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Merge pull request #7954 from freqtrade/new_release

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New release 2022.12
This commit is contained in:
Matthias 2022-12-29 17:45:08 +01:00 committed by GitHub
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2
.github/ISSUE_TEMPLATE/bug_report.md vendored
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@ -20,7 +20,7 @@ Please do not use bug reports to request new features.
* Operating system: ____
* Python Version: _____ (`python -V`)
* CCXT version: _____ (`pip freeze | grep ccxt`)
* Freqtrade Version: ____ (`freqtrade -V` or `docker-compose run --rm freqtrade -V` for Freqtrade running in docker)
* Freqtrade Version: ____ (`freqtrade -V` or `docker compose run --rm freqtrade -V` for Freqtrade running in docker)
Note: All issues other than enhancement requests will be closed without further comment if the above template is deleted or not filled out.

2
.github/ISSUE_TEMPLATE/feature_request.md vendored
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@ -18,7 +18,7 @@ Have you search for this feature before requesting it? It's highly likely that a
* Operating system: ____
* Python Version: _____ (`python -V`)
* CCXT version: _____ (`pip freeze | grep ccxt`)
* Freqtrade Version: ____ (`freqtrade -V` or `docker-compose run --rm freqtrade -V` for Freqtrade running in docker)
* Freqtrade Version: ____ (`freqtrade -V` or `docker compose run --rm freqtrade -V` for Freqtrade running in docker)
## Describe the enhancement

2
.github/ISSUE_TEMPLATE/question.md vendored
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@ -18,7 +18,7 @@ Please do not use the question template to report bugs or to request new feature
* Operating system: ____
* Python Version: _____ (`python -V`)
* CCXT version: _____ (`pip freeze | grep ccxt`)
* Freqtrade Version: ____ (`freqtrade -V` or `docker-compose run --rm freqtrade -V` for Freqtrade running in docker)
* Freqtrade Version: ____ (`freqtrade -V` or `docker compose run --rm freqtrade -V` for Freqtrade running in docker)
## Your question

82
.github/workflows/ci.yml vendored
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@ -66,12 +66,6 @@ jobs:
- name: Tests
run: |
pytest --random-order --cov=freqtrade --cov-config=.coveragerc
if: matrix.python-version != '3.9' || matrix.os != 'ubuntu-22.04'
- name: Tests incl. ccxt compatibility tests
run: |
pytest --random-order --cov=freqtrade --cov-config=.coveragerc --longrun
if: matrix.python-version == '3.9' && matrix.os == 'ubuntu-22.04'
- name: Coveralls
if: (runner.os == 'Linux' && matrix.python-version == '3.10' && matrix.os == 'ubuntu-22.04')
@ -94,7 +88,7 @@ jobs:
run: |
cp config_examples/config_bittrex.example.json config.json
freqtrade create-userdir --userdir user_data
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
freqtrade hyperopt --datadir tests/testdata -e 6 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
- name: Flake8
run: |
@ -154,6 +148,19 @@ jobs:
if: runner.os == 'macOS'
run: |
brew update
# homebrew fails to update python due to unlinking failures
# https://github.com/actions/runner-images/issues/6817
rm /usr/local/bin/2to3 || true
rm /usr/local/bin/2to3-3.11 || true
rm /usr/local/bin/idle3 || true
rm /usr/local/bin/idle3.11 || true
rm /usr/local/bin/pydoc3 || true
rm /usr/local/bin/pydoc3.11 || true
rm /usr/local/bin/python3 || true
rm /usr/local/bin/python3.11 || true
rm /usr/local/bin/python3-config || true
rm /usr/local/bin/python3.11-config || true
brew install hdf5 c-blosc
python -m pip install --upgrade pip wheel
export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
@ -310,9 +317,64 @@ jobs:
details: Freqtrade doc test failed!
webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
build_linux_online:
# Run pytest with "live" checks
runs-on: ubuntu-22.04
# permissions:
steps:
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: "3.9"
- name: Cache_dependencies
uses: actions/cache@v3
id: cache
with:
path: ~/dependencies/
key: ${{ runner.os }}-dependencies
- name: pip cache (linux)
uses: actions/cache@v3
if: runner.os == 'Linux'
with:
path: ~/.cache/pip
key: test-${{ 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 ${HOME}/dependencies/; cd ..
- name: Installation - *nix
if: runner.os == 'Linux'
run: |
python -m pip install --upgrade pip wheel
export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
export TA_LIBRARY_PATH=${HOME}/dependencies/lib
export TA_INCLUDE_PATH=${HOME}/dependencies/include
pip install -r requirements-dev.txt
pip install -e .
- name: Tests incl. ccxt compatibility tests
run: |
pytest --random-order --cov=freqtrade --cov-config=.coveragerc --longrun
# Notify only once - when CI completes (and after deploy) in case it's successfull
notify-complete:
needs: [ build_linux, build_macos, build_windows, docs_check, mypy_version_check, pre-commit ]
needs: [
build_linux,
build_macos,
build_windows,
docs_check,
mypy_version_check,
pre-commit,
build_linux_online
]
runs-on: ubuntu-22.04
# Discord notification can't handle schedule events
if: (github.event_name != 'schedule')
@ -361,7 +423,7 @@ jobs:
python setup.py sdist bdist_wheel
- name: Publish to PyPI (Test)
uses: pypa/gh-action-pypi-publish@v1.5.1
uses: pypa/gh-action-pypi-publish@v1.6.4
if: (github.event_name == 'release')
with:
user: __token__
@ -369,7 +431,7 @@ jobs:
repository_url: https://test.pypi.org/legacy/
- name: Publish to PyPI
uses: pypa/gh-action-pypi-publish@v1.5.1
uses: pypa/gh-action-pypi-publish@v1.6.4
if: (github.event_name == 'release')
with:
user: __token__

4
.pre-commit-config.yaml
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@ -15,9 +15,9 @@ repos:
additional_dependencies:
- types-cachetools==5.2.1
- types-filelock==3.2.7
- types-requests==2.28.11.5
- types-requests==2.28.11.7
- types-tabulate==0.9.0.0
- types-python-dateutil==2.8.19.4
- types-python-dateutil==2.8.19.5
# stages: [push]
- repo: https://github.com/pycqa/isort

1
README.md
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@ -1,6 +1,7 @@
# ![freqtrade](https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docs/assets/freqtrade_poweredby.svg)
[![Freqtrade CI](https://github.com/freqtrade/freqtrade/workflows/Freqtrade%20CI/badge.svg)](https://github.com/freqtrade/freqtrade/actions/)
[![DOI](https://joss.theoj.org/papers/10.21105/joss.04864/status.svg)](https://doi.org/10.21105/joss.04864)
[![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)

