcommand-convert-data) and [convert-trade-data](#subcommand-convert-trade-data) methods.
+
+#### Subcommand convert data
+
+```
+usage: freqtrade convert-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                              [-d PATH] [--userdir PATH]
+                              [-p PAIRS [PAIRS ...]] --format-from
+                              {json,jsongz} --format-to {json,jsongz}
+                              [--erase]
+                              [-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Show profits for only these pairs. Pairs are space-
+                        separated.
+  --format-from {json,jsongz}
+                        Source format for data conversion.
+  --format-to {json,jsongz}
+                        Destination format for data conversion.
+  --erase               Clean all existing data for the selected
+                        exchange/pairs/timeframes.
+  -t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]
+                        Specify which tickers to download. Space-separated
+                        list. Default: `1m 5m`.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`).
+                        Multiple --config options may be used. Can be set to
+                        `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+```
+
+##### Example converting data
+
+The following command will convert all candle (OHLCV) data available in `~/.freqtrade/data/binance` from json to jsongz, saving diskspace in the process.
+It'll also remove original json data files (`--erase` parameter).
+
+``` bash
+freqtrade convert-data --format-from json --format-to jsongz --datadir ~/.freqtrade/data/binance -t 5m 15m --erase
+```
+
+#### Subcommand convert-trade data
+
+```
+usage: freqtrade convert-trade-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                                    [-d PATH] [--userdir PATH]
+                                    [-p PAIRS [PAIRS ...]] --format-from
+                                    {json,jsongz} --format-to {json,jsongz}
+                                    [--erase]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Show profits for only these pairs. Pairs are space-
+                        separated.
+  --format-from {json,jsongz}
+                        Source format for data conversion.
+  --format-to {json,jsongz}
+                        Destination format for data conversion.
+  --erase               Clean all existing data for the selected
+                        exchange/pairs/timeframes.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`).
+                        Multiple --config options may be used. Can be set to
+                        `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+```
+
+##### Example converting trades
+
+The following command will convert all available trade-data in `~/.freqtrade/data/kraken` from jsongz to json.
+It'll also remove original jsongz data files (`--erase` parameter).
+
+``` bash
+freqtrade convert-trade-data --format-from jsongz --format-to json --datadir ~/.freqtrade/data/kraken --erase
+```
+
+### Subcommand list-data
+
+You can get a list of downloaded data using the `list-data` subcommand.
+
+```
+usage: freqtrade list-data [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
+                           [--userdir PATH] [--exchange EXCHANGE]
+                           [--data-format-ohlcv {json,jsongz}]
+                           [-p PAIRS [PAIRS ...]]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --exchange EXCHANGE   Exchange name (default: `bittrex`). Only valid if no
+                        config is provided.
+  --data-format-ohlcv {json,jsongz}
+                        Storage format for downloaded candle (OHLCV) data.
+                        (default: `json`).
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Show profits for only these pairs. Pairs are space-
+                        separated.
+
+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.
+```
+
+#### Example list-data
+
+```bash
+> freqtrade list-data --userdir ~/.freqtrade/user_data/
+
+Found 33 pair / timeframe combinations.
+pairs       timeframe
+----------  -----------------------------------------
+ADA/BTC     5m, 15m, 30m, 1h, 2h, 4h, 6h, 12h, 1d
+ADA/ETH     5m, 15m, 30m, 1h, 2h, 4h, 6h, 12h, 1d
+ETH/BTC     5m, 15m, 30m, 1h, 2h, 4h, 6h, 12h, 1d
+ETH/USDT    5m, 15m, 30m, 1h, 2h, 4h
+```
+
 ### Pairs file
 
 In alternative to the whitelist from `config.json`, a `pairs.json` file can be used.
@@ -46,15 +244,15 @@ Then run:
 freqtrade download-data --exchange binance
 ```
 
-This will download ticker data for all the currency pairs you defined in `pairs.json`.
+This will download historical candle (OHLCV) data for all the currency pairs you defined in `pairs.json`.
 
 ### Other Notes
 
 - To use a different directory than the exchange specific default, use `--datadir user_data/data/some_directory`.
-- To change the exchange used to download the tickers, please use a different configuration file (you'll probably need to adjust ratelimits etc.)
+- To change the exchange used to download the historical data from, please use a different configuration file (you'll probably need to adjust ratelimits etc.)
 - To use `pairs.json` from some other directory, use `--pairs-file some_other_dir/pairs.json`.
-- To download ticker data for only 10 days, use `--days 10` (defaults to 30 days).
-- Use `--timeframes` to specify which tickers to download. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute tickers.
+- To download historical candle (OHLCV) data for only 10 days, use `--days 10` (defaults to 30 days).
+- Use `--timeframes` to specify what timeframe download the historical candle (OHLCV) data for. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute data.
 - To use exchange, timeframe and list of pairs as defined in your configuration file, use the `-c/--config` option. With this, the script uses the whitelist defined in the config as the list of currency pairs to download data for and does not require the pairs.json file. You can combine `-c/--config` with most other options.
 
 ### Trades (tick) data

docs/deprecated.md

@@ -9,18 +9,27 @@ and are no longer supported. Please avoid their usage in your configuration.
 ### the `--refresh-pairs-cached` command line option
 
 `--refresh-pairs-cached` in the context of backtesting, hyperopt and edge allows to refresh candle data for backtesting.
-Since this leads to much confusion, and slows down backtesting (while not being part of backtesting) this has been singled out 
-as a seperate freqtrade subcommand `freqtrade download-data`.
+Since this leads to much confusion, and slows down backtesting (while not being part of backtesting) this has been singled out as a separate freqtrade sub-command `freqtrade download-data`.
 
-This command line option was deprecated in 2019.7-dev (develop branch) and removed in 2019.9 (master branch).
+This command line option was deprecated in 2019.7-dev (develop branch) and removed in 2019.9.
 
 ### The **--dynamic-whitelist** command line option
 
 This command line option was deprecated in 2018 and removed freqtrade 2019.6-dev (develop branch)
-and in freqtrade 2019.7 (master branch).
+and in freqtrade 2019.7.
 
 ### the `--live` command line option
 
 `--live` in the context of backtesting allowed to download the latest tick data for backtesting.
 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 (master branch)
+Removed in 2019-7-dev (develop branch) and in freqtrade 2019.8.
+
+### 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.
+
+The old section of configuration parameters (`"pairlist"`) has been deprecated in 2019.11 and has been removed in 2020.4.
+
+### deprecation of bidVolume and askVolume from volume-pairlist
+
+Since only quoteVolume can be compared between assets, the other options (bidVolume, askVolume) have been deprecated in 2020.4.

docs/developer.md

@@ -1,6 +1,6 @@
 # Development Help
 
-This page is intended for developers of FreqTrade, people who want to contribute to the FreqTrade codebase or documentation, or people who want to understand the source code of the application they're running.
+This page is intended for developers of Freqtrade, people who want to contribute to the Freqtrade codebase or documentation, or people who want to understand the source code of the application they're running.
 
 All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. We [track issues](https://github.com/freqtrade/freqtrade/issues) on [GitHub](https://github.com) and also have a dev channel in [slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) where you can ask questions.
 
@@ -85,6 +85,35 @@ docker-compose exec freqtrade_develop /bin/bash
 
 ![image](https://user-images.githubusercontent.com/419355/65456522-ba671a80-de06-11e9-9598-df9ca0d8dcac.png)
 
+## ErrorHandling
+
+Freqtrade Exceptions all inherit from `FreqtradeException`.
+This general class of error should however not be used directly. Instead, multiple specialized sub-Exceptions exist.
+
+Below is an outline of exception inheritance hierarchy:
+
+```
++ FreqtradeException
+|
++---+ OperationalException
+|
++---+ DependencyException
+|   |
+|   +---+ PricingError
+|   |
+|   +---+ ExchangeError
+|       |
+|       +---+ TemporaryError
+|       |
+|       +---+ DDosProtection
+|       |
+|       +---+ InvalidOrderException
+|           |
+|           +---+ RetryableOrderError
+|
++---+ StrategyError
+```
+
 ## Modules
 
 ### Dynamic Pairlist
@@ -92,13 +121,13 @@ docker-compose exec freqtrade_develop /bin/bash
 You have a great idea for a new pair selection algorithm you would like to try out? Great.
 Hopefully you also want to contribute this back upstream.
 
-Whatever your motivations are - This should get you off the ground in trying to develop a new Pairlist provider.
+Whatever your motivations are - This should get you off the ground in trying to develop a new Pairlist Handler.
 
-First of all, have a look at the [VolumePairList](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/pairlist/VolumePairList.py) provider, and best copy this file with a name of your new Pairlist Provider.
+First of all, have a look at the [VolumePairList](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/pairlist/VolumePairList.py) Handler, and best copy this file with a name of your new Pairlist Handler.
 
-This is a simple provider, which however serves as a good example on how to start developing.
+This is a simple Handler, which however serves as a good example on how to start developing.
 
-Next, modify the classname of the provider (ideally align this with the Filename).
+Next, modify the classname of the Handler (ideally align this with the module filename).
 
 The base-class provides an instance of the exchange (`self._exchange`) the pairlist manager (`self._pairlistmanager`), as well as the main configuration (`self._config`), the pairlist dedicated configuration (`self._pairlistconfig`) and the absolute position within the list of pairlists.
 
@@ -114,28 +143,44 @@ Now, let's step through the methods which require actions:
 
 #### Pairlist configuration
 
-Configuration for PairListProvider is done in the bot configuration file in the element `"pairlist"`.
-This Pairlist-object may contain configurations with additional configurations for the configured pairlist.
-By convention, `"number_assets"` is used to specify the maximum number of pairs to keep in the whitelist. Please follow this to ensure a consistent user experience.
+Configuration for the chain of Pairlist Handlers is done in the bot configuration file in the element `"pairlists"`, an array of configuration parameters for each Pairlist Handlers in the chain.
 
-Additional elements can be configured as needed. `VolumePairList` uses `"sort_key"` to specify the sorting value - however feel free to specify whatever is necessary for your great algorithm to be successfull and dynamic.
+By convention, `"number_assets"` is used to specify the maximum number of pairs to keep in the pairlist. Please follow this to ensure a consistent user experience.
+
+Additional parameters can be configured as needed. For instance, `VolumePairList` uses `"sort_key"` to specify the sorting value - however feel free to specify whatever is necessary for your great algorithm to be successfull and dynamic.
 
 #### short_desc
 
 Returns a description used for Telegram messages.
-This should contain the name of the Provider, as well as a short description containing the number of assets. Please follow the format `"PairlistName - top/bottom X pairs"`.
+
+This should contain the name of the Pairlist Handler, as well as a short description containing the number of assets. Please follow the format `"PairlistName - top/bottom X pairs"`.
+
+#### gen_pairlist
+
+Override this method if the Pairlist Handler can be used as the leading Pairlist Handler in the chain, defining the initial pairlist which is then handled by all Pairlist Handlers in the chain. Examples are `StaticPairList` and `VolumePairList`.
+
+This is called with each iteration of the bot (only if the Pairlist Handler is at the first location) - so consider implementing caching for compute/network heavy calculations.
+
+It must return the resulting pairlist (which may then be passed into the chain of Pairlist Handlers).
+
+Validations are optional, the parent class exposes a `_verify_blacklist(pairlist)` and `_whitelist_for_active_markets(pairlist)` to do default filtering. Use this if you limit your result to a certain number of pairs - so the endresult is not shorter than expected.
 
 #### filter_pairlist
 
-Override this method and run all calculations needed in this method.
+This method is called for each Pairlist Handler in the chain by the pairlist manager.
+
 This is called with each iteration of the bot - so consider implementing caching for compute/network heavy calculations.
 
 It get's passed a pairlist (which can be the result of previous pairlists) as well as `tickers`, a pre-fetched version of `get_tickers()`.
 
-It must return the resulting pairlist (which may then be passed into the next pairlist filter).
+The default implementation in the base class simply calls the `_validate_pair()` method for each pair in the pairlist, but you may override it. So you should either implement the `_validate_pair()` in your Pairlist Handler or override `filter_pairlist()` to do something else.
+
+If overridden, it must return the resulting pairlist (which may then be passed into the next Pairlist Handler in the chain).
 
 Validations are optional, the parent class exposes a `_verify_blacklist(pairlist)` and `_whitelist_for_active_markets(pairlist)` to do default filters. Use this if you limit your result to a certain number of pairs - so the endresult is not shorter than expected.
 
+In `VolumePairList`, this implements different methods of sorting, does early validation so only the expected number of pairs is returned.
+
 ##### sample
 
 ``` python
@@ -145,15 +190,10 @@ Validations are optional, the parent class exposes a `_verify_blacklist(pairlist
         return pairs
 ```
 
-#### _gen_pair_whitelist
-
-This is a simple method used by `VolumePairList` - however serves as a good example.
-In VolumePairList, this implements different methods of sorting, does early validation so only the expected number of pairs is returned.
-
 ## Implement a new Exchange (WIP)
 
 !!! Note
-    This section is a Work in Progress and is not a complete guide on how to test a new exchange with FreqTrade.
+    This section is a Work in Progress and is not a complete guide on how to test a new exchange with Freqtrade.
 
 Most exchanges supported by CCXT should work out of the box.
 
@@ -165,7 +205,7 @@ Since CCXT does not provide unification for Stoploss On Exchange yet, we'll need
 
 ### Incomplete candles
 
-While fetching OHLCV data, we're may end up getting incomplete candles (Depending on the exchange).
+While fetching candle (OHLCV) data, we may end up getting incomplete candles (depending on the exchange).
 To demonstrate this, we'll use daily candles (`"1d"`) to keep things simple.
 We query the api (`ct.fetch_ohlcv()`) for the timeframe and look at the date of the last entry. If this entry changes or shows the date of a "incomplete" candle, then we should drop this since having incomplete candles is problematic because indicators assume that only complete candles are passed to them, and will generate a lot of false buy signals. By default, we're therefore removing the last candle assuming it's incomplete.
 
@@ -174,26 +214,28 @@ To check how the new exchange behaves, you can use the following snippet:
 ``` python
 import ccxt
 from datetime import datetime
-from freqtrade.data.converter import parse_ticker_dataframe
+from freqtrade.data.converter import ohlcv_to_dataframe
 ct = ccxt.binance()
 timeframe = "1d"
 pair = "XLM/BTC"  # Make sure to use a pair that exists on that exchange!
 raw = ct.fetch_ohlcv(pair, timeframe=timeframe)
 
 # convert to dataframe
-df1 = parse_ticker_dataframe(raw, timeframe, pair=pair, drop_incomplete=False)
+df1 = ohlcv_to_dataframe(raw, timeframe, pair=pair, drop_incomplete=False)
 
-print(df1["date"].tail(1))
+print(df1.tail(1))
 print(datetime.utcnow())
 ```
 
 ``` output
-19   2019-06-08 00:00:00+00:00
+                         date      open      high       low     close  volume  
+499 2019-06-08 00:00:00+00:00  0.000007  0.000007  0.000007  0.000007   26264344.0  
 2019-06-09 12:30:27.873327
 ```
 
 The output will show the last entry from the Exchange as well as the current UTC date.
 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).
 
 ## Updating example notebooks
 
@@ -232,7 +274,7 @@ git checkout -b new_release <commitid>
 
 Determine if crucial bugfixes have been made between this commit and the current state, and eventually cherry-pick these.
 
-* Edit `freqtrade/__init__.py` and add the version matching the current date (for example `2019.7` for July 2019). Minor versions can be `2019.7-1` should we need to do a second release that month.
+* Edit `freqtrade/__init__.py` and add the version matching the current date (for example `2019.7` for July 2019). Minor versions can be `2019.7.1` should we need to do a second release that month. Version numbers must follow allowed versions from PEP0440 to avoid failures pushing to pypi.
 * Commit this part
 * push that branch to the remote and create a PR against the master branch
 
@@ -246,6 +288,17 @@ Determine if crucial bugfixes have been made between this commit and the current
 git log --oneline --no-decorate --no-merges master..new_release
 ```
 
+To keep the release-log short, best wrap the full git changelog into a collapsible details secction.
+
+```markdown
+<details>
+<summary>Expand full changelog</summary>
+
+... Full git changelog
+
+</details>
+```
+
 ### Create github release / tag
 
 Once the PR against master is merged (best right after merging):
@@ -253,4 +306,24 @@ Once the PR against master is merged (best right after merging):
 * Use the button "Draft a new release" in the Github UI (subsection releases).
 * Use the version-number specified as tag.
 * Use "master" as reference (this step comes after the above PR is merged).
-* Use the above changelog as release comment (as codeblock).
+* Use the above changelog as release comment (as codeblock)
+
+## Releases
+
+### pypi
+
+To create a pypi release, please run the following commands:
+
+Additional requirement: `wheel`, `twine` (for uploading), account on pypi with proper permissions.
+
+``` bash
+python setup.py sdist bdist_wheel
+
+# For pypi test (to check if some change to the installation did work)
+twine upload --repository-url https://test.pypi.org/legacy/ dist/*
+
+# For production:
+twine upload dist/*
+```
+
+Please don't push non-releases to the productive / real pypi instance.

docs/docker.md

@@ -1,4 +1,4 @@
-# Using FreqTrade with Docker
+# Using Freqtrade with Docker
 
 ## Install Docker
 
@@ -8,13 +8,144 @@ Start by downloading and installing Docker CE for your platform:
 * [Windows](https://docs.docker.com/docker-for-windows/install/)
 * [Linux](https://docs.docker.com/install/)
 
+Optionally, [docker-compose](https://docs.docker.com/compose/install/) should be installed and available to follow the [docker quick start guide](#docker-quick-start).
+
 Once you have Docker installed, simply prepare the config file (e.g. `config.json`) and run the image for `freqtrade` as explained below.
 
-## Download the official FreqTrade docker image
+## Freqtrade with docker-compose
+
+Freqtrade provides an official Docker image on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/), as well as a [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) ready for usage.
+
+!!! Note
+    The following section assumes that docker and docker-compose is installed and available to the logged in user.
+
+!!! Note
+    All below comands use relative directories and will have to be executed from the directory containing the `docker-compose.yml` file.
+
+!!! Note "Docker on Raspberry"
+    If you're running freqtrade on a Raspberry PI, you must change the image from `freqtradeorg/freqtrade:master` to `freqtradeorg/freqtrade:master_pi` or `freqtradeorg/freqtrade:develop_pi`, otherwise the image will not work.
+
+### Docker quick start
+
+Create a new directory and place the [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) in this directory.
+
+``` bash
+mkdir ft_userdata
+cd ft_userdata/
+# Download the docker-compose file from the repository
+curl https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docker-compose.yml -o docker-compose.yml
+
+# Pull the freqtrade image
+docker-compose pull
+
+# Create user directory structure
+docker-compose run --rm freqtrade create-userdir --userdir user_data
+
+# Create configuration - Requires answering interactive questions
+docker-compose run --rm freqtrade new-config --config user_data/config.json
+```
+
+The above snippet creates a new directory called "ft_userdata", downloads the latest compose file and pulls the freqtrade image.
+The last 2 steps in the snippet create the directory with user-data, as well as (interactively) the default configuration based on your selections.
+
+!!! Note
+    You can edit the configuration at any time, which is available as `user_data/config.json` (within the directory `ft_userdata`) when using the above configuration.
+
+#### Adding your strategy
+
+The configuration is now available as `user_data/config.json`.
+You should now copy your strategy to `user_data/strategies/` - and add the Strategy class name to the `docker-compose.yml` file, replacing `SampleStrategy`. If you wish to run the bot with the SampleStrategy, just leave it as it is.
+
+!!! Warning
+    The `SampleStrategy` is there for your reference and give you ideas for your own strategy.
+    Please always backtest the strategy and use dry-run for some time before risking real money!
+
+Once this is done, you're ready to launch the bot in trading mode (Dry-run or Live-trading, depending on your answer to the corresponding question you made above).
+
+``` bash
+docker-compose up -d
+```
+
+#### Docker-compose logs
+
+Logs will be written to `user_data/logs/freqtrade.log`. 
+Alternatively, you can check the latest logs using `docker-compose logs -f`.
+
+#### Database
+
+The database will be in the user_data directory as well, and will be called `user_data/tradesv3.sqlite`.
+
+#### Updating freqtrade with docker-compose
+
+To update freqtrade when using docker-compose is as simple as running the following 2 commands:
+
+``` bash
+# Download the latest image
+docker-compose pull
+# Restart the image
+docker-compose up -d
+```
+
+This will first pull the latest image, and will then restart the container with the just pulled version.
+
+!!! Note
+    You should always check the changelog for breaking changes / manual interventions required and make sure the bot starts correctly after the update.
+
+#### Going from here
+
+Advanced users may edit the docker-compose file further to include all possible options or arguments.
+
+All possible freqtrade arguments will be available by running `docker-compose run --rm freqtrade <command> <optional arguments>`.
+
+!!! Note "`docker-compose run --rm`"
+    Including `--rm` will clean up the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
+
+##### Example: Download data with docker-compose
+
+Download backtesting data for 5 days for the pair ETH/BTC and 1h timeframe from Binance. The data will be stored in the directory `user_data/data/` on the host.
+
+``` bash
+docker-compose run --rm freqtrade download-data --pairs ETH/BTC --exchange binance --days 5 -t 1h
+```
+
+Head over to the [Data Downloading Documentation](data-download.md) for more details on downloading data.
+
+##### Example: Backtest with docker-compose
+
+Run backtesting in docker-containers for SampleStrategy and specified timerange of historical data, on 5m timeframe:
+
+``` bash
+docker-compose run --rm freqtrade backtesting --config user_data/config.json --strategy SampleStrategy --timerange 20190801-20191001 -i 5m
+```
+
+Head over to the [Backtesting Documentation](backtesting.md) to learn more.
+
+#### Additional dependencies with docker-compose
+
+If your strategy requires dependencies not included in the default image (like [technical](https://github.com/freqtrade/technical)) - it will be necessary to build the image on your host.
+For this, please create a Dockerfile containing installation steps for the additional dependencies (have a look at [Dockerfile.technical](https://github.com/freqtrade/freqtrade/blob/develop/Dockerfile.technical) for an example).
+
+You'll then also need to modify the `docker-compose.yml` file and uncomment the build step, as well as rename the image to avoid naming collisions.
+
+``` yaml
+    image: freqtrade_custom
+    build:
+      context: .
+      dockerfile: "./Dockerfile.<yourextension>"
+```
+
+You can then run `docker-compose build` to build the docker image, and run it using the commands described above.
+
+## Freqtrade with docker without docker-compose
+
+!!! Warning
+    The below documentation is provided for completeness and assumes that you are somewhat familiar with running docker containers. If you're just starting out with docker, we recommend to follow the [Freqtrade with docker-compose](#freqtrade-with-docker-compose) instructions.
+
+### Download the official Freqtrade docker image
 
 Pull the image from docker hub.
 
-Branches / tags available can be checked out on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/tags/).
+Branches / tags available can be checked out on [Dockerhub tags page](https://hub.docker.com/r/freqtradeorg/freqtrade/tags/).
 
 ```bash
 docker pull freqtradeorg/freqtrade:develop
@@ -164,8 +295,7 @@ docker run -d \
 ```
 
 !!! Note
-    db-url defaults to `sqlite:///tradesv3.sqlite` but it defaults to `sqlite://` if `dry_run=True` is being used.
-    To override this behaviour use a custom db-url value: i.e.: `--db-url sqlite:///tradesv3.dryrun.sqlite`
+    When using docker, it's best to specify `--db-url` explicitly to ensure that the database URL and the mounted database file match.
 
 !!! Note
     All available bot command line parameters can be added to the end of the `docker run` command.

docs/edge.md

@@ -1,4 +1,4 @@
-#  Edge positioning
+# Edge positioning
 
 This page explains how to use Edge Positioning module in your bot in order to enter into a trade only if the trade has a reasonable win rate and risk reward ratio, and consequently adjust your position size and stoploss.
 
@@ -6,9 +6,11 @@ This page explains how to use Edge Positioning module in your bot in order to en
     Edge positioning is not compatible with dynamic (volume-based) whitelist.
 
 !!! Note
-    Edge does not consider anything else than buy/sell/stoploss signals. So trailing stoploss, ROI, and everything else are ignored in its calculation.
+    Edge does not consider anything other than *its own* buy/sell/stoploss signals. It ignores the stoploss, trailing stoploss, and ROI settings in the strategy configuration file.
+    Therefore, it is important to understand that Edge can improve the performance of some trading strategies but *decrease* the performance of others.
 
 ## Introduction
+
 Trading is all about probability. No one can claim that he has a strategy working all the time. You have to assume that sometimes you lose.
 
 But it doesn't mean there is no rule, it only means rules should work "most of the time". Let's play a game: we toss a coin, heads: I give you 10$, tails: you give me 10$. Is it an interesting game? No, it's quite boring, isn't it?
@@ -22,45 +24,63 @@ Let's complicate it more: you win 80% of the time but only 2$, I win 20% of the
 The question is: How do you calculate that? How do you know if you wanna play?
 
 The answer comes to two factors:
+
 - Win Rate
 - Risk Reward Ratio
 
 ### Win Rate
+
 Win Rate (*W*) is is the mean over some amount of trades (*N*) what is the percentage of winning trades to total number of trades (note that we don't consider how much you gained but only if you won or not).
 
-    W = (Number of winning trades) / (Total number of trades) = (Number of winning trades) / N
+```
+W = (Number of winning trades) / (Total number of trades) = (Number of winning trades) / N
+```
 
 Complementary Loss Rate (*L*) is defined as
 
-    L = (Number of losing trades) / (Total number of trades) = (Number of losing trades) / N
+```
+L = (Number of losing trades) / (Total number of trades) = (Number of losing trades) / N
+```
 
 or, which is the same, as
 
-    L = 1 – W
+```
+L = 1 – W
+```
 
 ### Risk Reward Ratio
+
 Risk Reward Ratio (*R*) is a formula used to measure the expected gains of a given investment against the risk of loss. It is basically what you potentially win divided by what you potentially lose:
 
-    R = Profit / Loss
+```
+R = Profit / Loss
+```
 
 Over time, on many trades, you can calculate your risk reward by dividing your average profit on winning trades by your average loss on losing trades:
 
-    Average profit = (Sum of profits) / (Number of winning trades)
+```
+Average profit = (Sum of profits) / (Number of winning trades)
 
-    Average loss = (Sum of losses) / (Number of losing trades)
+Average loss = (Sum of losses) / (Number of losing trades)
 
-    R = (Average profit) / (Average loss)
+R = (Average profit) / (Average loss)
+```
 
 ### Expectancy
+
 At this point we can combine *W* and *R* to create an expectancy ratio. This is a simple process of multiplying the risk reward ratio by the percentage of winning trades and subtracting the percentage of losing trades, which is calculated as follows:
 
-    Expectancy Ratio = (Risk Reward Ratio X Win Rate) – Loss Rate = (R X W) – L
+```
+Expectancy Ratio = (Risk Reward Ratio X Win Rate) – Loss Rate = (R X W) – L
+```
 
 So lets say your Win rate is 28% and your Risk Reward Ratio is 5:
 
-    Expectancy = (5 X 0.28) – 0.72 = 0.68
+```
+Expectancy = (5 X 0.28) – 0.72 = 0.68
+```
 
-Superficially, this means that on average you expect this strategy’s trades to return .68 times the size of your loses. This is important for two reasons: First, it may seem obvious, but you know right away that you have a positive return. Second, you now have a number you can compare to other candidate systems to make decisions about which ones you employ.
+Superficially, this means that on average you expect this strategy’s trades to return 1.68 times the size of your loses. Said another way, you can expect to win $1.68 for every $1 you lose. This is important for two reasons: First, it may seem obvious, but you know right away that you have a positive return. Second, you now have a number you can compare to other candidate systems to make decisions about which ones you employ.
 
 It is important to remember that any system with an expectancy greater than 0 is profitable using past data. The key is finding one that will be profitable in the future.
 
@@ -69,7 +89,8 @@ You can also use this value to evaluate the effectiveness of modifications to th
 **NOTICE:** It's important to keep in mind that Edge is testing your expectancy using historical data, there's no guarantee that you will have a similar edge in the future. It's still vital to do this testing in order to build confidence in your methodology, but be wary of "curve-fitting" your approach to the historical data as things are unlikely to play out the exact same way for future trades.
 
 ## How does it work?
-If enabled in config, Edge will go through historical data with a range of stoplosses in order to find buy and sell/stoploss signals. It then calculates win rate and expectancy over *N* trades for each stoploss. Here is an example:
+
+Edge combines dynamic stoploss, dynamic positions, and whitelist generation into one isolated module which is then applied to the trading strategy. If enabled in config, Edge will go through historical data with a range of stoplosses in order to find buy and sell/stoploss signals. It then calculates win rate and expectancy over *N* trades for each stoploss. Here is an example:
 
 | Pair   |      Stoploss      |  Win Rate | Risk Reward Ratio | Expectancy |
 |----------|:-------------:|-------------:|------------------:|-----------:|
@@ -83,6 +104,7 @@ The goal here is to find the best stoploss for the strategy in order to have the
 Edge module then forces stoploss value it evaluated to your strategy dynamically.
 
 ### Position size
+
 Edge also dictates the stake amount for each trade to the bot according to the following factors:
 
 - Allowed capital at risk
@@ -90,13 +112,17 @@ Edge also dictates the stake amount for each trade to the bot according to the f
 
 Allowed capital at risk is calculated as follows:
 
-    Allowed capital at risk = (Capital available_percentage) X (Allowed risk per trade)
+```
+Allowed capital at risk = (Capital available_percentage) X (Allowed risk per trade)
+```
 
 Stoploss is calculated as described above against historical data.
 
 Your position size then will be:
 
-    Position size = (Allowed capital at risk) / Stoploss
+```
+Position size = (Allowed capital at risk) / Stoploss
+```
 
 Example:
 
@@ -115,100 +141,29 @@ Available capital doesn’t change before a position is sold. Let’s assume tha
 So the Bot receives another buy signal for trade 4 with a stoploss at 2% then your position size would be **0.055 / 0.02 = 2.75 ETH**.
 
 ## Configurations
+
 Edge module has following configuration options:
 
-#### enabled
-If true, then Edge will run periodically.
-
-(defaults to false)
-
-#### process_throttle_secs
-How often should Edge run in seconds?
-
-(defaults to 3600 so one hour)
-
-#### calculate_since_number_of_days
-Number of days of data against which Edge calculates Win Rate, Risk Reward and Expectancy
-Note that it downloads historical data so increasing this number would lead to slowing down the bot.
-
-(defaults to 7)
-
-#### capital_available_percentage
-This is the percentage of the total capital on exchange in stake currency.
-
-As an example if you have 10 ETH available in your wallet on the exchange and this value is 0.5 (which is 50%), then the bot will use a maximum amount of 5 ETH for trading and considers it as available capital.
-
-(defaults to 0.5)
-
-#### allowed_risk
-Percentage of allowed risk per trade.
-
-(defaults to 0.01 so 1%)
-
-#### stoploss_range_min
-
-Minimum stoploss.
-
-(defaults to -0.01)
-
-#### stoploss_range_max
-
-Maximum stoploss.
-
-(defaults to -0.10)
-
-#### stoploss_range_step
-
-As an example if this is set to -0.01 then Edge will test the strategy for \[-0.01, -0,02, -0,03 ..., -0.09, -0.10\] ranges.
-Note than having a smaller step means having a bigger range which could lead to slow calculation.
-
-If you set this parameter to -0.001, you then slow down the Edge calculation by a factor of 10.
-
-(defaults to -0.01)
-
-#### minimum_winrate
-
-It filters out pairs which don't have at least minimum_winrate.
-
-This comes handy if you want to be conservative and don't comprise win rate in favour of risk reward ratio.
-
-(defaults to 0.60)
-
-#### minimum_expectancy
-
-It filters out pairs which have the expectancy lower than this number.
-
-Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ return.
-
-(defaults to 0.20)
-
-#### min_trade_number
-
-When calculating *W*, *R* and *E* (expectancy) against historical data, you always want to have a minimum number of trades. The more this number is the more Edge is reliable.
-
-Having a win rate of 100% on a single trade doesn't mean anything at all. But having a win rate of 70% over past 100 trades means clearly something.
-
-(defaults to 10, it is highly recommended not to decrease this number)
-
-#### max_trade_duration_minute
-
-Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.
-
-**NOTICE:** While configuring this value, you should take into consideration your ticker interval. As an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. Default value is set assuming your strategy interval is relatively small (1m or 5m, etc.).
-
-(defaults to 1 day, i.e. to 60 * 24 = 1440 minutes)
-
-#### remove_pumps
-
-Edge will remove sudden pumps in a given market while going through historical data. However, given that pumps happen very often in crypto markets, we recommend you keep this off.
-
-(defaults to false)
+|  Parameter | Description |
+|------------|-------------|
+| `enabled` | If true, then Edge will run periodically. <br>*Defaults to `false`.* <br> **Datatype:** Boolean
+| `process_throttle_secs` | How often should Edge run in seconds. <br>*Defaults to `3600` (once per hour).* <br> **Datatype:** Integer
+| `calculate_since_number_of_days` | Number of days of data against which Edge calculates Win Rate, Risk Reward and Expectancy. <br> **Note** that it downloads historical data so increasing this number would lead to slowing down the bot. <br>*Defaults to `7`.* <br> **Datatype:** Integer
+| `allowed_risk` | Ratio of allowed risk per trade. <br>*Defaults to `0.01` (1%)).* <br> **Datatype:** Float
+| `stoploss_range_min` | Minimum stoploss. <br>*Defaults to `-0.01`.* <br> **Datatype:** Float
+| `stoploss_range_max` | Maximum stoploss. <br>*Defaults to `-0.10`.* <br> **Datatype:** Float
+| `stoploss_range_step` | As an example if this is set to -0.01 then Edge will test the strategy for `[-0.01, -0,02, -0,03 ..., -0.09, -0.10]` ranges. <br> **Note** than having a smaller step means having a bigger range which could lead to slow calculation. <br> If you set this parameter to -0.001, you then slow down the Edge calculation by a factor of 10. <br>*Defaults to `-0.001`.* <br> **Datatype:** Float
+| `minimum_winrate` | It filters out pairs which don't have at least minimum_winrate. <br>This comes handy if you want to be conservative and don't comprise win rate in favour of risk reward ratio. <br>*Defaults to `0.60`.* <br> **Datatype:** Float
+| `minimum_expectancy` | It filters out pairs which have the expectancy lower than this number. <br>Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ return. <br>*Defaults to `0.20`.* <br> **Datatype:** Float
+| `min_trade_number` | When calculating *W*, *R* and *E* (expectancy) against historical data, you always want to have a minimum number of trades. The more this number is the more Edge is reliable. <br>Having a win rate of 100% on a single trade doesn't mean anything at all. But having a win rate of 70% over past 100 trades means clearly something. <br>*Defaults to `10` (it is highly recommended not to decrease this number).* <br> **Datatype:** Integer
+| `max_trade_duration_minute` | Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.<br>**NOTICE:** While configuring this value, you should take into consideration your timeframe. As an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. Default value is set assuming your strategy interval is relatively small (1m or 5m, etc.).<br>*Defaults to `1440` (one day).* <br> **Datatype:** Integer
+| `remove_pumps` | Edge will remove sudden pumps in a given market while going through historical data. However, given that pumps happen very often in crypto markets, we recommend you keep this off.<br>*Defaults to `false`.* <br> **Datatype:** Boolean
 
 ## Running Edge independently
 
 You can run Edge independently in order to see in details the result. Here is an example:
 
-```bash
+``` bash
 freqtrade edge
 ```
 
@@ -232,6 +187,12 @@ An example of its output:
 | APPC/BTC  |      -0.02 |       0.44 |                2.28 |                   1.27 |         0.44 |                       25 |                       43 |
 | NEBL/BTC  |      -0.03 |       0.63 |                1.29 |                   0.58 |         0.44 |                       19 |                       59 |
 
+Edge produced the above table by comparing `calculate_since_number_of_days` to `minimum_expectancy` to find `min_trade_number` historical information based on the config file. The timerange Edge uses for its comparisons can be further limited by using the `--timerange` switch.
+
+In live and dry-run modes, after the `process_throttle_secs` has passed, Edge will again process `calculate_since_number_of_days` against `minimum_expectancy` to find `min_trade_number`. If no `min_trade_number` is found, the bot will return "whitelist empty". Depending on the trade strategy being deployed, "whitelist empty" may be return much of the time - or *all* of the time. The use of Edge may also cause trading to occur in bursts, though this is rare.
+
+If you encounter "whitelist empty" a lot, condsider tuning `calculate_since_number_of_days`, `minimum_expectancy`  and `min_trade_number` to align to the trading frequency of your strategy.
+
 ### Update cached pairs with the latest data
 
 Edge requires historic data the same way as backtesting does.

docs/exchanges.md

@@ -5,7 +5,7 @@ This page combines common gotchas and informations which are exchange-specific a
 ## Binance
 
 !!! Tip "Stoploss on Exchange"
-    Binance is currently the only exchange supporting `stoploss_on_exchange`. It provides great advantages, so we recommend to benefit from it.
+    Binance supports `stoploss_on_exchange` and uses stop-loss-limit orders. It provides great advantages, so we recommend to benefit from it.
 
 ### Blacklists
 
@@ -22,13 +22,29 @@ Binance has been split into 3, and users must use the correct ccxt exchange ID f
 
 ## Kraken
 
+!!! Tip "Stoploss on Exchange"
+    Kraken supports `stoploss_on_exchange` and uses stop-loss-market orders. It provides great advantages, so we recommend to benefit from it, however since the resulting order is a stoploss-market order, sell-rates are not guaranteed, which makes this feature less secure than on other exchanges. This limitation is based on kraken's policy [source](https://blog.kraken.com/post/1234/announcement-delisting-pairs-and-temporary-suspension-of-advanced-order-types/) and [source2](https://blog.kraken.com/post/1494/kraken-enables-advanced-orders-and-adds-10-currency-pairs/) - which has stoploss-limit orders disabled.
+
 ### Historic Kraken data
 
 The Kraken API does only provide 720 historic candles, which is sufficient for Freqtrade dry-run and live trade modes, but is a problem for backtesting.
 To download data for the Kraken exchange, using `--dl-trades` is mandatory, otherwise the bot will download the same 720 candles over and over, and you'll not have enough backtest data.
 
+Due to the heavy rate-limiting applied by Kraken, the following configuration section should be used to download data:
+
+``` json
+    "ccxt_async_config": {
+        "enableRateLimit": true,
+        "rateLimit": 3100
+    },
+```
+
 ## Bittrex
 
+### Order types
+
+Bittrex does not support market orders. If you have a message at the bot startup about this, you should change order type values set in your configuration and/or in the strategy from `"market"` to `"limit"`. See some more details on this [here in the FAQ](faq.md#im-getting-the-exchange-bittrex-does-not-support-market-orders-message-and-cannot-run-my-strategy).
+
 ### Restricted markets
 
 Bittrex split its exchange into US and International versions.
@@ -55,9 +71,49 @@ res = [ f"{x['MarketCurrency']}/{x['BaseCurrency']}" for x in ct.publicGetMarket
 print(res)
 ```
 
+## FTX
+
+!!! Tip "Stoploss on Exchange"
+    FTX 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.
+
+
+### Using subaccounts
+
+To use subaccounts with FTX, you need to edit the configuration and add the following:
+
+``` json
+"exchange": {
+    "ccxt_config": {
+        "headers": {
+            "FTX-SUBACCOUNT": "name"
+        }
+    },
+}
+```
+
+!!! Note
+    Older versions of freqtrade may require this key to be added to `"ccxt_async_config"` as well.
+
+## All exchanges
+
+Should you experience constant errors with Nonce (like `InvalidNonce`), it is best to regenerate the API keys. Resetting Nonce is difficult and it's usually easier to regenerate the API keys.
+
+
 ## Random notes for other exchanges
 
 * The Ocean (exchange id: `theocean`) exchange uses Web3 functionality and requires `web3` python package to be installed:
 ```shell
 $ pip3 install web3
 ```
+
+### Getting latest price / Incomplete candles
+
+Most exchanges return current incomplete candle via their OHLCV/klines API interface.
+By default, Freqtrade assumes that incomplete candle is fetched from the exchange and removes the last candle assuming it's the incomplete candle.
+
+Whether your exchange returns incomplete candles or not can be checked using [the helper script](developer.md#Incomplete-candles) from the Contributor documentation.
+
+Due to the danger of repainting, Freqtrade does not allow you to use this incomplete candle.
+
+However, if it is based on the need for the latest price for your strategy - then this requirement can be acquired using the [data provider](strategy-customization.md#possible-options-for-dataprovider) from within the strategy.

docs/faq.md

@@ -1,5 +1,9 @@
 # Freqtrade FAQ
 
+## Beginner Tips & Tricks
+
+* When you work with your strategy & hyperopt file you should use a proper code editor like vscode or Pycharm. A good code editor will provide syntax highlighting as well as line numbers, making it easy to find syntax errors (most likely, pointed out by Freqtrade during startup).
+
 ## Freqtrade common issues
 
 ### The bot does not start
@@ -15,10 +19,12 @@ This could have the following reasons:
 
 ### I have waited 5 minutes, why hasn't the bot made any trades yet?!
 
-Depending on the buy strategy, the amount of whitelisted coins, the
+* Depending on the buy strategy, the amount of whitelisted coins, the
 situation of the market etc, it can take up to hours to find good entry
 position for a trade. Be patient!
 
+* Or it may because of a configuration error? Best check the logs, it's usually telling you if the bot is simply not getting buy signals (only heartbeat messages), or if there is something wrong (errors / exceptions in the log).
+
 ### I have made 12 trades already, why is my total profit negative?!
 
 I understand your disappointment but unfortunately 12 trades is just
@@ -45,12 +51,42 @@ the tutorial [here|Testing-new-strategies-with-Hyperopt](bot-usage.md#hyperopt-c
 
 You can use the `/forcesell all` command from Telegram.
 
-### I get the message "RESTRICTED_MARKET"
+### I want to run multiple bots on the same machine
+
+Please look at the [advanced setup documentation Page](advanced-setup.md#running-multiple-instances-of-freqtrade).
+
+### I'm getting "Missing data fillup" messages in the log
+
+This message is just a warning that the latest candles had missing candles in them.
+Depending on the exchange, this can indicate that the pair didn't have a trade for the timeframe you are using - and the exchange does only return candles with volume.
+On low volume pairs, this is a rather common occurance.
+
+If this happens for all pairs in the pairlist, this might indicate a recent exchange downtime. Please check your exchange's public channels for details.
+
+Irrespectively of the reason, Freqtrade will fill up these candles with "empty" candles, where open, high, low and close are set to the previous candle close - and volume is empty. In a chart, this will look like a `_` - and is aligned with how exchanges usually represent 0 volume candles.
+
+### I'm getting the "RESTRICTED_MARKET" message in the log
 
 Currently known to happen for US Bittrex users.  
 
 Read [the Bittrex section about restricted markets](exchanges.md#restricted-markets) for more information.
 
+### I'm getting the "Exchange Bittrex does not support market orders." message and cannot run my strategy
+
+As the message says, Bittrex does not support market orders and you have one of the [order types](configuration.md/#understand-order_types) set to "market". Probably your strategy was written with other exchanges in mind and sets "market" orders for "stoploss" orders, which is correct and preferable for most of the exchanges supporting market orders (but not for Bittrex).
+
+To fix it for Bittrex, redefine order types in the strategy to use "limit" instead of "market":
+
+```
+    order_types = {
+        ...
+        'stoploss': 'limit',
+        ...
+    }
+```
+
+Same fix should be done in the configuration file, if order types are defined in your custom config rather than in the strategy.
+
 ### How do I search the bot logs for something?
 
 By default, the bot writes its log into stderr stream. This is implemented this way so that you can easily separate the bot's diagnostics messages from Backtesting, Edge and Hyperopt results, output from other various Freqtrade utility subcommands, as well as from the output of your custom `print()`'s you may have inserted into your strategy. So if you need to search the log messages with the grep utility, you need to redirect stderr to stdout and disregard stdout.
@@ -84,7 +120,7 @@ $ tail -f /path/to/mylogfile.log | grep 'something'
 ```
 from a separate terminal window.
 
-On Windows, the `--logfilename` option is also supported by Freqtrade and you can use the `findstr` command to search the log for the string of interest:
+On Windows, the `--logfile` option is also supported by Freqtrade and you can use the `findstr` command to search the log for the string of interest:
 ```
 > type \path\to\mylogfile.log | findstr "something"
 ```
@@ -99,25 +135,27 @@ to find a great result (unless if you are very lucky), so you probably
 have to run it for 10.000 or more. But it will take an eternity to
 compute.
 
-We recommend you to run it at least 10.000 epochs:
+Since hyperopt uses Bayesian search, running for too many epochs may not produce greater results.
+
+It's therefore recommended to run between 500-1000 epochs over and over until you hit at least 10.000 epocs in total (or are satisfied with the result). You can best judge by looking at the results - if the bot keeps discovering better strategies, it's best to keep on going. 
 
 ```bash
-freqtrade hyperopt -e 10000
+freqtrade hyperopt -e 1000
 ```
 
 or if you want intermediate result to see
 
 ```bash
-for i in {1..100}; do freqtrade hyperopt -e 100; done
+for i in {1..100}; do freqtrade hyperopt -e 1000; done
 ```
 
-### Why it is so long to run hyperopt?
+### Why does it take a long time to run hyperopt?
 
-Finding a great Hyperopt results takes time.
+* Discovering a great strategy with Hyperopt takes time. Study www.freqtrade.io, the Freqtrade Documentation page, join the Freqtrade [Slack community](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) - or the Freqtrade [discord community](https://discord.gg/X89cVG). While you patiently wait for the most advanced, free crypto bot in the world, to hand you a possible golden strategy specially designed just for you.
 
-If you wonder why it takes a while to find great hyperopt results
+* If you wonder why it can take from 20 minutes to days to do 1000 epocs here are some answers:
 
-This answer was written during the under the release 0.15.1, when we had:
+This answer was written during the release 0.15.1, when we had:
 
 - 8 triggers
 - 9 guards: let's say we evaluate even 10 values from each
@@ -127,7 +165,14 @@ The following calculation is still very rough and not very precise
 but it will give the idea. With only these triggers and guards there is
 already 8\*10^9\*10 evaluations. A roughly total of 80 billion evals.
 Did you run 100 000 evals? Congrats, you've done roughly 1 / 100 000 th
-of the search space.
+of the search space, assuming that the bot never tests the same parameters more than once.
+
+* The time it takes to run 1000 hyperopt epocs depends on things like: The available cpu, harddisk, ram, timeframe, timerange, indicator settings, indicator count, amount of coins that hyperopt test strategies on and the resulting trade count - which can be 650 trades in a year or 10.0000 trades depending if the strategy aims for big profits by trading rarely or for many low profit trades. 
+
+Example: 4% profit 650 times vs 0,3% profit a trade 10.000 times in a year. If we assume you set the --timerange to 365 days. 
+
+Example: 
+`freqtrade --config config.json --strategy SampleStrategy --hyperopt SampleHyperopt -e 1000 --timerange 20190601-20200601`
 
 ## Edge module
 

docs/hyperopt.md

@@ -6,12 +6,32 @@ algorithms included in the `scikit-optimize` package to accomplish this. The
 search will burn all your CPU cores, make your laptop sound like a fighter jet
 and still take a long time.
 
+In general, the search for best parameters starts with a few random combinations (see [below](#reproducible-results) for more details) and then uses Bayesian search with a ML regressor algorithm (currently ExtraTreesRegressor) to quickly find a combination of parameters in the search hyperspace that minimizes the value of the [loss function](#loss-functions).
+
 Hyperopt requires historic data to be available, just as backtesting does.
-To learn how to get data for the pairs and exchange you're interrested in, head over to the [Data Downloading](data-download.md) section of the documentation.
+To learn how to get data for the pairs and exchange you're interested in, head over to the [Data Downloading](data-download.md) section of the documentation.
 
 !!! Bug
     Hyperopt can crash when used with only 1 CPU Core as found out in [Issue #1133](https://github.com/freqtrade/freqtrade/issues/1133)
 
+## Install hyperopt dependencies
+
+Since Hyperopt dependencies are not needed to run the bot itself, are heavy, can not be easily built on some platforms (like Raspberry PI), they are not installed by default. Before you run Hyperopt, you need to install the corresponding dependencies, as described in this section below.
+
+!!! Note
+    Since Hyperopt is a resource intensive process, running it on a Raspberry Pi is not recommended nor supported.
+
+### Docker
+
+The docker-image includes hyperopt dependencies, no further action needed.
+
+### Easy installation script (setup.sh) / Manual installation
+
+```bash
+source .env/bin/activate
+pip install -r requirements-hyperopt.txt
+```
+
 ## Prepare Hyperopting
 
 Before we start digging into Hyperopt, we recommend you to take a look at
@@ -27,9 +47,9 @@ This will create a new hyperopt file from a template, which will be located unde
 Depending on the space you want to optimize, only some of the below are required:
 
 * fill `buy_strategy_generator` - for buy signal optimization
-* fill `indicator_space` - for buy signal optimzation
+* fill `indicator_space` - for buy signal optimization
 * fill `sell_strategy_generator` - for sell signal optimization
-* fill `sell_indicator_space` - for sell signal optimzation
+* fill `sell_indicator_space` - for sell signal optimization
 
 !!! Note
     `populate_indicators` needs to create all indicators any of thee spaces may use, otherwise hyperopt will not work.
@@ -43,6 +63,9 @@ Optional - can also be loaded from a strategy:
 !!! Note
     Assuming the optional methods are not in your hyperopt file, please use `--strategy AweSomeStrategy` which contains these methods so hyperopt can use these methods instead.
 
+!!! Note
+    You always have to provide a strategy to Hyperopt, even if your custom Hyperopt class contains all methods.
+
 Rarely you may also need to override:
 
 * `roi_space` - for custom ROI optimization (if you need the ranges for the ROI parameters in the optimization hyperspace that differ from default)
@@ -53,7 +76,7 @@ Rarely you may also need to override:
 !!! Tip "Quickly optimize ROI, stoploss and trailing stoploss"
     You can quickly optimize the spaces `roi`, `stoploss` and `trailing` without changing anything (i.e. without creation of a "complete" Hyperopt class with dimensions, parameters, triggers and guards, as described in this document) from the default hyperopt template by relying on your strategy to do most of the calculations.
 
