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Merge pull request #1 from freqtrade/develop

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pull from freqtrade/freqtrade
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svalovic 2020-04-01 22:18:37 +02:00 committed by GitHub
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1
.coveragerc
View File

@ -1,6 +1,7 @@
[run]
omit =
scripts/*
freqtrade/templates/*
freqtrade/vendor/*
freqtrade/__main__.py
tests/*

259
.github/workflows/ci.yml vendored Normal file
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@ -0,0 +1,259 @@
name: Freqtrade CI
on:
push:
branches:
- master
- develop
- github_actions_tests
tags:
release:
types: [published]
pull_request:
schedule:
- cron: '0 5 * * 4'
jobs:
build:
runs-on: ${{ matrix.os }}
strategy:
matrix:
os: [ ubuntu-18.04, macos-latest ]
python-version: [3.7, 3.8]
steps:
- uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v1
with:
python-version: ${{ matrix.python-version }}
- name: Cache_dependencies
uses: actions/cache@v1
id: cache
with:
path: ~/dependencies/
key: ${{ runner.os }}-dependencies
- name: pip cache (linux)
uses: actions/cache@preview
if: startsWith(matrix.os, 'ubuntu')
with:
path: ~/.cache/pip
key: test-${{ matrix.os }}-${{ matrix.python-version }}-pip
- name: pip cache (macOS)
uses: actions/cache@preview
if: startsWith(matrix.os, 'macOS')
with:
path: ~/Library/Caches/pip
key: test-${{ matrix.os }}-${{ matrix.python-version }}-pip
- name: TA binary *nix
if: steps.cache.outputs.cache-hit != 'true'
run: |
cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
- name: Installation - *nix
run: |
python -m pip install --upgrade pip
export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
export TA_LIBRARY_PATH=${HOME}/dependencies/lib
export TA_INCLUDE_PATH=${HOME}/dependencies/include
pip install -r requirements-dev.txt
pip install -e .
- name: Tests
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
coveralls -v || true
- name: Backtesting
run: |
cp config.json.example config.json
freqtrade create-userdir --userdir user_data
freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
- name: Hyperopt
run: |
cp config.json.example config.json
freqtrade create-userdir --userdir user_data
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt
- name: Flake8
run: |
flake8
- name: Mypy
run: |
mypy freqtrade scripts
- 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 ${{ matrix.os }}*'
mention: 'here'
mention_if: 'failure'
channel: '#notifications'
url: ${{ secrets.SLACK_WEBHOOK }}
build_windows:
runs-on: ${{ matrix.os }}
strategy:
matrix:
os: [ windows-latest ]
python-version: [3.7, 3.8]
steps:
- uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v1
with:
python-version: ${{ matrix.python-version }}
- name: Pip cache (Windows)
uses: actions/cache@preview
if: startsWith(runner.os, 'Windows')
with:
path: ~\AppData\Local\pip\Cache
key: ${{ matrix.os }}-${{ matrix.python-version }}-pip
- name: Installation
run: |
./build_helpers/install_windows.ps1
- name: Tests
run: |
pytest --random-order --cov=freqtrade --cov-config=.coveragerc
- name: Backtesting
run: |
cp config.json.example config.json
freqtrade create-userdir --userdir user_data
freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
- name: Hyperopt
run: |
cp config.json.example config.json
freqtrade create-userdir --userdir user_data
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt
- name: Flake8
run: |
flake8
- name: Mypy
run: |
mypy freqtrade scripts
- 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 windows*'
mention: 'here'
mention_if: 'failure'
channel: '#notifications'
url: ${{ secrets.SLACK_WEBHOOK }}
docs_check:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Documentation syntax
run: |
./tests/test_docs.sh
- name: Slack Notification
uses: homoluctus/slatify@v1.8.0
if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
with:
type: ${{ job.status }}
job_name: '*Freqtrade Docs*'
channel: '#notifications'
url: ${{ secrets.SLACK_WEBHOOK }}
deploy:
needs: [ build, build_windows, docs_check ]
runs-on: ubuntu-18.04
if: (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'release') && github.repository == 'freqtrade/freqtrade'
steps:
- uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v1
with:
python-version: 3.8
- name: Extract branch name
shell: bash
run: echo "##[set-output name=branch;]$(echo ${GITHUB_REF#refs/heads/})"
id: extract_branch
- name: Build distribution
run: |
pip install -U setuptools wheel
python setup.py sdist bdist_wheel
- name: Publish to PyPI (Test)
uses: pypa/gh-action-pypi-publish@master
if: (steps.extract_branch.outputs.branch == 'master' || github.event_name == 'release')
with:
user: __token__
password: ${{ secrets.pypi_test_password }}
repository_url: https://test.pypi.org/legacy/
- name: Publish to PyPI
uses: pypa/gh-action-pypi-publish@master
if: (steps.extract_branch.outputs.branch == 'master' || github.event_name == 'release')
with:
user: __token__
password: ${{ secrets.pypi_password }}
- name: Build and test and push docker image
env:
IMAGE_NAME: freqtradeorg/freqtrade
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 ${{ steps.extract_branch.outputs.branch }}_pi
uses: elgohr/Publish-Docker-Github-Action@2.7
with:
name: freqtradeorg/freqtrade:${{ 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
- 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 Deploy*'
mention: 'here'
mention_if: 'failure'
channel: '#notifications'
url: ${{ secrets.SLACK_WEBHOOK }}

18
.github/workflows/docker_update_readme.yml vendored Normal file
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@ -0,0 +1,18 @@
name: Update Docker Hub Description
on:
push:
branches:
- master
jobs:
dockerHubDescription:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v1
- name: Docker Hub Description
uses: peter-evans/dockerhub-description@v2.1.0
env:
DOCKERHUB_USERNAME: ${{ secrets.DOCKER_USERNAME }}
DOCKERHUB_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}
DOCKERHUB_REPOSITORY: freqtradeorg/freqtrade

1
.gitignore vendored
View File

@ -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

26
.travis.yml
View File

@ -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:
@ -24,31 +23,34 @@ jobs:
script:
- pytest --random-order --cov=freqtrade --cov-config=.coveragerc
# Allow failure for coveralls
- coveralls || true
# - coveralls || true
name: pytest
- script:
- cp config.json.example config.json
- freqtrade --datadir tests/testdata backtesting
- freqtrade create-userdir --userdir user_data
- freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
name: backtest
- script:
- cp config.json.example config.json
- freqtrade --datadir tests/testdata hyperopt -e 5
- freqtrade create-userdir --userdir user_data
- freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt
name: hyperopt
- script: flake8
name: flake8
- script:
# Test Documentation boxes -
# !!! <TYPE>: is not allowed!
- grep -Er '^!{3}\s\S+:' docs/*; test $? -ne 0
# !!! <TYPE> "title" - Title needs to be quoted!
- grep -Er '^!{3}\s\S+:|^!{3}\s\S+\s[^"]' docs/*; test $? -ne 0
name: doc syntax
- script: mypy freqtrade scripts
name: mypy
- stage: docker
if: branch in (master, develop, feat/improve_travis) AND (type in (push, cron))
script:
- build_helpers/publish_docker.sh
name: "Build and test and push docker image"
# - stage: docker
# if: branch in (master, develop, feat/improve_travis) AND (type in (push, cron))
# script:
# - build_helpers/publish_docker.sh
# name: "Build and test and push docker image"
notifications:
slack:

10
CONTRIBUTING.md
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@ -11,7 +11,7 @@ Few pointers for contributions:
- Create your PR against the `develop` branch, not `master`.
- New features need to contain unit tests and must be PEP8 conformant (max-line-length = 100).
If you are unsure, discuss the feature on our [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LWEyODBiNzkzNzcyNzU0MWYyYzE5NjIyOTQxMzBmMGUxOTIzM2YyN2Y4NWY1YTEwZDgwYTRmMzE2NmM5ZmY2MTg)
If you are unsure, discuss the feature on our [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE)
or in a [issue](https://github.com/freqtrade/freqtrade/issues) before a PR.
## Getting started
@ -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.
Beeing 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 beeing Committer for some time, a Committer may be named Core Committer and given full repository access.
After being Committer for some time, a Committer may be named Core Committer and given full repository access.

8
Dockerfile
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@ -1,4 +1,4 @@
FROM python:3.7.4-slim-stretch
FROM python:3.8.2-slim-buster
RUN apt-get update \
&& apt-get -y install curl build-essential libssl-dev \
@ -16,11 +16,13 @@ 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/
COPY requirements.txt requirements-common.txt requirements-hyperopt.txt /freqtrade/
RUN pip install numpy --no-cache-dir \
&& pip install -r requirements.txt --no-cache-dir
&& pip install -r requirements-hyperopt.txt --no-cache-dir
# Install and execute
COPY . /freqtrade/
RUN pip install -e . --no-cache-dir
ENTRYPOINT ["freqtrade"]
# Default to trade mode
CMD [ "trade" ]

5
Dockerfile.pi
View File

@ -22,13 +22,13 @@ RUN tar -xzf /freqtrade/ta-lib-0.4.0-src.tar.gz \
ENV LD_LIBRARY_PATH /usr/local/lib
# Install berryconda
RUN wget https://github.com/jjhelmus/berryconda/releases/download/v2.0.0/Berryconda3-2.0.0-Linux-armv7l.sh \
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 scipy \
RUN ~/berryconda3/bin/conda install -y numpy pandas \
&& ~/berryconda3/bin/pip install -r requirements-common.txt --no-cache-dir
# Install and execute
@ -38,3 +38,4 @@ RUN ~/berryconda3/bin/pip install -e . --no-cache-dir
RUN [ "cross-build-end" ]
ENTRYPOINT ["/root/berryconda3/bin/python","./freqtrade/main.py"]
CMD [ "trade" ]

1
MANIFEST.in
View File

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

17
README.md
View File

@ -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
@ -62,7 +63,6 @@ git checkout develop
For any other type of installation please refer to [Installation doc](https://www.freqtrade.io/en/latest/installation/).
## Basic Usage
### Bot commands
@ -106,7 +106,7 @@ optional arguments:
### Telegram RPC commands
Telegram is not mandatory. However, this is a great way to control your bot. More details on our [documentation](https://www.freqtrade.io/en/latest/telegram-usage/)
Telegram is not mandatory. However, this is a great way to control your bot. More details and the full command list on our [documentation](https://www.freqtrade.io/en/latest/telegram-usage/)
- `/start`: Starts the trader
- `/stop`: Stops the trader
@ -129,11 +129,6 @@ The project is currently setup in two main branches:
- `master` - This branch contains the latest stable release. The bot 'should' be stable on this branch, and is generally well tested.
- `feat/*` - These are feature branches, which are being worked on heavily. Please don't use these unless you want to test a specific feature.
## A note on Binance
For Binance, please add `"BNB/<STAKE>"` to your blacklist to avoid issues.
Accounts having BNB accounts use this to pay for fees - if your first trade happens to be on `BNB`, further trades will consume this position and make the initial BNB order unsellable as the expected amount is not there anymore.
## Support
### Help / Slack
@ -141,7 +136,7 @@ Accounts having BNB accounts use this to pay for fees - if your first trade happ
For any questions not covered by the documentation or for further
information about the bot, we encourage you to join our slack channel.
- [Click here to join Slack channel](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LWEyODBiNzkzNzcyNzU0MWYyYzE5NjIyOTQxMzBmMGUxOTIzM2YyN2Y4NWY1YTEwZDgwYTRmMzE2NmM5ZmY2MTg).
- [Click here to join Slack channel](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE).
### [Bugs / Issues](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)
@ -172,7 +167,7 @@ to understand the requirements before sending your pull-requests.
Coding is not a neccessity to contribute - maybe start with improving our documentation?
Issues labeled [good first issue](https://github.com/freqtrade/freqtrade/labels/good%20first%20issue) can be good first contributions, and will help get you familiar with the codebase.
**Note** before starting any major new feature work, *please open an issue describing what you are planning to do* or talk to us on [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LWEyODBiNzkzNzcyNzU0MWYyYzE5NjIyOTQxMzBmMGUxOTIzM2YyN2Y4NWY1YTEwZDgwYTRmMzE2NmM5ZmY2MTg). This will ensure that interested parties can give valuable feedback on the feature, and let others know that you are working on it.
**Note** before starting any major new feature work, *please open an issue describing what you are planning to do* or talk to us on [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE). This will ensure that interested parties can give valuable feedback on the feature, and let others know that you are working on it.
**Important:** Always create your PR against the `develop` branch, not `master`.

12
bin/freqtrade
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@ -1,11 +1,11 @@
#!/usr/bin/env python3
import sys
import warnings
import logging
from freqtrade.main import main
logger = logging.getLogger(__name__)
warnings.warn(
"Deprecated - To continue to run the bot like this, please run `pip install -e .` again.",
DeprecationWarning)
main(sys.argv[1:])
logger.error("DEPRECATED installation detected, please run `pip install -e .` again.")
sys.exit(2)

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build_helpers/install_windows.ps1 Normal file
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@ -0,0 +1,17 @@
# Downloads don't work automatically, since the URL is regenerated via javascript.
# 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"
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-0.4.17-cp37-cp37m-win_amd64.whl
}
if ($pyv -eq '3.8') {
pip install build_helpers\TA_Lib-0.4.17-cp38-cp38-win_amd64.whl
}
pip install -r requirements-dev.txt
pip install -e .

20
build_helpers/publish_docker.sh
View File

@ -1,17 +1,17 @@
#!/bin/sh
# - export TAG=`if [ "$TRAVIS_BRANCH" == "develop" ]; then echo "latest"; else echo $TRAVIS_BRANCH ; fi`
# Replace / with _ to create a valid tag
TAG=$(echo "${TRAVIS_BRANCH}" | sed -e "s/\//_/")
# Replace / with _ to create a valid tag
TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g")
echo "Running for ${TAG}"
# Add commit and commit_message to docker container
echo "${TRAVIS_COMMIT} ${TRAVIS_COMMIT_MESSAGE}" > freqtrade_commit
echo "${GITHUB_SHA}" > freqtrade_commit
if [ "${TRAVIS_EVENT_TYPE}" = "cron" ]; then
echo "event ${TRAVIS_EVENT_TYPE}: full rebuild - skipping cache"
if [ "${GITHUB_EVENT_NAME}" = "schedule" ]; then
echo "event ${GITHUB_EVENT_NAME}: full rebuild - skipping cache"
docker build -t freqtrade:${TAG} .
else
echo "event ${TRAVIS_EVENT_TYPE}: building with cache"
echo "event ${GITHUB_EVENT_NAME}: building with cache"
# Pull last build to avoid rebuilding the whole image
docker pull ${IMAGE_NAME}:${TAG}
docker build --cache-from ${IMAGE_NAME}:${TAG} -t freqtrade:${TAG} .
@ -23,7 +23,7 @@ if [ $? -ne 0 ]; then
fi
# Run backtest
docker run --rm -it -v $(pwd)/config.json.example:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG} --datadir /tests/testdata backtesting
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"
@ -38,12 +38,12 @@ if [ $? -ne 0 ]; then
fi
# Tag as latest for develop builds
if [ "${TRAVIS_BRANCH}" = "develop" ]; then
if [ "${TAG}" = "develop" ]; then
docker tag freqtrade:$TAG ${IMAGE_NAME}:latest
fi
# Login
echo "$DOCKER_PASS" | docker login -u $DOCKER_USER --password-stdin
docker login -u $DOCKER_USERNAME -p $DOCKER_PASSWORD
if [ $? -ne 0 ]; then
echo "failed login"

23
config.json.example
View File

@ -2,8 +2,9 @@
"max_open_trades": 3,
"stake_currency": "BTC",
"stake_amount": 0.05,
"tradable_balance_ratio": 0.99,
"fiat_display_currency": "USD",
"ticker_interval" : "5m",
"ticker_interval": "5m",
"dry_run": false,
"trailing_stop": false,
"unfilledtimeout": {
@ -22,7 +23,10 @@
"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
},
"exchange": {
"name": "bittrex",
@ -40,8 +44,8 @@
"DASH/BTC",
"ZEC/BTC",
"XLM/BTC",
"NXT/BTC",
"POWR/BTC",
"XRP/BTC",
"TRX/BTC",
"ADA/BTC",
"XMR/BTC"
],
@ -49,16 +53,13 @@
"DOGE/BTC"
]
},
"experimental": {
"use_sell_signal": false,
"sell_profit_only": false,
"ignore_roi_if_buy_signal": false
},
"pairlists": [
{"method": "StaticPairList"}
],
"edge": {
"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,
@ -70,7 +71,7 @@
"remove_pumps": false
},
"telegram": {
"enabled": true,
"enabled": false,
"token": "your_telegram_token",
"chat_id": "your_telegram_chat_id"
},

36
config_binance.json.example
View File

@ -2,8 +2,9 @@
"max_open_trades": 3,
"stake_currency": "BTC",
"stake_amount": 0.05,
"tradable_balance_ratio": 0.99,
"fiat_display_currency": "USD",
"ticker_interval" : "5m",
"ticker_interval": "5m",
"dry_run": true,
"trailing_stop": false,
"unfilledtimeout": {
@ -22,7 +23,10 @@
"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
},
"exchange": {
"name": "binance",
@ -34,33 +38,33 @@
"rateLimit": 200
},
"pair_whitelist": [
"AST/BTC",
"ETC/BTC",
"ETH/BTC",
"ALGO/BTC",
"ATOM/BTC",
"BAT/BTC",
"BCH/BTC",
"BRD/BTC",
"EOS/BTC",
"ETH/BTC",
"IOTA/BTC",
"LINK/BTC",
"LTC/BTC",
"MTH/BTC",
"NCASH/BTC",
"TNT/BTC",
"NEO/BTC",
"NXS/BTC",
"XMR/BTC",
"XLM/BTC",
"XRP/BTC"
"XRP/BTC",
"XTZ/BTC"
],
"pair_blacklist": [
"BNB/BTC"
]
},
"experimental": {
"use_sell_signal": false,
"sell_profit_only": false,
"ignore_roi_if_buy_signal": false
},
"pairlists": [
{"method": "StaticPairList"}
],
"edge": {
"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,

43
config_full.json.example
View File

@ -2,8 +2,11 @@
"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",
"trailing_stop": false,
@ -22,6 +25,7 @@
"sell": 30
},
"bid_strategy": {
"price_side": "bid",
"use_order_book": false,
"ask_last_balance": 0.0,
"order_book_top": 1,
@ -31,9 +35,13 @@
}
},
"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
},
"order_types": {
"buy": "limit",
@ -47,14 +55,18 @@
"buy": "gtc",
"sell": "gtc"
},
"pairlist": {
"method": "VolumePairList",
"config": {
"pairlists": [
{"method": "StaticPairList"},
{
"method": "VolumePairList",
"number_assets": 20,
"sort_key": "quoteVolume",
"precision_filter": false
}
},
"refresh_period": 1800
},
{"method": "PrecisionFilter"},
{"method": "PriceFilter", "low_price_ratio": 0.01},
{"method": "SpreadFilter", "max_spread_ratio": 0.005}
],
"exchange": {
"name": "bittrex",
"sandbox": false,
@ -75,7 +87,7 @@
"ZEC/BTC",
"XLM/BTC",
"NXT/BTC",
"POWR/BTC",
"TRX/BTC",
"ADA/BTC",
"XMR/BTC"
],
@ -89,7 +101,6 @@
"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,
@ -100,11 +111,6 @@
"max_trade_duration_minute": 1440,
"remove_pumps": false
},
"experimental": {
"use_sell_signal": false,
"sell_profit_only": false,
"ignore_roi_if_buy_signal": false
},
"telegram": {
"enabled": true,
"token": "your_telegram_token",
@ -121,8 +127,11 @@
"initial_state": "running",
"forcebuy_enable": false,
"internals": {
"process_throttle_secs": 5
"process_throttle_secs": 5,
"heartbeat_interval": 60
},
"strategy": "DefaultStrategy",
"strategy_path": "user_data/strategies/"
"strategy_path": "user_data/strategies/",
"dataformat_ohlcv": "json",
"dataformat_trades": "jsongz"
}

37
config_kraken.json.example
View File

@ -2,8 +2,9 @@
"max_open_trades": 5,
"stake_currency": "EUR",
"stake_amount": 10,
"tradable_balance_ratio": 0.99,
"fiat_display_currency": "EUR",
"ticker_interval" : "5m",
"ticker_interval": "5m",
"dry_run": true,
"trailing_stop": false,
"unfilledtimeout": {
@ -22,7 +23,11 @@
"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
},
"exchange": {
"name": "kraken",
@ -34,19 +39,38 @@
"rateLimit": 1000
},
"pair_whitelist": [
"ETH/EUR",
"ADA/EUR",
"ATOM/EUR",
"BAT/EUR",
"BCH/EUR",
"BTC/EUR",
"BCH/EUR"
"DAI/EUR",
"DASH/EUR",
"EOS/EUR",
"ETC/EUR",
"ETH/EUR",
"LINK/EUR",
"LTC/EUR",
"QTUM/EUR",
"REP/EUR",
"WAVES/EUR",
"XLM/EUR",
"XMR/EUR",
"XRP/EUR",
"XTZ/EUR",
"ZEC/EUR"
],
"pair_blacklist": [
]
},
"pairlists": [
{"method": "StaticPairList"}
],
"edge": {
"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,
@ -66,5 +90,6 @@
"forcebuy_enable": false,
"internals": {
"process_throttle_secs": 5
}
},
"download_trades": true
}

20
docker-compose.develop.yml Normal file
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@ -0,0 +1,20 @@
---
version: '3'
services:
freqtrade_develop:
build:
context: .
dockerfile: "./Dockerfile.develop"
volumes:
- ".:/freqtrade"
entrypoint:
- "freqtrade"
freqtrade_bash:
build:
context: .
dockerfile: "./Dockerfile.develop"
volumes:
- ".:/freqtrade"
entrypoint:
- "/bin/bash"

20
docker-compose.yml Normal file
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@ -0,0 +1,20 @@
---
version: '3'
services:
freqtrade:
image: freqtradeorg/freqtrade:master
# Build step - only needed when additional dependencies are needed
# build:
# context: .
# dockerfile: "./Dockerfile.technical"
restart: unless-stopped
container_name: freqtrade
volumes:
- "./user_data:/freqtrade/user_data"
# Default command used when running `docker compose up`
command: >
trade
--logfile /freqtrade/user_data/freqtrade.log
--db-url sqlite:////freqtrade/user_data/tradesv3.sqlite
--config /freqtrade/user_data/config.json
--strategy SampleStrategy

91
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@ -0,0 +1,91 @@
# Advanced Hyperopt
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.
For the sample below, you then need to add the command line parameter `--hyperopt-loss SuperDuperHyperOptLoss` to your hyperopt call so this function is being used.
A sample of this can be found below, which is identical to the Default Hyperopt loss implementation. A full sample can be found in [userdata/hyperopts](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_loss.py).
``` python
from freqtrade.optimize.hyperopt import IHyperOptLoss
TARGET_TRADES = 600
EXPECTED_MAX_PROFIT = 3.0
MAX_ACCEPTED_TRADE_DURATION = 300
class SuperDuperHyperOptLoss(IHyperOptLoss):
"""
Defines the default loss function for hyperopt
"""
@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
*args, **kwargs) -> float:
"""
Objective function, returns smaller number for better results
This is the legacy algorithm (used until now in freqtrade).
Weights are distributed as follows:
* 0.4 to trade duration
* 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()
trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
result = trade_loss + profit_loss + duration_loss
return result
```
Currently, the arguments are:
* `results`: DataFrame containing the result
The following columns are available in results (corresponds to the output-file of backtesting when used with `--export trades`):
`pair, profit_percent, profit_abs, open_time, close_time, open_index, close_index, trade_duration, open_at_end, open_rate, close_rate, sell_reason`
* `trade_count`: Amount of trades (identical to `len(results)`)
* `min_date`: Start date of the hyperopting TimeFrame
* `min_date`: End date of the hyperopting TimeFrame
This function needs to return a floating point number (`float`). Smaller numbers will be interpreted as better results. The parameters and balancing for this is up to you.
!!! Note
This function is called once per iteration - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.
!!! Note
Please keep the arguments `*args` and `**kwargs` in the interface to allow us to extend this interface later.

92
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@ -0,0 +1,92 @@
# Advanced Post-installation Tasks
This page explains some advanced tasks and configuration options that can be performed after the bot installation and may be uselful in some environments.
If you do not know what things mentioned here mean, you probably do not need it.
## 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.
!!! Note
Certain systems (like Raspbian) don't load service unit files from the user directory. In this case, copy `freqtrade.service` into `/etc/systemd/user/` (requires superuser permissions).
After that you can start the daemon with:
```bash
systemctl --user start freqtrade
```
For this to be persistent (run when user is logged out) you'll need to enable `linger` for your freqtrade user.
```bash
sudo loginctl enable-linger "$USER"
```
If you run the bot as a service, you can use systemd service manager as a software watchdog monitoring freqtrade bot
state and restarting it in the case of failures. If the `internals.sd_notify` parameter is set to true in the
configuration or the `--sd-notify` command line option is used, the bot will send keep-alive ping messages to systemd
using the sd_notify (systemd notifications) protocol and will also tell systemd its current state (Running or Stopped)
when it changes.
The `freqtrade.service.watchdog` file contains an example of the service unit configuration file which uses systemd
as the watchdog.
!!! Note
The sd_notify communication between the bot and the systemd service manager will not work if the bot runs in a Docker container.
## 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.
### 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:
* `--logfilename 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.
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.
For `rsyslog` the messages from the bot can be redirected into a separate dedicated log file. To achieve this, add
```
if $programname startswith "freqtrade" then -/var/log/freqtrade.log
```
to one of the rsyslog configuration files, for example at the end of the `/etc/rsyslog.d/50-default.conf`.
For `syslog` (`rsyslog`), the reduction mode can be switched on. This will reduce the number of repeating messages. For instance, multiple bot Heartbeat messages will be reduced to a single message when nothing else happens with the bot. To achieve this, set in `/etc/rsyslog.conf`:
```
# Filter duplicated messages
$RepeatedMsgReduction on
```
### Logging to journald
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:
* `--logfilename 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:
* `journalctl -f` -- shows Freqtrade log messages sent to `journald` along with other log messages fetched by `journald`.
* `journalctl -f -u freqtrade.service` -- this command can be used when the bot is run as a `systemd` service.
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.

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@ -1,45 +1,9 @@
# Backtesting
This page explains how to validate your strategy performance by using
Backtesting.
This page explains how to validate your strategy performance by using Backtesting.
## Getting data for backtesting and hyperopt
To download data (candles / OHLCV) needed for backtesting and hyperoptimization use the `freqtrade download-data` command.
If no additional parameter is specified, freqtrade will download data for `"1m"` and `"5m"` timeframes for 30 days.
Exchange and pairs will come from `config.json` (if specified using `-c/--config`). Otherwise `--exchange` becomes mandatory.
Alternatively, a `pairs.json` file can be used.
If you are using Binance for example:
- create a directory `user_data/data/binance` and copy `pairs.json` in that directory.
- update the `pairs.json` to contain the currency pairs you are interested in.
```bash
mkdir -p user_data/data/binance
cp freqtrade/tests/testdata/pairs.json user_data/data/binance
```
Then run:
```bash
freqtrade download-data --exchange binance
```
This will download ticker data for all the currency pairs you defined in `pairs.json`.
- 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 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 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.
!!! Tip Updating existing data
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.
Backtesting requires historic data to be available.
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.
## Test your strategy with Backtesting
@ -47,42 +11,42 @@ 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`.
For details on downloading, please refer to the [relevant section](#Getting-data-for-backtesting-and-hyperopt) in the documentation.
Backtesting will use the crypto-currencies (pairs) from your config file and load historical candle (OHCLV) data from `user_data/data/<exchange>` by default.
If no data is available for the exchange / pair / timeframe (ticker interval) combination, backtesting will ask you to download them first using `freqtrade download-data`.
For details on downloading, please refer to the [Data Downloading](data-download.md) section in the documentation.
The result of backtesting will confirm you if your bot has better odds of making a profit than a loss.
The result of backtesting will confirm if your bot has better odds of making a profit than a loss.
The backtesting is very easy with freqtrade.
!!! 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.
### Run a backtesting against the currencies listed in your config file
#### With 5 min tickers (Per default)
#### With 5 min candle (OHLCV) data (per default)
```bash
freqtrade backtesting
```
#### With 1 min tickers
#### With 1 min candle (OHLCV) data
```bash
freqtrade backtesting --ticker-interval 1m
```
#### Using a different on-disk ticker-data source
#### Using a different on-disk historical candle (OHLCV) data source
Assume you downloaded the history data from the Bittrex exchange and kept it in the `user_data/data/bittrex-20180101` directory.
You can then use this data for backtesting as follows:
```bash
freqtrade backtesting --datadir user_data/data/bittrex-20180101
freqtrade --datadir user_data/data/bittrex-20180101 backtesting
```
#### With a (custom) strategy file
```bash
freqtrade -s SampleStrategy backtesting
freqtrade backtesting -s SampleStrategy
```
Where `-s SampleStrategy` refers to the class name within the strategy file `sample_strategy.py` found in the `freqtrade/user_data/strategies` directory.
@ -109,28 +73,38 @@ The exported trades can be used for [further analysis](#further-backtest-result-
freqtrade backtesting --export trades --export-filename=backtest_samplestrategy.json
```
#### Running backtest with smaller testset
Please also read about the [strategy startup period](strategy-customization.md#strategy-startup-period).
Use the `--timerange` argument to change how much of the testset
you want to use. The last N ticks/timeframes will be used.
#### Supplying custom fee value
Example:
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 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 --timerange=-200
freqtrade backtesting --fee 0.001
```
#### Advanced use of timerange
!!! 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.
Doing `--timerange=-200` will get the last 200 timeframes
from your inputdata. You can also specify specific dates,
or a range span indexed by start and stop.
#### Running backtest with smaller testset by using timerange
Use the `--timerange` argument to change how much of the testset you want to use.
For example, running backtesting with the `--timerange=20190501-` option will use all available data starting with May 1st, 2019 from your inputdata.
```bash
freqtrade backtesting --timerange=20190501-
```
You can also specify particular dates or a range span indexed by start and stop.
The full timerange specification:
- Use last 123 tickframes of data: `--timerange=-123`
- Use first 123 tickframes of data: `--timerange=123-`
- Use tickframes from line 123 through 456: `--timerange=123-456`
- Use tickframes till 2018/01/31: `--timerange=-20180131`
- Use tickframes since 2018/01/31: `--timerange=20180131-`
- Use tickframes since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
@ -145,47 +119,49 @@ 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 |
```
The 1st table will contain all trades the bot made.
The 1st table contains all trades the bot made, including "left open trades".
The 2nd table will contain a recap of sell reasons.
The 2nd table contains a recap of sell reasons.
This table can tell you which area needs some additional work (i.e. all `sell_signal` trades are losses, so we should disable the sell-signal or work on improving that).
The 3rd table will contain all trades the bot had to `forcesell` at the end of the backtest period to present a full picture.
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,
@ -195,22 +171,16 @@ here:
| TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 243 |
```
We understand the bot has made `429` trades for an average duration of
`4:12:00`, with a performance of `76.20%` (profit), that means it has
The bot has made `429` trades for an average duration of `4:12:00`, with a performance of `76.20%` (profit), that means it has
earned a total of `0.00762792 BTC` starting with a capital of 0.01 BTC.
The column `avg profit %` shows the average profit for all trades made while the column `cum profit %` sums all the profits/losses.
The column `tot profit %` shows instead the total profit % in relation to allocated capital
(`max_open_trades * stake_amount`). In the above results we have `max_open_trades=2 stake_amount=0.005` in config
so `(76.20/100) * (0.005 * 2) =~ 0.00762792 BTC`.
The column `avg profit %` shows the average profit for all trades made while the column `cum profit %` sums up all the profits/losses.
The column `tot profit %` shows instead the total profit % in relation to allocated capital (`max_open_trades * stake_amount`).
In the above results we have `max_open_trades=2` and `stake_amount=0.005` in config so `tot_profit %` will be `(76.20/100) * (0.005 * 2) =~ 0.00762792 BTC`.
As you will see your strategy performance will be influenced by your buy
strategy, your sell strategy, and also by the `minimal_roi` and
`stop_loss` you have set.
Your strategy performance is influenced by your buy strategy, your sell strategy, and also by the `minimal_roi` and `stop_loss` you have set.
As for an example if your minimal_roi is only `"0": 0.01`. You cannot
expect the bot to make more profit than 1% (because it will sell every
time a trade will reach 1%).
For example, if your `minimal_roi` is only `"0": 0.01` you cannot expect the bot to make more profit than 1% (because it will sell every time a trade reaches 1%).
```json
"minimal_roi": {
@ -219,22 +189,42 @@ time a trade will reach 1%).
```
On the other hand, if you set a too high `minimal_roi` like `"0": 0.55`
(55%), there is a lot of chance that the bot will never reach this
profit. Hence, keep in mind that your performance is a mix of your
strategies, your configuration, and the crypto-currency you have set up.
(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.
### 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%)
- 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)
Taking these assumptions, backtesting tries to mirror real trading as closely as possible. However, backtesting will **never** replace running a strategy in dry-run mode.
Also, keep in mind that past results don't guarantee future success.
In addition to the above assumptions, strategy authors should carefully read the [Common Mistakes](strategy-customization.md#common-mistakes-when-developing-strategies) section, to avoid using data in backtesting which is not available in real market conditions.
### Further backtest-result analysis
To further analyze your backtest results, you can [export the trades](#exporting-trades-to-file).
You can then load the trades to perform further analysis as shown in our [data analysis](data-analysis.md#backtesting) backtesting section.
## Backtesting multiple strategies
To backtest multiple strategies, a list of Strategies can be provided.
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
strategies you'd like to compare, this should give a nice runtime boost.
This is limited to 1 timeframe (ticker interval) value per run. However, data is only loaded once from disk so if you have multiple
strategies you'd like to compare, this will give a nice runtime boost.
All listed Strategies need to be in the same directory.
@ -244,14 +234,14 @@ freqtrade backtesting --timerange 20180401-20180410 --ticker-interval 5m --strat
This will save the results to `user_data/backtest_results/backtest-result-<strategy>.json`, injecting the strategy-name into the target filename.
There will be an additional table comparing win/losses of the different strategies (identical to the "Total" row in the first table).
Detailed output for all strategies one after the other will be available, so make sure to scroll up.
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