8
build_helpers/publish_docker_arm64.sh
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@ -7,11 +7,13 @@ export DOCKER_BUILDKIT=1
TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g")
TAG_PLOT=${TAG}_plot
TAG_FREQAI=${TAG}_freqai
TAG_FREQAI_RL=${TAG_FREQAI}rl
TAG_PI="${TAG}_pi"
TAG_ARM=${TAG}_arm
TAG_PLOT_ARM=${TAG_PLOT}_arm
TAG_FREQAI_ARM=${TAG_FREQAI}_arm
TAG_FREQAI_RL_ARM=${TAG_FREQAI_RL}_arm
CACHE_IMAGE=freqtradeorg/freqtrade_cache
echo "Running for ${TAG}"
@ -41,9 +43,11 @@ docker tag freqtrade:$TAG_ARM ${CACHE_IMAGE}:$TAG_ARM
docker build --cache-from freqtrade:${TAG_ARM} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG_ARM} -t freqtrade:${TAG_PLOT_ARM} -f docker/Dockerfile.plot .
docker build --cache-from freqtrade:${TAG_ARM} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG_ARM} -t freqtrade:${TAG_FREQAI_ARM} -f docker/Dockerfile.freqai .
docker build --cache-from freqtrade:${TAG_ARM} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG_ARM} -t freqtrade:${TAG_FREQAI_RL_ARM} -f docker/Dockerfile.freqai_rl .
docker tag freqtrade:$TAG_PLOT_ARM ${CACHE_IMAGE}:$TAG_PLOT_ARM
docker tag freqtrade:$TAG_FREQAI_ARM ${CACHE_IMAGE}:$TAG_FREQAI_ARM
docker tag freqtrade:$TAG_FREQAI_RL_ARM ${CACHE_IMAGE}:$TAG_FREQAI_RL_ARM
# Run backtest
docker run --rm -v $(pwd)/config_examples/config_bittrex.example.json:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG_ARM} backtesting --datadir /tests/testdata --strategy-path /tests/strategy/strats/ --strategy StrategyTestV3
@ -58,6 +62,7 @@ docker images
# docker push ${IMAGE_NAME}
docker push ${CACHE_IMAGE}:$TAG_PLOT_ARM
docker push ${CACHE_IMAGE}:$TAG_FREQAI_ARM
docker push ${CACHE_IMAGE}:$TAG_FREQAI_RL_ARM
docker push ${CACHE_IMAGE}:$TAG_ARM
# Create multi-arch image
@ -74,6 +79,9 @@ docker manifest push -p ${IMAGE_NAME}:${TAG_PLOT}
docker manifest create ${IMAGE_NAME}:${TAG_FREQAI} ${CACHE_IMAGE}:${TAG_FREQAI_ARM} ${CACHE_IMAGE}:${TAG_FREQAI}
docker manifest push -p ${IMAGE_NAME}:${TAG_FREQAI}
docker manifest create ${IMAGE_NAME}:${TAG_FREQAI_RL} ${CACHE_IMAGE}:${TAG_FREQAI_RL_ARM} ${CACHE_IMAGE}:${TAG_FREQAI_RL}
docker manifest push -p ${IMAGE_NAME}:${TAG_FREQAI_RL}
# Tag as latest for develop builds
if [ "${TAG}" = "develop" ]; then
docker manifest create ${IMAGE_NAME}:latest ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI} ${CACHE_IMAGE}:${TAG}

4
build_helpers/publish_docker_multi.sh
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@ -6,6 +6,7 @@
TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g")
TAG_PLOT=${TAG}_plot
TAG_FREQAI=${TAG}_freqai
TAG_FREQAI_RL=${TAG_FREQAI}rl
TAG_PI="${TAG}_pi"
PI_PLATFORM="linux/arm/v7"
@ -51,9 +52,11 @@ docker tag freqtrade:$TAG ${CACHE_IMAGE}:$TAG
docker build --cache-from freqtrade:${TAG} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG} -t freqtrade:${TAG_PLOT} -f docker/Dockerfile.plot .
docker build --cache-from freqtrade:${TAG} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG} -t freqtrade:${TAG_FREQAI} -f docker/Dockerfile.freqai .
docker build --cache-from freqtrade:${TAG_FREQAI} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG_FREQAI} -t freqtrade:${TAG_FREQAI_RL} -f docker/Dockerfile.freqai_rl .
docker tag freqtrade:$TAG_PLOT ${CACHE_IMAGE}:$TAG_PLOT
docker tag freqtrade:$TAG_FREQAI ${CACHE_IMAGE}:$TAG_FREQAI
docker tag freqtrade:$TAG_FREQAI_RL ${CACHE_IMAGE}:$TAG_FREQAI_RL
# Run backtest
docker run --rm -v $(pwd)/config_examples/config_bittrex.example.json:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG} backtesting --datadir /tests/testdata --strategy-path /tests/strategy/strats/ --strategy StrategyTestV3
@ -68,6 +71,7 @@ docker images
docker push ${CACHE_IMAGE}
docker push ${CACHE_IMAGE}:$TAG_PLOT
docker push ${CACHE_IMAGE}:$TAG_FREQAI
docker push ${CACHE_IMAGE}:$TAG_FREQAI_RL
docker push ${CACHE_IMAGE}:$TAG

4
config_examples/config_freqai.example.json
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@ -79,9 +79,7 @@
"test_size": 0.33,
"random_state": 1
},
"model_training_parameters": {
"n_estimators": 1000
}
"model_training_parameters": {}
},
"bot_name": "",
"force_entry_enable": true,

8
docker/Dockerfile.freqai_rl Normal file
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@ -0,0 +1,8 @@
ARG sourceimage=freqtradeorg/freqtrade
ARG sourcetag=develop_freqai
FROM ${sourceimage}:${sourcetag}
# Install dependencies
COPY requirements-freqai.txt requirements-freqai-rl.txt /freqtrade/
RUN pip install -r requirements-freqai-rl.txt --user --no-cache-dir

14
docs/advanced-backtesting.md
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@ -100,3 +100,17 @@ freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 2 --enter-re
The indicators have to be present in your strategy's main DataFrame (either for your main
timeframe or for informative timeframes) otherwise they will simply be ignored in the script
output.
### Filtering the trade output by date
To show only trades between dates within your backtested timerange, supply the usual `timerange` option in `YYYYMMDD-[YYYYMMDD]` format:
```
--timerange : Timerange to filter output trades, start date inclusive, end date exclusive. e.g. 20220101-20221231
```
For example, if your backtest timerange was `20220101-20221231` but you only want to output trades in January:
```bash
freqtrade backtesting-analysis -c <config.json> --timerange 20220101-20220201
```