-    ``` python
+    ```python
     # Have a working strategy at hand.
     freqtrade new-hyperopt --hyperopt EmptyHyperopt
 
@@ -71,17 +94,17 @@ Copy the file `user_data/hyperopts/sample_hyperopt.py` into `user_data/hyperopts
 
 There are two places you need to change in your hyperopt file to add a new buy hyperopt for testing:
 
-- Inside `indicator_space()` - the parameters hyperopt shall be optimizing.
-- Inside `populate_buy_trend()` - applying the parameters.
+* Inside `indicator_space()` - the parameters hyperopt shall be optimizing.
+* Inside `populate_buy_trend()` - applying the parameters.
 
 There you have two different types of indicators: 1. `guards` and 2. `triggers`.
 
 1. Guards are conditions like "never buy if ADX < 10", or never buy if current price is over EMA10.
-2. Triggers are ones that actually trigger buy in specific moment, like "buy when EMA5 crosses over EMA10" or "buy when close price touches lower bollinger band".
+2. Triggers are ones that actually trigger buy in specific moment, like "buy when EMA5 crosses over EMA10" or "buy when close price touches lower Bollinger band".
 
 Hyperoptimization will, for each eval round, pick one trigger and possibly
 multiple guards. The constructed strategy will be something like
-"*buy exactly when close price touches lower bollinger band, BUT only if
+"*buy exactly when close price touches lower Bollinger band, BUT only if
 ADX > 10*".
 
 If you have updated the buy strategy, i.e. changed the contents of
@@ -99,10 +122,11 @@ Place the corresponding settings into the following methods
 The configuration and rules are the same than for buy signals.
 To avoid naming collisions in the search-space, please prefix all sell-spaces with `sell-`.
 
-#### Using ticker-interval as part of the Strategy
+#### Using timeframe as a part of the Strategy
 
-The Strategy exposes the ticker-interval as `self.ticker_interval`. The same value is available as class-attribute `HyperoptName.ticker_interval`.
-In the case of the linked sample-value this would be `SampleHyperOpt.ticker_interval`.
+The Strategy class exposes the timeframe value as the `self.timeframe` attribute.
+The same value is available as class-attribute `HyperoptName.timeframe`.
+In the case of the linked sample-value this would be `SampleHyperOpt.timeframe`.
 
 ## Solving a Mystery
 
@@ -137,7 +161,7 @@ one we call `trigger` and use it to decide which buy trigger we want to use.
 
 So let's write the buy strategy using these values:
 
-``` python
+```python
         def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
             conditions = []
             # GUARDS AND TRENDS
@@ -155,6 +179,9 @@ So let's write the buy strategy using these values:
                         dataframe['macd'], dataframe['macdsignal']
                     ))
 
+            # Check that volume is not 0
+            conditions.append(dataframe['volume'] > 0)
+
             if conditions:
                 dataframe.loc[
                     reduce(lambda x, y: x & y, conditions),
@@ -168,11 +195,7 @@ So let's write the buy strategy using these values:
 Hyperopting will now call this `populate_buy_trend` as many times you ask it (`epochs`)
 with different value combinations. It will then use the given historical data and make
 buys based on the buy signals generated with the above function and based on the results
-it will end with telling you which paramter combination produced the best profits.
-
-The search for best parameters starts with a few random combinations and then uses a
-regressor algorithm (currently ExtraTreesRegressor) to quickly find a parameter combination
-that minimizes the value of the [loss function](#loss-functions).
+it will end with telling you which parameter combination produced the best profits.
 
 The above setup expects to find ADX, RSI and Bollinger Bands in the populated indicators.
 When you want to test an indicator that isn't used by the bot currently, remember to
@@ -182,7 +205,7 @@ add it to the `populate_indicators()` method in your custom hyperopt file.
 
 Each hyperparameter tuning requires a target. This is usually defined as a loss function (sometimes also called objective function), which should decrease for more desirable results, and increase for bad results.
 
-By default, FreqTrade uses a loss function, which has been with freqtrade since the beginning and optimizes mostly for short trade duration and avoiding losses.
+By default, Freqtrade uses a loss function, which has been with freqtrade since the beginning and optimizes mostly for short trade duration and avoiding losses.
 
 A different loss function can be specified by using the `--hyperopt-loss <Class-name>` argument.
 This class should be in its own file within the `user_data/hyperopts/` directory.
@@ -191,7 +214,10 @@ Currently, the following loss functions are builtin:
 
 * `DefaultHyperOptLoss` (default legacy Freqtrade hyperoptimization loss function)
 * `OnlyProfitHyperOptLoss` (which takes only amount of profit into consideration)
-* `SharpeHyperOptLoss` (optimizes Sharpe Ratio calculated on the trade returns)
+* `SharpeHyperOptLoss` (optimizes Sharpe Ratio calculated on trade returns relative to standard deviation)
+* `SharpeHyperOptLossDaily` (optimizes Sharpe Ratio calculated on **daily** trade returns relative to standard deviation)
+* `SortinoHyperOptLoss` (optimizes Sortino Ratio calculated on trade returns relative to **downside** standard deviation)
+* `SortinoHyperOptLossDaily` (optimizes Sortino Ratio calculated on **daily** trade returns relative to **downside** standard deviation)
 
 Creation of a custom loss function is covered in the [Advanced Hyperopt](advanced-hyperopt.md) part of the documentation.
 
@@ -206,7 +232,7 @@ We strongly recommend to use `screen` or `tmux` to prevent any connection loss.
 freqtrade hyperopt --config config.json --hyperopt <hyperoptname> -e 5000 --spaces all
 ```
 
-Use  `<hyperoptname>` as the name of the custom hyperopt used.
+Use `<hyperoptname>` as the name of the custom hyperopt used.
 
 The `-e` option will set how many evaluations hyperopt will do. We recommend
 running at least several thousand evaluations.
@@ -219,11 +245,11 @@ The `--spaces all` option determines that all possible parameters should be opti
 !!! Warning
     When switching parameters or changing configuration options, make sure to not use the argument `--continue` so temporary results can be removed.
 
-### Execute Hyperopt with Different Ticker-Data Source
+### Execute Hyperopt with different historical data source
 
-If you would like to hyperopt parameters using an alternate ticker data that
-you have on-disk, use the `--datadir PATH` option. Default hyperopt will
-use data from directory `user_data/data`.
+If you would like to hyperopt parameters using an alternate historical data set that
+you have on-disk, use the `--datadir PATH` option. By default, hyperopt
+uses data from directory `user_data/data`.
 
 ### Running Hyperopt with Smaller Testset
 
@@ -239,7 +265,7 @@ freqtrade hyperopt --timerange 20180401-20180501
 Hyperopt can reuse `populate_indicators`, `populate_buy_trend`, `populate_sell_trend` from your strategy, assuming these methods are **not** in your custom hyperopt file, and a strategy is provided.
 
 ```bash
-freqtrade hyperopt --strategy SampleStrategy --customhyperopt SampleHyperopt
+freqtrade hyperopt --strategy SampleStrategy --hyperopt SampleHyperopt
 ```
 
 ### Running Hyperopt with Smaller Search Space
@@ -271,7 +297,7 @@ In some situations, you may need to run Hyperopt (and Backtesting) with the
 By default, hyperopt emulates the behavior of the Freqtrade Live Run/Dry Run, where only one
 open trade is allowed for every traded pair. The total number of trades open for all pairs
 is also limited by the `max_open_trades` setting. During Hyperopt/Backtesting this may lead to
-some potential trades to be hidden (or masked) by previosly open trades.
+some potential trades to be hidden (or masked) by previously open trades.
 
 The `--eps`/`--enable-position-stacking` argument allows emulation of buying the same pair multiple times,
 while `--dmmp`/`--disable-max-market-positions` disables applying `max_open_trades`
@@ -284,6 +310,16 @@ number).
 You can also enable position stacking in the configuration file by explicitly setting
 `"position_stacking"=true`.
 
+### Reproducible results
+
+The search for optimal parameters starts with a few (currently 30) random combinations in the hyperspace of parameters, random Hyperopt epochs. These random epochs are marked with an asterisk character (`*`) in the first column in the Hyperopt output.
+
+The initial state for generation of these random values (random state) is controlled by the value of the `--random-state` command line option. You can set it to some arbitrary value of your choice to obtain reproducible results.
+
+If you have not set this value explicitly in the command line options, Hyperopt seeds the random state with some random value for you. The random state value for each Hyperopt run is shown in the log, so you can copy and paste it into the `--random-state` command line option to repeat the set of the initial random epochs used.
+
+If you have not changed anything in the command line options, configuration, timerange, Strategy and Hyperopt classes, historical data and the Loss Function -- you should obtain same hyperoptimization results with same random state value used.
+
 ## Understand the Hyperopt Result
 
 Once Hyperopt is completed you can use the result to create a new strategy.
@@ -313,7 +349,7 @@ method, what those values match to.
 
 So for example you had `rsi-value: 29.0` so we would look at `rsi`-block, that translates to the following code block:
 
-``` python
+```python
 (dataframe['rsi'] < 29.0)
 ```
 
@@ -334,6 +370,9 @@ By default, hyperopt prints colorized results -- epochs with positive profit are
 
 You can use the `--print-all` command line option if you would like to see all results in the hyperopt output, not only the best ones. When `--print-all` is used, current best results are also colorized by default -- they are printed in bold (bright) style. This can also be switched off with the `--no-color` command line option.
 
+!!! Note "Windows and color output"
+    Windows does not support color-output nativly, therefore it is automatically disabled. To have color-output for hyperopt running under windows, please consider using WSL.
+
 ### Understand Hyperopt ROI results
 
 If you are optimizing ROI (i.e. if optimization search-space contains 'all', 'default' or 'roi'), your result will look as follows and include a ROI table:
@@ -362,20 +401,21 @@ In order to use this best ROI table found by Hyperopt in backtesting and for liv
         118: 0
     }
 ```
+
 As stated in the comment, you can also use it as the value of the `minimal_roi` setting in the configuration file.
 
 #### Default ROI Search Space
 
-If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps). Hyperopt implements adaptive ranges for ROI tables with ranges for values in the ROI steps that depend on the ticker_interval used. By default the values vary in the following ranges (for some of the most used ticker intervals, values are rounded to 5 digits after the decimal point):
+If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps). Hyperopt implements adaptive ranges for ROI tables with ranges for values in the ROI steps that depend on the timeframe used. By default the values vary in the following ranges (for some of the most used timeframes, values are rounded to 5 digits after the decimal point):
 
-| # step | 1m |  | 5m |  | 1h |  | 1d |  |
-|---|---|---|---|---|---|---|---|---|
-| 1 | 0 | 0.01161...0.11992 | 0 | 0.03...0.31 | 0 | 0.06883...0.71124 | 0 | 0.12178...1.25835 |
-| 2 | 2...8 | 0.00774...0.04255 | 10...40 | 0.02...0.11 | 120...480 | 0.04589...0.25238 | 2880...11520 | 0.08118...0.44651 |
-| 3 | 4...20 | 0.00387...0.01547 | 20...100 | 0.01...0.04 | 240...1200 | 0.02294...0.09177 | 5760...28800 | 0.04059...0.16237 |
-| 4 | 6...44 | 0.0 | 30...220 | 0.0 | 360...2640 | 0.0 | 8640...63360 | 0.0 |
+| # step | 1m     |                   | 5m       |             | 1h         |                   | 1d           |                   |
+| ------ | ------ | ----------------- | -------- | ----------- | ---------- | ----------------- | ------------ | ----------------- |
+| 1      | 0      | 0.01161...0.11992 | 0        | 0.03...0.31 | 0          | 0.06883...0.71124 | 0            | 0.12178...1.25835 |
+| 2      | 2...8  | 0.00774...0.04255 | 10...40  | 0.02...0.11 | 120...480  | 0.04589...0.25238 | 2880...11520 | 0.08118...0.44651 |
+| 3      | 4...20 | 0.00387...0.01547 | 20...100 | 0.01...0.04 | 240...1200 | 0.02294...0.09177 | 5760...28800 | 0.04059...0.16237 |
+| 4      | 6...44 | 0.0               | 30...220 | 0.0         | 360...2640 | 0.0               | 8640...63360 | 0.0               |
 
-These ranges should be sufficient in most cases. The minutes in the steps (ROI dict keys) are scaled linearly depending on the ticker interval used. The ROI values in the steps (ROI dict values) are scaled logarithmically depending on the ticker interval used.
+These ranges should be sufficient in most cases. The minutes in the steps (ROI dict keys) are scaled linearly depending on the timeframe used. The ROI values in the steps (ROI dict values) are scaled logarithmically depending on the timeframe used.
 
 If you have the `generate_roi_table()` and `roi_space()` methods in your custom hyperopt file, remove them in order to utilize these adaptive ROI tables and the ROI hyperoptimization space generated by Freqtrade by default.
 
@@ -406,6 +446,7 @@ In order to use this best stoploss value found by Hyperopt in backtesting and fo
     # This attribute will be overridden if the config file contains "stoploss"
     stoploss = -0.27996
 ```
+
 As stated in the comment, you can also use it as the value of the `stoploss` setting in the configuration file.
 
 #### Default Stoploss Search Space
@@ -442,13 +483,14 @@ In order to use these best trailing stop parameters found by Hyperopt in backtes
     trailing_stop_positive_offset = 0.06038
     trailing_only_offset_is_reached = True
 ```
+
 As stated in the comment, you can also use it as the values of the corresponding settings in the configuration file.
 
 #### Default Trailing Stop Search Space
 
 If you are optimizing trailing stop values, Freqtrade creates the 'trailing' optimization hyperspace for you. By default, the `trailing_stop` parameter is always set to True in that hyperspace, the value of the `trailing_only_offset_is_reached` vary between True and False, the values of the `trailing_stop_positive` and `trailing_stop_positive_offset` parameters vary in the ranges 0.02...0.35 and 0.01...0.1 correspondingly, which is sufficient in most cases.
 
-Override the `trailing_space()` method and define the desired range in it if you need values of the trailing stop parameters to vary in other ranges during hyperoptimization. A sample for this method can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py).
+Override the `trailing_space()` method and define the desired range in it if you need values of the trailing stop parameters to vary in other ranges during hyperoptimization. A sample for this method can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py).
 
 ## Show details of Hyperopt results
 
@@ -459,8 +501,3 @@ After you run Hyperopt for the desired amount of epochs, you can later list all
 Once the optimized strategy has been implemented into your strategy, you should backtest this strategy to make sure everything is working as expected.
 
 To achieve same results (number of trades, their durations, profit, etc.) than during Hyperopt, please use same set of arguments `--dmmp`/`--disable-max-market-positions` and `--eps`/`--enable-position-stacking` for Backtesting.
-
-## Next Step
-
-Now you have a perfect bot and want to control it from Telegram. Your
-next step is to learn the [Telegram usage](telegram-usage.md).

docs/index.md

@@ -1,5 +1,5 @@
 # Freqtrade
-[![Build Status](https://travis-ci.org/freqtrade/freqtrade.svg?branch=develop)](https://travis-ci.org/freqtrade/freqtrade)
+[![Freqtrade CI](https://github.com/freqtrade/freqtrade/workflows/Freqtrade%20CI/badge.svg)](https://github.com/freqtrade/freqtrade/actions/)
 [![Coverage Status](https://coveralls.io/repos/github/freqtrade/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/freqtrade/freqtrade?branch=develop)
 [![Maintainability](https://api.codeclimate.com/v1/badges/5737e6d668200b7518ff/maintainability)](https://codeclimate.com/github/freqtrade/freqtrade/maintainability)
 
@@ -11,8 +11,10 @@
 <a class="github-button" href="https://github.com/freqtrade/freqtrade/archive/master.zip" data-icon="octicon-cloud-download" data-size="large" aria-label="Download freqtrade/freqtrade on GitHub">Download</a>
 <!-- Place this tag where you want the button to render. -->
 <a class="github-button" href="https://github.com/freqtrade" data-size="large" aria-label="Follow @freqtrade on GitHub">Follow @freqtrade</a>
+
 ## Introduction
-Freqtrade is a cryptocurrency trading bot written in Python.
+
+Freqtrade is a crypto-currency algorithmic trading software developed in python (3.6+) and supported on Windows, macOS and Linux.
 
 !!! Danger "DISCLAIMER"
     This software is for educational purposes only. Do not risk money which you are afraid to lose. USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS AND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR TRADING RESULTS.
@@ -23,18 +25,15 @@ Freqtrade is a cryptocurrency trading bot written in Python.
 
 ## Features
 
- - Based on Python 3.6+: For botting on any operating system — Windows, macOS and Linux.
- - Persistence: Persistence is achieved through sqlite database.
- - Dry-run mode: Run the bot without playing money.
- - Backtesting: Run a simulation of your buy/sell strategy with historical data.
- - Strategy Optimization by machine learning: Use machine learning to optimize your buy/sell strategy parameters with real exchange data.
- - 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.
- - Whitelist crypto-currencies: Select which crypto-currency you want to trade or use dynamic whitelists based on market (pair) trade volume.
- - Blacklist crypto-currencies: Select which crypto-currency you want to avoid.
- - Manageable via Telegram or REST APi: Manage the bot with Telegram or via the builtin REST API.
- - Display profit/loss in fiat: Display your profit/loss in any of 33 fiat currencies supported.
- - Daily summary of profit/loss: Receive the daily summary of your profit/loss.
- - Performance status report: Receive the performance status of your current trades.
+- 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).
+- Download market data: Download historical data of the exchange and the markets your may want to trade with.
+- Backtest: Test your strategy on downloaded historical data.
+- Optimize: Find the best parameters for your strategy using hyperoptimization which employs machining learning methods. You can optimize buy, sell, take profit (ROI), stop-loss and trailing stop-loss parameters for your strategy.
+- Select markets: Create your static list or use an automatic one based on top traded volumes and/or prices (not available during backtesting). You can also explicitly blacklist markets you don't want to trade.
+- Run: Test your strategy with simulated money (Dry-Run mode) or deploy it with real money (Live-Trade mode).
+- Run using Edge (optional module): The concept is to find the best historical [trade expectancy](edge.md#expectancy) by markets based on variation of the stop-loss and then allow/reject markets to trade. The sizing of the trade is based on a risk of a percentage of your capital.
+- Control/Monitor: Use Telegram or a REST API (start/stop the bot, show profit/loss, daily summary, current open trades results, etc.).
+- Analyse: Further analysis can be performed on either Backtesting data or Freqtrade trading history (SQL database), including automated standard plots, and methods to load the data into [interactive environments](data-analysis.md).
 
 ## Requirements
 
@@ -52,20 +51,23 @@ To run this bot we recommend you a cloud instance with a minimum of:
 
 ### Software requirements
 
+- Docker (Recommended)
+
+Alternatively
+
 - Python 3.6.x
 - pip (pip3)
 - git
 - TA-Lib
 - virtualenv (Recommended)
-- Docker (Recommended)
 
 ## Support
 
-Help / Slack
-For any questions not covered by the documentation or for further information about the bot, we encourage you to join our Slack channel.
+### Help / Slack
+For any questions not covered by the documentation or for further information about the bot, we encourage you to join our passionate  Slack community.
 
-Click [here](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) to join Slack channel.
+Click [here](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) to join the Freqtrade Slack channel.
 
 ## Ready to try?
 
-Begin by reading our installation guide [here](installation).
+Begin by reading our installation guide [for docker](docker.md), or for [installation without docker](installation.md).

docs/installation.md

@@ -2,6 +2,8 @@
 
 This page explains how to prepare your environment for running the bot.
 
+Please consider using the prebuilt [docker images](docker.md) to get started quickly while trying out freqtrade evaluating how it operates.
+
 ## Prerequisite
 
 ### Requirements
@@ -11,18 +13,10 @@ Click each one for install guide:
 * [Python >= 3.6.x](http://docs.python-guide.org/en/latest/starting/installation/)
 * [pip](https://pip.pypa.io/en/stable/installing/)
 * [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
-* [virtualenv](https://virtualenv.pypa.io/en/stable/installation/) (Recommended)
+* [virtualenv](https://virtualenv.pypa.io/en/stable/installation.html) (Recommended)
 * [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html) (install instructions below)
 
-### API keys
-
-Before running your bot in production you will need to setup few
-external API. In production mode, the bot will require valid Exchange API
-credentials. We also recommend a [Telegram bot](telegram-usage.md#setup-your-telegram-bot) (optional but recommended).
-
-### Setup your exchange account
-
-You will need to create API Keys (Usually you get `key` and `secret`) from the Exchange website and insert this into the appropriate fields in the configuration or when asked by the installation script.
+ We also recommend a [Telegram bot](telegram-usage.md#setup-your-telegram-bot), which is optional but recommended.
 
 ## Quick start
 
@@ -31,7 +25,7 @@ Freqtrade provides the Linux/MacOS Easy Installation script to install all depen
 !!! Note
     Windows installation is explained [here](#windows).
 
-The easiest way to install and run Freqtrade is to clone the bot GitHub repository and then run the Easy Installation script, if it's available for your platform.
+The easiest way to install and run Freqtrade is to clone the bot Github repository and then run the Easy Installation script, if it's available for your platform.
 
 !!! Note "Version considerations"
     When cloning the repository the default working branch has the name `develop`. This branch contains all last features (can be considered as relatively stable, thanks to automated tests). The `master` branch contains the code of the last release (done usually once per month on an approximately one week old snapshot of the `develop` branch to prevent packaging bugs, so potentially it's more stable).
@@ -42,11 +36,12 @@ The easiest way to install and run Freqtrade is to clone the bot GitHub reposito
 This can be achieved with the following commands:
 
 ```bash
-git clone git@github.com:freqtrade/freqtrade.git
+git clone https://github.com/freqtrade/freqtrade.git
 cd freqtrade
 git checkout master  # Optional, see (1)
 ./setup.sh --install
 ```
+
 (1) This command switches the cloned repository to the use of the `master` branch. It's not needed if you wish to stay on the `develop` branch. You may later switch between branches at any time with the `git checkout master`/`git checkout develop` commands.
 
 ## Easy Installation Script (Linux/MacOS)
@@ -64,11 +59,11 @@ usage:
 
 ** --install **
 
-With this option, the script will install everything you need to run the bot:
+With this option, the script will install the bot and most dependencies:
+You will need to have git and python3.6+ installed beforehand for this to work.
 
 * Mandatory software as: `ta-lib`
-* Setup your virtualenv
-* Configure your `config.json` file
+* Setup your virtualenv under `.env/`
 
 This option is a combination of installation tasks, `--reset` and `--config`.
 
@@ -82,7 +77,7 @@ This option will hard reset your branch (only if you are on either `master` or `
 
 ** --config **
 
-Use this option to configure the `config.json` configuration file. The script will interactively ask you questions to setup your bot and create your `config.json`.
+DEPRECATED - use `freqtrade new-config -c config.json` instead.
 
 ------
 
@@ -129,6 +124,17 @@ bash setup.sh -i
 
 #### 1. Install TA-Lib
 
+Use the provided ta-lib installation script
+
+```bash
+sudo ./build_helpers/install_ta-lib.sh
+```
+
+!!! Note
+    This will use the ta-lib tar.gz included in this repository.
+
+##### TA-Lib manual installation
+
 Official webpage: https://mrjbq7.github.io/ta-lib/install.html
 
 ```bash
@@ -184,7 +190,8 @@ python3 -m pip install -e .
 # Initialize the user_directory
 freqtrade create-userdir --userdir user_data/
 
-cp config.json.example config.json
+# Create a new configuration file
+freqtrade new-config --config config.json
 ```
 
 > *To edit the config please refer to [Bot Configuration](configuration.md).*
@@ -241,14 +248,14 @@ git clone https://github.com/freqtrade/freqtrade.git
 
 Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7/ta-lib#windows).
 
-As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial precompiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which needs to be downloaded and installed using `pip install TA_Lib‑0.4.17‑cp36‑cp36m‑win32.whl` (make sure to use the version matching your python version)
+As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial precompiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which needs to be downloaded and installed using `pip install TA_Lib‑0.4.18‑cp38‑cp38‑win_amd64.whl` (make sure to use the version matching your python version)
 
 ```cmd
 >cd \path\freqtrade-develop
 >python -m venv .env
 >.env\Scripts\activate.bat
 REM optionally install ta-lib from wheel
-REM >pip install TA_Lib‑0.4.17‑cp36‑cp36m‑win32.whl
+REM >pip install TA_Lib‑0.4.18‑cp38‑cp38‑win_amd64.whl
 >pip install -r requirements.txt
 >pip install -e .
 >freqtrade
@@ -270,3 +277,18 @@ The easiest way is to download install Microsoft Visual Studio Community [here](
 
 Now you have an environment ready, the next step is
 [Bot Configuration](configuration.md).
+
+## Troubleshooting
+
+### MacOS installation error
+
+Newer versions of MacOS may have installation failed with errors like `error: command 'g++' failed with exit status 1`.
+
+This error will require explicit installation of the SDK Headers, which are not installed by default in this version of MacOS.
+For MacOS 10.14, this can be accomplished with the below command.
+
+``` bash
+open /Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_10.14.pkg
+```
+
+If this file is inexistant, then you're probably on a different version of MacOS, so you may need to consult the internet for specific resolution details.

docs/plotting.md

@@ -31,7 +31,8 @@ usage: freqtrade plot-dataframe [-h] [-v] [--logfile FILE] [-V] [-c PATH]
                                 [--plot-limit INT] [--db-url PATH]
                                 [--trade-source {DB,file}] [--export EXPORT]
                                 [--export-filename PATH]
-                                [--timerange TIMERANGE] [-i TICKER_INTERVAL]
+                                [--timerange TIMERANGE] [-i TIMEFRAME]
+                                [--no-trades]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -50,32 +51,36 @@ optional arguments:
                         values cause huge files. Default: 750.
   --db-url PATH         Override trades database URL, this is useful in custom
                         deployments (default: `sqlite:///tradesv3.sqlite` for
-                        Live Run mode, `sqlite://` for Dry Run).
+                        Live Run mode, `sqlite:///tradesv3.dryrun.sqlite` for
+                        Dry Run).
   --trade-source {DB,file}
                         Specify the source for trades (Can be DB or file
                         (backtest file)) Default: file
   --export EXPORT       Export backtest results, argument are: trades.
                         Example: `--export=trades`
   --export-filename PATH
-                        Save backtest results to the file with this filename
-                        (default: `user_data/backtest_results/backtest-
-                        result.json`). Requires `--export` to be set as well.
-                        Example: `--export-filename=user_data/backtest_results
-                        /backtest_today.json`
+                        Save backtest results to the file with this filename.
+                        Requires `--export` to be set as well. Example:
+                        `--export-filename=user_data/backtest_results/backtest
+                        _today.json`
   --timerange TIMERANGE
                         Specify what timerange of data to use.
-  -i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
+  -i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
                         Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
                         `1d`).
+  --no-trades           Skip using trades from backtesting file and DB.
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
-  --logfile FILE        Log to the file specified.
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
   -V, --version         show program's version number and exit
   -c PATH, --config PATH
-                        Specify configuration file (default: `config.json`).
-                        Multiple --config options may be used. Can be set to
-                        `-` to read config from stdin.
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
   -d PATH, --datadir PATH
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
@@ -83,10 +88,9 @@ Common arguments:
 
 Strategy arguments:
   -s NAME, --strategy NAME
-                        Specify strategy class name (default:
-                        `DefaultStrategy`).
+                        Specify strategy class name which will be used by the
+                        bot.
   --strategy-path PATH  Specify additional strategy lookup path.
-
 ```
 
 Example:
@@ -136,21 +140,83 @@ To plot trades from a backtesting result, use `--export-filename <filename>`
 freqtrade plot-dataframe --strategy AwesomeStrategy --export-filename user_data/backtest_results/backtest-result.json -p BTC/ETH
 ```
 
+### Plot dataframe basics
+
+![plot-dataframe2](assets/plot-dataframe2.png)
+
+The `plot-dataframe` subcommand requires backtesting data, a strategy and either a backtesting-results file or a database, containing trades corresponding to the strategy.
+
+The resulting plot will have the following elements:
+
+* Green triangles: Buy signals from the strategy. (Note: not every buy signal generates a trade, compare to cyan circles.)
+* Red triangles: Sell signals from the strategy. (Also, not every sell signal terminates a trade, compare to red and green squares.)
+* Cyan circles: Trade entry points.
+* Red squares: Trade exit points for trades with loss or 0% profit.
+* Green squares: Trade exit points for profitable trades.
+* Indicators with values corresponding to the candle scale (e.g. SMA/EMA), as specified with `--indicators1`.
+* Volume (bar chart at the bottom of the main chart).
+* Indicators with values in different scales (e.g. MACD, RSI) below the volume bars, as specified with `--indicators2`.
+
+!!! Note "Bollinger Bands"
+    Bollinger bands are automatically added to the plot if the columns `bb_lowerband` and `bb_upperband` exist, and are painted as a light blue area spanning from the lower band to the upper band.
+
+#### Advanced plot configuration
+
+An advanced plot configuration can be specified in the strategy in the `plot_config` parameter.
+
+Additional features when using plot_config include:
+
+* Specify colors per indicator
+* Specify additional subplots
+
+The sample plot configuration below specifies fixed colors for the indicators. Otherwise consecutive plots may produce different colorschemes each time, making comparisons difficult.
+It also allows multiple subplots to display both MACD and RSI at the same time.
+
+Sample configuration with inline comments explaining the process:
+
+``` python
+    plot_config = {
+        'main_plot': {
+            # Configuration for main plot indicators.
+            # Specifies `ema10` to be red, and `ema50` to be a shade of gray
+            'ema10': {'color': 'red'},
+            'ema50': {'color': '#CCCCCC'},
+            # By omitting color, a random color is selected.
+            'sar': {},
+        },
+        'subplots': {
+            # Create subplot MACD
+            "MACD": {
+                'macd': {'color': 'blue'},
+                'macdsignal': {'color': 'orange'},
+            },
+            # Additional subplot RSI
+            "RSI": {
+                'rsi': {'color': 'red'},
+            }
+        }
+    }
+```
+
+!!! Note
+    The above configuration assumes that `ema10`, `ema50`, `macd`, `macdsignal` and `rsi` are columns in the DataFrame created by the strategy.
+
 ## Plot profit
 
 ![plot-profit](assets/plot-profit.png)
 
-The `freqtrade plot-profit` subcommand shows an interactive graph with three plots:
+The `plot-profit` subcommand shows an interactive graph with three plots:
 
-1) Average closing price for all pairs
-2) The summarized profit made by backtesting.
-   Note that this is not the real-world profit, but more of an estimate.
-3) Profit for each individual pair
+* Average closing price for all pairs.
+* The summarized profit made by backtesting.
+Note that this is not the real-world profit, but more of an estimate.
+* Profit for each individual pair.
 
 The first graph is good to get a grip of how the overall market progresses.
 
 The second graph will show if your algorithm works or doesn't.
 Perhaps you want an algorithm that steadily makes small profits, or one that acts less often, but makes big swings.
+This graph will also highlight the start (and end) of the Max drawdown period.
 
 The third graph can be useful to spot outliers, events in pairs that cause profit spikes.
 
@@ -158,10 +224,11 @@ Possible options for the `freqtrade plot-profit` subcommand:
 
 ```
 usage: freqtrade plot-profit [-h] [-v] [--logfile FILE] [-V] [-c PATH]
-                             [-d PATH] [--userdir PATH] [-p PAIRS [PAIRS ...]]
+                             [-d PATH] [--userdir PATH] [-s NAME]
+                             [--strategy-path PATH] [-p PAIRS [PAIRS ...]]
                              [--timerange TIMERANGE] [--export EXPORT]
                              [--export-filename PATH] [--db-url PATH]
-                             [--trade-source {DB,file}] [-i TICKER_INTERVAL]
+                             [--trade-source {DB,file}] [-i TIMEFRAME]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -173,34 +240,42 @@ optional arguments:
   --export EXPORT       Export backtest results, argument are: trades.
                         Example: `--export=trades`
   --export-filename PATH
-                        Save backtest results to the file with this filename
-                        (default: `user_data/backtest_results/backtest-
-                        result.json`). Requires `--export` to be set as well.
-                        Example: `--export-filename=user_data/backtest_results
-                        /backtest_today.json`
+                        Save backtest results to the file with this filename.
+                        Requires `--export` to be set as well. Example:
+                        `--export-filename=user_data/backtest_results/backtest
+                        _today.json`
   --db-url PATH         Override trades database URL, this is useful in custom
                         deployments (default: `sqlite:///tradesv3.sqlite` for
-                        Live Run mode, `sqlite://` for Dry Run).
+                        Live Run mode, `sqlite:///tradesv3.dryrun.sqlite` for
+                        Dry Run).
   --trade-source {DB,file}
                         Specify the source for trades (Can be DB or file
                         (backtest file)) Default: file
-  -i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
+  -i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
                         Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
                         `1d`).
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
-  --logfile FILE        Log to the file specified.
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
   -V, --version         show program's version number and exit
   -c PATH, --config PATH
-                        Specify configuration file (default: `config.json`).
-                        Multiple --config options may be used. Can be set to
-                        `-` to read config from stdin.
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
   -d PATH, --datadir PATH
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
                         Path to userdata directory.
 
+Strategy arguments:
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --strategy-path PATH  Specify additional strategy lookup path.
 ```
 
 The `-p/--pairs`  argument, can be used to limit the pairs that are considered for this calculation.
@@ -210,7 +285,7 @@ Examples:
 Use custom backtest-export file
 
 ``` bash
-freqtrade plot-profit  -p LTC/BTC --export-filename user_data/backtest_results/backtest-result-Strategy005.json
+freqtrade plot-profit  -p LTC/BTC --export-filename user_data/backtest_results/backtest-result.json
 ```
 
 Use custom database

docs/requirements-docs.txt

@@ -1,2 +1,2 @@
-mkdocs-material==4.5.1
+mkdocs-material==5.5.8
 mdx_truly_sane_lists==1.2

docs/rest-api.md

@@ -11,6 +11,9 @@ Sample configuration:
         "enabled": true,
         "listen_ip_address": "127.0.0.1",
         "listen_port": 8080,
+        "verbosity": "info",
+        "jwt_secret_key": "somethingrandom",
+        "CORS_origins": [],
         "username": "Freqtrader",
         "password": "SuperSecret1!"
     },
@@ -29,7 +32,7 @@ This should return the response:
 {"status":"pong"}
 ```
 
-All other endpoints return sensitive info and require authentication, so are not available through a web browser.
+All other endpoints return sensitive info and require authentication and are therefore not available through a web browser.
 
 To generate a secure password, either use a password manager, or use the below code snipped.
 
@@ -38,9 +41,12 @@ import secrets
 secrets.token_hex()
 ```
 
+!!! Hint
+    Use the same method to also generate a JWT secret key (`jwt_secret_key`).
+
 ### Configuration with docker
 
-If you run your bot using docker, you'll need to have the bot listen to incomming connections. The security is then handled by docker.
+If you run your bot using docker, you'll need to have the bot listen to incoming connections. The security is then handled by docker.
 
 ``` json
     "api_server": {
@@ -74,7 +80,7 @@ docker run -d \
 ## Consuming the API
 
 You can consume the API by using the script `scripts/rest_client.py`.
-The client script only requires the `requests` module, so FreqTrade does not need to be installed on the system.
+The client script only requires the `requests` module, so Freqtrade does not need to be installed on the system.
 
 ``` bash
 python3 scripts/rest_client.py <command> [optional parameters]
@@ -100,26 +106,30 @@ python3 scripts/rest_client.py --config rest_config.json <command> [optional par
 
 ## Available commands
 
-|  Command | Default | Description |
-|----------|---------|-------------|
-| `start` | | Starts the trader
-| `stop` | | Stops the trader
-| `stopbuy` | | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
-| `reload_conf` | | Reloads the configuration file
-| `show_config` | | Shows part of the current configuration with relevant settings to operation
-| `status` | | Lists all open trades
-| `count` | | Displays number of trades used and available
-| `profit` | | Display a summary of your profit/loss from close trades and some stats about your performance
-| `forcesell <trade_id>` | | Instantly sells the given trade  (Ignoring `minimum_roi`).
-| `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>` | 7 | Shows profit or loss per day, over the last n days
-| `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.
-| `version` | | Show version
+|  Command | Description |
+|----------|-------------|
+| `ping` | Simple command testing the API Readiness - requires no authentication.
+| `start` | Starts the trader
+| `stop` | Stops the trader
+| `stopbuy` | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
+| `reload_config` | Reloads the configuration file
+| `trades` | List last trades.
+| `delete_trade <trade_id>` | Remove trade from the database. Tries to close open orders. Requires manual handling of this trade on the exchange.
+| `show_config` | Shows part of the current configuration with relevant settings to operation
+| `logs` | Shows last log messages
+| `status` | Lists all open trades
+| `count` | Displays number of trades used and available
+| `profit` | Display a summary of your profit/loss from close trades and some stats about your performance
+| `forcesell <trade_id>` | Instantly sells the given trade  (Ignoring `minimum_roi`).
+| `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)
+| `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.
+| `version` | Show version
 
 Possible commands can be listed from the rest-client script using the `help` command.
 
@@ -129,76 +139,129 @@ python3 scripts/rest_client.py help
 
 ``` output
 Possible commands:
+
 balance
-        Get the account balance
-        :returns: json object
+	Get the account balance.
 
 blacklist
-        Show the current blacklist
+	Show the current blacklist.
+
         :param add: List of coins to add (example: "BNB/BTC")
-        :returns: json object
 
 count
-        Returns the amount of open trades
-        :returns: json object
+	Return the amount of open trades.
 
 daily
-        Returns the amount of open trades
-        :returns: json object
+	Return the amount of open trades.
+
+delete_trade
+	Delete trade from the database.
+        Tries to close open orders. Requires manual handling of this asset on the exchange.
+
+        :param trade_id: Deletes the trade with this ID from the database.
 
 edge
-        Returns information about edge
-        :returns: json object
+	Return information about edge.
 
 forcebuy
-        Buy an asset
+	Buy an asset.
+
         :param pair: Pair to buy (ETH/BTC)
         :param price: Optional - price to buy
-        :returns: json object of the trade
 
 forcesell
-        Force-sell a trade
+	Force-sell a trade.
+
         :param tradeid: Id of the trade (can be received via status command)
-        :returns: json object
+
+logs
+	Show latest logs.
+
+        :param limit: Limits log messages to the last <limit> logs. No limit to get all the trades.
 
 performance
-        Returns the performance of the different coins
-        :returns: json object
+	Return the performance of the different coins.
 
 profit
-        Returns the profit summary
-        :returns: json object
+	Return the profit summary.
 
-reload_conf
-        Reload configuration
-        :returns: json object
+reload_config
+	Reload configuration.
 
 show_config
+	
         Returns part of the configuration, relevant for trading operations.
-        :return: json object containing the version
 
 start
-        Start the bot if it's in stopped state.
-        :returns: json object
+	Start the bot if it's in the stopped state.
 
 status
-        Get the status of open trades
-        :returns: json object
+	Get the status of open trades.
 
 stop
-        Stop the bot. Use start to restart
-        :returns: json object
+	Stop the bot. Use `start` to restart.
 
 stopbuy
-        Stop buying (but handle sells gracefully).
-        use reload_conf to reset
-        :returns: json object
+	Stop buying (but handle sells gracefully). Use `reload_config` to reset.
+
+trades
+	Return trades history.
+
+        :param limit: Limits trades to the X last trades. No limit to get all the trades.
 
 version
-        Returns the version of the bot
-        :returns: json object containing the version
+	Return the version of the bot.
 
 whitelist
-        Show the current whitelist
-        :returns: json object
+	Show the current whitelist.
+
+
 ```
+
+## Advanced API usage using JWT tokens
+
+!!! Note
+    The below should be done in an application (a Freqtrade REST API client, which fetches info via API), and is not intended to be used on a regular basis.
+
+Freqtrade's REST API also offers JWT (JSON Web Tokens).
+You can login using the following command, and subsequently use the resulting access_token.
+
+``` bash
+> curl -X POST --user Freqtrader http://localhost:8080/api/v1/token/login
+{"access_token":"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpYXQiOjE1ODkxMTk2ODEsIm5iZiI6MTU4OTExOTY4MSwianRpIjoiMmEwYmY0NWUtMjhmOS00YTUzLTlmNzItMmM5ZWVlYThkNzc2IiwiZXhwIjoxNTg5MTIwNTgxLCJpZGVudGl0eSI6eyJ1IjoiRnJlcXRyYWRlciJ9LCJmcmVzaCI6ZmFsc2UsInR5cGUiOiJhY2Nlc3MifQ.qt6MAXYIa-l556OM7arBvYJ0SDI9J8bIk3_glDujF5g","refresh_token":"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpYXQiOjE1ODkxMTk2ODEsIm5iZiI6MTU4OTExOTY4MSwianRpIjoiZWQ1ZWI3YjAtYjMwMy00YzAyLTg2N2MtNWViMjIxNWQ2YTMxIiwiZXhwIjoxNTkxNzExNjgxLCJpZGVudGl0eSI6eyJ1IjoiRnJlcXRyYWRlciJ9LCJ0eXBlIjoicmVmcmVzaCJ9.d1AT_jYICyTAjD0fiQAr52rkRqtxCjUGEMwlNuuzgNQ"}
+
+> access_token="eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpYXQiOjE1ODkxMTk2ODEsIm5iZiI6MTU4OTExOTY4MSwianRpIjoiMmEwYmY0NWUtMjhmOS00YTUzLTlmNzItMmM5ZWVlYThkNzc2IiwiZXhwIjoxNTg5MTIwNTgxLCJpZGVudGl0eSI6eyJ1IjoiRnJlcXRyYWRlciJ9LCJmcmVzaCI6ZmFsc2UsInR5cGUiOiJhY2Nlc3MifQ.qt6MAXYIa-l556OM7arBvYJ0SDI9J8bIk3_glDujF5g"
+# Use access_token for authentication
+> curl -X GET --header "Authorization: Bearer ${access_token}" http://localhost:8080/api/v1/count
+
+```
+
+Since the access token has a short timeout (15 min) - the `token/refresh` request should be used periodically to get a fresh access token:
+
+``` bash
+> curl -X POST --header "Authorization: Bearer ${refresh_token}"http://localhost:8080/api/v1/token/refresh
+{"access_token":"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpYXQiOjE1ODkxMTk5NzQsIm5iZiI6MTU4OTExOTk3NCwianRpIjoiMDBjNTlhMWUtMjBmYS00ZTk0LTliZjAtNWQwNTg2MTdiZDIyIiwiZXhwIjoxNTg5MTIwODc0LCJpZGVudGl0eSI6eyJ1IjoiRnJlcXRyYWRlciJ9LCJmcmVzaCI6ZmFsc2UsInR5cGUiOiJhY2Nlc3MifQ.1seHlII3WprjjclY6DpRhen0rqdF4j6jbvxIhUFaSbs"}
+```
+
+## CORS
+
+All web-based frontends are subject to [CORS](https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS) - Cross-Origin Resource Sharing.
+Since most of the requests to the Freqtrade API must be authenticated, a proper CORS policy is key to avoid security problems.
+Also, the standard disallows `*` CORS policies for requests with credentials, so this setting must be set appropriately.
+
+Users can configure this themselves via the `CORS_origins` configuration setting.
+It consists of a list of allowed sites that are allowed to consume resources from the bot's API.
+
+Assuming your application is deployed as `https://frequi.freqtrade.io/home/` - this would mean that the following configuration becomes necessary:
+
+```jsonc
+{
+    //...
+    "jwt_secret_key": "somethingrandom",
+    "CORS_origins": ["https://frequi.freqtrade.io"],
+    //...
+}
+```
+
+!!! Note
+    We strongly recommend to also set `jwt_secret_key` to something random and known only to yourself to avoid unauthorized access to your bot.

docs/sandbox-testing.md

@@ -1,104 +1,59 @@
 # Sandbox API testing
 
-Where an exchange provides a sandbox for risk-free integration, or end-to-end, testing CCXT provides access to these.
+Some exchanges provide sandboxes or testbeds for risk-free testing, while running the bot against a real exchange.
+With some configuration, freqtrade (in combination with ccxt) provides access to these.
 
-This document is a *light overview of configuring Freqtrade and GDAX sandbox.
-This can be useful to developers and trader alike as Freqtrade is quite customisable.
+This document is an overview to configure Freqtrade to be used with sandboxes.
+This can be useful to developers and trader alike.
 
-When testing your API connectivity, make sure to use the following URLs.
-***Website**
-https://public.sandbox.gdax.com
-***REST API**
-https://api-public.sandbox.gdax.com
+## Exchanges known to have a sandbox / testnet
+
+* [binance](https://testnet.binance.vision/)
+* [coinbasepro](https://public.sandbox.pro.coinbase.com)
+* [gemini](https://exchange.sandbox.gemini.com/)
+* [huobipro](https://www.testnet.huobi.pro/)
+* [kucoin](https://sandbox.kucoin.com/)
+* [phemex](https://testnet.phemex.com/) 
+
+!!! Note
+    We did not test correct functioning of all of the above testnets. Please report your experiences with each sandbox.
 
 ---
 
-# Configure a Sandbox account on Gdax
+## Configure a Sandbox account
 
-Aim of this document section
+When testing your API connectivity, make sure to use the appropriate sandbox / testnet URL.
 
-- An sanbox account
-- create 2FA (needed to create an API)
-- Add test 50BTC to account 
-- Create :
-- - API-KEY
-- - API-Secret
-- - API Password
+In general, you should follow these steps to enable an exchange's sandbox:
 
-## Acccount
+* Figure out if an exchange has a sandbox (most likely by using google or the exchange's support documents)
+* Create a sandbox account (often the sandbox-account requires separate registration)
+* [Add some test assets to account](#add-test-funds)
+* Create API keys
 
-This link will redirect to the sandbox main page to login / create account dialogues:
-https://public.sandbox.pro.coinbase.com/orders/
+### Add test funds
 
-After registration and Email confimation you wil be redirected into your sanbox account.  It is easy to verify you're in sandbox by checking the URL bar.
-> https://public.sandbox.pro.coinbase.com/
+Usually, sandbox exchanges allow depositing funds directly via web-interface.
+You should make sure to have a realistic amount of funds available to your test-account, so results are representable of your real account funds.
 
-## Enable 2Fa (a prerequisite to creating sandbox API Keys)
+!!! Warning
+    Test exchanges will **NEVER** require your real credit card or banking details!
 
-From within sand box site select your profile, top right.
->Or as a direct link: https://public.sandbox.pro.coinbase.com/profile
+## Configure freqtrade to use a exchange's sandbox
 
-From the menu panel to the left of the screen select
-
-> Security: "*View or Update*"
-
-In the new site select "enable authenticator" as typical google Authenticator.
-
-- open Google Authenticator on your phone
-- scan barcode
-- enter your generated 2fa
-
-## Enable API Access
-
-From within sandbox select profile>api>create api-keys
->or as a direct link: https://public.sandbox.pro.coinbase.com/profile/api
-
-Click on "create one" and ensure  **view** and **trade**  are "checked" and sumbit your 2FA
-
-- **Copy and paste the Passphase** into a notepade this will be needed later
-- **Copy and paste the API Secret** popup into a notepad this will needed later
-- **Copy and paste the API Key** into a notepad this will needed later
-
-## Add 50 BTC test funds
-
-To add funds, use the web interface deposit and withdraw buttons.
-
-To begin select 'Wallets' from the top menu.
-> Or as a direct link: https://public.sandbox.pro.coinbase.com/wallets
-
-- Deposits (bottom left of screen)
-- - Deposit Funds Bitcoin
-- - - Coinbase BTC Wallet
-- - - - Max (50 BTC)
-- - - - - Deposit
-
-*This process may be repeated for other currencies, ETH as example*
-
----
-
-# Configure Freqtrade to use Gax Sandbox 
-
-The aim of this document section
-
-- Enable sandbox URLs in Freqtrade
-- Configure API
-- - secret
-- - key
-- - passphrase
- 
-## Sandbox URLs
+### Sandbox URLs
 
 Freqtrade makes use of CCXT which in turn provides a list of URLs to Freqtrade.
 These include `['test']` and `['api']`.
 
-- `[Test]` if available will point to an Exchanges sandbox. 
-- `[Api]` normally used, and resolves to live API target on the exchange 
+* `[Test]` if available will point to an Exchanges sandbox.
+* `[Api]` normally used, and resolves to live API target on the exchange.
 
 To make use of sandbox / test add "sandbox": true, to your config.json
 
 ```json
   "exchange": {
-        "name": "gdax",
+        "name": "coinbasepro",
         "sandbox": true,
         "key": "5wowfxemogxeowo;heiohgmd",
         "secret": "/ZMH1P62rCVmwefewrgcewX8nh4gob+lywxfwfxwwfxwfNsH1ySgvWCUR/w==",
@@ -106,36 +61,57 @@ To make use of sandbox / test add "sandbox": true, to your config.json
         "outdated_offset": 5
         "pair_whitelist": [
             "BTC/USD"
+        ]
+  },
+  "datadir": "user_data/data/coinbasepro_sandbox"
 ```
 
-Also insert your 
+Also the following information:
 
-- api-key  (noted earlier)
-- api-secret (noted earlier)
-- password (the passphrase - noted earlier)
+* api-key (created for the sandbox webpage)
+* api-secret (noted earlier)
+* password (the passphrase - noted earlier)
+
+!!! Tip "Different data directory"
+    We also recommend to set `datadir` to something identifying downloaded data as sandbox data, to avoid having sandbox data mixed with data from the real exchange.
+    This can be done by adding the `"datadir"` key to the configuration.
+    Now, whenever you use this configuration, your data directory will be set to this directory.
 
 ---
 
 ## You should now be ready to test your sandbox
 
-Ensure Freqtrade logs show the sandbox URL, and trades made are shown in sandbox.
-** Typically the  BTC/USD has the most activity in sandbox to test against.
+Ensure Freqtrade logs show the sandbox URL, and trades made are shown in sandbox. Also make sure to select a pair which shows at least some decent value (which very often is BTC/<somestablecoin>).
 
-## GDAX - Old Candles problem
+## Common problems with sandbox exchanges
 
-It is my experience that GDAX sandbox candles may be 20+- minutes out of date. This can cause trades to fail as one of Freqtrades safety checks.
+Sandbox exchange instances often have very low volume, which can cause some problems which usually are not seen on a real exchange instance.
 
-To disable this check, add / change the `"outdated_offset"` parameter in the exchange section of your configuration to adjust for this delay.
-Example based on the above configuration:
+### Old Candles problem
 
-```json
-  "exchange": {
-        "name": "gdax",
-        "sandbox": true,
-        "key": "5wowfxemogxeowo;heiohgmd",
-        "secret": "/ZMH1P62rCVmwefewrgcewX8nh4gob+lywxfwfxwwfxwfNsH1ySgvWCUR/w==",
-        "password": "1bkjfkhfhfu6sr",
-        "outdated_offset": 30
-        "pair_whitelist": [
-            "BTC/USD"
-```
+Since Sandboxes often have low volume, candles can be quite old and show no volume.
+To disable the error "Outdated history for pair ...", best increase the parameter `"outdated_offset"` to a number that seems realistic for the sandbox you're using.
+
+### Unfilled orders
+
+Sandboxes often have very low volumes - which means that many trades can go unfilled, or can go unfilled for a very long time.
+
+To mitigate this, you can try to match the first order on the opposite orderbook side using the following configuration:
+
+``` jsonc
+  "order_types": {
+    "buy": "limit",
+    "sell": "limit"
+    // ...
+  },
+  "bid_strategy": {
+    "price_side": "ask",
+    // ...
+  },
+  "ask_strategy":{
+    "price_side": "bid",
+    // ...
+  },
+  ```
+
+  The configuration is similar to the suggested configuration for market orders - however by using limit-orders you can avoid moving the price too much, and you can set the worst price you might get.

docs/sql_cheatsheet.md

@@ -1,13 +1,29 @@
 # SQL Helper
+
 This page contains some help if you want to edit your sqlite db.
 