292
docs/bot-usage.md
View File

@ -5,46 +5,74 @@ This page explains the different parameters of the bot and how to run it.
!!! Note
If you've used `setup.sh`, don't forget to activate your virtual environment (`source .env/bin/activate`) before running freqtrade commands.
## Bot commands
```
usage: freqtrade [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--userdir PATH] [-s NAME] [--strategy-path PATH]
[--db-url PATH] [--sd-notify]
{backtesting,edge,hyperopt,create-userdir,list-exchanges} ...
usage: freqtrade [-h] [-V]
{trade,backtesting,edge,hyperopt,create-userdir,list-exchanges,list-timeframes,download-data,plot-dataframe,plot-profit}
...
Free, open source crypto trading bot
positional arguments:
{backtesting,edge,hyperopt,create-userdir,list-exchanges}
{trade,backtesting,edge,hyperopt,create-userdir,list-exchanges,list-timeframes,download-data,plot-dataframe,plot-profit}
trade Trade module.
backtesting Backtesting module.
edge Edge module.
hyperopt Hyperopt module.
create-userdir Create user-data directory.
list-exchanges Print available exchanges.
list-timeframes Print available ticker intervals (timeframes) for the
exchange.
download-data Download backtesting data.
plot-dataframe Plot candles with indicators.
plot-profit Generate plot showing profits.
optional arguments:
-h, --help show this help message and exit
-V, --version show program's version number and exit
```
### Bot trading commands
```
usage: freqtrade trade [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--userdir PATH] [-s NAME] [--strategy-path PATH]
[--db-url PATH] [--sd-notify] [--dry-run]
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).
--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
Path to userdata directory.
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.
--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).
--sd-notify Notify systemd service manager.
.
```
@ -54,7 +82,7 @@ The bot allows you to select which configuration file you want to use by means o
the `-c/--config` command line option:
```bash
freqtrade -c path/far/far/away/config.json
freqtrade trade -c path/far/far/away/config.json
```
Per default, the bot loads the `config.json` configuration file from the current
@ -67,22 +95,22 @@ The bot allows you to use multiple configuration files by specifying multiple
defined in the latter configuration files override parameters with the same name
defined in the previous configuration files specified in the command line earlier.
For example, you can make a separate configuration file with your key and secrete
For example, you can make a separate configuration file with your key and secret
for the Exchange you use for trading, specify default configuration file with
empty key and secrete values while running in the Dry Mode (which does not actually
empty key and secret values while running in the Dry Mode (which does not actually
require them):
```bash
freqtrade -c ./config.json
freqtrade trade -c ./config.json
```
and specify both configuration files when running in the normal Live Trade Mode:
```bash
freqtrade -c ./config.json -c path/to/secrets/keys.config.json
freqtrade trade -c ./config.json -c path/to/secrets/keys.config.json
```
This could help you hide your private Exchange key and Exchange secrete on you local machine
This could help you hide your private Exchange key and Exchange secret on you local machine
by setting appropriate file permissions for the file which contains actual secrets and, additionally,
prevent unintended disclosure of sensitive private data when you publish examples
of your configuration in the project issues or in the Internet.
@ -100,7 +128,7 @@ user_data/
├── backtest_results
├── data
├── hyperopts
├── hyperopts_results
├── hyperopt_results
├── plot
└── strategies
```
@ -116,10 +144,10 @@ It is recommended to use version control to keep track of changes to your strate
### How to use **--strategy**?
This parameter will allow you to load your custom strategy class.
Per default without `--strategy` or `-s` the bot will load the
`DefaultStrategy` included with the bot (`freqtrade/strategy/default_strategy.py`).
To test the bot installation, you can use the `SampleStrategy` installed by the `create-userdir` subcommand (usually `user_data/strategy/sample_strategy.py`).
The bot will search your strategy file within `user_data/strategies` and `freqtrade/strategy`.
The bot will search your strategy file within `user_data/strategies`.
To use other directories, please read the next section about `--strategy-path`.
To load a strategy, simply pass the class name (e.g.: `CustomStrategy`) in this parameter.
@ -128,7 +156,7 @@ In `user_data/strategies` you have a file `my_awesome_strategy.py` which has
a strategy class called `AwesomeStrategy` to load it:
```bash
freqtrade --strategy AwesomeStrategy
freqtrade trade --strategy AwesomeStrategy
```
If the bot does not find your strategy file, it will display in an error
@ -143,7 +171,7 @@ This parameter allows you to add an additional strategy lookup path, which gets
checked before the default locations (The passed path must be a directory!):
```bash
freqtrade --strategy AwesomeStrategy --strategy-path /some/directory
freqtrade trade --strategy AwesomeStrategy --strategy-path /some/directory
```
#### How to install a strategy?
@ -159,7 +187,7 @@ using `--db-url`. This can also be used to specify a custom database
in production mode. Example command:
```bash
freqtrade -c config.json --db-url sqlite:///tradesv3.dry_run.sqlite
freqtrade trade -c config.json --db-url sqlite:///tradesv3.dry_run.sqlite
```
## Backtesting commands
@ -167,23 +195,30 @@ freqtrade -c config.json --db-url sqlite:///tradesv3.dry_run.sqlite
Backtesting also uses the config specified via `-c/--config`.
```
usage: freqtrade backtesting [-h] [-i TICKER_INTERVAL] [--timerange TIMERANGE]
[--max_open_trades MAX_OPEN_TRADES]
[--stake_amount STAKE_AMOUNT] [-r] [--eps] [--dmmp]
[-l]
[--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
[--export EXPORT] [--export-filename PATH]
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]
[--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
Specify ticker interval (1m, 5m, 30m, 1h, 1d).
Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
`1d`).
--timerange TIMERANGE
Specify what timerange of data to use.
--max_open_trades MAX_OPEN_TRADES
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
Allow buying the same pair multiple times (position
stacking).
@ -193,42 +228,67 @@ optional arguments:
number).
--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]
Provide a space-separated list of strategies to
backtest Please note that ticker-interval needs to be
backtest. Please note that ticker-interval needs to be
set either in config or via command line. When using
this together with --export trades, the strategy-name
is injected into the filename (so backtest-data.json
becomes backtest-data-DefaultStrategy.json
--export EXPORT Export backtest results, argument are: trades. Example
--export=trades
this together with `--export trades`, the strategy-
name is injected into the filename (so `backtest-
data.json` becomes `backtest-data-
DefaultStrategy.json`
--export EXPORT Export backtest results, argument are: trades.
Example: `--export=trades`
--export-filename PATH
Save backtest results to this filename requires
--export to be set as well Example --export-
filename=user_data/backtest_results/backtest_today.json
(default: user_data/backtest_results/backtest-
result.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`
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.
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.
```
### Getting historic data for backtesting
The first time your run Backtesting, you will need to download some historic data first.
This can be accomplished by using `freqtrade download-data`.
Check the corresponding [help page section](backtesting.md#Getting-data-for-backtesting-and-hyperopt) for more details
Check the corresponding [Data Downloading](data-download.md) section for more details
## 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] [-i TICKER_INTERVAL] [--timerange TIMERANGE]
[--max_open_trades INT]
[--stake_amount STAKE_AMOUNT] [-r]
[--customhyperopt NAME] [--hyperopt-path PATH]
[--eps] [-e INT]
[-s {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]]
[--dmmp] [--print-all] [--no-color] [-j JOBS]
[--random-state INT] [--min-trades INT] [--continue]
[--hyperopt-loss NAME]
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]
[--hyperopt NAME] [--hyperopt-path PATH] [--eps]
[-e INT]
[--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
@ -237,22 +297,25 @@ optional arguments:
`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.
--customhyperopt NAME
Specify hyperopt class name (default:
`DefaultHyperOpts`).
--hyperopt-path PATH Specify additional lookup path for Hyperopts and
--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
bot.
--hyperopt-path PATH Specify additional lookup path for Hyperopt and
Hyperopt Loss functions.
--eps, --enable-position-stacking
Allow buying the same pair multiple times (position
stacking).
-e INT, --epochs INT Specify number of epochs (default: 100).
-s {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...], --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
@ -260,6 +323,7 @@ optional arguments:
--print-all Print all results, not only the best ones.
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--print-json Print best results in JSON format.
-j JOBS, --job-workers JOBS
The number of concurrently running jobs for
hyperoptimization (hyperopt worker processes). If -1
@ -277,9 +341,34 @@ optional arguments:
class (IHyperOptLoss). Different functions can
generate completely different results, since the
target for optimization is different. Built-in
Hyperopt-loss-functions are: DefaultHyperOptLoss,
OnlyProfitHyperOptLoss, SharpeHyperOptLoss.
Hyperopt-loss-functions are:
DefaultHyperOptLoss, OnlyProfitHyperOptLoss,
SharpeHyperOptLoss, SharpeHyperOptLossDaily,
SortinoHyperOptLoss, SortinoHyperOptLossDaily.
(default: `DefaultHyperOptLoss`).
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. 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.
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.
```
## Edge commands
@ -287,26 +376,55 @@ optional arguments:
To know your trade expectancy and winrate against historical data, you can use Edge.
```
usage: freqtrade edge [-h] [-i TICKER_INTERVAL] [--timerange TIMERANGE]
[--max_open_trades MAX_OPEN_TRADES]
[--stake_amount STAKE_AMOUNT] [-r]
[--stoplosses STOPLOSS_RANGE]
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]
[--fee FLOAT] [--stoplosses STOPLOSS_RANGE]
optional arguments:
-h, --help show this help message and exit
-i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
Specify ticker interval (1m, 5m, 30m, 1h, 1d).
Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
`1d`).
--timerange TIMERANGE
Specify what timerange of data to use.
--max_open_trades MAX_OPEN_TRADES
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
Defines a range of stoploss against which edge will
assess the strategy the format is "min,max,step"
(without any space).example:
--stoplosses=-0.01,-0.1,-0.001
Defines a range of stoploss values against which edge
will assess the strategy. The format is "min,max,step"
(without any space). Example:
`--stoplosses=-0.01,-0.1,-0.001`
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.
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.
```
To understand edge and how to read the results, please read the [edge documentation](edge.md).

523
docs/configuration.md
View File

@ -38,104 +38,177 @@ The prevelance for all Options is as follows:
Mandatory parameters are marked as **Required**, which means that they are required to be set in one of the possible ways.
| Command | Default | Description |
|----------|---------|-------------|
| `max_open_trades` | 3 | **Required.** Number of trades open your bot will have. If -1 then it is ignored (i.e. potentially unlimited open trades)
| `stake_currency` | BTC | **Required.** Crypto-currency used for trading.
| `stake_amount` | 0.05 | **Required.** Amount of crypto-currency your bot will use for each trade. Per default, the bot will use (0.05 BTC x 3) = 0.15 BTC in total will be always engaged. Set it to `"unlimited"` to allow the bot to use all available balance.
| `amount_reserve_percent` | 0.05 | Reserve some amount in min pair stake amount. Default is 5%. The bot will reserve `amount_reserve_percent` + stop-loss value when calculating min pair stake amount in order to avoid possible trade refusals.
| `ticker_interval` | [1m, 5m, 15m, 30m, 1h, 1d, ...] | The ticker interval to use (1min, 5 min, 15 min, 30 min, 1 hour or 1 day). Default is 5 minutes. [Strategy Override](#parameters-in-the-strategy).
| `fiat_display_currency` | USD | **Required.** Fiat currency used to show your profits. More information below.
| `dry_run` | true | **Required.** Define if the bot must be in Dry-run or production mode.
| `dry_run_wallet` | 999.9 | 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.
| `process_only_new_candles` | false | If set to true indicators are processed only once a new candle arrives. 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).
| `minimal_roi` | See below | Set the threshold in percent the bot will use to sell a trade. More information below. [Strategy Override](#parameters-in-the-strategy).
| `stoploss` | -0.10 | Value of the stoploss in percent used by the bot. More information below. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy).
| `trailing_stop` | false | Enables trailing stop-loss (based on `stoploss` in either configuration or strategy file). More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy).
| `trailing_stop_positive` | 0 | Changes stop-loss once profit has been reached. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy).
| `trailing_stop_positive_offset` | 0 | 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).
| `trailing_only_offset_is_reached` | false | Only apply trailing stoploss when the offset is reached. [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy).
| `unfilledtimeout.buy` | 10 | **Required.** How long (in minutes) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled.
| `unfilledtimeout.sell` | 10 | **Required.** How long (in minutes) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled.
| `bid_strategy.ask_last_balance` | 0.0 | **Required.** Set the bidding price. More information [below](#understand-ask_last_balance).
| `bid_strategy.use_order_book` | false | Allows buying of pair using the rates in Order Book Bids.
| `bid_strategy.order_book_top` | 0 | Bot will use the top N rate in Order Book Bids. Ie. a value of 2 will allow the bot to pick the 2nd bid rate in Order Book Bids.
| `bid_strategy. check_depth_of_market.enabled` | false | Does not buy if the % difference of buy orders and sell orders is met in Order Book.
| `bid_strategy. check_depth_of_market.bids_to_ask_delta` | 0 | 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.
| `ask_strategy.use_order_book` | false | Allows selling of open traded pair using the rates in Order Book Asks.
| `ask_strategy.order_book_min` | 0 | Bot will scan from the top min to max Order Book Asks searching for a profitable rate.
| `ask_strategy.order_book_max` | 0 | Bot will scan from the top min to max Order Book Asks searching for a profitable rate.
| `order_types` | None | 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).
| `order_time_in_force` | None | Configure time in force for buy and sell orders. [More information below](#understand-order_time_in_force). [Strategy Override](#parameters-in-the-strategy).
| `exchange.name` | | **Required.** Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename).
| `exchange.sandbox` | false | 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.
| `exchange.key` | '' | API key to use for the exchange. Only required when you are in production mode. ***Keep it in secrete, do not disclose publicly.***
| `exchange.secret` | '' | API secret to use for the exchange. Only required when you are in production mode. ***Keep it in secrete, do not disclose publicly.***
| `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 secrete, do not disclose publicly.***
| `exchange.pair_whitelist` | [] | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Can be overriden by dynamic pairlists (see [below](#dynamic-pairlists)).
| `exchange.pair_blacklist` | [] | List of pairs the bot must absolutely avoid for trading and backtesting. Can be overriden by dynamic pairlists (see [below](#dynamic-pairlists)).
| `exchange.ccxt_config` | None | 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)
| `exchange.ccxt_async_config` | None | 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)
| `exchange.markets_refresh_interval` | 60 | The interval in minutes in which markets are reloaded.
| `edge` | false | Please refer to [edge configuration document](edge.md) for detailed explanation.
| `experimental.use_sell_signal` | false | Use your sell strategy in addition of the `minimal_roi`. [Strategy Override](#parameters-in-the-strategy).
| `experimental.sell_profit_only` | false | Waits until you have made a positive profit before taking a sell decision. [Strategy Override](#parameters-in-the-strategy).
| `experimental.ignore_roi_if_buy_signal` | false | Does not sell if the buy-signal is still active. Takes preference over `minimal_roi` and `use_sell_signal`. [Strategy Override](#parameters-in-the-strategy).
| `experimental.block_bad_exchanges` | true | Block exchanges known to not work with freqtrade. Leave on default unless you want to test if that exchange works now.
| `pairlist.method` | StaticPairList | Use static or dynamic volume-based pairlist. [More information below](#dynamic-pairlists).
| `pairlist.config` | None | Additional configuration for dynamic pairlists. [More information below](#dynamic-pairlists).
| `telegram.enabled` | true | **Required.** Enable or not the usage of Telegram.
| `telegram.token` | token | Your Telegram bot token. Only required if `telegram.enabled` is `true`. ***Keep it in secrete, do not disclose publicly.***
| `telegram.chat_id` | chat_id | Your personal Telegram account id. Only required if `telegram.enabled` is `true`. ***Keep it in secrete, do not disclose publicly.***
| `webhook.enabled` | false | Enable usage of Webhook notifications
| `webhook.url` | false | URL for the webhook. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details.
| `webhook.webhookbuy` | false | Payload to send on buy. Only required if `webhook.enabled` is `true`. See the [webhook documentationV](webhook-config.md) for more details.
| `webhook.webhooksell` | false | Payload to send on sell. Only required if `webhook.enabled` is `true`. See the [webhook documentationV](webhook-config.md) for more details.
| `webhook.webhookstatus` | false | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentationV](webhook-config.md) for more details.
| `db_url` | `sqlite:///tradesv3.sqlite`| Declares database URL to use. NOTE: This defaults to `sqlite://` if `dry_run` is `True`.
| `initial_state` | running | Defines the initial application state. More information below.
| `forcebuy_enable` | false | Enables the RPC Commands to force a buy. More information below.
| `strategy` | DefaultStrategy | Defines Strategy class to use.
| `strategy_path` | null | Adds an additional strategy lookup path (must be a directory).
| `internals.process_throttle_secs` | 5 | **Required.** Set the process throttle. Value in second.
| `internals.sd_notify` | false | 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.
| `logfile` | | Specify Logfile. Uses a rolling strategy of 10 files, with 1Mb per file.
| `user_data_dir` | cwd()/user_data | Directory containing user data. Defaults to `./user_data/`.
| 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.
| `ticker_interval` | The timeframe (ticker interval) to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
| `fiat_display_currency` | Fiat currency used to show your profits. [More information below](#what-values-can-be-used-for-fiat_display_currency). <br> **Datatype:** String
| `dry_run` | **Required.** Define if the bot must be in Dry Run or production mode. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
| `dry_run_wallet` | Define the starting amount in stake currency for the simulated wallet used by the bot running in the Dry Run mode.<br>*Defaults to `1000`.* <br> **Datatype:** Float
| `process_only_new_candles` | Enable processing of indicators only when new candles arrive. If false each loop populates the indicators, this will mean the same candle is processed many times creating system load but can be useful of your strategy depends on tick data not only candle. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `minimal_roi` | **Required.** Set the threshold in percent the bot will use to sell a trade. [More information below](#understand-minimal_roi). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
| `stoploss` | **Required.** Value of the stoploss in percent used by the bot. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Float (as ratio)
| `trailing_stop` | Enables trailing stoploss (based on `stoploss` in either configuration or strategy file). More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Boolean
| `trailing_stop_positive` | Changes stoploss once profit has been reached. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Float
| `trailing_stop_positive_offset` | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0` (no offset).* <br> **Datatype:** Float
| `trailing_only_offset_is_reached` | Only apply trailing stoploss when the offset is reached. [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `unfilledtimeout.buy` | **Required.** How long (in minutes) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
| `unfilledtimeout.sell` | **Required.** How long (in minutes) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
| `bid_strategy.price_side` | Select the side of the spread the bot should look at to get the buy rate. [More information below](#buy-price-side).<br> *Defaults to `bid`.* <br> **Datatype:** String (either `ask` or `bid`).
| `bid_strategy.ask_last_balance` | **Required.** Set the bidding price. More information [below](#buy-price-without-orderbook-enabled).
| `bid_strategy.use_order_book` | Enable buying using the rates in [Order Book Bids](#buy-price-with-orderbook-enabled). <br> **Datatype:** Boolean
| `bid_strategy.order_book_top` | Bot will use the top N rate in Order Book Bids to buy. I.e. a value of 2 will allow the bot to pick the 2nd bid rate in [Order Book Bids](#buy-price-with-orderbook-enabled). <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
| `bid_strategy. check_depth_of_market.enabled` | Do not buy if the difference of buy orders and sell orders is met in Order Book. [Check market depth](#check-depth-of-market). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `bid_strategy. check_depth_of_market.bids_to_ask_delta` | The difference ratio of buy orders and sell orders found in Order Book. A value below 1 means sell order size is greater, while value greater than 1 means buy order size is higher. [Check market depth](#check-depth-of-market) <br> *Defaults to `0`.* <br> **Datatype:** Float (as ratio)
| `ask_strategy.price_side` | Select the side of the spread the bot should look at to get the sell rate. [More information below](#sell-price-side).<br> *Defaults to `ask`.* <br> **Datatype:** String (either `ask` or `bid`).
| `ask_strategy.use_order_book` | Enable selling of open trades using [Order Book Asks](#sell-price-with-orderbook-enabled). <br> **Datatype:** Boolean
| `ask_strategy.order_book_min` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
| `ask_strategy.order_book_max` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
| `ask_strategy.use_sell_signal` | Use sell signals produced by the strategy in addition to the `minimal_roi`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `true`.* <br> **Datatype:** Boolean
| `ask_strategy.sell_profit_only` | Wait until the bot makes a positive profit before taking a sell decision. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `ask_strategy.ignore_roi_if_buy_signal` | Do not sell if the buy signal is still active. This setting takes preference over `minimal_roi` and `use_sell_signal`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `order_types` | Configure order-types depending on the action (`"buy"`, `"sell"`, `"stoploss"`, `"stoploss_on_exchange"`). [More information below](#understand-order_types). [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Dict
| `order_time_in_force` | Configure time in force for buy and sell orders. [More information below](#understand-order_time_in_force). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
| `exchange.name` | **Required.** Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename). <br> **Datatype:** String
| `exchange.sandbox` | Use the 'sandbox' version of the exchange, where the exchange provides a sandbox for risk-free integration. See [here](sandbox-testing.md) in more details.<br> **Datatype:** Boolean
| `exchange.key` | API key to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
| `exchange.secret` | API secret to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
| `exchange.password` | API password to use for the exchange. Only required when you are in production mode and for exchanges that use password for API requests.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
| `exchange.pair_whitelist` | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Not used by VolumePairList (see [below](#dynamic-pairlists)). <br> **Datatype:** List
| `exchange.pair_blacklist` | List of pairs the bot must absolutely avoid for trading and backtesting (see [below](#dynamic-pairlists)). <br> **Datatype:** List
| `exchange.ccxt_config` | Additional CCXT parameters passed to the regular ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
| `exchange.ccxt_async_config` | Additional CCXT parameters passed to the async ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
| `exchange.markets_refresh_interval` | The interval in minutes in which markets are reloaded. <br>*Defaults to `60` minutes.* <br> **Datatype:** Positive Integer
| `edge.*` | Please refer to [edge configuration document](edge.md) for detailed explanation.
| `experimental.block_bad_exchanges` | Block exchanges known to not work with freqtrade. Leave on default unless you want to test if that exchange works now. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
| `pairlists` | Define one or more pairlists to be used. [More information below](#dynamic-pairlists). <br>*Defaults to `StaticPairList`.* <br> **Datatype:** List of Dicts
| `telegram.enabled` | Enable the usage of Telegram. <br> **Datatype:** Boolean
| `telegram.token` | Your Telegram bot token. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
| `telegram.chat_id` | Your personal Telegram account id. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
| `webhook.enabled` | Enable usage of Webhook notifications <br> **Datatype:** Boolean
| `webhook.url` | URL for the webhook. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
| `webhook.webhookbuy` | Payload to send on buy. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
| `webhook.webhookbuycancel` | Payload to send on buy order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
| `webhook.webhooksell` | Payload to send on sell. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
| `webhook.webhooksellcancel` | Payload to send on sell order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
| `webhook.webhookstatus` | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
| `api_server.enabled` | Enable usage of API Server. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** Boolean
| `api_server.listen_ip_address` | Bind IP address. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** IPv4
| `api_server.listen_port` | Bind Port. See the [API Server documentation](rest-api.md) for more details. <br>**Datatype:** Integer between 1024 and 65535
| `api_server.username` | Username for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
| `api_server.password` | Password for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
| `db_url` | Declares database URL to use. NOTE: This defaults to `sqlite:///tradesv3.dryrun.sqlite` if `dry_run` is `true`, and to `sqlite:///tradesv3.sqlite` for production instances. <br> **Datatype:** String, SQLAlchemy connect string
| `initial_state` | Defines the initial application state. More information below. <br>*Defaults to `stopped`.* <br> **Datatype:** Enum, either `stopped` or `running`
| `forcebuy_enable` | Enables the RPC Commands to force a buy. More information below. <br> **Datatype:** Boolean
| `strategy` | **Required** Defines Strategy class to use. Recommended to be set via `--strategy NAME`. <br> **Datatype:** ClassName
| `strategy_path` | Adds an additional strategy lookup path (must be a directory). <br> **Datatype:** String
| `internals.process_throttle_secs` | Set the process throttle. Value in second. <br>*Defaults to `5` seconds.* <br> **Datatype:** Positive Intege
| `internals.heartbeat_interval` | Print heartbeat message every N seconds. Set to 0 to disable heartbeat messages. <br>*Defaults to `60` seconds.* <br> **Datatype:** Positive Integer or 0
| `internals.sd_notify` | Enables use of the sd_notify protocol to tell systemd service manager about changes in the bot state and issue keep-alive pings. See [here](installation.md#7-optional-configure-freqtrade-as-a-systemd-service) for more details. <br> **Datatype:** Boolean
| `logfile` | Specifies logfile name. Uses a rolling strategy for log file rotation for 10 files with the 1MB limit per file. <br> **Datatype:** String
| `user_data_dir` | Directory containing user data. <br> *Defaults to `./user_data/`*. <br> **Datatype:** String
| `dataformat_ohlcv` | Data format to use to store historical candle (OHLCV) data. <br> *Defaults to `json`*. <br> **Datatype:** String
| `dataformat_trades` | Data format to use to store historical trades data. <br> *Defaults to `jsongz`*. <br> **Datatype:** String
### Parameters in the strategy
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.
* `ticker_interval`
* `minimal_roi`
* `ticker_interval`
* `stoploss`
* `trailing_stop`
* `trailing_stop_positive`
* `trailing_stop_positive_offset`
* `trailing_only_offset_is_reached`
* `process_only_new_candles`
* `order_types`
* `order_time_in_force`
* `use_sell_signal` (experimental)
* `sell_profit_only` (experimental)
* `ignore_roi_if_buy_signal` (experimental)
* `stake_currency`
* `stake_amount`
* `unfilledtimeout`
* `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.
The minimal value is 0.0005. If there is not enough crypto-currency in
the account an exception is generated.
To allow the bot to trade all the available `stake_currency` in your account set
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.
#### 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`.
!!! Note
This setting respects the [available balance configuration](#available-balance).
#### 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:
```python
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,
```
In this case a trade amount is calclulated as:
!!! 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).
```python
currency_balanse / (max_open_trades - current_open_trades)
```
!!! 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
@ -157,6 +230,9 @@ 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.
@ -189,13 +265,6 @@ 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.
@ -214,6 +283,11 @@ If this is configured, the following 4 values (`buy`, `sell`, `stoploss` and
`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% (configurable via `stoploss_on_exchange_limit_ratio`).
Calculation example: we bought the asset at 100$.
Stop-price is 95$, then limit would be `95 * 0.99 = 94.05$` - so the stoploss will happen between 95$ and 94.05$.
Syntax for Strategy:
```python
@ -223,7 +297,8 @@ order_types = {
"emergencysell": "market",
"stoploss": "market",
"stoploss_on_exchange": False,
"stoploss_on_exchange_interval": 60
"stoploss_on_exchange_interval": 60,
"stoploss_on_exchange_limit_ratio": 0.99,
}
```
@ -253,7 +328,7 @@ Configuration:
!!! Note
If `stoploss_on_exchange` is enabled and the stoploss is cancelled manually on the exchange, then the bot will create a new order.
!!! Warning stoploss_on_exchange failures
!!! 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.
### Understand order_time_in_force
@ -267,7 +342,7 @@ This is most of the time the default time in force. It means the order will rema
on exchange till it is canceled by user. It can be fully or partially fulfilled.
If partially fulfilled, the remaining will stay on the exchange till cancelled.
**FOK (Full Or Kill):**
**FOK (Fill Or Kill):**
It means if the order is not executed immediately AND fully then it is canceled by the exchange.
@ -297,16 +372,18 @@ The possible values are: `gtc` (default), `fok` or `ioc`.
Freqtrade is based on [CCXT library](https://github.com/ccxt/ccxt) that supports over 100 cryptocurrency
exchange markets and trading APIs. The complete up-to-date list can be found in the
[CCXT repo homepage](https://github.com/ccxt/ccxt/tree/master/python). However, the bot was tested
with only Bittrex and Binance.
The bot was tested with the following exchanges:
[CCXT repo homepage](https://github.com/ccxt/ccxt/tree/master/python).
However, the bot was tested by the development team with only Bittrex, Binance and Kraken,
so the these are the only officially supported exhanges:
- [Bittrex](https://bittrex.com/): "bittrex"
- [Binance](https://www.binance.com/): "binance"
- [Kraken](https://kraken.com/): "kraken"
Feel free to test other exchanges and submit your PR to improve the bot.
Some exchanges require special configuration, which can be found on the [Exchange-specific Notes](exchanges.md) documentation page.
#### Sample exchange configuration
A exchange configuration for "binance" would look as follows:
@ -330,13 +407,13 @@ This configuration enables binance, as well as rate limiting to avoid bans from
Optimal settings for rate limiting depend on the exchange and the size of the whitelist, so an ideal parameter will vary on many other settings.
We try to provide sensible defaults per exchange where possible, if you encounter bans please make sure that `"enableRateLimit"` is enabled and increase the `"rateLimit"` parameter step by step.
#### Advanced FreqTrade Exchange configuration
#### Advanced Freqtrade Exchange configuration
Advanced options can be configured using the `_ft_has_params` setting, which will override Defaults and exchange-specific behaviours.
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": {
@ -369,6 +446,206 @@ The valid values are:
"BTC", "ETH", "XRP", "LTC", "BCH", "USDT"
```
## Prices used for orders
Prices for regular orders can be controlled via the parameter structures `bid_strategy` for buying and `ask_strategy` for selling.
Prices are always retrieved right before an order is placed, either by querying the exchange tickers or by using the orderbook data.
!!! Note
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.
### Buy price
#### 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.
## Pairlists
Pairlists define the list of pairs that the bot should trade.
There are [`StaticPairList`](#static-pair-list) and dynamic Whitelists available.
[`PrecisionFilter`](#precision-filter) and [`PriceFilter`](#price-pair-filter) act as filters, removing low-value pairs.
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.
Inactive markets and blacklisted pairs are always removed from the resulting `pair_whitelist`.
### Available Pairlists
* [`StaticPairList`](#static-pair-list) (default, if not configured differently)
* [`VolumePairList`](#volume-pair-list)
* [`PrecisionFilter`](#precision-filter)
* [`PriceFilter`](#price-pair-filter)
* [`SpreadFilter`](#spread-filter)
!!! Tip "Testing pairlists"
Pairlist configurations can be quite tricky to get right. Best use the [`test-pairlist`](utils.md#test-pairlist) subcommand to test your configuration quickly.
#### Static Pair List
By default, the `StaticPairList` method is used, which uses a statically defined pair whitelist from the configuration.
It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklist`.
```json
"pairlists": [
{"method": "StaticPairList"}
],
```
#### 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` 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.
`refresh_period` allows setting the period (in seconds), at which the pairlist will be refreshed. Defaults to 1800s (30 minutes).
`VolumePairList` is based on the ticker data, as reported by the ccxt library:
* The `bidVolume` is the volume (amount) of current best bid in the orderbook.
* The `askVolume` is the volume (amount) of current best ask in the orderbook.
* The `quoteVolume` is the amount of quote (stake) currency traded (bought or sold) in last 24 hours.
```json
"pairlists": [{
"method": "VolumePairList",
"number_assets": 20,
"sort_key": "quoteVolume",
"refresh_period": 1800,
],
```
#### Precision Filter
Filters low-value coins which would not allow setting a stoploss.
#### Price Pair Filter
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.
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.
#### Spread Filter
Removes pairs that have a difference between asks and bids above the specified ratio (default `0.005`).
Example:
If `DOGE/BTC` maximum bid is 0.00000026 and minimum ask is 0.00000027 the ratio is calculated as: `1 - bid/ask ~= 0.037` which is `> 0.005`
### Full Pairlist example
The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets, sorting by `quoteVolume` and applies both [`PrecisionFilter`](#precision-filter) and [`PriceFilter`](#price-pair-filter), filtering all assets where 1 priceunit is > 1%.
```json
"exchange": {
"pair_whitelist": [],
"pair_blacklist": ["BNB/BTC"]
},
"pairlists": [
{
"method": "VolumePairList",
"number_assets": 20,
"sort_key": "quoteVolume",
},
{"method": "PrecisionFilter"},
{"method": "PriceFilter", "low_price_ratio": 0.01}
],
```
## Switch to Dry-run mode
We recommend starting the bot in the Dry-run mode to see how your bot will
@ -384,7 +661,7 @@ creating trades on the exchange.
"db_url": "sqlite:///tradesv3.dryrun.sqlite",
```
3. Remove your Exchange API key and secrete (change them by empty values or fake credentials):
3. Remove your Exchange API key and secret (change them by empty values or fake credentials):
```json
"exchange": {
@ -395,41 +672,18 @@ 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.
### Dynamic Pairlists
!!! Note
A simulated wallet is available during dry-run mode, and will assume a starting capital of `dry_run_wallet` (defaults to 1000).
Dynamic pairlists select pairs for you based on the logic configured.
The bot runs against all pairs (with that stake) on the exchange, and a number of assets
(`number_assets`) is selected based on the selected criteria.
### Considerations for dry-run
By default, the `StaticPairList` method is used.
The Pairlist method is configured as `pair_whitelist` parameter under the `exchange`
section of the configuration.
**Available Pairlist methods:**
* `StaticPairList`
* It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklist`.
* `VolumePairList`
* It selects `number_assets` top pairs based on `sort_key`, which can be one of
`askVolume`, `bidVolume` and `quoteVolume`, defaults to `quoteVolume`.
* There is a possibility to filter low-value coins that would not allow setting a stop loss
(set `precision_filter` parameter to `true` for this).
Example:
```json
"pairlist": {
"method": "VolumePairList",
"config": {
"number_assets": 20,
"sort_key": "quoteVolume",
"precision_filter": false
}
},
```
* 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
@ -437,6 +691,11 @@ 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.**
@ -456,12 +715,11 @@ you run it in production mode.
"secret": "08a9dc6db3d7b53e1acebd9275677f4b0a04f1a5",
...
}
```
!!! Note
If you have an exchange API key yet, [see our tutorial](/pre-requisite).
### Using proxy with FreqTrade
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
To use a proxy with freqtrade, add the kwarg `"aiohttp_trust_env"=true` to the `"ccxt_async_kwargs"` dict in the exchange section of the configuration.
@ -481,14 +739,13 @@ export HTTPS_PROXY="http://addr:port"
freqtrade
```
## Embedding Strategies
### 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.
#### Encoding a string as BASE64
### Encoding a string as BASE64
This is a quick example, how to generate the BASE64 string in python

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@ -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.
@ -61,34 +82,6 @@ except:
print(Path.cwd())
```
## Load existing objects into a Jupyter notebook
These examples assume that you have already generated data using the cli. They will allow you to drill deeper into your results, and perform analysis which otherwise would make the output very difficult to digest due to information overload.
### Load backtest results into a pandas dataframe
```python
from freqtrade.data.btanalysis import load_backtest_data
# Load backtest results
df = load_backtest_data("user_data/backtest_results/backtest-result.json")
# Show value-counts per pair
df.groupby("pair")["sell_reason"].value_counts()
```
### Load live trading results into a pandas dataframe
``` python
from freqtrade.data.btanalysis import load_trades_from_db
# Fetch trades from database
df = load_trades_from_db("sqlite:///tradesv3.sqlite")
# Display results
df.groupby("pair")["sell_reason"].value_counts()
```
### Load multiple configuration files
This option can be useful to inspect the results of passing in multiple configs.
@ -114,99 +107,9 @@ Best avoid relative paths, since this starts at the storage location of the jupy
}
```
### Load exchange data to a pandas dataframe
### Further Data analysis documentation
This loads candle data to a dataframe
```python
from pathlib import Path
from freqtrade.data.history import load_pair_history
# Load data using values passed to function
ticker_interval = "5m"
data_location = Path('user_data', 'data', 'bitrex')
pair = "BTC_USDT"
candles = load_pair_history(datadir=data_location,
ticker_interval=ticker_interval,
pair=pair)
# Confirm success
print(f"Loaded len(candles) rows of data for {pair} from {data_location}")
candles.head()
```
## Strategy debugging example
Debugging a strategy can be time-consuming. FreqTrade offers helper functions to visualize raw data.
### Define variables used in analyses
You can override strategy settings as demonstrated below.
```python
# Customize these according to your needs.
# Define some constants
ticker_interval = "5m"
# Name of the strategy class
strategy_name = 'SampleStrategy'
# Path to user data
user_data_dir = 'user_data'
# Location of the strategy
strategy_location = Path(user_data_dir, 'strategies')
# Location of the data
data_location = Path(user_data_dir, 'data', 'binance')
# Pair to analyze - Only use one pair here
pair = "BTC_USDT"
```
### Load exchange data
```python
from pathlib import Path
from freqtrade.data.history import load_pair_history
# Load data using values set above
candles = load_pair_history(datadir=data_location,
ticker_interval=ticker_interval,
pair=pair)
# Confirm success
print(f"Loaded {len(candles)} rows of data for {pair} from {data_location}")
candles.head()
```
### Load and run strategy
* Rerun each time the strategy file is changed
```python
from freqtrade.resolvers import StrategyResolver
# Load strategy using values set above
strategy = StrategyResolver({'strategy': strategy_name,
'user_data_dir': user_data_dir,
'strategy_path': strategy_location}).strategy
# Generate buy/sell signals using strategy
df = strategy.analyze_ticker(candles, {'pair': pair})
```
### Display the trade details
* Note that using `data.tail()` is preferable to `data.head()` as most indicators have some "startup" data at the top of the dataframe.
* Some possible problems
* Columns with NaN values at the end of the dataframe
* Columns used in `crossed*()` functions with completely different units
* Comparison with full backtest
* having 200 buy signals as output for one pair from `analyze_ticker()` does not necessarily mean that 200 trades will be made during backtesting.
* Assuming you use only one condition such as, `df['rsi'] < 30` as buy condition, this will generate multiple "buy" signals for each pair in sequence (until rsi returns > 29). The bot will only buy on the first of these signals (and also only if a trade-slot ("max_open_trades") is still available), or on one of the middle signals, as soon as a "slot" becomes available.
```python
# Report results
print(f"Generated {df['buy'].sum()} buy signals")
data = df.set_index('date', drop=True)
data.tail()
```
* [Strategy debugging](strategy_analysis_example.md) - also available as Jupyter notebook (`user_data/notebooks/strategy_analysis_example.ipynb`)
* [Plotting](plotting.md)
Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data.

232
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@ -0,0 +1,232 @@
# Data Downloading
## Getting data for backtesting and hyperopt
To download data (candles / OHLCV) needed for backtesting and hyperoptimization use the `freqtrade download-data` command.
If no additional parameter is specified, freqtrade will download data for `"1m"` and `"5m"` timeframes for the last 30 days.
Exchange and pairs will come from `config.json` (if specified using `-c/--config`).
Otherwise `--exchange` becomes mandatory.
!!! Tip "Tip: Updating existing data"
If you already have backtesting data available in your data-directory and would like to refresh this data up to today, use `--days xx` with a number slightly higher than the missing number of days. Freqtrade will keep the available data and only download the missing data.
Be carefull though: If the number is too small (which would result in a few missing days), the whole dataset will be removed and only xx days will be downloaded.
### Usage
```
usage: freqtrade download-data [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [-p PAIRS [PAIRS ...]]
[--pairs-file FILE] [--days INT] [--dl-trades] [--exchange EXCHANGE]
[-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]]
[--erase] [--data-format-ohlcv {json,jsongz}] [--data-format-trades {json,jsongz}]
optional arguments:
-h, --help show this help message and exit
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Show profits for only these pairs. Pairs are space-separated.
--pairs-file FILE File containing a list of pairs to download.
--days INT Download data for given number of days.
--dl-trades Download trades instead of OHLCV data. The bot will resample trades to the desired timeframe as specified as
--timeframes/-t.
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no config is provided.
-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]
Specify which tickers to download. Space-separated list. Default: `1m 5m`.
--erase Clean all existing data for the selected exchange/pairs/timeframes.
--data-format-ohlcv {json,jsongz}
Storage format for downloaded candle (OHLCV) data. (default: `json`).
--data-format-trades {json,jsongz}
Storage format for downloaded trades data. (default: `jsongz`).
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are: 'syslog', 'journald'. See the documentation for more details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: `config.json`). Multiple --config options may be used. Can be set to `-`
to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```
### Data format
Freqtrade currently supports 2 dataformats, `json` (plain "text" json files) and `jsongz` (a gzipped version of json files).
By default, OHLCV data is stored as `json` data, while trades data is stored as `jsongz` data.
This can be changed via the `--data-format-ohlcv` and `--data-format-trades` parameters respectivly.
If the default dataformat has been changed during download, then the keys `dataformat_ohlcv` and `dataformat_trades` in the configuration file need to be adjusted to the selected dataformat as well.
!!! Note
You can convert between data-formats using the [convert-data](#subcommand-convert-data) and [convert-trade-data](#subcommand-convert-trade-data) methods.
#### Subcommand convert data
```
usage: freqtrade convert-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH]
[-p PAIRS [PAIRS ...]] --format-from
{json,jsongz} --format-to {json,jsongz}
[--erase]
[-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]]
optional arguments:
-h, --help show this help message and exit
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Show profits for only these pairs. Pairs are space-
separated.
--format-from {json,jsongz}
Source format for data conversion.
--format-to {json,jsongz}
Destination format for data conversion.
--erase Clean all existing data for the selected
exchange/pairs/timeframes.
-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]
Specify which tickers to download. Space-separated
list. Default: `1m 5m`.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: `config.json`).
Multiple --config options may be used. Can be set to
`-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```
##### Example converting data
The following command will convert all candle (OHLCV) data available in `~/.freqtrade/data/binance` from json to jsongz, saving diskspace in the process.
It'll also remove original json data files (`--erase` parameter).
``` bash
freqtrade convert-data --format-from json --format-to jsongz --data-dir ~/.freqtrade/data/binance -t 5m 15m --erase
```
#### Subcommand convert-trade data
```
usage: freqtrade convert-trade-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH]
[-p PAIRS [PAIRS ...]] --format-from
{json,jsongz} --format-to {json,jsongz}
[--erase]
optional arguments:
-h, --help show this help message and exit
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Show profits for only these pairs. Pairs are space-
separated.
--format-from {json,jsongz}
Source format for data conversion.
--format-to {json,jsongz}
Destination format for data conversion.
--erase Clean all existing data for the selected
exchange/pairs/timeframes.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: `config.json`).
Multiple --config options may be used. Can be set to
`-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```
##### Example converting trades
The following command will convert all available trade-data in `~/.freqtrade/data/kraken` from jsongz to json.
It'll also remove original jsongz data files (`--erase` parameter).
``` bash
freqtrade convert-trade-data --format-from jsongz --format-to json --data-dir ~/.freqtrade/data/kraken --erase
```
### Pairs file
In alternative to the whitelist from `config.json`, a `pairs.json` file can be used.
If you are using Binance for example:
- create a directory `user_data/data/binance` and copy or create the `pairs.json` file in that directory.
- update the `pairs.json` file to contain the currency pairs you are interested in.
```bash
mkdir -p user_data/data/binance
cp freqtrade/tests/testdata/pairs.json user_data/data/binance
```
The format of the `pairs.json` file is a simple json list.
Mixing different stake-currencies is allowed for this file, since it's only used for downloading.
``` json
[
"ETH/BTC",
"ETH/USDT",
"BTC/USDT",
"XRP/ETH"
]
```
### Start download
Then run:
```bash
freqtrade download-data --exchange binance
```
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 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 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
By default, `download-data` subcommand downloads Candles (OHLCV) data. Some exchanges also provide historic trade-data via their API.
This data can be useful if you need many different timeframes, since it is only downloaded once, and then resampled locally to the desired timeframes.
Since this data is large by default, the files use gzip by default. They are stored in your data-directory with the naming convention of `<pair>-trades.json.gz` (`ETH_BTC-trades.json.gz`). Incremental mode is also supported, as for historic OHLCV data, so downloading the data once per week with `--days 8` will create an incremental data-repository.
To use this mode, simply add `--dl-trades` to your call. This will swap the download method to download trades, and resamples the data locally.
Example call:
```bash
freqtrade download-data --exchange binance --pairs XRP/ETH ETH/BTC --days 20 --dl-trades
```
!!! Note
While this method uses async calls, it will be slow, since it requires the result of the previous call to generate the next request to the exchange.
!!! Warning
The historic trades are not available during Freqtrade dry-run and live trade modes because all exchanges tested provide this data with a delay of few 100 candles, so it's not suitable for real-time trading.
!!! Note "Kraken user"
Kraken users should read [this](exchanges.md#historic-kraken-data) before starting to download data.
## Next step
Great, you now have backtest data downloaded, so you can now start [backtesting](backtesting.md) your strategy.