3
docs/backtesting.md
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@ -583,7 +583,8 @@ To utilize this, you can append `--timeframe-detail 5m` to your regular backtest
freqtrade backtesting --strategy AwesomeStrategy --timeframe 1h --timeframe-detail 5m
```
This will load 1h data as well as 5m data for the timeframe. The strategy will be analyzed with the 1h timeframe - and for every "open trade candle" (candles where a trade is open) the 5m data will be used to simulate intra-candle movements.
This will load 1h data as well as 5m data for the timeframe. The strategy will be analyzed with the 1h timeframe, and Entry orders will only be placed at the main timeframe, however Order fills and exit signals will be evaluated at the 5m candle, simulating intra-candle movements.
All callback functions (`custom_exit()`, `custom_stoploss()`, ... ) will be running for each 5m candle once the trade is opened (so 12 times in the above example of 1h timeframe, and 5m detailed timeframe).
`--timeframe-detail` must be smaller than the original timeframe, otherwise backtesting will fail to start.

4
docs/data-analysis.md
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@ -5,7 +5,7 @@ You can analyze the results of backtests and trading history easily using Jupyte
## Quick start with docker
Freqtrade provides a docker-compose file which starts up a jupyter lab server.
You can run this server using the following command: `docker-compose -f docker/docker-compose-jupyter.yml up`
You can run this server using the following command: `docker compose -f docker/docker-compose-jupyter.yml up`
This will create a dockercontainer running jupyter lab, which will be accessible using `https://127.0.0.1:8888/lab`.
Please use the link that's printed in the console after startup for simplified login.
@ -83,7 +83,7 @@ from pathlib import Path
project_root = "somedir/freqtrade"
i=0
try:
os.chdirdir(project_root)
os.chdir(project_root)
assert Path('LICENSE').is_file()
except:
while i<4 and (not Path('LICENSE').is_file()):

7
docs/developer.md
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@ -49,6 +49,13 @@ For more information about the [Remote container extension](https://code.visuals
New code should be covered by basic unittests. Depending on the complexity of the feature, Reviewers may request more in-depth unittests.
If necessary, the Freqtrade team can assist and give guidance with writing good tests (however please don't expect anyone to write the tests for you).
#### How to run tests
Use `pytest` in root folder to run all available testcases and confirm your local environment is setup correctly
!!! Note "feature branches"
Tests are expected to pass on the `develop` and `stable` branches. Other branches may be work in progress with tests not working yet.
#### Checking log content in tests
Freqtrade uses 2 main methods to check log content in tests, `log_has()` and `log_has_re()` (to check using regex, in case of dynamic log-messages).

66
docs/docker_quickstart.md
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@ -4,20 +4,22 @@ This page explains how to run the bot with Docker. It is not meant to work out o
## Install Docker
Start by downloading and installing Docker CE for your platform:
Start by downloading and installing Docker / Docker Desktop for your platform:
* [Mac](https://docs.docker.com/docker-for-mac/install/)
* [Windows](https://docs.docker.com/docker-for-windows/install/)
* [Linux](https://docs.docker.com/install/)
To simplify running freqtrade, [`docker-compose`](https://docs.docker.com/compose/install/) should be installed and available to follow the below [docker quick start guide](#docker-quick-start).
!!! Info "Docker compose install"
Freqtrade documentation assumes the use of Docker desktop (or the docker compose plugin).
While the docker-compose standalone installation still works, it will require changing all `docker compose` commands from `docker compose` to `docker-compose` to work (e.g. `docker compose up -d` will become `docker-compose up -d`).
## Freqtrade with docker-compose
## Freqtrade with docker
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/stable/docker-compose.yml) ready for usage.
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/stable/docker-compose.yml) ready for usage.
!!! Note
- The following section assumes that `docker` and `docker-compose` are installed and available to the logged in user.
- The following section assumes that `docker` is installed and available to the logged in user.
- All below commands use relative directories and will have to be executed from the directory containing the `docker-compose.yml` file.
### Docker quick start
@ -31,13 +33,13 @@ cd ft_userdata/
curl https://raw.githubusercontent.com/freqtrade/freqtrade/stable/docker-compose.yml -o docker-compose.yml
# Pull the freqtrade image
docker-compose pull
docker compose pull
# Create user directory structure
docker-compose run --rm freqtrade create-userdir --userdir user_data
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
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.
@ -64,7 +66,7 @@ The `SampleStrategy` is run by default.
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 up -d
```
!!! Warning "Default configuration"
@ -84,27 +86,27 @@ You can now access the UI by typing localhost:8080 in your browser.
#### Monitoring the bot
You can check for running instances with `docker-compose ps`.
You can check for running instances with `docker compose ps`.
This should list the service `freqtrade` as `running`. If that's not the case, best check the logs (see next point).
#### Docker-compose logs
#### Docker compose logs
Logs will be written to: `user_data/logs/freqtrade.log`.
You can also check the latest log with the command `docker-compose logs -f`.
You can also check the latest log with the command `docker compose logs -f`.
#### Database
The database will be located at: `user_data/tradesv3.sqlite`
#### Updating freqtrade with docker-compose
#### Updating freqtrade with docker
Updating freqtrade when using `docker-compose` is as simple as running the following 2 commands:
Updating freqtrade when using `docker` is as simple as running the following 2 commands:
``` bash
# Download the latest image
docker-compose pull
docker compose pull
# Restart the image
docker-compose up -d
docker compose up -d
```
This will first pull the latest image, and will then restart the container with the just pulled version.
@ -116,43 +118,43 @@ This will first pull the latest image, and will then restart the container with
Advanced users may edit the docker-compose file further to include all possible options or arguments.
All freqtrade arguments will be available by running `docker-compose run --rm freqtrade <command> <optional arguments>`.
All freqtrade arguments will be available by running `docker compose run --rm freqtrade <command> <optional arguments>`.
!!! Warning "`docker-compose` for trade commands"
Trade commands (`freqtrade trade <...>`) should not be ran via `docker-compose run` - but should use `docker-compose up -d` instead.
!!! Warning "`docker compose` for trade commands"
Trade commands (`freqtrade trade <...>`) should not be ran via `docker compose run` - but should use `docker compose up -d` instead.
This makes sure that the container is properly started (including port forwardings) and will make sure that the container will restart after a system reboot.
If you intend to use freqUI, please also ensure to adjust the [configuration accordingly](rest-api.md#configuration-with-docker), otherwise the UI will not be available.
!!! Note "`docker-compose run --rm`"
!!! Note "`docker compose run --rm`"
Including `--rm` will remove the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
??? Note "Using docker without docker-compose"
"`docker-compose run --rm`" will require a compose file to be provided.
??? Note "Using docker without docker"
"`docker compose run --rm`" will require a compose file to be provided.
Some freqtrade commands that don't require authentication such as `list-pairs` can be run with "`docker run --rm`" instead.
For example `docker run --rm freqtradeorg/freqtrade:stable list-pairs --exchange binance --quote BTC --print-json`.
This can be useful for fetching exchange information to add to your `config.json` without affecting your running containers.
#### Example: Download data with docker-compose
#### Example: Download data with docker
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
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
#### Example: Backtest with docker
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
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
### Additional dependencies with docker
If your strategy requires dependencies not included in the default image - 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 [docker/Dockerfile.custom](https://github.com/freqtrade/freqtrade/blob/develop/docker/Dockerfile.custom) for an example).
@ -166,15 +168,15 @@ You'll then also need to modify the `docker-compose.yml` file and uncomment the
dockerfile: "./Dockerfile.<yourextension>"
```
You can then run `docker-compose build --pull` to build the docker image, and run it using the commands described above.
You can then run `docker compose build --pull` to build the docker image, and run it using the commands described above.
### Plotting with docker-compose
### Plotting with docker
Commands `freqtrade plot-profit` and `freqtrade plot-dataframe` ([Documentation](plotting.md)) are available by changing the image to `*_plot` in your docker-compose.yml file.
You can then use these commands as follows:
``` bash
docker-compose run --rm freqtrade plot-dataframe --strategy AwesomeStrategy -p BTC/ETH --timerange=20180801-20180805
docker compose run --rm freqtrade plot-dataframe --strategy AwesomeStrategy -p BTC/ETH --timerange=20180801-20180805
```
The output will be stored in the `user_data/plot` directory, and can be opened with any modern browser.
@ -185,7 +187,7 @@ Freqtrade provides a docker-compose file which starts up a jupyter lab server.
You can run this server using the following command:
``` bash
docker-compose -f docker/docker-compose-jupyter.yml up
docker compose -f docker/docker-compose-jupyter.yml up
```
This will create a docker-container running jupyter lab, which will be accessible using `https://127.0.0.1:8888/lab`.
@ -194,7 +196,7 @@ Please use the link that's printed in the console after startup for simplified l
Since part of this image is built on your machine, it is recommended to rebuild the image from time to time to keep freqtrade (and dependencies) up-to-date.
``` bash
docker-compose -f docker/docker-compose-jupyter.yml build --no-cache
docker compose -f docker/docker-compose-jupyter.yml build --no-cache
```
## Troubleshooting

3
docs/exchanges.md
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@ -54,6 +54,9 @@ This configuration enables kraken, as well as rate-limiting to avoid bans from t
## Binance
!!! Warning "Server location and geo-ip restrictions"
Please be aware that binance restrict api access regarding the server country. The currents and non exhaustive countries blocked are United States, Malaysia (Singapour), Ontario (Canada). Please go to [binance terms > b. Eligibility](https://www.binance.com/en/terms) to find up to date list.
Binance supports [time_in_force](configuration.md#understand-order_time_in_force).
!!! Tip "Stoploss on Exchange"

5
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@ -26,10 +26,7 @@ FreqAI is configured through the typical [Freqtrade config file](configuration.m
},
"data_split_parameters" : {
"test_size": 0.25
},
"model_training_parameters" : {
"n_estimators": 100
},
}
}
```