 ## Install sqlite3
-**Ubuntu/Debian installation**
+
+Sqlite3 is a terminal based sqlite application.
+Feel free to use a visual Database editor like SqliteBrowser if you feel more comfortable with that.
+
+### Ubuntu/Debian installation
+
 ```bash
 sudo apt-get install sqlite3
 ```
 
+### Using sqlite3 via docker-compose
+
+The freqtrade docker image does contain sqlite3, so you can edit the database without having to install anything on the host system.
+
+``` bash
+docker-compose exec freqtrade /bin/bash
+sqlite3 <databasefile>.sqlite
+```
+
 ## Open the DB
+
 ```bash
 sqlite3
 .open <filepath>
@@ -16,45 +32,61 @@ sqlite3
 ## Table structure
 
 ### List tables
+
 ```bash
 .tables
 ```
 
 ### Display table structure
+
 ```bash
 .schema <table_name>
 ```
 
 ### Trade table structure
+
 ```sql
-CREATE TABLE trades (
-	id INTEGER NOT NULL,
-	exchange VARCHAR NOT NULL,
-	pair VARCHAR NOT NULL,
-	is_open BOOLEAN NOT NULL,
-	fee_open FLOAT NOT NULL,
-	fee_close FLOAT NOT NULL,
-	open_rate FLOAT,
-	open_rate_requested FLOAT,
-	close_rate FLOAT,
-	close_rate_requested FLOAT,
-	close_profit FLOAT,
-	stake_amount FLOAT NOT NULL,
-	amount FLOAT,
-	open_date DATETIME NOT NULL,
-	close_date DATETIME,
-	open_order_id VARCHAR,
-	stop_loss FLOAT,
-	initial_stop_loss FLOAT,
-	stoploss_order_id VARCHAR,
-	stoploss_last_update DATETIME,
-	max_rate FLOAT,
-	sell_reason VARCHAR,
-	strategy VARCHAR,
-	ticker_interval INTEGER,
-	PRIMARY KEY (id),
-	CHECK (is_open IN (0, 1))
+CREATE TABLE trades
+    id INTEGER NOT NULL,
+    exchange VARCHAR NOT NULL,
+    pair VARCHAR NOT NULL,
+    is_open BOOLEAN NOT NULL,
+    fee_open FLOAT NOT NULL,
+    fee_open_cost FLOAT,
+    fee_open_currency VARCHAR,
+    fee_close FLOAT NOT NULL,
+    fee_close_cost FLOAT,
+    fee_close_currency VARCHAR,
+    open_rate FLOAT,
+    open_rate_requested FLOAT,
+    open_trade_price FLOAT,
+    close_rate FLOAT,
+    close_rate_requested FLOAT,
+    close_profit FLOAT,
+    close_profit_abs FLOAT,
+    stake_amount FLOAT NOT NULL,
+    amount FLOAT,
+    open_date DATETIME NOT NULL,
+    close_date DATETIME,
+    open_order_id VARCHAR,
+    stop_loss FLOAT,
+    stop_loss_pct FLOAT,
+    initial_stop_loss FLOAT,
+    initial_stop_loss_pct FLOAT,
+    stoploss_order_id VARCHAR,
+    stoploss_last_update DATETIME,
+    max_rate FLOAT,
+    min_rate FLOAT,
+    sell_reason VARCHAR,
+    strategy VARCHAR,
+    timeframe INTEGER,
+    PRIMARY KEY (id),
+    CHECK (is_open IN (0, 1))
 );
+CREATE INDEX ix_trades_stoploss_order_id ON trades (stoploss_order_id);
+CREATE INDEX ix_trades_pair ON trades (pair);
+CREATE INDEX ix_trades_is_open ON trades (is_open);
+
 ```
 
 ## Get all trades in the table
@@ -67,42 +99,60 @@ SELECT * FROM trades;
 
 !!! Warning
   	Manually selling a pair on the exchange will not be detected by the bot and it will try to sell anyway. Whenever possible, forcesell <tradeid> should be used to accomplish the same thing.  
-		It is strongly advised to backup your database file before making any manual changes.
+	It is strongly advised to backup your database file before making any manual changes.
 
 !!! Note
   	This should not be necessary after /forcesell, as forcesell orders are closed automatically by the bot on the next iteration.
 
 ```sql
 UPDATE trades
-SET is_open=0, close_date=<close_date>, close_rate=<close_rate>, close_profit=close_rate/open_rate-1, sell_reason=<sell_reason>  
+SET is_open=0,
+  close_date=<close_date>,
+  close_rate=<close_rate>,
+  close_profit = close_rate / open_rate - 1,
+  close_profit_abs = (amount * <close_rate> * (1 - fee_close) - (amount * (open_rate * (1 - fee_open)))),
+  sell_reason=<sell_reason>
 WHERE id=<trade_ID_to_update>;
 ```
 
-##### Example
+### Example
 
 ```sql
 UPDATE trades
-SET is_open=0, close_date='2017-12-20 03:08:45.103418', close_rate=0.19638016, close_profit=0.0496, sell_reason='force_sell'  
+SET is_open=0,
+  close_date='2020-06-20 03:08:45.103418',
+  close_rate=0.19638016,
+  close_profit=0.0496,
+  close_profit_abs = (amount * 0.19638016 * (1 - fee_close) - (amount * (open_rate * (1 - fee_open)))),
+  sell_reason='force_sell'  
 WHERE id=31;
 ```
 
-## Insert manually a new trade
+## Manually insert a new trade
 
 ```sql
 INSERT INTO trades (exchange, pair, is_open, fee_open, fee_close, open_rate, stake_amount, amount, open_date)
-VALUES ('bittrex', 'ETH/BTC', 1, 0.0025, 0.0025, <open_rate>, <stake_amount>, <amount>, '<datetime>')
+VALUES ('binance', 'ETH/BTC', 1, 0.0025, 0.0025, <open_rate>, <stake_amount>, <amount>, '<datetime>')
 ```
 
-##### Example:
+### Insert trade example
 
 ```sql
 INSERT INTO trades (exchange, pair, is_open, fee_open, fee_close, open_rate, stake_amount, amount, open_date)
-VALUES ('bittrex', 'ETH/BTC', 1, 0.0025, 0.0025, 0.00258580, 0.002, 0.7715262081, '2017-11-28 12:44:24.000000')
+VALUES ('binance', 'ETH/BTC', 1, 0.0025, 0.0025, 0.00258580, 0.002, 0.7715262081, '2020-06-28 12:44:24.000000')
 ```
 
-## Fix wrong fees in the table
-If your DB was created before [PR#200](https://github.com/freqtrade/freqtrade/pull/200) was merged (before 12/23/17).
+## Remove trade from the database
+
+Maybe you'd like to remove a trade from the database, because something went wrong.
 
 ```sql
-UPDATE trades SET fee=0.0025 WHERE fee=0.005;
+DELETE FROM trades WHERE id = <tradeid>;
 ```
+
+```sql
+DELETE FROM trades WHERE id = 31;
+```
+
+!!! Warning
+    This will remove this trade from the database. Please make sure you got the correct id and **NEVER** run this query without the `where` clause.

docs/stoploss.md

@@ -1,12 +1,68 @@
 # Stop Loss
 
-The `stoploss` configuration parameter is loss in percentage that should trigger a sale.
+The `stoploss` configuration parameter is loss as ratio that should trigger a sale.
 For example, value `-0.10` will cause immediate sell if the profit dips below -10% for a given trade. This parameter is optional.
 
 Most of the strategy files already include the optimal `stoploss` value.
 
 !!! Info
-    All stoploss properties mentioned in this file can be set in the Strategy, or in the configuration. Configuration values will override the strategy values.
+    All stoploss properties mentioned in this file can be set in the Strategy, or in the configuration.  
+    <ins>Configuration values will override the strategy values.</ins>
+
+## Stop Loss On-Exchange/Freqtrade
+
+Those stoploss modes can be *on exchange* or *off exchange*.
+
+These modes can be configured with these values:
+
+``` python
+    '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), Kraken (stop-loss-market) and FTX (stop limit and stop-market) as of now.  
+    <ins>Do not set too low 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
+
+### stoploss_on_exchange and stoploss_on_exchange_limit_ratio
+Enable or Disable stop loss on exchange.
+If the stoploss is *on exchange* it means a stoploss limit order is placed on the exchange immediately after buy order happens successfully. This will protect you against sudden crashes in market as the order will be in the queue immediately and if market goes down then the order has more chance of being fulfilled.
+
+If `stoploss_on_exchange` uses limit orders, the exchange needs 2 prices, the stoploss_price and the Limit price.  
+`stoploss` defines the stop-price where the limit order is placed - and limit should be slightly below this.  
+If an exchange supports both limit and market stoploss orders, then the value of `stoploss` will be used to determine the stoploss type.  
+
+Calculation example: we bought the asset at 100$.  
+Stop-price is 95$, then limit would be `95 * 0.99 = 94.05$` - so the limit order fill can happen between 95$ and 94.05$.  
+
+For example, assuming the stoploss is on exchange, and trailing stoploss is enabled, and the market is going up, then the bot automatically cancels the previous stoploss order and puts a new one with a stop value higher than the previous stoploss order.
+
+### stoploss_on_exchange_interval
+In case of stoploss on exchange there is another parameter called `stoploss_on_exchange_interval`. This configures the interval in seconds at which the bot will check the stoploss and update it if necessary.  
+The bot cannot do these every 5 seconds (at each iteration), otherwise it would get banned by the exchange.
+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.
+
+### emergencysell
+`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 = {
+    'buy': 'limit',
+    'sell': 'limit',
+    'emergencysell': 'market',
+    'stoploss': 'market',
+    'stoploss_on_exchange': True,
+    'stoploss_on_exchange_interval': 60,
+    'stoploss_on_exchange_limit_ratio': 0.99
+}
+```
 
 ## Stop Loss Types
 
@@ -17,29 +73,29 @@ At this stage the bot contains the following stoploss support modes:
 3. Trailing stop loss, custom positive loss.
 4. Trailing stop loss only once the trade has reached a certain offset.
 
-Those stoploss modes can be *on exchange* or *off exchange*. If the stoploss is *on exchange* it means a stoploss limit order is placed on the exchange immediately after buy order happens successfully. This will protect you against sudden crashes in market as the order will be in the queue immediately and if market goes down then the order has more chance of being fulfilled.
-
-In case of stoploss on exchange there is another parameter called `stoploss_on_exchange_interval`. This configures the interval in seconds at which the bot will check the stoploss and update it if necessary.
-
-For example, assuming the stoploss is on exchange, and trailing stoploss is enabled, and the market is going up, then the bot automatically cancels the previous stoploss order and puts a new one with a stop value higher than the previous stoploss order.
-The bot cannot do this every 5 seconds (at each iteration), otherwise it would get banned by the exchange.
-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.
-
-!!! Note
-    Stoploss on exchange is only supported for Binance as of now.
-
-## Static Stop Loss
+### Static Stop Loss
 
 This is very simple, you define a stop loss of x (as a ratio of price, i.e. x * 100% of price). This will try to sell the asset once the loss exceeds the defined loss.
 
-## Trailing Stop Loss
+Example of stop loss:
+
+``` python
+    stoploss = -0.10
+```
+
+For example, simplified math:
+* the bot buys an asset at a price of 100$
+* the stop loss is defined at -10%
+* the stop loss would get triggered once the asset drops below 90$
+
+### Trailing Stop Loss
 
 The initial value for this is `stoploss`, just as you would define your static Stop loss.
 To enable trailing stoploss:
 
 ``` python
-trailing_stop = True
+    stoploss = -0.10
+    trailing_stop = True
 ```
 
 This will now activate an algorithm, which automatically moves the stop loss up every time the price of your asset increases.
@@ -47,35 +103,43 @@ This will now activate an algorithm, which automatically moves the stop loss up
 For example, simplified math:
 
 * the bot buys an asset at a price of 100$
-* the stop loss is defined at 2%
-* the stop loss would get triggered once the asset dropps below 98$
+* the stop loss is defined at -10%
+* the stop loss would get triggered once the asset drops below 90$
 * assuming the asset now increases to 102$
-* the stop loss will now be 2% of 102$ or 99.96$
-* now the asset drops in value to 101$, the stop loss will still be 99.96$ and would trigger at 99.96$.
+* the stop loss will now be -10% of 102$ = 91.8$
+* now the asset drops in value to 101$, the stop loss will still be 91.8$ and would trigger at 91.8$.
 
-In summary: The stoploss will be adjusted to be always be 2% of the highest observed price.
+In summary: The stoploss will be adjusted to be always be -10% of the highest observed price.
 
-### Custom positive stoploss
+### Trailing stop loss, custom positive loss
 
-It is also possible to have a default stop loss, when you are in the red with your buy, but once your profit surpasses a certain percentage, the system will utilize a new stop loss, which can have a different value.
-For example your default stop loss is 5%, but once you have 1.1% profit, it will be changed to be only a 1% stop loss, which trails the green candles until it goes below them.
+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.
 
-Both values require `trailing_stop` to be set to true.
+!!! Note
+    If you want the stoploss to only be changed when you break even of making a profit (what most users want) please refer to next section with [offset enabled](#Trailing-stop-loss-only-once-the-trade-has-reached-a-certain-offset).
+
+Both values require `trailing_stop` to be set to true and `trailing_stop_positive` with a value.
 
 ``` python
-    trailing_stop_positive = 0.01
-    trailing_stop_positive_offset = 0.011
+    stoploss = -0.10
+    trailing_stop = True
+    trailing_stop_positive = 0.02
 ```
 
-The 0.01 would translate to a 1% stop loss, once you hit 1.1% profit.
+For example, simplified math:
+
+* the bot buys an asset at a price of 100$
+* the stop loss is defined at -10%
+* the stop loss would get triggered once the asset drops below 90$
+* assuming the asset now increases to 102$
+* the stop loss will now be -2% of 102$ = 99.96$ (99.96$ stop loss will be locked in and will follow asset price increasements with -2%)
+* now the asset drops in value to 101$, the stop loss will still be 99.96$ and would trigger at 99.96$
+
+The 0.02 would translate to a -2% stop loss.
 Before this, `stoploss` is used for the trailing stoploss.
 
-Read the [next section](#trailing-only-once-offset-is-reached) to keep stoploss at 5% of the entry point.
-
-!!! 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.
-
-### Trailing only once offset is reached
+### Trailing stop loss only once the trade has reached a certain offset
 
 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.
 
@@ -84,24 +148,35 @@ This option can be used with or without `trailing_stop_positive`, but uses `trai
 
 ``` python
     trailing_stop_positive_offset = 0.011
-    trailing_only_offset_is_reached = true
+    trailing_only_offset_is_reached = True
 ```
 
-Simplified example:
+Configuration (offset is buyprice + 3%):
 
 ``` python
-    stoploss = 0.05
+    stoploss = -0.10
+    trailing_stop = True
+    trailing_stop_positive = 0.02
     trailing_stop_positive_offset = 0.03
     trailing_only_offset_is_reached = True
 ```
 
+For example, simplified math:
+
 * the bot buys an asset at a price of 100$
-* the stop loss is defined at 5%
-* the stop loss will remain at 95% until profit reaches +3%
+* the stop loss is defined at -10%
+* the stop loss would get triggered once the asset drops below 90$
+* stoploss will remain at 90$ unless asset increases to or above our configured offset
+* assuming the asset now increases to 103$ (where we have the offset configured)
+* the stop loss will now be -2% of 103$ = 100.94$
+* now the asset drops in value to 101$, the stop loss will still be 100.94$ and would trigger at 100.94$
+
+!!! 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.
 
 ## 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_conf` command (alternatively, completely stopping and restarting the bot also works).
+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).
 
 The new stoploss value will be applied to open trades (and corresponding log-messages will be generated).
 

docs/strategy-advanced.md

@@ -0,0 +1,222 @@
+# Advanced Strategies
+
+This page explains some advanced concepts available for strategies.
+If you're just getting started, please be familiar with the methods described in the [Strategy Customization](strategy-customization.md) documentation and with the [Freqtrade basics](bot-basics.md) first.
+
+[Freqtrade basics](bot-basics.md) describes in which sequence each method described below is called, which can be helpful to understand which method to use for your custom needs.
+
+!!! Note
+    All callback methods described below should only be implemented in a strategy if they are actually used.
+
+## Custom order timeout rules
+
+Simple, timebased order-timeouts can be configured either via strategy or in the configuration in the `unfilledtimeout` section.
+
+However, freqtrade also offers a custom callback for both ordertypes, which allows you to decide based on custom criteria if a order did time out or not.
+
+!!! Note
+    Unfilled order timeouts are not relevant during backtesting or hyperopt, and are only relevant during real (live) trading. Therefore these methods are only called in these circumstances.
+
+### Custom order timeout example
+
+A simple example, which applies different unfilled-timeouts depending on the price of the asset can be seen below.
+It applies a tight timeout for higher priced assets, while allowing more time to fill on cheap coins.
+
+The function must return either `True` (cancel order) or `False` (keep order alive).
+
+``` python
+from datetime import datetime, timedelta
+from freqtrade.persistence import Trade
+
+class Awesomestrategy(IStrategy):
+
+    # ... populate_* methods
+
+    # Set unfilledtimeout to 25 hours, since our maximum timeout from below is 24 hours.
+    unfilledtimeout = {
+        'buy': 60 * 25,
+        'sell': 60 * 25
+    }
+
+    def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
+        if trade.open_rate > 100 and trade.open_date < datetime.utcnow() - timedelta(minutes=5):
+            return True
+        elif trade.open_rate > 10 and trade.open_date < datetime.utcnow() - timedelta(minutes=3):
+            return True
+        elif trade.open_rate < 1 and trade.open_date < datetime.utcnow() - timedelta(hours=24):
+           return True
+        return False
+
+
+    def check_sell_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
+        if trade.open_rate > 100 and trade.open_date < datetime.utcnow() - timedelta(minutes=5):
+            return True
+        elif trade.open_rate > 10 and trade.open_date < datetime.utcnow() - timedelta(minutes=3):
+            return True
+        elif trade.open_rate < 1 and trade.open_date < datetime.utcnow() - timedelta(hours=24):
+           return True
+        return False
+```
+
+!!! Note
+    For the above example, `unfilledtimeout` must be set to something bigger than 24h, otherwise that type of timeout will apply first.
+
+### Custom order timeout example (using additional data)
+
+``` python
+from datetime import datetime
+from freqtrade.persistence import Trade
+
+class Awesomestrategy(IStrategy):
+
+    # ... populate_* methods
+
+    # Set unfilledtimeout to 25 hours, since our maximum timeout from below is 24 hours.
+    unfilledtimeout = {
+        'buy': 60 * 25,
+        'sell': 60 * 25
+    }
+
+    def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
+        ob = self.dp.orderbook(pair, 1)
+        current_price = ob['bids'][0][0]
+        # Cancel buy order if price is more than 2% above the order.
+        if current_price > order['price'] * 1.02:
+            return True
+        return False
+
+
+    def check_sell_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
+        ob = self.dp.orderbook(pair, 1)
+        current_price = ob['asks'][0][0]
+        # Cancel sell order if price is more than 2% below the order.
+        if current_price < order['price'] * 0.98:
+            return True
+        return False
+```
+
+## Bot loop start callback
+
+A simple callback which is called once at the start of every bot throttling iteration.
+This can be used to perform calculations which are pair independent (apply to all pairs), loading of external data, etc.
+
+``` python
+import requests
+
+class Awesomestrategy(IStrategy):
+
+    # ... populate_* methods
+
+    def bot_loop_start(self, **kwargs) -> None:
+        """
+        Called at the start of the bot iteration (one loop).
+        Might be used to perform pair-independent tasks
+        (e.g. gather some remote resource for comparison)
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        """
+        if self.config['runmode'].value in ('live', 'dry_run'):
+            # Assign this to the class by using self.*
+            # can then be used by populate_* methods
+            self.remote_data = requests.get('https://some_remote_source.example.com')
+
+```
+
+## Bot order confirmation
+
+### Trade entry (buy order) confirmation
+
+`confirm_trade_entry()` can be used to abort a trade entry at the latest second (maybe because the price is not what we expect).
+
+``` python
+class Awesomestrategy(IStrategy):
+
+    # ... populate_* methods
+
+    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
+                            time_in_force: str, **kwargs) -> bool:
+        """
+        Called right before placing a buy order.
+        Timing for this function is critical, so avoid doing heavy computations or
+        network requests in this method.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+        When not implemented by a strategy, returns True (always confirming).
+
+        :param pair: Pair that's about to be bought.
+        :param order_type: Order type (as configured in order_types). usually limit or market.
+        :param amount: Amount in target (quote) currency that's going to be traded.
+        :param rate: Rate that's going to be used when using limit orders
+        :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return bool: When True is returned, then the buy-order is placed on the exchange.
+            False aborts the process
+        """
+        return True
+
+```
+
+### Trade exit (sell order) confirmation
+
+`confirm_trade_exit()` can be used to abort a trade exit (sell) at the latest second (maybe because the price is not what we expect).
+
+``` python
+from freqtrade.persistence import Trade
+
+
+class Awesomestrategy(IStrategy):
+
+    # ... populate_* methods
+
+    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
+                           rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool:
+        """
+        Called right before placing a regular sell order.
+        Timing for this function is critical, so avoid doing heavy computations or
+        network requests in this method.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+        When not implemented by a strategy, returns True (always confirming).
+
+        :param pair: Pair that's about to be sold.
+        :param order_type: Order type (as configured in order_types). usually limit or market.
+        :param amount: Amount in quote currency.
+        :param rate: Rate that's going to be used when using limit orders
+        :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
+        :param sell_reason: Sell reason.
+            Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
+                           'sell_signal', 'force_sell', 'emergency_sell']
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return bool: When True is returned, then the sell-order is placed on the exchange.
+            False aborts the process
+        """
+        if sell_reason == 'force_sell' and trade.calc_profit_ratio(rate) < 0:
+            # Reject force-sells with negative profit
+            # This is just a sample, please adjust to your needs
+            # (this does not necessarily make sense, assuming you know when you're force-selling)
+            return False
+        return True
+
+```
+
+## Derived strategies
+
+The strategies can be derived from other strategies. This avoids duplication of your custom strategy code. You can use this technique to override small parts of your main strategy, leaving the rest untouched:
+
+``` python
+class MyAwesomeStrategy(IStrategy):
+    ...
+    stoploss = 0.13
+    trailing_stop = False
+    # All other attributes and methods are here as they
+    # should be in any custom strategy...
+    ...
+
+class MyAwesomeStrategy2(MyAwesomeStrategy):
+    # Override something
+    stoploss = 0.08
+    trailing_stop = True
+```
+
+Both attributes and methods may be overriden, altering behavior of the original strategy in a way you need.

docs/strategy-customization.md

@@ -1,7 +1,8 @@
 # Strategy Customization
 
-This page explains where to customize your strategies, and add new
-indicators.
+This page explains how to customize your strategies, add new indicators and set up trading rules.
+
+Please familiarize yourself with [Freqtrade basics](bot-basics.md) first, which provides overall info on how the bot operates.
 
 ## Install a custom strategy file
 
@@ -57,12 +58,12 @@ file as reference.**
 
 !!! Note "Strategies and Backtesting"
     To avoid problems and unexpected differences between Backtesting and dry/live modes, please be aware
-    that during backtesting the full time-interval is passed to the `populate_*()` methods at once.
+    that during backtesting the full time range is passed to the `populate_*()` methods at once.
     It is therefore best to use vectorized operations (across the whole dataframe, not loops) and
     avoid index referencing (`df.iloc[-1]`), but instead use `df.shift()` to get to the previous candle.
 
 !!! Warning "Warning: Using future data"
-    Since backtesting passes the full time interval to the `populate_*()` methods, the strategy author
+    Since backtesting passes the full time range to the `populate_*()` methods, the strategy author
     needs to take care to avoid having the strategy utilize data from the future.
     Some common patterns for this are listed in the [Common Mistakes](#common-mistakes-when-developing-strategies) section of this document.
 
@@ -84,7 +85,7 @@ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame
     Performance Note: For the best performance be frugal on the number of indicators
     you are using. Let uncomment only the indicator you are using in your strategies
     or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
-    :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
+    :param dataframe: Dataframe with data from the exchange
     :param metadata: Additional information, like the currently traded pair
     :return: a Dataframe with all mandatory indicators for the strategies
     """
@@ -140,10 +141,10 @@ By letting the bot know how much history is needed, backtest trades can start at
 
 #### Example
 
-Let's try to backtest 1 month (January 2019) of 5m candles using the an example strategy with EMA100, as above.
+Let's try to backtest 1 month (January 2019) of 5m candles using an example strategy with EMA100, as above.
 
 ``` bash
-freqtrade backtesting --timerange 20190101-20190201 --ticker-interval 5m
+freqtrade backtesting --timerange 20190101-20190201 --timeframe 5m
 ```
 
 Assuming `startup_candle_count` is set to 100, backtesting knows it needs 100 candles to generate valid buy signals. It will load data from `20190101 - (100 * 5m)` - which is ~2019-12-31 15:30:00.
@@ -249,6 +250,23 @@ minimal_roi = {
 
 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 ...)
+
+``` python
+from freqtrade.exchange import timeframe_to_minutes
+
+class AwesomeStrategy(IStrategy):
+
+    timeframe = "1d"
+    timeframe_mins = timeframe_to_minutes(timeframe)
+    minimal_roi = {
+        "0": 0.05,                             # 5% for the first 3 candles
+        str(timeframe_mins * 3)): 0.02,  # 2% after 3 candles
+        str(timeframe_mins * 6)): 0.01,  # 1% After 6 candles
+    }
+```
+
 ### Stoploss
 
 Setting a stoploss is highly recommended to protect your capital from strong moves against you.
@@ -267,13 +285,14 @@ If your exchange supports it, it's recommended to also set `"stoploss_on_exchang
 
 For more information on order_types please look [here](configuration.md#understand-order_types).
 
-### Ticker interval
+### Timeframe (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 buy/sell signals may work with one interval, but not the other.
-This setting is accessible within the strategy by using `self.ticker_interval`.
+Please note that the same buy/sell signals may work well with one timeframe, but not with the others.
+
+This setting is accessible within the strategy methods as the `self.timeframe` attribute.
 
 ### Metadata dict
 
@@ -307,70 +326,17 @@ class Awesomestrategy(IStrategy):
 !!! Note
     If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
 
-### Additional data (DataProvider)
+***
 
-The strategy provides access to the `DataProvider`. This allows you to get additional data to use in your strategy.
+## Additional data (informative_pairs)
 
-All methods return `None` in case of failure (do not raise an exception).
-
-Please always check the mode of operation to select the correct method to get data (samples see below).
-
-#### Possible options for DataProvider
-
-- `available_pairs` - Property with tuples listing cached pairs with their intervals (pair, interval).
-- `ohlcv(pair, timeframe)` - Currently cached ticker data for the pair, returns DataFrame or empty DataFrame.
-- `historic_ohlcv(pair, timeframe)` - Returns historical data stored on disk.
-- `get_pair_dataframe(pair, timeframe)` - This is a universal method, which returns either historical data (for backtesting) or cached live data (for the Dry-Run and Live-Run modes).
-- `orderbook(pair, maximum)` - Returns latest orderbook data for the pair, a dict with bids/asks with a total of `maximum` entries.
-- `market(pair)` - Returns market data for the pair: fees, limits, precisions, activity flag, etc. See [ccxt documentation](https://github.com/ccxt/ccxt/wiki/Manual#markets) for more details on Market data structure.
-- `runmode` - Property containing the current runmode.
-
-#### Example: fetch live ohlcv / historic data for the first informative pair
-
-``` python
-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"
-    Be carefull when using dataprovider in backtesting. `historic_ohlcv()` (and `get_pair_dataframe()`
-    for the backtesting runmode) provides the full time-range in one go,
-    so please be aware of it and make sure to not "look into the future" to avoid surprises when running in dry/live mode).
-
-!!! Warning "Warning in hyperopt"
-    This option cannot currently be used during hyperopt.
-
-#### Orderbook
-
-``` python
-if self.dp:
-    if self.dp.runmode 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]
-```
-
-!!! Warning
-    The order book is not part of the historic data which means backtesting and hyperopt will not work if this
-    method is used.
-
-#### Available Pairs
-
-``` python
-if self.dp:
-    for pair, ticker in self.dp.available_pairs:
-        print(f"available {pair}, {ticker}")
-```
-
-#### Get data for non-tradeable pairs
+### Get data for non-tradeable pairs
 
 Data for additional, informative pairs (reference pairs) can be beneficial for some strategies.
-Ohlcv data for these pairs will be downloaded as part of the regular whitelist refresh process and is available via `DataProvider` just as other pairs (see above).
+OHLCV data for these pairs will be downloaded as part of the regular whitelist refresh process and is available via `DataProvider` just as other pairs (see below).
 These parts will **not** be traded unless they are also specified in the pair whitelist, or have been selected by Dynamic Whitelisting.
 
-The pairs need to be specified as tuples in the format `("pair", "interval")`, with pair as the first and time interval as the second argument.
+The pairs need to be specified as tuples in the format `("pair", "timeframe")`, with pair as the first and timeframe as the second argument.
 
 Sample:
 
@@ -381,13 +347,207 @@ def informative_pairs(self):
             ]
 ```
 
+A full sample can be found [in the DataProvider section](#complete-data-provider-sample).
+
 !!! Warning
     As these pairs will be refreshed as part of the regular whitelist refresh, it's best to keep this list short.
-    All intervals and all pairs can be specified as long as they are available (and active) on the used exchange.
-    It is however better to use resampling to longer time-intervals when possible
+    All timeframes and all pairs can be specified as long as they are available (and active) on the used exchange.
+    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.
 
-### Additional data (Wallets)
+***
+
+## Additional data (DataProvider)
+
+The strategy provides access to the `DataProvider`. This allows you to get additional data to use in your strategy.
+
+All methods return `None` in case of failure (do not raise an exception).
+
+Please always check the mode of operation to select the correct method to get data (samples see below).
+
+!!! Warning "Hyperopt"
+    Dataprovider is available during hyperopt, however it can only be used in `populate_indicators()` within a strategy.
+    It is not available in `populate_buy()` and `populate_sell()` methods, nor in `populate_indicators()`, if this method located in the hyperopt file.
+
+### Possible options for DataProvider
+
+- [`available_pairs`](#available_pairs) - Property with tuples listing cached pairs with their timeframe (pair, timeframe).
+- [`current_whitelist()`](#current_whitelist) - Returns a current list of whitelisted pairs. Useful for accessing dynamic whitelists (i.e. VolumePairlist)
+- [`get_pair_dataframe(pair, timeframe)`](#get_pair_dataframepair-timeframe) - This is a universal method, which returns either historical data (for backtesting) or cached live data (for the Dry-Run and Live-Run modes).
+- [`get_analyzed_dataframe(pair, timeframe)`](#get_analyzed_dataframepair-timeframe) - Returns the analyzed dataframe (after calling `populate_indicators()`, `populate_buy()`, `populate_sell()`) and the time of the latest analysis.
+- `historic_ohlcv(pair, timeframe)` - Returns historical data stored on disk.
+- `market(pair)` - Returns market data for the pair: fees, limits, precisions, activity flag, etc. See [ccxt documentation](https://github.com/ccxt/ccxt/wiki/Manual#markets) for more details on the Market data structure.
+- `ohlcv(pair, timeframe)` - Currently cached candle (OHLCV) data for the pair, returns DataFrame or empty DataFrame.
+- [`orderbook(pair, maximum)`](#orderbookpair-maximum) - Returns latest orderbook data for the pair, a dict with bids/asks with a total of `maximum` entries.
+- [`ticker(pair)`](#tickerpair) - Returns current ticker data for the pair. See [ccxt documentation](https://github.com/ccxt/ccxt/wiki/Manual#price-tickers) for more details on the Ticker data structure.
+- `runmode` - Property containing the current runmode.
+
+### Example Usages
+
+### *available_pairs*
+
+``` python
+if self.dp:
+    for pair, timeframe in self.dp.available_pairs:
+        print(f"available {pair}, {timeframe}")
+```
+
+### *current_whitelist()*
+
+Imagine you've developed a strategy that trades the `5m` timeframe using signals generated from a `1d` timeframe on the top 10 volume pairs by volume. 
+
+The strategy might look something like this:
+
+*Scan through the top 10 pairs by volume using the `VolumePairList` every 5 minutes and use a 14 day RSI to buy and sell.*
+
+Due to the limited available data, it's very difficult to resample our `5m` candles into daily candles for use in a 14 day RSI. Most exchanges limit us to just 500 candles which effectively gives us around 1.74 daily candles. We need 14 days at least!
+
+Since we can't resample our data we will have to use an informative pair; and since our whitelist will be dynamic we don't know which pair(s) to use.
+
+This is where calling `self.dp.current_whitelist()` comes in handy.
+
+```python
+    def informative_pairs(self):
+
+        # get access to all pairs available in whitelist.
+        pairs = self.dp.current_whitelist()
+        # Assign tf to each pair so they can be downloaded and cached for strategy.
+        informative_pairs = [(pair, '1d') for pair in pairs]
+        return informative_pairs   
+```
+
+### *get_pair_dataframe(pair, timeframe)*
+
+``` python
+# fetch live / historical candle (OHLCV) data for the first informative pair
+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"
+    Be careful when using dataprovider in backtesting. `historic_ohlcv()` (and `get_pair_dataframe()`
+    for the backtesting runmode) provides the full time-range in one go,
+    so please be aware of it and make sure to not "look into the future" to avoid surprises when running in dry/live mode.
+
+### *get_analyzed_dataframe(pair, timeframe)*
+
+This method is used by freqtrade internally to determine the last signal.
+It can also be used in specific callbacks to get the signal that caused the action (see [Advanced Strategy Documentation](strategy-advanced.md) for more details on available callbacks).
+
+``` python
+# fetch current dataframe
+if self.dp:
+    dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=metadata['pair'],
+                                                             timeframe=self.timeframe)
+```
+
+!!! Note "No data available"
+    Returns an empty dataframe if the requested pair was not cached.
+    This should not happen when using whitelisted pairs.
+
+### *orderbook(pair, maximum)*
+
+``` python
+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]
+```
+
+!!! Warning
+    The order book is not part of the historic data which means backtesting and hyperopt will not work correctly if this method is used.
+
+### *ticker(pair)*
+
+``` python
+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
+    Although the ticker data structure is a part of the ccxt Unified Interface, the values returned by this method can
+    vary for different exchanges. For instance, many exchanges do not return `vwap` values, the FTX exchange
+    does not always fills in the `last` field (so it can be None), etc. So you need to carefully verify the ticker
+    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 will lead to wrong results.
+
+### Complete Data-provider sample
+
+```python
+class SampleStrategy(IStrategy):
+    # strategy init stuff...
+
+    timeframe = '5m'
+
+    # more strategy init stuff..
+
+    def informative_pairs(self):
+
+        # get access to all pairs available in whitelist.
+        pairs = self.dp.current_whitelist()
+        # Assign tf to each pair so they can be downloaded and cached for strategy.
+        informative_pairs = [(pair, '1d') for pair in pairs]
+        # Optionally Add additional "static" pairs
+        informative_pairs += [("ETH/USDT", "5m"),
+                              ("BTC/TUSD", "15m"),
+                            ]
+        return informative_pairs
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        if not self.dp:
+            # Don't do anything if DataProvider is not available.
+            return dataframe
+
+        inf_tf = '1d'
+        # Get the informative pair
+        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
+        # Get the 14 day rsi
+        informative['rsi'] = ta.RSI(informative, timeperiod=14)
+
+        # Rename columns to be unique
+        informative.columns = [f"{col}_{inf_tf}" for col in informative.columns]
+        # Assuming inf_tf = '1d' - then the columns will now be:
+        # date_1d, open_1d, high_1d, low_1d, close_1d, rsi_1d
+
+        # Combine the 2 dataframes
+        # all indicators on the informative sample MUST be calculated before this point
+        dataframe = pd.merge(dataframe, informative, left_on='date', right_on=f'date_{inf_tf}', how='left')
+        # FFill to have the 1d value available in every row throughout the day.
+        # Without this, comparisons would only work once per day.
+        dataframe = dataframe.ffill()
+
+        # Calculate rsi of the original dataframe (5m timeframe)
+        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+
+        # Do other stuff
+        # ...
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        dataframe.loc[
+            (
+                (qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
+                (dataframe['rsi_1d'] < 30) &                     # Ensure daily RSI is < 30
+                (dataframe['volume'] > 0)                        # Ensure this candle had volume (important for backtesting)
+            ),
+            'buy'] = 1
+
+```
+
+***
+
+## Additional data (Wallets)
 
 The strategy provides access to the `Wallets` object. This contains the current balances on the exchange.
 
@@ -403,13 +563,15 @@ if self.wallets:
     total_eth = self.wallets.get_total('ETH')
 ```
 
-#### Possible options for Wallets
+### Possible options for Wallets
 
 - `get_free(asset)` - currently available balance to trade
 - `get_used(asset)` - currently tied up balance (open orders)
 - `get_total(asset)` - total available balance - sum of the 2 above
 
-### Additional data (Trades)
+***
+
+## Additional data (Trades)
 
 A history of Trades can be retrieved in the strategy by querying the database.
 
@@ -422,7 +584,7 @@ from freqtrade.persistence import Trade
 The following example queries for the current pair and trades from today, however other filters can easily be added.
 
 ``` python
-if self.config['runmode'] in ('live', 'dry_run'):
+if self.config['runmode'].value in ('live', 'dry_run'):
     trades = Trade.get_trades([Trade.pair == metadata['pair'],
                                Trade.open_date > datetime.utcnow() - timedelta(days=1),
                                Trade.is_open == False,
@@ -434,7 +596,7 @@ if self.config['runmode'] in ('live', 'dry_run'):
 Get amount of stake_currency currently invested in Trades:
 
 ``` python
-if self.config['runmode'] in ('live', 'dry_run'):
+if self.config['runmode'].value in ('live', 'dry_run'):
     total_stakes = Trade.total_open_trades_stakes()
 ```
 
@@ -442,7 +604,7 @@ Retrieve performance per pair.
 Returns a List of dicts per pair.
 
 ``` python
-if self.config['runmode'] in ('live', 'dry_run'):
+if self.config['runmode'].value in ('live', 'dry_run'):
     performance = Trade.get_overall_performance()
 ```
 
@@ -455,7 +617,52 @@ Sample return value: ETH/BTC had 5 trades, with a total profit of 1.5% (ratio of
 !!! Warning
     Trade history is not available during backtesting or hyperopt.
 
-### Print created dataframe
+## Prevent trades from happening for a specific pair
+
+Freqtrade locks pairs automatically for the current candle (until that candle is over) when a pair is sold, preventing an immediate re-buy of that pair.
+
+Locked pairs will show the message `Pair <pair> is currently locked.`.
+
+### Locking pairs from within the strategy
+
+Sometimes it may be desired to lock a pair after certain events happen (e.g. multiple losing trades in a row).
+
+Freqtrade has an easy method to do this from within the strategy, by calling `self.lock_pair(pair, until)`.
+`until` must be a datetime object in the future, after which trading will be reenabled for that pair.
+
+Locks can also be lifted manually, by calling `self.unlock_pair(pair)`.
+
+To verify if a pair is currently locked, use `self.is_pair_locked(pair)`.
+
+!!! Note
+    Locked pairs are not persisted, so a restart of the bot, or calling `/reload_config` will reset locked pairs.
+
+!!! Warning
+    Locking pairs is not functioning during backtesting.
+
+#### Pair locking example
+
+``` python
+from freqtrade.persistence import Trade
+from datetime import timedelta, datetime, timezone
+# Put the above lines a the top of the strategy file, next to all the other imports
+# --------
+
+# Within populate indicators (or populate_buy):
+if self.config['runmode'].value in ('live', 'dry_run'):
+   # fetch closed trades for the last 2 days
+    trades = Trade.get_trades([Trade.pair == metadata['pair'],
+                               Trade.open_date > datetime.utcnow() - timedelta(days=2),
+                               Trade.is_open == False,
+                ]).all()
+    # Analyze the conditions you'd like to lock the pair .... will probably be different for every strategy
+    sumprofit = sum(trade.close_profit for trade in trades)
+    if sumprofit < 0:
+        # Lock pair for 12 hours
+        self.lock_pair(metadata['pair'], until=datetime.now(timezone.utc) + timedelta(hours=12))
+```
+
+## Print created dataframe
 
 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.
@@ -479,20 +686,7 @@ def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
 
 Printing more than a few rows is also possible (simply use  `print(dataframe)` instead of `print(dataframe.tail())`), however not recommended, as that will be very verbose (~500 lines per pair every 5 seconds).
 
-### Where can i find a strategy template?
-
-The strategy template is located in the file
-[user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_strategy.py).
-
-### Specify custom strategy location
-
-If you want to use a strategy from a different directory you can pass `--strategy-path`
-
-```bash
-freqtrade trade --strategy AwesomeStrategy --strategy-path /some/directory
-```
-
-### Common mistakes when developing strategies
+## Common mistakes when developing strategies
 
 Backtesting analyzes the whole time-range at once for performance reasons. Because of this, strategy authors need to make sure that strategies do not look-ahead into the future.
 This is a common pain-point, which can cause huge differences between backtesting and dry/live run methods, since they all use data which is not available during dry/live runs, so these strategies will perform well during backtesting, but will fail / perform badly in real conditions.
@@ -504,7 +698,7 @@ The following lists some common patterns which should be avoided to prevent frus
 - don't use `dataframe['volume'].mean()`. This uses the full DataFrame for backtesting, including data from the future. Use `dataframe['volume'].rolling(<window>).mean()` instead
 - don't use `.resample('1h')`. This uses the left border of the interval, so moves data from an hour to the start of the hour. Use `.resample('1h', label='right')` instead.
 
-### Further strategy ideas
+## Further strategy ideas
 
 To get additional Ideas for strategies, head over to our [strategy repository](https://github.com/freqtrade/freqtrade-strategies). Feel free to use them as they are - but results will depend on the current market situation, pairs used etc. - therefore please backtest the strategy for your exchange/desired pairs first, evaluate carefully, use at your own risk.
 Feel free to use any of them as inspiration for your own strategies.

docs/strategy_analysis_example.md

@@ -1,24 +1,28 @@
 # Strategy analysis example
 
-Debugging a strategy can be time-consuming. FreqTrade offers helper functions to visualize raw data.
+Debugging a strategy can be time-consuming. Freqtrade offers helper functions to visualize raw data.
+The following assumes you work with SampleStrategy, data for 5m timeframe from Binance and have downloaded them into the data directory in the default location.
 