165
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@ -1,8 +1,8 @@
# 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/enQtNjU5ODcwNjI1MDU3LWEyODBiNzkzNzcyNzU0MWYyYzE5NjIyOTQxMzBmMGUxOTIzM2YyN2Y4NWY1YTEwZDgwYTRmMzE2NmM5ZmY2MTg) where you can ask questions.
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.
## Documentation
@ -38,8 +38,53 @@ def test_method_to_test(caplog):
assert log_has("This event happened", caplog)
# Check regex with trailing number ...
assert log_has_re(r"This dynamic event happened and produced \d+", caplog)
```
### Local docker usage
The fastest and easiest way to start up is to use docker-compose.develop which gives developers the ability to start the bot up with all the required dependencies, *without* needing to install any freqtrade specific dependencies on your local machine.
#### Install
* [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
* [docker](https://docs.docker.com/install/)
* [docker-compose](https://docs.docker.com/compose/install/)
#### Starting the bot
##### Use the develop dockerfile
``` bash
rm docker-compose.yml && mv docker-compose.develop.yml docker-compose.yml
```
#### Docker Compose
##### Starting
``` bash
docker-compose up
```
![Docker compose up](https://user-images.githubusercontent.com/419355/65456322-47f63a80-de06-11e9-90c6-3c74d1bad0b8.png)
##### Rebuilding
``` bash
docker-compose build
```
##### Execing (effectively SSH into the container)
The `exec` command requires that the container already be running, if you want to start it
that can be effected by `docker-compose up` or `docker-compose run freqtrade_develop`
``` bash
docker-compose exec freqtrade_develop /bin/bash
```
![image](https://user-images.githubusercontent.com/419355/65456522-ba671a80-de06-11e9-9598-df9ca0d8dcac.png)
## Modules
### Dynamic Pairlist
@ -55,22 +100,22 @@ This is a simple provider, which however serves as a good example on how to star
Next, modify the classname of the provider (ideally align this with the Filename).
The base-class provides the an instance of the bot (`self._freqtrade`), as well as the configuration (`self._config`), and initiates both `_blacklist` and `_whitelist`.
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.
```python
self._freqtrade = freqtrade
self._exchange = exchange
self._pairlistmanager = pairlistmanager
self._config = config
self._whitelist = self._config['exchange']['pair_whitelist']
self._blacklist = self._config['exchange'].get('pair_blacklist', [])
self._pairlistconfig = pairlistconfig
self._pairlist_pos = pairlist_pos
```
Now, let's step through the methods which require actions:
#### configuration
#### Pairlist configuration
Configuration for PairListProvider is done in the bot configuration file in the element `"pairlist"`.
This Pairlist-object may contain a `"config"` dict with additional configurations for the configured 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.
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.
@ -80,34 +125,35 @@ Additional elements can be configured as needed. `VolumePairList` uses `"sort_ke
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"`.
#### refresh_pairlist
#### filter_pairlist
Override this method and run all calculations needed in this method.
This is called with each iteration of the bot - so consider implementing caching for compute/network heavy calculations.
Assign the resulting whiteslist to `self._whitelist` and `self._blacklist` respectively. These will then be used to run the bot in this iteration. Pairs with open trades will be added to the whitelist to have the sell-methods run correctly.
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()`.
Please also run `self._validate_whitelist(pairs)` and to check and remove pairs with inactive markets. This function is available in the Parent class (`StaticPairList`) and should ideally not be overwritten.
It must return the resulting pairlist (which may then be passed into the next pairlist filter).
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.
##### sample
``` python
def refresh_pairlist(self) -> None:
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
# Generate dynamic whitelist
pairs = self._gen_pair_whitelist(self._config['stake_currency'], self._sort_key)
# Validate whitelist to only have active market pairs
self._whitelist = self._validate_whitelist(pairs)[:self._number_pairs]
pairs = self._calculate_pairlist(pairlist, tickers)
return pairs
```
#### _gen_pair_whitelist
This is a simple method used by `VolumePairList` - however serves as a good example.
It implements caching (`@cached(TTLCache(maxsize=1, ttl=1800))`) as well as a configuration option to allow different (but similar) strategies to work with the same PairListProvider.
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.
@ -119,7 +165,7 @@ Since CCXT does not provide unification for Stoploss On Exchange yet, we'll need
### Incomplete candles
While fetching OHLCV data, we're may end up getting incomplete candles (Depending on the exchange).
While fetching candle (OHLCV) data, we may end up getting incomplete candles (depending on the exchange).
To demonstrate this, we'll use daily candles (`"1d"`) to keep things simple.
We query the api (`ct.fetch_ohlcv()`) for the timeframe and look at the date of the last entry. If this entry changes or shows the date of a "incomplete" candle, then we should drop this since having incomplete candles is problematic because indicators assume that only complete candles are passed to them, and will generate a lot of false buy signals. By default, we're therefore removing the last candle assuming it's incomplete.
@ -128,26 +174,50 @@ 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
To keep the jupyter notebooks aligned with the documentation, the following should be ran after updating a example notebook.
``` bash
jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace freqtrade/templates/strategy_analysis_example.ipynb
jupyter nbconvert --ClearOutputPreprocessor.enabled=True --to markdown freqtrade/templates/strategy_analysis_example.ipynb --stdout > docs/strategy_analysis_example.md
```
## Continuous integration
This documents some decisions taken for the CI Pipeline.
* CI runs on all OS variants, Linux (ubuntu), macOS and Windows.
* Docker images are build for the branches `master` and `develop`.
* Raspberry PI Docker images are postfixed with `_pi` - so tags will be `:master_pi` and `develop_pi`.
* Docker images contain a file, `/freqtrade/freqtrade_commit` containing the commit this image is based of.
* Full docker image rebuilds are run once a week via schedule.
* Deployments run on ubuntu.
* ta-lib binaries are contained in the build_helpers directory to avoid fails related to external unavailability.
* All tests must pass for a PR to be merged to `master` or `develop`.
## Creating a release
@ -155,38 +225,65 @@ This part of the documentation is aimed at maintainers, and shows how to create
### Create release branch
``` bash
# make sure you're in develop branch
git checkout develop
First, pick a commit that's about one week old (to not include latest additions to releases).
``` bash
# create new branch
git checkout -b new_release
git checkout -b new_release <commitid>
```
* 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.
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. 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
### Create changelog from git commits
!!! Note
Make sure that both master and develop are up-todate!.
Make sure that the master branch is uptodate!
``` bash
# Needs to be done before merging / pulling that branch.
git log --oneline --no-decorate --no-merges master..develop
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):
* Use the button "Draft a new release" in the Github UI (subsection releases)
* 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)
### After-release
## Releases
* Update version in develop by postfixing that with `-dev` (`2019.6 -> 2019.6-dev`).
* Create a PR against develop to update that branch.
### 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.

146
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@ -1,4 +1,4 @@
# Using FreqTrade with Docker
# Using Freqtrade with Docker
## Install Docker
@ -8,13 +8,141 @@ Start by downloading and installing Docker CE for your platform:
* [Windows](https://docs.docker.com/docker-for-windows/install/)
* [Linux](https://docs.docker.com/install/)
Optionally, [docker-compose](https://docs.docker.com/compose/install/) should be installed and available to follow the [docker quick start guide](#docker-quick-start).
Once you have Docker installed, simply prepare the config file (e.g. `config.json`) and run the image for `freqtrade` as explained below.
## Download the official FreqTrade docker image
## Freqtrade with docker-compose
Freqtrade provides an official Docker image on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/), as well as a [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) ready for usage.
!!! Note
The following section assumes that docker and docker-compose is installed and available to the logged in user.
!!! Note
All below comands use relative directories and will have to be executed from the directory containing the `docker-compose.yml` file.
### Docker quick start
Create a new directory and place the [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) in this directory.
``` bash
mkdir ft_userdata
cd ft_userdata/
# Download the docker-compose file from the repository
curl https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docker-compose.yml -o docker-compose.yml
# Pull the freqtrade image
docker-compose pull
# Create user directory structure
docker-compose run --rm freqtrade create-userdir --userdir user_data
# Create configuration - Requires answering interactive questions
docker-compose run --rm freqtrade new-config --config user_data/config.json
```
The above snippet creates a new directory called "ft_userdata", downloads the latest compose file and pulls the freqtrade image.
The last 2 steps in the snippet create the directory with user-data, as well as (interactively) the default configuration based on your selections.
!!! Note
You can edit the configuration at any time, which is available as `user_data/config.json` (within the directory `ft_userdata`) when using the above configuration.
#### Adding your strategy
The configuration is now available as `user_data/config.json`.
You should now copy your strategy to `user_data/strategies/` - and add the Strategy class name to the `docker-compose.yml` file, replacing `SampleStrategy`. If you wish to run the bot with the SampleStrategy, just leave it as it is.
!!! Warning
The `SampleStrategy` is there for your reference and give you ideas for your own strategy.
Please always backtest the strategy and use dry-run for some time before risking real money!
Once this is done, you're ready to launch the bot in trading mode (Dry-run or Live-trading, depending on your answer to the corresponding question you made above).
``` bash
docker-compose up -d
```
#### Docker-compose logs
Logs will be written to `user_data/freqtrade.log`.
Alternatively, you can check the latest logs using `docker-compose logs -f`.
#### Database
The database will be in the user_data directory as well, and will be called `user_data/tradesv3.sqlite`.
#### Updating freqtrade with docker-compose
To update freqtrade when using docker-compose is as simple as running the following 2 commands:
``` bash
# Download the latest image
docker-compose pull
# Restart the image
docker-compose up -d
```
This will first pull the latest image, and will then restart the container with the just pulled version.
!!! Note
You should always check the changelog for breaking changes / manual interventions required and make sure the bot starts correctly after the update.
#### Going from here
Advanced users may edit the docker-compose file further to include all possible options or arguments.
All possible freqtrade arguments will be available by running `docker-compose run --rm freqtrade <command> <optional arguments>`.
!!! Note "`docker-compose run --rm`"
Including `--rm` will clean up the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
##### Example: Download data with docker-compose
Download backtesting data for 5 days for the pair ETH/BTC and 1h timeframe from Binance. The data will be stored in the directory `user_data/data/` on the host.
``` bash
docker-compose run --rm freqtrade download-data --pairs ETH/BTC --exchange binance --days 5 -t 1h
```
Head over to the [Data Downloading Documentation](data-download.md) for more details on downloading data.
##### Example: Backtest with docker-compose
Run backtesting in docker-containers for SampleStrategy and specified timerange of historical data, on 5m timeframe:
``` bash
docker-compose run --rm freqtrade backtesting --config user_data/config.json --strategy SampleStrategy --timerange 20190801-20191001 -i 5m
```
Head over to the [Backtesting Documentation](backtesting.md) to learn more.
#### Additional dependencies with docker-compose
If your strategy requires dependencies not included in the default image (like [technical](https://github.com/freqtrade/technical)) - it will be necessary to build the image on your host.
For this, please create a Dockerfile containing installation steps for the additional dependencies (have a look at [Dockerfile.technical](https://github.com/freqtrade/freqtrade/blob/develop/Dockerfile.technical) for an example).
You'll then also need to modify the `docker-compose.yml` file and uncomment the build step, as well as rename the image to avoid naming collisions.
``` yaml
image: freqtrade_custom
build:
context: .
dockerfile: "./Dockerfile.<yourextension>"
```
You can then run `docker-compose build` to build the docker image, and run it using the commands described above.
## Freqtrade with docker without docker-compose
!!! Warning
The below documentation is provided for completeness and assumes that you are somewhat familiar with running docker containers. If you're just starting out with docker, we recommend to follow the [Freqtrade with docker-compose](#freqtrade-with-docker-compose) instructions.
### Download the official Freqtrade docker image
Pull the image from docker hub.
Branches / tags available can be checked out on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/tags/).
Branches / tags available can be checked out on [Dockerhub tags page](https://hub.docker.com/r/freqtradeorg/freqtrade/tags/).
```bash
docker pull freqtradeorg/freqtrade:develop
@ -26,7 +154,7 @@ To update the image, simply run the above commands again and restart your runnin
Should you require additional libraries, please [build the image yourself](#build-your-own-docker-image).
!!! Note Docker image update frequency
!!! Note "Docker image update frequency"
The official docker images with tags `master`, `develop` and `latest` are automatically rebuild once a week to keep the base image uptodate.
In addition to that, every merge to `develop` will trigger a rebuild for `develop` and `latest`.
@ -160,16 +288,18 @@ docker run -d \
-v ~/.freqtrade/config.json:/freqtrade/config.json \
-v ~/.freqtrade/user_data/:/freqtrade/user_data \
-v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
freqtrade --db-url sqlite:///tradesv3.sqlite --strategy MyAwesomeStrategy
freqtrade trade --db-url sqlite:///tradesv3.sqlite --strategy MyAwesomeStrategy
```
!!! 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.
!!! Note
You can define a [restart policy](https://docs.docker.com/config/containers/start-containers-automatically/) in docker. It can be useful in some cases to use the `--restart unless-stopped` flag (crash of freqtrade or reboot of your system).
### Monitor your Docker instance
You can use the following commands to monitor and manage your container:
@ -199,7 +329,7 @@ docker run -d \
-v ~/.freqtrade/config.json:/freqtrade/config.json \
-v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
-v ~/.freqtrade/user_data/:/freqtrade/user_data/ \
freqtrade --strategy AwsomelyProfitableStrategy backtesting
freqtrade backtesting --strategy AwsomelyProfitableStrategy
```
Head over to the [Backtesting Documentation](backtesting.md) for more details.