59
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View File

@ -4,22 +4,30 @@ The table below will list all configuration parameters available for FreqAI. Som
Mandatory parameters are marked as **Required** and have to be set in one of the suggested ways.
### General configuration parameters
| Parameter | Description |
|------------|-------------|
| | **General configuration parameters**
| | **General configuration parameters within the `config.freqai` tree**
| `freqai` | **Required.** <br> The parent dictionary containing all the parameters for controlling FreqAI. <br> **Datatype:** Dictionary.
| `train_period_days` | **Required.** <br> Number of days to use for the training data (width of the sliding window). <br> **Datatype:** Positive integer.
| `backtest_period_days` | **Required.** <br> Number of days to inference from the trained model before sliding the `train_period_days` window defined above, and retraining the model during backtesting (more info [here](freqai-running.md#backtesting)). This can be fractional days, but beware that the provided `timerange` will be divided by this number to yield the number of trainings necessary to complete the backtest. <br> **Datatype:** Float.
| `identifier` | **Required.** <br> A unique ID for the current model. If models are saved to disk, the `identifier` allows for reloading specific pre-trained models/data. <br> **Datatype:** String.
| `live_retrain_hours` | Frequency of retraining during dry/live runs. <br> **Datatype:** Float > 0. <br> Default: `0` (models retrain as often as possible).
| `expiration_hours` | Avoid making predictions if a model is more than `expiration_hours` old. <br> **Datatype:** Positive integer. <br> Default: `0` (models never expire).
| `purge_old_models` | Delete obsolete models. <br> **Datatype:** Boolean. <br> Default: `False` (all historic models remain on disk).
| `purge_old_models` | Delete all unused models during live runs (not relevant to backtesting). If set to false (not default), dry/live runs will accumulate all unused models to disk. If <br> **Datatype:** Boolean. <br> Default: `True`.
| `save_backtest_models` | Save models to disk when running backtesting. Backtesting operates most efficiently by saving the prediction data and reusing them directly for subsequent runs (when you wish to tune entry/exit parameters). Saving backtesting models to disk also allows to use the same model files for starting a dry/live instance with the same model `identifier`. <br> **Datatype:** Boolean. <br> Default: `False` (no models are saved).
| `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training dataset (more information can be found [here](freqai-configuration.md#creating-a-dynamic-target-threshold)). <br> **Datatype:** Positive integer.
| `follow_mode` | Use a `follower` that will look for models associated with a specific `identifier` and load those for inferencing. A `follower` will **not** train new models. <br> **Datatype:** Boolean. <br> Default: `False`.
| `continual_learning` | Use the final state of the most recently trained model as starting point for the new model, allowing for incremental learning (more information can be found [here](freqai-running.md#continual-learning)). <br> **Datatype:** Boolean. <br> Default: `False`.
| `write_metrics_to_disk` | Collect train timings, inference timings and cpu usage in json file. <br> **Datatype:** Boolean. <br> Default: `False`
| | **Feature parameters**
| `data_kitchen_thread_count` | <br> Designate the number of threads you want to use for data processing (outlier methods, normalization, etc.). This has no impact on the number of threads used for training. If user does not set it (default), FreqAI will use max number of threads - 2 (leaving 1 physical core available for Freqtrade bot and FreqUI) <br> **Datatype:** Positive integer.
### Feature parameters
| Parameter | Description |
|------------|-------------|
| | **Feature parameters within the `freqai.feature_parameters` sub dictionary**
| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples are shown [here](freqai-feature-engineering.md). <br> **Datatype:** Dictionary.
| `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for. The list is added as features to the base indicators dataset. <br> **Datatype:** List of timeframes (strings).
| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` during feature engineering (see details [here](freqai-feature-engineering.md)) will be created for each correlated coin. The correlated coins features are added to the base indicators dataset. <br> **Datatype:** List of assets (strings).
@ -29,7 +37,7 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
| `indicator_max_period_candles` | **No longer used (#7325)**. Replaced by `startup_candle_count` which is set in the [strategy](freqai-configuration.md#building-a-freqai-strategy). `startup_candle_count` is timeframe independent and defines the maximum *period* used in `populate_any_indicators()` for indicator creation. FreqAI uses this parameter together with the maximum timeframe in `include_time_frames` to calculate how many data points to download such that the first data point does not include a NaN. <br> **Datatype:** Positive integer.
| `indicator_periods_candles` | Time periods to calculate indicators for. The indicators are added to the base indicator dataset. <br> **Datatype:** List of positive integers.
| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis) <br> **Datatype:** Boolean. <br> Default: `False`.
| `plot_feature_importances` | Create a feature importance plot for each model for the top/bottom `plot_feature_importances` number of features. <br> **Datatype:** Integer. <br> Default: `0`.
| `plot_feature_importances` | Create a feature importance plot for each model for the top/bottom `plot_feature_importances` number of features. Plot is stored in `user_data/models/<identifier>/sub-train-<COIN>_<timestamp>.html`. <br> **Datatype:** Integer. <br> Default: `0`.
| `DI_threshold` | Activates the use of the Dissimilarity Index for outlier detection when set to > 0. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di). <br> **Datatype:** Positive float (typically < 1).
| `use_SVM_to_remove_outliers` | Train a support vector machine to detect and remove outliers from the training dataset, as well as from incoming data points. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Boolean.
| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Dictionary.
@ -38,16 +46,49 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
| `noise_standard_deviation` | If set, FreqAI adds noise to the training features with the aim of preventing overfitting. FreqAI generates random deviates from a gaussian distribution with a standard deviation of `noise_standard_deviation` and adds them to all data points. `noise_standard_deviation` should be kept relative to the normalized space, i.e., between -1 and 1. In other words, since data in FreqAI is always normalized to be between -1 and 1, `noise_standard_deviation: 0.05` would result in 32% of the data being randomly increased/decreased by more than 2.5% (i.e., the percent of data falling within the first standard deviation). <br> **Datatype:** Integer. <br> Default: `0`.
| `outlier_protection_percentage` | Enable to prevent outlier detection methods from discarding too much data. If more than `outlier_protection_percentage` % of points are detected as outliers by the SVM or DBSCAN, FreqAI will log a warning message and ignore outlier detection, i.e., the original dataset will be kept intact. If the outlier protection is triggered, no predictions will be made based on the training dataset. <br> **Datatype:** Float. <br> Default: `30`.
| `reverse_train_test_order` | Split the feature dataset (see below) and use the latest data split for training and test on historical split of the data. This allows the model to be trained up to the most recent data point, while avoiding overfitting. However, you should be careful to understand the unorthodox nature of this parameter before employing it. <br> **Datatype:** Boolean. <br> Default: `False` (no reversal).
| | **Data split parameters**
### Data split parameters
| Parameter | Description |
|------------|-------------|
| | **Data split parameters within the `freqai.data_split_parameters` sub dictionary**
| `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website). <br> **Datatype:** Dictionary.
| `test_size` | The fraction of data that should be used for testing instead of training. <br> **Datatype:** Positive float < 1.
| `shuffle` | Shuffle the training data points during training. Typically, to not remove the chronological order of data in time-series forecasting, this is set to `False`. <br> **Datatype:** Boolean. <br> Defaut: `False`.
| | **Model training parameters**
### Model training parameters
| Parameter | Description |
|------------|-------------|
| | **Model training parameters within the `freqai.model_training_parameters` sub dictionary**
| `model_training_parameters` | A flexible dictionary that includes all parameters available by the selected model library. For example, if you use `LightGBMRegressor`, this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html) (external website). If you select a different model, this dictionary can contain any parameter from that model. A list of the currently available models can be found [here](freqai-configuration.md#using-different-prediction-models). <br> **Datatype:** Dictionary.
| `n_estimators` | The number of boosted trees to fit in the training of the model. <br> **Datatype:** Integer.
| `learning_rate` | Boosting learning rate during training of the model. <br> **Datatype:** Float.
| `n_jobs`, `thread_count`, `task_type` | Set the number of threads for parallel processing and the `task_type` (`gpu` or `cpu`). Different model libraries use different parameter names. <br> **Datatype:** Float.
### Reinforcement Learning parameters
| Parameter | Description |
|------------|-------------|
| | **Reinforcement Learning Parameters within the `freqai.rl_config` sub dictionary**
| `rl_config` | A dictionary containing the control parameters for a Reinforcement Learning model. <br> **Datatype:** Dictionary.
| `train_cycles` | Training time steps will be set based on the `train_cycles * number of training data points. <br> **Datatype:** Integer.
| `cpu_count` | Number of processors to dedicate to the Reinforcement Learning training process. <br> **Datatype:** int.
| `max_trade_duration_candles`| Guides the agent training to keep trades below desired length. Example usage shown in `prediction_models/ReinforcementLearner.py` within the customizable `calculate_reward()` function. <br> **Datatype:** int.
| `model_type` | Model string from stable_baselines3 or SBcontrib. Available strings include: `'TRPO', 'ARS', 'RecurrentPPO', 'MaskablePPO', 'PPO', 'A2C', 'DQN'`. User should ensure that `model_training_parameters` match those available to the corresponding stable_baselines3 model by visiting their documentaiton. [PPO doc](https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html) (external website) <br> **Datatype:** string.
| `policy_type` | One of the available policy types from stable_baselines3 <br> **Datatype:** string.
| `max_training_drawdown_pct` | The maximum drawdown that the agent is allowed to experience during training. <br> **Datatype:** float. <br> Default: 0.8
| `cpu_count` | Number of threads/cpus to dedicate to the Reinforcement Learning training process (depending on if `ReinforcementLearning_multiproc` is selected or not). Recommended to leave this untouched, by default, this value is set to the total number of physical cores minus 1. <br> **Datatype:** int.
| `model_reward_parameters` | Parameters used inside the customizable `calculate_reward()` function in `ReinforcementLearner.py` <br> **Datatype:** int.
| `add_state_info` | Tell FreqAI to include state information in the feature set for training and inferencing. The current state variables include trade duration, current profit, trade position. This is only available in dry/live runs, and is automatically switched to false for backtesting. <br> **Datatype:** bool. <br> Default: `False`.
| `net_arch` | Network architecture which is well described in [`stable_baselines3` doc](https://stable-baselines3.readthedocs.io/en/master/guide/custom_policy.html#examples). In summary: `[<shared layers>, dict(vf=[<non-shared value network layers>], pi=[<non-shared policy network layers>])]`. By default this is set to `[128, 128]`, which defines 2 shared hidden layers with 128 units each.
| `randomize_starting_position` | Randomize the starting point of each episode to avoid overfitting. <br> **Datatype:** bool. <br> Default: `False`.
### Additional parameters
| Parameter | Description |
|------------|-------------|
| | **Extraneous parameters**
| `keras` | If the selected model makes use of Keras (typical for Tensorflow-based prediction models), this flag needs to be activated so that the model save/loading follows Keras standards. <br> **Datatype:** Boolean. <br> Default: `False`.
| `conv_width` | The width of a convolutional neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. <br> **Datatype:** Integer. <br> Default: `2`.
| `reduce_df_footprint` | Recast all numeric columns to float32/int32, with the objective of reducing ram/disk usage and decreasing train/inference timing. This parameter is set in the main level of the Freqtrade configuration file (not inside FreqAI). <br> **Datatype:** Boolean. <br> Default: `False`.
| `freqai.keras` | If the selected model makes use of Keras (typical for Tensorflow-based prediction models), this flag needs to be activated so that the model save/loading follows Keras standards. <br> **Datatype:** Boolean. <br> Default: `False`.
| `freqai.conv_width` | The width of a convolutional neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. <br> **Datatype:** Integer. <br> Default: `2`.
| `freqai.reduce_df_footprint` | Recast all numeric columns to float32/int32, with the objective of reducing ram/disk usage and decreasing train/inference timing. This parameter is set in the main level of the Freqtrade configuration file (not inside FreqAI). <br> **Datatype:** Boolean. <br> Default: `False`.