 ## Setup
 
 
 ```python
 from pathlib import Path
+from freqtrade.configuration import Configuration
+
 # Customize these according to your needs.
 
+# Initialize empty configuration object
+config = Configuration.from_files([])
+# Optionally, use existing configuration file
+# config = Configuration.from_files(["config.json"])
+
 # Define some constants
-timeframe = "5m"
+config["timeframe"] = "5m"
 # Name of the strategy class
-strategy_name = 'SampleStrategy'
-# Path to user data
-user_data_dir = Path('user_data')
-# Location of the strategy
-strategy_location = user_data_dir / 'strategies'
+config["strategy"] = "SampleStrategy"
 # Location of the data
-data_location = Path(user_data_dir, 'data', 'binance')
+data_location = Path(config['user_data_dir'], 'data', 'binance')
 # Pair to analyze - Only use one pair here
 pair = "BTC_USDT"
 ```
@@ -29,7 +33,7 @@ pair = "BTC_USDT"
 from freqtrade.data.history import load_pair_history
 
 candles = load_pair_history(datadir=data_location,
-                            timeframe=timeframe,
+                            timeframe=config["timeframe"],
                             pair=pair)
 
 # Confirm success
@@ -44,9 +48,7 @@ candles.head()
 ```python
 # Load strategy using values set above
 from freqtrade.resolvers import StrategyResolver
-strategy = StrategyResolver({'strategy': strategy_name,
-                            'user_data_dir': user_data_dir,
-                            'strategy_path': strategy_location}).strategy
+strategy = StrategyResolver.load_strategy(config)
 
 # Generate buy/sell signals using strategy
 df = strategy.analyze_ticker(candles, {'pair': pair})
@@ -83,10 +85,44 @@ Analyze a trades dataframe (also used below for plotting)
 
 
 ```python
-from freqtrade.data.btanalysis import load_backtest_data
+from freqtrade.data.btanalysis import load_backtest_data, load_backtest_stats
 
-# Load backtest results
-trades = load_backtest_data(user_data_dir / "backtest_results/backtest-result.json")
+# if backtest_dir points to a directory, it'll automatically load the last backtest file.
+backtest_dir = config["user_data_dir"] / "backtest_results"
+# backtest_dir can also point to a specific file 
+# backtest_dir = config["user_data_dir"] / "backtest_results/backtest-result-2020-07-01_20-04-22.json"
+```
+
+
+```python
+# You can get the full backtest statistics by using the following command.
+# This contains all information used to generate the backtest result.
+stats = load_backtest_stats(backtest_dir)
+
+strategy = 'SampleStrategy'
+# All statistics are available per strategy, so if `--strategy-list` was used during backtest, this will be reflected here as well.
+# Example usages:
+print(stats['strategy'][strategy]['results_per_pair'])
+# Get pairlist used for this backtest
+print(stats['strategy'][strategy]['pairlist'])
+# Get market change (average change of all pairs from start to end of the backtest period)
+print(stats['strategy'][strategy]['market_change'])
+# Maximum drawdown ()
+print(stats['strategy'][strategy]['max_drawdown'])
+# Maximum drawdown start and end
+print(stats['strategy'][strategy]['drawdown_start'])
+print(stats['strategy'][strategy]['drawdown_end'])
+
+
+# Get strategy comparison (only relevant if multiple strategies were compared)
+print(stats['strategy_comparison'])
+
+```
+
+
+```python
+# Load backtested trades as dataframe
+trades = load_backtest_data(backtest_dir)
 
 # Show value-counts per pair
 trades.groupby("pair")["sell_reason"].value_counts()
@@ -119,7 +155,6 @@ from freqtrade.data.btanalysis import analyze_trade_parallelism
 # Analyze the above
 parallel_trades = analyze_trade_parallelism(trades, '5m')
 
-
 parallel_trades.plot()
 ```
 
@@ -132,11 +167,14 @@ Freqtrade offers interactive plotting capabilities based on plotly.
 from freqtrade.plot.plotting import  generate_candlestick_graph
 # Limit graph period to keep plotly quick and reactive
 
+# Filter trades to one pair
+trades_red = trades.loc[trades['pair'] == pair]
+
 data_red = data['2019-06-01':'2019-06-10']
 # Generate candlestick graph
 graph = generate_candlestick_graph(pair=pair,
                                    data=data_red,
-                                   trades=trades,
+                                   trades=trades_red,
                                    indicators1=['sma20', 'ema50', 'ema55'],
                                    indicators2=['rsi', 'macd', 'macdsignal', 'macdhist']
                                   )

docs/telegram-usage.md

@@ -47,28 +47,31 @@ Per default, the Telegram bot shows predefined commands. Some commands
 are only available by sending them to the bot. The table below list the
 official commands. You can ask at any moment for help with `/help`.
 
-|  Command | Default | Description |
-|----------|---------|-------------|
-| `/start` | | Starts the trader
-| `/stop` | | Stops the trader
-| `/stopbuy` | | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
-| `/reload_conf` | | Reloads the configuration file
-| `/show_config` | | Shows part of the current configuration with relevant settings to operation
-| `/status` | | Lists all open trades
-| `/status table` | | List all open trades in a table format
-| `/count` | | Displays number of trades used and available
-| `/profit` | | Display a summary of your profit/loss from close trades and some stats about your performance
-| `/forcesell <trade_id>` | | Instantly sells the given trade  (Ignoring `minimum_roi`).
-| `/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>` | 7 | Shows profit or loss per day, over the last n days
-| `/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
-| `/version` | | Show version
+|  Command | Description |
+|----------|-------------|
+| `/start` | Starts the trader
+| `/stop` | Stops the trader
+| `/stopbuy` | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
+| `/reload_config` | Reloads the configuration file
+| `/show_config` | Shows part of the current configuration with relevant settings to operation
+| `/logs [limit]` | Show last log messages.
+| `/status` | Lists all open trades
+| `/status table` | List all open trades in a table format. Pending buy orders are marked with an asterisk (*) Pending sell orders are marked with a double asterisk (**)
+| `/trades [limit]` | List all recently closed trades in a table format.
+| `/delete <trade_id>` | Delete a specific trade from the Database. Tries to close open orders. Requires manual handling of this trade on the exchange.
+| `/count` | Displays number of trades used and available
+| `/profit` | Display a summary of your profit/loss from close trades and some stats about your performance
+| `/forcesell <trade_id>` | Instantly sells the given trade  (Ignoring `minimum_roi`).
+| `/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)
+| `/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
+| `/version` | Show version
 
 ## Telegram commands in action
 
@@ -85,14 +88,14 @@ Below, example of Telegram message you will receive for each command.
 
 ### /stopbuy
 
-> **status:** `Setting max_open_trades to 0. Run /reload_conf to reset.`
+> **status:** `Setting max_open_trades to 0. Run /reload_config to reset.`
 
 Prevents the bot from opening new trades by temporarily setting "max_open_trades" to 0. Open trades will be handled via their regular rules (ROI / Sell-signal, stoploss, ...).
 
 After this, give the bot time to close off open trades (can be checked via `/status table`).
 Once all positions are sold, run `/stop` to completely stop the bot.
 
-`/reload_conf` resets "max_open_trades" to the value set in the configuration and resets this command. 
+`/reload_config` resets "max_open_trades" to the value set in the configuration and resets this command.
 
 !!! Warning
    The stop-buy signal is ONLY active while the bot is running, and is not persisted anyway, so restarting the bot will cause this to reset.
@@ -113,6 +116,7 @@ For each open trade, the bot will send you the following message.
 ### /status table
 
 Return the status of all open trades in a table format.
+
 ```
    ID  Pair      Since    Profit
 ----  --------  -------  --------
@@ -123,6 +127,7 @@ Return the status of all open trades in a table format.
 ### /count
 
 Return the number of trades used and available.
+
 ```
 current    max
 ---------  -----
@@ -208,15 +213,15 @@ Shows the current whitelist
 
 Shows the current blacklist.
 If Pair is set, then this pair will be added to the pairlist.
-Also supports multiple pairs, seperated by a space.
-Use `/reload_conf` to reset the blacklist.
+Also supports multiple pairs, separated by a space.
+Use `/reload_config` to reset the blacklist.
 
 > Using blacklist `StaticPairList` with 2 pairs  
 >`DODGE/BTC`, `HOT/BTC`.
 
 ### /edge
 
-Shows pairs validated by Edge along with their corresponding winrate, expectancy and stoploss values.
+Shows pairs validated by Edge along with their corresponding win-rate, expectancy and stoploss values.
 
 > **Edge only validated following pairs:**
 ```

docs/utils.md

@@ -36,6 +36,38 @@ optional arguments:
     └── sample_strategy.py
 ```
 
+## Create new config
+
+Creates a new configuration file, asking some questions which are important selections for a configuration.
+
+```
+usage: freqtrade new-config [-h] [-c PATH]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`). Multiple --config options may be used. Can be set to `-`
+                        to read config from stdin.
+```
+
+!!! Warning
+    Only vital questions are asked. Freqtrade offers a lot more configuration possibilities, which are listed in the [Configuration documentation](configuration.md#configuration-parameters)
+
+### Create config examples
+
+```
+$ freqtrade new-config --config config_binance.json
+
+? Do you want to enable Dry-run (simulated trades)?  Yes
+? Please insert your stake currency: BTC
+? Please insert your stake amount: 0.05
+? Please insert max_open_trades (Integer or 'unlimited'): 3
+? Please insert your desired timeframe (e.g. 5m): 5m
+? Please insert your display Currency (for reporting): USD
+? Select exchange  binance
+? Do you want to enable Telegram?  No
+```
+
 ## Create new strategy
 
 Creates a new strategy from a template similar to SampleStrategy.
@@ -45,7 +77,7 @@ Results will be located in `user_data/strategies/<strategyclassname>.py`.
 
 ``` output
 usage: freqtrade new-strategy [-h] [--userdir PATH] [-s NAME]
-                              [--template {full,minimal}]
+                              [--template {full,minimal,advanced}]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -54,10 +86,10 @@ optional arguments:
   -s NAME, --strategy NAME
                         Specify strategy class name which will be used by the
                         bot.
-  --template {full,minimal}
-                        Use a template which is either `minimal` or `full`
-                        (containing multiple sample indicators). Default:
-                        `full`.
+  --template {full,minimal,advanced}
+                        Use a template which is either `minimal`, `full`
+                        (containing multiple sample indicators) or `advanced`.
+                        Default: `full`.
 
 ```
 
@@ -73,6 +105,12 @@ With custom user directory
 freqtrade new-strategy --userdir ~/.freqtrade/ --strategy AwesomeStrategy
 ```
 
+Using the advanced template (populates all optional functions and methods)
+
+```bash
+freqtrade new-strategy --strategy AwesomeStrategy --template advanced
+```
+
 ## Create new hyperopt
 
 Creates a new hyperopt from a template similar to SampleHyperopt.
@@ -82,7 +120,7 @@ Results will be located in `user_data/hyperopts/<classname>.py`.
 
 ``` output
 usage: freqtrade new-hyperopt [-h] [--userdir PATH] [--hyperopt NAME]
-                              [--template {full,minimal}]
+                              [--template {full,minimal,advanced}]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -90,10 +128,10 @@ optional arguments:
                         Path to userdata directory.
   --hyperopt NAME       Specify hyperopt class name which will be used by the
                         bot.
-  --template {full,minimal}
-                        Use a template which is either `minimal` or `full`
-                        (containing multiple sample indicators). Default:
-                        `full`.
+  --template {full,minimal,advanced}
+                        Use a template which is either `minimal`, `full`
+                        (containing multiple sample indicators) or `advanced`.
+                        Default: `full`.
 ```
 
 ### Sample usage of new-hyperopt
@@ -108,6 +146,97 @@ With custom user directory
 freqtrade new-hyperopt --userdir ~/.freqtrade/ --hyperopt AwesomeHyperopt
 ```
 
+## List Strategies and List Hyperopts
+
+Use the `list-strategies` subcommand to see all strategies in one particular directory and the `list-hyperopts` subcommand to list custom Hyperopts.
+
+These subcommands are useful for finding problems in your environment with loading strategies or hyperopt classes: modules with strategies or hyperopt classes that contain errors and failed to load are printed in red (LOAD FAILED), while strategies or hyperopt classes with duplicate names are printed in yellow (DUPLICATE NAME).
+
+```
+usage: freqtrade list-strategies [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                                 [-d PATH] [--userdir PATH]
+                                 [--strategy-path PATH] [-1] [--no-color]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --strategy-path PATH  Specify additional strategy lookup path.
+  -1, --one-column      Print output in one column.
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`).
+                        Multiple --config options may be used. Can be set to
+                        `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+```
+```
+usage: freqtrade list-hyperopts [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                                [-d PATH] [--userdir PATH]
+                                [--hyperopt-path PATH] [-1] [--no-color]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --hyperopt-path PATH  Specify additional lookup path for Hyperopt and
+                        Hyperopt Loss functions.
+  -1, --one-column      Print output in one column.
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`).
+                        Multiple --config options may be used. Can be set to
+                        `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+```
+
+!!! Warning
+    Using these commands will try to load all python files from a directory. This can be a security risk if untrusted files reside in this directory, since all module-level code is executed.
+
+Example: Search default strategies and hyperopts directories (within the default userdir).
+
+``` bash
+freqtrade list-strategies
+freqtrade list-hyperopts
+```
+
+Example: Search strategies and hyperopts directory within the userdir.
+
+``` bash
+freqtrade list-strategies --userdir ~/.freqtrade/
+freqtrade list-hyperopts --userdir ~/.freqtrade/
+```
+
+Example: Search dedicated strategy path.
+
+``` bash
+freqtrade list-strategies --strategy-path ~/.freqtrade/strategies/
+```
+
+Example: Search dedicated hyperopt path.
+
+``` bash
+freqtrade list-hyperopt --hyperopt-path ~/.freqtrade/hyperopts/
+```
+
 ## List Exchanges
 
 Use the `list-exchanges` subcommand to see the exchanges available for the bot.
@@ -135,23 +264,34 @@ All exchanges supported by the ccxt library: _1btcxe, acx, adara, allcoin, anxpr
 
 ## List Timeframes
 
-Use the `list-timeframes` subcommand to see the list of ticker intervals (timeframes) available for the exchange.
+Use the `list-timeframes` subcommand to see the list of timeframes (ticker intervals) available for the exchange.
 
 ```
-usage: freqtrade list-timeframes [-h] [--exchange EXCHANGE] [-1]
+usage: freqtrade list-timeframes [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [--exchange EXCHANGE] [-1]
 
 optional arguments:
-  -h, --help           show this help message and exit
-  --exchange EXCHANGE  Exchange name (default: `bittrex`). Only valid if no
-                       config is provided.
-  -1, --one-column     Print output in one column.
+  -h, --help            show this help message and exit
+  --exchange EXCHANGE   Exchange name (default: `bittrex`). Only valid if no config is provided.
+  -1, --one-column      Print output in one column.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are: 'syslog', 'journald'. See the documentation for more details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`). Multiple --config options may be used. Can be set to `-`
+                        to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
 
 ```
 
 * Example: see the timeframes for the 'binance' exchange, set in the configuration file:
 
 ```
-$ freqtrade -c config_binance.json list-timeframes
+$ freqtrade list-timeframes -c config_binance.json
 ...
 Timeframes available for the exchange `binance`: 1m, 3m, 5m, 15m, 30m, 1h, 2h, 4h, 6h, 8h, 12h, 1d, 3d, 1w, 1M
 ```
@@ -175,14 +315,16 @@ You can print info about any pair/market with these subcommands - and you can fi
 These subcommands have same usage and same set of available options:
 
 ```
-usage: freqtrade list-markets [-h] [--exchange EXCHANGE] [--print-list]
-                              [--print-json] [-1] [--print-csv]
+usage: freqtrade list-markets [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                              [-d PATH] [--userdir PATH] [--exchange EXCHANGE]
+                              [--print-list] [--print-json] [-1] [--print-csv]
                               [--base BASE_CURRENCY [BASE_CURRENCY ...]]
                               [--quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]]
                               [-a]
 
-usage: freqtrade list-pairs [-h] [--exchange EXCHANGE] [--print-list]
-                            [--print-json] [-1] [--print-csv]
+usage: freqtrade list-pairs [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                            [-d PATH] [--userdir PATH] [--exchange EXCHANGE]
+                            [--print-list] [--print-json] [-1] [--print-csv]
                             [--base BASE_CURRENCY [BASE_CURRENCY ...]]
                             [--quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]] [-a]
 
@@ -201,6 +343,22 @@ optional arguments:
                         Specify quote currency(-ies). Space-separated list.
   -a, --all             Print all pairs or market symbols. By default only
                         active ones are shown.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`).
+                        Multiple --config options may be used. Can be set to
+                        `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
 ```
 
 By default, only active pairs/markets are shown. Active pairs/markets are those that can currently be traded
@@ -222,7 +380,7 @@ $ freqtrade list-pairs --quote USD --print-json
 human-readable list with summary:
 
 ```
-$ freqtrade -c config_binance.json list-pairs --all --base BTC ETH --quote USDT USD --print-list
+$ freqtrade list-pairs -c config_binance.json --all --base BTC ETH --quote USDT USD --print-list
 ```
 
 * Print all markets on exchange "Kraken", in the tabular format:
@@ -270,17 +428,57 @@ You can list the hyperoptimization epochs the Hyperopt module evaluated previous
 ```
 usage: freqtrade hyperopt-list [-h] [-v] [--logfile FILE] [-V] [-c PATH]
                                [-d PATH] [--userdir PATH] [--best]
-                               [--profitable] [--no-color] [--print-json]
-                               [--no-details]
+                               [--profitable] [--min-trades INT]
+                               [--max-trades INT] [--min-avg-time FLOAT]
+                               [--max-avg-time FLOAT] [--min-avg-profit FLOAT]
+                               [--max-avg-profit FLOAT]
+                               [--min-total-profit FLOAT] [--max-total-profit FLOAT]
+                               [--min-objective FLOAT] [--max-objective FLOAT]
+                               [--no-color] [--print-json] [--no-details]
+                               [--export-csv FILE]
 