160
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@ -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.
@ -9,6 +9,7 @@ This page explains how to use Edge Positioning module in your bot in order to en
Edge does not consider anything else than buy/sell/stoploss signals. So trailing stoploss, ROI, and everything else are ignored in its calculation.
## 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?
@ -22,43 +23,61 @@ 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.
@ -69,6 +88,7 @@ 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:
| Pair | Stoploss | Win Rate | Risk Reward Ratio | Expectancy |
@ -83,6 +103,7 @@ 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 +111,17 @@ 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 +140,30 @@ 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
| `capital_available_percentage` | **DEPRECATED - [replaced with `tradable_balance_ratio`](configuration.md#Available balance)** This is the percentage of the total capital on exchange in stake currency. <br>As an example if you have 10 ETH available in your wallet on the exchange and this value is 0.5 (which is 50%), then the bot will use a maximum amount of 5 ETH for trading and considers it as available capital. <br>*Defaults to `0.5`.* <br> **Datatype:** Float
| `allowed_risk` | Ratio of allowed risk per trade. <br>*Defaults to `0.01` (1%)).* <br> **Datatype:** Float
| `stoploss_range_min` | Minimum stoploss. <br>*Defaults to `-0.01`.* <br> **Datatype:** Float
| `stoploss_range_max` | Maximum stoploss. <br>*Defaults to `-0.10`.* <br> **Datatype:** Float
| `stoploss_range_step` | As an example if this is set to -0.01 then Edge will test the strategy for `[-0.01, -0,02, -0,03 ..., -0.09, -0.10]` ranges. <br> **Note** than having a smaller step means having a bigger range which could lead to slow calculation. <br> If you set this parameter to -0.001, you then slow down the Edge calculation by a factor of 10. <br>*Defaults to `-0.001`.* <br> **Datatype:** Float
| `minimum_winrate` | It filters out pairs which don't have at least minimum_winrate. <br>This comes handy if you want to be conservative and don't comprise win rate in favour of risk reward ratio. <br>*Defaults to `0.60`.* <br> **Datatype:** Float
| `minimum_expectancy` | It filters out pairs which have the expectancy lower than this number. <br>Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ return. <br>*Defaults to `0.20`.* <br> **Datatype:** Float
| `min_trade_number` | When calculating *W*, *R* and *E* (expectancy) against historical data, you always want to have a minimum number of trades. The more this number is the more Edge is reliable. <br>Having a win rate of 100% on a single trade doesn't mean anything at all. But having a win rate of 70% over past 100 trades means clearly something. <br>*Defaults to `10` (it is highly recommended not to decrease this number).* <br> **Datatype:** Integer
| `max_trade_duration_minute` | Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.<br>**NOTICE:** While configuring this value, you should take into consideration your timeframe (ticker interval). As an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. Default value is set assuming your strategy interval is relatively small (1m or 5m, etc.).<br>*Defaults to `1440` (one day).* <br> **Datatype:** Integer
| `remove_pumps` | Edge will remove sudden pumps in a given market while going through historical data. However, given that pumps happen very often in crypto markets, we recommend you keep this off.<br>*Defaults to `false`.* <br> **Datatype:** Boolean
## Running Edge independently
You can run Edge independently in order to see in details the result. Here is an example:
```bash
``` bash
freqtrade edge
```
@ -235,7 +190,7 @@ An example of its output:
### Update cached pairs with the latest data
Edge requires historic data the same way as backtesting does.
Please refer to the [download section](backtesting.md#Getting-data-for-backtesting-and-hyperopt) of the documentation for details.
Please refer to the [Data Downloading](data-download.md) section of the documentation for details.
### Precising stoploss range
@ -249,13 +204,10 @@ freqtrade edge --stoplosses=-0.01,-0.1,-0.001 #min,max,step
freqtrade edge --timerange=20181110-20181113
```
Doing `--timerange=-200` will get the last 200 timeframes from your inputdata. You can also specify specific dates, or a range span indexed by start and stop.
Doing `--timerange=-20190901` will get all available data until September 1st (excluding September 1st 2019).
The full timerange specification:
* Use last 123 tickframes of data: `--timerange=-123`
* Use first 123 tickframes of data: `--timerange=123-`
* Use tickframes from line 123 through 456: `--timerange=123-456`
* Use tickframes till 2018/01/31: `--timerange=-20180131`
* Use tickframes since 2018/01/31: `--timerange=20180131-`
* Use tickframes since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`

86
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@ -0,0 +1,86 @@
# Exchange-specific Notes
This page combines common gotchas and informations which are exchange-specific and most likely don't apply to other exchanges.
## Binance
!!! Tip "Stoploss on Exchange"
Binance supports `stoploss_on_exchange` and uses stop-loss-limit orders. It provides great advantages, so we recommend to benefit from it.
### Blacklists
For Binance, please add `"BNB/<STAKE>"` to your blacklist to avoid issues.
Accounts having BNB accounts use this to pay for fees - if your first trade happens to be on `BNB`, further trades will consume this position and make the initial BNB order unsellable as the expected amount is not there anymore.
### Binance sites
Binance has been split into 3, and users must use the correct ccxt exchange ID for their exchange, otherwise API keys are not recognized.
* [binance.com](https://www.binance.com/) - International users. Use exchange id: `binance`.
* [binance.us](https://www.binance.us/) - US based users. Use exchange id: `binanceus`.
* [binance.je](https://www.binance.je/) - Binance Jersey, trading fiat currencies. Use exchange id: `binanceje`.
## 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.
## 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.
The International version has more pairs available, however the API always returns all pairs, so there is currently no automated way to detect if you're affected by the restriction.
If you have restricted pairs in your whitelist, you'll get a warning message in the log on Freqtrade startup for each restricted pair.
The warning message will look similar to the following:
``` output
[...] Message: bittrex {"success":false,"message":"RESTRICTED_MARKET","result":null,"explanation":null}"
```
If you're an "International" customer on the Bittrex exchange, then this warning will probably not impact you.
If you're a US customer, the bot will fail to create orders for these pairs, and you should remove them from your whitelist.
You can get a list of restricted markets by using the following snippet:
``` python
import ccxt
ct = ccxt.bittrex()
_ = ct.load_markets()
res = [ f"{x['MarketCurrency']}/{x['BaseCurrency']}" for x in ct.publicGetMarkets()['result'] if x['IsRestricted']]
print(res)
```
## All exchanges
Should you experience constant errors with Nonce (like `InvalidNonce`), it is best to regenerate the API keys. Resetting Nonce is difficult and it's usually easier to regenerate the API keys.
## Random notes for other exchanges
* The Ocean (exchange id: `theocean`) exchange uses Web3 functionality and requires `web3` python package to be installed:
```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.

68
docs/faq.md
View File

@ -4,7 +4,7 @@
### The bot does not start
Running the bot with `freqtrade --config config.json` does show the output `freqtrade: command not found`.
Running the bot with `freqtrade trade --config config.json` does show the output `freqtrade: command not found`.
This could have the following reasons:
@ -38,28 +38,78 @@ like pauses. You can stop your bot, adjust settings and start it again.
### I want to improve the bot with a new strategy
That's great. We have a nice backtesting and hyperoptimizing setup. See
That's great. We have a nice backtesting and hyperoptimization setup. See
the tutorial [here|Testing-new-strategies-with-Hyperopt](bot-usage.md#hyperopt-commands).
### Is there a setting to only SELL the coins being held and not perform anymore BUYS?
You can use the `/forcesell all` command from Telegram.
### I get the message "RESTRICTED_MARKET"
### I'm getting the "RESTRICTED_MARKET" message in the log
Currently known to happen for US Bittrex users.
Bittrex split its exchange into US and International versions.
The International version has more pairs available, however the API always returns all pairs, so there is currently no automated way to detect if you're affected by the restriction.
If you have restricted pairs in your whitelist, you'll get a warning message in the log on FreqTrade startup for each restricted pair.
If you're an "International" Customer on the Bittrex exchange, then this warning will probably not impact you.
If you're a US customer, the bot will fail to create orders for these pairs, and you should remove them from your Whitelist.
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.
* In unix shells, this normally can be done as simple as:
```shell
$ freqtrade --some-options 2>&1 >/dev/null | grep 'something'
```
(note, `2>&1` and `>/dev/null` should be written in this order)
* Bash interpreter also supports so called process substitution syntax, you can grep the log for a string with it as:
```shell
$ freqtrade --some-options 2> >(grep 'something') >/dev/null
```
or
```shell
$ freqtrade --some-options 2> >(grep -v 'something' 1>&2)
```
* You can also write the copy of Freqtrade log messages to a file with the `--logfile` option:
```shell
$ freqtrade --logfile /path/to/mylogfile.log --some-options
```
and then grep it as:
```shell
$ cat /path/to/mylogfile.log | grep 'something'
```
or even on the fly, as the bot works and the logfile grows:
```shell
$ 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:
```
> type \path\to\mylogfile.log | findstr "something"
```
## Hyperopt module
### How many epoch do I need to get a good Hyperopt result?
Per default Hyperopts without `-e` or `--epochs` parameter will only
Per default Hyperopt called without the `-e`/`--epochs` command line option will only
run 100 epochs, means 100 evals of your triggers, guards, ... Too few
to find a great result (unless if you are very lucky), so you probably
have to run it for 10.000 or more. But it will take an eternity to

300
docs/hyperopt.md
View File

@ -6,36 +6,63 @@ algorithms included in the `scikit-optimize` package to accomplish this. The
search will burn all your CPU cores, make your laptop sound like a fighter jet
and still take a long time.
In general, the search for best parameters starts with a few random combinations and then uses Bayesian search with a
ML regressor algorithm (currently ExtraTreesRegressor) to quickly find a combination of parameters in the search hyperspace
that minimizes the value of the [loss function](#loss-functions).
Hyperopt requires historic data to be available, just as backtesting does.
To learn how to get data for the pairs and exchange you're interested in, head over to the [Data Downloading](data-download.md) section of the documentation.
!!! Bug
Hyperopt will crash when used with only 1 CPU Core as found out in [Issue #1133](https://github.com/freqtrade/freqtrade/issues/1133)
Hyperopt can crash when used with only 1 CPU Core as found out in [Issue #1133](https://github.com/freqtrade/freqtrade/issues/1133)
## Prepare Hyperopting
Before we start digging into Hyperopt, we recommend you to take a look at
an example hyperopt file located into [user_data/hyperopts/](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt.py)
the sample hyperopt file located in [user_data/hyperopts/](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt.py).
Configuring hyperopt is similar to writing your own strategy, and many tasks will be similar and a lot of code can be copied across from the strategy.
The simplest way to get started is to use `freqtrade new-hyperopt --hyperopt AwesomeHyperopt`.
This will create a new hyperopt file from a template, which will be located under `user_data/hyperopts/AwesomeHyperopt.py`.
### Checklist on all tasks / possibilities in hyperopt
Depending on the space you want to optimize, only some of the below are required:
* fill `populate_indicators` - probably a copy from your strategy
* 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
Optional, but recommended:
!!! Note
`populate_indicators` needs to create all indicators any of thee spaces may use, otherwise hyperopt will not work.
Optional - can also be loaded from a strategy:
* copy `populate_indicators` from your strategy - otherwise default-strategy will be used
* copy `populate_buy_trend` from your strategy - otherwise default-strategy will be used
* copy `populate_sell_trend` from your strategy - otherwise default-strategy will be used
!!! 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.
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)
* `generate_roi_table` - for custom ROI optimization (if you need more than 4 entries in the ROI table)
* `generate_roi_table` - for custom ROI optimization (if you need the ranges for the values in the ROI table that differ from default or the number of entries (steps) in the ROI table which differs from the default 4 steps)
* `stoploss_space` - for custom stoploss optimization (if you need the range for the stoploss parameter in the optimization hyperspace that differs from default)
* `trailing_space` - for custom trailing stop optimization (if you need the ranges for the trailing stop parameters in the optimization hyperspace that differ from default)
!!! 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
# Have a working strategy at hand.
freqtrade new-hyperopt --hyperopt EmptyHyperopt
freqtrade hyperopt --hyperopt EmptyHyperopt --spaces roi stoploss trailing --strategy MyWorkingStrategy --config config.json -e 100
```
### 1. Install a Custom Hyperopt File
@ -48,22 +75,22 @@ 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, ie. changed the contents of
`populate_buy_trend()` method you have to update the `guards` and
`triggers` hyperopts must use.
If you have updated the buy strategy, i.e. changed the contents of
`populate_buy_trend()` method, you have to update the `guards` and
`triggers` your hyperopt must use correspondingly.
#### Sell optimization
@ -76,10 +103,11 @@ 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 `SampleHyperOpts.ticker_interval`.
The Strategy class exposes the timeframe (ticker interval) value as the `self.ticker_interval` attribute.
The same value is available as class-attribute `HyperoptName.ticker_interval`.
In the case of the linked sample-value this would be `SampleHyperOpt.ticker_interval`.
## Solving a Mystery
@ -114,7 +142,7 @@ one we call `trigger` and use it to decide which buy trigger we want to use.
So let's write the buy strategy using these values:
``` python
```python
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
conditions = []
# GUARDS AND TRENDS
@ -132,6 +160,9 @@ So let's write the buy strategy using these values:
dataframe['macd'], dataframe['macdsignal']
))
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
@ -145,21 +176,17 @@ 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
add it to the `populate_indicators()` method in `hyperopt.py`.
add it to the `populate_indicators()` method in your custom hyperopt file.
## Loss-functions
Each hyperparameter tuning requires a target. This is usually defined as a loss function (sometimes also called objective function), which should decrease for more desirable results, and increase for bad results.
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.
@ -168,65 +195,12 @@ 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)
### 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.
For the sample below, you then need to add the command line parameter `--hyperopt-loss SuperDuperHyperOptLoss` to your hyperopt call so this fuction is being used.
A sample of this can be found below, which is identical to the Default Hyperopt loss implementation. A full sample can be found [user_data/hyperopts/](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_loss.py)
``` python
from freqtrade.optimize.hyperopt import IHyperOptLoss
TARGET_TRADES = 600
EXPECTED_MAX_PROFIT = 3.0
MAX_ACCEPTED_TRADE_DURATION = 300
class SuperDuperHyperOptLoss(IHyperOptLoss):
"""
Defines the default loss function for hyperopt
"""
@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
*args, **kwargs) -> float:
"""
Objective function, returns smaller number for better results
This is the legacy algorithm (used until now in freqtrade).
Weights are distributed as follows:
* 0.4 to trade duration
* 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()
trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
result = trade_loss + profit_loss + duration_loss
return result
```
Currently, the arguments are:
* `results`: DataFrame containing the result
The following columns are available in results (corresponds to the output-file of backtesting when used with `--export trades`):
`pair, profit_percent, profit_abs, open_time, close_time, open_index, close_index, trade_duration, open_at_end, open_rate, close_rate, sell_reason`
* `trade_count`: Amount of trades (identical to `len(results)`)
* `min_date`: Start date of the hyperopting TimeFrame
* `min_date`: End date of the hyperopting TimeFrame
This function needs to return a floating point number (`float`). Smaller numbers will be interpreted as better results. The parameters and balancing for this is up to you.
!!! Note
This function is called once per iteration - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.
!!! Note
Please keep the arguments `*args` and `**kwargs` in the interface to allow us to extend this interface later.
Creation of a custom loss function is covered in the [Advanced Hyperopt](advanced-hyperopt.md) part of the documentation.
## Execute Hyperopt
@ -236,15 +210,15 @@ Because hyperopt tries a lot of combinations to find the best parameters it will
We strongly recommend to use `screen` or `tmux` to prevent any connection loss.
```bash
freqtrade -c config.json hyperopt --customhyperopt <hyperoptname> -e 5000 --spaces all
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` flag will set how many evaluations hyperopt will do. We recommend
The `-e` option will set how many evaluations hyperopt will do. We recommend
running at least several thousand evaluations.
The `--spaces all` flag determines that all possible parameters should be optimized. Possibilities are listed below.
The `--spaces all` option determines that all possible parameters should be optimized. Possibilities are listed below.
!!! Note
By default, hyperopt will erase previous results and start from scratch. Continuation can be archived by using `--continue`.
@ -252,11 +226,11 @@ The `--spaces all` flag determines that all possible parameters should be optimi
!!! 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
@ -267,9 +241,17 @@ For example, to use one month of data, pass the following parameter to the hyper
freqtrade hyperopt --timerange 20180401-20180501
```
### Running Hyperopt using methods from a strategy
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
```
### Running Hyperopt with Smaller Search Space
Use the `--spaces` argument to limit the search space used by hyperopt.
Use the `--spaces` option to limit the search space used by hyperopt.
Letting Hyperopt optimize everything is a huuuuge search space. Often it
might make more sense to start by just searching for initial buy algorithm.
Or maybe you just want to optimize your stoploss or roi table for that awesome
@ -282,8 +264,12 @@ Legal values are:
* `sell`: just search for a new sell strategy
* `roi`: just optimize the minimal profit table for your strategy
* `stoploss`: search for the best stoploss value
* `trailing`: search for the best trailing stop values
* `default`: `all` except `trailing`
* space-separated list of any of the above values for example `--spaces roi stoploss`
The default Hyperopt Search Space, used when no `--space` command line option is specified, does not include the `trailing` hyperspace. We recommend you to run optimization for the `trailing` hyperspace separately, when the best parameters for other hyperspaces were found, validated and pasted into your custom strategy.
### Position stacking and disabling max market positions
In some situations, you may need to run Hyperopt (and Backtesting) with the
@ -292,7 +278,7 @@ In some situations, you may need to run Hyperopt (and Backtesting) with the
By default, hyperopt emulates the behavior of the Freqtrade Live Run/Dry Run, where only one
open trade is allowed for every traded pair. The total number of trades open for all pairs
is also limited by the `max_open_trades` setting. During Hyperopt/Backtesting this may lead to
some potential trades to be hidden (or masked) by previosly open trades.
some potential trades to be hidden (or masked) by previously open trades.
The `--eps`/`--enable-position-stacking` argument allows emulation of buying the same pair multiple times,
while `--dmmp`/`--disable-max-market-positions` disables applying `max_open_trades`
@ -305,6 +291,16 @@ 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 a leading asterisk sign at 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.
@ -334,12 +330,11 @@ 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)
```
Translating your whole hyperopt result as the new buy-signal
would then look like:
Translating your whole hyperopt result as the new buy-signal would then look like:
```python
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
@ -358,55 +353,55 @@ You can use the `--print-all` command line option if you would like to see all r
### Understand Hyperopt ROI results
If you are optimizing ROI (i.e. if optimization search-space contains 'all' or 'roi'), your result will look as follows and include a ROI table:
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:
```
Best result:
44/100: 135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins. Objective: 1.94367
Buy hyperspace params:
{ 'adx-value': 44,
'rsi-value': 29,
'adx-enabled': False,
'rsi-enabled': True,
'trigger': 'bb_lower'}
ROI table:
{ 0: 0.10674752302642071,
21: 0.09158372701087236,
78: 0.03634636907306948,
{ 0: 0.10674,
21: 0.09158,
78: 0.03634,
118: 0}
```
This would translate to the following ROI table:
In order to use this best ROI table found by Hyperopt in backtesting and for live trades/dry-run, copy-paste it as the value of the `minimal_roi` attribute of your custom strategy:
``` python
minimal_roi = {
"118": 0,
"78": 0.0363,
"21": 0.0915,
"0": 0.106
```
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
0: 0.10674,
21: 0.09158,
78: 0.03634,
118: 0
}
```
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 can vary in the following ranges (for some of the most used ticker intervals, values are rounded to 5 digits after the decimal point):
As stated in the comment, you can also use it as the value of the `minimal_roi` setting in the configuration file.
| # 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 |
#### Default ROI Search Space
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.
If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps). Hyperopt implements adaptive ranges for ROI tables with ranges for values in the ROI steps that depend on the ticker_interval used. By default the values vary in the following ranges (for some of the most used timeframes, values are rounded to 5 digits after the decimal point):
| # step | 1m | | 5m | | 1h | | 1d | |
| ------ | ------ | ----------------- | -------- | ----------- | ---------- | ----------------- | ------------ | ----------------- |
| 1 | 0 | 0.01161...0.11992 | 0 | 0.03...0.31 | 0 | 0.06883...0.71124 | 0 | 0.12178...1.25835 |
| 2 | 2...8 | 0.00774...0.04255 | 10...40 | 0.02...0.11 | 120...480 | 0.04589...0.25238 | 2880...11520 | 0.08118...0.44651 |
| 3 | 4...20 | 0.00387...0.01547 | 20...100 | 0.01...0.04 | 240...1200 | 0.02294...0.09177 | 5760...28800 | 0.04059...0.16237 |
| 4 | 6...44 | 0.0 | 30...220 | 0.0 | 360...2640 | 0.0 | 8640...63360 | 0.0 |
These ranges should be sufficient in most cases. The minutes in the steps (ROI dict keys) are scaled linearly depending on the timeframe (ticker interval) used. The ROI values in the steps (ROI dict values) are scaled logarithmically depending on the timeframe used.
If you have the `generate_roi_table()` and `roi_space()` methods in your custom hyperopt file, remove them in order to utilize these adaptive ROI tables and the ROI hyperoptimization space generated by Freqtrade by default.
Override the `roi_space()` method if you need components of the ROI tables to vary in other ranges. Override the `generate_roi_table()` and `roi_space()` methods and implement your own custom approach for generation of the ROI tables during hyperoptimization if you need a different structure of the ROI tables or other amount of rows (steps). A sample for these methods 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 `roi_space()` method if you need components of the ROI tables to vary in other ranges. Override the `generate_roi_table()` and `roi_space()` methods and implement your own custom approach for generation of the ROI tables during hyperoptimization if you need a different structure of the ROI tables or other amount of rows (steps). A sample for these methods can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py).
### Understand Hyperopt Stoploss results
If you are optimizing stoploss values (i.e. if optimization search-space contains 'all' or 'stoploss'), your result will look as follows and include stoploss:
If you are optimizing stoploss values (i.e. if optimization search-space contains 'all', 'default' or 'stoploss'), your result will look as follows and include stoploss:
```
Best result:
@ -419,16 +414,67 @@ Buy hyperspace params:
'adx-enabled': False,
'rsi-enabled': True,
'trigger': 'bb_lower'}
Stoploss: -0.37996664668703606
Stoploss: -0.27996
```
If you are optimizing stoploss values, Freqtrade creates the 'stoploss' optimization hyperspace for you. By default, the stoploss values in that hyperspace can vary in the range -0.5...-0.02, which is sufficient in most cases.
In order to use this best stoploss value found by Hyperopt in backtesting and for live trades/dry-run, copy-paste it as the value of the `stoploss` attribute of your custom strategy:
```
# Optimal stoploss designed for the strategy
# 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
If you are optimizing stoploss values, Freqtrade creates the 'stoploss' optimization hyperspace for you. By default, the stoploss values in that hyperspace vary in the range -0.35...-0.02, which is sufficient in most cases.
If you have the `stoploss_space()` method in your custom hyperopt file, remove it in order to utilize Stoploss hyperoptimization space generated by Freqtrade by default.
Override the `stoploss_space()` method and define the desired range in it if you need stoploss values to vary in other range 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 `stoploss_space()` method and define the desired range in it if you need stoploss values to vary in other range 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).
### Validate backtesting results
### Understand Hyperopt Trailing Stop results
If you are optimizing trailing stop values (i.e. if optimization search-space contains 'all' or 'trailing'), your result will look as follows and include trailing stop parameters:
```
Best result:
45/100: 606 trades. Avg profit 1.04%. Total profit 0.31555614 BTC ( 630.48Σ%). Avg duration 150.3 mins. Objective: -1.10161
Trailing stop:
{ 'trailing_only_offset_is_reached': True,
'trailing_stop': True,
'trailing_stop_positive': 0.02001,
'trailing_stop_positive_offset': 0.06038}
```
In order to use these best trailing stop parameters found by Hyperopt in backtesting and for live trades/dry-run, copy-paste them as the values of the corresponding attributes of your custom strategy:
```
# Trailing stop
# These attributes will be overridden if the config file contains corresponding values.
trailing_stop = True
trailing_stop_positive = 0.02001
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).
## Show details of Hyperopt results
After you run Hyperopt for the desired amount of epochs, you can later list all results for analysis, select only best or profitable once, and show the details for any of the epochs previously evaluated. This can be done with the `hyperopt-list` and `hyperopt-show` subcommands. The usage of these subcommands is described in the [Utils](utils.md#list-hyperopt-results) chapter.
## Validate backtesting results
Once the optimized strategy has been implemented into your strategy, you should backtest this strategy to make sure everything is working as expected.

40
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View File

@ -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/enQtNjU5ODcwNjI1MDU3LWEyODBiNzkzNzcyNzU0MWYyYzE5NjIyOTQxMzBmMGUxOTIzM2YyN2Y4NWY1YTEwZDgwYTRmMzE2NmM5ZmY2MTg) 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).

186
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@ -2,6 +2,8 @@
This page explains how to prepare your environment for running the bot.
Please consider using the prebuilt [docker images](docker.md) to get started quickly while trying out freqtrade evaluating how it operates.
## Prerequisite
### Requirements
@ -14,36 +16,37 @@ Click each one for install guide:
* [virtualenv](https://virtualenv.pypa.io/en/stable/installation/) (Recommended)
* [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html) (install instructions below)
### API keys
Before running your bot in production you will need to setup few
external API. In production mode, the bot will require valid Exchange API
credentials. We also recommend a [Telegram bot](telegram-usage.md#setup-your-telegram-bot) (optional but recommended).
### Setup your exchange account
You will need to create API Keys (Usually you get `key` and `secret`) from the Exchange website and insert this into the appropriate fields in the configuration or when asked by the installation script.
We also recommend a [Telegram bot](telegram-usage.md#setup-your-telegram-bot), which is optional but recommended.
## Quick start
Freqtrade provides a Linux/MacOS script to install all dependencies and help you to configure the bot.
!!! Note
Python3.6 or higher and the corresponding pip are assumed to be available. The install-script will warn and stop if that's not the case.
```bash
git clone git@github.com:freqtrade/freqtrade.git
cd freqtrade
git checkout develop
./setup.sh --install
```
Freqtrade provides the Linux/MacOS Easy Installation script to install all dependencies and help you configure the bot.
!!! Note
Windows installation is explained [here](#windows).
## Easy Installation - Linux Script
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.
If you are on Debian, Ubuntu or MacOS freqtrade provides a script to Install, Update, Configure, and Reset your bot.
!!! 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).
!!! Note
Python3.6 or higher and the corresponding `pip` are assumed to be available. The install-script will warn you and stop if that's not the case. `git` is also needed to clone the Freqtrade repository.
This can be achieved with the following commands:
```bash
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)
If you are on Debian, Ubuntu or MacOS Freqtrade provides the script to install, update, configure and reset the codebase of your bot.
```bash
$ ./setup.sh
@ -56,25 +59,25 @@ usage:
** --install **
This 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 script is a combination of `install script` `--reset`, `--config`
This option is a combination of installation tasks, `--reset` and `--config`.
** --update **
Update parameter will pull the last version of your current branch and update your virtualenv.
This option will pull the last version of your current branch and update your virtualenv. Run the script with this option periodically to update your bot.
** --reset **
Reset parameter will hard reset your branch (only if you are on `master` or `develop`) and recreate your virtualenv.
This option will hard reset your branch (only if you are on either `master` or `develop`) and recreate your virtualenv.
** --config **
Config parameter is a `config.json` configurator. This script will ask you questions to setup your bot and create your `config.json`.
DEPRECATED - use `freqtrade new-config -c config.json` instead.
------
@ -95,31 +98,43 @@ sudo apt-get update
sudo apt-get install build-essential git
```
#### Raspberry Pi / Raspbian
### Raspberry Pi / Raspbian
Before installing FreqTrade on a Raspberry Pi running the official Raspbian Image, make sure you have at least Python 3.6 installed. The default image only provides Python 3.5. Probably the easiest way to get a recent version of python is [miniconda](https://repo.continuum.io/miniconda/).
The following assumes the latest [Raspbian Buster lite image](https://www.raspberrypi.org/downloads/raspbian/) from at least September 2019.
This image comes with python3.7 preinstalled, making it easy to get freqtrade up and running.
The following assumes that miniconda3 is installed and available in your environment. Last miniconda3 installation file use python 3.4, we will update to python 3.6 on this installation.
It's recommended to use (mini)conda for this as installation/compilation of `numpy`, `scipy` and `pandas` takes a long time.
Additional package to install on your Raspbian, `libffi-dev` required by cryptography (from python-telegram-bot).
Tested using a Raspberry Pi 3 with the Raspbian Buster lite image, all updates applied.
``` bash
conda config --add channels rpi
conda install python=3.6
conda create -n freqtrade python=3.6
conda activate freqtrade
conda install scipy pandas numpy
sudo apt-get install python3-venv libatlas-base-dev
git clone https://github.com/freqtrade/freqtrade.git
cd freqtrade
sudo apt install libffi-dev
python3 -m pip install -r requirements-common.txt
python3 -m pip install -e .
bash setup.sh -i
```
!!! Note "Installation duration"
Depending on your internet speed and the Raspberry Pi version, installation can take multiple hours to complete.
!!! Note
The above does not install hyperopt dependencies. To install these, please use `python3 -m pip install -e .[hyperopt]`.
We do not advise to run hyperopt on a Raspberry Pi, since this is a very resource-heavy operation, which should be done on powerful machine.
### Common
#### 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
@ -147,13 +162,13 @@ python3 -m venv .env
source .env/bin/activate
```
#### 3. Install FreqTrade
#### 3. Install Freqtrade
Clone the git repository:
```bash
git clone https://github.com/freqtrade/freqtrade.git
cd freqtrade
```
Optionally checkout the master branch to get the latest stable release:
@ -162,60 +177,38 @@ Optionally checkout the master branch to get the latest stable release:
git checkout master
```
#### 4. Initialize the configuration
```bash
cd freqtrade
cp config.json.example config.json
```
> *To edit the config please refer to [Bot Configuration](configuration.md).*
#### 5. Install python dependencies
#### 4. Install python dependencies
``` bash
python3 -m pip install --upgrade pip
python3 -m pip install -r requirements.txt
python3 -m pip install -e .
```
#### 5. Initialize the configuration
```bash
# Initialize the user_directory
freqtrade create-userdir --userdir user_data/
# Create a new configuration file
freqtrade new-config --config config.json
```
> *To edit the config please refer to [Bot Configuration](configuration.md).*
#### 6. Run the Bot
If this is the first time you run the bot, ensure you are running it in Dry-run `"dry_run": true,` otherwise it will start to buy and sell coins.
```bash
freqtrade -c config.json
freqtrade trade -c config.json
```
*Note*: If you run the bot on a server, you should consider using [Docker](docker.md) or a terminal multiplexer like `screen` or [`tmux`](https://en.wikipedia.org/wiki/Tmux) to avoid that the bot is stopped on logout.
#### 7. [Optional] Configure `freqtrade` as a `systemd` service
#### 7. (Optional) Post-installation Tasks
From the freqtrade repo... copy `freqtrade.service` to your systemd user directory (usually `~/.config/systemd/user`) and update `WorkingDirectory` and `ExecStart` to match your setup.
After that you can start the daemon with:
```bash
systemctl --user start freqtrade
```
For this to be persistent (run when user is logged out) you'll need to enable `linger` for your freqtrade user.
```bash
sudo loginctl enable-linger "$USER"
```
If you run the bot as a service, you can use systemd service manager as a software watchdog monitoring freqtrade bot
state and restarting it in the case of failures. If the `internals.sd_notify` parameter is set to true in the
configuration or the `--sd-notify` command line option is used, the bot will send keep-alive ping messages to systemd
using the sd_notify (systemd notifications) protocol and will also tell systemd its current state (Running or Stopped)
when it changes.
The `freqtrade.service.watchdog` file contains an example of the service unit configuration file which uses systemd
as the watchdog.
!!! Note
The sd_notify communication between the bot and the systemd service manager will not work if the bot runs in a Docker container.
On Linux, as an optional post-installation task, you may wish to setup the bot to run as a `systemd` service or configure it to send the log messages to the `syslog`/`rsyslog` or `journald` daemons. See [Advanced Logging](advanced-setup.md#advanced-logging) for details.
------
@ -239,6 +232,12 @@ If that is not available on your system, feel free to try the instructions below
### Install freqtrade manually
!!! Note
Make sure to use 64bit Windows and 64bit Python to avoid problems with backtesting or hyperopt due to the memory constraints 32bit applications have under Windows.
!!! Hint
Using the [Anaconda Distribution](https://www.anaconda.com/distribution/) under Windows can greatly help with installation problems. Check out the [Conda section](#using-conda) in this document for more information.
#### Clone the git repository
```bash
@ -254,14 +253,12 @@ As compiling from source on windows has heavy dependencies (requires a partial v
```cmd
>cd \path\freqtrade-develop
>python -m venv .env
>cd .env\Scripts
>activate.bat
>cd \path\freqtrade-develop
>.env\Scripts\activate.bat
REM optionally install ta-lib from wheel
REM >pip install TA_Lib‑0.4.17‑cp36‑cp36m‑win32.whl
>pip install -r requirements.txt
>pip install -e .
>python freqtrade\main.py
>freqtrade
```
> Thanks [Owdr](https://github.com/Owdr) for the commands. Source: [Issue #222](https://github.com/freqtrade/freqtrade/issues/222)
@ -280,3 +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.

2
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@ -49,4 +49,6 @@
</nav>
<!-- Place this tag in your head or just before your close body tag. -->
<script async defer src="https://buttons.github.io/buttons.js"></script>
<script src="https://code.jquery.com/jquery-3.4.1.min.js"
integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script>
</header>

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@ -23,13 +23,16 @@ The `freqtrade plot-dataframe` subcommand shows an interactive graph with three
Possible arguments:
```
usage: freqtrade plot-dataframe [-h] [-p PAIRS [PAIRS ...]]
usage: freqtrade plot-dataframe [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH] [-s NAME]
[--strategy-path PATH] [-p PAIRS [PAIRS ...]]
[--indicators1 INDICATORS1 [INDICATORS1 ...]]
[--indicators2 INDICATORS2 [INDICATORS2 ...]]
[--plot-limit INT] [--db-url PATH]
[--trade-source {DB,file}] [--export EXPORT]
[--export-filename PATH]
[--timerange TIMERANGE]
[--timerange TIMERANGE] [-i TICKER_INTERVAL]
[--no-trades]
optional arguments:
-h, --help show this help message and exit
@ -48,21 +51,46 @@ 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
Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
`1d`).
--no-trades Skip using trades from backtesting file and DB.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
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.
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.
```
Example:
@ -79,11 +107,11 @@ The `-p/--pairs` argument can be used to specify pairs you would like to plot.
Specify custom indicators.
Use `--indicators1` for the main plot and `--indicators2` for the subplot below (if values are in a different range than prices).
!!! tip
!!! Tip
You will almost certainly want to specify a custom strategy! This can be done by adding `-s Classname` / `--strategy ClassName` to the command.
``` bash
freqtrade --strategy AwesomeStrategy plot-dataframe -p BTC/ETH --indicators1 sma ema --indicators2 macd
freqtrade plot-dataframe --strategy AwesomeStrategy -p BTC/ETH --indicators1 sma ema --indicators2 macd
```
### Further usage examples
@ -91,52 +119,115 @@ freqtrade --strategy AwesomeStrategy plot-dataframe -p BTC/ETH --indicators1 sma
To plot multiple pairs, separate them with a space:
``` bash
freqtrade --strategy AwesomeStrategy plot-dataframe -p BTC/ETH XRP/ETH
freqtrade plot-dataframe --strategy AwesomeStrategy -p BTC/ETH XRP/ETH
```
To plot a timerange (to zoom in)
``` bash
freqtrade --strategy AwesomeStrategy plot-dataframe -p BTC/ETH --timerange=20180801-20180805
freqtrade plot-dataframe --strategy AwesomeStrategy -p BTC/ETH --timerange=20180801-20180805
```
To plot trades stored in a database use `--db-url` in combination with `--trade-source DB`:
``` bash
freqtrade --strategy AwesomeStrategy plot-dataframe --db-url sqlite:///tradesv3.dry_run.sqlite -p BTC/ETH --trade-source DB
freqtrade plot-dataframe --strategy AwesomeStrategy --db-url sqlite:///tradesv3.dry_run.sqlite -p BTC/ETH --trade-source DB
```
To plot trades from a backtesting result, use `--export-filename <filename>`
``` bash
freqtrade --strategy AwesomeStrategy plot-dataframe --export-filename user_data/backtest_results/backtest-result.json -p BTC/ETH
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.
Possible options for the `freqtrade plot-profit` subcommand:
```
usage: freqtrade plot-profit [-h] [-p PAIRS [PAIRS ...]]
usage: freqtrade plot-profit [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH] [-p PAIRS [PAIRS ...]]
[--timerange TIMERANGE] [--export EXPORT]
[--export-filename PATH] [--db-url PATH]
[--trade-source {DB,file}]
[--trade-source {DB,file}] [-i TICKER_INTERVAL]
optional arguments:
-h, --help show this help message and exit
@ -148,17 +239,35 @@ 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
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. 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.
```
@ -179,5 +288,5 @@ freqtrade plot-profit -p LTC/BTC --db-url sqlite:///tradesv3.sqlite --trade-sou
```
``` bash
freqtrade plot-profit --datadir user_data/data/binance_save/ -p LTC/BTC
freqtrade --datadir user_data/data/binance_save/ plot-profit -p LTC/BTC
```

3
docs/requirements-docs.txt
View File

@ -1 +1,2 @@
mkdocs-material==4.4.2
mkdocs-material==4.6.3
mdx_truly_sane_lists==1.2

27
docs/rest-api.md
View File

@ -16,13 +16,20 @@ Sample configuration:
},
```
!!! Danger Security warning
By default, the configuration listens on localhost only (so it's not reachable from other systems). We strongly recommend to not expose this API to the internet and choose a strong, unique password, since others will potentially be able to control your bot.
!!! Danger "Security warning"
By default, the configuration listens on localhost only (so it's not reachable from other systems). We strongly recommend to not expose this API to the internet and choose a strong, unique password, since others will potentially be able to control your bot.
!!! Danger Password selection
Please make sure to select a very strong, unique password to protect your bot from unauthorized access.
!!! Danger "Password selection"
Please make sure to select a very strong, unique password to protect your bot from unauthorized access.
You can then access the API by going to `http://127.0.0.1:8080/api/v1/version` to check if the API is running correctly.
You can then access the API by going to `http://127.0.0.1:8080/api/v1/ping` in a browser to check if the API is running correctly.
This should return the response:
``` output
{"status":"pong"}
```
All other endpoints return sensitive info and require authentication, so are not available through a web browser.
To generate a secure password, either use a password manager, or use the below code snipped.
@ -58,7 +65,7 @@ docker run -d \
-v ~/.freqtrade/user_data/:/freqtrade/user_data \
-v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
-p 127.0.0.1:8080:8080 \
freqtrade --db-url sqlite:///tradesv3.sqlite --strategy MyAwesomeStrategy
freqtrade trade --db-url sqlite:///tradesv3.sqlite --strategy MyAwesomeStrategy
```
!!! Danger "Security warning"
@ -67,7 +74,7 @@ docker run -d \
## Consuming the API
You can consume the API by using the script `scripts/rest_client.py`.
The client script only requires the `requests` module, so FreqTrade does not need to be installed on the system.
The client script only requires the `requests` module, so Freqtrade does not need to be installed on the system.
``` bash
python3 scripts/rest_client.py <command> [optional parameters]
@ -99,8 +106,8 @@ python3 scripts/rest_client.py --config rest_config.json <command> [optional par
| `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`).
@ -166,6 +173,10 @@ reload_conf
Reload configuration
:returns: json object
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