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@ -0,0 +1,286 @@
# Reinforcement Learning
!!! Note "Installation size"
Reinforcement learning dependencies include large packages such as `torch`, which should be explicitly requested during `./setup.sh -i` by answering "y" to the question "Do you also want dependencies for freqai-rl (~700mb additional space required) [y/N]?".
Users who prefer docker should ensure they use the docker image appended with `_freqairl`.
## Background and terminology
### What is RL and why does FreqAI need it?
Reinforcement learning involves two important components, the *agent* and the training *environment*. During agent training, the agent moves through historical data candle by candle, always making 1 of a set of actions: Long entry, long exit, short entry, short exit, neutral). During this training process, the environment tracks the performance of these actions and rewards the agent according to a custom user made `calculate_reward()` (here we offer a default reward for users to build on if they wish [details here](#creating-a-custom-reward-function)). The reward is used to train weights in a neural network.
A second important component of the FreqAI RL implementation is the use of *state* information. State information is fed into the network at each step, including current profit, current position, and current trade duration. These are used to train the agent in the training environment, and to reinforce the agent in dry/live (this functionality is not available in backtesting). *FreqAI + Freqtrade is a perfect match for this reinforcing mechanism since this information is readily available in live deployments.*
Reinforcement learning is a natural progression for FreqAI, since it adds a new layer of adaptivity and market reactivity that Classifiers and Regressors cannot match. However, Classifiers and Regressors have strengths that RL does not have such as robust predictions. Improperly trained RL agents may find "cheats" and "tricks" to maximize reward without actually winning any trades. For this reason, RL is more complex and demands a higher level of understanding than typical Classifiers and Regressors.
### The RL interface
With the current framework, we aim to expose the training environment via the common "prediction model" file, which is a user inherited `BaseReinforcementLearner` object (e.g. `freqai/prediction_models/ReinforcementLearner`). Inside this user class, the RL environment is available and customized via `MyRLEnv` as [shown below](#creating-a-custom-reward-function).
We envision the majority of users focusing their effort on creative design of the `calculate_reward()` function [details here](#creating-a-custom-reward-function), while leaving the rest of the environment untouched. Other users may not touch the environment at all, and they will only play with the configuration settings and the powerful feature engineering that already exists in FreqAI. Meanwhile, we enable advanced users to create their own model classes entirely.
The framework is built on stable_baselines3 (torch) and OpenAI gym for the base environment class. But generally speaking, the model class is well isolated. Thus, the addition of competing libraries can be easily integrated into the existing framework. For the environment, it is inheriting from `gym.env` which means that it is necessary to write an entirely new environment in order to switch to a different library.
### Important considerations
As explained above, the agent is "trained" in an artificial trading "environment". In our case, that environment may seem quite similar to a real Freqtrade backtesting environment, but it is *NOT*. In fact, the RL training environment is much more simplified. It does not incorporate any of the complicated strategy logic, such as callbacks like `custom_exit`, `custom_stoploss`, leverage controls, etc. The RL environment is instead a very "raw" representation of the true market, where the agent has free-will to learn the policy (read: stoploss, take profit, etc.) which is enforced by the `calculate_reward()`. Thus, it is important to consider that the agent training environment is not identical to the real world.
## Running Reinforcement Learning
Setting up and running a Reinforcement Learning model is the same as running a Regressor or Classifier. The same two flags, `--freqaimodel` and `--strategy`, must be defined on the command line:
```bash
freqtrade trade --freqaimodel ReinforcementLearner --strategy MyRLStrategy --config config.json
```
where `ReinforcementLearner` will use the templated `ReinforcementLearner` from `freqai/prediction_models/ReinforcementLearner` (or a custom user defined one located in `user_data/freqaimodels`). The strategy, on the other hand, follows the same base [feature engineering](freqai-feature-engineering.md) with `populate_any_indicators` as a typical Regressor:
```python
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
# first loop is automatically duplicating indicators for time periods
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
t = int(t)
informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, window=t)
# The following raw price values are necessary for RL models
informative[f"%-{pair}raw_close"] = informative["close"]
informative[f"%-{pair}raw_open"] = informative["open"]
informative[f"%-{pair}raw_high"] = informative["high"]
informative[f"%-{pair}raw_low"] = informative["low"]
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
]
df = df.drop(columns=skip_columns)
# Add generalized indicators here (because in live, it will call this
# function to populate indicators during training). Notice how we ensure not to
# add them multiple times
if set_generalized_indicators:
# For RL, there are no direct targets to set. This is filler (neutral)
# until the agent sends an action.
df["&-action"] = 0
return df
```
Most of the function remains the same as for typical Regressors, however, the function above shows how the strategy must pass the raw price data to the agent so that it has access to raw OHLCV in the training environment:
```python
# The following features are necessary for RL models
informative[f"%-{pair}raw_close"] = informative["close"]
informative[f"%-{pair}raw_open"] = informative["open"]
informative[f"%-{pair}raw_high"] = informative["high"]
informative[f"%-{pair}raw_low"] = informative["low"]
```
Finally, there is no explicit "label" to make - instead it is necessary to assign the `&-action` column which will contain the agent's actions when accessed in `populate_entry/exit_trends()`. In the present example, the neutral action to 0. This value should align with the environment used. FreqAI provides two environments, both use 0 as the neutral action.
After users realize there are no labels to set, they will soon understand that the agent is making its "own" entry and exit decisions. This makes strategy construction rather simple. The entry and exit signals come from the agent in the form of an integer - which are used directly to decide entries and exits in the strategy:
```python
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
enter_long_conditions = [df["do_predict"] == 1, df["&-action"] == 1]
if enter_long_conditions:
df.loc[
reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
] = (1, "long")
enter_short_conditions = [df["do_predict"] == 1, df["&-action"] == 3]
if enter_short_conditions:
df.loc[
reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
] = (1, "short")
return df
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
exit_long_conditions = [df["do_predict"] == 1, df["&-action"] == 2]
if exit_long_conditions:
df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
exit_short_conditions = [df["do_predict"] == 1, df["&-action"] == 4]
if exit_short_conditions:
df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
return df
```
It is important to consider that `&-action` depends on which environment they choose to use. The example above shows 5 actions, where 0 is neutral, 1 is enter long, 2 is exit long, 3 is enter short and 4 is exit short.
## Configuring the Reinforcement Learner
In order to configure the `Reinforcement Learner` the following dictionary must exist in the `freqai` config:
```json
"rl_config": {
"train_cycles": 25,
"add_state_info": true,
"max_trade_duration_candles": 300,
"max_training_drawdown_pct": 0.02,
"cpu_count": 8,
"model_type": "PPO",
"policy_type": "MlpPolicy",
"model_reward_parameters": {
"rr": 1,