 optional arguments:
   -h, --help            show this help message and exit
   --best                Select only best epochs.
   --profitable          Select only profitable epochs.
+  --min-trades INT      Select epochs with more than INT trades.
+  --max-trades INT      Select epochs with less than INT trades.
+  --min-avg-time FLOAT  Select epochs on above average time.
+  --max-avg-time FLOAT  Select epochs on under average time.
+  --min-avg-profit FLOAT
+                        Select epochs on above average profit.
+  --max-avg-profit FLOAT
+                        Select epochs on below average profit.
+  --min-total-profit FLOAT
+                        Select epochs on above total profit.
+  --max-total-profit FLOAT
+                        Select epochs on below total profit.
+  --min-objective FLOAT
+                        Select epochs on above objective (- is added by default).
+  --max-objective FLOAT
+                        Select epochs on below objective (- is added by default).
   --no-color            Disable colorization of hyperopt results. May be
                         useful if you are redirecting output to a file.
   --print-json          Print best result detailization in JSON format.
   --no-details          Do not print best epoch details.
+  --export-csv FILE     Export to CSV-File. This will disable table print.
+                        Example: --export-csv hyperopt.csv
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
 ```
  
 ### Examples
@@ -327,3 +525,48 @@ Prints JSON data with details for the last best epoch (i.e., the best of all epo
 ```
 freqtrade hyperopt-show --best -n -1 --print-json --no-header
 ```
+
+## Show trades
+
+Print selected (or all) trades from database to screen.
+
+```
+usage: freqtrade show-trades [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                             [-d PATH] [--userdir PATH] [--db-url PATH]
+                             [--trade-ids TRADE_IDS [TRADE_IDS ...]]
+                             [--print-json]
+
+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).
+  --trade-ids TRADE_IDS [TRADE_IDS ...]
+                        Specify the list of trade ids.
+  --print-json          Print output in JSON format.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+```
+
+### Examples
+
+Print trades with id 2 and 3 as json
+
+``` bash
+freqtrade show-trades --db-url sqlite:///tradesv3.sqlite --trade-ids 2 3 --print-json
+```

docs/webhook-config.md

@@ -15,10 +15,20 @@ Sample configuration (tested using IFTTT).
             "value2": "limit {limit:8f}",
             "value3": "{stake_amount:8f} {stake_currency}"
         },
+        "webhookbuycancel": {
+            "value1": "Cancelling Open Buy Order for {pair}",
+            "value2": "limit {limit:8f}",
+            "value3": "{stake_amount:8f} {stake_currency}"
+        },
         "webhooksell": {
             "value1": "Selling {pair}",
             "value2": "limit {limit:8f}",
-            "value3": "profit: {profit_amount:8f} {stake_currency}"
+            "value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
+        },
+        "webhooksellcancel": {
+            "value1": "Cancelling Open Sell Order for {pair}",
+            "value2": "limit {limit:8f}",
+            "value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
         },
         "webhookstatus": {
             "value1": "Status: {status}",
@@ -37,19 +47,41 @@ Different payloads can be configured for different events. Not all fields are ne
 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`
 * `limit`
+* `amount`
+* `open_date`
 * `stake_amount`
 * `stake_currency`
 * `fiat_currency`
 * `order_type`
+* `current_rate`
+
+### Webhookbuycancel
+
+The fields in `webhook.webhookbuycancel` are filled when the bot cancels a buy order. Parameters are filled using string.format.
+Possible parameters are:
+
+* `trade_id`
+* `exchange`
+* `pair`
+* `limit`
+* `amount`
+* `open_date`
+* `stake_amount`
+* `stake_currency`
+* `fiat_currency`
+* `order_type`
+* `current_rate`
 
 ### Webhooksell
 
 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`
 * `gain`
@@ -58,11 +90,35 @@ Possible parameters are:
 * `open_rate`
 * `current_rate`
 * `profit_amount`
-* `profit_percent`
+* `profit_ratio`
 * `stake_currency`
 * `fiat_currency`
 * `sell_reason`
 * `order_type`
+* `open_date`
+* `close_date`
+
+### Webhooksellcancel
+
+The fields in `webhook.webhooksellcancel` are filled when the bot cancels a sell order. Parameters are filled using string.format.
+Possible parameters are:
+
+* `trade_id`
+* `exchange`
+* `pair`
+* `gain`
+* `limit`
+* `amount`
+* `open_rate`
+* `current_rate`
+* `profit_amount`
+* `profit_ratio`
+* `stake_currency`
+* `fiat_currency`
+* `sell_reason`
+* `order_type`
+* `open_date`
+* `close_date`
 
 ### Webhookstatus
 

environment.yml

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

freqtrade/__init__.py

@@ -1,44 +1,34 @@
-""" FreqTrade bot """
-__version__ = '2019.11'
+""" Freqtrade bot """
+__version__ = '2020.8'
 
 if __version__ == 'develop':
 
     try:
         import subprocess
+
         __version__ = 'develop-' + subprocess.check_output(
             ['git', 'log', '--format="%h"', '-n 1'],
             stderr=subprocess.DEVNULL).decode("utf-8").rstrip().strip('"')
+
+        # from datetime import datetime
+        # last_release = subprocess.check_output(
+        #     ['git', 'tag']
+        # ).decode('utf-8').split()[-1].split(".")
+        # # Releases are in the format "2020.1" - we increment the latest version for dev.
+        # prefix = f"{last_release[0]}.{int(last_release[1]) + 1}"
+        # dev_version = int(datetime.now().timestamp() // 1000)
+        # __version__ = f"{prefix}.dev{dev_version}"
+
+        #  subprocess.check_output(
+        #     ['git', 'log', '--format="%h"', '-n 1'],
+        #     stderr=subprocess.DEVNULL).decode("utf-8").rstrip().strip('"')
     except Exception:
         # git not available, ignore
-        pass
-
-
-class DependencyException(Exception):
-    """
-    Indicates that an assumed dependency is not met.
-    This could happen when there is currently not enough money on the account.
-    """
-
-
-class OperationalException(Exception):
-    """
-    Requires manual intervention and will usually stop the bot.
-    This happens when an exchange returns an unexpected error during runtime
-    or given configuration is invalid.
-    """
-
-
-class InvalidOrderException(Exception):
-    """
-    This is returned when the order is not valid. Example:
-    If stoploss on exchange order is hit, then trying to cancel the order
-    should return this exception.
-    """
-
-
-class TemporaryError(Exception):
-    """
-    Temporary network or exchange related error.
-    This could happen when an exchange is congested, unavailable, or the user
-    has networking problems. Usually resolves itself after a time.
-    """
+        try:
+            # Try Fallback to freqtrade_commit file (created by CI while building docker image)
+            from pathlib import Path
+            versionfile = Path('./freqtrade_commit')
+            if versionfile.is_file():
+                __version__ = f"docker-{versionfile.read_text()[:8]}"
+        except Exception:
+            pass

freqtrade/commands/__init__.py

@@ -0,0 +1,30 @@
+# flake8: noqa: F401
+"""
+Commands module.
+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.arguments import Arguments
+from freqtrade.commands.build_config_commands import start_new_config
+from freqtrade.commands.data_commands import (start_convert_data,
+                                              start_download_data,
+                                              start_list_data)
+from freqtrade.commands.deploy_commands import (start_create_userdir,
+                                                start_new_hyperopt,
+                                                start_new_strategy)
+from freqtrade.commands.hyperopt_commands import (start_hyperopt_list,
+                                                  start_hyperopt_show)
+from freqtrade.commands.list_commands import (start_list_exchanges,
+                                              start_list_hyperopts,
+                                              start_list_markets,
+                                              start_list_strategies,
+                                              start_list_timeframes,
+                                              start_show_trades)
+from freqtrade.commands.optimize_commands import (start_backtesting,
+                                                  start_edge, start_hyperopt)
+from freqtrade.commands.pairlist_commands import start_test_pairlist
+from freqtrade.commands.plot_commands import (start_plot_dataframe,
+                                              start_plot_profit)
+from freqtrade.commands.trade_commands import start_trading

freqtrade/commands/arguments.py

@@ -6,8 +6,8 @@ from functools import partial
 from pathlib import Path
 from typing import Any, Dict, List, Optional
 
-from freqtrade import constants
-from freqtrade.configuration.cli_options import AVAILABLE_CLI_OPTIONS
+from freqtrade.commands.cli_options import AVAILABLE_CLI_OPTIONS
+from freqtrade.constants import DEFAULT_CONFIG
 
 ARGS_COMMON = ["verbosity", "logfile", "version", "config", "datadir", "user_data_dir"]
 
@@ -15,7 +15,7 @@ ARGS_STRATEGY = ["strategy", "strategy_path"]
 
 ARGS_TRADE = ["db_url", "sd_notify", "dry_run"]
 
-ARGS_COMMON_OPTIMIZE = ["ticker_interval", "timerange",
+ARGS_COMMON_OPTIMIZE = ["timeframe", "timerange",
                         "max_open_trades", "stake_amount", "fee"]
 
 ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + ["position_stacking", "use_max_market_positions",
@@ -30,6 +30,10 @@ 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"]
+
+ARGS_LIST_HYPEROPTS = ["hyperopt_path", "print_one_column", "print_colorized"]
+
 ARGS_LIST_EXCHANGES = ["print_one_column", "list_exchanges_all"]
 
 ARGS_LIST_TIMEFRAMES = ["exchange", "print_one_column"]
@@ -41,28 +45,45 @@ ARGS_TEST_PAIRLIST = ["config", "quote_currencies", "print_one_column", "list_pa
 
 ARGS_CREATE_USERDIR = ["user_data_dir", "reset"]
 
+ARGS_BUILD_CONFIG = ["config"]
+
 ARGS_BUILD_STRATEGY = ["user_data_dir", "strategy", "template"]
 
 ARGS_BUILD_HYPEROPT = ["user_data_dir", "hyperopt", "template"]
 
+ARGS_CONVERT_DATA = ["pairs", "format_from", "format_to", "erase"]
+ARGS_CONVERT_DATA_OHLCV = ARGS_CONVERT_DATA + ["timeframes"]
+
+ARGS_LIST_DATA = ["exchange", "dataformat_ohlcv", "pairs"]
+
 ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "download_trades", "exchange",
-                      "timeframes", "erase"]
+                      "timeframes", "erase", "dataformat_ohlcv", "dataformat_trades"]
 
 ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",
                        "db_url", "trade_source", "export", "exportfilename",
-                       "timerange", "ticker_interval"]
+                       "timerange", "timeframe", "no_trades"]
 
 ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
-                    "trade_source", "ticker_interval"]
+                    "trade_source", "timeframe"]
 
-ARGS_HYPEROPT_LIST = ["hyperopt_list_best", "hyperopt_list_profitable", "print_colorized",
-                      "print_json", "hyperopt_list_no_details"]
+ARGS_SHOW_TRADES = ["db_url", "trade_ids", "print_json"]
+
+ARGS_HYPEROPT_LIST = ["hyperopt_list_best", "hyperopt_list_profitable",
+                      "hyperopt_list_min_trades", "hyperopt_list_max_trades",
+                      "hyperopt_list_min_avg_time", "hyperopt_list_max_avg_time",
+                      "hyperopt_list_min_avg_profit", "hyperopt_list_max_avg_profit",
+                      "hyperopt_list_min_total_profit", "hyperopt_list_max_total_profit",
+                      "hyperopt_list_min_objective", "hyperopt_list_max_objective",
+                      "print_colorized", "print_json", "hyperopt_list_no_details",
+                      "export_csv"]
 
 ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperopt_show_index",
                       "print_json", "hyperopt_show_no_header"]
 
-NO_CONF_REQURIED = ["download-data", "list-timeframes", "list-markets", "list-pairs",
-                    "hyperopt-list", "hyperopt-show", "plot-dataframe", "plot-profit"]
+NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
+                    "list-markets", "list-pairs", "list-strategies", "list-data",
+                    "list-hyperopts", "hyperopt-list", "hyperopt-show",
+                    "plot-dataframe", "plot-profit", "show-trades"]
 
 NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-hyperopt", "new-strategy"]
 
@@ -96,10 +117,23 @@ class Arguments:
         # Workaround issue in argparse with action='append' and default value
         # (see https://bugs.python.org/issue16399)
         # Allow no-config for certain commands (like downloading / plotting)
-        if ('config' in parsed_arg and parsed_arg.config is None and
-            ((Path.cwd() / constants.DEFAULT_CONFIG).is_file() or
-             not ('command' in parsed_arg and parsed_arg.command in NO_CONF_REQURIED))):
-            parsed_arg.config = [constants.DEFAULT_CONFIG]
+        if ('config' in parsed_arg and parsed_arg.config is None):
+            conf_required = ('command' in parsed_arg and parsed_arg.command in NO_CONF_REQURIED)
+
+            if 'user_data_dir' in parsed_arg and parsed_arg.user_data_dir is not None:
+                user_dir = parsed_arg.user_data_dir
+            else:
+                # Default case
+                user_dir = 'user_data'
+                # Try loading from "user_data/config.json"
+            cfgfile = Path(user_dir) / DEFAULT_CONFIG
+            if cfgfile.is_file():
+                parsed_arg.config = [str(cfgfile)]
+            else:
+                # Else use "config.json".
+                cfgfile = Path.cwd() / DEFAULT_CONFIG
+                if cfgfile.is_file() or not conf_required:
+                    parsed_arg.config = [DEFAULT_CONFIG]
 
         return parsed_arg
 
@@ -127,13 +161,16 @@ class Arguments:
         self.parser = argparse.ArgumentParser(description='Free, open source crypto trading bot')
         self._build_args(optionlist=['version'], parser=self.parser)
 
-        from freqtrade.optimize import start_backtesting, start_hyperopt, start_edge
-        from freqtrade.utils import (start_create_userdir, start_download_data,
-                                     start_hyperopt_list, start_hyperopt_show,
-                                     start_list_exchanges, start_list_markets,
-                                     start_new_hyperopt, start_new_strategy,
-                                     start_list_timeframes, start_test_pairlist, start_trading)
-        from freqtrade.plot.plot_utils import start_plot_dataframe, start_plot_profit
+        from freqtrade.commands import (start_create_userdir, start_convert_data,
+                                        start_download_data, start_list_data,
+                                        start_hyperopt_list, start_hyperopt_show,
+                                        start_list_exchanges, start_list_hyperopts,
+                                        start_list_markets, start_list_strategies,
+                                        start_list_timeframes, start_new_config,
+                                        start_new_hyperopt, start_new_strategy,
+                                        start_plot_dataframe, start_plot_profit, start_show_trades,
+                                        start_backtesting, start_hyperopt, start_edge,
+                                        start_test_pairlist, start_trading)
 
         subparsers = self.parser.add_subparsers(dest='command',
                                                 # Use custom message when no subhandler is added
@@ -147,6 +184,67 @@ class Arguments:
         trade_cmd.set_defaults(func=start_trading)
         self._build_args(optionlist=ARGS_TRADE, parser=trade_cmd)
 
+        # add create-userdir subcommand
+        create_userdir_cmd = subparsers.add_parser('create-userdir',
+                                                   help="Create user-data directory.",
+                                                   )
+        create_userdir_cmd.set_defaults(func=start_create_userdir)
+        self._build_args(optionlist=ARGS_CREATE_USERDIR, parser=create_userdir_cmd)
+
+        # add new-config subcommand
+        build_config_cmd = subparsers.add_parser('new-config',
+                                                 help="Create new config")
+        build_config_cmd.set_defaults(func=start_new_config)
+        self._build_args(optionlist=ARGS_BUILD_CONFIG, parser=build_config_cmd)
+
+        # add new-hyperopt subcommand
+        build_hyperopt_cmd = subparsers.add_parser('new-hyperopt',
+                                                   help="Create new hyperopt")
+        build_hyperopt_cmd.set_defaults(func=start_new_hyperopt)
+        self._build_args(optionlist=ARGS_BUILD_HYPEROPT, parser=build_hyperopt_cmd)
+
+        # add new-strategy subcommand
+        build_strategy_cmd = subparsers.add_parser('new-strategy',
+                                                   help="Create new strategy")
+        build_strategy_cmd.set_defaults(func=start_new_strategy)
+        self._build_args(optionlist=ARGS_BUILD_STRATEGY, parser=build_strategy_cmd)
+
+        # Add download-data subcommand
+        download_data_cmd = subparsers.add_parser(
+            'download-data',
+            help='Download backtesting data.',
+            parents=[_common_parser],
+        )
+        download_data_cmd.set_defaults(func=start_download_data)
+        self._build_args(optionlist=ARGS_DOWNLOAD_DATA, parser=download_data_cmd)
+
+        # Add convert-data subcommand
+        convert_data_cmd = subparsers.add_parser(
+            'convert-data',
+            help='Convert candle (OHLCV) data from one format to another.',
+            parents=[_common_parser],
+        )
+        convert_data_cmd.set_defaults(func=partial(start_convert_data, ohlcv=True))
+        self._build_args(optionlist=ARGS_CONVERT_DATA_OHLCV, parser=convert_data_cmd)
+
+        # Add convert-trade-data subcommand
+        convert_trade_data_cmd = subparsers.add_parser(
+            'convert-trade-data',
+            help='Convert trade data from one format to another.',
+            parents=[_common_parser],
+        )
+        convert_trade_data_cmd.set_defaults(func=partial(start_convert_data, ohlcv=False))
+        self._build_args(optionlist=ARGS_CONVERT_DATA, parser=convert_trade_data_cmd)
+
+        # Add list-data subcommand
+        list_data_cmd = subparsers.add_parser(
+            'list-data',
+            help='List downloaded data.',
+            parents=[_common_parser],
+        )
+        list_data_cmd.set_defaults(func=start_list_data)
+        self._build_args(optionlist=ARGS_LIST_DATA, parser=list_data_cmd)
+
         # Add backtesting subcommand
         backtesting_cmd = subparsers.add_parser('backtesting', help='Backtesting module.',
                                                 parents=[_common_parser, _strategy_parser])
@@ -166,24 +264,23 @@ class Arguments:
         hyperopt_cmd.set_defaults(func=start_hyperopt)
         self._build_args(optionlist=ARGS_HYPEROPT, parser=hyperopt_cmd)
 
-        # add create-userdir subcommand
-        create_userdir_cmd = subparsers.add_parser('create-userdir',
-                                                   help="Create user-data directory.",
-                                                   )
-        create_userdir_cmd.set_defaults(func=start_create_userdir)
-        self._build_args(optionlist=ARGS_CREATE_USERDIR, parser=create_userdir_cmd)
+        # Add hyperopt-list subcommand
+        hyperopt_list_cmd = subparsers.add_parser(
+            'hyperopt-list',
+            help='List Hyperopt results',
+            parents=[_common_parser],
+        )
+        hyperopt_list_cmd.set_defaults(func=start_hyperopt_list)
+        self._build_args(optionlist=ARGS_HYPEROPT_LIST, parser=hyperopt_list_cmd)
 
-        # add new-strategy subcommand
-        build_strategy_cmd = subparsers.add_parser('new-strategy',
-                                                   help="Create new strategy")
-        build_strategy_cmd.set_defaults(func=start_new_strategy)
-        self._build_args(optionlist=ARGS_BUILD_STRATEGY, parser=build_strategy_cmd)
-
-        # add new-hyperopt subcommand
-        build_hyperopt_cmd = subparsers.add_parser('new-hyperopt',
-                                                   help="Create new hyperopt")
-        build_hyperopt_cmd.set_defaults(func=start_new_hyperopt)
-        self._build_args(optionlist=ARGS_BUILD_HYPEROPT, parser=build_hyperopt_cmd)
+        # Add hyperopt-show subcommand
+        hyperopt_show_cmd = subparsers.add_parser(
+            'hyperopt-show',
+            help='Show details of Hyperopt results',
+            parents=[_common_parser],
+        )
+        hyperopt_show_cmd.set_defaults(func=start_hyperopt_show)
+        self._build_args(optionlist=ARGS_HYPEROPT_SHOW, parser=hyperopt_show_cmd)
 
         # Add list-exchanges subcommand
         list_exchanges_cmd = subparsers.add_parser(
@@ -194,14 +291,14 @@ class Arguments:
         list_exchanges_cmd.set_defaults(func=start_list_exchanges)
         self._build_args(optionlist=ARGS_LIST_EXCHANGES, parser=list_exchanges_cmd)
 
-        # Add list-timeframes subcommand
-        list_timeframes_cmd = subparsers.add_parser(
-            'list-timeframes',
-            help='Print available ticker intervals (timeframes) for the exchange.',
+        # Add list-hyperopts subcommand
+        list_hyperopts_cmd = subparsers.add_parser(
+            'list-hyperopts',
+            help='Print available hyperopt classes.',
             parents=[_common_parser],
         )
-        list_timeframes_cmd.set_defaults(func=start_list_timeframes)
-        self._build_args(optionlist=ARGS_LIST_TIMEFRAMES, parser=list_timeframes_cmd)
+        list_hyperopts_cmd.set_defaults(func=start_list_hyperopts)
+        self._build_args(optionlist=ARGS_LIST_HYPEROPTS, parser=list_hyperopts_cmd)
 
         # Add list-markets subcommand
         list_markets_cmd = subparsers.add_parser(
@@ -221,6 +318,33 @@ class Arguments:
         list_pairs_cmd.set_defaults(func=partial(start_list_markets, pairs_only=True))
         self._build_args(optionlist=ARGS_LIST_PAIRS, parser=list_pairs_cmd)
 
+        # Add list-strategies subcommand
+        list_strategies_cmd = subparsers.add_parser(
+            'list-strategies',
+            help='Print available strategies.',
+            parents=[_common_parser],
+        )
+        list_strategies_cmd.set_defaults(func=start_list_strategies)
+        self._build_args(optionlist=ARGS_LIST_STRATEGIES, parser=list_strategies_cmd)
+
+        # Add list-timeframes subcommand
+        list_timeframes_cmd = subparsers.add_parser(
+            'list-timeframes',
+            help='Print available timeframes for the exchange.',
+            parents=[_common_parser],
+        )
+        list_timeframes_cmd.set_defaults(func=start_list_timeframes)
+        self._build_args(optionlist=ARGS_LIST_TIMEFRAMES, parser=list_timeframes_cmd)
+
+        # Add show-trades subcommand
+        show_trades = subparsers.add_parser(
+            'show-trades',
+            help='Show trades.',
+            parents=[_common_parser],
+        )
+        show_trades.set_defaults(func=start_show_trades)
+        self._build_args(optionlist=ARGS_SHOW_TRADES, parser=show_trades)
+
         # Add test-pairlist subcommand
         test_pairlist_cmd = subparsers.add_parser(
             'test-pairlist',
@@ -229,15 +353,6 @@ class Arguments:
         test_pairlist_cmd.set_defaults(func=start_test_pairlist)
         self._build_args(optionlist=ARGS_TEST_PAIRLIST, parser=test_pairlist_cmd)
 
-        # Add download-data subcommand
-        download_data_cmd = subparsers.add_parser(
-            'download-data',
-            help='Download backtesting data.',
-            parents=[_common_parser],
-        )
-        download_data_cmd.set_defaults(func=start_download_data)
-        self._build_args(optionlist=ARGS_DOWNLOAD_DATA, parser=download_data_cmd)
-
         # Add Plotting subcommand
         plot_dataframe_cmd = subparsers.add_parser(
             'plot-dataframe',
@@ -251,25 +366,7 @@ class Arguments:
         plot_profit_cmd = subparsers.add_parser(
             'plot-profit',
             help='Generate plot showing profits.',
-            parents=[_common_parser],
+            parents=[_common_parser, _strategy_parser],
         )
         plot_profit_cmd.set_defaults(func=start_plot_profit)
         self._build_args(optionlist=ARGS_PLOT_PROFIT, parser=plot_profit_cmd)
-
-        # Add hyperopt-list subcommand
-        hyperopt_list_cmd = subparsers.add_parser(
-            'hyperopt-list',
-            help='List Hyperopt results',
-            parents=[_common_parser],
-        )
-        hyperopt_list_cmd.set_defaults(func=start_hyperopt_list)
-        self._build_args(optionlist=ARGS_HYPEROPT_LIST, parser=hyperopt_list_cmd)
-
-        # Add hyperopt-show subcommand
-        hyperopt_show_cmd = subparsers.add_parser(
-            'hyperopt-show',
-            help='Show details of Hyperopt results',
-            parents=[_common_parser],
-        )
-        hyperopt_show_cmd.set_defaults(func=start_hyperopt_show)
-        self._build_args(optionlist=ARGS_HYPEROPT_SHOW, parser=hyperopt_show_cmd)

freqtrade/commands/build_config_commands.py

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

freqtrade/commands/cli_options.py

@@ -1,7 +1,7 @@
 """
 Definition of cli arguments used in arguments.py
 """
-import argparse
+from argparse import ArgumentTypeError
 
 from freqtrade import __version__, constants
 
@@ -12,7 +12,7 @@ def check_int_positive(value: str) -> int:
         if uint <= 0:
             raise ValueError
     except ValueError:
-        raise argparse.ArgumentTypeError(
+        raise ArgumentTypeError(
             f"{value} is invalid for this parameter, should be a positive integer value"
         )
     return uint
@@ -24,7 +24,7 @@ def check_int_nonzero(value: str) -> int:
         if uint == 0:
             raise ValueError
     except ValueError:
-        raise argparse.ArgumentTypeError(
+        raise ArgumentTypeError(
             f"{value} is invalid for this parameter, should be a non-zero integer value"
         )
     return uint
@@ -59,7 +59,8 @@ AVAILABLE_CLI_OPTIONS = {
     ),
     "config": Arg(
         '-c', '--config',
-        help=f'Specify configuration file (default: `{constants.DEFAULT_CONFIG}`). '
+        help=f'Specify configuration file (default: `userdir/{constants.DEFAULT_CONFIG}` '
+        f'or `config.json` whichever exists). '
         f'Multiple --config options may be used. '
         f'Can be set to `-` to read config from stdin.',
         action='append',
@@ -109,8 +110,8 @@ AVAILABLE_CLI_OPTIONS = {
         action='store_true',
     ),
     # Optimize common
-    "ticker_interval": Arg(
-        '-i', '--ticker-interval',
+    "timeframe": Arg(
+        '-i', '--timeframe', '--ticker-interval',
         help='Specify ticker interval (`1m`, `5m`, `30m`, `1h`, `1d`).',
     ),
     "timerange": Arg(
@@ -118,14 +119,14 @@ AVAILABLE_CLI_OPTIONS = {
         help='Specify what timerange of data to use.',
     ),
     "max_open_trades": Arg(
-        '--max_open_trades',
-        help='Specify max_open_trades to use.',
+        '--max-open-trades',
+        help='Override the value of the `max_open_trades` configuration setting.',
         type=int,
         metavar='INT',
     ),
     "stake_amount": Arg(
-        '--stake_amount',
-        help='Specify stake_amount.',
+        '--stake-amount',
+        help='Override the value of the `stake_amount` configuration setting.',
         type=float,
     ),
     # Backtesting
@@ -216,10 +217,17 @@ AVAILABLE_CLI_OPTIONS = {
     ),
     "print_json": Arg(
         '--print-json',
-        help='Print best result detailization in JSON format.',
+        help='Print output in JSON format.',
         action='store_true',
         default=False,
     ),
+    "export_csv": Arg(
+        '--export-csv',
+        help='Export to CSV-File.'
+        ' This will disable table print.'
+        ' Example: --export-csv hyperopt.csv',
+        metavar='FILE',
+    ),
     "hyperopt_jobs": Arg(
         '-j', '--job-workers',
         help='The number of concurrently running jobs for hyperoptimization '
@@ -256,7 +264,8 @@ AVAILABLE_CLI_OPTIONS = {
         help='Specify the class name of the hyperopt loss function class (IHyperOptLoss). '
         'Different functions can generate completely different results, '
         'since the target for optimization is different. Built-in Hyperopt-loss-functions are: '
-        'DefaultHyperOptLoss, OnlyProfitHyperOptLoss, SharpeHyperOptLoss.'
+        'DefaultHyperOptLoss, OnlyProfitHyperOptLoss, SharpeHyperOptLoss, SharpeHyperOptLossDaily, '
+        'SortinoHyperOptLoss, SortinoHyperOptLossDaily.'
         '(default: `%(default)s`).',
         metavar='NAME',
         default=constants.DEFAULT_HYPEROPT_LOSS,
@@ -332,6 +341,30 @@ AVAILABLE_CLI_OPTIONS = {
              'desired timeframe as specified as --timeframes/-t.',
         action='store_true',
     ),
+    "format_from": Arg(
+        '--format-from',
+        help='Source format for data conversion.',
+        choices=constants.AVAILABLE_DATAHANDLERS,
+        required=True,
+    ),
+    "format_to": Arg(
+        '--format-to',
+        help='Destination format for data conversion.',
+        choices=constants.AVAILABLE_DATAHANDLERS,
+        required=True,
+    ),
+    "dataformat_ohlcv": Arg(
+        '--data-format-ohlcv',
+        help='Storage format for downloaded candle (OHLCV) data. (default: `%(default)s`).',
+        choices=constants.AVAILABLE_DATAHANDLERS,
+        default='json'
+    ),
+    "dataformat_trades": Arg(
+        '--data-format-trades',
+        help='Storage format for downloaded trades data. (default: `%(default)s`).',
+        choices=constants.AVAILABLE_DATAHANDLERS,
+        default='jsongz'
+    ),
     "exchange": Arg(
         '--exchange',
         help=f'Exchange name (default: `{constants.DEFAULT_EXCHANGE}`). '
@@ -339,8 +372,8 @@ AVAILABLE_CLI_OPTIONS = {
     ),
     "timeframes": Arg(
         '-t', '--timeframes',
-        help=f'Specify which tickers to download. Space-separated list. '
-        f'Default: `1m 5m`.',
+        help='Specify which tickers to download. Space-separated list. '
+        'Default: `1m 5m`.',
         choices=['1m', '3m', '5m', '15m', '30m', '1h', '2h', '4h',
                  '6h', '8h', '12h', '1d', '3d', '1w'],
         default=['1m', '5m'],
@@ -354,24 +387,22 @@ AVAILABLE_CLI_OPTIONS = {
     # Templating options
     "template": Arg(
         '--template',
-        help='Use a template which is either `minimal` or '
-        '`full` (containing multiple sample indicators). Default: `%(default)s`.',
-        choices=['full', 'minimal'],
+        help='Use a template which is either `minimal`, '
+        '`full` (containing multiple sample indicators) or `advanced`. Default: `%(default)s`.',
+        choices=['full', 'minimal', 'advanced'],
         default='full',
     ),
     # Plot dataframe
     "indicators1": Arg(
         '--indicators1',
         help='Set indicators from your strategy you want in the first row of the graph. '
-        'Space-separated list. Example: `ema3 ema5`. Default: `%(default)s`.',
-        default=['sma', 'ema3', 'ema5'],
+        "Space-separated list. Example: `ema3 ema5`. Default: `['sma', 'ema3', 'ema5']`.",
         nargs='+',
     ),
     "indicators2": Arg(
         '--indicators2',
         help='Set indicators from your strategy you want in the third row of the graph. '
-        'Space-separated list. Example: `fastd fastk`. Default: `%(default)s`.',
-        default=['macd', 'macdsignal'],
+        "Space-separated list. Example: `fastd fastk`. Default: `['macd', 'macdsignal']`.",
         nargs='+',
     ),
     "plot_limit": Arg(
@@ -382,6 +413,11 @@ AVAILABLE_CLI_OPTIONS = {
         metavar='INT',
         default=750,
     ),
+    "no_trades": Arg(
+        '--no-trades',
+        help='Skip using trades from backtesting file and DB.',
+        action='store_true',
+    ),
     "trade_source": Arg(
         '--trade-source',
         help='Specify the source for trades (Can be DB or file (backtest file)) '
@@ -389,6 +425,11 @@ AVAILABLE_CLI_OPTIONS = {
         choices=["DB", "file"],
         default="file",
     ),
+    "trade_ids": Arg(
+        '--trade-ids',
+        help='Specify the list of trade ids.',
+        nargs='+',
+    ),
     # hyperopt-list, hyperopt-show
     "hyperopt_list_profitable": Arg(
         '--profitable',
@@ -400,6 +441,66 @@ AVAILABLE_CLI_OPTIONS = {
         help='Select only best epochs.',
         action='store_true',
     ),
+    "hyperopt_list_min_trades": Arg(
+        '--min-trades',
+        help='Select epochs with more than INT trades.',
+        type=check_int_positive,
+        metavar='INT',
+    ),
+    "hyperopt_list_max_trades": Arg(
+        '--max-trades',
+        help='Select epochs with less than INT trades.',
+        type=check_int_positive,
+        metavar='INT',
+    ),
+    "hyperopt_list_min_avg_time": Arg(
+        '--min-avg-time',
+        help='Select epochs above average time.',
+        type=float,
+        metavar='FLOAT',
+    ),
+    "hyperopt_list_max_avg_time": Arg(
+        '--max-avg-time',
+        help='Select epochs below average time.',
+        type=float,
+        metavar='FLOAT',
+    ),
+    "hyperopt_list_min_avg_profit": Arg(
+        '--min-avg-profit',
+        help='Select epochs above average profit.',
+        type=float,
+        metavar='FLOAT',
+    ),
+    "hyperopt_list_max_avg_profit": Arg(
+        '--max-avg-profit',
+        help='Select epochs below average profit.',
+        type=float,
+        metavar='FLOAT',
+    ),
+    "hyperopt_list_min_total_profit": Arg(
+        '--min-total-profit',
+        help='Select epochs above total profit.',
+        type=float,
+        metavar='FLOAT',
+    ),
+    "hyperopt_list_max_total_profit": Arg(
+        '--max-total-profit',
+        help='Select epochs below total profit.',
+        type=float,
+        metavar='FLOAT',
+    ),
+    "hyperopt_list_min_objective": Arg(
+        '--min-objective',
+        help='Select epochs above objective.',
+        type=float,
+        metavar='FLOAT',
+    ),
+    "hyperopt_list_max_objective": Arg(
+        '--max-objective',
+        help='Select epochs below objective.',
+        type=float,
+        metavar='FLOAT',
+    ),
     "hyperopt_list_no_details": Arg(
         '--no-details',
         help='Do not print best epoch details.',

freqtrade/commands/data_commands.py

@@ -0,0 +1,119 @@
+import logging
+import sys
+from collections import defaultdict
+from typing import Any, Dict, List
+
+import arrow
+
+from freqtrade.configuration import TimeRange, setup_utils_configuration
+from freqtrade.data.converter import (convert_ohlcv_format,
+                                      convert_trades_format)
+from freqtrade.data.history import (convert_trades_to_ohlcv,
+                                    refresh_backtest_ohlcv_data,
+                                    refresh_backtest_trades_data)
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange import timeframe_to_minutes
+from freqtrade.resolvers import ExchangeResolver
+from freqtrade.state import RunMode
+
+logger = logging.getLogger(__name__)
+
+
+def start_download_data(args: Dict[str, Any]) -> None:
+    """
+    Download data (former download_backtest_data.py script)
+    """
+    config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
+
+    timerange = TimeRange()
+    if 'days' in config:
+        time_since = arrow.utcnow().shift(days=-config['days']).strftime("%Y%m%d")
+        timerange = TimeRange.parse_timerange(f'{time_since}-')
+
+    if 'pairs' not in config:
+        raise OperationalException(
+            "Downloading data requires a list of pairs. "
+            "Please check the documentation on how to configure this.")
+
+    logger.info(f"About to download pairs: {config['pairs']}, "
+                f"intervals: {config['timeframes']} to {config['datadir']}")
+
+    pairs_not_available: List[str] = []
+
+    # Init exchange
+    exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
+    # Manual validations of relevant settings
+    exchange.validate_pairs(config['pairs'])
+    for timeframe in config['timeframes']:
+        exchange.validate_timeframes(timeframe)
+
+    try:
+
+        if config.get('download_trades'):
+            pairs_not_available = refresh_backtest_trades_data(
+                exchange, pairs=config['pairs'], datadir=config['datadir'],
+                timerange=timerange, erase=bool(config.get('erase')),
+                data_format=config['dataformat_trades'])
+
+            # Convert downloaded trade data to different timeframes
+            convert_trades_to_ohlcv(
+                pairs=config['pairs'], timeframes=config['timeframes'],
+                datadir=config['datadir'], timerange=timerange, erase=bool(config.get('erase')),
+                data_format_ohlcv=config['dataformat_ohlcv'],
+                data_format_trades=config['dataformat_trades'],
+                )
+        else:
+            pairs_not_available = refresh_backtest_ohlcv_data(
+                exchange, pairs=config['pairs'], timeframes=config['timeframes'],
+                datadir=config['datadir'], timerange=timerange, erase=bool(config.get('erase')),
+                data_format=config['dataformat_ohlcv'])
+
+    except KeyboardInterrupt:
+        sys.exit("SIGINT received, aborting ...")
+
+    finally:
+        if pairs_not_available:
+            logger.info(f"Pairs [{','.join(pairs_not_available)}] not available "
+                        f"on exchange {exchange.name}.")
+
+
+def start_convert_data(args: Dict[str, Any], ohlcv: bool = True) -> None:
+    """
+    Convert data from one format to another
+    """
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+    if ohlcv:
+        convert_ohlcv_format(config,
+                             convert_from=args['format_from'], convert_to=args['format_to'],
+                             erase=args['erase'])
+    else:
+        convert_trades_format(config,
+                              convert_from=args['format_from'], convert_to=args['format_to'],
+                              erase=args['erase'])
+
+
+def start_list_data(args: Dict[str, Any]) -> None:
+    """
+    List available backtest data
+    """
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    from freqtrade.data.history.idatahandler import get_datahandler
+    from tabulate import tabulate
+    dhc = get_datahandler(config['datadir'], config['dataformat_ohlcv'])
+
+    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 in sorted(paircombs, key=lambda x: (x[0], timeframe_to_minutes(x[1]))):
+        groupedpair[pair].append(timeframe)
+
+    if groupedpair:
+        print(tabulate([(pair, ', '.join(timeframes)) for pair, timeframes in groupedpair.items()],
+                       headers=("Pair", "Timeframe"),
+                       tablefmt='psql', stralign='right'))

freqtrade/commands/deploy_commands.py

@@ -0,0 +1,139 @@
+import logging
+import sys
+from pathlib import Path
+from typing import Any, Dict
+
+from freqtrade.configuration import setup_utils_configuration
+from freqtrade.configuration.directory_operations import (copy_sample_files,
+                                                          create_userdata_dir)
+from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGIES
+from freqtrade.exceptions import OperationalException
+from freqtrade.misc import render_template, render_template_with_fallback
+from freqtrade.state import RunMode
+
+logger = logging.getLogger(__name__)
+
+
+def start_create_userdir(args: Dict[str, Any]) -> None:
+    """
+    Create "user_data" directory to contain user data strategies, hyperopt, ...)
+    :param args: Cli args from Arguments()
+    :return: None
+    """
+    if "user_data_dir" in args and args["user_data_dir"]:
+        userdir = create_userdata_dir(args["user_data_dir"], create_dir=True)
+        copy_sample_files(userdir, overwrite=args["reset"])
+    else:
+        logger.warning("`create-userdir` requires --userdir to be set.")
+        sys.exit(1)
+
+
+def deploy_new_strategy(strategy_name: str, strategy_path: Path, subtemplate: str) -> None:
+    """
+    Deploy new strategy from template to strategy_path
+    """
+    fallback = 'full'
+    indicators = render_template_with_fallback(
+        templatefile=f"subtemplates/indicators_{subtemplate}.j2",
+        templatefallbackfile=f"subtemplates/indicators_{fallback}.j2",
+        )
+    buy_trend = render_template_with_fallback(
+        templatefile=f"subtemplates/buy_trend_{subtemplate}.j2",
+        templatefallbackfile=f"subtemplates/buy_trend_{fallback}.j2",
+        )
+    sell_trend = render_template_with_fallback(
+        templatefile=f"subtemplates/sell_trend_{subtemplate}.j2",
+        templatefallbackfile=f"subtemplates/sell_trend_{fallback}.j2",
+        )
+    plot_config = render_template_with_fallback(
+        templatefile=f"subtemplates/plot_config_{subtemplate}.j2",
+        templatefallbackfile=f"subtemplates/plot_config_{fallback}.j2",
+    )
+    additional_methods = render_template_with_fallback(
+        templatefile=f"subtemplates/strategy_methods_{subtemplate}.j2",
+        templatefallbackfile="subtemplates/strategy_methods_empty.j2",
+    )
+
+    strategy_text = render_template(templatefile='base_strategy.py.j2',
+                                    arguments={"strategy": strategy_name,
+                                               "indicators": indicators,
+                                               "buy_trend": buy_trend,
+                                               "sell_trend": sell_trend,
+                                               "plot_config": plot_config,
+                                               "additional_methods": additional_methods,
+                                               })
+
+    logger.info(f"Writing strategy to `{strategy_path}`.")
+    strategy_path.write_text(strategy_text)
+
+
+def start_new_strategy(args: Dict[str, Any]) -> None:
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    if "strategy" in args and args["strategy"]:
+        if args["strategy"] == "DefaultStrategy":
+            raise OperationalException("DefaultStrategy is not allowed as name.")
+
+        new_path = config['user_data_dir'] / USERPATH_STRATEGIES / (args['strategy'] + '.py')
+
+        if new_path.exists():
+            raise OperationalException(f"`{new_path}` already exists. "
+                                       "Please choose another Strategy Name.")
+
+        deploy_new_strategy(args['strategy'], new_path, args['template'])
+
+    else:
+        raise OperationalException("`new-strategy` requires --strategy to be set.")
+
+
+def deploy_new_hyperopt(hyperopt_name: str, hyperopt_path: Path, subtemplate: str) -> None:
+    """
+    Deploys a new hyperopt template to hyperopt_path
+    """
+    fallback = 'full'
+    buy_guards = render_template_with_fallback(
+        templatefile=f"subtemplates/hyperopt_buy_guards_{subtemplate}.j2",
+        templatefallbackfile=f"subtemplates/hyperopt_buy_guards_{fallback}.j2",
+        )
+    sell_guards = render_template_with_fallback(
+        templatefile=f"subtemplates/hyperopt_sell_guards_{subtemplate}.j2",
+        templatefallbackfile=f"subtemplates/hyperopt_sell_guards_{fallback}.j2",
+        )
+    buy_space = render_template_with_fallback(
+        templatefile=f"subtemplates/hyperopt_buy_space_{subtemplate}.j2",
+        templatefallbackfile=f"subtemplates/hyperopt_buy_space_{fallback}.j2",
+        )
+    sell_space = render_template_with_fallback(
+        templatefile=f"subtemplates/hyperopt_sell_space_{subtemplate}.j2",
+        templatefallbackfile=f"subtemplates/hyperopt_sell_space_{fallback}.j2",
+        )
+
+    strategy_text = render_template(templatefile='base_hyperopt.py.j2',
+                                    arguments={"hyperopt": hyperopt_name,
+                                               "buy_guards": buy_guards,
+                                               "sell_guards": sell_guards,
+                                               "buy_space": buy_space,
+                                               "sell_space": sell_space,
+                                               })
+
+    logger.info(f"Writing hyperopt to `{hyperopt_path}`.")
+    hyperopt_path.write_text(strategy_text)
+
+
+def start_new_hyperopt(args: Dict[str, Any]) -> None:
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    if 'hyperopt' in args and args['hyperopt']:
+        if args['hyperopt'] == 'DefaultHyperopt':
+            raise OperationalException("DefaultHyperopt is not allowed as name.")
+
+        new_path = config['user_data_dir'] / USERPATH_HYPEROPTS / (args['hyperopt'] + '.py')
+
+        if new_path.exists():
+            raise OperationalException(f"`{new_path}` already exists. "
+                                       "Please choose another Strategy Name.")
+        deploy_new_hyperopt(args['hyperopt'], new_path, args['template'])
+    else:
+        raise OperationalException("`new-hyperopt` requires --hyperopt to be set.")

freqtrade/commands/hyperopt_commands.py

@@ -0,0 +1,223 @@
+import logging
+from operator import itemgetter
+from typing import Any, Dict, List
+
+from colorama import init as colorama_init
+
+from freqtrade.configuration import setup_utils_configuration
+from freqtrade.exceptions import OperationalException
+from freqtrade.state import RunMode
+
+logger = logging.getLogger(__name__)
+
+
+def start_hyperopt_list(args: Dict[str, Any]) -> None:
+    """
+    List hyperopt epochs previously evaluated
+    """
+    from freqtrade.optimize.hyperopt import Hyperopt
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    print_colorized = config.get('print_colorized', False)
+    print_json = config.get('print_json', False)
+    export_csv = config.get('export_csv', None)
+    no_details = config.get('hyperopt_list_no_details', False)
+    no_header = False
+
+    filteroptions = {
+        'only_best': config.get('hyperopt_list_best', False),
+        'only_profitable': config.get('hyperopt_list_profitable', False),
+        'filter_min_trades': config.get('hyperopt_list_min_trades', 0),
+        'filter_max_trades': config.get('hyperopt_list_max_trades', 0),
+        'filter_min_avg_time': config.get('hyperopt_list_min_avg_time', None),
+        'filter_max_avg_time': config.get('hyperopt_list_max_avg_time', None),
+        'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit', None),
+        'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit', None),
+        'filter_min_total_profit': config.get('hyperopt_list_min_total_profit', None),
+        'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None),
+        'filter_min_objective': config.get('hyperopt_list_min_objective', None),
+        'filter_max_objective': config.get('hyperopt_list_max_objective', None),
+    }
+
+    results_file = (config['user_data_dir'] /
+                    'hyperopt_results' / 'hyperopt_results.pickle')
+
+    # Previous evaluations
+    epochs = Hyperopt.load_previous_results(results_file)
+    total_epochs = len(epochs)
+
+    epochs = hyperopt_filter_epochs(epochs, filteroptions)
+
+    if print_colorized:
+        colorama_init(autoreset=True)
+
+    if not export_csv:
+        try:
+            print(Hyperopt.get_result_table(config, epochs, total_epochs,
+                                            not filteroptions['only_best'], print_colorized, 0))
+        except KeyboardInterrupt:
+            print('User interrupted..')
+
+    if epochs and not no_details:
+        sorted_epochs = sorted(epochs, key=itemgetter('loss'))
+        results = sorted_epochs[0]
+        Hyperopt.print_epoch_details(results, total_epochs, print_json, no_header)
+
+    if epochs and export_csv:
+        Hyperopt.export_csv_file(
+            config, epochs, total_epochs, not filteroptions['only_best'], export_csv
+        )
+
+
+def start_hyperopt_show(args: Dict[str, Any]) -> None:
+    """
+    Show details of a hyperopt epoch previously evaluated
+    """
+    from freqtrade.optimize.hyperopt import Hyperopt
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    print_json = config.get('print_json', False)
+    no_header = config.get('hyperopt_show_no_header', False)
+    results_file = (config['user_data_dir'] /
+                    'hyperopt_results' / 'hyperopt_results.pickle')
+    n = config.get('hyperopt_show_index', -1)
+
+    filteroptions = {
+        'only_best': config.get('hyperopt_list_best', False),
+        'only_profitable': config.get('hyperopt_list_profitable', False),
+        'filter_min_trades': config.get('hyperopt_list_min_trades', 0),
+        'filter_max_trades': config.get('hyperopt_list_max_trades', 0),
+        'filter_min_avg_time': config.get('hyperopt_list_min_avg_time', None),
+        'filter_max_avg_time': config.get('hyperopt_list_max_avg_time', None),
+        'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit', None),
+        'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit', None),
+        'filter_min_total_profit': config.get('hyperopt_list_min_total_profit', None),
+        'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None),
+        'filter_min_objective': config.get('hyperopt_list_min_objective', None),
+        'filter_max_objective': config.get('hyperopt_list_max_objective', None)
+    }
+
+    # Previous evaluations
+    epochs = Hyperopt.load_previous_results(results_file)
+    total_epochs = len(epochs)
+
+    epochs = hyperopt_filter_epochs(epochs, filteroptions)
+    filtered_epochs = len(epochs)
+
+    if n > filtered_epochs:
+        raise OperationalException(
+            f"The index of the epoch to show should be less than {filtered_epochs + 1}.")
+    if n < -filtered_epochs:
+        raise OperationalException(
+            f"The index of the epoch to show should be greater than {-filtered_epochs - 1}.")
+
+    # Translate epoch index from human-readable format to pythonic
+    if n > 0:
+        n -= 1
+
+    if epochs:
+        val = epochs[n]
+        Hyperopt.print_epoch_details(val, total_epochs, print_json, no_header,
+                                     header_str="Epoch details")
+
+
+def hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
+    """
+    Filter our items from the list of hyperopt results
+    """
+    if filteroptions['only_best']:
+        epochs = [x for x in epochs if x['is_best']]
+    if filteroptions['only_profitable']:
+        epochs = [x for x in epochs if x['results_metrics']['profit'] > 0]
+
+    epochs = _hyperopt_filter_epochs_trade_count(epochs, filteroptions)
+
+    epochs = _hyperopt_filter_epochs_duration(epochs, filteroptions)
+
+    epochs = _hyperopt_filter_epochs_profit(epochs, filteroptions)
+
+    epochs = _hyperopt_filter_epochs_objective(epochs, filteroptions)
+
+    logger.info(f"{len(epochs)} " +
+                ("best " if filteroptions['only_best'] else "") +
+                ("profitable " if filteroptions['only_profitable'] else "") +
+                "epochs found.")
+    return epochs
+
+
+def _hyperopt_filter_epochs_trade_count(epochs: List, filteroptions: dict) -> List:
+
+    if filteroptions['filter_min_trades'] > 0:
+        epochs = [
+            x for x in epochs
+            if x['results_metrics']['trade_count'] > filteroptions['filter_min_trades']
+        ]
+    if filteroptions['filter_max_trades'] > 0:
+        epochs = [
+            x for x in epochs
+            if x['results_metrics']['trade_count'] < filteroptions['filter_max_trades']
+        ]
+    return epochs
+
+
+def _hyperopt_filter_epochs_duration(epochs: List, filteroptions: dict) -> List:
+
+    if filteroptions['filter_min_avg_time'] is not None:
+        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
+        epochs = [
+            x for x in epochs
+            if x['results_metrics']['duration'] > filteroptions['filter_min_avg_time']
+        ]
+    if filteroptions['filter_max_avg_time'] is not None:
+        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
+        epochs = [
+            x for x in epochs
+            if x['results_metrics']['duration'] < filteroptions['filter_max_avg_time']
+        ]
+
+    return epochs
+
+
+def _hyperopt_filter_epochs_profit(epochs: List, filteroptions: dict) -> List:
+
+    if filteroptions['filter_min_avg_profit'] is not None:
+        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
+        epochs = [
+            x for x in epochs
+            if x['results_metrics']['avg_profit'] > filteroptions['filter_min_avg_profit']
+        ]
+    if filteroptions['filter_max_avg_profit'] is not None:
+        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
+        epochs = [
+            x for x in epochs
+            if x['results_metrics']['avg_profit'] < filteroptions['filter_max_avg_profit']
+        ]
+    if filteroptions['filter_min_total_profit'] is not None:
+        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
+        epochs = [
+            x for x in epochs
+            if x['results_metrics']['profit'] > filteroptions['filter_min_total_profit']
+        ]
+    if filteroptions['filter_max_total_profit'] is not None:
+        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
+        epochs = [
+            x for x in epochs
+            if x['results_metrics']['profit'] < filteroptions['filter_max_total_profit']
+        ]
+    return epochs
+
+
+def _hyperopt_filter_epochs_objective(epochs: List, filteroptions: dict) -> List:
+
+    if filteroptions['filter_min_objective'] is not None:
+        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
+
+        epochs = [x for x in epochs if x['loss'] < filteroptions['filter_min_objective']]
+    if filteroptions['filter_max_objective'] is not None:
+        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
+
+        epochs = [x for x in epochs if x['loss'] > filteroptions['filter_max_objective']]
+
+    return epochs

freqtrade/commands/list_commands.py

@@ -0,0 +1,226 @@
+import csv
+import logging
+import sys
+from collections import OrderedDict
+from pathlib import Path
+from typing import Any, Dict, List
+
+from colorama import init as colorama_init
+from colorama import Fore, Style
+import rapidjson
+from tabulate import tabulate
+
+from freqtrade.configuration import setup_utils_configuration
+from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGIES
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange import (available_exchanges, ccxt_exchanges,
+                                market_is_active)
+from freqtrade.misc import plural
+from freqtrade.resolvers import ExchangeResolver, StrategyResolver
+from freqtrade.state import RunMode
+
+logger = logging.getLogger(__name__)
+
+
+def start_list_exchanges(args: Dict[str, Any]) -> None:
+    """
+    Print available exchanges
+    :param args: Cli args from Arguments()
+    :return: None
+    """
+    exchanges = ccxt_exchanges() if args['list_exchanges_all'] else available_exchanges()
+    if args['print_one_column']:
+        print('\n'.join(exchanges))
+    else:
+        if args['list_exchanges_all']:
+            print(f"All exchanges supported by the ccxt library: {', '.join(exchanges)}")
+        else:
+            print(f"Exchanges available for Freqtrade: {', '.join(exchanges)}")
+
+
+def _print_objs_tabular(objs: List, print_colorized: bool) -> None:
+    if print_colorized:
+        colorama_init(autoreset=True)
+        red = Fore.RED
+        yellow = Fore.YELLOW
+        reset = Style.RESET_ALL
+    else:
+        red = ''
+        yellow = ''
+        reset = ''
+
+    names = [s['name'] for s in objs]
+    objss_to_print = [{
+        'name': s['name'] if s['name'] else "--",
+        'location': s['location'].name,
+        'status': (red + "LOAD FAILED" + reset if s['class'] is None
+                   else "OK" if names.count(s['name']) == 1
+                   else yellow + "DUPLICATE NAME" + reset)
+    } for s in objs]
+
+    print(tabulate(objss_to_print, headers='keys', tablefmt='psql', stralign='right'))
+
+
+def start_list_strategies(args: Dict[str, Any]) -> None:
+    """
+    Print files with Strategy custom classes available in the directory
+    """
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    directory = Path(config.get('strategy_path', config['user_data_dir'] / USERPATH_STRATEGIES))
+    strategy_objs = StrategyResolver.search_all_objects(directory, not args['print_one_column'])
+    # Sort alphabetically
+    strategy_objs = sorted(strategy_objs, key=lambda x: x['name'])
+
+    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))
+
+
+def start_list_hyperopts(args: Dict[str, Any]) -> None:
+    """
+    Print files with HyperOpt custom classes available in the directory
+    """
+    from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    directory = Path(config.get('hyperopt_path', config['user_data_dir'] / USERPATH_HYPEROPTS))
+    hyperopt_objs = HyperOptResolver.search_all_objects(directory, not args['print_one_column'])
+    # Sort alphabetically
+    hyperopt_objs = sorted(hyperopt_objs, key=lambda x: x['name'])
+
+    if args['print_one_column']:
+        print('\n'.join([s['name'] for s in hyperopt_objs]))
+    else:
+        _print_objs_tabular(hyperopt_objs, config.get('print_colorized', False))
+
+
+def start_list_timeframes(args: Dict[str, Any]) -> None:
+    """
+    Print ticker intervals (timeframes) available on Exchange
+    """
+    config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
+    # Do not use timeframe set in the config
+    config['timeframe'] = None
+
+    # Init exchange
+    exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
+
+    if args['print_one_column']:
+        print('\n'.join(exchange.timeframes))
+    else:
+        print(f"Timeframes available for the exchange `{exchange.name}`: "
+              f"{', '.join(exchange.timeframes)}")
+
+
+def start_list_markets(args: Dict[str, Any], pairs_only: bool = False) -> None:
+    """
+    Print pairs/markets on the exchange
+    :param args: Cli args from Arguments()
+    :param pairs_only: if True print only pairs, otherwise print all instruments (markets)
+    :return: None
+    """
+    config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
+
+    # Init exchange
+    exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
+
+    # By default only active pairs/markets are to be shown
+    active_only = not args.get('list_pairs_all', False)
+
+    base_currencies = args.get('base_currencies', [])
+    quote_currencies = args.get('quote_currencies', [])
+
+    try:
+        pairs = exchange.get_markets(base_currencies=base_currencies,
+                                     quote_currencies=quote_currencies,
+                                     pairs_only=pairs_only,
+                                     active_only=active_only)
+        # Sort the pairs/markets by symbol
+        pairs = OrderedDict(sorted(pairs.items()))
+    except Exception as e:
+        raise OperationalException(f"Cannot get markets. Reason: {e}") from e
+
+    else:
+        summary_str = ((f"Exchange {exchange.name} has {len(pairs)} ") +
+                       ("active " if active_only else "") +
+                       (plural(len(pairs), "pair" if pairs_only else "market")) +
+                       (f" with {', '.join(base_currencies)} as base "
+                        f"{plural(len(base_currencies), 'currency', 'currencies')}"
+                        if base_currencies else "") +
+                       (" and" if base_currencies and quote_currencies else "") +
+                       (f" with {', '.join(quote_currencies)} as quote "
+                        f"{plural(len(quote_currencies), 'currency', 'currencies')}"
+                        if quote_currencies else ""))
+
+        headers = ["Id", "Symbol", "Base", "Quote", "Active",
+                   *(['Is pair'] if not pairs_only else [])]
+
+        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
+                args.get('print_csv', False)):
+            # Print summary string in the log in case of machine-readable
+            # regular formats.
+            logger.info(f"{summary_str}.")
+        else:
+            # Print empty string separating leading logs and output in case of
+            # human-readable formats.
+            print()
+
+        if len(pairs):
+            if args.get('print_list', False):
+                # print data as a list, with human-readable summary
+                print(f"{summary_str}: {', '.join(pairs.keys())}.")
+            elif args.get('print_one_column', False):
+                print('\n'.join(pairs.keys()))
+            elif args.get('list_pairs_print_json', False):
+                print(rapidjson.dumps(list(pairs.keys()), default=str))
+            elif args.get('print_csv', False):
+                writer = csv.DictWriter(sys.stdout, fieldnames=headers)
+                writer.writeheader()
+                writer.writerows(tabular_data)
+            else:
+                # print data as a table, with the human-readable summary
+                print(f"{summary_str}:")