109
docs/stoploss.md
View File

@ -3,74 +3,101 @@
The `stoploss` configuration parameter is loss in percentage 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. This parameter is optional. If you use it in the configuration file, it will take over the
`stoploss` value from the strategy file.
Most of the strategy files already include the optimal `stoploss` value.
## Stop Loss support
!!! 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.
## Stop Loss Types
At this stage the bot contains the following stoploss support modes:
1. static stop loss, defined in either the strategy or configuration.
2. trailing stop loss, defined in the configuration.
3. trailing stop loss, custom positive loss, defined in configuration.
1. Static stop loss.
2. Trailing stop loss.
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
All stoploss properties can be configured in either Strategy or configuration. Configuration values override strategy values.
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 successfuly. 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. As an example in case of trailing stoploss if the order is on the exchange and the market is going up then the bot automatically cancels the previous stoploss order and put a new one with a stop value higher than previous one. It is clear that the bot cannot do it every 5 seconds otherwise it gets banned. So this parameter will tell the bot how often it should update the stoploss order. The default value is 60 (1 minute).
!!! Note
Stoploss on exchange is only supported for Binance as of now.
Stoploss on exchange is only supported for Binance (stop-loss-limit) and Kraken (stop-loss-market) as of now.
## Static Stop Loss
This is very simple, basically you define a stop loss of x in your strategy file or alternative in the configuration, which
will overwrite the strategy definition. This will basically try to sell your asset, the second the loss exceeds the defined 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
The initial value for this stop loss, is defined in your strategy or configuration. Just as you would define your Stop Loss normally.
To enable this Feauture all you have to do is to define the configuration element:
The initial value for this is `stoploss`, just as you would define your static Stop loss.
To enable trailing stoploss:
``` json
"trailing_stop" : True
``` python
trailing_stop = True
```
This will now activate an algorithm, which automatically moves your stop loss up every time the price of your asset increases.
This will now activate an algorithm, which automatically moves the stop loss up every time the price of your asset increases.
For example, simplified math,
For example, simplified math:
* you buy an asset at a price of 100$
* your stop loss is defined at 2%
* which means your stop loss, gets triggered once your asset dropped below 98$
* assuming your asset now increases to 102$
* your stop loss, will now be 2% of 102$ or 99.96$
* now your asset drops in value to 101$, your stop loss, will still be 99.96$
* 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$
* 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$.
basically what this means is that your stop loss will be adjusted to be always be 2% of the highest observed price
In summary: The stoploss will be adjusted to be always be 2% of the highest observed price.
### Custom positive loss
### Custom positive stoploss
Due to demand, it is 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 be 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, 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.
Both values can be configured in the main configuration file and requires `"trailing_stop": true` to be set to true.
Both values require `trailing_stop` to be set to true.
``` json
"trailing_stop_positive": 0.01,
"trailing_stop_positive_offset": 0.011,
"trailing_only_offset_is_reached": false
``` python
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.011
```
The 0.01 would translate to a 1% stop loss, once you hit 1.1% profit.
Before this, `stoploss` is used for the trailing stoploss.
You should also make sure to have this value (`trailing_stop_positive_offset`) lower than your minimal ROI, otherwise minimal ROI will apply first and sell your trade.
Read the [next section](#trailing-only-once-offset-is-reached) to keep stoploss at 5% of the entry point.
If `"trailing_only_offset_is_reached": true` then the trailing stoploss is only activated once the offset is reached. Until then, the stoploss remains at the configured`stoploss`.
!!! 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
It is also possible to use a static stoploss until the offset is reached, and then trail the trade to take profits once the market turns.
If `"trailing_only_offset_is_reached": true` then the trailing stoploss is only activated once the offset is reached. Until then, the stoploss remains at the configured `stoploss`.
This option can be used with or without `trailing_stop_positive`, but uses `trailing_stop_positive_offset` as offset.
``` python
trailing_stop_positive_offset = 0.011
trailing_only_offset_is_reached = true
```
Simplified example:
``` python
stoploss = 0.05
trailing_stop_positive_offset = 0.03
trailing_only_offset_is_reached = True
```
* 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%
## Changing stoploss on open trades

280
docs/strategy-customization.md
View File

@ -1,4 +1,4 @@
# Optimization
# Strategy Customization
This page explains where to customize your strategies, and add new
indicators.
@ -7,24 +7,28 @@ indicators.
This is very simple. Copy paste your strategy file into the directory `user_data/strategies`.
Let assume you have a class called `AwesomeStrategy` in the file `awesome-strategy.py`:
Let assume you have a class called `AwesomeStrategy` in the file `AwesomeStrategy.py`:
1. Move your file into `user_data/strategies` (you should have `user_data/strategies/awesome-strategy.py`
1. Move your file into `user_data/strategies` (you should have `user_data/strategies/AwesomeStrategy.py`
2. Start the bot with the param `--strategy AwesomeStrategy` (the parameter is the class name)
```bash
freqtrade --strategy AwesomeStrategy
freqtrade trade --strategy AwesomeStrategy
```
## Change your strategy
## Develop your own strategy
The bot includes a default strategy file. However, we recommend you to
use your own file to not have to lose your parameters every time the default
strategy file will be updated on Github. Put your custom strategy file
into the directory `user_data/strategies`.
The bot includes a default strategy file.
Also, several other strategies are available in the [strategy repository](https://github.com/freqtrade/freqtrade-strategies).
Best copy the test-strategy and modify this copy to avoid having bot-updates override your changes.
`cp user_data/strategies/sample_strategy.py user_data/strategies/awesome-strategy.py`
You will however most likely have your own idea for a strategy.
This document intends to help you develop one for yourself.
To get started, use `freqtrade new-strategy --strategy AwesomeStrategy`.
This will create a new strategy file from a template, which will be located under `user_data/strategies/AwesomeStrategy.py`.
!!! Note
This is just a template file, which will most likely not be profitable out of the box.
### Anatomy of a strategy
@ -45,23 +49,22 @@ The current version is 2 - which is also the default when it's not set explicitl
Future versions will require this to be set.
```bash
freqtrade --strategy AwesomeStrategy
freqtrade trade --strategy AwesomeStrategy
```
**For the following section we will use the [user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/sample_strategy.py)
**For the following section we will use the [user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_strategy.py)
file as reference.**
!!! Note Strategies and Backtesting
!!! 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.
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 Using future data
!!! Warning "Warning: Using future data"
Since backtesting passes the full time interval to the `populate_*()` methods, the strategy author
needs to take care to avoid having the strategy utilize data from the future.
Samples for usage of future data are `dataframe.shift(-1)`, `dataframe.resample("1h")` (this uses the left border of the interval, so moves data from an hour to the start of the hour).
They all use data which is not available during regular operations, so these strategies will perform well during backtesting, but will fail / perform badly in dry-runs.
Some common patterns for this are listed in the [Common Mistakes](#common-mistakes-when-developing-strategies) section of this document.
### Customize Indicators
@ -81,7 +84,7 @@ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param dataframe: Dataframe with data from the exchange
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
@ -115,9 +118,40 @@ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame
```
!!! Note "Want more indicator examples?"
Look into the [user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/sample_strategy.py).
Look into the [user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_strategy.py).
Then uncomment indicators you need.
### Strategy startup period
Most indicators have an instable startup period, in which they are either not available, or the calculation is incorrect. This can lead to inconsistencies, since Freqtrade does not know how long this instable period should be.
To account for this, the strategy can be assigned the `startup_candle_count` attribute.
This should be set to the maximum number of candles that the strategy requires to calculate stable indicators.
In this example strategy, this should be set to 100 (`startup_candle_count = 100`), since the longest needed history is 100 candles.
``` python
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
```
By letting the bot know how much history is needed, backtest trades can start at the specified timerange during backtesting and hyperopt.
!!! Warning
`startup_candle_count` should be below `ohlcv_candle_limit` (which is 500 for most exchanges) - since only this amount of candles will be available during Dry-Run/Live Trade operations.
#### Example
Let's try to backtest 1 month (January 2019) of 5m candles using the an example strategy with EMA100, as above.
``` bash
freqtrade backtesting --timerange 20190101-20190201 --ticker-interval 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.
If this data is available, indicators will be calculated with this extended timerange. The instable startup period (up to 2019-01-01 00:00:00) will then be removed before starting backtesting.
!!! Note
If data for the startup period is not available, then the timerange will be adjusted to account for this startup period - so Backtesting would start at 2019-01-01 08:30:00.
### Buy signal rules
Edit the method `populate_buy_trend()` in your strategy file to update your buy strategy.
@ -138,15 +172,19 @@ def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
dataframe.loc[
(
(dataframe['adx'] > 30) &
(dataframe['tema'] <= dataframe['bb_middleband']) &
(dataframe['tema'] > dataframe['tema'].shift(1))
(qtpylib.crossed_above(dataframe['rsi'], 30)) & # Signal: RSI crosses above 30
(dataframe['tema'] <= dataframe['bb_middleband']) & # Guard
(dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'buy'] = 1
return dataframe
```
!!! Note
Buying requires sellers to buy from - therefore volume needs to be > 0 (`dataframe['volume'] > 0`) to make sure that the bot does not buy/sell in no-activity periods.
### Sell signal rules
Edit the method `populate_sell_trend()` into your strategy file to update your sell strategy.
@ -168,9 +206,10 @@ def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame
"""
dataframe.loc[
(
(dataframe['adx'] > 70) &
(dataframe['tema'] > dataframe['bb_middleband']) &
(dataframe['tema'] < dataframe['tema'].shift(1))
(qtpylib.crossed_above(dataframe['rsi'], 70)) & # Signal: RSI crosses above 70
(dataframe['tema'] > dataframe['bb_middleband']) & # Guard
(dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe
@ -210,6 +249,23 @@ minimal_roi = {
While technically not completely disabled, this would sell once the trade reaches 10000% Profit.
To use times based on candle duration (ticker_interval or timeframe), the following snippet can be handy.
This will allow you to change the ticket_interval for the strategy, and ROI times will still be set as candles (e.g. after 3 candles ...)
``` python
from freqtrade.exchange import timeframe_to_minutes
class AwesomeStrategy(IStrategy):
ticker_interval = "1d"
ticker_interval_mins = timeframe_to_minutes(ticker_interval)
minimal_roi = {
"0": 0.05, # 5% for the first 3 candles
str(ticker_interval_mins * 3)): 0.02, # 2% after 3 candles
str(ticker_interval_mins * 6)): 0.01, # 1% After 6 candles
}
```
### Stoploss
Setting a stoploss is highly recommended to protect your capital from strong moves against you.
@ -228,13 +284,14 @@ If your exchange supports it, it's recommended to also set `"stoploss_on_exchang
For more information on order_types please look [here](configuration.md#understand-order_types).
### Ticker interval
### Timeframe (ticker interval)
This is the set of candles the bot should download and use for the analysis.
Common values are `"1m"`, `"5m"`, `"15m"`, `"1h"`, however all values supported by your exchange should work.
Please note that the same buy/sell signals may work with one interval, but not the other.
This setting is accessible within the strategy by using `self.ticker_interval`.
Please note that the same buy/sell signals may work well with one timeframe, but not with the others.
This setting is accessible within the strategy methods as the `self.ticker_interval` attribute.
### Metadata dict
@ -246,9 +303,9 @@ Instead, have a look at the section [Storing information](#Storing-information)
### Storing information
Storing information can be accomplished by crating a new dictionary within the strategy class.
Storing information can be accomplished by creating a new dictionary within the strategy class.
The name of the variable can be choosen at will, but should be prefixed with `cust_` to avoid naming collisions with predefined strategy variables.
The name of the variable can be chosen at will, but should be prefixed with `cust_` to avoid naming collisions with predefined strategy variables.
```python
class Awesomestrategy(IStrategy):
@ -263,10 +320,10 @@ class Awesomestrategy(IStrategy):
```
!!! Warning
The data is not persisted after a bot-restart (or config-reload). Also, the amount of data should be kept smallish (no DataFrames and such), otherwise the bot will start to consume a lot of memory and eventually run out of memory and crash.
The data is not persisted after a bot-restart (or config-reload). Also, the amount of data should be kept smallish (no DataFrames and such), otherwise the bot will start to consume a lot of memory and eventually run out of memory and crash.
!!! Note
If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
### Additional data (DataProvider)
@ -279,33 +336,35 @@ Please always check the mode of operation to select the correct method to get da
#### Possible options for DataProvider
- `available_pairs` - Property with tuples listing cached pairs with their intervals (pair, interval).
- `ohlcv(pair, ticker_interval)` - Currently cached ticker data for the pair, returns DataFrame or empty DataFrame.
- `historic_ohlcv(pair, ticker_interval)` - Returns historical data stored on disk.
- `get_pair_dataframe(pair, ticker_interval)` - This is a universal method, which returns either historical data (for backtesting) or cached live data (for the Dry-Run and Live-Run modes).
- `ohlcv(pair, timeframe)` - Currently cached candle (OHLCV) data for the pair, returns DataFrame or empty DataFrame.
- `historic_ohlcv(pair, timeframe)` - Returns historical data stored on disk.
- `get_pair_dataframe(pair, timeframe)` - This is a universal method, which returns either historical data (for backtesting) or cached live data (for the Dry-Run and Live-Run modes).
- `orderbook(pair, maximum)` - Returns latest orderbook data for the pair, a dict with bids/asks with a total of `maximum` entries.
- `market(pair)` - Returns market data for the pair: fees, limits, precisions, activity flag, etc. See [ccxt documentation](https://github.com/ccxt/ccxt/wiki/Manual#markets) for more details on Market data structure.
- `runmode` - Property containing the current runmode.
#### Example: fetch live ohlcv / historic data for the first informative pair
#### Example: fetch live / historical candle (OHLCV) data for the first informative pair
``` python
if self.dp:
inf_pair, inf_timeframe = self.informative_pairs()[0]
informative = self.dp.get_pair_dataframe(pair=inf_pair,
ticker_interval=inf_timeframe)
timeframe=inf_timeframe)
```
!!! Warning Warning about backtesting
!!! 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
!!! 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'):
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]
@ -319,8 +378,8 @@ if self.dp:
``` python
if self.dp:
for pair, ticker in self.dp.available_pairs:
print(f"available {pair}, {ticker}")
for pair, timeframe in self.dp.available_pairs:
print(f"available {pair}, {timeframe}")
```
#### Get data for non-tradeable pairs
@ -344,9 +403,9 @@ def informative_pairs(self):
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
to avoid hammering the exchange with too many requests and risk beeing blocked.
to avoid hammering the exchange with too many requests and risk being blocked.
### Additional data - Wallets
### Additional data (Wallets)
The strategy provides access to the `Wallets` object. This contains the current balances on the exchange.
@ -368,6 +427,97 @@ if self.wallets:
- `get_used(asset)` - currently tied up balance (open orders)
- `get_total(asset)` - total available balance - sum of the 2 above
### Additional data (Trades)
A history of Trades can be retrieved in the strategy by querying the database.
At the top of the file, import Trade.
```python
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'].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,
]).order_by(Trade.close_date).all()
# Summarize profit for this pair.
curdayprofit = sum(trade.close_profit for trade in trades)
```
Get amount of stake_currency currently invested in Trades:
``` python
if self.config['runmode'].value in ('live', 'dry_run'):
total_stakes = Trade.total_open_trades_stakes()
```
Retrieve performance per pair.
Returns a List of dicts per pair.
``` python
if self.config['runmode'].value in ('live', 'dry_run'):
performance = Trade.get_overall_performance()
```
Sample return value: ETH/BTC had 5 trades, with a total profit of 1.5% (ratio of 0.015).
``` json
{'pair': "ETH/BTC", 'profit': 0.015, 'count': 5}
```
!!! Warning
Trade history is not available during backtesting or hyperopt.
### 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_conf` 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()`.
@ -392,26 +542,54 @@ 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 is the default strategy?
The default buy strategy is located in the file
[freqtrade/default_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/strategy/default_strategy.py).
### Specify custom strategy location
If you want to use a strategy from a different directory you can pass `--strategy-path`
```bash
freqtrade --strategy AwesomeStrategy --strategy-path /some/directory
freqtrade trade --strategy AwesomeStrategy --strategy-path /some/directory
```
### Derived strategies
The strategies can be derived from other strategies. This avoids duplication of your custom strategy code. You can use this technique to override small parts of your main strategy, leaving the rest untouched:
``` python
class MyAwesomeStrategy(IStrategy):
...
stoploss = 0.13
trailing_stop = False
# All other attributes and methods are here as they
# should be in any custom strategy...
...
class MyAwesomeStrategy2(MyAwesomeStrategy):
# Override something
stoploss = 0.08
trailing_stop = True
```
Both attributes and methods may be overriden, altering behavior of the original strategy in a way you need.
### Common mistakes when developing strategies
Backtesting analyzes the whole time-range at once for performance reasons. Because of this, strategy authors need to make sure that strategies do not look-ahead into the future.
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.
The following lists some common patterns which should be avoided to prevent frustration:
- don't use `shift(-1)`. This uses data from the future, which is not available.
- don't use `.iloc[-1]` or any other absolute position in the dataframe, this will be different between dry-run and backtesting.
- 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
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.
We're happy to accept Pull Requests containing new Strategies to that repo.
We also got a *strategy-sharing* channel in our [Slack community](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LWEyODBiNzkzNzcyNzU0MWYyYzE5NjIyOTQxMzBmMGUxOTIzM2YyN2Y4NWY1YTEwZDgwYTRmMzE2NmM5ZmY2MTg) which is a great place to get and/or share ideas.
We also got a *strategy-sharing* channel in our [Slack community](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) which is a great place to get and/or share ideas.
## Next step

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@ -0,0 +1,162 @@
# Strategy analysis example
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
config["ticker_interval"] = "5m"
# Name of the strategy class
config["strategy"] = "SampleStrategy"
# Location of the data
data_location = Path(config['user_data_dir'], 'data', 'binance')
# Pair to analyze - Only use one pair here
pair = "BTC_USDT"
```
```python
# Load data using values set above
from freqtrade.data.history import load_pair_history
candles = load_pair_history(datadir=data_location,
timeframe=config["ticker_interval"],
pair=pair)
# Confirm success
print("Loaded " + str(len(candles)) + f" rows of data for {pair} from {data_location}")
candles.head()
```
## Load and run strategy
* Rerun each time the strategy file is changed
```python
# Load strategy using values set above
from freqtrade.resolvers import StrategyResolver
strategy = StrategyResolver.load_strategy(config)
# Generate buy/sell signals using strategy
df = strategy.analyze_ticker(candles, {'pair': pair})
df.tail()
```
### Display the trade details
* Note that using `data.head()` would also work, however most indicators have some "startup" data at the top of the dataframe.
* Some possible problems
* Columns with NaN values at the end of the dataframe
* Columns used in `crossed*()` functions with completely different units
* Comparison with full backtest
* having 200 buy signals as output for one pair from `analyze_ticker()` does not necessarily mean that 200 trades will be made during backtesting.
* Assuming you use only one condition such as, `df['rsi'] < 30` as buy condition, this will generate multiple "buy" signals for each pair in sequence (until rsi returns > 29). The bot will only buy on the first of these signals (and also only if a trade-slot ("max_open_trades") is still available), or on one of the middle signals, as soon as a "slot" becomes available.
```python
# Report results
print(f"Generated {df['buy'].sum()} buy signals")
data = df.set_index('date', drop=False)
data.tail()
```
## Load existing objects into a Jupyter notebook
The following cells assume that you have already generated data using the cli.
They will allow you to drill deeper into your results, and perform analysis which otherwise would make the output very difficult to digest due to information overload.
### Load backtest results to pandas dataframe
Analyze a trades dataframe (also used below for plotting)
```python
from freqtrade.data.btanalysis import load_backtest_data
# Load backtest results
trades = load_backtest_data(config["user_data_dir"] / "backtest_results/backtest-result.json")
# Show value-counts per pair
trades.groupby("pair")["sell_reason"].value_counts()
```
### Load live trading results into a pandas dataframe
In case you did already some trading and want to analyze your performance
```python
from freqtrade.data.btanalysis import load_trades_from_db
# Fetch trades from database
trades = load_trades_from_db("sqlite:///tradesv3.sqlite")
# Display results
trades.groupby("pair")["sell_reason"].value_counts()
```
## Analyze the loaded trades for trade parallelism
This can be useful to find the best `max_open_trades` parameter, when used with backtesting in conjunction with `--disable-max-market-positions`.
`analyze_trade_parallelism()` returns a timeseries dataframe with an "open_trades" column, specifying the number of open trades for each candle.
```python
from freqtrade.data.btanalysis import analyze_trade_parallelism
# Analyze the above
parallel_trades = analyze_trade_parallelism(trades, '5m')
parallel_trades.plot()
```
## Plot results
Freqtrade offers interactive plotting capabilities based on plotly.
```python
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_red,
indicators1=['sma20', 'ema50', 'ema55'],
indicators2=['rsi', 'macd', 'macdsignal', 'macdhist']
)
```
```python
# Show graph inline
# graph.show()
# Render graph in a seperate window
graph.show(renderer="browser")
```
Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data.