"profit_aim": 0.025
}
}
```
Parameter details can be found [here](freqai-parameter-table.md), but in general the `train_cycles` decides how many times the agent should cycle through the candle data in its artificial environment to train weights in the model. `model_type` is a string which selects one of the available models in [stable_baselines](https://stable-baselines3.readthedocs.io/en/master/)(external link).
!!! Note
If you would like to experiment with `continual_learning`, then you should set that value to `true` in the main `freqai` configuration dictionary. This will tell the Reinforcement Learning library to continue training new models from the final state of previous models, instead of retraining new models from scratch each time a retrain is initiated.
!!! Note
Remember that the general `model_training_parameters` dictionary should contain all the model hyperparameter customizations for the particular `model_type`. For example, `PPO` parameters can be found [here](https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html).
## Creating a custom reward function
As you begin to modify the strategy and the prediction model, you will quickly realize some important differences between the Reinforcement Learner and the Regressors/Classifiers. Firstly, the strategy does not set a target value (no labels!). Instead, you set the `calculate_reward()` function inside the `MyRLEnv` class (see below). A default `calculate_reward()` is provided inside `prediction_models/ReinforcementLearner.py` to demonstrate the necessary building blocks for creating rewards, but users are encouraged to create their own custom reinforcement learning model class (see below) and save it to `user_data/freqaimodels`. It is inside the `calculate_reward()` where creative theories about the market can be expressed. For example, you can reward your agent when it makes a winning trade, and penalize the agent when it makes a losing trade. Or perhaps, you wish to reward the agent for entering trades, and penalize the agent for sitting in trades too long. Below we show examples of how these rewards are all calculated:
```python
from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
class MyCoolRLModel(ReinforcementLearner):
"""
User created RL prediction model.
Save this file to `freqtrade/user_data/freqaimodels`
then use it with:
freqtrade trade --freqaimodel MyCoolRLModel --config config.json --strategy SomeCoolStrat
Here the users can override any of the functions
available in the `IFreqaiModel` inheritance tree. Most importantly for RL, this
is where the user overrides `MyRLEnv` (see below), to define custom
`calculate_reward()` function, or to override any other parts of the environment.
This class also allows users to override any other part of the IFreqaiModel tree.
For example, the user can override `def fit()` or `def train()` or `def predict()`
to take fine-tuned control over these processes.
Another common override may be `def data_cleaning_predict()` where the user can
take fine-tuned control over the data handling pipeline.
"""
class MyRLEnv(Base5ActionRLEnv):
"""
User made custom environment. This class inherits from BaseEnvironment and gym.env.
Users can override any functions from those parent classes. Here is an example
of a user customized `calculate_reward()` function.
"""
def calculate_reward(self, action: int) -> float:
# first, penalize if the action is not valid
if not self._is_valid(action):
return -2
pnl = self.get_unrealized_profit()
factor = 100
# reward agent for entering trades
if action in (Actions.Long_enter.value, Actions.Short_enter.value) \
and self._position == Positions.Neutral:
return 25
# discourage agent from not entering trades
if action == Actions.Neutral.value and self._position == Positions.Neutral:
return -1
max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
trade_duration = self._current_tick - self._last_trade_tick
if trade_duration <= max_trade_duration:
factor *= 1.5
elif trade_duration > max_trade_duration:
factor *= 0.5
# discourage sitting in position
if self._position in (Positions.Short, Positions.Long) and \
action == Actions.Neutral.value:
return -1 * trade_duration / max_trade_duration
# close long
if action == Actions.Long_exit.value and self._position == Positions.Long:
if pnl > self.profit_aim * self.rr:
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
return float(pnl * factor)
# close short
if action == Actions.Short_exit.value and self._position == Positions.Short:
if pnl > self.profit_aim * self.rr:
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
return float(pnl * factor)
return 0.
```
### Using Tensorboard
Reinforcement Learning models benefit from tracking training metrics. FreqAI has integrated Tensorboard to allow users to track training and evaluation performance across all coins and across all retrainings. Tensorboard is activated via the following command:
```bash
cd freqtrade
tensorboard --logdir user_data/models/unique-id
```
where `unique-id` is the `identifier` set in the `freqai` configuration file. This command must be run in a separate shell to view the output in their browser at 127.0.0.1:6006 (6006 is the default port used by Tensorboard).
![tensorboard](assets/tensorboard.jpg)
### Custom logging
FreqAI also provides a built in episodic summary logger called `self.tensorboard_log` for adding custom information to the Tensorboard log. By default, this function is already called once per step inside the environment to record the agent actions. All values accumulated for all steps in a single episode are reported at the conclusion of each episode, followed by a full reset of all metrics to 0 in preparation for the subsequent episode.
`self.tensorboard_log` can also be used anywhere inside the environment, for example, it can be added to the `calculate_reward` function to collect more detailed information about how often various parts of the reward were called:
```py
class MyRLEnv(Base5ActionRLEnv):
"""
User made custom environment. This class inherits from BaseEnvironment and gym.env.
Users can override any functions from those parent classes. Here is an example
of a user customized `calculate_reward()` function.
"""
def calculate_reward(self, action: int) -> float:
if not self._is_valid(action):
self.tensorboard_log("is_valid")
return -2
```
!!! Note
The `self.tensorboard_log()` function is designed for tracking incremented objects only i.e. events, actions inside the training environment. If the event of interest is a float, the float can be passed as the second argument e.g. `self.tensorboard_log("float_metric1", 0.23)` would add 0.23 to `float_metric`. In this case you can also disable incrementing using `inc=False` parameter.
### Choosing a base environment
FreqAI provides three base environments, `Base3ActionRLEnvironment`, `Base4ActionEnvironment` and `Base5ActionEnvironment`. As the names imply, the environments are customized for agents that can select from 3, 4 or 5 actions. The `Base3ActionEnvironment` is the simplest, the agent can select from hold, long, or short. This environment can also be used for long-only bots (it automatically follows the `can_short` flag from the strategy), where long is the enter condition and short is the exit condition. Meanwhile, in the `Base4ActionEnvironment`, the agent can enter long, enter short, hold neutral, or exit position. Finally, in the `Base5ActionEnvironment`, the agent has the same actions as Base4, but instead of a single exit action, it separates exit long and exit short. The main changes stemming from the environment selection include:
* the actions available in the `calculate_reward`
* the actions consumed by the user strategy
All of the FreqAI provided environments inherit from an action/position agnostic environment object called the `BaseEnvironment`, which contains all shared logic. The architecture is designed to be easily customized. The simplest customization is the `calculate_reward()` (see details [here](#creating-a-custom-reward-function)). However, the customizations can be further extended into any of the functions inside the environment. You can do this by simply overriding those functions inside your `MyRLEnv` in the prediction model file. Or for more advanced customizations, it is encouraged to create an entirely new environment inherited from `BaseEnvironment`.
!!! Note
Only the `Base3ActionRLEnv` can do long-only training/trading (set the user strategy attribute `can_short = False`).