+                print(tabulate(tabular_data, headers='keys', tablefmt='psql', stralign='right'))
+        elif not (args.get('print_one_column', False) or
+                  args.get('list_pairs_print_json', False) or
+                  args.get('print_csv', False)):
+            print(f"{summary_str}.")
+
+
+def start_show_trades(args: Dict[str, Any]) -> None:
+    """
+    Show trades
+    """
+    from freqtrade.persistence import init, Trade
+    import json
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    if 'db_url' not in config:
+        raise OperationalException("--db-url is required for this command.")
+
+    logger.info(f'Using DB: "{config["db_url"]}"')
+    init(config['db_url'], clean_open_orders=False)
+    tfilter = []
+
+    if config.get('trade_ids'):
+        tfilter.append(Trade.id.in_(config['trade_ids']))
+
+    trades = Trade.get_trades(tfilter).all()
+    logger.info(f"Printing {len(trades)} Trades: ")
+    if config.get('print_json', False):
+        print(json.dumps([trade.to_json() for trade in trades], indent=4))
+    else:
+        for trade in trades:
+            print(trade)

freqtrade/commands/optimize_commands.py

@@ -0,0 +1,107 @@
+import logging
+from typing import Any, Dict
+
+from freqtrade import constants
+from freqtrade.configuration import setup_utils_configuration
+from freqtrade.exceptions import DependencyException, OperationalException
+from freqtrade.state import RunMode
+
+logger = logging.getLogger(__name__)
+
+
+def setup_optimize_configuration(args: Dict[str, Any], method: RunMode) -> Dict[str, Any]:
+    """
+    Prepare the configuration for the Hyperopt module
+    :param args: Cli args from Arguments()
+    :return: Configuration
+    """
+    config = setup_utils_configuration(args, method)
+
+    no_unlimited_runmodes = {
+        RunMode.BACKTEST: 'backtesting',
+        RunMode.HYPEROPT: 'hyperoptimization',
+    }
+    if (method in no_unlimited_runmodes.keys() and
+            config['stake_amount'] == constants.UNLIMITED_STAKE_AMOUNT):
+        raise DependencyException(
+            f'The value of `stake_amount` cannot be set as "{constants.UNLIMITED_STAKE_AMOUNT}" '
+            f'for {no_unlimited_runmodes[method]}')
+
+    return config
+
+
+def start_backtesting(args: Dict[str, Any]) -> None:
+    """
+    Start Backtesting script
+    :param args: Cli args from Arguments()
+    :return: None
+    """
+    # Import here to avoid loading backtesting module when it's not used
+    from freqtrade.optimize.backtesting import Backtesting
+
+    # Initialize configuration
+    config = setup_optimize_configuration(args, RunMode.BACKTEST)
+
+    logger.info('Starting freqtrade in Backtesting mode')
+
+    # Initialize backtesting object
+    backtesting = Backtesting(config)
+    backtesting.start()
+
+
+def start_hyperopt(args: Dict[str, Any]) -> None:
+    """
+    Start hyperopt script
+    :param args: Cli args from Arguments()
+    :return: None
+    """
+    # Import here to avoid loading hyperopt module when it's not used
+    try:
+        from filelock import FileLock, Timeout
+        from freqtrade.optimize.hyperopt import Hyperopt
+    except ImportError as e:
+        raise OperationalException(
+            f"{e}. Please ensure that the hyperopt dependencies are installed.") from e
+    # Initialize configuration
+    config = setup_optimize_configuration(args, RunMode.HYPEROPT)
+
+    logger.info('Starting freqtrade in Hyperopt mode')
+
+    lock = FileLock(Hyperopt.get_lock_filename(config))
+
+    try:
+        with lock.acquire(timeout=1):
+
+            # Remove noisy log messages
+            logging.getLogger('hyperopt.tpe').setLevel(logging.WARNING)
+            logging.getLogger('filelock').setLevel(logging.WARNING)
+
+            # Initialize backtesting object
+            hyperopt = Hyperopt(config)
+            hyperopt.start()
+
+    except Timeout:
+        logger.info("Another running instance of freqtrade Hyperopt detected.")
+        logger.info("Simultaneous execution of multiple Hyperopt commands is not supported. "
+                    "Hyperopt module is resource hungry. Please run your Hyperopt sequentially "
+                    "or on separate machines.")
+        logger.info("Quitting now.")
+        # TODO: return False here in order to help freqtrade to exit
+        # with non-zero exit code...
+        # Same in Edge and Backtesting start() functions.
+
+
+def start_edge(args: Dict[str, Any]) -> None:
+    """
+    Start Edge script
+    :param args: Cli args from Arguments()
+    :return: None
+    """
+    from freqtrade.optimize.edge_cli import EdgeCli
+    # Initialize configuration
+    config = setup_optimize_configuration(args, RunMode.EDGE)
+    logger.info('Starting freqtrade in Edge mode')
+
+    # Initialize Edge object
+    edge_cli = EdgeCli(config)
+    edge_cli.start()

freqtrade/commands/pairlist_commands.py

@@ -0,0 +1,41 @@
+import logging
+from typing import Any, Dict
+
+import rapidjson
+
+from freqtrade.configuration import setup_utils_configuration
+from freqtrade.resolvers import ExchangeResolver
+from freqtrade.state import RunMode
+
+logger = logging.getLogger(__name__)
+
+
+def start_test_pairlist(args: Dict[str, Any]) -> None:
+    """
+    Test Pairlist configuration
+    """
+    from freqtrade.pairlist.pairlistmanager import PairListManager
+    config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
+
+    exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
+
+    quote_currencies = args.get('quote_currencies')
+    if not quote_currencies:
+        quote_currencies = [config.get('stake_currency')]
+    results = {}
+    for curr in quote_currencies:
+        config['stake_currency'] = curr
+        pairlists = PairListManager(exchange, config)
+        pairlists.refresh_pairlist()
+        results[curr] = pairlists.whitelist
+
+    for curr, pairlist in results.items():
+        if not args.get('print_one_column', False):
+            print(f"Pairs for {curr}: ")
+
+        if args.get('print_one_column', False):
+            print('\n'.join(pairlist))
+        elif args.get('list_pairs_print_json', False):
+            print(rapidjson.dumps(list(pairlist), default=str))
+        else:
+            print(pairlist)

freqtrade/commands/plot_commands.py

@@ -1,11 +1,11 @@
 from typing import Any, Dict
 
-from freqtrade import OperationalException
+from freqtrade.configuration import setup_utils_configuration
+from freqtrade.exceptions import OperationalException
 from freqtrade.state import RunMode
-from freqtrade.utils import setup_utils_configuration
 
 
-def validate_plot_args(args: Dict[str, Any]):
+def validate_plot_args(args: Dict[str, Any]) -> None:
     if not args.get('datadir') and not args.get('config'):
         raise OperationalException(
             "You need to specify either `--datadir` or `--config` "

freqtrade/commands/trade_commands.py

@@ -0,0 +1,30 @@
+import logging
+
+from typing import Any, Dict
+
+
+logger = logging.getLogger(__name__)
+
+
+def start_trading(args: Dict[str, Any]) -> int:
+    """
+    Main entry point for trading mode
+    """
+    # Import here to avoid loading worker module when it's not used
+    from freqtrade.worker import Worker
+
+    # Create and run worker
+    worker = None
+    try:
+        worker = Worker(args)
+        worker.run()
+    except Exception as e:
+        logger.error(str(e))
+        logger.exception("Fatal exception!")
+    except KeyboardInterrupt:
+        logger.info('SIGINT received, aborting ...')
+    finally:
+        if worker:
+            logger.info("worker found ... calling exit")
+            worker.exit()
+    return 0

freqtrade/configuration/__init__.py

@@ -1,5 +1,7 @@
-from freqtrade.configuration.arguments import Arguments  # noqa: F401
-from freqtrade.configuration.check_exchange import check_exchange, remove_credentials  # noqa: F401
-from freqtrade.configuration.timerange import TimeRange  # noqa: F401
-from freqtrade.configuration.configuration import Configuration  # noqa: F401
-from freqtrade.configuration.config_validation import validate_config_consistency  # noqa: F401
+# flake8: noqa: F401
+
+from freqtrade.configuration.config_setup import setup_utils_configuration
+from freqtrade.configuration.check_exchange import check_exchange, remove_credentials
+from freqtrade.configuration.timerange import TimeRange
+from freqtrade.configuration.configuration import Configuration
+from freqtrade.configuration.config_validation import validate_config_consistency

freqtrade/configuration/check_exchange.py

@@ -1,16 +1,16 @@
 import logging
 from typing import Any, Dict
 
-from freqtrade import OperationalException
+from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import (available_exchanges, get_exchange_bad_reason,
-                                is_exchange_known_ccxt, is_exchange_bad,
+                                is_exchange_bad, is_exchange_known_ccxt,
                                 is_exchange_officially_supported)
 from freqtrade.state import RunMode
 
 logger = logging.getLogger(__name__)
 
 
-def remove_credentials(config: Dict[str, Any]):
+def remove_credentials(config: Dict[str, Any]) -> None:
     """
     Removes exchange keys from the configuration and specifies dry-run
     Used for backtesting / hyperopt / edge and utils.

freqtrade/configuration/config_setup.py

@@ -0,0 +1,25 @@
+import logging
+from typing import Any, Dict
+
+from .config_validation import validate_config_consistency
+from .configuration import Configuration
+from .check_exchange import remove_credentials
+from freqtrade.state import RunMode
+
+logger = logging.getLogger(__name__)
+
+
+def setup_utils_configuration(args: Dict[str, Any], method: RunMode) -> Dict[str, Any]:
+    """
+    Prepare the configuration for utils subcommands
+    :param args: Cli args from Arguments()
+    :return: Configuration
+    """
+    configuration = Configuration(args, method)
+    config = configuration.get_config()
+
+    # Ensure we do not use Exchange credentials
+    remove_credentials(config)
+    validate_config_consistency(config)
+
+    return config

freqtrade/configuration/config_validation.py

@@ -1,10 +1,12 @@
 import logging
+from copy import deepcopy
 from typing import Any, Dict
 
 from jsonschema import Draft4Validator, validators
 from jsonschema.exceptions import ValidationError, best_match
 
-from freqtrade import constants, OperationalException
+from freqtrade import constants
+from freqtrade.exceptions import OperationalException
 from freqtrade.state import RunMode
 
 logger = logging.getLogger(__name__)
@@ -41,15 +43,20 @@ def validate_config_schema(conf: Dict[str, Any]) -> Dict[str, Any]:
     :param conf: Config in JSON format
     :return: Returns the config if valid, otherwise throw an exception
     """
+    conf_schema = deepcopy(constants.CONF_SCHEMA)
+    if conf.get('runmode', RunMode.OTHER) in (RunMode.DRY_RUN, RunMode.LIVE):
+        conf_schema['required'] = constants.SCHEMA_TRADE_REQUIRED
+    else:
+        conf_schema['required'] = constants.SCHEMA_MINIMAL_REQUIRED
     try:
-        FreqtradeValidator(constants.CONF_SCHEMA).validate(conf)
+        FreqtradeValidator(conf_schema).validate(conf)
         return conf
     except ValidationError as e:
         logger.critical(
             f"Invalid configuration. See config.json.example. Reason: {e}"
         )
         raise ValidationError(
-            best_match(Draft4Validator(constants.CONF_SCHEMA).iter_errors(conf)).message
+            best_match(Draft4Validator(conf_schema).iter_errors(conf)).message
         )
 
 
@@ -66,12 +73,24 @@ def validate_config_consistency(conf: Dict[str, Any]) -> None:
     _validate_trailing_stoploss(conf)
     _validate_edge(conf)
     _validate_whitelist(conf)
+    _validate_unlimited_amount(conf)
 
     # validate configuration before returning
     logger.info('Validating configuration ...')
     validate_config_schema(conf)
 
 
+def _validate_unlimited_amount(conf: Dict[str, Any]) -> None:
+    """
+    If edge is disabled, either max_open_trades or stake_amount need to be set.
+    :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):
+        raise OperationalException("`max_open_trades` and `stake_amount` cannot both be unlimited.")
+
+
 def _validate_trailing_stoploss(conf: Dict[str, Any]) -> None:
 
     if conf.get('stoploss') == 0.0:
@@ -131,15 +150,3 @@ def _validate_whitelist(conf: Dict[str, Any]) -> None:
         if (pl.get('method') == 'StaticPairList'
                 and not conf.get('exchange', {}).get('pair_whitelist')):
             raise OperationalException("StaticPairList requires pair_whitelist to be set.")
-
-        if pl.get('method') == 'StaticPairList':
-            stake = conf['stake_currency']
-            invalid_pairs = []
-            for pair in conf['exchange'].get('pair_whitelist'):
-                if not pair.endswith(f'/{stake}'):
-                    invalid_pairs.append(pair)
-
-            if invalid_pairs:
-                raise OperationalException(
-                    f"Stake-currency '{stake}' not compatible with pair-whitelist. "
-                    f"Please remove the following pairs: {invalid_pairs}")

freqtrade/configuration/configuration.py

@@ -7,15 +7,16 @@ from copy import deepcopy
 from pathlib import Path
 from typing import Any, Callable, Dict, List, Optional
 
-from freqtrade import OperationalException, constants
+from freqtrade import constants
 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.load_config import load_config_file
+from freqtrade.exceptions import OperationalException
 from freqtrade.loggers import setup_logging
 from freqtrade.misc import deep_merge_dicts, json_load
-from freqtrade.state import RunMode, TRADING_MODES, NON_UTIL_MODES
+from freqtrade.state import NON_UTIL_MODES, TRADING_MODES, RunMode
 
 logger = logging.getLogger(__name__)
 
@@ -53,7 +54,7 @@ class Configuration:
         :param files: List of file paths
         :return: configuration dictionary
         """
-        c = Configuration({"config": files}, RunMode.OTHER)
+        c = Configuration({'config': files}, RunMode.OTHER)
         return c.get_config()
 
     def load_from_files(self, files: List[str]) -> Dict[str, Any]:
@@ -95,6 +96,8 @@ class Configuration:
         # Keep a copy of the original configuration file
         config['original_config'] = deepcopy(config)
 
+        self._process_logging_options(config)
+
         self._process_runmode(config)
 
         self._process_common_options(config)
@@ -120,10 +123,10 @@ class Configuration:
         the -v/--verbose, --logfile options
         """
         # Log level
-        config.update({'verbosity': self.args.get("verbosity", 0)})
+        config.update({'verbosity': self.args.get('verbosity', 0)})
 
-        if 'logfile' in self.args and self.args["logfile"]:
-            config.update({'logfile': self.args["logfile"]})
+        if 'logfile' in self.args and self.args['logfile']:
+            config.update({'logfile': self.args['logfile']})
 
         setup_logging(config)
 
@@ -145,70 +148,65 @@ class Configuration:
 
     def _process_common_options(self, config: Dict[str, Any]) -> None:
 
-        self._process_logging_options(config)
-
         # Set strategy if not specified in config and or if it's non default
-        if self.args.get("strategy") or not config.get('strategy'):
-            config.update({'strategy': self.args.get("strategy")})
+        if self.args.get('strategy') or not config.get('strategy'):
+            config.update({'strategy': self.args.get('strategy')})
 
         self._args_to_config(config, argname='strategy_path',
                              logstring='Using additional Strategy lookup path: {}')
 
-        if ('db_url' in self.args and self.args["db_url"] and
-                self.args["db_url"] != constants.DEFAULT_DB_PROD_URL):
-            config.update({'db_url': self.args["db_url"]})
+        if ('db_url' in self.args and self.args['db_url'] and
+                self.args['db_url'] != constants.DEFAULT_DB_PROD_URL):
+            config.update({'db_url': self.args['db_url']})
             logger.info('Parameter --db-url detected ...')
 
         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"]:
+        if 'sd_notify' in self.args and self.args['sd_notify']:
             config['internals'].update({'sd_notify': True})
 
-        self._args_to_config(config, argname='dry_run',
-                             logstring='Parameter --dry-run detected, '
-                             'overriding dry_run to: {} ...')
-
     def _process_datadir_options(self, config: Dict[str, Any]) -> None:
         """
         Extract information for sys.argv and load directory configurations
         --user-data, --datadir
         """
         # Check exchange parameter here - otherwise `datadir` might be wrong.
-        if "exchange" in self.args and self.args["exchange"]:
-            config['exchange']['name'] = self.args["exchange"]
+        if 'exchange' in self.args and self.args['exchange']:
+            config['exchange']['name'] = self.args['exchange']
             logger.info(f"Using exchange {config['exchange']['name']}")
 
         if 'pair_whitelist' not in config['exchange']:
             config['exchange']['pair_whitelist'] = []
 
-        if 'user_data_dir' in self.args and self.args["user_data_dir"]:
-            config.update({'user_data_dir': self.args["user_data_dir"]})
+        if 'user_data_dir' in self.args and self.args['user_data_dir']:
+            config.update({'user_data_dir': self.args['user_data_dir']})
         elif 'user_data_dir' not in config:
             # Default to cwd/user_data (legacy option ...)
-            config.update({'user_data_dir': str(Path.cwd() / "user_data")})
+            config.update({'user_data_dir': str(Path.cwd() / 'user_data')})
 
         # reset to user_data_dir so this contains the absolute path.
         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", None))})
+        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'):
             self._args_to_config(config, argname='exportfilename',
                                  logstring='Storing backtest results to {} ...')
+            config['exportfilename'] = Path(config['exportfilename'])
         else:
             config['exportfilename'] = (config['user_data_dir']
-                                        / 'backtest_results/backtest-result.json')
+                                        / 'backtest_results')
 
     def _process_optimize_options(self, config: Dict[str, Any]) -> None:
 
         # This will override the strategy configuration
-        self._args_to_config(config, argname='ticker_interval',
-                             logstring='Parameter -i/--ticker-interval detected ... '
-                             'Using ticker_interval: {} ...')
+        self._args_to_config(config, argname='timeframe',
+                             logstring='Parameter -i/--timeframe detected ... '
+                             'Using timeframe: {} ...')
 
         self._args_to_config(config, argname='position_stacking',
                              logstring='Parameter --enable-position-stacking detected ...')
@@ -221,15 +219,15 @@ class Configuration:
             config.update({'use_max_market_positions': False})
             logger.info('Parameter --disable-max-market-positions detected ...')
             logger.info('max_open_trades set to unlimited ...')
-        elif 'max_open_trades' in self.args and self.args["max_open_trades"]:
-            config.update({'max_open_trades': self.args["max_open_trades"]})
-            logger.info('Parameter --max_open_trades detected, '
+        elif 'max_open_trades' in self.args and self.args['max_open_trades']:
+            config.update({'max_open_trades': self.args['max_open_trades']})
+            logger.info('Parameter --max-open-trades detected, '
                         'overriding max_open_trades to: %s ...', config.get('max_open_trades'))
         elif config['runmode'] in NON_UTIL_MODES:
             logger.info('Using max_open_trades: %s ...', config.get('max_open_trades'))
 
         self._args_to_config(config, argname='stake_amount',
-                             logstring='Parameter --stake_amount detected, '
+                             logstring='Parameter --stake-amount detected, '
                              'overriding stake_amount to: {} ...')
 
         self._args_to_config(config, argname='fee',
@@ -244,8 +242,8 @@ class Configuration:
         self._args_to_config(config, argname='strategy_list',
                              logstring='Using strategy list of {} strategies', logfun=len)
 
-        self._args_to_config(config, argname='ticker_interval',
-                             logstring='Overriding ticker interval with Command line argument')
+        self._args_to_config(config, argname='timeframe',
+                             logstring='Overriding timeframe with Command line argument')
 
         self._args_to_config(config, argname='export',
                              logstring='Parameter --export detected: {} ...')
@@ -285,6 +283,9 @@ class Configuration:
         self._args_to_config(config, argname='print_json',
                              logstring='Parameter --print-json detected ...')
 
+        self._args_to_config(config, argname='export_csv',
+                             logstring='Parameter --export-csv detected: {}')
+
         self._args_to_config(config, argname='hyperopt_jobs',
                              logstring='Parameter -j/--job-workers detected: {}')
 
@@ -309,6 +310,36 @@ class Configuration:
         self._args_to_config(config, argname='hyperopt_list_profitable',
                              logstring='Parameter --profitable detected: {}')
 
+        self._args_to_config(config, argname='hyperopt_list_min_trades',
+                             logstring='Parameter --min-trades detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_max_trades',
+                             logstring='Parameter --max-trades detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_min_avg_time',
+                             logstring='Parameter --min-avg-time detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_max_avg_time',
+                             logstring='Parameter --max-avg-time detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_min_avg_profit',
+                             logstring='Parameter --min-avg-profit detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_max_avg_profit',
+                             logstring='Parameter --max-avg-profit detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_min_total_profit',
+                             logstring='Parameter --min-total-profit detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_max_total_profit',
+                             logstring='Parameter --max-total-profit detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_min_objective',
+                             logstring='Parameter --min-objective detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_max_objective',
+                             logstring='Parameter --max-objective detected: {}')
+
         self._args_to_config(config, argname='hyperopt_list_no_details',
                              logstring='Parameter --no-details detected: {}')
 
@@ -326,28 +357,46 @@ class Configuration:
         self._args_to_config(config, argname='indicators2',
                              logstring='Using indicators2: {}')
 
+        self._args_to_config(config, argname='trade_ids',
+                             logstring='Filtering on trade_ids: {}')
+
         self._args_to_config(config, argname='plot_limit',
                              logstring='Limiting plot to: {}')
+
         self._args_to_config(config, argname='trade_source',
                              logstring='Using trades from: {}')
 
         self._args_to_config(config, argname='erase',
                              logstring='Erase detected. Deleting existing data.')
 
+        self._args_to_config(config, argname='no_trades',
+                             logstring='Parameter --no-trades detected.')
+
         self._args_to_config(config, argname='timeframes',
                              logstring='timeframes --timeframes: {}')
 
         self._args_to_config(config, argname='days',
                              logstring='Detected --days: {}')
+
         self._args_to_config(config, argname='download_trades',
                              logstring='Detected --dl-trades: {}')
 
+        self._args_to_config(config, argname='dataformat_ohlcv',
+                             logstring='Using "{}" to store OHLCV data.')
+
+        self._args_to_config(config, argname='dataformat_trades',
+                             logstring='Using "{}" to store trades data.')
+
     def _process_runmode(self, config: Dict[str, Any]) -> None:
 
+        self._args_to_config(config, argname='dry_run',
+                             logstring='Parameter --dry-run detected, '
+                             'overriding dry_run to: {} ...')
+
         if not self.runmode:
             # Handle real mode, infer dry/live from config
             self.runmode = RunMode.DRY_RUN if config.get('dry_run', True) else RunMode.LIVE
-            logger.info(f"Runmode set to {self.runmode}.")
+            logger.info(f"Runmode set to {self.runmode.value}.")
 
         config.update({'runmode': self.runmode})
 
@@ -398,12 +447,12 @@ class Configuration:
                 config['pairs'].sort()
             return
 
-        if "config" in self.args and self.args["config"]:
+        if 'config' in self.args and self.args['config']:
             logger.info("Using pairlist from configuration.")
             config['pairs'] = config.get('exchange', {}).get('pair_whitelist')
         else:
             # Fall back to /dl_path/pairs.json
-            pairs_file = Path(config['datadir']) / "pairs.json"
+            pairs_file = config['datadir'] / 'pairs.json'
             if pairs_file.exists():
                 with pairs_file.open('r') as f:
                     config['pairs'] = json_load(f)

freqtrade/configuration/deprecated_settings.py

@@ -5,7 +5,7 @@ Functions to handle deprecated settings
 import logging
 from typing import Any, Dict
 
-from freqtrade import OperationalException
+from freqtrade.exceptions import OperationalException
 
 
 logger = logging.getLogger(__name__)
@@ -13,7 +13,7 @@ logger = logging.getLogger(__name__)
 
 def check_conflicting_settings(config: Dict[str, Any],
                                section1: str, name1: str,
-                               section2: str, name2: str):
+                               section2: str, name2: str) -> None:
     section1_config = config.get(section1, {})
     section2_config = config.get(section2, {})
     if name1 in section1_config and name2 in section2_config:
@@ -28,7 +28,7 @@ def check_conflicting_settings(config: Dict[str, Any],
 
 def process_deprecated_setting(config: Dict[str, Any],
                                section1: str, name1: str,
-                               section2: str, name2: str):
+                               section2: str, name2: str) -> None:
     section2_config = config.get(section2, {})
 
     if name2 in section2_config:
@@ -58,25 +58,23 @@ def process_temporary_deprecated_settings(config: Dict[str, Any]) -> None:
     process_deprecated_setting(config, 'ask_strategy', 'ignore_roi_if_buy_signal',
                                'experimental', 'ignore_roi_if_buy_signal')
 
-    if not config.get('pairlists') and not config.get('pairlists'):
-        config['pairlists'] = [{'method': 'StaticPairList'}]
+    if (config.get('edge', {}).get('enabled', False)
+       and 'capital_available_percentage' in config.get('edge', {})):
+        raise OperationalException(
+            "DEPRECATED: "
+            "Using 'edge.capital_available_percentage' has been deprecated in favor of "
+            "'tradable_balance_ratio'. Please migrate your configuration to "
+            "'tradable_balance_ratio' and remove 'capital_available_percentage' "
+            "from the edge configuration."
+        )
+    if 'ticker_interval' in config:
         logger.warning(
             "DEPRECATED: "
-            "Pairlists must be defined explicitly in the future."
-            "Defaulting to StaticPairList for now.")
-
-    if config.get('pairlist', {}).get("method") == 'VolumePairList':
-        logger.warning(
-            "DEPRECATED: "
-            f"Using VolumePairList in pairlist is deprecated and must be moved to pairlists. "
-            "Please refer to the docs on configuration details")
-        pl = {'method': 'VolumePairList'}
-        pl.update(config.get('pairlist', {}).get('config'))
-        config['pairlists'].append(pl)
-
-    if config.get('pairlist', {}).get('config', {}).get('precision_filter'):
-        logger.warning(
-            "DEPRECATED: "
-            f"Using precision_filter setting is deprecated and has been replaced by"
-            "PrecisionFilter. Please refer to the docs on configuration details")
-        config['pairlists'].append({'method': 'PrecisionFilter'})
+            "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']

freqtrade/configuration/directory_operations.py

@@ -3,13 +3,13 @@ import shutil
 from pathlib import Path
 from typing import Any, Dict, Optional
 
-from freqtrade import OperationalException
+from freqtrade.exceptions import OperationalException
 from freqtrade.constants import USER_DATA_FILES
 
 logger = logging.getLogger(__name__)
 
 
-def create_datadir(config: Dict[str, Any], datadir: Optional[str] = None) -> str:
+def create_datadir(config: Dict[str, Any], datadir: Optional[str] = None) -> Path:
 
     folder = Path(datadir) if datadir else Path(f"{config['user_data_dir']}/data")
     if not datadir:
@@ -20,10 +20,10 @@ def create_datadir(config: Dict[str, Any], datadir: Optional[str] = None) -> str
     if not folder.is_dir():
         folder.mkdir(parents=True)
         logger.info(f'Created data directory: {datadir}')
-    return str(folder)
+    return folder
 
 
-def create_userdata_dir(directory: str, create_dir=False) -> Path:
+def create_userdata_dir(directory: str, create_dir: bool = False) -> Path:
     """
     Create userdata directory structure.
     if create_dir is True, then the parent-directory will be created if it does not exist.
@@ -33,8 +33,8 @@ def create_userdata_dir(directory: str, create_dir=False) -> Path:
     :param create_dir: Create directory if it does not exist.
     :return: Path object containing the directory
     """
-    sub_dirs = ["backtest_results", "data", "hyperopts", "hyperopt_results", "notebooks",
-                "plot", "strategies", ]
+    sub_dirs = ["backtest_results", "data", "hyperopts", "hyperopt_results", "logs",
+                "notebooks", "plot", "strategies", ]
     folder = Path(directory)
     if not folder.is_dir():
         if create_dir:

freqtrade/configuration/load_config.py

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

freqtrade/configuration/timerange.py

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

freqtrade/constants.py

@@ -3,6 +3,9 @@
 """
 bot constants
 """
+from typing import List, Tuple
+
+
 DEFAULT_CONFIG = 'config.json'
 DEFAULT_EXCHANGE = 'bittrex'
 PROCESS_THROTTLE_SECS = 5  # sec
@@ -10,41 +13,47 @@ HYPEROPT_EPOCH = 100  # epochs
 RETRY_TIMEOUT = 30  # sec
 DEFAULT_HYPEROPT_LOSS = 'DefaultHyperOptLoss'
 DEFAULT_DB_PROD_URL = 'sqlite:///tradesv3.sqlite'
-DEFAULT_DB_DRYRUN_URL = 'sqlite://'
+DEFAULT_DB_DRYRUN_URL = 'sqlite:///tradesv3.dryrun.sqlite'
 UNLIMITED_STAKE_AMOUNT = 'unlimited'
 DEFAULT_AMOUNT_RESERVE_PERCENT = 0.05
 REQUIRED_ORDERTIF = ['buy', 'sell']
 REQUIRED_ORDERTYPES = ['buy', 'sell', 'stoploss', 'stoploss_on_exchange']
+ORDERBOOK_SIDES = ['ask', 'bid']
 ORDERTYPE_POSSIBILITIES = ['limit', 'market']
 ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
-AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList', 'PrecisionFilter', 'PriceFilter']
-DRY_RUN_WALLET = 999.9
+AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
+                       'AgeFilter', 'PrecisionFilter', 'PriceFilter',
+                       'ShuffleFilter', 'SpreadFilter']
+AVAILABLE_DATAHANDLERS = ['json', 'jsongz']
+DRY_RUN_WALLET = 1000
+DATETIME_PRINT_FORMAT = '%Y-%m-%d %H:%M:%S'
 MATH_CLOSE_PREC = 1e-14  # Precision used for float comparisons
+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']
+
+LAST_BT_RESULT_FN = '.last_result.json'
 
 USERPATH_HYPEROPTS = 'hyperopts'
-USERPATH_STRATEGY = 'strategies'
+USERPATH_STRATEGIES = 'strategies'
+USERPATH_NOTEBOOKS = 'notebooks'
 
 # Soure files with destination directories within user-directory
 USER_DATA_FILES = {
-    'sample_strategy.py': USERPATH_STRATEGY,
+    'sample_strategy.py': USERPATH_STRATEGIES,
     'sample_hyperopt_advanced.py': USERPATH_HYPEROPTS,
     'sample_hyperopt_loss.py': USERPATH_HYPEROPTS,
     'sample_hyperopt.py': USERPATH_HYPEROPTS,
-    'strategy_analysis_example.ipynb': 'notebooks',
+    'strategy_analysis_example.ipynb': USERPATH_NOTEBOOKS,
 }
 
-TIMEFRAMES = [
-    '1m', '3m', '5m', '15m', '30m',
-    '1h', '2h', '4h', '6h', '8h', '12h',
-    '1d', '3d', '1w',
-]
-
 SUPPORTED_FIAT = [
     "AUD", "BRL", "CAD", "CHF", "CLP", "CNY", "CZK", "DKK",
     "EUR", "GBP", "HKD", "HUF", "IDR", "ILS", "INR", "JPY",
     "KRW", "MXN", "MYR", "NOK", "NZD", "PHP", "PKR", "PLN",
     "RUB", "SEK", "SGD", "THB", "TRY", "TWD", "ZAR", "USD",
-    "BTC", "XBT", "ETH", "XRP", "LTC", "BCH", "USDT"
+    "BTC", "ETH", "XRP", "LTC", "BCH"
 ]
 
 MINIMAL_CONFIG = {
@@ -66,16 +75,27 @@ CONF_SCHEMA = {
     'type': 'object',
     'properties': {
         'max_open_trades': {'type': ['integer', 'number'], 'minimum': -1},
-        'ticker_interval': {'type': 'string', 'enum': TIMEFRAMES},
-        'stake_currency': {'type': 'string', 'enum': ['BTC', 'XBT', 'ETH', 'USDT', 'EUR', 'USD']},
+        'timeframe': {'type': 'string'},
+        'stake_currency': {'type': 'string'},
         'stake_amount': {
             'type': ['number', 'string'],
             'minimum': 0.0001,
             'pattern': UNLIMITED_STAKE_AMOUNT
         },
+        'tradable_balance_ratio': {
+            'type': 'number',
+            'minimum': 0.1,
+            'maximum': 1,
+            'default': 0.99
+        },
+        'amend_last_stake_amount': {'type': 'boolean', 'default': False},
+        'last_stake_amount_min_ratio': {
+            'type': 'number', 'minimum': 0.0, 'maximum': 1.0, 'default': 0.5
+        },
         'fiat_display_currency': {'type': 'string', 'enum': SUPPORTED_FIAT},
         'dry_run': {'type': 'boolean'},
-        'dry_run_wallet': {'type': 'number'},
+        'dry_run_wallet': {'type': 'number', 'default': DRY_RUN_WALLET},
+        'cancel_open_orders_on_exit': {'type': 'boolean', 'default': False},
         'process_only_new_candles': {'type': 'boolean'},
         'minimal_roi': {
             'type': 'object',
@@ -105,15 +125,16 @@ CONF_SCHEMA = {
                     'minimum': 0,
                     'maximum': 1,
                     'exclusiveMaximum': False,
-                    'use_order_book': {'type': 'boolean'},
-                    'order_book_top': {'type': 'integer', 'maximum': 20, 'minimum': 1},
-                    'check_depth_of_market': {
-                        'type': 'object',
-                        'properties': {
-                            'enabled': {'type': 'boolean'},
-                            'bids_to_ask_delta': {'type': 'number', 'minimum': 0},
-                        }
-                    },
+                },
+                'price_side': {'type': 'string', 'enum': ORDERBOOK_SIDES, 'default': 'bid'},
+                'use_order_book': {'type': 'boolean'},
+                'order_book_top': {'type': 'integer', 'maximum': 20, 'minimum': 1},
+                'check_depth_of_market': {
+                    'type': 'object',
+                    'properties': {
+                        'enabled': {'type': 'boolean'},
+                        'bids_to_ask_delta': {'type': 'number', 'minimum': 0},
+                    }
                 },
             },
             'required': ['ask_last_balance']
@@ -121,6 +142,7 @@ CONF_SCHEMA = {
         'ask_strategy': {
             'type': 'object',
             'properties': {
+                'price_side': {'type': 'string', 'enum': ORDERBOOK_SIDES, 'default': 'ask'},
                 'use_order_book': {'type': 'boolean'},
                 'order_book_min': {'type': 'integer', 'minimum': 1},
                 'order_book_max': {'type': 'integer', 'minimum': 1, 'maximum': 50},
@@ -137,7 +159,9 @@ CONF_SCHEMA = {
                 'emergencysell': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
                 'stoploss': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
                 'stoploss_on_exchange': {'type': 'boolean'},
-                'stoploss_on_exchange_interval': {'type': 'number'}
+                'stoploss_on_exchange_interval': {'type': 'number'},
+                'stoploss_on_exchange_limit_ratio': {'type': 'number', 'minimum': 0.0,
+                                                     'maximum': 1.0}
             },
             'required': ['buy', 'sell', 'stoploss', 'stoploss_on_exchange']
         },
@@ -185,7 +209,9 @@ CONF_SCHEMA = {
             'properties': {
                 'enabled': {'type': 'boolean'},
                 'webhookbuy': {'type': 'object'},
+                'webhookbuycancel': {'type': 'object'},
                 'webhooksell': {'type': 'object'},
+                'webhooksellcancel': {'type': 'object'},
                 'webhookstatus': {'type': 'object'},
             },
         },
@@ -201,19 +227,34 @@ CONF_SCHEMA = {
                 },
                 'username': {'type': 'string'},
                 'password': {'type': 'string'},
+                'jwt_secret_key': {'type': 'string'},
+                'CORS_origins': {'type': 'array', 'items': {'type': 'string'}},
+                'verbosity': {'type': 'string', 'enum': ['error', 'info']},
             },
             'required': ['enabled', 'listen_ip_address', 'listen_port', 'username', 'password']
         },
         'db_url': {'type': 'string'},
         'initial_state': {'type': 'string', 'enum': ['running', 'stopped']},
         'forcebuy_enable': {'type': 'boolean'},
+        'disable_dataframe_checks': {'type': 'boolean'},
         'internals': {
             'type': 'object',
+            'default': {},
             'properties': {
                 'process_throttle_secs': {'type': 'integer'},
                 'interval': {'type': 'integer'},
                 'sd_notify': {'type': 'boolean'},
             }
+        },
+        'dataformat_ohlcv': {
+            'type': 'string',
+                    'enum': AVAILABLE_DATAHANDLERS,
+                    'default': 'json'
+        },
+        'dataformat_trades': {
+            'type': 'string',
+                    'enum': AVAILABLE_DATAHANDLERS,
+                    'default': 'jsongz'
         }
     },
     'definitions': {
@@ -230,7 +271,6 @@ CONF_SCHEMA = {
                     'type': 'array',
                     'items': {
                         'type': 'string',
-                        'pattern': '^[0-9A-Z]+/[0-9A-Z]+$'
                     },
                     'uniqueItems': True
                 },
@@ -238,7 +278,6 @@ CONF_SCHEMA = {
                     'type': 'array',
                     'items': {
                         'type': 'string',
-                        'pattern': '^[0-9A-Z]+/[0-9A-Z]+$'
                     },
                     'uniqueItems': True
                 },
@@ -256,7 +295,6 @@ CONF_SCHEMA = {
                 'process_throttle_secs': {'type': 'integer', 'minimum': 600},
                 'calculate_since_number_of_days': {'type': 'integer'},
                 'allowed_risk': {'type': 'number'},
-                'capital_available_percentage': {'type': 'number'},
                 'stoploss_range_min': {'type': 'number'},
                 'stoploss_range_max': {'type': 'number'},
                 'stoploss_range_step': {'type': 'number'},
@@ -266,18 +304,45 @@ CONF_SCHEMA = {
                 'max_trade_duration_minute': {'type': 'integer'},
                 'remove_pumps': {'type': 'boolean'}
             },
-            'required': ['process_throttle_secs', 'allowed_risk', 'capital_available_percentage']
+            'required': ['process_throttle_secs', 'allowed_risk']
         }
     },
-    'required': [
-        'exchange',
-        'max_open_trades',
-        'stake_currency',
-        'stake_amount',
-        'dry_run',
-        'bid_strategy',
-        'unfilledtimeout',
-        'stoploss',
-        'minimal_roi',
-    ]
 }
+
+SCHEMA_TRADE_REQUIRED = [
+    'exchange',
+    'timeframe',
+    'max_open_trades',
+    'stake_currency',
+    'stake_amount',
+    'tradable_balance_ratio',
+    'last_stake_amount_min_ratio',
+    'dry_run',
+    'dry_run_wallet',
+    'ask_strategy',
+    'bid_strategy',
+    'unfilledtimeout',
+    'stoploss',
+    'minimal_roi',
+    'internals',
+    'dataformat_ohlcv',
+    'dataformat_trades',
+]
+
+SCHEMA_MINIMAL_REQUIRED = [
+    'exchange',
+    'dry_run',
+    'dataformat_ohlcv',
+    'dataformat_trades',
+]
+
+CANCEL_REASON = {
+    "TIMEOUT": "cancelled due to timeout",
+    "PARTIALLY_FILLED": "partially filled - keeping order open",
+    "ALL_CANCELLED": "cancelled (all unfilled and partially filled open orders cancelled)",
+    "CANCELLED_ON_EXCHANGE": "cancelled on exchange",
+}
+
+# List of pairs with their timeframes
+PairWithTimeframe = Tuple[str, str]
+ListPairsWithTimeframes = List[PairWithTimeframe]

freqtrade/data/btanalysis.py

@@ -3,52 +3,123 @@ Helpers when analyzing backtest data
 """
 import logging
 from pathlib import Path
-from typing import Dict
+from typing import Dict, Union, Tuple, Any, Optional
 
 import numpy as np
 import pandas as pd
 from datetime import timezone
 
 from freqtrade import persistence
+from freqtrade.constants import LAST_BT_RESULT_FN
 from freqtrade.misc import json_load
 from freqtrade.persistence import Trade
 
 logger = logging.getLogger(__name__)
 
 # must align with columns in backtest.py
-BT_DATA_COLUMNS = ["pair", "profitperc", "open_time", "close_time", "index", "duration",
+BT_DATA_COLUMNS = ["pair", "profit_percent", "open_date", "close_date", "index", "trade_duration",
                    "open_rate", "close_rate", "open_at_end", "sell_reason"]
 
 
-def load_backtest_data(filename) -> pd.DataFrame:
+def get_latest_backtest_filename(directory: Union[Path, str]) -> str:
     """
-    Load backtest data file.
-    :param filename: pathlib.Path object, or string pointing to the file.
-    :return: a dataframe with the analysis results
+    Get latest backtest export based on '.last_result.json'.
+    :param directory: Directory to search for last result
+    :return: string containing the filename of the latest backtest result
+    :raises: ValueError in the following cases:
+        * Directory does not exist
+        * `directory/.last_result.json` does not exist
+        * `directory/.last_result.json` has the wrong content
     """
-    if isinstance(filename, str):
-        filename = Path(filename)
+    if isinstance(directory, str):
+        directory = Path(directory)
+    if not directory.is_dir():
+        raise ValueError(f"Directory '{directory}' does not exist.")
+    filename = directory / LAST_BT_RESULT_FN
 
     if not filename.is_file():
-        raise ValueError(f"File {filename} does not exist.")
+        raise ValueError(
+            f"Directory '{directory}' does not seem to contain backtest statistics yet.")
 
     with filename.open() as file:
         data = json_load(file)
 
-    df = pd.DataFrame(data, columns=BT_DATA_COLUMNS)
+    if 'latest_backtest' not in data:
+        raise ValueError(f"Invalid '{LAST_BT_RESULT_FN}' format.")
 
-    df['open_time'] = pd.to_datetime(df['open_time'],
-                                     unit='s',
-                                     utc=True,
-                                     infer_datetime_format=True
-                                     )
-    df['close_time'] = pd.to_datetime(df['close_time'],
-                                      unit='s',
-                                      utc=True,
-                                      infer_datetime_format=True
-                                      )
-    df['profitabs'] = df['close_rate'] - df['open_rate']
-    df = df.sort_values("open_time").reset_index(drop=True)
+    return data['latest_backtest']
+
+
+def load_backtest_stats(filename: Union[Path, str]) -> Dict[str, Any]:
+    """
+    Load backtest statistics file.
+    :param filename: pathlib.Path object, or string pointing to the file.
+    :return: a dictionary containing the resulting file.
+    """
+    if isinstance(filename, str):
+        filename = Path(filename)
+    if filename.is_dir():
+        filename = filename / get_latest_backtest_filename(filename)
+    if not filename.is_file():
+        raise ValueError(f"File {filename} does not exist.")
+    logger.info(f"Loading backtest result from {filename}")
+    with filename.open() as file:
+        data = json_load(file)
+
+    return data
+
+
+def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = None) -> pd.DataFrame:
+    """
+    Load backtest data file.
+    :param filename: pathlib.Path object, or string pointing to a file or directory
+    :param strategy: Strategy to load - mainly relevant for multi-strategy backtests
+                     Can also serve as protection to load the correct result.
+    :return: a dataframe with the analysis results
+    :raise: ValueError if loading goes wrong.
+    """
+    data = load_backtest_stats(filename)
+    if not isinstance(data, list):
+        # new, nested format
+        if 'strategy' not in data:
+            raise ValueError("Unknown dataformat.")
+
+        if not strategy:
+            if len(data['strategy']) == 1:
+                strategy = list(data['strategy'].keys())[0]
+            else:
+                raise ValueError("Detected backtest result with more than one strategy. "
+                                 "Please specify a strategy.")
+
+        if strategy not in data['strategy']:
+            raise ValueError(f"Strategy {strategy} not available in the backtest result.")
+
+        data = data['strategy'][strategy]['trades']
+        df = pd.DataFrame(data)
+        df['open_date'] = pd.to_datetime(df['open_date'],
+                                         utc=True,
+                                         infer_datetime_format=True
+                                         )
+        df['close_date'] = pd.to_datetime(df['close_date'],
+                                          utc=True,
+                                          infer_datetime_format=True
+                                          )
+    else:
+        # old format - only with lists.
+        df = pd.DataFrame(data, columns=BT_DATA_COLUMNS)
+
+        df['open_date'] = pd.to_datetime(df['open_date'],
+                                         unit='s',
+                                         utc=True,
+                                         infer_datetime_format=True
+                                         )
+        df['close_date'] = pd.to_datetime(df['close_date'],
+                                          unit='s',
+                                          utc=True,
+                                          infer_datetime_format=True
+                                          )
+        df['profit_abs'] = df['close_rate'] - df['open_rate']
+    df = df.sort_values("open_date").reset_index(drop=True)
     return df
 
 
@@ -62,9 +133,9 @@ def analyze_trade_parallelism(results: pd.DataFrame, timeframe: str) -> pd.DataF
     """
     from freqtrade.exchange import timeframe_to_minutes
     timeframe_min = timeframe_to_minutes(timeframe)
-    dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time,
+    dates = [pd.Series(pd.date_range(row[1]['open_date'], row[1]['close_date'],
                                      freq=f"{timeframe_min}min"))
-             for row in results[['open_time', 'close_time']].iterrows()]
+             for row in results[['open_date', 'close_date']].iterrows()]
     deltas = [len(x) for x in dates]
     dates = pd.Series(pd.concat(dates).values, name='date')
     df2 = pd.DataFrame(np.repeat(results.values, deltas, axis=0), columns=results.columns)
@@ -90,28 +161,33 @@ def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
     return df_final[df_final['open_trades'] > max_open_trades]
 
 
-def load_trades_from_db(db_url: str) -> pd.DataFrame:
+def load_trades_from_db(db_url: str, strategy: Optional[str] = None) -> pd.DataFrame:
     """
     Load trades from a DB (using dburl)
     :param db_url: Sqlite url (default format sqlite:///tradesv3.dry-run.sqlite)
+    :param strategy: Strategy to load - mainly relevant for multi-strategy backtests
+                     Can also serve as protection to load the correct result.
     :return: Dataframe containing Trades
     """
-    trades: pd.DataFrame = pd.DataFrame([], columns=BT_DATA_COLUMNS)
     persistence.init(db_url, clean_open_orders=False)
 
-    columns = ["pair", "open_time", "close_time", "profit", "profitperc",
-               "open_rate", "close_rate", "amount", "duration", "sell_reason",
+    columns = ["pair", "open_date", "close_date", "profit", "profit_percent",
+               "open_rate", "close_rate", "amount", "trade_duration", "sell_reason",
                "fee_open", "fee_close", "open_rate_requested", "close_rate_requested",
                "stake_amount", "max_rate", "min_rate", "id", "exchange",
-               "stop_loss", "initial_stop_loss", "strategy", "ticker_interval"]
+               "stop_loss", "initial_stop_loss", "strategy", "timeframe"]
+
+    filters = []
+    if strategy:
+        filters.append(Trade.strategy == strategy)
 
     trades = pd.DataFrame([(t.pair,
                             t.open_date.replace(tzinfo=timezone.utc),
                             t.close_date.replace(tzinfo=timezone.utc) if t.close_date else None,
-                            t.calc_profit(), t.calc_profit_percent(),
+                            t.calc_profit(), t.calc_profit_ratio(),
                             t.open_rate, t.close_rate, t.amount,
                             (round((t.close_date.timestamp() - t.open_date.timestamp()) / 60, 2)
-                                if t.close_date else None),
+                             if t.close_date else None),
                             t.sell_reason,
                             t.fee_open, t.fee_close,
                             t.open_rate_requested,
@@ -121,46 +197,84 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
                             t.min_rate,
                             t.id, t.exchange,
                             t.stop_loss, t.initial_stop_loss,
-                            t.strategy, t.ticker_interval
+                            t.strategy, t.timeframe
                             )
-                           for t in Trade.get_trades().all()],
+                           for t in Trade.get_trades(filters).all()],
                           columns=columns)
 
     return trades
 
 
-def load_trades(source: str, db_url: str, exportfilename: str) -> pd.DataFrame:
+def load_trades(source: str, db_url: str, exportfilename: Path,
+                no_trades: bool = False, strategy: Optional[str] = None) -> pd.DataFrame:
     """
-    Based on configuration option "trade_source":
+    Based on configuration option 'trade_source':
     * loads data from DB (using `db_url`)
     * loads data from backtestfile (using `exportfilename`)
+    :param source: "DB" or "file" - specify source to load from
+    :param db_url: sqlalchemy formatted url to a database
+    :param exportfilename: Json file generated by backtesting
+    :param no_trades: Skip using trades, only return backtesting data columns
+    :return: DataFrame containing trades
     """
+    if no_trades:
+        df = pd.DataFrame(columns=BT_DATA_COLUMNS)
+        return df
+
     if source == "DB":
         return load_trades_from_db(db_url)
     elif source == "file":
-        return load_backtest_data(Path(exportfilename))
+        return load_backtest_data(exportfilename, strategy)
 
 
-def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame) -> pd.DataFrame:
+def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame,
+                             date_index=False) -> pd.DataFrame:
     """
     Compare trades and backtested pair DataFrames to get trades performed on backtested period
     :return: the DataFrame of a trades of period
     """
-    trades = trades.loc[(trades['open_time'] >= dataframe.iloc[0]['date']) &
-                        (trades['close_time'] <= dataframe.iloc[-1]['date'])]
+    if date_index:
+        trades_start = dataframe.index[0]
+        trades_stop = dataframe.index[-1]
+    else:
+        trades_start = dataframe.iloc[0]['date']
+        trades_stop = dataframe.iloc[-1]['date']
+    trades = trades.loc[(trades['open_date'] >= trades_start) &
+                        (trades['close_date'] <= trades_stop)]
     return trades
 
 
-def combine_tickers_with_mean(tickers: Dict[str, pd.DataFrame], column: str = "close"):
+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 np.mean(tmp_means)
+
+
+def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
+                                 column: str = "close") -> pd.DataFrame:
     """
     Combine multiple dataframes "column"
-    :param tickers: Dict of Dataframes, dict key should be pair.
+    :param data: Dict of Dataframes, dict key should be pair.
     :param column: Column in the original dataframes to use
     :return: DataFrame with the column renamed to the dict key, and a column
         named mean, containing the mean of all pairs.
     """
-    df_comb = pd.concat([tickers[pair].set_index('date').rename(
-        {column: pair}, axis=1)[pair] for pair in tickers], axis=1)
+    df_comb = pd.concat([data[pair].set_index('date').rename(
+        {column: pair}, axis=1)[pair] for pair in data], axis=1)
 
     df_comb['mean'] = df_comb.mean(axis=1)
 
@@ -172,18 +286,49 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
     """
     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_time and profitperc)
+    :param trades: DataFrame containing trades (requires columns close_date and profit_percent)
     :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_time')[['profitperc']].sum()
+    _trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_date'
+                                  )[['profit_percent']].sum()
     df.loc[:, col_name] = _trades_sum.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 calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
+                           value_col: str = 'profit_percent'
+                           ) -> Tuple[float, pd.Timestamp, pd.Timestamp]:
+    """
+    Calculate max drawdown and the corresponding close dates
+    :param trades: DataFrame containing trades (requires columns close_date and profit_percent)
+    :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_percent')
+    :return: Tuple (float, highdate, lowdate) with absolute max drawdown, high and low time
+    :raise: ValueError if trade-dataframe was found empty.
+    """
+    if len(trades) == 0:
+        raise ValueError("Trade dataframe empty.")
+    profit_results = trades.sort_values(date_col).reset_index(drop=True)
+    max_drawdown_df = pd.DataFrame()
+    max_drawdown_df['cumulative'] = profit_results[value_col].cumsum()
+    max_drawdown_df['high_value'] = max_drawdown_df['cumulative'].cummax()
+    max_drawdown_df['drawdown'] = max_drawdown_df['cumulative'] - max_drawdown_df['high_value']
+
+    idxmin = max_drawdown_df['drawdown'].idxmin()
+    if idxmin == 0:
+        raise ValueError("No losing trade, therefore no drawdown.")
+    high_date = profit_results.loc[max_drawdown_df.iloc[:idxmin]['high_value'].idxmax(), date_col]
+    low_date = profit_results.loc[idxmin, date_col]
+    return abs(min(max_drawdown_df['drawdown'])), high_date, low_date

freqtrade/data/converter.py

@@ -1,21 +1,27 @@
 """
 Functions to convert data from one format to another
 """
+import itertools
 import logging
+from datetime import datetime, timezone
+from operator import itemgetter
+from typing import Any, Dict, List
 
 import pandas as pd
 from pandas import DataFrame, to_datetime
 
+from freqtrade.constants import (DEFAULT_DATAFRAME_COLUMNS,
+                                 DEFAULT_TRADES_COLUMNS)
 
 logger = logging.getLogger(__name__)
 
 
-def parse_ticker_dataframe(ticker: list, timeframe: str, pair: str, *,
-                           fill_missing: bool = True,
-                           drop_incomplete: bool = True) -> DataFrame:
+def ohlcv_to_dataframe(ohlcv: list, timeframe: str, pair: str, *,
+                       fill_missing: bool = True, drop_incomplete: bool = True) -> DataFrame:
     """
-    Converts a ticker-list (format ccxt.fetch_ohlcv) to a Dataframe
-    :param ticker: ticker list, as returned by exchange.async_get_candle_history
+    Converts a list with candle (OHLCV) data (in format returned by ccxt.fetch_ohlcv)
+    to a Dataframe
+    :param ohlcv: list with candle (OHLCV) data, as returned by exchange.async_get_candle_history
     :param timeframe: timeframe (e.g. 5m). Used to fill up eventual missing data
     :param pair: Pair this data is for (used to warn if fillup was necessary)
     :param fill_missing: fill up missing candles with 0 candles
@@ -23,23 +29,40 @@ def parse_ticker_dataframe(ticker: list, timeframe: str, pair: str, *,
     :param drop_incomplete: Drop the last candle of the dataframe, assuming it's incomplete
     :return: DataFrame
     """
-    logger.debug("Parsing tickerlist to dataframe")
-    cols = ['date', 'open', 'high', 'low', 'close', 'volume']
-    frame = DataFrame(ticker, columns=cols)
+    logger.debug(f"Converting candle (OHLCV) data to dataframe for pair {pair}.")
+    cols = DEFAULT_DATAFRAME_COLUMNS
+    df = DataFrame(ohlcv, columns=cols)
 
-    frame['date'] = to_datetime(frame['date'],
-                                unit='ms',
-                                utc=True,
-                                infer_datetime_format=True)
+    df['date'] = to_datetime(df['date'], unit='ms', utc=True, infer_datetime_format=True)
 
-    # Some exchanges return int values for volume and even for ohlc.
+    # Some exchanges return int values for Volume and even for OHLC.
     # Convert them since TA-LIB indicators used in the strategy assume floats
     # and fail with exception...
-    frame = frame.astype(dtype={'open': 'float', 'high': 'float', 'low': 'float', 'close': 'float',
-                                'volume': 'float'})
+    df = df.astype(dtype={'open': 'float', 'high': 'float', 'low': 'float', 'close': 'float',
+                          'volume': 'float'})
+    return clean_ohlcv_dataframe(df, timeframe, pair,
+                                 fill_missing=fill_missing,
+                                 drop_incomplete=drop_incomplete)
 
+
+def clean_ohlcv_dataframe(data: DataFrame, timeframe: str, pair: str, *,
+                          fill_missing: bool = True,
+                          drop_incomplete: bool = True) -> DataFrame:
+    """
+    Clense a OHLCV dataframe by
+      * Grouping it by date (removes duplicate tics)
+      * dropping last candles if requested
+      * Filling up missing data (if requested)
+    :param data: DataFrame containing candle (OHLCV) data.
+    :param timeframe: timeframe (e.g. 5m). Used to fill up eventual missing data
+    :param pair: Pair this data is for (used to warn if fillup was necessary)
+    :param fill_missing: fill up missing candles with 0 candles
+                         (see ohlcv_fill_up_missing_data for details)
+    :param drop_incomplete: Drop the last candle of the dataframe, assuming it's incomplete
+    :return: DataFrame
+    """
     # group by index and aggregate results to eliminate duplicate ticks
-    frame = frame.groupby(by='date', as_index=False, sort=True).agg({
+    data = data.groupby(by='date', as_index=False, sort=True).agg({
         'open': 'first',
         'high': 'max',
         'low': 'min',
@@ -48,13 +71,13 @@ def parse_ticker_dataframe(ticker: list, timeframe: str, pair: str, *,
     })
     # eliminate partial candle
     if drop_incomplete:
-        frame.drop(frame.tail(1).index, inplace=True)
+        data.drop(data.tail(1).index, inplace=True)
         logger.debug('Dropping last candle')
 
     if fill_missing:
-        return ohlcv_fill_up_missing_data(frame, timeframe, pair)
+        return ohlcv_fill_up_missing_data(data, timeframe, pair)
     else:
-        return frame
+        return data
 
 
 def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str) -> DataFrame:
@@ -65,16 +88,16 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str)
     """
     from freqtrade.exchange import timeframe_to_minutes
 
-    ohlc_dict = {
+    ohlcv_dict = {
         'open': 'first',
         'high': 'max',
         'low': 'min',
         'close': 'last',
         'volume': 'sum'
     }
-    ticker_minutes = timeframe_to_minutes(timeframe)
+    timeframe_minutes = timeframe_to_minutes(timeframe)
     # Resample to create "NAN" values
-    df = dataframe.resample(f'{ticker_minutes}min', on='date').agg(ohlc_dict)
+    df = dataframe.resample(f'{timeframe_minutes}min', on='date').agg(ohlcv_dict)
 
     # Forwardfill close for missing columns
     df['close'] = df['close'].fillna(method='ffill')