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@ -0,0 +1,13 @@
.rst-versions {
font-size: .7rem;
color: white;
}
.rst-versions.rst-badge .rst-current-version {
font-size: .7rem;
color: white;
}
.rst-versions .rst-other-versions {
color: white;
}

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@ -53,8 +53,9 @@ official commands. You can ask at any moment for help with `/help`.
| `/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
| `/status table` | | List all open trades in a table format. Pending buy orders are marked with an asterisk (*) Pending sell orders are marked with a double asterisk (**)
| `/count` | | Displays number of trades used and available
| `/profit` | | Display a summary of your profit/loss from close trades and some stats about your performance
| `/forcesell <trade_id>` | | Instantly sells the given trade (Ignoring `minimum_roi`).
@ -93,7 +94,7 @@ 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.
!!! warning
!!! 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.
### /status

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@ -0,0 +1,517 @@
# Utility Subcommands
Besides the Live-Trade and Dry-Run run modes, the `backtesting`, `edge` and `hyperopt` optimization subcommands, and the `download-data` subcommand which prepares historical data, the bot contains a number of utility subcommands. They are described in this section.
## Create userdir
Creates the directory structure to hold your files for freqtrade.
Will also create strategy and hyperopt examples for you to get started.
Can be used multiple times - using `--reset` will reset the sample strategy and hyperopt files to their default state.
```
usage: freqtrade create-userdir [-h] [--userdir PATH] [--reset]
optional arguments:
-h, --help show this help message and exit
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
--reset Reset sample files to their original state.
```
!!! Warning
Using `--reset` may result in loss of data, since this will overwrite all sample files without asking again.
```
├── backtest_results
├── data
├── hyperopt_results
├── hyperopts
│   ├── sample_hyperopt_advanced.py
│   ├── sample_hyperopt_loss.py
│   └── sample_hyperopt.py
├── notebooks
│   └── strategy_analysis_example.ipynb
├── plot
└── strategies
└── sample_strategy.py
```
## Create new config
Creates a new configuration file, asking some questions which are important selections for a configuration.
```
usage: freqtrade new-config [-h] [-c PATH]
optional arguments:
-h, --help show this help message and exit
-c PATH, --config PATH
Specify configuration file (default: `config.json`). Multiple --config options may be used. Can be set to `-`
to read config from stdin.
```
!!! Warning
Only vital questions are asked. Freqtrade offers a lot more configuration possibilities, which are listed in the [Configuration documentation](configuration.md#configuration-parameters)
### Create config examples
```
$ freqtrade new-config --config config_binance.json
? Do you want to enable Dry-run (simulated trades)? Yes
? Please insert your stake currency: BTC
? Please insert your stake amount: 0.05
? Please insert max_open_trades (Integer or 'unlimited'): 3
? Please insert your timeframe (ticker interval): 5m
? Please insert your display Currency (for reporting): USD
? Select exchange binance
? Do you want to enable Telegram? No
```
## Create new strategy
Creates a new strategy from a template similar to SampleStrategy.
The file will be named inline with your class name, and will not overwrite existing files.
Results will be located in `user_data/strategies/<strategyclassname>.py`.
``` output
usage: freqtrade new-strategy [-h] [--userdir PATH] [-s NAME]
[--template {full,minimal}]
optional arguments:
-h, --help show this help message and exit
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
-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`.
```
### Sample usage of new-strategy
```bash
freqtrade new-strategy --strategy AwesomeStrategy
```
With custom user directory
```bash
freqtrade new-strategy --userdir ~/.freqtrade/ --strategy AwesomeStrategy
```
## Create new hyperopt
Creates a new hyperopt from a template similar to SampleHyperopt.
The file will be named inline with your class name, and will not overwrite existing files.
Results will be located in `user_data/hyperopts/<classname>.py`.
``` output
usage: freqtrade new-hyperopt [-h] [--userdir PATH] [--hyperopt NAME]
[--template {full,minimal}]
optional arguments:
-h, --help show this help message and exit
--userdir PATH, --user-data-dir PATH
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`.
```
### Sample usage of new-hyperopt
```bash
freqtrade new-hyperopt --hyperopt AwesomeHyperopt
```
With custom user directory
```bash
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.
```
usage: freqtrade list-exchanges [-h] [-1] [-a]
optional arguments:
-h, --help show this help message and exit
-1, --one-column Print output in one column.
-a, --all Print all exchanges known to the ccxt library.
```
* Example: see exchanges available for the bot:
```
$ freqtrade list-exchanges
Exchanges available for Freqtrade: _1btcxe, acx, allcoin, bequant, bibox, binance, binanceje, binanceus, bitbank, bitfinex, bitfinex2, bitkk, bitlish, bitmart, bittrex, bitz, bleutrade, btcalpha, btcmarkets, btcturk, buda, cex, cobinhood, coinbaseprime, coinbasepro, coinex, cointiger, coss, crex24, digifinex, dsx, dx, ethfinex, fcoin, fcoinjp, gateio, gdax, gemini, hitbtc2, huobipro, huobiru, idex, kkex, kraken, kucoin, kucoin2, kuna, lbank, mandala, mercado, oceanex, okcoincny, okcoinusd, okex, okex3, poloniex, rightbtc, theocean, tidebit, upbit, zb
```
* Example: see all exchanges supported by the ccxt library (including 'bad' ones, i.e. those that are known to not work with Freqtrade):
```
$ freqtrade list-exchanges -a
All exchanges supported by the ccxt library: _1btcxe, acx, adara, allcoin, anxpro, bcex, bequant, bibox, bigone, binance, binanceje, binanceus, bit2c, bitbank, bitbay, bitfinex, bitfinex2, bitflyer, bitforex, bithumb, bitkk, bitlish, bitmart, bitmex, bitso, bitstamp, bitstamp1, bittrex, bitz, bl3p, bleutrade, braziliex, btcalpha, btcbox, btcchina, btcmarkets, btctradeim, btctradeua, btcturk, buda, bxinth, cex, chilebit, cobinhood, coinbase, coinbaseprime, coinbasepro, coincheck, coinegg, coinex, coinexchange, coinfalcon, coinfloor, coingi, coinmarketcap, coinmate, coinone, coinspot, cointiger, coolcoin, coss, crex24, crypton, deribit, digifinex, dsx, dx, ethfinex, exmo, exx, fcoin, fcoinjp, flowbtc, foxbit, fybse, gateio, gdax, gemini, hitbtc, hitbtc2, huobipro, huobiru, ice3x, idex, independentreserve, indodax, itbit, kkex, kraken, kucoin, kucoin2, kuna, lakebtc, latoken, lbank, liquid, livecoin, luno, lykke, mandala, mercado, mixcoins, negociecoins, nova, oceanex, okcoincny, okcoinusd, okex, okex3, paymium, poloniex, rightbtc, southxchange, stronghold, surbitcoin, theocean, therock, tidebit, tidex, upbit, vaultoro, vbtc, virwox, xbtce, yobit, zaif, zb
```
## List Timeframes
Use the `list-timeframes` subcommand to see the list of timeframes (ticker intervals) available for the exchange.
```
usage: freqtrade list-timeframes [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [--exchange EXCHANGE] [-1]
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.
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 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
```
* Example: enumerate exchanges available for Freqtrade and print timeframes supported by each of them:
```
$ for i in `freqtrade list-exchanges -1`; do freqtrade list-timeframes --exchange $i; done
```
## List pairs/list markets
The `list-pairs` and `list-markets` subcommands allow to see the pairs/markets available on exchange.
Pairs are markets with the '/' character between the base currency part and the quote currency part in the market symbol.
For example, in the 'ETH/BTC' pair 'ETH' is the base currency, while 'BTC' is the quote currency.
For pairs traded by Freqtrade the pair quote currency is defined by the value of the `stake_currency` configuration setting.
You can print info about any pair/market with these subcommands - and you can filter output by quote-currency using `--quote BTC`, or by base-currency using `--base ETH` options correspondingly.
These subcommands have same usage and same set of available options:
```
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] [-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]
optional arguments:
-h, --help show this help message and exit
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no
config is provided.
--print-list Print list of pairs or market symbols. By default data
is printed in the tabular format.
--print-json Print list of pairs or market symbols in JSON format.
-1, --one-column Print output in one column.
--print-csv Print exchange pair or market data in the csv format.
--base BASE_CURRENCY [BASE_CURRENCY ...]
Specify base currency(-ies). Space-separated list.
--quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]
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
on the exchange. The see the list of all pairs/markets (not only the active ones), use the `-a`/`-all` option.
Pairs/markets are sorted by its symbol string in the printed output.
### Examples
* Print the list of active pairs with quote currency USD on exchange, specified in the default
configuration file (i.e. pairs on the "Bittrex" exchange) in JSON format:
```
$ freqtrade list-pairs --quote USD --print-json
```
* Print the list of all pairs on the exchange, specified in the `config_binance.json` configuration file
(i.e. on the "Binance" exchange) with base currencies BTC or ETH and quote currencies USDT or USD, as the
human-readable list with summary:
```
$ 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:
```
$ freqtrade list-markets --exchange kraken --all
```
## Test pairlist
Use the `test-pairlist` subcommand to test the configuration of [dynamic pairlists](configuration.md#pairlists).
Requires a configuration with specified `pairlists` attribute.
Can be used to generate static pairlists to be used during backtesting / hyperopt.
```
usage: freqtrade test-pairlist [-h] [-c PATH]
[--quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]]
[-1] [--print-json]
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.
--quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]
Specify quote currency(-ies). Space-separated list.
-1, --one-column Print output in one column.
--print-json Print list of pairs or market symbols in JSON format.
```
### Examples
Show whitelist when using a [dynamic pairlist](configuration.md#pairlists).
```
freqtrade test-pairlist --config config.json --quote USDT BTC
```
## List Hyperopt results
You can list the hyperoptimization epochs the Hyperopt module evaluated previously with the `hyperopt-list` subcommand.
```
usage: freqtrade hyperopt-list [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH] [--best]
[--profitable] [--min-trades INT]
[--max-trades INT] [--min-avg-time FLOAT]
[--max-avg-time FLOAT] [--min-avg-profit FLOAT]
[--max-avg-profit FLOAT]
[--min-total-profit FLOAT]
[--max-total-profit FLOAT] [--no-color]
[--print-json] [--no-details]
[--export-csv FILE]
optional arguments:
-h, --help show this help message and exit
--best Select only best epochs.
--profitable Select only profitable epochs.
--min-trades INT Select epochs with more than INT trades.
--max-trades INT Select epochs with less than INT trades.
--min-avg-time FLOAT Select epochs on above average time.
--max-avg-time FLOAT Select epochs on under average time.
--min-avg-profit FLOAT
Select epochs on above average profit.
--max-avg-profit FLOAT
Select epochs on below average profit.
--min-total-profit FLOAT
Select epochs on above total profit.
--max-total-profit FLOAT
Select epochs on below total profit.
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--print-json Print best result detailization in JSON format.
--no-details Do not print best epoch details.
--export-csv FILE Export to CSV-File. This will disable table print.
Example: --export-csv hyperopt.csv
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```
### Examples
List all results, print details of the best result at the end:
```
freqtrade hyperopt-list
```
List only epochs with positive profit. Do not print the details of the best epoch, so that the list can be iterated in a script:
```
freqtrade hyperopt-list --profitable --no-details
```
## Show details of Hyperopt results
You can show the details of any hyperoptimization epoch previously evaluated by the Hyperopt module with the `hyperopt-show` subcommand.
```
usage: freqtrade hyperopt-show [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH] [--best]
[--profitable] [-n INT] [--print-json]
[--no-header]
optional arguments:
-h, --help show this help message and exit
--best Select only best epochs.
--profitable Select only profitable epochs.
-n INT, --index INT Specify the index of the epoch to print details for.
--print-json Print best result detailization in JSON format.
--no-header Do not print epoch details header.
```
### Examples
Print details for the epoch 168 (the number of the epoch is shown by the `hyperopt-list` subcommand or by Hyperopt itself during hyperoptimization run):
```
freqtrade hyperopt-show -n 168
```
Prints JSON data with details for the last best epoch (i.e., the best of all epochs):
```
freqtrade hyperopt-show --best -n -1 --print-json --no-header
```

56
docs/webhook-config.md
View File

@ -15,10 +15,20 @@ Sample configuration (tested using IFTTT).
"value2": "limit {limit:8f}",
"value3": "{stake_amount:8f} {stake_currency}"
},
"webhookbuycancel": {
"value1": "Cancelling Open Buy Order for {pair}",
"value2": "limit {limit:8f}",
"value3": "{stake_amount:8f} {stake_currency}"
},
"webhooksell": {
"value1": "Selling {pair}",
"value2": "limit {limit:8f}",
"value3": "profit: {profit_amount:8f} {stake_currency}"
"value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
},
"webhooksellcancel": {
"value1": "Cancelling Open Sell Order for {pair}",
"value2": "limit {limit:8f}",
"value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
},
"webhookstatus": {
"value1": "Status: {status}",
@ -40,10 +50,29 @@ Possible parameters are:
* `exchange`
* `pair`
* `limit`
* `amount`
* `open_date`
* `stake_amount`
* `stake_currency`
* `fiat_currency`
* `order_type`
* `current_rate`
### Webhookbuycancel
The fields in `webhook.webhookbuycancel` are filled when the bot cancels a buy order. Parameters are filled using string.format.
Possible parameters are:
* `exchange`
* `pair`
* `limit`
* `amount`
* `open_date`
* `stake_amount`
* `stake_currency`
* `fiat_currency`
* `order_type`
* `current_rate`
### Webhooksell
@ -58,11 +87,34 @@ Possible parameters are:
* `open_rate`
* `current_rate`
* `profit_amount`
* `profit_percent`
* `profit_ratio`
* `stake_currency`
* `fiat_currency`
* `sell_reason`
* `order_type`
* `open_date`
* `close_date`
### Webhooksellcancel
The fields in `webhook.webhooksellcancel` are filled when the bot cancels a sell order. Parameters are filled using string.format.
Possible parameters are:
* `exchange`
* `pair`
* `gain`
* `limit`
* `amount`
* `open_rate`
* `current_rate`
* `profit_amount`
* `profit_ratio`
* `stake_currency`
* `fiat_currency`
* `sell_reason`
* `order_type`
* `open_date`
* `close_date`
### Webhookstatus

13
environment.yml
View File

@ -9,25 +9,26 @@ dependencies:
- wheel
- numpy
- pandas
- scipy
- SQLAlchemy
- scikit-learn
- arrow
- requests
- urllib3
- wrapt
- joblib
- jsonschema
- tabulate
- python-rapidjson
- filelock
- flask
- python-dotenv
- cachetools
- scikit-optimize
- python-telegram-bot
# Optional for plotting
- plotly
# Optional for hyperopt
- scipy
- scikit-optimize
- scikit-learn
- filelock
- joblib
# Optional for development
- flake8
- pytest
@ -44,7 +45,7 @@ dependencies:
- pip:
# Required for app
- cython
- coinmarketcap
- pycoingecko
- ccxt
- TA-Lib
- py_find_1st

2
freqtrade.service
View File

@ -6,7 +6,7 @@ After=network.target
# Set WorkingDirectory and ExecStart to your file paths accordingly
# NOTE: %h will be resolved to /home/<username>
WorkingDirectory=%h/freqtrade
ExecStart=/usr/bin/freqtrade
ExecStart=/usr/bin/freqtrade trade
Restart=on-failure
[Install]

2
freqtrade.service.watchdog
View File

@ -6,7 +6,7 @@ After=network.target
# Set WorkingDirectory and ExecStart to your file paths accordingly
# NOTE: %h will be resolved to /home/<username>
WorkingDirectory=%h/freqtrade
ExecStart=/usr/bin/freqtrade --sd-notify
ExecStart=/usr/bin/freqtrade trade --sd-notify
Restart=always
#Restart=on-failure

47
freqtrade/__init__.py
View File

@ -1,44 +1,27 @@
""" FreqTrade bot """
""" Freqtrade bot """
__version__ = 'develop'
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.
"""

28
freqtrade/commands/__init__.py Normal file
View File

@ -0,0 +1,28 @@
# 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)
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)
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

349
freqtrade/commands/arguments.py Normal file
View File

@ -0,0 +1,349 @@
"""
This module contains the argument manager class
"""
import argparse
from functools import partial
from pathlib import Path
from typing import Any, Dict, List, Optional
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"]
ARGS_STRATEGY = ["strategy", "strategy_path"]
ARGS_TRADE = ["db_url", "sd_notify", "dry_run"]
ARGS_COMMON_OPTIMIZE = ["ticker_interval", "timerange",
"max_open_trades", "stake_amount", "fee"]
ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + ["position_stacking", "use_max_market_positions",
"strategy_list", "export", "exportfilename"]
ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
"position_stacking", "epochs", "spaces",
"use_max_market_positions", "print_all",
"print_colorized", "print_json", "hyperopt_jobs",
"hyperopt_random_state", "hyperopt_min_trades",
"hyperopt_continue", "hyperopt_loss"]
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"]
ARGS_LIST_PAIRS = ["exchange", "print_list", "list_pairs_print_json", "print_one_column",
"print_csv", "base_currencies", "quote_currencies", "list_pairs_all"]
ARGS_TEST_PAIRLIST = ["config", "quote_currencies", "print_one_column", "list_pairs_print_json"]
ARGS_CREATE_USERDIR = ["user_data_dir", "reset"]
ARGS_BUILD_CONFIG = ["config"]
ARGS_BUILD_STRATEGY = ["user_data_dir", "strategy", "template"]
ARGS_BUILD_HYPEROPT = ["user_data_dir", "hyperopt", "template"]
ARGS_CONVERT_DATA = ["pairs", "format_from", "format_to", "erase"]
ARGS_CONVERT_DATA_OHLCV = ARGS_CONVERT_DATA + ["timeframes"]
ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "download_trades", "exchange",
"timeframes", "erase", "dataformat_ohlcv", "dataformat_trades"]
ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",
"db_url", "trade_source", "export", "exportfilename",
"timerange", "ticker_interval", "no_trades"]
ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
"trade_source", "ticker_interval"]
ARGS_HYPEROPT_LIST = ["hyperopt_list_best", "hyperopt_list_profitable",
"hyperopt_list_min_trades", "hyperopt_list_max_trades",
"hyperopt_list_min_avg_time", "hyperopt_list_max_avg_time",
"hyperopt_list_min_avg_profit", "hyperopt_list_max_avg_profit",
"hyperopt_list_min_total_profit", "hyperopt_list_max_total_profit",
"print_colorized", "print_json", "hyperopt_list_no_details",
"export_csv"]
ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperopt_show_index",
"print_json", "hyperopt_show_no_header"]
NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
"list-markets", "list-pairs", "list-strategies",
"list-hyperopts", "hyperopt-list", "hyperopt-show",
"plot-dataframe", "plot-profit"]
NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-hyperopt", "new-strategy"]
class Arguments:
"""
Arguments Class. Manage the arguments received by the cli
"""
def __init__(self, args: Optional[List[str]]) -> None:
self.args = args
self._parsed_arg: Optional[argparse.Namespace] = None
def get_parsed_arg(self) -> Dict[str, Any]:
"""
Return the list of arguments
:return: List[str] List of arguments
"""
if self._parsed_arg is None:
self._build_subcommands()
self._parsed_arg = self._parse_args()
return vars(self._parsed_arg)
def _parse_args(self) -> argparse.Namespace:
"""
Parses given arguments and returns an argparse Namespace instance.
"""
parsed_arg = self.parser.parse_args(self.args)
# 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):
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
def _build_args(self, optionlist, parser):
for val in optionlist:
opt = AVAILABLE_CLI_OPTIONS[val]
parser.add_argument(*opt.cli, dest=val, **opt.kwargs)
def _build_subcommands(self) -> None:
"""
Builds and attaches all subcommands.
:return: None
"""
# Build shared arguments (as group Common Options)
_common_parser = argparse.ArgumentParser(add_help=False)
group = _common_parser.add_argument_group("Common arguments")
self._build_args(optionlist=ARGS_COMMON, parser=group)
_strategy_parser = argparse.ArgumentParser(add_help=False)
strategy_group = _strategy_parser.add_argument_group("Strategy arguments")
self._build_args(optionlist=ARGS_STRATEGY, parser=strategy_group)
# Build main command
self.parser = argparse.ArgumentParser(description='Free, open source crypto trading bot')
self._build_args(optionlist=['version'], parser=self.parser)
from freqtrade.commands import (start_create_userdir, start_convert_data,
start_download_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_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
# shown from `main.py`
# required=True
)
# Add trade subcommand
trade_cmd = subparsers.add_parser('trade', help='Trade module.',
parents=[_common_parser, _strategy_parser])
trade_cmd.set_defaults(func=start_trading)
self._build_args(optionlist=ARGS_TRADE, parser=trade_cmd)
# Add backtesting subcommand
backtesting_cmd = subparsers.add_parser('backtesting', help='Backtesting module.',
parents=[_common_parser, _strategy_parser])
backtesting_cmd.set_defaults(func=start_backtesting)
self._build_args(optionlist=ARGS_BACKTEST, parser=backtesting_cmd)
# Add edge subcommand
edge_cmd = subparsers.add_parser('edge', help='Edge module.',
parents=[_common_parser, _strategy_parser])
edge_cmd.set_defaults(func=start_edge)
self._build_args(optionlist=ARGS_EDGE, parser=edge_cmd)
# Add hyperopt subcommand
hyperopt_cmd = subparsers.add_parser('hyperopt', help='Hyperopt module.',
parents=[_common_parser, _strategy_parser],
)
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 new-config subcommand
build_config_cmd = subparsers.add_parser('new-config',
help="Create new config")
build_config_cmd.set_defaults(func=start_new_config)
self._build_args(optionlist=ARGS_BUILD_CONFIG, parser=build_config_cmd)
# add new-strategy subcommand
build_strategy_cmd = subparsers.add_parser('new-strategy',
help="Create new strategy")
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 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-hyperopts subcommand
list_hyperopts_cmd = subparsers.add_parser(
'list-hyperopts',
help='Print available hyperopt classes.',
parents=[_common_parser],
)
list_hyperopts_cmd.set_defaults(func=start_list_hyperopts)
self._build_args(optionlist=ARGS_LIST_HYPEROPTS, parser=list_hyperopts_cmd)
# Add list-exchanges subcommand
list_exchanges_cmd = subparsers.add_parser(
'list-exchanges',
help='Print available exchanges.',
parents=[_common_parser],
)
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.',
parents=[_common_parser],
)
list_timeframes_cmd.set_defaults(func=start_list_timeframes)
self._build_args(optionlist=ARGS_LIST_TIMEFRAMES, parser=list_timeframes_cmd)
# Add list-markets subcommand
list_markets_cmd = subparsers.add_parser(
'list-markets',
help='Print markets on exchange.',
parents=[_common_parser],
)
list_markets_cmd.set_defaults(func=partial(start_list_markets, pairs_only=False))
self._build_args(optionlist=ARGS_LIST_PAIRS, parser=list_markets_cmd)
# Add list-pairs subcommand
list_pairs_cmd = subparsers.add_parser(
'list-pairs',
help='Print pairs on exchange.',
parents=[_common_parser],
)
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 test-pairlist subcommand
test_pairlist_cmd = subparsers.add_parser(
'test-pairlist',
help='Test your pairlist configuration.',
)
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 convert-data subcommand
convert_data_cmd = subparsers.add_parser(
'convert-data',
help='Convert candle (OHLCV) data from one format to another.',
parents=[_common_parser],
)
convert_data_cmd.set_defaults(func=partial(start_convert_data, ohlcv=True))
self._build_args(optionlist=ARGS_CONVERT_DATA_OHLCV, parser=convert_data_cmd)
# Add convert-trade-data subcommand
convert_trade_data_cmd = subparsers.add_parser(
'convert-trade-data',
help='Convert trade data from one format to another.',
parents=[_common_parser],
)
convert_trade_data_cmd.set_defaults(func=partial(start_convert_data, ohlcv=False))
self._build_args(optionlist=ARGS_CONVERT_DATA, parser=convert_trade_data_cmd)
# Add Plotting subcommand
plot_dataframe_cmd = subparsers.add_parser(
'plot-dataframe',
help='Plot candles with indicators.',
parents=[_common_parser, _strategy_parser],
)
plot_dataframe_cmd.set_defaults(func=start_plot_dataframe)
self._build_args(optionlist=ARGS_PLOT_DATAFRAME, parser=plot_dataframe_cmd)
# Plot profit
plot_profit_cmd = subparsers.add_parser(
'plot-profit',
help='Generate plot showing profits.',
parents=[_common_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)

193
freqtrade/commands/build_config_commands.py Normal file
View File

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

247
freqtrade/configuration/cli_options.py → freqtrade/commands/cli_options.py
View File

@ -1,8 +1,7 @@
"""
Definition of cli arguments used in arguments.py
"""
import argparse
import os
from argparse import ArgumentTypeError
from freqtrade import __version__, constants
@ -13,12 +12,24 @@ 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
def check_int_nonzero(value: str) -> int:
try:
uint = int(value)
if uint == 0:
raise ValueError
except ValueError:
raise ArgumentTypeError(
f"{value} is invalid for this parameter, should be a non-zero integer value"
)
return uint
class Arg:
# Optional CLI arguments
def __init__(self, *args, **kwargs):
@ -37,7 +48,8 @@ AVAILABLE_CLI_OPTIONS = {
),
"logfile": Arg(
'--logfile',
help='Log to the file specified.',
help="Log to the file specified. Special values are: 'syslog', 'journald'. "
"See the documentation for more details.",
metavar='FILE',
),
"version": Arg(
@ -47,7 +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',
@ -63,12 +76,16 @@ AVAILABLE_CLI_OPTIONS = {
help='Path to userdata directory.',
metavar='PATH',
),
"reset": Arg(
'--reset',
help='Reset sample files to their original state.',
action='store_true',
),
# Main options
"strategy": Arg(
'-s', '--strategy',
help='Specify strategy class name (default: `%(default)s`).',
help='Specify strategy class name which will be used by the bot.',
metavar='NAME',
default='DefaultStrategy',
),
"strategy_path": Arg(
'--strategy-path',
@ -87,6 +104,11 @@ AVAILABLE_CLI_OPTIONS = {
help='Notify systemd service manager.',
action='store_true',
),
"dry_run": Arg(
'--dry-run',
help='Enforce dry-run for trading (removes Exchange secrets and simulates trades).',
action='store_true',
),
# Optimize common
"ticker_interval": Arg(
'-i', '--ticker-interval',
@ -97,14 +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
@ -137,12 +159,16 @@ AVAILABLE_CLI_OPTIONS = {
),
"exportfilename": Arg(
'--export-filename',
help='Save backtest results to the file with this filename (default: `%(default)s`). '
help='Save backtest results to the file with this filename. '
'Requires `--export` to be set as well. '
'Example: `--export-filename=user_data/backtest_results/backtest_today.json`',
metavar='PATH',
default=os.path.join('user_data', 'backtest_results',
'backtest-result.json'),
),
"fee": Arg(
'--fee',
help='Specify fee ratio. Will be applied twice (on trade entry and exit).',
type=float,
metavar='FLOAT',
),
# Edge
"stoploss_range": Arg(
@ -153,14 +179,13 @@ AVAILABLE_CLI_OPTIONS = {
),
# Hyperopt
"hyperopt": Arg(
'--customhyperopt',
help='Specify hyperopt class name (default: `%(default)s`).',
'--hyperopt',
help='Specify hyperopt class name which will be used by the bot.',
metavar='NAME',
default=constants.DEFAULT_HYPEROPT,
),
"hyperopt_path": Arg(
'--hyperopt-path',
help='Specify additional lookup path for Hyperopts and Hyperopt Loss functions.',
help='Specify additional lookup path for Hyperopt and Hyperopt Loss functions.',
metavar='PATH',
),
"epochs": Arg(
@ -171,12 +196,11 @@ AVAILABLE_CLI_OPTIONS = {
default=constants.HYPEROPT_EPOCH,
),
"spaces": Arg(
'-s', '--spaces',
help='Specify which parameters to hyperopt. Space-separated list. '
'Default: `%(default)s`.',
choices=['all', 'buy', 'sell', 'roi', 'stoploss'],
'--spaces',
help='Specify which parameters to hyperopt. Space-separated list.',
choices=['all', 'buy', 'sell', 'roi', 'stoploss', 'trailing', 'default'],
nargs='+',
default='all',
default='default',
),
"print_all": Arg(
'--print-all',
@ -197,6 +221,13 @@ AVAILABLE_CLI_OPTIONS = {
action='store_true',
default=False,
),
"export_csv": Arg(
'--export-csv',
help='Export to CSV-File.'
' This will disable table print.'
' Example: --export-csv hyperopt.csv',
metavar='FILE',
),
"hyperopt_jobs": Arg(
'-j', '--job-workers',
help='The number of concurrently running jobs for hyperoptimization '
@ -233,7 +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,
@ -241,9 +273,50 @@ AVAILABLE_CLI_OPTIONS = {
# List exchanges
"print_one_column": Arg(
'-1', '--one-column',
help='Print exchanges in one column.',
help='Print output in one column.',
action='store_true',
),
"list_exchanges_all": Arg(
'-a', '--all',
help='Print all exchanges known to the ccxt library.',
action='store_true',
),
# List pairs / markets
"list_pairs_all": Arg(
'-a', '--all',
help='Print all pairs or market symbols. By default only active '
'ones are shown.',
action='store_true',
),
"print_list": Arg(
'--print-list',
help='Print list of pairs or market symbols. By default data is '
'printed in the tabular format.',
action='store_true',
),
"list_pairs_print_json": Arg(
'--print-json',
help='Print list of pairs or market symbols in JSON format.',
action='store_true',
default=False,
),
"print_csv": Arg(
'--print-csv',
help='Print exchange pair or market data in the csv format.',
action='store_true',
),
"quote_currencies": Arg(
'--quote',
help='Specify quote currency(-ies). Space-separated list.',
nargs='+',
metavar='QUOTE_CURRENCY',
),
"base_currencies": Arg(
'--base',
help='Specify base currency(-ies). Space-separated list.',
nargs='+',
metavar='BASE_CURRENCY',
),
# Script options
"pairs": Arg(
'-p', '--pairs',
@ -262,6 +335,36 @@ AVAILABLE_CLI_OPTIONS = {
type=check_int_positive,
metavar='INT',
),
"download_trades": Arg(
'--dl-trades',
help='Download trades instead of OHLCV data. The bot will resample trades to the '
'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}`). '
@ -281,19 +384,25 @@ AVAILABLE_CLI_OPTIONS = {
help='Clean all existing data for the selected exchange/pairs/timeframes.',
action='store_true',
),
# 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'],
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(
@ -304,6 +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)) '
@ -311,4 +425,79 @@ AVAILABLE_CLI_OPTIONS = {
choices=["DB", "file"],
default="file",
),
# hyperopt-list, hyperopt-show
"hyperopt_list_profitable": Arg(
'--profitable',
help='Select only profitable epochs.',
action='store_true',
),
"hyperopt_list_best": Arg(
'--best',
help='Select only best epochs.',
action='store_true',
),
"hyperopt_list_min_trades": Arg(
'--min-trades',
help='Select epochs with more than INT trades.',
type=check_int_positive,
metavar='INT',
),
"hyperopt_list_max_trades": Arg(
'--max-trades',
help='Select epochs with less than INT trades.',
type=check_int_positive,
metavar='INT',
),
"hyperopt_list_min_avg_time": Arg(
'--min-avg-time',
help='Select epochs on above average time.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_max_avg_time": Arg(
'--max-avg-time',
help='Select epochs on under average time.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_min_avg_profit": Arg(
'--min-avg-profit',
help='Select epochs on above average profit.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_max_avg_profit": Arg(
'--max-avg-profit',
help='Select epochs on below average profit.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_min_total_profit": Arg(
'--min-total-profit',
help='Select epochs on above total profit.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_max_total_profit": Arg(
'--max-total-profit',
help='Select epochs on below total profit.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_no_details": Arg(
'--no-details',
help='Do not print best epoch details.',
action='store_true',
),
"hyperopt_show_index": Arg(
'-n', '--index',
help='Specify the index of the epoch to print details for.',
type=check_int_nonzero,
metavar='INT',
),
"hyperopt_show_no_header": Arg(
'--no-header',
help='Do not print epoch details header.',
action='store_true',
),
}

90
freqtrade/commands/data_commands.py Normal file
View File

@ -0,0 +1,90 @@
import logging
import sys
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.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'])

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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
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
"""
indicators = render_template(templatefile=f"subtemplates/indicators_{subtemplate}.j2",)
buy_trend = render_template(templatefile=f"subtemplates/buy_trend_{subtemplate}.j2",)
sell_trend = render_template(templatefile=f"subtemplates/sell_trend_{subtemplate}.j2",)
plot_config = render_template(templatefile=f"subtemplates/plot_config_{subtemplate}.j2",)
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,
})
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
"""
buy_guards = render_template(
templatefile=f"subtemplates/hyperopt_buy_guards_{subtemplate}.j2",)
sell_guards = render_template(
templatefile=f"subtemplates/hyperopt_sell_guards_{subtemplate}.j2",)
buy_space = render_template(
templatefile=f"subtemplates/hyperopt_buy_space_{subtemplate}.j2",)
sell_space = render_template(
templatefile=f"subtemplates/hyperopt_sell_space_{subtemplate}.j2",)
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.")

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

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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, symbol_is_pair)
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 ticker_interval set in the config
config['ticker_interval'] = 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': symbol_is_pair(v['symbol'])}
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}.")