11
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View File

@ -79,16 +79,11 @@ To change your **features**, you **must** set a new `identifier` in the config t
To save the models generated during a particular backtest so that you can start a live deployment from one of them instead of training a new model, you must set `save_backtest_models` to `True` in the config.
### Backtest live models
### Backtest live collected predictions
FreqAI allow you to reuse ready models through the backtest parameter `--freqai-backtest-live-models`. This can be useful when you want to reuse models generated in dry/run for comparison or other study. For that, you must set `"purge_old_models"` to `True` in the config.
FreqAI allow you to reuse live historic predictions through the backtest parameter `--freqai-backtest-live-models`. This can be useful when you want to reuse predictions generated in dry/run for comparison or other study.
The `--timerange` parameter must not be informed, as it will be automatically calculated through the training end dates of the models.
Each model has an identifier derived from the training end date. If you have only 1 model trained, FreqAI will backtest from the training end date until the current date. If you have more than 1 model, each model will perform the backtesting according to the training end date until the training end date of the next model and so on. For the last model, the period of the previous model will be used for the execution.
!!! Note
Currently, there is no checking for expired models, even if the `expired_hours` parameter is set.
The `--timerange` parameter must not be informed, as it will be automatically calculated through the data in the historic predictions file.
### Downloading data to cover the full backtest period

20
docs/freqai.md
View File

@ -72,11 +72,25 @@ pip install -r requirements-freqai.txt
If you are using docker, a dedicated tag with FreqAI dependencies is available as `:freqai`. As such - you can replace the image line in your docker-compose file with `image: freqtradeorg/freqtrade:develop_freqai`. This image contains the regular FreqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices.
### FreqAI position in open-source machine learning landscape
Forecasting chaotic time-series based systems, such as equity/cryptocurrency markets, requires a broad set of tools geared toward testing a wide range of hypotheses. Fortunately, a recent maturation of robust machine learning libraries (e.g. `scikit-learn`) has opened up a wide range of research possibilities. Scientists from a diverse range of fields can now easily prototype their studies on an abundance of established machine learning algorithms. Similarly, these user-friendly libraries enable "citzen scientists" to use their basic Python skills for data-exploration. However, leveraging these machine learning libraries on historical and live chaotic data sources can be logistically difficult and expensive. Additionally, robust data-collection, storage, and handling presents a disparate challenge. [`FreqAI`](#freqai) aims to provide a generalized and extensible open-sourced framework geared toward live deployments of adaptive modeling for market forecasting. The `FreqAI` framework is effectively a sandbox for the rich world of open-source machine learning libraries. Inside the `FreqAI` sandbox, users find they can combine a wide variety of third-party libraries to test creative hypotheses on a free live 24/7 chaotic data source - cryptocurrency exchange data.
### Citing FreqAI
FreqAI is [published in the Journal of Open Source Software](https://joss.theoj.org/papers/10.21105/joss.04864). If you find FreqAI useful in your research, please use the following citation:
```bibtex
@article{Caulk2022,
doi = {10.21105/joss.04864},
url = {https://doi.org/10.21105/joss.04864},
year = {2022}, publisher = {The Open Journal},
volume = {7}, number = {80}, pages = {4864},
author = {Robert A. Caulk and Elin Törnquist and Matthias Voppichler and Andrew R. Lawless and Ryan McMullan and Wagner Costa Santos and Timothy C. Pogue and Johan van der Vlugt and Stefan P. Gehring and Pascal Schmidt},
title = {FreqAI: generalizing adaptive modeling for chaotic time-series market forecasts},
journal = {Journal of Open Source Software} }
```
## Common pitfalls
FreqAI cannot be combined with dynamic `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically).
@ -99,6 +113,8 @@ Code review and software architecture brainstorming:
Software development:
Wagner Costa @wagnercosta
Emre Suzen @aemr3
Timothy Pogue @wizrds
Beta testing and bug reporting:
Stefan Gehring @bloodhunter4rc, @longyu, Andrew Lawless @paranoidandy, Pascal Schmidt @smidelis, Ryan McMullan @smarmau, Juha Nykänen @suikula, Johan van der Vlugt @jooopiert, Richárd Józsa @richardjosza, Timothy Pogue @wizrds
Stefan Gehring @bloodhunter4rc, @longyu, Andrew Lawless @paranoidandy, Pascal Schmidt @smidelis, Ryan McMullan @smarmau, Juha Nykänen @suikula, Johan van der Vlugt @jooopiert, Richárd Józsa @richardjosza