@@ -92,8 +115,26 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str)
     return df
 
 
+def trim_dataframe(df: DataFrame, timerange, df_date_col: str = 'date') -> DataFrame:
+    """
+    Trim dataframe based on given timerange
+    :param df: Dataframe to trim
+    :param timerange: timerange (use start and end date if available)
+    :param: df_date_col: Column in the dataframe to use as Date column
+    :return: trimmed dataframe
+    """
+    if timerange.starttype == 'date':
+        start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
+        df = df.loc[df[df_date_col] >= start, :]
+    if timerange.stoptype == 'date':
+        stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
+        df = df.loc[df[df_date_col] <= stop, :]
+    return df
+
+
 def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
     """
+    TODO: This should get a dedicated test
     Gets order book list, returns dataframe with below format per suggested by creslin
     -------------------------------------------------------------------
      b_sum       b_size       bids       asks       a_size       a_sum
@@ -116,23 +157,105 @@ def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
     return frame
 
 
-def trades_to_ohlcv(trades: list, timeframe: str) -> list:
+def trades_remove_duplicates(trades: List[List]) -> List[List]:
     """
-    Converts trades list to ohlcv list
+    Removes duplicates from the trades list.
+    Uses itertools.groupby to avoid converting to pandas.
+    Tests show it as being pretty efficient on lists of 4M Lists.
+    :param trades: List of Lists with constants.DEFAULT_TRADES_COLUMNS as columns
+    :return: same format as above, but with duplicates removed
+    """
+    return [i for i, _ in itertools.groupby(sorted(trades, key=itemgetter(0)))]
+
+
+def trades_dict_to_list(trades: List[Dict]) -> List[List]:
+    """
+    Convert fetch_trades result into a List (to be more memory efficient).
     :param trades: List of trades, as returned by ccxt.fetch_trades.
-    :param timeframe: Ticker timeframe to resample data to
-    :return: ohlcv timeframe as list (as returned by ccxt.fetch_ohlcv)
+    :return: List of Lists, with constants.DEFAULT_TRADES_COLUMNS as columns
+    """
+    return [[t[col] for col in DEFAULT_TRADES_COLUMNS] for t in trades]
+
+
+def trades_to_ohlcv(trades: List, timeframe: str) -> DataFrame:
+    """
+    Converts trades list to OHLCV list
+    TODO: This should get a dedicated test
+    :param trades: List of trades, as returned by ccxt.fetch_trades.
+    :param timeframe: Timeframe to resample data to
+    :return: OHLCV Dataframe.
     """
     from freqtrade.exchange import timeframe_to_minutes
-    ticker_minutes = timeframe_to_minutes(timeframe)
-    df = pd.DataFrame(trades)
-    df['datetime'] = pd.to_datetime(df['datetime'])
-    df = df.set_index('datetime')
+    timeframe_minutes = timeframe_to_minutes(timeframe)
+    df = pd.DataFrame(trades, columns=DEFAULT_TRADES_COLUMNS)
+    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms',
+                                     utc=True,)
+    df = df.set_index('timestamp')
 
-    df_new = df['price'].resample(f'{ticker_minutes}min').ohlc()
-    df_new['volume'] = df['amount'].resample(f'{ticker_minutes}min').sum()
-    df_new['date'] = df_new.index.astype("int64") // 10 ** 6
+    df_new = df['price'].resample(f'{timeframe_minutes}min').ohlc()
+    df_new['volume'] = df['amount'].resample(f'{timeframe_minutes}min').sum()
+    df_new['date'] = df_new.index
     # Drop 0 volume rows
     df_new = df_new.dropna()
-    columns = ["date", "open", "high", "low", "close", "volume"]
-    return list(zip(*[df_new[x].values.tolist() for x in columns]))
+    return df_new.loc[:, DEFAULT_DATAFRAME_COLUMNS]
+
+
+def convert_trades_format(config: Dict[str, Any], convert_from: str, convert_to: str, erase: bool):
+    """
+    Convert trades from one format to another format.
+    :param config: Config dictionary
+    :param convert_from: Source format
+    :param convert_to: Target format
+    :param erase: Erase souce data (does not apply if source and target format are identical)
+    """
+    from freqtrade.data.history.idatahandler import get_datahandler
+    src = get_datahandler(config['datadir'], convert_from)
+    trg = get_datahandler(config['datadir'], convert_to)
+
+    if 'pairs' not in config:
+        config['pairs'] = src.trades_get_pairs(config['datadir'])
+    logger.info(f"Converting trades for {config['pairs']}")
+
+    for pair in config['pairs']:
+        data = src.trades_load(pair=pair)
+        logger.info(f"Converting {len(data)} trades for {pair}")
+        trg.trades_store(pair, data)
+        if erase and convert_from != convert_to:
+            logger.info(f"Deleting source Trade data for {pair}.")
+            src.trades_purge(pair=pair)
+
+
+def convert_ohlcv_format(config: Dict[str, Any], convert_from: str, convert_to: str, erase: bool):
+    """
+    Convert OHLCV from one format to another
+    :param config: Config dictionary
+    :param convert_from: Source format
+    :param convert_to: Target format
+    :param erase: Erase souce data (does not apply if source and target format are identical)
+    """
+    from freqtrade.data.history.idatahandler import get_datahandler
+    src = get_datahandler(config['datadir'], convert_from)
+    trg = get_datahandler(config['datadir'], convert_to)
+    timeframes = config.get('timeframes', [config.get('timeframe')])
+    logger.info(f"Converting candle (OHLCV) for timeframe {timeframes}")
+
+    if 'pairs' not in config:
+        config['pairs'] = []
+        # Check timeframes or fall back to timeframe.
+        for timeframe in timeframes:
+            config['pairs'].extend(src.ohlcv_get_pairs(config['datadir'],
+                                                       timeframe))
+    logger.info(f"Converting candle (OHLCV) data for {config['pairs']}")
+
+    for timeframe in timeframes:
+        for pair in config['pairs']:
+            data = src.ohlcv_load(pair=pair, timeframe=timeframe,
+                                  timerange=None,
+                                  fill_missing=False,
+                                  drop_incomplete=False,
+                                  startup_candles=0)
+            logger.info(f"Converting {len(data)} candles for {pair}")
+            trg.ohlcv_store(pair=pair, timeframe=timeframe, data=data)
+            if erase and convert_from != convert_to:
+                logger.info(f"Deleting source data for {pair} / {timeframe}")
+                src.ohlcv_purge(pair=pair, timeframe=timeframe)

freqtrade/data/dataprovider.py

@@ -1,16 +1,19 @@
 """
 Dataprovider
 Responsible to provide data to the bot
-including Klines, tickers, historic data
+including ticker and orderbook data, live and historical candle (OHLCV) data
 Common Interface for bot and strategy to access data.
 """
 import logging
-from pathlib import Path
+from datetime import datetime, timezone
 from typing import Any, Dict, List, Optional, Tuple
 
+from arrow import Arrow
 from pandas import DataFrame
 
+from freqtrade.constants import ListPairsWithTimeframes, PairWithTimeframe
 from freqtrade.data.history import load_pair_history
+from freqtrade.exceptions import ExchangeError, OperationalException
 from freqtrade.exchange import Exchange
 from freqtrade.state import RunMode
 
@@ -19,13 +22,26 @@ logger = logging.getLogger(__name__)
 
 class DataProvider:
 
-    def __init__(self, config: dict, exchange: Exchange) -> None:
+    def __init__(self, config: dict, exchange: Exchange, pairlists=None) -> None:
         self._config = config
         self._exchange = exchange
+        self._pairlists = pairlists
+        self.__cached_pairs: Dict[PairWithTimeframe, Tuple[DataFrame, datetime]] = {}
+
+    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
+        (but the class is exposed via `self.dp` to the strategy)
+        :param pair: pair to get the data for
+        :param timeframe: Timeframe to get data for
+        :param dataframe: analyzed dataframe
+        """
+        self.__cached_pairs[(pair, timeframe)] = (dataframe, Arrow.utcnow().datetime)
 
     def refresh(self,
-                pairlist: List[Tuple[str, str]],
-                helping_pairs: List[Tuple[str, str]] = None) -> None:
+                pairlist: ListPairsWithTimeframes,
+                helping_pairs: ListPairsWithTimeframes = None) -> None:
         """
         Refresh data, called with each cycle
         """
@@ -35,7 +51,7 @@ class DataProvider:
             self._exchange.refresh_latest_ohlcv(pairlist)
 
     @property
-    def available_pairs(self) -> List[Tuple[str, str]]:
+    def available_pairs(self) -> ListPairsWithTimeframes:
         """
         Return a list of tuples containing (pair, timeframe) for which data is currently cached.
         Should be whitelist + open trades.
@@ -44,48 +60,62 @@ class DataProvider:
 
     def ohlcv(self, pair: str, timeframe: str = None, copy: bool = True) -> DataFrame:
         """
-        Get ohlcv data for the given pair as DataFrame
+        Get candle (OHLCV) data for the given pair as DataFrame
         Please use the `available_pairs` method to verify which pairs are currently cached.
         :param pair: pair to get the data for
-        :param timeframe: Ticker timeframe to get data for
+        :param timeframe: Timeframe to get data for
         :param copy: copy dataframe before returning if True.
                      Use False only for read-only operations (where the dataframe is not modified)
         """
         if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
-            return self._exchange.klines((pair, timeframe or self._config['ticker_interval']),
+            return self._exchange.klines((pair, timeframe or self._config['timeframe']),
                                          copy=copy)
         else:
             return DataFrame()
 
     def historic_ohlcv(self, pair: str, timeframe: str = None) -> DataFrame:
         """
-        Get stored historic ohlcv data
+        Get stored historical candle (OHLCV) data
         :param pair: pair to get the data for
         :param timeframe: timeframe to get data for
         """
         return load_pair_history(pair=pair,
-                                 timeframe=timeframe or self._config['ticker_interval'],
-                                 datadir=Path(self._config['datadir'])
+                                 timeframe=timeframe or self._config['timeframe'],
+                                 datadir=self._config['datadir']
                                  )
 
     def get_pair_dataframe(self, pair: str, timeframe: str = None) -> DataFrame:
         """
-        Return pair ohlcv data, either live or cached historical -- depending
+        Return pair candle (OHLCV) data, either live or cached historical -- depending
         on the runmode.
         :param pair: pair to get the data for
         :param timeframe: timeframe to get data for
         :return: Dataframe for this pair
         """
         if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
-            # Get live ohlcv data.
+            # Get live OHLCV data.
             data = self.ohlcv(pair=pair, timeframe=timeframe)
         else:
-            # Get historic ohlcv data (cached on disk).
+            # Get historical OHLCV data (cached on disk).
             data = self.historic_ohlcv(pair=pair, timeframe=timeframe)
         if len(data) == 0:
             logger.warning(f"No data found for ({pair}, {timeframe}).")
         return data
 
+    def get_analyzed_dataframe(self, pair: str, timeframe: str) -> Tuple[DataFrame, datetime]:
+        """
+        :param pair: pair to get the data for
+        :param timeframe: timeframe to get data for
+        :return: Tuple of (Analyzed Dataframe, lastrefreshed) for the requested pair / timeframe
+            combination.
+            Returns empty dataframe and Epoch 0 (1970-01-01) if no dataframe was cached.
+        """
+        if (pair, timeframe) in self.__cached_pairs:
+            return self.__cached_pairs[(pair, timeframe)]
+        else:
+
+            return (DataFrame(), datetime.fromtimestamp(0, tz=timezone.utc))
+
     def market(self, pair: str) -> Optional[Dict[str, Any]]:
         """
         Return market data for the pair
@@ -96,19 +126,24 @@ class DataProvider:
 
     def ticker(self, pair: str):
         """
-        Return last ticker data
+        Return last ticker data from exchange
+        :param pair: Pair to get the data for
+        :return: Ticker dict from exchange or empty dict if ticker is not available for the pair
         """
-        # TODO: Implement me
-        pass
+        try:
+            return self._exchange.fetch_ticker(pair)
+        except ExchangeError:
+            return {}
 
     def orderbook(self, pair: str, maximum: int) -> Dict[str, List]:
         """
-        fetch latest orderbook data
+        Fetch latest l2 orderbook data
+        Warning: Does a network request - so use with common sense.
         :param pair: pair to get the data for
         :param maximum: Maximum number of orderbook entries to query
         :return: dict including bids/asks with a total of `maximum` entries.
         """
-        return self._exchange.get_order_book(pair, maximum)
+        return self._exchange.fetch_l2_order_book(pair, maximum)
 
     @property
     def runmode(self) -> RunMode:
@@ -117,3 +152,17 @@ class DataProvider:
         can be "live", "dry-run", "backtest", "edgecli", "hyperopt" or "other".
         """
         return RunMode(self._config.get('runmode', RunMode.OTHER))
+
+    def current_whitelist(self) -> List[str]:
+        """
+        fetch latest available whitelist.
+
+        Useful when you have a large whitelist and need to call each pair as an informative pair.
+        As available pairs does not show whitelist until after informative pairs have been cached.
+        :return: list of pairs in whitelist
+        """
+
+        if self._pairlists:
+            return self._pairlists.whitelist
+        else:
+            raise OperationalException("Dataprovider was not initialized with a pairlist provider.")

freqtrade/data/history.py

@@ -1,484 +0,0 @@
-"""
-Handle historic data (ohlcv).
-
-Includes:
-* load data for a pair (or a list of pairs) from disk
-* download data from exchange and store to disk
-"""
-
-import logging
-import operator
-from copy import deepcopy
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Any, Dict, List, Optional, Tuple
-
-import arrow
-from pandas import DataFrame
-
-from freqtrade import OperationalException, misc
-from freqtrade.configuration import TimeRange
-from freqtrade.data.converter import parse_ticker_dataframe, trades_to_ohlcv
-from freqtrade.exchange import Exchange, timeframe_to_minutes, timeframe_to_seconds
-
-logger = logging.getLogger(__name__)
-
-
-def trim_tickerlist(tickerlist: List[Dict], timerange: TimeRange) -> List[Dict]:
-    """
-    Trim tickerlist based on given timerange
-    """
-    if not tickerlist:
-        return tickerlist
-
-    start_index = 0
-    stop_index = len(tickerlist)
-
-    if timerange.starttype == 'date':
-        while (start_index < len(tickerlist) and
-               tickerlist[start_index][0] < timerange.startts * 1000):
-            start_index += 1
-
-    if timerange.stoptype == 'date':
-        while (stop_index > 0 and
-               tickerlist[stop_index-1][0] > timerange.stopts * 1000):
-            stop_index -= 1
-
-    if start_index > stop_index:
-        raise ValueError(f'The timerange [{timerange.startts},{timerange.stopts}] is incorrect')
-
-    return tickerlist[start_index:stop_index]
-
-
-def trim_dataframe(df: DataFrame, timerange: TimeRange, df_date_col: str = 'date') -> DataFrame:
-    """
-    Trim dataframe based on given timerange
-    :param df: Dataframe to trim
-    :param timerange: timerange (use start and end date if available)
-    :param: df_date_col: Column in the dataframe to use as Date column
-    :return: trimmed dataframe
-    """
-    if timerange.starttype == 'date':
-        start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
-        df = df.loc[df[df_date_col] >= start, :]
-    if timerange.stoptype == 'date':
-        stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
-        df = df.loc[df[df_date_col] <= stop, :]
-    return df
-
-
-def load_tickerdata_file(datadir: Path, pair: str, timeframe: str,
-                         timerange: Optional[TimeRange] = None) -> Optional[list]:
-    """
-    Load a pair from file, either .json.gz or .json
-    :return: tickerlist or None if unsuccessful
-    """
-    filename = pair_data_filename(datadir, pair, timeframe)
-    pairdata = misc.file_load_json(filename)
-    if not pairdata:
-        return []
-
-    if timerange:
-        pairdata = trim_tickerlist(pairdata, timerange)
-    return pairdata
-
-
-def store_tickerdata_file(datadir: Path, pair: str,
-                          timeframe: str, data: list, is_zip: bool = False):
-    """
-    Stores tickerdata to file
-    """
-    filename = pair_data_filename(datadir, pair, timeframe)
-    misc.file_dump_json(filename, data, is_zip=is_zip)
-
-
-def load_trades_file(datadir: Path, pair: str,
-                     timerange: Optional[TimeRange] = None) -> List[Dict]:
-    """
-    Load a pair from file, either .json.gz or .json
-    :return: tradelist or empty list if unsuccesful
-    """
-    filename = pair_trades_filename(datadir, pair)
-    tradesdata = misc.file_load_json(filename)
-    if not tradesdata:
-        return []
-
-    return tradesdata
-
-
-def store_trades_file(datadir: Path, pair: str,
-                      data: list, is_zip: bool = True):
-    """
-    Stores tickerdata to file
-    """
-    filename = pair_trades_filename(datadir, pair)
-    misc.file_dump_json(filename, data, is_zip=is_zip)
-
-
-def _validate_pairdata(pair, pairdata, timerange: TimeRange):
-    if timerange.starttype == 'date' and pairdata[0][0] > timerange.startts * 1000:
-        logger.warning('Missing data at start for pair %s, data starts at %s',
-                       pair, arrow.get(pairdata[0][0] // 1000).strftime('%Y-%m-%d %H:%M:%S'))
-    if timerange.stoptype == 'date' and pairdata[-1][0] < timerange.stopts * 1000:
-        logger.warning('Missing data at end for pair %s, data ends at %s',
-                       pair, arrow.get(pairdata[-1][0] // 1000).strftime('%Y-%m-%d %H:%M:%S'))
-
-
-def load_pair_history(pair: str,
-                      timeframe: str,
-                      datadir: Path,
-                      timerange: Optional[TimeRange] = None,
-                      refresh_pairs: bool = False,
-                      exchange: Optional[Exchange] = None,
-                      fill_up_missing: bool = True,
-                      drop_incomplete: bool = True,
-                      startup_candles: int = 0,
-                      ) -> DataFrame:
-    """
-    Loads cached ticker history for the given pair.
-    :param pair: Pair to load data for
-    :param timeframe: Ticker timeframe (e.g. "5m")
-    :param datadir: Path to the data storage location.
-    :param timerange: Limit data to be loaded to this timerange
-    :param refresh_pairs: Refresh pairs from exchange.
-        (Note: Requires exchange to be passed as well.)
-    :param exchange: Exchange object (needed when using "refresh_pairs")
-    :param fill_up_missing: Fill missing values with "No action"-candles
-    :param drop_incomplete: Drop last candle assuming it may be incomplete.
-    :param startup_candles: Additional candles to load at the start of the period
-    :return: DataFrame with ohlcv data, or empty DataFrame
-    """
-
-    timerange_startup = deepcopy(timerange)
-    if startup_candles > 0 and timerange_startup:
-        timerange_startup.subtract_start(timeframe_to_seconds(timeframe) * startup_candles)
-
-    # The user forced the refresh of pairs
-    if refresh_pairs:
-        download_pair_history(datadir=datadir,
-                              exchange=exchange,
-                              pair=pair,
-                              timeframe=timeframe,
-                              timerange=timerange)
-
-    pairdata = load_tickerdata_file(datadir, pair, timeframe, timerange=timerange_startup)
-
-    if pairdata:
-        if timerange_startup:
-            _validate_pairdata(pair, pairdata, timerange_startup)
-        return parse_ticker_dataframe(pairdata, timeframe, pair=pair,
-                                      fill_missing=fill_up_missing,
-                                      drop_incomplete=drop_incomplete)
-    else:
-        logger.warning(
-            f'No history data for pair: "{pair}", timeframe: {timeframe}. '
-            'Use `freqtrade download-data` to download the data'
-        )
-        return DataFrame()
-
-
-def load_data(datadir: Path,
-              timeframe: str,
-              pairs: List[str],
-              refresh_pairs: bool = False,
-              exchange: Optional[Exchange] = None,
-              timerange: Optional[TimeRange] = None,
-              fill_up_missing: bool = True,
-              startup_candles: int = 0,
-              fail_without_data: bool = False
-              ) -> Dict[str, DataFrame]:
-    """
-    Loads ticker history data for a list of pairs
-    :param datadir: Path to the data storage location.
-    :param timeframe: Ticker Timeframe (e.g. "5m")
-    :param pairs: List of pairs to load
-    :param refresh_pairs: Refresh pairs from exchange.
-        (Note: Requires exchange to be passed as well.)
-    :param exchange: Exchange object (needed when using "refresh_pairs")
-    :param timerange: Limit data to be loaded to this timerange
-    :param fill_up_missing: Fill missing values with "No action"-candles
-    :param startup_candles: Additional candles to load at the start of the period
-    :param fail_without_data: Raise OperationalException if no data is found.
-    :return: dict(<pair>:<Dataframe>)
-    TODO: refresh_pairs is still used by edge to keep the data uptodate.
-        This should be replaced in the future. Instead, writing the current candles to disk
-        from dataprovider should be implemented, as this would avoid loading ohlcv data twice.
-        exchange and refresh_pairs are then not needed here nor in load_pair_history.
-    """
-    result: Dict[str, DataFrame] = {}
-    if startup_candles > 0 and timerange:
-        logger.info(f'Using indicator startup period: {startup_candles} ...')
-
-    for pair in pairs:
-        hist = load_pair_history(pair=pair, timeframe=timeframe,
-                                 datadir=datadir, timerange=timerange,
-                                 refresh_pairs=refresh_pairs,
-                                 exchange=exchange,
-                                 fill_up_missing=fill_up_missing,
-                                 startup_candles=startup_candles)
-        if not hist.empty:
-            result[pair] = hist
-
-    if fail_without_data and not result:
-        raise OperationalException("No data found. Terminating.")
-    return result
-
-
-def pair_data_filename(datadir: Path, pair: str, timeframe: str) -> Path:
-    pair_s = pair.replace("/", "_")
-    filename = datadir.joinpath(f'{pair_s}-{timeframe}.json')
-    return filename
-
-
-def pair_trades_filename(datadir: Path, pair: str) -> Path:
-    pair_s = pair.replace("/", "_")
-    filename = datadir.joinpath(f'{pair_s}-trades.json.gz')
-    return filename
-
-
-def _load_cached_data_for_updating(datadir: Path, pair: str, timeframe: str,
-                                   timerange: Optional[TimeRange]) -> Tuple[List[Any],
-                                                                            Optional[int]]:
-    """
-    Load cached data to download more data.
-    If timerange is passed in, checks whether data from an before the stored data will be
-    downloaded.
-    If that's the case then what's available should be completely overwritten.
-    Only used by download_pair_history().
-    """
-
-    since_ms = None
-
-    # user sets timerange, so find the start time
-    if timerange:
-        if timerange.starttype == 'date':
-            since_ms = timerange.startts * 1000
-        elif timerange.stoptype == 'line':
-            num_minutes = timerange.stopts * timeframe_to_minutes(timeframe)
-            since_ms = arrow.utcnow().shift(minutes=num_minutes).timestamp * 1000
-
-    # read the cached file
-    # Intentionally don't pass timerange in - since we need to load the full dataset.
-    data = load_tickerdata_file(datadir, pair, timeframe)
-    # remove the last item, could be incomplete candle
-    if data:
-        data.pop()
-    else:
-        data = []
-
-    if data:
-        if since_ms and since_ms < data[0][0]:
-            # Earlier data than existing data requested, redownload all
-            data = []
-        else:
-            # a part of the data was already downloaded, so download unexist data only
-            since_ms = data[-1][0] + 1
-
-    return (data, since_ms)
-
-
-def download_pair_history(datadir: Path,
-                          exchange: Optional[Exchange],
-                          pair: str,
-                          timeframe: str = '5m',
-                          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: Ticker Timeframe (e.g 5m)
-    :param timerange: range of time to download
-    :return: bool with success state
-    """
-    if not exchange:
-        raise OperationalException(
-            "Exchange needs to be initialized when downloading pair history data"
-        )
-
-    try:
-        logger.info(
-            f'Download history data for pair: "{pair}", timeframe: {timeframe} '
-            f'and store in {datadir}.'
-        )
-
-        data, since_ms = _load_cached_data_for_updating(datadir, pair, timeframe, timerange)
-
-        logger.debug("Current Start: %s", misc.format_ms_time(data[1][0]) if data else 'None')
-        logger.debug("Current End: %s", misc.format_ms_time(data[-1][0]) if data else 'None')
-
-        # Default since_ms to 30 days if nothing is given
-        new_data = exchange.get_historic_ohlcv(pair=pair, timeframe=timeframe,
-                                               since_ms=since_ms if since_ms
-                                               else
-                                               int(arrow.utcnow().shift(
-                                                   days=-30).float_timestamp) * 1000)
-        data.extend(new_data)
-
-        logger.debug("New Start: %s", misc.format_ms_time(data[0][0]))
-        logger.debug("New End: %s", misc.format_ms_time(data[-1][0]))
-
-        store_tickerdata_file(datadir, pair, timeframe, data=data)
-        return True
-
-    except Exception as e:
-        logger.error(
-            f'Failed to download history data for pair: "{pair}", timeframe: {timeframe}. '
-            f'Error: {e}'
-        )
-        return False
-
-
-def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes: List[str],
-                                dl_path: Path, timerange: Optional[TimeRange] = None,
-                                erase=False) -> List[str]:
-    """
-    Refresh stored ohlcv data for backtesting and hyperopt operations.
-    Used by freqtrade download-data
-    :return: Pairs not available
-    """
-    pairs_not_available = []
-    for pair in pairs:
-        if pair not in exchange.markets:
-            pairs_not_available.append(pair)
-            logger.info(f"Skipping pair {pair}...")
-            continue
-        for timeframe in timeframes:
-
-            dl_file = pair_data_filename(dl_path, pair, timeframe)
-            if erase and dl_file.exists():
-                logger.info(
-                    f'Deleting existing data for pair {pair}, interval {timeframe}.')
-                dl_file.unlink()
-
-            logger.info(f'Downloading pair {pair}, interval {timeframe}.')
-            download_pair_history(datadir=dl_path, exchange=exchange,
-                                  pair=pair, timeframe=str(timeframe),
-                                  timerange=timerange)
-    return pairs_not_available
-
-
-def download_trades_history(datadir: Path,
-                            exchange: Exchange,
-                            pair: str,
-                            timerange: Optional[TimeRange] = None) -> bool:
-    """
-    Download trade history from the exchange.
-    Appends to previously downloaded trades data.
-    """
-    try:
-
-        since = timerange.startts * 1000 if timerange and timerange.starttype == 'date' else None
-
-        trades = load_trades_file(datadir, pair)
-
-        from_id = trades[-1]['id'] if trades else None
-
-        logger.debug("Current Start: %s", trades[0]['datetime'] if trades else 'None')
-        logger.debug("Current End: %s", trades[-1]['datetime'] if trades else 'None')
-
-        new_trades = exchange.get_historic_trades(pair=pair,
-                                                  since=since if since else
-                                                  int(arrow.utcnow().shift(
-                                                      days=-30).float_timestamp) * 1000,
-                                                  #  until=xxx,
-                                                  from_id=from_id,
-                                                  )
-        trades.extend(new_trades[1])
-        store_trades_file(datadir, pair, trades)
-
-        logger.debug("New Start: %s", trades[0]['datetime'])
-        logger.debug("New End: %s", trades[-1]['datetime'])
-        logger.info(f"New Amount of trades: {len(trades)}")
-        return True
-
-    except Exception as e:
-        logger.error(
-            f'Failed to download historic trades for pair: "{pair}". '
-            f'Error: {e}'
-        )
-        return False
-
-
-def refresh_backtest_trades_data(exchange: Exchange, pairs: List[str], datadir: Path,
-                                 timerange: TimeRange, erase=False) -> List[str]:
-    """
-    Refresh stored trades data.
-    Used by freqtrade download-data
-    :return: Pairs not available
-    """
-    pairs_not_available = []
-    for pair in pairs:
-        if pair not in exchange.markets:
-            pairs_not_available.append(pair)
-            logger.info(f"Skipping pair {pair}...")
-            continue
-
-        dl_file = pair_trades_filename(datadir, pair)
-        if erase and dl_file.exists():
-            logger.info(
-                f'Deleting existing data for pair {pair}.')
-            dl_file.unlink()
-
-        logger.info(f'Downloading trades for pair {pair}.')
-        download_trades_history(datadir=datadir, exchange=exchange,
-                                pair=pair,
-                                timerange=timerange)
-    return pairs_not_available
-
-
-def convert_trades_to_ohlcv(pairs: List[str], timeframes: List[str],
-                            datadir: Path, timerange: TimeRange, erase=False) -> None:
-    """
-    Convert stored trades data to ohlcv data
-    """
-    for pair in pairs:
-        trades = load_trades_file(datadir, pair)
-        for timeframe in timeframes:
-            ohlcv_file = pair_data_filename(datadir, pair, timeframe)
-            if erase and ohlcv_file.exists():
-                logger.info(f'Deleting existing data for pair {pair}, interval {timeframe}.')
-                ohlcv_file.unlink()
-            ohlcv = trades_to_ohlcv(trades, timeframe)
-            # Store ohlcv
-            store_tickerdata_file(datadir, pair, timeframe, data=ohlcv)
-
-
-def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
-    """
-    Get the maximum timeframe for the given backtest data
-    :param data: dictionary with preprocessed backtesting data
-    :return: tuple containing min_date, max_date
-    """
-    timeframe = [
-        (arrow.get(frame['date'].min()), arrow.get(frame['date'].max()))
-        for frame in data.values()
-    ]
-    return min(timeframe, key=operator.itemgetter(0))[0], \
-        max(timeframe, key=operator.itemgetter(1))[1]
-
-
-def validate_backtest_data(data: DataFrame, pair: str, min_date: datetime,
-                           max_date: datetime, timeframe_mins: int) -> bool:
-    """
-    Validates preprocessed backtesting data for missing values and shows warnings about it that.
-
-    :param data: preprocessed backtesting data (as DataFrame)
-    :param pair: pair used for log output.
-    :param min_date: start-date of the data
-    :param max_date: end-date of the data
-    :param timeframe_mins: ticker Timeframe in minutes
-    """
-    # total difference in minutes / timeframe-minutes
-    expected_frames = int((max_date - min_date).total_seconds() // 60 // timeframe_mins)
-    found_missing = False
-    dflen = len(data)
-    if dflen < expected_frames:
-        found_missing = True
-        logger.warning("%s has missing frames: expected %s, got %s, that's %s missing values",
-                       pair, expected_frames, dflen, expected_frames - dflen)
-    return found_missing

freqtrade/data/history/__init__.py

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

freqtrade/data/history/history_utils.py

@@ -0,0 +1,396 @@
+import logging
+import operator
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+import arrow
+from pandas import DataFrame
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
+from freqtrade.data.converter import (ohlcv_to_dataframe,
+                                      trades_remove_duplicates,
+                                      trades_to_ohlcv)
+from freqtrade.data.history.idatahandler import IDataHandler, get_datahandler
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange import Exchange
+from freqtrade.misc import format_ms_time
+
+logger = logging.getLogger(__name__)
+
+
+def load_pair_history(pair: str,
+                      timeframe: str,
+                      datadir: Path, *,
+                      timerange: Optional[TimeRange] = None,
+                      fill_up_missing: bool = True,
+                      drop_incomplete: bool = True,
+                      startup_candles: int = 0,
+                      data_format: str = None,
+                      data_handler: IDataHandler = None,
+                      ) -> DataFrame:
+    """
+    Load cached ohlcv history for the given pair.
+
+    :param pair: Pair to load data for
+    :param timeframe: Timeframe (e.g. "5m")
+    :param datadir: Path to the data storage location.
+    :param data_format: Format of the data. Ignored if data_handler is set.
+    :param timerange: Limit data to be loaded to this timerange
+    :param fill_up_missing: Fill missing values with "No action"-candles
+    :param drop_incomplete: Drop last candle assuming it may be incomplete.
+    :param startup_candles: Additional candles to load at the start of the period
+    :param data_handler: Initialized data-handler to use.
+                         Will be initialized from data_format if not set
+    :return: DataFrame with ohlcv data, or empty DataFrame
+    """
+    data_handler = get_datahandler(datadir, data_format, data_handler)
+
+    return data_handler.ohlcv_load(pair=pair,
+                                   timeframe=timeframe,
+                                   timerange=timerange,
+                                   fill_missing=fill_up_missing,
+                                   drop_incomplete=drop_incomplete,
+                                   startup_candles=startup_candles,
+                                   )
+
+
+def load_data(datadir: Path,
+              timeframe: str,
+              pairs: List[str], *,
+              timerange: Optional[TimeRange] = None,
+              fill_up_missing: bool = True,
+              startup_candles: int = 0,
+              fail_without_data: bool = False,
+              data_format: str = 'json',
+              ) -> Dict[str, DataFrame]:
+    """
+    Load ohlcv history data for a list of pairs.
+
+    :param datadir: Path to the data storage location.
+    :param timeframe: Timeframe (e.g. "5m")
+    :param pairs: List of pairs to load
+    :param timerange: Limit data to be loaded to this timerange
+    :param fill_up_missing: Fill missing values with "No action"-candles
+    :param startup_candles: Additional candles to load at the start of the period
+    :param fail_without_data: Raise OperationalException if no data is found.
+    :param data_format: Data format which should be used. Defaults to json
+    :return: dict(<pair>:<Dataframe>)
+    """
+    result: Dict[str, DataFrame] = {}
+    if startup_candles > 0 and timerange:
+        logger.info(f'Using indicator startup period: {startup_candles} ...')
+
+    data_handler = get_datahandler(datadir, data_format)
+
+    for pair in pairs:
+        hist = load_pair_history(pair=pair, timeframe=timeframe,
+                                 datadir=datadir, timerange=timerange,
+                                 fill_up_missing=fill_up_missing,
+                                 startup_candles=startup_candles,
+                                 data_handler=data_handler
+                                 )
+        if not hist.empty:
+            result[pair] = hist
+
+    if fail_without_data and not result:
+        raise OperationalException("No data found. Terminating.")
+    return result
+
+
+def refresh_data(datadir: Path,
+                 timeframe: str,
+                 pairs: List[str],
+                 exchange: Exchange,
+                 data_format: str = None,
+                 timerange: Optional[TimeRange] = None,
+                 ) -> None:
+    """
+    Refresh ohlcv history data for a list of pairs.
+
+    :param datadir: Path to the data storage location.
+    :param timeframe: Timeframe (e.g. "5m")
+    :param pairs: List of pairs to load
+    :param exchange: Exchange object
+    :param timerange: Limit data to be loaded to this timerange
+    """
+    data_handler = get_datahandler(datadir, data_format)
+    for pair in pairs:
+        _download_pair_history(pair=pair, timeframe=timeframe,
+                               datadir=datadir, timerange=timerange,
+                               exchange=exchange, data_handler=data_handler)
+
+
+def _load_cached_data_for_updating(pair: str, timeframe: str, timerange: Optional[TimeRange],
+                                   data_handler: IDataHandler) -> Tuple[DataFrame, Optional[int]]:
+    """
+    Load cached data to download more data.
+    If timerange is passed in, checks whether data from an before the stored data will be
+    downloaded.
+    If that's the case then what's available should be completely overwritten.
+    Otherwise downloads always start at the end of the available data to avoid data gaps.
+    Note: Only used by download_pair_history().
+    """
+    start = None
+    if timerange:
+        if timerange.starttype == 'date':
+            # TODO: convert to date for conversion
+            start = datetime.fromtimestamp(timerange.startts, 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)
+    if not data.empty:
+        if start and start < data.iloc[0]['date']:
+            # Earlier data than existing data requested, redownload all
+            data = DataFrame(columns=DEFAULT_DATAFRAME_COLUMNS)
+        else:
+            start = data.iloc[-1]['date']
+
+    start_ms = int(start.timestamp() * 1000) if start else None
+    return data, start_ms
+
+
+def _download_pair_history(datadir: Path,
+                           exchange: Exchange,
+                           pair: str, *,
+                           timeframe: str = '5m',
+                           timerange: Optional[TimeRange] = None,
+                           data_handler: IDataHandler = None) -> bool:
+    """
+    Download latest candles from the exchange for the pair and timeframe passed in parameters
+    The data is downloaded starting from the last correct data that
+    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
+    :return: bool with success state
+    """
+    data_handler = get_datahandler(datadir, data_handler=data_handler)
+
+    try:
+        logger.info(
+            f'Download history data for pair: "{pair}", timeframe: {timeframe} '
+            f'and store in {datadir}.'
+        )
+
+        # data, since_ms = _load_cached_data_for_updating_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')
+        logger.debug("Current End: %s",
+                     f"{data.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')
+
+        # Default since_ms to 30 days if nothing is given
+        new_data = exchange.get_historic_ohlcv(pair=pair,
+                                               timeframe=timeframe,
+                                               since_ms=since_ms if since_ms else
+                                               int(arrow.utcnow().shift(
+                                                   days=-30).float_timestamp) * 1000
+                                               )
+        # TODO: Maybe move parsing to exchange class (?)
+        new_dataframe = ohlcv_to_dataframe(new_data, timeframe, pair,
+                                           fill_missing=False, drop_incomplete=True)
+        if data.empty:
+            data = new_dataframe
+        else:
+            data = data.append(new_dataframe)
+
+        logger.debug("New  Start: %s",
+                     f"{data.iloc[0]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')
+        logger.debug("New End: %s",
+                     f"{data.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')
+
+        data_handler.ohlcv_store(pair, timeframe, data=data)
+        return True
+
+    except Exception as e:
+        logger.error(
+            f'Failed to download history data for pair: "{pair}", timeframe: {timeframe}. '
+            f'Error: {e}'
+        )
+        return False
+
+
+def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes: List[str],
+                                datadir: Path, timerange: Optional[TimeRange] = None,
+                                erase: bool = False, data_format: str = None) -> List[str]:
+    """
+    Refresh stored ohlcv data for backtesting and hyperopt operations.
+    Used by freqtrade download-data subcommand.
+    :return: List of pairs that are not available.
+    """
+    pairs_not_available = []
+    data_handler = get_datahandler(datadir, data_format)
+    for pair in pairs:
+        if pair not in exchange.markets:
+            pairs_not_available.append(pair)
+            logger.info(f"Skipping pair {pair}...")
+            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}.')
+            _download_pair_history(datadir=datadir, exchange=exchange,
+                                   pair=pair, timeframe=str(timeframe),
+                                   timerange=timerange, data_handler=data_handler)
+    return pairs_not_available
+
+
+def _download_trades_history(exchange: Exchange,
+                             pair: str, *,
+                             timerange: Optional[TimeRange] = None,
+                             data_handler: IDataHandler
+                             ) -> bool:
+    """
+    Download trade history from the exchange.
+    Appends to previously downloaded trades data.
+    """
+    try:
+
+        since = timerange.startts * 1000 if \
+            (timerange and timerange.starttype == 'date') else int(arrow.utcnow().shift(
+                days=-30).float_timestamp) * 1000
+
+        trades = data_handler.trades_load(pair)
+
+        # TradesList columns are defined in constants.DEFAULT_TRADES_COLUMNS
+        # DEFAULT_TRADES_COLUMNS: 0 -> timestamp
+        # DEFAULT_TRADES_COLUMNS: 1 -> id
+
+        if trades and since < trades[0][0]:
+            # since is before the first trade
+            logger.info(f"Start earlier than available data. Redownloading trades for {pair}...")
+            trades = []
+
+        from_id = trades[-1][1] if trades else None
+        if trades and since < trades[-1][0]:
+            # Reset since to the last available point
+            # - 5 seconds (to ensure we're getting all trades)
+            since = trades[-1][0] - (5 * 1000)
+            logger.info(f"Using last trade date -5s - Downloading trades for {pair} "
+                        f"since: {format_ms_time(since)}.")
+
+        logger.debug(f"Current Start: {format_ms_time(trades[0][0]) if trades else 'None'}")
+        logger.debug(f"Current End: {format_ms_time(trades[-1][0]) if trades else 'None'}")
+        logger.info(f"Current Amount of trades: {len(trades)}")
+
+        # Default since_ms to 30 days if nothing is given
+        new_trades = exchange.get_historic_trades(pair=pair,
+                                                  since=since,
+                                                  from_id=from_id,
+                                                  )
+        trades.extend(new_trades[1])
+        # Remove duplicates to make sure we're not storing data we don't need
+        trades = trades_remove_duplicates(trades)
+        data_handler.trades_store(pair, data=trades)
+
+        logger.debug(f"New Start: {format_ms_time(trades[0][0])}")
+        logger.debug(f"New End: {format_ms_time(trades[-1][0])}")
+        logger.info(f"New Amount of trades: {len(trades)}")
+        return True
+
+    except Exception as e:
+        logger.error(
+            f'Failed to download historic trades for pair: "{pair}". '
+            f'Error: {e}'
+        )
+        return False
+
+
+def refresh_backtest_trades_data(exchange: Exchange, pairs: List[str], datadir: Path,
+                                 timerange: TimeRange, erase: bool = False,
+                                 data_format: str = 'jsongz') -> List[str]:
+    """
+    Refresh stored trades data for backtesting and hyperopt operations.
+    Used by freqtrade download-data subcommand.
+    :return: List of pairs that are not available.
+    """
+    pairs_not_available = []
+    data_handler = get_datahandler(datadir, data_format=data_format)
+    for pair in pairs:
+        if pair not in exchange.markets:
+            pairs_not_available.append(pair)
+            logger.info(f"Skipping pair {pair}...")
+            continue
+
+        if erase:
+            if data_handler.trades_purge(pair):
+                logger.info(f'Deleting existing data for pair {pair}.')
+
+        logger.info(f'Downloading trades for pair {pair}.')
+        _download_trades_history(exchange=exchange,
+                                 pair=pair,
+                                 timerange=timerange,
+                                 data_handler=data_handler)
+    return pairs_not_available
+
+
+def convert_trades_to_ohlcv(pairs: List[str], timeframes: List[str],
+                            datadir: Path, timerange: TimeRange, erase: bool = False,
+                            data_format_ohlcv: str = 'json',
+                            data_format_trades: str = 'jsongz') -> None:
+    """
+    Convert stored trades data to ohlcv data
+    """
+    data_handler_trades = get_datahandler(datadir, data_format=data_format_trades)
+    data_handler_ohlcv = get_datahandler(datadir, data_format=data_format_ohlcv)
+
+    for pair in pairs:
+        trades = data_handler_trades.trades_load(pair)
+        for timeframe in timeframes:
+            if erase:
+                if data_handler_ohlcv.ohlcv_purge(pair, timeframe):
+                    logger.info(f'Deleting existing data for pair {pair}, interval {timeframe}.')
+            ohlcv = trades_to_ohlcv(trades, timeframe)
+            # Store ohlcv
+            data_handler_ohlcv.ohlcv_store(pair, timeframe, data=ohlcv)
+
+
+def get_timerange(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
+    """
+    Get the maximum common timerange for the given backtest data.
+
+    :param data: dictionary with preprocessed backtesting data
+    :return: tuple containing min_date, max_date
+    """
+    timeranges = [
+        (arrow.get(frame['date'].min()), arrow.get(frame['date'].max()))
+        for frame in data.values()
+    ]
+    return (min(timeranges, key=operator.itemgetter(0))[0],
+            max(timeranges, key=operator.itemgetter(1))[1])
+
+
+def validate_backtest_data(data: DataFrame, pair: str, min_date: datetime,
+                           max_date: datetime, timeframe_min: int) -> bool:
+    """
+    Validates preprocessed backtesting data for missing values and shows warnings about it that.
+
+    :param data: preprocessed backtesting data (as DataFrame)
+    :param pair: pair used for log output.
+    :param min_date: start-date of the data
+    :param max_date: end-date of the data
+    :param timeframe_min: Timeframe in minutes
+    """
+    # total difference in minutes / timeframe-minutes
+    expected_frames = int((max_date - min_date).total_seconds() // 60 // timeframe_min)
+    found_missing = False
+    dflen = len(data)
+    if dflen < expected_frames:
+        found_missing = True
+        logger.warning("%s has missing frames: expected %s, got %s, that's %s missing values",
+                       pair, expected_frames, dflen, expected_frames - dflen)
+    return found_missing

freqtrade/data/history/idatahandler.py

@@ -0,0 +1,257 @@
+"""
+Abstract datahandler interface.
+It's subclasses handle and storing data from disk.
+
+"""
+import logging
+from abc import ABC, abstractclassmethod, abstractmethod
+from copy import deepcopy
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import List, Optional, Type
+
+from pandas import DataFrame
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import ListPairsWithTimeframes
+from freqtrade.data.converter import (clean_ohlcv_dataframe,
+                                      trades_remove_duplicates, trim_dataframe)
+from freqtrade.exchange import timeframe_to_seconds
+
+logger = logging.getLogger(__name__)
+
+# Type for trades list
+TradeList = List[List]
+
+
+class IDataHandler(ABC):
+
+    def __init__(self, datadir: Path) -> None:
+        self._datadir = datadir
+
+    @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
+        :return: List of Tuples of (pair, timeframe)
+        """
+
+    @abstractclassmethod
+    def ohlcv_get_pairs(cls, datadir: Path, timeframe: str) -> List[str]:
+        """
+        Returns a list of all pairs with ohlcv data available in this datadir
+        for the specified timeframe
+        :param datadir: Directory to search for ohlcv files
+        :param timeframe: Timeframe to search pairs for
+        :return: List of Pairs
+        """
+
+    @abstractmethod
+    def ohlcv_store(self, pair: str, timeframe: str, data: DataFrame) -> None:
+        """
+        Store data in json format "values".
+            format looks as follows:
+            [[<date>,<open>,<high>,<low>,<close>]]
+        :param pair: Pair - used to generate filename
+        :timeframe: Timeframe - used to generate filename
+        :data: Dataframe containing OHLCV data
+        :return: None
+        """
+
+    @abstractmethod
+    def _ohlcv_load(self, pair: str, timeframe: str,
+                    timerange: Optional[TimeRange] = None,
+                    ) -> DataFrame:
+        """
+        Internal method used to load data for one pair from disk.
+        Implements the loading and conversion to a Pandas dataframe.
+        Timerange trimming and dataframe validation happens outside of this method.
+        :param pair: Pair to load data
+        :param timeframe: Timeframe (e.g. "5m")
+        :param timerange: Limit data to be loaded to this timerange.
+                        Optionally implemented by subclasses to avoid loading
+                        all data where possible.
+        :return: DataFrame with ohlcv data, or empty DataFrame
+        """
+
+    @abstractmethod
+    def ohlcv_purge(self, pair: str, timeframe: str) -> bool:
+        """
+        Remove data for this pair
+        :param pair: Delete data for this pair.
+        :param timeframe: Timeframe (e.g. "5m")
+        :return: True when deleted, false if file did not exist.
+        """
+
+    @abstractmethod
+    def ohlcv_append(self, pair: str, timeframe: str, data: DataFrame) -> None:
+        """
+        Append data to existing data structures
+        :param pair: Pair
+        :param timeframe: Timeframe this ohlcv data is for
+        :param data: Data to append.
+        """
+
+    @abstractclassmethod
+    def trades_get_pairs(cls, datadir: Path) -> List[str]:
+        """
+        Returns a list of all pairs for which trade data is available in this
+        :param datadir: Directory to search for ohlcv files
+        :return: List of Pairs
+        """
+
+    @abstractmethod
+    def trades_store(self, pair: str, data: TradeList) -> None:
+        """
+        Store trades data (list of Dicts) to file
+        :param pair: Pair - used for filename
+        :param data: List of Lists containing trade data,
+                     column sequence as in DEFAULT_TRADES_COLUMNS
+        """
+
+    @abstractmethod
+    def trades_append(self, pair: str, data: TradeList):
+        """
+        Append data to existing files
+        :param pair: Pair - used for filename
+        :param data: List of Lists containing trade data,
+                     column sequence as in DEFAULT_TRADES_COLUMNS
+        """
+
+    @abstractmethod
+    def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
+        """
+        Load a pair from file, either .json.gz or .json
+        :param pair: Load trades for this pair
+        :param timerange: Timerange to load trades for - currently not implemented
+        :return: List of trades
+        """
+
+    @abstractmethod
+    def trades_purge(self, pair: str) -> bool:
+        """
+        Remove data for this pair
+        :param pair: Delete data for this pair.
+        :return: True when deleted, false if file did not exist.
+        """
+
+    def trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
+        """
+        Load a pair from file, either .json.gz or .json
+        Removes duplicates in the process.
+        :param pair: Load trades for this pair
+        :param timerange: Timerange to load trades for - currently not implemented
+        :return: List of trades
+        """
+        return trades_remove_duplicates(self._trades_load(pair, timerange=timerange))
+
+    def ohlcv_load(self, pair, timeframe: str,
+                   timerange: Optional[TimeRange] = None,
+                   fill_missing: bool = True,
+                   drop_incomplete: bool = True,
+                   startup_candles: int = 0,
+                   warn_no_data: bool = True
+                   ) -> DataFrame:
+        """
+        Load cached candle (OHLCV) data for the given pair.
+
+        :param pair: Pair to load data for
+        :param timeframe: Timeframe (e.g. "5m")
+        :param timerange: Limit data to be loaded to this timerange
+        :param fill_missing: Fill missing values with "No action"-candles
+        :param drop_incomplete: Drop last candle assuming it may be incomplete.
+        :param startup_candles: Additional candles to load at the start of the period
+        :param warn_no_data: Log a warning message when no data is found
+        :return: DataFrame with ohlcv data, or empty DataFrame
+        """
+        # Fix startup period
+        timerange_startup = deepcopy(timerange)
+        if startup_candles > 0 and timerange_startup:
+            timerange_startup.subtract_start(timeframe_to_seconds(timeframe) * startup_candles)
+
+        pairdf = self._ohlcv_load(pair, timeframe,
+                                  timerange=timerange_startup)
+        if self._check_empty_df(pairdf, pair, timeframe, warn_no_data):
+            return pairdf
+        else:
+            enddate = pairdf.iloc[-1]['date']
+
+            if timerange_startup:
+                self._validate_pairdata(pair, pairdf, timerange_startup)
+                pairdf = trim_dataframe(pairdf, timerange_startup)
+                if self._check_empty_df(pairdf, pair, timeframe, warn_no_data):
+                    return pairdf
+
+            # incomplete candles should only be dropped if we didn't trim the end beforehand.
+            pairdf = clean_ohlcv_dataframe(pairdf, timeframe,
+                                           pair=pair,
+                                           fill_missing=fill_missing,
+                                           drop_incomplete=(drop_incomplete and
+                                                            enddate == pairdf.iloc[-1]['date']))
+            self._check_empty_df(pairdf, pair, timeframe, warn_no_data)
+            return pairdf
+
+    def _check_empty_df(self, pairdf: DataFrame, pair: str, timeframe: str, warn_no_data: bool):
+        """
+        Warn on empty dataframe
+        """
+        if pairdf.empty:
+            if warn_no_data:
+                logger.warning(
+                    f'No history data for pair: "{pair}", timeframe: {timeframe}. '
+                    'Use `freqtrade download-data` to download the data'
+                )
+            return True
+        return False
+
+    def _validate_pairdata(self, pair, pairdata: DataFrame, timerange: TimeRange):
+        """
+        Validates pairdata for missing data at start end end and logs warnings.
+        :param pairdata: Dataframe to validate
+        :param timerange: Timerange specified for start and end dates
+        """
+
+        if timerange.starttype == 'date':
+            start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
+            if pairdata.iloc[0]['date'] > start:
+                logger.warning(f"Missing data at start for pair {pair}, "
+                               f"data starts at {pairdata.iloc[0]['date']:%Y-%m-%d %H:%M:%S}")
+        if timerange.stoptype == 'date':
+            stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
+            if pairdata.iloc[-1]['date'] < stop:
+                logger.warning(f"Missing data at end for pair {pair}, "
+                               f"data ends at {pairdata.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}")
+
+
+def get_datahandlerclass(datatype: str) -> Type[IDataHandler]:
+    """
+    Get datahandler class.
+    Could be done using Resolvers, but since this may be called often and resolvers
+    are rather expensive, doing this directly should improve performance.
+    :param datatype: datatype to use.
+    :return: Datahandler class
+    """
+
+    if datatype == 'json':
+        from .jsondatahandler import JsonDataHandler
+        return JsonDataHandler
+    elif datatype == 'jsongz':
+        from .jsondatahandler import JsonGzDataHandler
+        return JsonGzDataHandler
+    else:
+        raise ValueError(f"No datahandler for datatype {datatype} available.")
+
+
+def get_datahandler(datadir: Path, data_format: str = None,
+                    data_handler: IDataHandler = None) -> IDataHandler:
+    """
+    :param datadir: Folder to save data
+    :data_format: dataformat to use
+    :data_handler: returns this datahandler if it exists or initializes a new one
+    """
+
+    if not data_handler:
+        HandlerClass = get_datahandlerclass(data_format or 'json')
+        data_handler = HandlerClass(datadir)
+    return data_handler

freqtrade/data/history/jsondatahandler.py

@@ -0,0 +1,204 @@
+import logging
+import re
+from pathlib import Path
+from typing import List, Optional
+
+import numpy as np
+from pandas import DataFrame, read_json, to_datetime
+
+from freqtrade import misc
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import (DEFAULT_DATAFRAME_COLUMNS,
+                                 ListPairsWithTimeframes)
+from freqtrade.data.converter import trades_dict_to_list
+
+from .idatahandler import IDataHandler, TradeList
+
+logger = logging.getLogger(__name__)
+
+
+class JsonDataHandler(IDataHandler):
+
+    _use_zip = False
+    _columns = DEFAULT_DATAFRAME_COLUMNS
+
+    @classmethod
+    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
+        :return: List of Pairs
+        """
+
+        _tmp = [re.search(r'^(\S+)(?=\-' + timeframe + '.json)', p.name)
+                for p in datadir.glob(f"*{timeframe}.{cls._get_file_extension()}")]
+        # Check if regex found something and only return these results
+        return [match[0].replace('_', '/') for match in _tmp if match]
+
+    def ohlcv_store(self, pair: str, timeframe: str, data: DataFrame) -> None:
+        """
+        Store data in json format "values".
+            format looks as follows:
+            [[<date>,<open>,<high>,<low>,<close>]]
+        :param pair: Pair - used to generate filename
+        :timeframe: Timeframe - used to generate filename
+        :data: Dataframe containing OHLCV data
+        :return: None
+        """
+        filename = self._pair_data_filename(self._datadir, pair, timeframe)
+        _data = data.copy()
+        # Convert date to int
+        _data['date'] = _data['date'].astype(np.int64) // 1000 // 1000
+
+        # Reset index, select only appropriate columns and save as json
+        _data.reset_index(drop=True).loc[:, self._columns].to_json(
+            filename, orient="values",
+            compression='gzip' if self._use_zip else None)
+
+    def _ohlcv_load(self, pair: str, timeframe: str,
+                    timerange: Optional[TimeRange] = None,
+                    ) -> DataFrame:
+        """
+        Internal method used to load data for one pair from disk.
+        Implements the loading and conversion to a Pandas dataframe.
+        Timerange trimming and dataframe validation happens outside of this method.
+        :param pair: Pair to load data
+        :param timeframe: Timeframe (e.g. "5m")