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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()

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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
# Do not use ticker_interval set in the config
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)

6
freqtrade/plot/plot_utils.py → freqtrade/commands/plot_commands.py
View File

@ -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` "

27
freqtrade/commands/trade_commands.py Normal file
View File

@ -0,0 +1,27 @@
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 KeyboardInterrupt:
logger.info('SIGINT received, aborting ...')
finally:
if worker:
logger.info("worker found ... calling exit")
worker.exit()
return 0

11
freqtrade/configuration/__init__.py
View File

@ -1,4 +1,7 @@
from freqtrade.configuration.arguments import Arguments # 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

157
freqtrade/configuration/arguments.py
View File

@ -1,157 +0,0 @@
"""
This module contains the argument manager class
"""
import argparse
from pathlib import Path
from typing import Any, Dict, List, Optional
from freqtrade import constants
from freqtrade.configuration.cli_options import AVAILABLE_CLI_OPTIONS
ARGS_COMMON = ["verbosity", "logfile", "version", "config", "datadir", "user_data_dir"]
ARGS_STRATEGY = ["strategy", "strategy_path"]
ARGS_MAIN = ARGS_COMMON + ARGS_STRATEGY + ["db_url", "sd_notify"]
ARGS_COMMON_OPTIMIZE = ["ticker_interval", "timerange",
"max_open_trades", "stake_amount"]
ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + ["position_stacking", "use_max_market_positions",
"strategy_list", "export", "exportfilename"]
ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
"position_stacking", "epochs", "spaces",
"use_max_market_positions", "print_all",
"print_colorized", "print_json", "hyperopt_jobs",
"hyperopt_random_state", "hyperopt_min_trades",
"hyperopt_continue", "hyperopt_loss"]
ARGS_EDGE = ARGS_COMMON_OPTIMIZE + ["stoploss_range"]
ARGS_LIST_EXCHANGES = ["print_one_column"]
ARGS_CREATE_USERDIR = ["user_data_dir"]
ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "exchange", "timeframes", "erase"]
ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit", "db_url",
"trade_source", "export", "exportfilename", "timerange", "ticker_interval"]
ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
"trade_source", "ticker_interval"]
NO_CONF_REQURIED = ["create-userdir", "download-data", "plot-dataframe", "plot-profit"]
class Arguments:
"""
Arguments Class. Manage the arguments received by the cli
"""
def __init__(self, args: Optional[List[str]]) -> None:
self.args = args
self._parsed_arg: Optional[argparse.Namespace] = None
self.parser = argparse.ArgumentParser(description='Free, open source crypto trading bot')
def _load_args(self) -> None:
self._build_args(optionlist=ARGS_MAIN)
self._build_subcommands()
def get_parsed_arg(self) -> Dict[str, Any]:
"""
Return the list of arguments
:return: List[str] List of arguments
"""
if self._parsed_arg is None:
self._load_args()
self._parsed_arg = self._parse_args()
return vars(self._parsed_arg)
def _parse_args(self) -> argparse.Namespace:
"""
Parses given arguments and returns an argparse Namespace instance.
"""
parsed_arg = self.parser.parse_args(self.args)
# When no config is provided, but a config exists, use that configuration!
# 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 (parsed_arg.config is None and ((Path.cwd() / constants.DEFAULT_CONFIG).is_file() or
not ('subparser' in parsed_arg and parsed_arg.subparser in NO_CONF_REQURIED))):
parsed_arg.config = [constants.DEFAULT_CONFIG]
return parsed_arg
def _build_args(self, optionlist, parser=None):
parser = parser or self.parser
for val in optionlist:
opt = AVAILABLE_CLI_OPTIONS[val]
parser.add_argument(*opt.cli, dest=val, **opt.kwargs)
def _build_subcommands(self) -> None:
"""
Builds and attaches all subcommands.
:return: None
"""
from freqtrade.optimize import start_backtesting, start_hyperopt, start_edge
from freqtrade.utils import start_create_userdir, start_download_data, start_list_exchanges
subparsers = self.parser.add_subparsers(dest='subparser')
# Add backtesting subcommand
backtesting_cmd = subparsers.add_parser('backtesting', help='Backtesting module.')
backtesting_cmd.set_defaults(func=start_backtesting)
self._build_args(optionlist=ARGS_BACKTEST, parser=backtesting_cmd)
# Add edge subcommand
edge_cmd = subparsers.add_parser('edge', help='Edge module.')
edge_cmd.set_defaults(func=start_edge)
self._build_args(optionlist=ARGS_EDGE, parser=edge_cmd)
# Add hyperopt subcommand
hyperopt_cmd = subparsers.add_parser('hyperopt', help='Hyperopt module.')
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 list-exchanges subcommand
list_exchanges_cmd = subparsers.add_parser(
'list-exchanges',
help='Print available exchanges.'
)
list_exchanges_cmd.set_defaults(func=start_list_exchanges)
self._build_args(optionlist=ARGS_LIST_EXCHANGES, parser=list_exchanges_cmd)
# Add download-data subcommand
download_data_cmd = subparsers.add_parser(
'download-data',
help='Download backtesting data.'
)
download_data_cmd.set_defaults(func=start_download_data)
self._build_args(optionlist=ARGS_DOWNLOAD_DATA, parser=download_data_cmd)
# Add Plotting subcommand
from freqtrade.plot.plot_utils import start_plot_dataframe, start_plot_profit
plot_dataframe_cmd = subparsers.add_parser(
'plot-dataframe',
help='Plot candles with indicators.'
)
plot_dataframe_cmd.set_defaults(func=start_plot_dataframe)
self._build_args(optionlist=ARGS_PLOT_DATAFRAME, parser=plot_dataframe_cmd)
# Plot profit
plot_profit_cmd = subparsers.add_parser(
'plot-profit',
help='Generate plot showing profits.'
)
plot_profit_cmd.set_defaults(func=start_plot_profit)
self._build_args(optionlist=ARGS_PLOT_PROFIT, parser=plot_profit_cmd)

32
freqtrade/configuration/check_exchange.py
View File

@ -1,15 +1,28 @@
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_available, 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]) -> None:
"""
Removes exchange keys from the configuration and specifies dry-run
Used for backtesting / hyperopt / edge and utils.
Modifies the input dict!
"""
config['exchange']['key'] = ''
config['exchange']['secret'] = ''
config['exchange']['password'] = ''
config['exchange']['uid'] = ''
config['dry_run'] = True
def check_exchange(config: Dict[str, Any], check_for_bad: bool = True) -> bool:
"""
Check if the exchange name in the config file is supported by Freqtrade
@ -21,7 +34,8 @@ def check_exchange(config: Dict[str, Any], check_for_bad: bool = True) -> bool:
and thus is not known for the Freqtrade at all.
"""
if config['runmode'] in [RunMode.PLOT] and not config.get('exchange', {}).get('name'):
if (config['runmode'] in [RunMode.PLOT, RunMode.UTIL_NO_EXCHANGE, RunMode.OTHER]
and not config.get('exchange', {}).get('name')):
# Skip checking exchange in plot mode, since it requires no exchange
return True
logger.info("Checking exchange...")
@ -31,15 +45,15 @@ def check_exchange(config: Dict[str, Any], check_for_bad: bool = True) -> bool:
raise OperationalException(
f'This command requires a configured exchange. You should either use '
f'`--exchange <exchange_name>` or specify a configuration file via `--config`.\n'
f'The following exchanges are supported by ccxt: '
f'The following exchanges are available for Freqtrade: '
f'{", ".join(available_exchanges())}'
)
if not is_exchange_available(exchange):
if not is_exchange_known_ccxt(exchange):
raise OperationalException(
f'Exchange "{exchange}" is not supported by ccxt '
f'Exchange "{exchange}" is not known to the ccxt library '
f'and therefore not available for the bot.\n'
f'The following exchanges are supported by ccxt: '
f'The following exchanges are available for Freqtrade: '
f'{", ".join(available_exchanges())}'
)
@ -51,8 +65,8 @@ def check_exchange(config: Dict[str, Any], check_for_bad: bool = True) -> bool:
logger.info(f'Exchange "{exchange}" is officially supported '
f'by the Freqtrade development team.')
else:
logger.warning(f'Exchange "{exchange}" is supported by ccxt '
f'and therefore available for the bot but not officially supported '
logger.warning(f'Exchange "{exchange}" is known to the the ccxt library, '
f'available for the bot, but not officially supported '
f'by the Freqtrade development team. '
f'It may work flawlessly (please report back) or have serious issues. '
f'Use it at your own discretion.')

25
freqtrade/configuration/config_setup.py Normal file
View File

@ -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

47
freqtrade/configuration/config_validation.py
View File

@ -1,11 +1,13 @@
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
)
@ -61,9 +68,27 @@ def validate_config_consistency(conf: Dict[str, Any]) -> None:
:param conf: Config in JSON format
:return: Returns None if everything is ok, otherwise throw an OperationalException
"""
# validating trailing stoploss
_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:
@ -111,3 +136,17 @@ def _validate_edge(conf: Dict[str, Any]) -> None:
"Edge and VolumePairList are incompatible, "
"Edge will override whatever pairs VolumePairlist selects."
)
def _validate_whitelist(conf: Dict[str, Any]) -> None:
"""
Dynamic whitelist does not require pair_whitelist to be set - however StaticWhitelist does.
"""
if conf.get('runmode', RunMode.OTHER) in [RunMode.OTHER, RunMode.PLOT,
RunMode.UTIL_NO_EXCHANGE, RunMode.UTIL_EXCHANGE]:
return
for pl in conf.get('pairlists', [{'method': 'StaticPairList'}]):
if (pl.get('method') == 'StaticPairList'
and not conf.get('exchange', {}).get('pair_whitelist')):
raise OperationalException("StaticPairList requires pair_whitelist to be set.")

158
freqtrade/configuration/configuration.py
View File

@ -7,16 +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.config_validation import (
validate_config_consistency, validate_config_schema)
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
from freqtrade.state import NON_UTIL_MODES, TRADING_MODES, RunMode
logger = logging.getLogger(__name__)
@ -75,10 +75,13 @@ class Configuration:
# Normalize config
if 'internals' not in config:
config['internals'] = {}
# TODO: This can be deleted along with removal of deprecated
# experimental settings
if 'ask_strategy' not in config:
config['ask_strategy'] = {}
# validate configuration before returning
logger.info('Validating configuration ...')
validate_config_schema(config)
if 'pairlists' not in config:
config['pairlists'] = []
return config
@ -88,25 +91,29 @@ class Configuration:
:return: Configuration dictionary
"""
# Load all configs
config: Dict[str, Any] = self.load_from_files(self.args["config"])
config: Dict[str, Any] = self.load_from_files(self.args.get("config", []))
# Keep a copy of the original configuration file
config['original_config'] = deepcopy(config)
self._process_logging_options(config)
self._process_runmode(config)
self._process_common_options(config)
self._process_trading_options(config)
self._process_optimize_options(config)
self._process_plot_options(config)
self._process_runmode(config)
# Check if the exchange set by the user is supported
check_exchange(config, config.get('experimental', {}).get('block_bad_exchanges', True))
self._resolve_pairs_list(config)
validate_config_consistency(config)
process_temporary_deprecated_settings(config)
return config
@ -123,21 +130,9 @@ class Configuration:
setup_logging(config)
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") != constants.DEFAULT_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"]})
logger.info('Parameter --db-url detected ...')
def _process_trading_options(self, config: Dict[str, Any]) -> None:
if config['runmode'] not in TRADING_MODES:
return
if config.get('dry_run', False):
logger.info('Dry run is enabled')
@ -151,13 +146,23 @@ class Configuration:
logger.info(f'Using DB: "{config["db_url"]}"')
def _process_common_options(self, config: Dict[str, Any]) -> None:
# 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")})
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"]})
logger.info('Parameter --db-url detected ...')
if config.get('forcebuy_enable', False):
logger.warning('`forcebuy` RPC message enabled.')
# Setting max_open_trades to infinite if -1
if config.get('max_open_trades') == -1:
config['max_open_trades'] = float('inf')
# Support for sd_notify
if 'sd_notify' in self.args and self.args["sd_notify"]:
config['internals'].update({'sd_notify': True})
@ -172,6 +177,9 @@ class Configuration:
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"]})
elif 'user_data_dir' not in config:
@ -185,6 +193,14 @@ class Configuration:
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')
def _process_optimize_options(self, config: Dict[str, Any]) -> None:
# This will override the strategy configuration
@ -195,21 +211,29 @@ class Configuration:
self._args_to_config(config, argname='position_stacking',
logstring='Parameter --enable-position-stacking detected ...')
# Setting max_open_trades to infinite if -1
if config.get('max_open_trades') == -1:
config['max_open_trades'] = float('inf')
if 'use_max_market_positions' in self.args and not self.args["use_max_market_positions"]:
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, '
logger.info('Parameter --max-open-trades detected, '
'overriding max_open_trades to: %s ...', config.get('max_open_trades'))
else:
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',
logstring='Parameter --fee detected, '
'setting fee to: {} ...')
self._args_to_config(config, argname='timerange',
logstring='Parameter --timerange detected: {} ...')
@ -224,9 +248,6 @@ class Configuration:
self._args_to_config(config, argname='export',
logstring='Parameter --export detected: {} ...')
self._args_to_config(config, argname='exportfilename',
logstring='Storing backtest results to {} ...')
# Edge section:
if 'stoploss_range' in self.args and self.args["stoploss_range"]:
txt_range = eval(self.args["stoploss_range"])
@ -262,6 +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: {}')
@ -277,6 +301,45 @@ class Configuration:
self._args_to_config(config, argname='hyperopt_loss',
logstring='Using Hyperopt loss class name: {}')
self._args_to_config(config, argname='hyperopt_show_index',
logstring='Parameter -n/--index detected: {}')
self._args_to_config(config, argname='hyperopt_list_best',
logstring='Parameter --best detected: {}')
self._args_to_config(config, argname='hyperopt_list_profitable',
logstring='Parameter --profitable detected: {}')
self._args_to_config(config, argname='hyperopt_list_min_trades',
logstring='Parameter --min-trades detected: {}')
self._args_to_config(config, argname='hyperopt_list_max_trades',
logstring='Parameter --max-trades detected: {}')
self._args_to_config(config, argname='hyperopt_list_min_avg_time',
logstring='Parameter --min-avg-time detected: {}')
self._args_to_config(config, argname='hyperopt_list_max_avg_time',
logstring='Parameter --max-avg-time detected: {}')
self._args_to_config(config, argname='hyperopt_list_min_avg_profit',
logstring='Parameter --min-avg-profit detected: {}')
self._args_to_config(config, argname='hyperopt_list_max_avg_profit',
logstring='Parameter --max-avg-profit detected: {}')
self._args_to_config(config, argname='hyperopt_list_min_total_profit',
logstring='Parameter --min-total-profit detected: {}')
self._args_to_config(config, argname='hyperopt_list_max_total_profit',
logstring='Parameter --max-total-profit detected: {}')
self._args_to_config(config, argname='hyperopt_list_no_details',
logstring='Parameter --no-details detected: {}')
self._args_to_config(config, argname='hyperopt_show_no_header',
logstring='Parameter --no-header detected: {}')
def _process_plot_options(self, config: Dict[str, Any]) -> None:
self._args_to_config(config, argname='pairs',
@ -296,18 +359,34 @@ class Configuration:
self._args_to_config(config, argname='erase',
logstring='Erase detected. Deleting existing data.')
self._args_to_config(config, argname='no_trades',
logstring='Parameter --no-trades detected.')
self._args_to_config(config, argname='timeframes',
logstring='timeframes --timeframes: {}')
self._args_to_config(config, argname='days',
logstring='Detected --days: {}')
self._args_to_config(config, argname='download_trades',
logstring='Detected --dl-trades: {}')
self._args_to_config(config, argname='dataformat_ohlcv',
logstring='Using "{}" to store OHLCV data.')
self._args_to_config(config, argname='dataformat_trades',
logstring='Using "{}" to store trades data.')
def _process_runmode(self, config: Dict[str, Any]) -> None:
self._args_to_config(config, argname='dry_run',
logstring='Parameter --dry-run detected, '
'overriding dry_run to: {} ...')
if not self.runmode:
# Handle real mode, infer dry/live from config
self.runmode = RunMode.DRY_RUN if config.get('dry_run', True) else RunMode.LIVE
logger.info(f"Runmode set to {self.runmode}.")
logger.info(f"Runmode set to {self.runmode.value}.")
config.update({'runmode': self.runmode})
@ -323,7 +402,8 @@ class Configuration:
sample: logfun=len (prints the length of the found
configuration instead of the content)
"""
if argname in self.args and self.args[argname]:
if (argname in self.args and self.args[argname] is not None
and self.args[argname] is not False):
config.update({argname: self.args[argname]})
if logfun:
@ -362,7 +442,7 @@ class 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)

92
freqtrade/configuration/deprecated_settings.py Normal file
View File

@ -0,0 +1,92 @@
"""
Functions to handle deprecated settings
"""
import logging
from typing import Any, Dict
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
def check_conflicting_settings(config: Dict[str, Any],
section1: str, name1: 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:
raise OperationalException(
f"Conflicting settings `{section1}.{name1}` and `{section2}.{name2}` "
"(DEPRECATED) detected in the configuration file. "
"This deprecated setting will be removed in the next versions of Freqtrade. "
f"Please delete it from your configuration and use the `{section1}.{name1}` "
"setting instead."
)
def process_deprecated_setting(config: Dict[str, Any],
section1: str, name1: str,
section2: str, name2: str) -> None:
section2_config = config.get(section2, {})
if name2 in section2_config:
logger.warning(
"DEPRECATED: "
f"The `{section2}.{name2}` setting is deprecated and "
"will be removed in the next versions of Freqtrade. "
f"Please use the `{section1}.{name1}` setting in your configuration instead."
)
section1_config = config.get(section1, {})
section1_config[name1] = section2_config[name2]
def process_temporary_deprecated_settings(config: Dict[str, Any]) -> None:
check_conflicting_settings(config, 'ask_strategy', 'use_sell_signal',
'experimental', 'use_sell_signal')
check_conflicting_settings(config, 'ask_strategy', 'sell_profit_only',
'experimental', 'sell_profit_only')
check_conflicting_settings(config, 'ask_strategy', 'ignore_roi_if_buy_signal',
'experimental', 'ignore_roi_if_buy_signal')
process_deprecated_setting(config, 'ask_strategy', 'use_sell_signal',
'experimental', 'use_sell_signal')
process_deprecated_setting(config, 'ask_strategy', 'sell_profit_only',
'experimental', 'sell_profit_only')
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'}]
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'})
if (config.get('edge', {}).get('enabled', False)
and 'capital_available_percentage' in config.get('edge', {})):
logger.warning(
"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."
)

38
freqtrade/configuration/directory_operations.py
View File

@ -1,13 +1,15 @@
import logging
from typing import Any, Dict, Optional
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:
@ -18,10 +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.
@ -31,7 +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", "plot", "strategies", ]
sub_dirs = ["backtest_results", "data", "hyperopts", "hyperopt_results", "notebooks",
"plot", "strategies", ]
folder = Path(directory)
if not folder.is_dir():
if create_dir:
@ -48,3 +51,26 @@ def create_userdata_dir(directory: str, create_dir=False) -> Path:
if not subfolder.is_dir():
subfolder.mkdir(parents=False)
return folder
def copy_sample_files(directory: Path, overwrite: bool = False) -> None:
"""
Copy files from templates to User data directory.
:param directory: Directory to copy data to
:param overwrite: Overwrite existing sample files
"""
if not directory.is_dir():
raise OperationalException(f"Directory `{directory}` does not exist.")
sourcedir = Path(__file__).parents[1] / "templates"
for source, target in USER_DATA_FILES.items():
targetdir = directory / target
if not targetdir.is_dir():
raise OperationalException(f"Directory `{targetdir}` does not exist.")
targetfile = targetdir / source
if targetfile.exists():
if not overwrite:
logger.warning(f"File `{targetfile}` exists already, not deploying sample file.")
continue
else:
logger.warning(f"File `{targetfile}` exists already, overwriting.")
shutil.copy(str(sourcedir / source), str(targetfile))

33
freqtrade/configuration/load_config.py
View File

@ -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

45
freqtrade/configuration/timerange.py
View File

@ -1,12 +1,16 @@
"""
This module contains the argument manager class
"""
import logging
import re
from typing import Optional
import arrow
logger = logging.getLogger(__name__)
class TimeRange:
"""
object defining timerange inputs.
@ -27,8 +31,36 @@ 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: int) -> None:
"""
Subtracts <seconds> from startts if startts is set.
:param seconds: Seconds to subtract from starttime
:return: None (Modifies the object in place)
"""
if self.startts:
self.startts = self.startts - seconds
def adjust_start_if_necessary(self, timeframe_secs: int, startup_candles: int,
min_date: arrow.Arrow) -> None:
"""
Adjust startts by <startup_candles> candles.
Applies only if no startup-candles have been available.
: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
:return: None (Modifies the object in place)
"""
if (not self.starttype or (startup_candles
and min_date.timestamp >= self.startts)):
# If no startts was defined, or backtest-data starts at the defined backtest-date
logger.warning("Moving start-date by %s candles to account for startup time.",
startup_candles)
self.startts = (min_date.timestamp + timeframe_secs * startup_candles)
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
@ -42,9 +74,10 @@ class TimeRange:
(r'^-(\d{10})$', (None, 'date')),
(r'^(\d{10})-$', ('date', None)),
(r'^(\d{10})-(\d{10})$', ('date', 'date')),
(r'^(-\d+)$', (None, 'line')),
(r'^(\d+)-$', ('line', None)),
(r'^(\d+)-(\d+)$', ('index', 'index'))]
(r'^-(\d{13})$', (None, 'date')),
(r'^(\d{13})-$', ('date', None)),
(r'^(\d{13})-(\d{13})$', ('date', 'date')),
]
for rex, stype in syntax:
# Apply the regular expression to text
match = re.match(rex, text)
@ -57,6 +90,8 @@ class TimeRange:
starts = rvals[index]
if stype[0] == 'date' and len(starts) == 8:
start = arrow.get(starts, 'YYYYMMDD').timestamp
elif len(starts) == 13:
start = int(starts) // 1000
else:
start = int(starts)
index += 1
@ -64,6 +99,8 @@ class TimeRange:
stops = rvals[index]
if stype[1] == 'date' and len(stops) == 8:
stop = arrow.get(stops, 'YYYYMMDD').timestamp
elif len(stops) == 13:
stop = int(stops) // 1000
else:
stop = int(stops)
return TimeRange(stype[0], stype[1], start, stop)

196
freqtrade/constants.py
View File

@ -6,35 +6,44 @@ bot constants
DEFAULT_CONFIG = 'config.json'
DEFAULT_EXCHANGE = 'bittrex'
PROCESS_THROTTLE_SECS = 5 # sec
DEFAULT_TICKER_INTERVAL = 5 # min
HYPEROPT_EPOCH = 100 # epochs
RETRY_TIMEOUT = 30 # sec
DEFAULT_STRATEGY = 'DefaultStrategy'
DEFAULT_HYPEROPT = 'DefaultHyperOpts'
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']
DRY_RUN_WALLET = 999.9
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
'PrecisionFilter', 'PriceFilter', 'SpreadFilter']
AVAILABLE_DATAHANDLERS = ['json', 'jsongz']
DRY_RUN_WALLET = 1000
MATH_CLOSE_PREC = 1e-14 # Precision used for float comparisons
DEFAULT_DATAFRAME_COLUMNS = ['date', 'open', 'high', 'low', 'close', 'volume']
TICKER_INTERVALS = [
'1m', '3m', '5m', '15m', '30m',
'1h', '2h', '4h', '6h', '8h', '12h',
'1d', '3d', '1w',
]
USERPATH_HYPEROPTS = 'hyperopts'
USERPATH_STRATEGIES = 'strategies'
USERPATH_NOTEBOOKS = 'notebooks'
# Soure files with destination directories within user-directory
USER_DATA_FILES = {
'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': USERPATH_NOTEBOOKS,
}
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 = {
@ -55,17 +64,27 @@ MINIMAL_CONFIG = {
CONF_SCHEMA = {
'type': 'object',
'properties': {
'max_open_trades': {'type': 'integer', 'minimum': -1},
'ticker_interval': {'type': 'string', 'enum': TICKER_INTERVALS},
'stake_currency': {'type': 'string', 'enum': ['BTC', 'XBT', 'ETH', 'USDT', 'EUR', 'USD']},
'max_open_trades': {'type': ['integer', 'number'], 'minimum': -1},
'ticker_interval': {'type': 'string'},
'stake_currency': {'type': 'string'},
'stake_amount': {
"type": ["number", "string"],
"minimum": 0.0005,
"pattern": UNLIMITED_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},
'process_only_new_candles': {'type': 'boolean'},
'minimal_roi': {
'type': 'object',
@ -83,8 +102,8 @@ CONF_SCHEMA = {
'unfilledtimeout': {
'type': 'object',
'properties': {
'buy': {'type': 'number', 'minimum': 3},
'sell': {'type': 'number', 'minimum': 10}
'buy': {'type': 'number', 'minimum': 1},
'sell': {'type': 'number', 'minimum': 1}
}
},
'bid_strategy': {
@ -95,15 +114,16 @@ CONF_SCHEMA = {
'minimum': 0,
'maximum': 1,
'exclusiveMaximum': False,
'use_order_book': {'type': 'boolean'},
'order_book_top': {'type': 'number', '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']
@ -111,9 +131,13 @@ 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': 'number', 'minimum': 1},
'order_book_max': {'type': 'number', 'minimum': 1, 'maximum': 50}
'order_book_min': {'type': 'integer', 'minimum': 1},
'order_book_max': {'type': 'integer', 'minimum': 1, 'maximum': 50},
'use_sell_signal': {'type': 'boolean'},
'sell_profit_only': {'type': 'boolean'},
'ignore_roi_if_buy_signal': {'type': 'boolean'}
}
},
'order_types': {
@ -143,16 +167,20 @@ CONF_SCHEMA = {
'properties': {
'use_sell_signal': {'type': 'boolean'},
'sell_profit_only': {'type': 'boolean'},
'ignore_roi_if_buy_signal_true': {'type': 'boolean'}
'ignore_roi_if_buy_signal': {'type': 'boolean'},
'block_bad_exchanges': {'type': 'boolean'}
}
},
'pairlist': {
'type': 'object',
'properties': {
'method': {'type': 'string', 'enum': AVAILABLE_PAIRLISTS},
'config': {'type': 'object'}
},
'required': ['method']
'pairlists': {
'type': 'array',
'items': {
'type': 'object',
'properties': {
'method': {'type': 'string', 'enum': AVAILABLE_PAIRLISTS},
'config': {'type': 'object'}
},
'required': ['method'],
}
},
'telegram': {
'type': 'object',
@ -168,7 +196,9 @@ CONF_SCHEMA = {
'properties': {
'enabled': {'type': 'boolean'},
'webhookbuy': {'type': 'object'},
'webhookbuycancel': {'type': 'object'},
'webhooksell': {'type': 'object'},
'webhooksellcancel': {'type': 'object'},
'webhookstatus': {'type': 'object'},
},
},
@ -179,8 +209,8 @@ CONF_SCHEMA = {
'listen_ip_address': {'format': 'ipv4'},
'listen_port': {
'type': 'integer',
"minimum": 1024,
"maximum": 65535
'minimum': 1024,
'maximum': 65535
},
'username': {'type': 'string'},
'password': {'type': 'string'},
@ -192,11 +222,22 @@ CONF_SCHEMA = {
'forcebuy_enable': {'type': 'boolean'},
'internals': {
'type': 'object',
'default': {},
'properties': {
'process_throttle_secs': {'type': 'number'},
'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': {
@ -213,7 +254,6 @@ CONF_SCHEMA = {
'type': 'array',
'items': {
'type': 'string',
'pattern': '^[0-9A-Z]+/[0-9A-Z]+$'
},
'uniqueItems': True
},
@ -221,7 +261,6 @@ CONF_SCHEMA = {
'type': 'array',
'items': {
'type': 'string',
'pattern': '^[0-9A-Z]+/[0-9A-Z]+$'
},
'uniqueItems': True
},
@ -230,37 +269,52 @@ CONF_SCHEMA = {
'ccxt_config': {'type': 'object'},
'ccxt_async_config': {'type': 'object'}
},
'required': ['name', 'pair_whitelist']
'required': ['name']
},
'edge': {
'type': 'object',
'properties': {
"enabled": {'type': 'boolean'},
"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'},
"minimum_winrate": {'type': 'number'},
"minimum_expectancy": {'type': 'number'},
"min_trade_number": {'type': 'number'},
"max_trade_duration_minute": {'type': 'integer'},
"remove_pumps": {'type': 'boolean'}
'enabled': {'type': 'boolean'},
'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'},
'minimum_winrate': {'type': 'number'},
'minimum_expectancy': {'type': 'number'},
'min_trade_number': {'type': 'number'},
'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']
}
},
'anyOf': [
{'required': ['exchange']}
],
'required': [
'max_open_trades',
'stake_currency',
'stake_amount',
'dry_run',
'bid_strategy',
'telegram'
]
}
SCHEMA_TRADE_REQUIRED = [
'exchange',
'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',
]

2
freqtrade/data/__init__.py
View File

@ -2,7 +2,7 @@
Module to handle data operations for freqtrade
"""
# limit what's imported when using `from freqtrad.data import *``
# limit what's imported when using `from freqtrade.data import *`
__all__ = [
'converter'
]

113
freqtrade/data/btanalysis.py
View File

@ -3,11 +3,11 @@ Helpers when analyzing backtest data
"""
import logging
from pathlib import Path
from typing import Dict
from typing import Dict, Union, Tuple
import numpy as np
import pandas as pd
import pytz
from datetime import timezone
from freqtrade import persistence
from freqtrade.misc import json_load
@ -20,7 +20,7 @@ BT_DATA_COLUMNS = ["pair", "profitperc", "open_time", "close_time", "index", "du
"open_rate", "close_rate", "open_at_end", "sell_reason"]
def load_backtest_data(filename) -> pd.DataFrame:
def load_backtest_data(filename: Union[Path, str]) -> pd.DataFrame:
"""
Load backtest data file.
:param filename: pathlib.Path object, or string pointing to the file.
@ -47,21 +47,23 @@ def load_backtest_data(filename) -> pd.DataFrame:
utc=True,
infer_datetime_format=True
)
df['profitabs'] = df['close_rate'] - df['open_rate']
df['profit'] = df['close_rate'] - df['open_rate']
df = df.sort_values("open_time").reset_index(drop=True)
return df
def evaluate_result_multi(results: pd.DataFrame, freq: str, max_open_trades: int) -> pd.DataFrame:
def analyze_trade_parallelism(results: pd.DataFrame, timeframe: str) -> pd.DataFrame:
"""
Find overlapping trades by expanding each trade once per period it was open
and then counting overlaps
and then counting overlaps.
:param results: Results Dataframe - can be loaded
:param freq: Frequency used for the backtest
:param max_open_trades: parameter max_open_trades used during backtest run
:return: dataframe with open-counts per time-period in freq
:param timeframe: Timeframe used for backtest
:return: dataframe with open-counts per time-period in timeframe
"""
dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time, freq=freq))
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,
freq=f"{timeframe_min}min"))
for row in results[['open_time', 'close_time']].iterrows()]
deltas = [len(x) for x in dates]
dates = pd.Series(pd.concat(dates).values, name='date')
@ -69,8 +71,23 @@ def evaluate_result_multi(results: pd.DataFrame, freq: str, max_open_trades: int
df2 = pd.concat([dates, df2], axis=1)
df2 = df2.set_index('date')
df_final = df2.resample(freq)[['pair']].count()
return df_final[df_final['pair'] > max_open_trades]
df_final = df2.resample(f"{timeframe_min}min")[['pair']].count()
df_final = df_final.rename({'pair': 'open_trades'}, axis=1)
return df_final
def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
max_open_trades: int) -> pd.DataFrame:
"""
Find overlapping trades by expanding each trade once per period it was open
and then counting overlaps
:param results: Results Dataframe - can be loaded
:param timeframe: Frequency used for the backtest
:param max_open_trades: parameter max_open_trades used during backtest run
:return: dataframe with open-counts per time-period in freq
"""
df_final = analyze_trade_parallelism(results, timeframe)
return df_final[df_final['open_trades'] > max_open_trades]
def load_trades_from_db(db_url: str) -> pd.DataFrame:
@ -89,12 +106,12 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
"stop_loss", "initial_stop_loss", "strategy", "ticker_interval"]
trades = pd.DataFrame([(t.pair,
t.open_date.replace(tzinfo=pytz.UTC),
t.close_date.replace(tzinfo=pytz.UTC) if t.close_date else None,
t.calc_profit(), t.calc_profit_percent(),
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_ratio(),
t.open_rate, t.close_rate, t.amount,
(t.close_date.timestamp() - t.open_date.timestamp()
if t.close_date else None),
(round((t.close_date.timestamp() - t.open_date.timestamp()) / 60, 2)
if t.close_date else None),
t.sell_reason,
t.fee_open, t.fee_close,
t.open_rate_requested,
@ -106,22 +123,32 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
t.stop_loss, t.initial_stop_loss,
t.strategy, t.ticker_interval
)
for t in Trade.query.all()],
for t in Trade.get_trades().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) -> pd.DataFrame:
"""
Based on configuration option "trade_source":
* loads data from DB (using `db_url`)
* loads data from backtestfile (using `exportfilename`)
:param source: "DB" or "file" - specify source to load from
:param db_url: sqlalchemy formatted url to a database
:param exportfilename: Json file generated by backtesting
:param no_trades: Skip using trades, only return backtesting data columns
:return: DataFrame containing trades
"""
if no_trades:
df = pd.DataFrame(columns=BT_DATA_COLUMNS)
return df
if source == "DB":
return load_trades_from_db(db_url)
elif source == "file":
return load_backtest_data(Path(exportfilename))
return load_backtest_data(exportfilename)
def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame) -> pd.DataFrame:
@ -134,33 +161,65 @@ def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame) -> p
return trades
def combine_tickers_with_mean(tickers: Dict[str, pd.DataFrame], column: str = "close"):
def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
column: str = "close") -> pd.DataFrame:
"""
Combine multiple dataframes "column"
:param tickers: Dict of Dataframes, dict key should be pair.
:param data: Dict of Dataframes, dict key should be pair.
:param column: Column in the original dataframes to use
:return: DataFrame with the column renamed to the dict key, and a column
named mean, containing the mean of all pairs.
"""
df_comb = pd.concat([tickers[pair].set_index('date').rename(
{column: pair}, axis=1)[pair] for pair in tickers], axis=1)
df_comb = pd.concat([data[pair].set_index('date').rename(
{column: pair}, axis=1)[pair] for pair in data], axis=1)
df_comb['mean'] = df_comb.mean(axis=1)
return df_comb
def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str) -> pd.DataFrame:
def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
timeframe: str) -> pd.DataFrame:
"""
Adds a column `col_name` with the cumulative profit for the given trades array.
:param df: DataFrame with date index
:param trades: DataFrame containing trades (requires columns close_time and profitperc)
: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.
"""
# Use groupby/sum().cumsum() to avoid errors when multiple trades sold at the same candle.
df[col_name] = trades.groupby('close_time')['profitperc'].sum().cumsum()
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()
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_time',
value_col: str = 'profitperc'
) -> Tuple[float, pd.Timestamp, pd.Timestamp]:
"""
Calculate max drawdown and the corresponding close dates
:param trades: DataFrame containing trades (requires columns close_time and profitperc)
:param date_col: Column in DataFrame to use for dates (defaults to 'close_time')
:param value_col: Column in DataFrame to use for values (defaults to 'profitperc')
:return: Tuple (float, highdate, lowdate) with absolute max drawdown, high and low time
:raise: ValueError if trade-dataframe was found empty.
"""
if len(trades) == 0:
raise ValueError("Trade dataframe empty.")
profit_results = trades.sort_values(date_col)
max_drawdown_df = pd.DataFrame()
max_drawdown_df['cumulative'] = profit_results[value_col].cumsum()
max_drawdown_df['high_value'] = max_drawdown_df['cumulative'].cummax()
max_drawdown_df['drawdown'] = max_drawdown_df['cumulative'] - max_drawdown_df['high_value']
high_date = profit_results.loc[max_drawdown_df['high_value'].idxmax(), date_col]
low_date = profit_results.loc[max_drawdown_df['drawdown'].idxmin(), date_col]
return abs(min(max_drawdown_df['drawdown'])), high_date, low_date

169
freqtrade/data/converter.py
View File

@ -2,44 +2,64 @@
Functions to convert data from one format to another
"""
import logging
from datetime import datetime, timezone
from typing import Any, Dict
import pandas as pd
from pandas import DataFrame, to_datetime
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
logger = logging.getLogger(__name__)
def parse_ticker_dataframe(ticker: list, ticker_interval: 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
:param ticker_interval: ticker_interval (e.g. 5m). Used to fill up eventual missing data
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
(see ohlcv_fill_up_missing_data for details)
: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',
@ -48,16 +68,16 @@ def parse_ticker_dataframe(ticker: list, ticker_interval: 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, ticker_interval, pair)
return ohlcv_fill_up_missing_data(data, timeframe, pair)
else:
return frame
return data
def ohlcv_fill_up_missing_data(dataframe: DataFrame, ticker_interval: str, pair: str) -> DataFrame:
def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str) -> DataFrame:
"""
Fills up missing data with 0 volume rows,
using the previous close as price for "open", "high" "low" and "close", volume is set to 0
@ -65,16 +85,16 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, ticker_interval: str, pair:
"""
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(ticker_interval)
timeframe_minutes = timeframe_to_minutes(timeframe)
# Resample to create "NAN" values
df = dataframe.resample(f'{ticker_minutes}min', on='date').agg(ohlc_dict)
df = dataframe.resample(f'{timeframe_minutes}min', on='date').agg(ohlcv_dict)
# Forwardfill close for missing columns
df['close'] = df['close'].fillna(method='ffill')
@ -92,8 +112,26 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, ticker_interval: str, pair:
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
@ -114,3 +152,86 @@ def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
keys=['b_sum', 'b_size', 'bids', 'asks', 'a_size', 'a_sum'])
# logger.info('order book %s', frame )
return frame
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
timeframe_minutes = timeframe_to_minutes(timeframe)
df = pd.DataFrame(trades)
df['datetime'] = pd.to_datetime(df['datetime'])
df = df.set_index('datetime')
df_new = df['price'].resample(f'{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()
return df_new[DEFAULT_DATAFRAME_COLUMNS]
def convert_trades_format(config: Dict[str, Any], convert_from: str, convert_to: str, erase: bool):
"""
Convert trades from one format to another format.
:param config: Config dictionary
:param convert_from: Source format
:param convert_to: Target format
:param erase: Erase souce data (does not apply if source and target format are identical)
"""
from freqtrade.data.history.idatahandler import get_datahandler
src = get_datahandler(config['datadir'], convert_from)
trg = get_datahandler(config['datadir'], convert_to)
if 'pairs' not in config:
config['pairs'] = src.trades_get_pairs(config['datadir'])
logger.info(f"Converting trades for {config['pairs']}")
for pair in config['pairs']:
data = src.trades_load(pair=pair)
logger.info(f"Converting {len(data)} trades for {pair}")
trg.trades_store(pair, data)
if erase and convert_from != convert_to:
logger.info(f"Deleting source Trade data for {pair}.")
src.trades_purge(pair=pair)
def convert_ohlcv_format(config: Dict[str, Any], convert_from: str, convert_to: str, erase: bool):
"""
Convert OHLCV from one format to another
:param config: Config dictionary
:param convert_from: Source format
:param convert_to: Target format
:param erase: Erase souce data (does not apply if source and target format are identical)
"""
from freqtrade.data.history.idatahandler import get_datahandler
src = get_datahandler(config['datadir'], convert_from)
trg = get_datahandler(config['datadir'], convert_to)
timeframes = config.get('timeframes', [config.get('ticker_interval')])
logger.info(f"Converting candle (OHLCV) for timeframe {timeframes}")
if 'pairs' not in config:
config['pairs'] = []
# Check timeframes or fall back to ticker_interval.
for timeframe in timeframes:
config['pairs'].extend(src.ohlcv_get_pairs(config['datadir'],
timeframe))
logger.info(f"Converting candle (OHLCV) data for {config['pairs']}")
for timeframe in timeframes:
for pair in config['pairs']:
data = src.ohlcv_load(pair=pair, timeframe=timeframe,
timerange=None,
fill_missing=False,
drop_incomplete=False,
startup_candles=0)
logger.info(f"Converting {len(data)} candles for {pair}")
trg.ohlcv_store(pair=pair, timeframe=timeframe, data=data)
if erase and convert_from != convert_to:
logger.info(f"Deleting source data for {pair} / {timeframe}")
src.ohlcv_purge(pair=pair, timeframe=timeframe)

54
freqtrade/data/dataprovider.py
View File

@ -1,12 +1,11 @@
"""
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 typing import List, Tuple
from typing import Any, Dict, List, Optional, Tuple
from pandas import DataFrame
@ -37,54 +36,63 @@ class DataProvider:
@property
def available_pairs(self) -> List[Tuple[str, str]]:
"""
Return a list of tuples containing pair, ticker_interval for which data is currently cached.
Return a list of tuples containing (pair, timeframe) for which data is currently cached.
Should be whitelist + open trades.
"""
return list(self._exchange._klines.keys())
def ohlcv(self, pair: str, ticker_interval: str = None, copy: bool = True) -> DataFrame:
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 ticker_interval: ticker interval 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, ticker_interval or self._config['ticker_interval']),
return self._exchange.klines((pair, timeframe or self._config['ticker_interval']),
copy=copy)
else:
return DataFrame()
def historic_ohlcv(self, pair: str, ticker_interval: str = None) -> 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 ticker_interval: ticker interval to get data for
:param timeframe: timeframe to get data for
"""
return load_pair_history(pair=pair,
ticker_interval=ticker_interval or self._config['ticker_interval'],
datadir=Path(self._config['datadir'])
timeframe=timeframe or self._config['ticker_interval'],
datadir=self._config['datadir']
)
def get_pair_dataframe(self, pair: str, ticker_interval: str = None) -> DataFrame:
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 ticker_interval: ticker interval to get 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.
data = self.ohlcv(pair=pair, ticker_interval=ticker_interval)
# Get live OHLCV data.
data = self.ohlcv(pair=pair, timeframe=timeframe)
else:
# Get historic ohlcv data (cached on disk).
data = self.historic_ohlcv(pair=pair, ticker_interval=ticker_interval)
# 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}, {ticker_interval}).")
logger.warning(f"No data found for ({pair}, {timeframe}).")
return data
def market(self, pair: str) -> Optional[Dict[str, Any]]:
"""
Return market data for the pair
:param pair: Pair to get the data for
:return: Market data dict from ccxt or None if market info is not available for the pair
"""
return self._exchange.markets.get(pair)
def ticker(self, pair: str):
"""
Return last ticker data
@ -92,9 +100,9 @@ class DataProvider:
# TODO: Implement me
pass
def orderbook(self, pair: str, maximum: int):
def orderbook(self, pair: str, maximum: int) -> Dict[str, List]:
"""
return latest orderbook data
fetch latest orderbook data
: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.

330
freqtrade/data/history.py
View File

@ -1,330 +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 datetime import datetime
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
from freqtrade.exchange import Exchange, timeframe_to_minutes
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 == 'line':
stop_index = timerange.startts
if timerange.starttype == 'index':
start_index = timerange.startts
elif timerange.starttype == 'date':
while (start_index < len(tickerlist) and
tickerlist[start_index][0] < timerange.startts * 1000):
start_index += 1
if timerange.stoptype == 'line':
start_index = max(len(tickerlist) + timerange.stopts, 0)
if timerange.stoptype == 'index':
stop_index = timerange.stopts
elif 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 load_tickerdata_file(datadir: Path, pair: str, ticker_interval: 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, ticker_interval)
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,
ticker_interval: str, data: list, is_zip: bool = False):
"""
Stores tickerdata to file
"""
filename = pair_data_filename(datadir, pair, ticker_interval)
misc.file_dump_json(filename, data, is_zip=is_zip)
def load_pair_history(pair: str,
ticker_interval: str,
datadir: Path,
timerange: TimeRange = TimeRange(None, None, 0, 0),
refresh_pairs: bool = False,
exchange: Optional[Exchange] = None,
fill_up_missing: bool = True,
drop_incomplete: bool = True
) -> DataFrame:
"""
Loads cached ticker history for the given pair.
:param pair: Pair to load data for
:param ticker_interval: Ticker-interval (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.
:return: DataFrame with ohlcv data
"""
# The user forced the refresh of pairs
if refresh_pairs:
download_pair_history(datadir=datadir,
exchange=exchange,
pair=pair,
ticker_interval=ticker_interval,
timerange=timerange)
pairdata = load_tickerdata_file(datadir, pair, ticker_interval, timerange=timerange)
if pairdata:
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'))
return parse_ticker_dataframe(pairdata, ticker_interval, pair=pair,
fill_missing=fill_up_missing,
drop_incomplete=drop_incomplete)
else:
logger.warning(
f'No history data for pair: "{pair}", interval: {ticker_interval}. '
'Use `freqtrade download-data` to download the data'
)
return None
def load_data(datadir: Path,
ticker_interval: str,
pairs: List[str],
refresh_pairs: bool = False,
exchange: Optional[Exchange] = None,
timerange: TimeRange = TimeRange(None, None, 0, 0),
fill_up_missing: bool = True,
) -> Dict[str, DataFrame]:
"""
Loads ticker history data for a list of pairs
:return: dict(<pair>:<tickerlist>)
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] = {}
for pair in pairs:
hist = load_pair_history(pair=pair, ticker_interval=ticker_interval,
datadir=datadir, timerange=timerange,
refresh_pairs=refresh_pairs,
exchange=exchange,
fill_up_missing=fill_up_missing)
if hist is not None:
result[pair] = hist
return result
def pair_data_filename(datadir: Path, pair: str, ticker_interval: str) -> Path:
pair_s = pair.replace("/", "_")
filename = datadir.joinpath(f'{pair_s}-{ticker_interval}.json')
return filename
def load_cached_data_for_updating(datadir: Path, pair: str, ticker_interval: str,
timerange: Optional[TimeRange]) -> Tuple[List[Any],
Optional[int]]:
"""
Load cached data to download more data.
If timerange is passed in, checks wether data from an before the stored data will be downloaded.
If that's the case than 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(ticker_interval)
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, ticker_interval)
# 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,
ticker_interval: str = '5m',
timerange: Optional[TimeRange] = None) -> bool:
"""
Download the latest ticker intervals from the exchange for the pair passed in parameters
The data is downloaded starting from the last correct ticker interval 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 ticker_interval: ticker interval
: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}", interval: {ticker_interval} '
f'and store in {datadir}.'
)
data, since_ms = load_cached_data_for_updating(datadir, pair, ticker_interval, 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, ticker_interval=ticker_interval,
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, ticker_interval, data=data)
return True
except Exception as e:
logger.error(
f'Failed to download history data for pair: "{pair}", interval: {ticker_interval}. '
f'Error: {e}'
)
return False
def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes: List[str],
dl_path: Path, timerange: TimeRange,
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 ticker_interval in timeframes:
dl_file = pair_data_filename(dl_path, pair, ticker_interval)
if erase and dl_file.exists():
logger.info(
f'Deleting existing data for pair {pair}, interval {ticker_interval}.')
dl_file.unlink()
logger.info(f'Downloading pair {pair}, interval {ticker_interval}.')
download_pair_history(datadir=dl_path, exchange=exchange,
pair=pair, ticker_interval=str(ticker_interval),
timerange=timerange)
return pairs_not_available
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, ticker_interval_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 ticker_interval_mins: ticker interval in minutes
"""
# total difference in minutes / interval-minutes
expected_frames = int((max_date - min_date).total_seconds() // 60 // ticker_interval_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

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freqtrade/data/history/__init__.py Normal file
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"""
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

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freqtrade/data/history/history_utils.py Normal file
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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_to_ohlcv
from freqtrade.data.history.idatahandler import IDataHandler, get_datahandler
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import Exchange
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 None
trades = data_handler.trades_load(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')
# Default since_ms to 30 days if nothing is given
new_trades = exchange.get_historic_trades(pair=pair,
since=since if since else
int(arrow.utcnow().shift(
days=-30).float_timestamp) * 1000,
from_id=from_id,
)
trades.extend(new_trades[1])
data_handler.trades_store(pair, data=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: 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

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

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

454
freqtrade/edge/__init__.py
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@ -1,453 +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'))
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,
ticker_interval=self.strategy.ticker_interval,
refresh_pairs=self._refresh_pairs,
exchange=self.exchange,
timerange=self._timerange
)
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

465
freqtrade/edge/edge_positioning.py Normal file
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@ -0,0 +1,465 @@
# 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
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')
# Deprecated capital_available_percentage. Will use tradable_balance_ratio in the future.
self._capital_percentage: float = self.edge_config.get(
'capital_available_percentage', 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.ticker_interval,
timerange=self._timerange,
)
data = load_data(
datadir=self.config['datadir'],
pairs=pairs,
timeframe=self.strategy.ticker_interval,
timerange=self._timerange,
startup_candles=self.strategy.startup_candle_count,
data_format=self.config.get('dataformat_ohlcv', 'json'),
)
if not data:
# 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(
'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)
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_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[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
"""
# 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 defined as ratios.
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_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.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, 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_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

37
freqtrade/exceptions.py Normal file
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@ -0,0 +1,37 @@
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 InvalidOrderException(FreqtradeException):
"""
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(FreqtradeException):
"""
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.
"""

19
freqtrade/exchange/__init__.py
View File

@ -1,13 +1,20 @@
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_available,
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.kraken import Kraken # noqa: F401
from freqtrade.exchange.binance import Binance # noqa: F401
from freqtrade.exchange.exchange import (market_is_active,
symbol_is_pair)
from freqtrade.exchange.kraken import Kraken
from freqtrade.exchange.binance import Binance
from freqtrade.exchange.bibox import Bibox
from freqtrade.exchange.ftx import Ftx

22
freqtrade/exchange/bibox.py Normal file
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@ -0,0 +1,22 @@
""" Bibox exchange subclass """
import logging
from typing import Dict
from freqtrade.exchange import Exchange
logger = logging.getLogger(__name__)
class Bibox(Exchange):
"""
Bibox exchange class. Contains adjustments needed for Freqtrade to work
with this exchange.
Please note that this exchange is not included in the list of exchanges
officially supported by the Freqtrade development team. So some features
may still not work as expected.
"""
# fetchCurrencies API point requires authentication for Bibox,
# so switch it off for Freqtrade load_markets()
_ccxt_config: Dict = {"has": {"fetchCurrencies": False}}

30
freqtrade/exchange/binance.py
View File

@ -4,8 +4,8 @@ from typing import Dict
import ccxt
from freqtrade import (DependencyException, InvalidOrderException,
OperationalException, TemporaryError)
from freqtrade.exceptions import (DependencyException, InvalidOrderException,
OperationalException, TemporaryError)
from freqtrade.exchange import Exchange
logger = logging.getLogger(__name__)
@ -16,6 +16,8 @@ class Binance(Exchange):
_ft_has: Dict = {
"stoploss_on_exchange": True,
"order_time_in_force": ['gtc', 'fok', 'ioc'],
"trades_pagination": "id",
"trades_pagination_arg": "fromId",
}
def get_order_book(self, pair: str, limit: int = 100) -> dict:
@ -30,16 +32,26 @@ class Binance(Exchange):
return super().get_order_book(pair, limit)
def stoploss_limit(self, pair: str, amount: float, stop_price: float, rate: float) -> Dict:
def stoploss_adjust(self, stop_loss: float, order: Dict) -> bool:
"""
Verify stop_loss against stoploss-order value (limit or price)
Returns True if adjustment is necessary.
"""
return order['type'] == 'stop_loss_limit' and stop_loss > float(order['info']['stopPrice'])
def stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict) -> Dict:
"""
creates a stoploss limit order.
this stoploss-limit is binance-specific.
It may work with a limited number of other exchanges, but this has not been tested yet.
"""
# Limit price threshold: As limit price should always be below stop-price
limit_price_pct = order_types.get('stoploss_on_exchange_limit_ratio', 0.99)
rate = stop_price * limit_price_pct
ordertype = "stop_loss_limit"
stop_price = self.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:
@ -55,12 +67,12 @@ 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=stop_price, params=params)
logger.info('stoploss limit order added for %s. '
'stop price: %s. limit: %s', pair, stop_price, rate)
return order

124
freqtrade/exchange/common.py Normal file
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@ -0,0 +1,124 @@
import logging
from freqtrade.exceptions import DependencyException, TemporaryError
logger = logging.getLogger(__name__)
API_RETRY_COUNT = 4
BAD_EXCHANGES = {
"bitmex": "Various reasons.",
"bitstamp": "Does not provide history. "
"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.",
**dict.fromkeys([
'adara',
'anxpro',
'bigone',
'coinbase',
'coinexchange',
'coinmarketcap',
'lykke',
'xbtce',
], "Does not provide timeframes. ccxt fetchOHLCV: False"),
**dict.fromkeys([
'bcex',
'bit2c',
'bitbay',
'bitflyer',
'bitforex',
'bithumb',
'bitso',
'bitstamp1',
'bl3p',
'braziliex',
'btcbox',
'btcchina',
'btctradeim',
'btctradeua',
'bxinth',
'chilebit',
'coincheck',
'coinegg',
'coinfalcon',
'coinfloor',
'coingi',
'coinmate',
'coinone',
'coinspot',
'coolcoin',
'crypton',
'deribit',
'exmo',
'exx',
'flowbtc',
'foxbit',
'fybse',
# 'hitbtc',
'ice3x',
'independentreserve',
'indodax',
'itbit',
'lakebtc',
'latoken',
'liquid',
'livecoin',
'luno',
'mixcoins',
'negociecoins',
'nova',
'paymium',
'southxchange',
'stronghold',
'surbitcoin',
'therock',
'tidex',
'vaultoro',
'vbtc',
'virwox',
'yobit',
'zaif',
], "Does not provide timeframes. ccxt fetchOHLCV: emulated"),
}
MAP_EXCHANGE_CHILDCLASS = {
'binanceus': 'binance',
'binanceje': 'binance',
}
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:
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 await wrapper(*args, **kwargs)
else:
logger.warning('Giving up retrying: %s()', f.__name__)
raise ex
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

668
freqtrade/exchange/exchange.py
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14
freqtrade/exchange/ftx.py Normal file
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@ -0,0 +1,14 @@
""" FTX exchange subclass """
import logging
from typing import Dict
from freqtrade.exchange import Exchange
logger = logging.getLogger(__name__)
class Ftx(Exchange):
_ft_has: Dict = {
"ohlcv_candle_limit": 1500,
}

56
freqtrade/exchange/kraken.py
View File

@ -4,7 +4,8 @@ from typing import Dict
import ccxt
from freqtrade import OperationalException, TemporaryError
from freqtrade.exceptions import (DependencyException, InvalidOrderException,
OperationalException, TemporaryError)
from freqtrade.exchange import Exchange
from freqtrade.exchange.exchange import retrier
@ -14,6 +15,11 @@ logger = logging.getLogger(__name__)
class Kraken(Exchange):
_params: Dict = {"trading_agreement": "agree"}
_ft_has: Dict = {
"stoploss_on_exchange": True,
"trades_pagination": "id",
"trades_pagination_arg": "since",
}
@retrier
def get_balances(self) -> dict:
@ -44,3 +50,51 @@ class Kraken(Exchange):
f'Could not get balance due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
def stoploss_adjust(self, stop_loss: float, order: Dict) -> bool:
"""
Verify stop_loss against stoploss-order value (limit or price)
Returns True if adjustment is necessary.
"""
return order['type'] == 'stop-loss' and stop_loss > float(order['price'])
def stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict) -> Dict:
"""
Creates a stoploss market order.
Stoploss market orders is the only stoploss type supported by kraken.
"""
ordertype = "stop-loss"
stop_price = self.price_to_precision(pair, stop_price)
if self._config['dry_run']:
dry_order = self.dry_run_order(
pair, ordertype, "sell", amount, stop_price)
return dry_order
try:
params = self._params.copy()
amount = self.amount_to_precision(pair, amount)
order = self._api.create_order(symbol=pair, type=ordertype, side='sell',
amount=amount, price=stop_price, params=params)
logger.info('stoploss order added for %s. '
'stop price: %s.', pair, stop_price)
return order
except ccxt.InsufficientFunds as e:
raise DependencyException(
f'Insufficient funds to create {ordertype} sell order on market {pair}.'
f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
f'Message: {e}') from e
except ccxt.InvalidOrder as e:
raise InvalidOrderException(
f'Could not create {ordertype} sell order on market {pair}. '
f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
f'Message: {e}') from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not place sell order due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e

995
freqtrade/freqtradebot.py
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40
freqtrade/indicator_helpers.py
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@ -1,40 +0,0 @@
from math import cos, exp, pi, sqrt
import numpy as np
import talib as ta
from pandas import Series
def went_up(series: Series) -> bool:
return series > series.shift(1)
def went_down(series: Series) -> bool:
return series < series.shift(1)
def ehlers_super_smoother(series: Series, smoothing: float = 6) -> Series:
magic = pi * sqrt(2) / smoothing
a1 = exp(-magic)
coeff2 = 2 * a1 * cos(magic)
coeff3 = -a1 * a1
coeff1 = (1 - coeff2 - coeff3) / 2
filtered = series.copy()
for i in range(2, len(series)):
filtered.iloc[i] = coeff1 * (series.iloc[i] + series.iloc[i-1]) + \
coeff2 * filtered.iloc[i-1] + coeff3 * filtered.iloc[i-2]
return filtered
def fishers_inverse(series: Series, smoothing: float = 0) -> np.ndarray:
""" Does a smoothed fishers inverse transformation.
Can be used with any oscillator that goes from 0 to 100 like RSI or MFI """
v1 = 0.1 * (series - 50)
if smoothing > 0:
v2 = ta.WMA(v1.values, timeperiod=smoothing)
else:
v2 = v1
return (np.exp(2 * v2)-1) / (np.exp(2 * v2) + 1)

45
freqtrade/loggers.py
View File

@ -1,9 +1,12 @@
import logging
import sys
from logging.handlers import RotatingFileHandler
from logging import Formatter
from logging.handlers import RotatingFileHandler, SysLogHandler
from typing import Any, Dict, List
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
@ -33,13 +36,41 @@ def setup_logging(config: Dict[str, Any]) -> None:
# Log level
verbosity = config['verbosity']
# Log to stdout, not stderr
log_handlers: List[logging.Handler] = [logging.StreamHandler(sys.stdout)]
# Log to stderr
log_handlers: List[logging.Handler] = [logging.StreamHandler(sys.stderr)]
if config.get('logfile'):
log_handlers.append(RotatingFileHandler(config['logfile'],
maxBytes=1024 * 1024, # 1Mb
backupCount=10))
logfile = config.get('logfile')
if logfile:
s = logfile.split(':')
if s[0] == 'syslog':
# Address can be either a string (socket filename) for Unix domain socket or
# a tuple (hostname, port) for UDP socket.
# Address can be omitted (i.e. simple 'syslog' used as the value of
# config['logfilename']), which defaults to '/dev/log', applicable for most
# of the systems.
address = (s[1], int(s[2])) if len(s) > 2 else s[1] if len(s) > 1 else '/dev/log'
handler = SysLogHandler(address=address)
# No datetime field for logging into syslog, to allow syslog
# to perform reduction of repeating messages if this is set in the
# syslog config. The messages should be equal for this.
handler.setFormatter(Formatter('%(name)s - %(levelname)s - %(message)s'))
log_handlers.append(handler)
elif s[0] == 'journald':
try:
from systemd.journal import JournaldLogHandler
except ImportError:
raise OperationalException("You need the systemd python package be installed in "
"order to use logging to journald.")
handler = JournaldLogHandler()
# No datetime field for logging into journald, to allow syslog
# to perform reduction of repeating messages if this is set in the
# syslog config. The messages should be equal for this.
handler.setFormatter(Formatter('%(name)s - %(levelname)s - %(message)s'))
log_handlers.append(handler)
else:
log_handlers.append(RotatingFileHandler(logfile,
maxBytes=1024 * 1024, # 1Mb
backupCount=10))
logging.basicConfig(
level=logging.INFO if verbosity < 1 else logging.DEBUG,

29
freqtrade/main.py
View File

@ -4,6 +4,7 @@ Main Freqtrade bot script.
Read the documentation to know what cli arguments you need.
"""
from freqtrade.exceptions import FreqtradeException, OperationalException
import sys
# check min. python version
if sys.version_info < (3, 6):
@ -13,9 +14,7 @@ if sys.version_info < (3, 6):
import logging
from typing import Any, List
from freqtrade import OperationalException
from freqtrade.configuration import Arguments
from freqtrade.worker import Worker
from freqtrade.commands import Arguments
logger = logging.getLogger('freqtrade')
@ -28,35 +27,35 @@ def main(sysargv: List[str] = None) -> None:
"""
return_code: Any = 1
worker = None
try:
arguments = Arguments(sysargv)
args = arguments.get_parsed_arg()
# A subcommand has been issued.
# Means if Backtesting or Hyperopt have been called we exit the bot
# Call subcommand.
if 'func' in args:
args['func'](args)
# TODO: fetch return_code as returned by the command function here
return_code = 0
return_code = args['func'](args)
else:
# Load and run worker
worker = Worker(args)
worker.run()
# No subcommand was issued.
raise OperationalException(
"Usage of Freqtrade requires a subcommand to be specified.\n"
"To have the bot executing trades in live/dry-run modes, "
"depending on the value of the `dry_run` setting in the config, run Freqtrade "
"as `freqtrade trade [options...]`.\n"
"To see the full list of options available, please use "
"`freqtrade --help` or `freqtrade <command> --help`."
)
except SystemExit as e:
return_code = e
except KeyboardInterrupt:
logger.info('SIGINT received, aborting ...')
return_code = 0
except OperationalException as e:
except FreqtradeException as e:
logger.error(str(e))
return_code = 2
except Exception:
logger.exception('Fatal exception!')
finally:
if worker:
worker.exit()
sys.exit(return_code)

49
freqtrade/misc.py
View File

@ -6,6 +6,7 @@ import logging
import re
from datetime import datetime
from pathlib import Path
from typing import Any
from typing.io import IO
import numpy as np
@ -40,28 +41,30 @@ def datesarray_to_datetimearray(dates: np.ndarray) -> np.ndarray:
return dates.dt.to_pydatetime()
def file_dump_json(filename: Path, data, is_zip=False) -> None:
def file_dump_json(filename: Path, data: Any, is_zip: bool = False) -> None:
"""
Dump JSON data into a file
:param filename: file to create
:param data: JSON Data to save
:return:
"""
logger.info(f'dumping json to "{filename}"')
if is_zip:
if filename.suffix != '.gz':
filename = filename.with_suffix('.gz')
logger.info(f'dumping json to "{filename}"')
with gzip.open(filename, 'w') as fp:
rapidjson.dump(data, fp, default=str, number_mode=rapidjson.NM_NATIVE)
else:
logger.info(f'dumping json to "{filename}"')
with open(filename, 'w') as fp:
rapidjson.dump(data, fp, default=str, number_mode=rapidjson.NM_NATIVE)
logger.debug(f'done json to "{filename}"')
def json_load(datafile: IO):
def json_load(datafile: IO) -> Any:
"""
load data with rapidjson
Use this to have a consistent experience,
@ -72,22 +75,30 @@ def json_load(datafile: IO):
def file_load_json(file):
gzipfile = file.with_suffix(file.suffix + '.gz')
if file.suffix != ".gz":
gzipfile = file.with_suffix(file.suffix + '.gz')
else:
gzipfile = file
# Try gzip file first, otherwise regular json file.
if gzipfile.is_file():
logger.debug('Loading ticker data from file %s', gzipfile)
with gzip.open(gzipfile) as tickerdata:
pairdata = json_load(tickerdata)
logger.debug(f"Loading historical data from file {gzipfile}")
with gzip.open(gzipfile) as datafile:
pairdata = json_load(datafile)
elif file.is_file():
logger.debug('Loading ticker data from file %s', file)
with open(file) as tickerdata:
pairdata = json_load(tickerdata)
logger.debug(f"Loading historical data from file {file}")
with open(file) as datafile:
pairdata = json_load(datafile)
else:
return None
return pairdata
def pair_to_filename(pair: str) -> str:
for ch in ['/', '-', ' ', '.', '@', '$', '+', ':']:
pair = pair.replace(ch, '_')
return pair
def format_ms_time(date: int) -> str:
"""
convert MS date to readable format.
@ -121,3 +132,19 @@ def round_dict(d, n):
Rounds float values in the dict to n digits after the decimal point.
"""
return {k: (round(v, n) if isinstance(v, float) else v) for k, v in d.items()}
def plural(num: float, singular: str, plural: str = None) -> str:
return singular if (num == 1 or num == -1) else plural or singular + 's'
def render_template(templatefile: str, arguments: dict = {}) -> str:
from jinja2 import Environment, PackageLoader, select_autoescape
env = Environment(
loader=PackageLoader('freqtrade', 'templates'),
autoescape=select_autoescape(['html', 'xml'])
)
template = env.get_template(templatefile)
return template.render(**arguments)

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