+        :param timerange: Limit data to be loaded to this timerange.
+                        Optionally implemented by subclasses to avoid loading
+                        all data where possible.
+        :return: DataFrame with ohlcv data, or empty DataFrame
+        """
+        filename = self._pair_data_filename(self._datadir, pair, timeframe)
+        if not filename.exists():
+            return DataFrame(columns=self._columns)
+        pairdata = read_json(filename, orient='values')
+        pairdata.columns = self._columns
+        pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
+                                          'low': 'float', 'close': 'float', 'volume': 'float'})
+        pairdata['date'] = to_datetime(pairdata['date'],
+                                       unit='ms',
+                                       utc=True,
+                                       infer_datetime_format=True)
+        return pairdata
+
+    def ohlcv_purge(self, pair: str, timeframe: str) -> bool:
+        """
+        Remove data for this pair
+        :param pair: Delete data for this pair.
+        :param timeframe: Timeframe (e.g. "5m")
+        :return: True when deleted, false if file did not exist.
+        """
+        filename = self._pair_data_filename(self._datadir, pair, timeframe)
+        if filename.exists():
+            filename.unlink()
+            return True
+        return False
+
+    def ohlcv_append(self, pair: str, timeframe: str, data: DataFrame) -> None:
+        """
+        Append data to existing data structures
+        :param pair: Pair
+        :param timeframe: Timeframe this ohlcv data is for
+        :param data: Data to append.
+        """
+        raise NotImplementedError()
+
+    @classmethod
+    def trades_get_pairs(cls, datadir: Path) -> List[str]:
+        """
+        Returns a list of all pairs for which trade data is available in this
+        :param datadir: Directory to search for ohlcv files
+        :return: List of Pairs
+        """
+        _tmp = [re.search(r'^(\S+)(?=\-trades.json)', p.name)
+                for p in datadir.glob(f"*trades.{cls._get_file_extension()}")]
+        # Check if regex found something and only return these results to avoid exceptions.
+        return [match[0].replace('_', '/') for match in _tmp if match]
+
+    def trades_store(self, pair: str, data: TradeList) -> None:
+        """
+        Store trades data (list of Dicts) to file
+        :param pair: Pair - used for filename
+        :param data: List of Lists containing trade data,
+                     column sequence as in DEFAULT_TRADES_COLUMNS
+        """
+        filename = self._pair_trades_filename(self._datadir, pair)
+        misc.file_dump_json(filename, data, is_zip=self._use_zip)
+
+    def trades_append(self, pair: str, data: TradeList):
+        """
+        Append data to existing files
+        :param pair: Pair - used for filename
+        :param data: List of Lists containing trade data,
+                     column sequence as in DEFAULT_TRADES_COLUMNS
+        """
+        raise NotImplementedError()
+
+    def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
+        """
+        Load a pair from file, either .json.gz or .json
+        # TODO: respect timerange ...
+        :param pair: Load trades for this pair
+        :param timerange: Timerange to load trades for - currently not implemented
+        :return: List of trades
+        """
+        filename = self._pair_trades_filename(self._datadir, pair)
+        tradesdata = misc.file_load_json(filename)
+
+        if not tradesdata:
+            return []
+
+        if isinstance(tradesdata[0], dict):
+            # Convert trades dict to list
+            logger.info("Old trades format detected - converting")
+            tradesdata = trades_dict_to_list(tradesdata)
+            pass
+        return tradesdata
+
+    def trades_purge(self, pair: str) -> bool:
+        """
+        Remove data for this pair
+        :param pair: Delete data for this pair.
+        :return: True when deleted, false if file did not exist.
+        """
+        filename = self._pair_trades_filename(self._datadir, pair)
+        if filename.exists():
+            filename.unlink()
+            return True
+        return False
+
+    @classmethod
+    def _pair_data_filename(cls, datadir: Path, pair: str, timeframe: str) -> Path:
+        pair_s = misc.pair_to_filename(pair)
+        filename = datadir.joinpath(f'{pair_s}-{timeframe}.{cls._get_file_extension()}')
+        return filename
+
+    @classmethod
+    def _get_file_extension(cls):
+        return "json.gz" if cls._use_zip else "json"
+
+    @classmethod
+    def _pair_trades_filename(cls, datadir: Path, pair: str) -> Path:
+        pair_s = misc.pair_to_filename(pair)
+        filename = datadir.joinpath(f'{pair_s}-trades.{cls._get_file_extension()}')
+        return filename
+
+
+class JsonGzDataHandler(JsonDataHandler):
+
+    _use_zip = True

freqtrade/edge/__init__.py

@@ -1,456 +1 @@
-# pragma pylint: disable=W0603
-""" Edge positioning package """
-import logging
-from pathlib import Path
-from typing import Any, Dict, NamedTuple
-
-import arrow
-import numpy as np
-import utils_find_1st as utf1st
-from pandas import DataFrame
-
-from freqtrade import constants, OperationalException
-from freqtrade.configuration import TimeRange
-from freqtrade.data import history
-from freqtrade.strategy.interface import SellType
-
-
-logger = logging.getLogger(__name__)
-
-
-class PairInfo(NamedTuple):
-    stoploss: float
-    winrate: float
-    risk_reward_ratio: float
-    required_risk_reward: float
-    expectancy: float
-    nb_trades: int
-    avg_trade_duration: float
-
-
-class Edge:
-    """
-    Calculates Win Rate, Risk Reward Ratio, Expectancy
-    against historical data for a give set of markets and a strategy
-    it then adjusts stoploss and position size accordingly
-    and force it into the strategy
-    Author: https://github.com/mishaker
-    """
-
-    config: Dict = {}
-    _cached_pairs: Dict[str, Any] = {}  # Keeps a list of pairs
-
-    def __init__(self, config: Dict[str, Any], exchange, strategy) -> None:
-
-        self.config = config
-        self.exchange = exchange
-        self.strategy = strategy
-
-        self.edge_config = self.config.get('edge', {})
-        self._cached_pairs: Dict[str, Any] = {}  # Keeps a list of pairs
-        self._final_pairs: list = []
-
-        # checking max_open_trades. it should be -1 as with Edge
-        # the number of trades is determined by position size
-        if self.config['max_open_trades'] != float('inf'):
-            logger.critical('max_open_trades should be -1 in config !')
-
-        if self.config['stake_amount'] != constants.UNLIMITED_STAKE_AMOUNT:
-            raise OperationalException('Edge works only with unlimited stake amount')
-
-        self._capital_percentage: float = self.edge_config.get('capital_available_percentage')
-        self._allowed_risk: float = self.edge_config.get('allowed_risk')
-        self._since_number_of_days: int = self.edge_config.get('calculate_since_number_of_days', 14)
-        self._last_updated: int = 0  # Timestamp of pairs last updated time
-        self._refresh_pairs = True
-
-        self._stoploss_range_min = float(self.edge_config.get('stoploss_range_min', -0.01))
-        self._stoploss_range_max = float(self.edge_config.get('stoploss_range_max', -0.05))
-        self._stoploss_range_step = float(self.edge_config.get('stoploss_range_step', -0.001))
-
-        # calculating stoploss range
-        self._stoploss_range = np.arange(
-            self._stoploss_range_min,
-            self._stoploss_range_max,
-            self._stoploss_range_step
-        )
-
-        self._timerange: TimeRange = TimeRange.parse_timerange("%s-" % arrow.now().shift(
-            days=-1 * self._since_number_of_days).format('YYYYMMDD'))
-        if config.get('fee'):
-            self.fee = config['fee']
-        else:
-            self.fee = self.exchange.get_fee()
-
-    def calculate(self) -> bool:
-        pairs = self.config['exchange']['pair_whitelist']
-        heartbeat = self.edge_config.get('process_throttle_secs')
-
-        if (self._last_updated > 0) and (
-                self._last_updated + heartbeat > arrow.utcnow().timestamp):
-            return False
-
-        data: Dict[str, Any] = {}
-        logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
-        logger.info('Using local backtesting data (using whitelist in given config) ...')
-
-        data = history.load_data(
-            datadir=Path(self.config['datadir']),
-            pairs=pairs,
-            timeframe=self.strategy.ticker_interval,
-            refresh_pairs=self._refresh_pairs,
-            exchange=self.exchange,
-            timerange=self._timerange,
-            startup_candles=self.strategy.startup_candle_count,
-        )
-
-        if not data:
-            # Reinitializing cached pairs
-            self._cached_pairs = {}
-            logger.critical("No data found. Edge is stopped ...")
-            return False
-
-        preprocessed = self.strategy.tickerdata_to_dataframe(data)
-
-        # Print timeframe
-        min_date, max_date = history.get_timeframe(preprocessed)
-        logger.info(
-            'Measuring data from %s up to %s (%s days) ...',
-            min_date.isoformat(),
-            max_date.isoformat(),
-            (max_date - min_date).days
-        )
-        headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low']
-
-        trades: list = []
-        for pair, pair_data in preprocessed.items():
-            # Sorting dataframe by date and reset index
-            pair_data = pair_data.sort_values(by=['date'])
-            pair_data = pair_data.reset_index(drop=True)
-
-            ticker_data = self.strategy.advise_sell(
-                self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
-
-            trades += self._find_trades_for_stoploss_range(ticker_data, pair, self._stoploss_range)
-
-        # If no trade found then exit
-        if len(trades) == 0:
-            logger.info("No trades found.")
-            return False
-
-        # Fill missing, calculable columns, profit, duration , abs etc.
-        trades_df = self._fill_calculable_fields(DataFrame(trades))
-        self._cached_pairs = self._process_expectancy(trades_df)
-        self._last_updated = arrow.utcnow().timestamp
-
-        return True
-
-    def stake_amount(self, pair: str, free_capital: float,
-                     total_capital: float, capital_in_trade: float) -> float:
-        stoploss = self.stoploss(pair)
-        available_capital = (total_capital + capital_in_trade) * self._capital_percentage
-        allowed_capital_at_risk = available_capital * self._allowed_risk
-        max_position_size = abs(allowed_capital_at_risk / stoploss)
-        position_size = min(max_position_size, free_capital)
-        if pair in self._cached_pairs:
-            logger.info(
-                'winrate: %s, expectancy: %s, position size: %s, pair: %s,'
-                ' capital in trade: %s, free capital: %s, total capital: %s,'
-                ' stoploss: %s, available capital: %s.',
-                self._cached_pairs[pair].winrate,
-                self._cached_pairs[pair].expectancy,
-                position_size, pair,
-                capital_in_trade, free_capital, total_capital,
-                stoploss, available_capital
-            )
-        return round(position_size, 15)
-
-    def stoploss(self, pair: str) -> float:
-        if pair in self._cached_pairs:
-            return self._cached_pairs[pair].stoploss
-        else:
-            logger.warning('tried to access stoploss of a non-existing pair, '
-                           'strategy stoploss is returned instead.')
-            return self.strategy.stoploss
-
-    def adjust(self, pairs) -> list:
-        """
-        Filters out and sorts "pairs" according to Edge calculated pairs
-        """
-        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:
-                final.append(pair)
-
-        if self._final_pairs != final:
-            self._final_pairs = final
-            if self._final_pairs:
-                logger.info(
-                    'Minimum expectancy and minimum winrate are met only for %s,'
-                    ' so other pairs are filtered out.',
-                    self._final_pairs
-                    )
-            else:
-                logger.info(
-                    'Edge removed all pairs as no pair with minimum expectancy '
-                    'and minimum winrate was found !'
-                    )
-
-        return self._final_pairs
-
-    def accepted_pairs(self) -> list:
-        """
-        return a list of accepted pairs along with their winrate, expectancy and stoploss
-        """
-        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)):
-                final.append({
-                    'Pair': pair,
-                    'Winrate': info.winrate,
-                    'Expectancy': info.expectancy,
-                    'Stoploss': info.stoploss,
-                })
-        return final
-
-    def _fill_calculable_fields(self, result: DataFrame) -> DataFrame:
-        """
-        The result frame contains a number of columns that are calculable
-        from other columns. These are left blank till all rows are added,
-        to be populated in single vector calls.
-
-        Columns to be populated are:
-        - Profit
-        - trade duration
-        - profit abs
-        :param result Dataframe
-        :return: result Dataframe
-        """
-
-        # stake and fees
-        # stake = 0.015
-        # 0.05% is 0.0005
-        # fee = 0.001
-
-        # we set stake amount to an arbitrary amount.
-        # as it doesn't change the calculation.
-        # all returned values are relative. they are percentages.
-        stake = 0.015
-        fee = self.fee
-        open_fee = fee / 2
-        close_fee = fee / 2
-
-        result['trade_duration'] = result['close_time'] - result['open_time']
-
-        result['trade_duration'] = result['trade_duration'].map(
-            lambda x: int(x.total_seconds() / 60))
-
-        # Spends, Takes, Profit, Absolute Profit
-
-        # Buy Price
-        result['buy_vol'] = stake / result['open_rate']  # How many target are we buying
-        result['buy_fee'] = stake * open_fee
-        result['buy_spend'] = stake + result['buy_fee']  # How much we're spending
-
-        # Sell price
-        result['sell_sum'] = result['buy_vol'] * result['close_rate']
-        result['sell_fee'] = result['sell_sum'] * close_fee
-        result['sell_take'] = result['sell_sum'] - result['sell_fee']
-
-        # profit_percent
-        result['profit_percent'] = (result['sell_take'] - result['buy_spend']) / result['buy_spend']
-
-        # Absolute profit
-        result['profit_abs'] = result['sell_take'] - result['buy_spend']
-
-        return result
-
-    def _process_expectancy(self, results: DataFrame) -> Dict[str, Any]:
-        """
-        This calculates WinRate, Required Risk Reward, Risk Reward and Expectancy of all pairs
-        The calulation will be done per pair and per strategy.
-        """
-        # Removing pairs having less than min_trades_number
-        min_trades_number = self.edge_config.get('min_trade_number', 10)
-        results = results.groupby(['pair', 'stoploss']).filter(lambda x: len(x) > min_trades_number)
-        ###################################
-
-        # Removing outliers (Only Pumps) from the dataset
-        # The method to detect outliers is to calculate standard deviation
-        # Then every value more than (standard deviation + 2*average) is out (pump)
-        #
-        # Removing Pumps
-        if self.edge_config.get('remove_pumps', False):
-            results = results.groupby(['pair', 'stoploss']).apply(
-                lambda x: x[x['profit_abs'] < 2 * x['profit_abs'].std() + x['profit_abs'].mean()])
-        ##########################################################################
-
-        # Removing trades having a duration more than X minutes (set in config)
-        max_trade_duration = self.edge_config.get('max_trade_duration_minute', 1440)
-        results = results[results.trade_duration < max_trade_duration]
-        #######################################################################
-
-        if results.empty:
-            return {}
-
-        groupby_aggregator = {
-            'profit_abs': [
-                ('nb_trades', 'count'),  # number of all trades
-                ('profit_sum', lambda x: x[x > 0].sum()),  # cumulative profit of all winning trades
-                ('loss_sum', lambda x: abs(x[x < 0].sum())),  # cumulative loss of all losing trades
-                ('nb_win_trades', lambda x: x[x > 0].count())  # number of winning trades
-            ],
-            'trade_duration': [('avg_trade_duration', 'mean')]
-        }
-
-        # Group by (pair and stoploss) by applying above aggregator
-        df = results.groupby(['pair', 'stoploss'])['profit_abs', 'trade_duration'].agg(
-            groupby_aggregator).reset_index(col_level=1)
-
-        # Dropping level 0 as we don't need it
-        df.columns = df.columns.droplevel(0)
-
-        # Calculating number of losing trades, average win and average loss
-        df['nb_loss_trades'] = df['nb_trades'] - df['nb_win_trades']
-        df['average_win'] = df['profit_sum'] / df['nb_win_trades']
-        df['average_loss'] = df['loss_sum'] / df['nb_loss_trades']
-
-        # Win rate = number of profitable trades / number of trades
-        df['winrate'] = df['nb_win_trades'] / df['nb_trades']
-
-        # risk_reward_ratio = average win / average loss
-        df['risk_reward_ratio'] = df['average_win'] / df['average_loss']
-
-        # required_risk_reward = (1 / winrate) - 1
-        df['required_risk_reward'] = (1 / df['winrate']) - 1
-
-        # expectancy = (risk_reward_ratio * winrate) - (lossrate)
-        df['expectancy'] = (df['risk_reward_ratio'] * df['winrate']) - (1 - df['winrate'])
-
-        # sort by expectancy and stoploss
-        df = df.sort_values(by=['expectancy', 'stoploss'], ascending=False).groupby(
-            'pair').first().sort_values(by=['expectancy'], ascending=False).reset_index()
-
-        final = {}
-        for x in df.itertuples():
-            final[x.pair] = PairInfo(
-                x.stoploss,
-                x.winrate,
-                x.risk_reward_ratio,
-                x.required_risk_reward,
-                x.expectancy,
-                x.nb_trades,
-                x.avg_trade_duration
-            )
-
-        # Returning a list of pairs in order of "expectancy"
-        return final
-
-    def _find_trades_for_stoploss_range(self, ticker_data, pair, stoploss_range):
-        buy_column = ticker_data['buy'].values
-        sell_column = ticker_data['sell'].values
-        date_column = ticker_data['date'].values
-        ohlc_columns = ticker_data[['open', 'high', 'low', 'close']].values
-
-        result: list = []
-        for stoploss in stoploss_range:
-            result += self._detect_next_stop_or_sell_point(
-                buy_column, sell_column, date_column, ohlc_columns, round(stoploss, 6), pair
-            )
-
-        return result
-
-    def _detect_next_stop_or_sell_point(self, buy_column, sell_column, date_column,
-                                        ohlc_columns, stoploss, pair):
-        """
-        Iterate through ohlc_columns in order to find the next trade
-        Next trade opens from the first buy signal noticed to
-        The sell or stoploss signal after it.
-        It then cuts OHLC, buy_column, sell_column and date_column.
-        Cut from (the exit trade index) + 1.
-
-        Author: https://github.com/mishaker
-        """
-
-        result: list = []
-        start_point = 0
-
-        while True:
-            open_trade_index = utf1st.find_1st(buy_column, 1, utf1st.cmp_equal)
-
-            # Return empty if we don't find trade entry (i.e. buy==1) or
-            # we find a buy but at the end of array
-            if open_trade_index == -1 or open_trade_index == len(buy_column) - 1:
-                break
-            else:
-                # When a buy signal is seen,
-                # trade opens in reality on the next candle
-                open_trade_index += 1
-
-            stop_price_percentage = stoploss + 1
-            open_price = ohlc_columns[open_trade_index, 0]
-            stop_price = (open_price * stop_price_percentage)
-
-            # Searching for the index where stoploss is hit
-            stop_index = utf1st.find_1st(
-                ohlc_columns[open_trade_index:, 2], stop_price, utf1st.cmp_smaller)
-
-            # If we don't find it then we assume stop_index will be far in future (infinite number)
-            if stop_index == -1:
-                stop_index = float('inf')
-
-            # Searching for the index where sell is hit
-            sell_index = utf1st.find_1st(sell_column[open_trade_index:], 1, utf1st.cmp_equal)
-
-            # If we don't find it then we assume sell_index will be far in future (infinite number)
-            if sell_index == -1:
-                sell_index = float('inf')
-
-            # Check if we don't find any stop or sell point (in that case trade remains open)
-            # It is not interesting for Edge to consider it so we simply ignore the trade
-            # And stop iterating there is no more entry
-            if stop_index == sell_index == float('inf'):
-                break
-
-            if stop_index <= sell_index:
-                exit_index = open_trade_index + stop_index
-                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
-                exit_index = open_trade_index + sell_index + 1
-
-                # Check if we have the next candle
-                if len(ohlc_columns) - 1 < exit_index:
-                    break
-
-                exit_type = SellType.SELL_SIGNAL
-                exit_price = ohlc_columns[exit_index, 0]
-
-            trade = {'pair': pair,
-                     'stoploss': stoploss,
-                     'profit_percent': '',
-                     'profit_abs': '',
-                     'open_time': date_column[open_trade_index],
-                     'close_time': date_column[exit_index],
-                     'open_index': start_point + open_trade_index,
-                     'close_index': start_point + exit_index,
-                     'trade_duration': '',
-                     'open_rate': round(open_price, 15),
-                     'close_rate': round(exit_price, 15),
-                     'exit_type': exit_type
-                     }
-
-            result.append(trade)
-
-            # Giving a view of exit_index till the end of array
-            buy_column = buy_column[exit_index:]
-            sell_column = sell_column[exit_index:]
-            date_column = date_column[exit_index:]
-            ohlc_columns = ohlc_columns[exit_index:]
-            start_point += exit_index
-
-        return result
+from .edge_positioning import Edge, PairInfo  # noqa: F401

freqtrade/edge/edge_positioning.py

@@ -0,0 +1,447 @@
+# pragma pylint: disable=W0603
+""" Edge positioning package """
+import logging
+from typing import Any, Dict, List, NamedTuple
+
+import arrow
+import numpy as np
+import utils_find_1st as utf1st
+from pandas import DataFrame
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import UNLIMITED_STAKE_AMOUNT, DATETIME_PRINT_FORMAT
+from freqtrade.exceptions import OperationalException
+from freqtrade.data.history import get_timerange, load_data, refresh_data
+from freqtrade.strategy.interface import SellType
+
+logger = logging.getLogger(__name__)
+
+
+class PairInfo(NamedTuple):
+    stoploss: float
+    winrate: float
+    risk_reward_ratio: float
+    required_risk_reward: float
+    expectancy: float
+    nb_trades: int
+    avg_trade_duration: float
+
+
+class Edge:
+    """
+    Calculates Win Rate, Risk Reward Ratio, Expectancy
+    against historical data for a give set of markets and a strategy
+    it then adjusts stoploss and position size accordingly
+    and force it into the strategy
+    Author: https://github.com/mishaker
+    """
+
+    config: Dict = {}
+    _cached_pairs: Dict[str, Any] = {}  # Keeps a list of pairs
+
+    def __init__(self, config: Dict[str, Any], exchange, strategy) -> None:
+
+        self.config = config
+        self.exchange = exchange
+        self.strategy = strategy
+
+        self.edge_config = self.config.get('edge', {})
+        self._cached_pairs: Dict[str, Any] = {}  # Keeps a list of pairs
+        self._final_pairs: list = []
+
+        # checking max_open_trades. it should be -1 as with Edge
+        # the number of trades is determined by position size
+        if self.config['max_open_trades'] != float('inf'):
+            logger.critical('max_open_trades should be -1 in config !')
+
+        if self.config['stake_amount'] != UNLIMITED_STAKE_AMOUNT:
+            raise OperationalException('Edge works only with unlimited stake amount')
+
+        self._capital_ratio: float = self.config['tradable_balance_ratio']
+        self._allowed_risk: float = self.edge_config.get('allowed_risk')
+        self._since_number_of_days: int = self.edge_config.get('calculate_since_number_of_days', 14)
+        self._last_updated: int = 0  # Timestamp of pairs last updated time
+        self._refresh_pairs = True
+
+        self._stoploss_range_min = float(self.edge_config.get('stoploss_range_min', -0.01))
+        self._stoploss_range_max = float(self.edge_config.get('stoploss_range_max', -0.05))
+        self._stoploss_range_step = float(self.edge_config.get('stoploss_range_step', -0.001))
+
+        # calculating stoploss range
+        self._stoploss_range = np.arange(
+            self._stoploss_range_min,
+            self._stoploss_range_max,
+            self._stoploss_range_step
+        )
+
+        self._timerange: TimeRange = TimeRange.parse_timerange("%s-" % arrow.now().shift(
+            days=-1 * self._since_number_of_days).format('YYYYMMDD'))
+        if config.get('fee'):
+            self.fee = config['fee']
+        else:
+            self.fee = self.exchange.get_fee(symbol=self.config['exchange']['pair_whitelist'][0])
+
+    def calculate(self) -> bool:
+        pairs = self.config['exchange']['pair_whitelist']
+        heartbeat = self.edge_config.get('process_throttle_secs')
+
+        if (self._last_updated > 0) and (
+                self._last_updated + heartbeat > arrow.utcnow().timestamp):
+            return False
+
+        data: Dict[str, Any] = {}
+        logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
+        logger.info('Using local backtesting data (using whitelist in given config) ...')
+
+        if self._refresh_pairs:
+            refresh_data(
+                datadir=self.config['datadir'],
+                pairs=pairs,
+                exchange=self.exchange,
+                timeframe=self.strategy.timeframe,
+                timerange=self._timerange,
+            )
+
+        data = load_data(
+            datadir=self.config['datadir'],
+            pairs=pairs,
+            timeframe=self.strategy.timeframe,
+            timerange=self._timerange,
+            startup_candles=self.strategy.startup_candle_count,
+            data_format=self.config.get('dataformat_ohlcv', 'json'),
+        )
+
+        if not data:
+            # Reinitializing cached pairs
+            self._cached_pairs = {}
+            logger.critical("No data found. Edge is stopped ...")
+            return False
+
+        preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
+
+        # Print timeframe
+        min_date, max_date = get_timerange(preprocessed)
+        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)..')
+        headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low']
+
+        trades: list = []
+        for pair, pair_data in preprocessed.items():
+            # Sorting dataframe by date and reset index
+            pair_data = pair_data.sort_values(by=['date'])
+            pair_data = pair_data.reset_index(drop=True)
+
+            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)
+
+        # If no trade found then exit
+        if len(trades) == 0:
+            logger.info("No trades found.")
+            return False
+
+        # Fill missing, calculable columns, profit, duration , abs etc.
+        trades_df = self._fill_calculable_fields(DataFrame(trades))
+        self._cached_pairs = self._process_expectancy(trades_df)
+        self._last_updated = arrow.utcnow().timestamp
+
+        return True
+
+    def stake_amount(self, pair: str, free_capital: float,
+                     total_capital: float, capital_in_trade: float) -> float:
+        stoploss = self.stoploss(pair)
+        available_capital = (total_capital + capital_in_trade) * self._capital_ratio
+        allowed_capital_at_risk = available_capital * self._allowed_risk
+        max_position_size = abs(allowed_capital_at_risk / stoploss)
+        position_size = min(max_position_size, free_capital)
+        if pair in self._cached_pairs:
+            logger.info(
+                'winrate: %s, expectancy: %s, position size: %s, pair: %s,'
+                ' capital in trade: %s, free capital: %s, total capital: %s,'
+                ' stoploss: %s, available capital: %s.',
+                self._cached_pairs[pair].winrate,
+                self._cached_pairs[pair].expectancy,
+                position_size, pair,
+                capital_in_trade, free_capital, total_capital,
+                stoploss, available_capital
+            )
+        return round(position_size, 15)
+
+    def stoploss(self, pair: str) -> float:
+        if pair in self._cached_pairs:
+            return self._cached_pairs[pair].stoploss
+        else:
+            logger.warning('tried to access stoploss of a non-existing pair, '
+                           'strategy stoploss is returned instead.')
+            return self.strategy.stoploss
+
+    def adjust(self, pairs: List[str]) -> list:
+        """
+        Filters out and sorts "pairs" according to Edge calculated pairs
+        """
+        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:
+                final.append(pair)
+
+        if self._final_pairs != final:
+            self._final_pairs = final
+            if self._final_pairs:
+                logger.info(
+                    'Minimum expectancy and minimum winrate are met only for %s,'
+                    ' so other pairs are filtered out.',
+                    self._final_pairs
+                    )
+            else:
+                logger.info(
+                    'Edge removed all pairs as no pair with minimum expectancy '
+                    'and minimum winrate was found !'
+                    )
+
+        return self._final_pairs
+
+    def accepted_pairs(self) -> list:
+        """
+        return a list of accepted pairs along with their winrate, expectancy and stoploss
+        """
+        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)):
+                final.append({
+                    'Pair': pair,
+                    'Winrate': info.winrate,
+                    'Expectancy': info.expectancy,
+                    'Stoploss': info.stoploss,
+                })
+        return final
+
+    def _fill_calculable_fields(self, result: DataFrame) -> DataFrame:
+        """
+        The result frame contains a number of columns that are calculable
+        from other columns. These are left blank till all rows are added,
+        to be populated in single vector calls.
+
+        Columns to be populated are:
+        - Profit
+        - trade duration
+        - profit abs
+        :param result Dataframe
+        :return: result Dataframe
+        """
+        # We set stake amount to an arbitrary amount, as it doesn't change the calculation.
+        # All returned values are relative, they are defined as ratios.
+        stake = 0.015
+
+        result['trade_duration'] = result['close_date'] - result['open_date']
+
+        result['trade_duration'] = result['trade_duration'].map(
+            lambda x: int(x.total_seconds() / 60))
+
+        # Spends, Takes, Profit, Absolute Profit
+
+        # Buy Price
+        result['buy_vol'] = stake / result['open_rate']  # How many target are we buying
+        result['buy_fee'] = stake * self.fee
+        result['buy_spend'] = stake + result['buy_fee']  # How much we're spending
+
+        # Sell price
+        result['sell_sum'] = result['buy_vol'] * result['close_rate']
+        result['sell_fee'] = result['sell_sum'] * self.fee
+        result['sell_take'] = result['sell_sum'] - result['sell_fee']
+
+        # profit_ratio
+        result['profit_ratio'] = (result['sell_take'] - result['buy_spend']) / result['buy_spend']
+
+        # Absolute profit
+        result['profit_abs'] = result['sell_take'] - result['buy_spend']
+
+        return result
+
+    def _process_expectancy(self, results: DataFrame) -> Dict[str, Any]:
+        """
+        This calculates WinRate, Required Risk Reward, Risk Reward and Expectancy of all pairs
+        The calulation will be done per pair and per strategy.
+        """
+        # Removing pairs having less than min_trades_number
+        min_trades_number = self.edge_config.get('min_trade_number', 10)
+        results = results.groupby(['pair', 'stoploss']).filter(lambda x: len(x) > min_trades_number)
+        ###################################
+
+        # Removing outliers (Only Pumps) from the dataset
+        # The method to detect outliers is to calculate standard deviation
+        # Then every value more than (standard deviation + 2*average) is out (pump)
+        #
+        # Removing Pumps
+        if self.edge_config.get('remove_pumps', False):
+            results = results[results['profit_abs'] < 2 * results['profit_abs'].std()
+                              + results['profit_abs'].mean()]
+        ##########################################################################
+
+        # Removing trades having a duration more than X minutes (set in config)
+        max_trade_duration = self.edge_config.get('max_trade_duration_minute', 1440)
+        results = results[results.trade_duration < max_trade_duration]
+        #######################################################################
+
+        if results.empty:
+            return {}
+
+        groupby_aggregator = {
+            'profit_abs': [
+                ('nb_trades', 'count'),  # number of all trades
+                ('profit_sum', lambda x: x[x > 0].sum()),  # cumulative profit of all winning trades
+                ('loss_sum', lambda x: abs(x[x < 0].sum())),  # cumulative loss of all losing trades
+                ('nb_win_trades', lambda x: x[x > 0].count())  # number of winning trades
+            ],
+            'trade_duration': [('avg_trade_duration', 'mean')]
+        }
+
+        # Group by (pair and stoploss) by applying above aggregator
+        df = results.groupby(['pair', 'stoploss'])[['profit_abs', 'trade_duration']].agg(
+            groupby_aggregator).reset_index(col_level=1)
+
+        # Dropping level 0 as we don't need it
+        df.columns = df.columns.droplevel(0)
+
+        # Calculating number of losing trades, average win and average loss
+        df['nb_loss_trades'] = df['nb_trades'] - df['nb_win_trades']
+        df['average_win'] = df['profit_sum'] / df['nb_win_trades']
+        df['average_loss'] = df['loss_sum'] / df['nb_loss_trades']
+
+        # Win rate = number of profitable trades / number of trades
+        df['winrate'] = df['nb_win_trades'] / df['nb_trades']
+
+        # risk_reward_ratio = average win / average loss
+        df['risk_reward_ratio'] = df['average_win'] / df['average_loss']
+
+        # required_risk_reward = (1 / winrate) - 1
+        df['required_risk_reward'] = (1 / df['winrate']) - 1
+
+        # expectancy = (risk_reward_ratio * winrate) - (lossrate)
+        df['expectancy'] = (df['risk_reward_ratio'] * df['winrate']) - (1 - df['winrate'])
+
+        # sort by expectancy and stoploss
+        df = df.sort_values(by=['expectancy', 'stoploss'], ascending=False).groupby(
+            'pair').first().sort_values(by=['expectancy'], ascending=False).reset_index()
+
+        final = {}
+        for x in df.itertuples():
+            final[x.pair] = PairInfo(
+                x.stoploss,
+                x.winrate,
+                x.risk_reward_ratio,
+                x.required_risk_reward,
+                x.expectancy,
+                x.nb_trades,
+                x.avg_trade_duration
+            )
+
+        # Returning a list of pairs in order of "expectancy"
+        return final
+
+    def _find_trades_for_stoploss_range(self, df, pair, stoploss_range):
+        buy_column = df['buy'].values
+        sell_column = df['sell'].values
+        date_column = df['date'].values
+        ohlc_columns = df[['open', 'high', 'low', 'close']].values
+
+        result: list = []
+        for stoploss in stoploss_range:
+            result += self._detect_next_stop_or_sell_point(
+                buy_column, sell_column, date_column, ohlc_columns, round(stoploss, 6), pair
+            )
+
+        return result
+
+    def _detect_next_stop_or_sell_point(self, buy_column, sell_column, date_column,
+                                        ohlc_columns, stoploss, pair):
+        """
+        Iterate through ohlc_columns in order to find the next trade
+        Next trade opens from the first buy signal noticed to
+        The sell or stoploss signal after it.
+        It then cuts OHLC, buy_column, sell_column and date_column.
+        Cut from (the exit trade index) + 1.
+
+        Author: https://github.com/mishaker
+        """
+
+        result: list = []
+        start_point = 0
+
+        while True:
+            open_trade_index = utf1st.find_1st(buy_column, 1, utf1st.cmp_equal)
+
+            # Return empty if we don't find trade entry (i.e. buy==1) or
+            # we find a buy but at the end of array
+            if open_trade_index == -1 or open_trade_index == len(buy_column) - 1:
+                break
+            else:
+                # When a buy signal is seen,
+                # trade opens in reality on the next candle
+                open_trade_index += 1
+
+            open_price = ohlc_columns[open_trade_index, 0]
+            stop_price = (open_price * (stoploss + 1))
+
+            # Searching for the index where stoploss is hit
+            stop_index = utf1st.find_1st(
+                ohlc_columns[open_trade_index:, 2], stop_price, utf1st.cmp_smaller)
+
+            # If we don't find it then we assume stop_index will be far in future (infinite number)
+            if stop_index == -1:
+                stop_index = float('inf')
+
+            # Searching for the index where sell is hit
+            sell_index = utf1st.find_1st(sell_column[open_trade_index:], 1, utf1st.cmp_equal)
+
+            # If we don't find it then we assume sell_index will be far in future (infinite number)
+            if sell_index == -1:
+                sell_index = float('inf')
+
+            # Check if we don't find any stop or sell point (in that case trade remains open)
+            # It is not interesting for Edge to consider it so we simply ignore the trade
+            # And stop iterating there is no more entry
+            if stop_index == sell_index == float('inf'):
+                break
+
+            if stop_index <= sell_index:
+                exit_index = open_trade_index + stop_index
+                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
+                exit_index = open_trade_index + sell_index + 1
+
+                # Check if we have the next candle
+                if len(ohlc_columns) - 1 < exit_index:
+                    break
+
+                exit_type = SellType.SELL_SIGNAL
+                exit_price = ohlc_columns[exit_index, 0]
+
+            trade = {'pair': pair,
+                     'stoploss': stoploss,
+                     'profit_ratio': '',
+                     'profit_abs': '',
+                     'open_date': date_column[open_trade_index],
+                     'close_date': date_column[exit_index],
+                     'trade_duration': '',
+                     'open_rate': round(open_price, 15),
+                     'close_rate': round(exit_price, 15),
+                     'exit_type': exit_type
+                     }
+
+            result.append(trade)
+
+            # Giving a view of exit_index till the end of array
+            buy_column = buy_column[exit_index:]
+            sell_column = sell_column[exit_index:]
+            date_column = date_column[exit_index:]
+            ohlc_columns = ohlc_columns[exit_index:]
+            start_point += exit_index
+
+        return result

freqtrade/exceptions.py

@@ -0,0 +1,73 @@
+
+
+class FreqtradeException(Exception):
+    """
+    Freqtrade base exception. Handled at the outermost level.
+    All other exception types are subclasses of this exception type.
+    """
+
+
+class OperationalException(FreqtradeException):
+    """
+    Requires manual intervention and will stop the bot.
+    Most of the time, this is caused by an invalid Configuration.
+    """
+
+
+class DependencyException(FreqtradeException):
+    """
+    Indicates that an assumed dependency is not met.
+    This could happen when there is currently not enough money on the account.
+    """
+
+
+class PricingError(DependencyException):
+    """
+    Subclass of DependencyException.
+    Indicates that the price could not be determined.
+    Implicitly a buy / sell operation.
+    """
+
+
+class ExchangeError(DependencyException):
+    """
+    Error raised out of the exchange.
+    Has multiple Errors to determine the appropriate error.
+    """
+
+
+class InvalidOrderException(ExchangeError):
+    """
+    This is returned when the order is not valid. Example:
+    If stoploss on exchange order is hit, then trying to cancel the order
+    should return this exception.
+    """
+
+
+class RetryableOrderError(InvalidOrderException):
+    """
+    This is returned when the order is not found.
+    This Error will be repeated with increasing backof (in line with DDosError).
+    """
+
+
+class TemporaryError(ExchangeError):
+    """
+    Temporary network or exchange related error.
+    This could happen when an exchange is congested, unavailable, or the user
+    has networking problems. Usually resolves itself after a time.
+    """
+
+
+class DDosProtection(TemporaryError):
+    """
+    Temporary error caused by DDOS protection.
+    Bot will wait for a second and then retry.
+    """
+
+
+class StrategyError(FreqtradeException):
+    """
+    Errors with custom user-code deteced.
+    Usually caused by errors in the strategy.
+    """

freqtrade/exchange/__init__.py

@@ -1,18 +1,19 @@
-from freqtrade.exchange.common import MAP_EXCHANGE_CHILDCLASS  # noqa: F401
-from freqtrade.exchange.exchange import Exchange  # noqa: F401
-from freqtrade.exchange.exchange import (get_exchange_bad_reason,  # noqa: F401
+# flake8: noqa: F401
+from freqtrade.exchange.common import MAP_EXCHANGE_CHILDCLASS
+from freqtrade.exchange.exchange import Exchange
+from freqtrade.exchange.exchange import (get_exchange_bad_reason,
                                          is_exchange_bad,
                                          is_exchange_known_ccxt,
                                          is_exchange_officially_supported,
                                          ccxt_exchanges,
                                          available_exchanges)
-from freqtrade.exchange.exchange import (timeframe_to_seconds,  # noqa: F401
+from freqtrade.exchange.exchange import (timeframe_to_seconds,
                                          timeframe_to_minutes,
                                          timeframe_to_msecs,
                                          timeframe_to_next_date,
                                          timeframe_to_prev_date)
-from freqtrade.exchange.exchange import (market_is_active,  # noqa: F401
-                                         symbol_is_pair)
-from freqtrade.exchange.kraken import Kraken  # noqa: F401
-from freqtrade.exchange.binance import Binance  # noqa: F401
-from freqtrade.exchange.bibox import Bibox  # noqa: F401
+from freqtrade.exchange.exchange import (market_is_active)
+from freqtrade.exchange.kraken import Kraken
+from freqtrade.exchange.binance import Binance
+from freqtrade.exchange.bibox import Bibox
+from freqtrade.exchange.ftx import Ftx

freqtrade/exchange/binance.py

@@ -4,9 +4,11 @@ from typing import Dict
 
 import ccxt
 
-from freqtrade import (DependencyException, InvalidOrderException,
-                       OperationalException, TemporaryError)
+from freqtrade.exceptions import (DDosProtection, ExchangeError,
+                                  InvalidOrderException, OperationalException,
+                                  TemporaryError)
 from freqtrade.exchange import Exchange
+from freqtrade.exchange.common import retrier
 
 logger = logging.getLogger(__name__)
 
@@ -20,7 +22,7 @@ class Binance(Exchange):
         "trades_pagination_arg": "fromId",
     }
 
-    def get_order_book(self, pair: str, limit: int = 100) -> dict:
+    def fetch_l2_order_book(self, pair: str, limit: int = 100) -> dict:
         """
         get order book level 2 from exchange
 
@@ -30,18 +32,29 @@ class Binance(Exchange):
         # get next-higher step in the limit_range list
         limit = min(list(filter(lambda x: limit <= x, limit_range)))
 
-        return super().get_order_book(pair, limit)
+        return super().fetch_l2_order_book(pair, limit)
 
-    def stoploss_limit(self, pair: str, amount: float, stop_price: float, rate: float) -> Dict:
+    def stoploss_adjust(self, stop_loss: float, order: Dict) -> bool:
+        """
+        Verify stop_loss against stoploss-order value (limit or price)
+        Returns True if adjustment is necessary.
+        """
+        return order['type'] == 'stop_loss_limit' and stop_loss > float(order['info']['stopPrice'])
+
+    @retrier(retries=0)
+    def stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict) -> Dict:
         """
         creates a stoploss limit order.
         this stoploss-limit is binance-specific.
         It may work with a limited number of other exchanges, but this has not been tested yet.
-
         """
+        # Limit price threshold: As limit price should always be below stop-price
+        limit_price_pct = order_types.get('stoploss_on_exchange_limit_ratio', 0.99)
+        rate = stop_price * limit_price_pct
+
         ordertype = "stop_loss_limit"
 
-        stop_price = self.symbol_price_prec(pair, stop_price)
+        stop_price = self.price_to_precision(pair, stop_price)
 
         # Ensure rate is less than stop price
         if stop_price <= rate:
@@ -57,18 +70,18 @@ class Binance(Exchange):
             params = self._params.copy()
             params.update({'stopPrice': stop_price})
 
-            amount = self.symbol_amount_prec(pair, amount)
+            amount = self.amount_to_precision(pair, amount)
 
-            rate = self.symbol_price_prec(pair, rate)
+            rate = self.price_to_precision(pair, rate)
 
-            order = self._api.create_order(pair, ordertype, 'sell',
-                                           amount, rate, params)
+            order = self._api.create_order(symbol=pair, type=ordertype, side='sell',
+                                           amount=amount, price=rate, params=params)
             logger.info('stoploss limit order added for %s. '
                         'stop price: %s. limit: %s', pair, stop_price, rate)
             return order
         except ccxt.InsufficientFunds as e:
-            raise DependencyException(
-                f'Insufficient funds to create {ordertype} sell order on market {pair}.'
+            raise ExchangeError(
+                f'Insufficient funds to create {ordertype} sell order on market {pair}. '
                 f'Tried to sell amount {amount} at rate {rate}. '
                 f'Message: {e}') from e
         except ccxt.InvalidOrder as e:
@@ -78,6 +91,8 @@ class Binance(Exchange):
                 f'Could not create {ordertype} sell order on market {pair}. '
                 f'Tried to sell amount {amount} at rate {rate}. '
                 f'Message: {e}') from e
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
         except (ccxt.NetworkError, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Could not place sell order due to {e.__class__.__name__}. Message: {e}') from e

freqtrade/exchange/common.py

@@ -1,6 +1,10 @@
+import asyncio
 import logging
+import time
+from functools import wraps
 
-from freqtrade import DependencyException, TemporaryError
+from freqtrade.exceptions import (DDosProtection, RetryableOrderError,
+                                  TemporaryError)
 
 logger = logging.getLogger(__name__)
 
@@ -12,6 +16,7 @@ BAD_EXCHANGES = {
                 "Details in https://github.com/freqtrade/freqtrade/issues/1983",
     "hitbtc": "This API cannot be used with Freqtrade. "
               "Use `hitbtc2` exchange id to access this exchange.",
+    "phemex": "Does not provide history. ",
     **dict.fromkeys([
         'adara',
         'anxpro',
@@ -88,17 +93,28 @@ MAP_EXCHANGE_CHILDCLASS = {
 }
 
 
+def calculate_backoff(retrycount, max_retries):
+    """
+    Calculate backoff
+    """
+    return (max_retries - retrycount) ** 2 + 1
+
+
 def retrier_async(f):
     async def wrapper(*args, **kwargs):
         count = kwargs.pop('count', API_RETRY_COUNT)
         try:
             return await f(*args, **kwargs)
-        except (TemporaryError, DependencyException) as ex:
+        except TemporaryError as ex:
             logger.warning('%s() returned exception: "%s"', f.__name__, ex)
             if count > 0:
+                logger.warning('retrying %s() still for %s times', f.__name__, count)
                 count -= 1
                 kwargs.update({'count': count})
-                logger.warning('retrying %s() still for %s times', f.__name__, count)
+                if isinstance(ex, DDosProtection):
+                    backoff_delay = calculate_backoff(count + 1, API_RETRY_COUNT)
+                    logger.info(f"Applying DDosProtection backoff delay: {backoff_delay}")
+                    await asyncio.sleep(backoff_delay)
                 return await wrapper(*args, **kwargs)
             else:
                 logger.warning('Giving up retrying: %s()', f.__name__)
@@ -106,19 +122,31 @@ def retrier_async(f):
     return wrapper
 
 
-def retrier(f):
-    def wrapper(*args, **kwargs):
-        count = kwargs.pop('count', API_RETRY_COUNT)
-        try:
-            return f(*args, **kwargs)
-        except (TemporaryError, DependencyException) as ex:
-            logger.warning('%s() returned exception: "%s"', f.__name__, ex)
-            if count > 0:
-                count -= 1
-                kwargs.update({'count': count})
-                logger.warning('retrying %s() still for %s times', f.__name__, count)
-                return wrapper(*args, **kwargs)
-            else:
-                logger.warning('Giving up retrying: %s()', f.__name__)
-                raise ex
-    return wrapper
+def retrier(_func=None, retries=API_RETRY_COUNT):
+    def decorator(f):
+        @wraps(f)
+        def wrapper(*args, **kwargs):
+            count = kwargs.pop('count', retries)
+            try:
+                return f(*args, **kwargs)
+            except (TemporaryError, RetryableOrderError) as ex:
+                logger.warning('%s() returned exception: "%s"', f.__name__, ex)
+                if count > 0:
+                    logger.warning('retrying %s() still for %s times', f.__name__, count)
+                    count -= 1
+                    kwargs.update({'count': count})
+                    if isinstance(ex, DDosProtection) or isinstance(ex, RetryableOrderError):
+                        # increasing backoff
+                        backoff_delay = calculate_backoff(count + 1, retries)
+                        logger.info(f"Applying DDosProtection backoff delay: {backoff_delay}")
+                        time.sleep(backoff_delay)
+                    return wrapper(*args, **kwargs)
+                else:
+                    logger.warning('Giving up retrying: %s()', f.__name__)
+                    raise ex
+        return wrapper
+    # Support both @retrier and @retrier(retries=2) syntax
+    if _func is None:
+        return decorator
+    else:
+        return decorator(_func)

freqtrade/exchange/ftx.py

@@ -0,0 +1,136 @@
+""" FTX exchange subclass """
+import logging
+from typing import Any, Dict
+
+import ccxt
+
+from freqtrade.exceptions import (DDosProtection, ExchangeError,
+                                  InvalidOrderException, OperationalException,
+                                  TemporaryError)
+from freqtrade.exchange import Exchange
+from freqtrade.exchange.common import retrier
+
+logger = logging.getLogger(__name__)
+
+
+class Ftx(Exchange):
+
+    _ft_has: Dict = {
+        "stoploss_on_exchange": True,
+        "ohlcv_candle_limit": 1500,
+    }
+
+    def market_is_tradable(self, market: Dict[str, Any]) -> bool:
+        """
+        Check if the market symbol is tradable by Freqtrade.
+        Default checks + check if pair is spot pair (no futures trading yet).
+        """
+        parent_check = super().market_is_tradable(market)
+
+        return (parent_check and
+                market.get('spot', False) is True)
+
+    def stoploss_adjust(self, stop_loss: float, order: Dict) -> bool:
+        """
+        Verify stop_loss against stoploss-order value (limit or price)
+        Returns True if adjustment is necessary.
+        """
+        return order['type'] == 'stop' and stop_loss > float(order['price'])
+
+    @retrier(retries=0)
+    def stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict) -> Dict:
+        """
+        Creates a stoploss order.
+        depending on order_types.stoploss configuration, uses 'market' or limit order.
+
+        Limit orders are defined by having orderPrice set, otherwise a market order is used.
+        """
+        limit_price_pct = order_types.get('stoploss_on_exchange_limit_ratio', 0.99)
+        limit_rate = stop_price * limit_price_pct
+
+        ordertype = "stop"
+
+        stop_price = self.price_to_precision(pair, stop_price)
+
+        if self._config['dry_run']:
+            dry_order = self.dry_run_order(
+                pair, ordertype, "sell", amount, stop_price)
+            return dry_order
+
+        try:
+            params = self._params.copy()
+            if order_types.get('stoploss', 'market') == 'limit':
+                # set orderPrice to place limit order, otherwise it's a market order
+                params['orderPrice'] = limit_rate
+
+            amount = self.amount_to_precision(pair, amount)
+
+            order = self._api.create_order(symbol=pair, type=ordertype, side='sell',
+                                           amount=amount, price=stop_price, params=params)
+            logger.info('stoploss order added for %s. '
+                        'stop price: %s.', pair, stop_price)
+            return order
+        except ccxt.InsufficientFunds as e:
+            raise ExchangeError(
+                f'Insufficient funds to create {ordertype} sell order on market {pair}. '
+                f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
+                f'Message: {e}') from e
+        except ccxt.InvalidOrder as e:
+            raise InvalidOrderException(
+                f'Could not create {ordertype} sell order on market {pair}. '
+                f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
+                f'Message: {e}') from e
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f'Could not place sell order due to {e.__class__.__name__}. Message: {e}') from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    @retrier(retries=5)
+    def fetch_stoploss_order(self, order_id: str, pair: str) -> Dict:
+        if self._config['dry_run']:
+            try:
+                order = self._dry_run_open_orders[order_id]
+                return order
+            except KeyError as e:
+                # Gracefully handle errors with dry-run orders.
+                raise InvalidOrderException(
+                    f'Tried to get an invalid dry-run-order (id: {order_id}). Message: {e}') from e
+        try:
+            orders = self._api.fetch_orders(pair, None, params={'type': 'stop'})
+
+            order = [order for order in orders if order['id'] == order_id]
+            if len(order) == 1:
+                return order[0]
+            else:
+                raise InvalidOrderException(f"Could not get stoploss order for id {order_id}")
+
+        except ccxt.InvalidOrder as e:
+            raise InvalidOrderException(
+                f'Tried to get an invalid order (id: {order_id}). Message: {e}') from e
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f'Could not get order due to {e.__class__.__name__}. Message: {e}') from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    @retrier
+    def cancel_stoploss_order(self, order_id: str, pair: str) -> Dict:
+        if self._config['dry_run']:
+            return {}
+        try:
+            return self._api.cancel_order(order_id, pair, params={'type': 'stop'})
+        except ccxt.InvalidOrder as e:
+            raise InvalidOrderException(
+                f'Could not cancel order. Message: {e}') from e
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f'Could not cancel order due to {e.__class__.__name__}. Message: {e}') from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e

freqtrade/exchange/kraken.py

@@ -1,12 +1,14 @@
 """ Kraken exchange subclass """
 import logging
-from typing import Dict
+from typing import Any, Dict
 
 import ccxt
 
-from freqtrade import OperationalException, TemporaryError
+from freqtrade.exceptions import (DDosProtection, ExchangeError,
+                                  InvalidOrderException, OperationalException,
+                                  TemporaryError)
 from freqtrade.exchange import Exchange
-from freqtrade.exchange.exchange import retrier
+from freqtrade.exchange.common import retrier
 
 logger = logging.getLogger(__name__)
 
@@ -15,10 +17,21 @@ class Kraken(Exchange):
 
     _params: Dict = {"trading_agreement": "agree"}
     _ft_has: Dict = {
+        "stoploss_on_exchange": True,
         "trades_pagination": "id",
         "trades_pagination_arg": "since",
     }
 
+    def market_is_tradable(self, market: Dict[str, Any]) -> bool:
+        """
+        Check if the market symbol is tradable by Freqtrade.
+        Default checks + check if pair is darkpool pair.
+        """
+        parent_check = super().market_is_tradable(market)
+
+        return (parent_check and
+                market.get('darkpool', False) is False)
+
     @retrier
     def get_balances(self) -> dict:
         if self._config['dry_run']:
@@ -43,8 +56,61 @@ class Kraken(Exchange):
                 balances[bal]['free'] = balances[bal]['total'] - balances[bal]['used']
 
             return balances
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
         except (ccxt.NetworkError, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Could not get balance due to {e.__class__.__name__}. Message: {e}') from e
         except ccxt.BaseError as e:
             raise OperationalException(e) from e
+
+    def stoploss_adjust(self, stop_loss: float, order: Dict) -> bool:
+        """
+        Verify stop_loss against stoploss-order value (limit or price)
+        Returns True if adjustment is necessary.
+        """
+        return order['type'] == 'stop-loss' and stop_loss > float(order['price'])
+
+    @retrier(retries=0)
+    def stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict) -> Dict:
+        """
+        Creates a stoploss market order.
+        Stoploss market orders is the only stoploss type supported by kraken.
+        """
+
+        ordertype = "stop-loss"
+
+        stop_price = self.price_to_precision(pair, stop_price)
+
+        if self._config['dry_run']:
+            dry_order = self.dry_run_order(
+                pair, ordertype, "sell", amount, stop_price)
+            return dry_order
+
+        try:
+            params = self._params.copy()
+
+            amount = self.amount_to_precision(pair, amount)
+
+            order = self._api.create_order(symbol=pair, type=ordertype, side='sell',
+                                           amount=amount, price=stop_price, params=params)
+            logger.info('stoploss order added for %s. '
+                        'stop price: %s.', pair, stop_price)
+            return order
+        except ccxt.InsufficientFunds as e:
+            raise ExchangeError(
+                f'Insufficient funds to create {ordertype} sell order on market {pair}. '
+                f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
+                f'Message: {e}') from e
+        except ccxt.InvalidOrder as e:
+            raise InvalidOrderException(
+                f'Could not create {ordertype} sell order on market {pair}. '
+                f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
+                f'Message: {e}') from e
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f'Could not place sell order due to {e.__class__.__name__}. Message: {e}') from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e