43
docs/includes/pairlists.md
View File

@ -23,6 +23,7 @@ You may also use something like `.*DOWN/BTC` or `.*UP/BTC` to exclude leveraged
* [`StaticPairList`](#static-pair-list) (default, if not configured differently)
* [`VolumePairList`](#volume-pair-list)
* [`ProducerPairList`](#producerpairlist)
* [`RemotePairList`](#remotepairlist)
* [`AgeFilter`](#agefilter)
* [`OffsetFilter`](#offsetfilter)
* [`PerformanceFilter`](#performancefilter)
@ -173,6 +174,48 @@ You can limit the length of the pairlist with the optional parameter `number_ass
`ProducerPairList` can also be used multiple times in sequence, combining the pairs from multiple producers.
Obviously in complex such configurations, the Producer may not provide data for all pairs, so the strategy must be fit for this.
#### RemotePairList
It allows the user to fetch a pairlist from a remote server or a locally stored json file within the freqtrade directory, enabling dynamic updates and customization of the trading pairlist.
The RemotePairList is defined in the pairlists section of the configuration settings. It uses the following configuration options:
```json
"pairlists": [
{
"method": "RemotePairList",
"pairlist_url": "https://example.com/pairlist",
"number_assets": 10,
"refresh_period": 1800,
"keep_pairlist_on_failure": true,
"read_timeout": 60,
"bearer_token": "my-bearer-token"
}
]
```
The `pairlist_url` option specifies the URL of the remote server where the pairlist is located, or the path to a local file (if file:/// is prepended). This allows the user to use either a remote server or a local file as the source for the pairlist.
The user is responsible for providing a server or local file that returns a JSON object with the following structure:
```json
{
"pairs": ["XRP/USDT", "ETH/USDT", "LTC/USDT"],
"refresh_period": 1800,
}
```
The `pairs` property should contain a list of strings with the trading pairs to be used by the bot. The `refresh_period` property is optional and specifies the number of seconds that the pairlist should be cached before being refreshed.
The optional `keep_pairlist_on_failure` specifies whether the previous received pairlist should be used if the remote server is not reachable or returns an error. The default value is true.
The optional `read_timeout` specifies the maximum amount of time (in seconds) to wait for a response from the remote source, The default value is 60.
The optional `bearer_token` will be included in the requests Authorization Header.
!!! Note
In case of a server error the last received pairlist will be kept if `keep_pairlist_on_failure` is set to true, when set to false a empty pairlist is returned.
#### AgeFilter
Removes pairs that have been listed on the exchange for less than `min_days_listed` days (defaults to `10`) or more than `max_days_listed` days (defaults `None` mean infinity).

1
docs/index.md
View File

@ -1,6 +1,7 @@
![freqtrade](assets/freqtrade_poweredby.svg)
[![Freqtrade CI](https://github.com/freqtrade/freqtrade/workflows/Freqtrade%20CI/badge.svg)](https://github.com/freqtrade/freqtrade/actions/)
[![DOI](https://joss.theoj.org/papers/10.21105/joss.04864/status.svg)](https://doi.org/10.21105/joss.04864)
[![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)

24
docs/overrides/main.html
View File

@ -11,9 +11,6 @@
{% endif %}
<div class="md-sidebar md-sidebar--primary" data-md-component="sidebar" data-md-type="navigation" {{ hidden }}>
<div class="md-sidebar__scrollwrap">
<div id="widget-wrapper">
</div>
<div class="md-sidebar__inner">
{% include "partials/nav.html" %}
</div>
@ -44,25 +41,4 @@
<script src="https://code.jquery.com/jquery-3.4.1.min.js"
integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script>
<!-- Load binance SDK -->
<script async defer src="https://public.bnbstatic.com/static/js/broker-sdk/broker-sdk@1.0.0.min.js"></script>
<script>
window.onload = function () {
var sidebar = document.getElementById('widget-wrapper')
var newDiv = document.createElement("div");
newDiv.id = "widget";
try {
sidebar.prepend(newDiv);
window.binanceBrokerPortalSdk.initBrokerSDK('#widget', {
apiHost: 'https://www.binance.com',
brokerId: 'R4BD3S82',
slideTime: 4e4,
});
} catch(err) {
console.log(err)
}
}
</script>
{% endblock %}

4
docs/requirements-docs.txt
View File

@ -1,6 +1,6 @@
markdown==3.3.7
mkdocs==1.4.2
mkdocs-material==8.5.10
mkdocs-material==8.5.11
mdx_truly_sane_lists==1.3
pymdown-extensions==9.8
pymdown-extensions==9.9
jinja2==3.1.2

4
docs/sql_cheatsheet.md
View File

@ -13,12 +13,12 @@ Feel free to use a visual Database editor like SqliteBrowser if you feel more co
sudo apt-get install sqlite3
```
### Using sqlite3 via docker-compose
### Using sqlite3 via docker
The freqtrade docker image does contain sqlite3, so you can edit the database without having to install anything on the host system.
``` bash
docker-compose exec freqtrade /bin/bash
docker compose exec freqtrade /bin/bash
sqlite3 <database-file>.sqlite
```

2
docs/strategy-callbacks.md
View File

@ -773,7 +773,7 @@ class DigDeeperStrategy(IStrategy):
* Sell 100@10\$ -> Avg price: 8.5\$, realized profit 150\$, 17.65%
* Buy 150@11\$ -> Avg price: 10\$, realized profit 150\$, 17.65%
* Sell 100@12\$ -> Avg price: 10\$, total realized profit 350\$, 20%
* Sell 150@14\$ -> Avg price: 10\$, total realized profit 950\$, 40%
* Sell 150@14\$ -> Avg price: 10\$, total realized profit 950\$, 40% <- *This will be the last "Exit" message*
The total profit for this trade was 950$ on a 3350$ investment (`100@8$ + 100@9$ + 150@11$`). As such - the final relative profit is 28.35% (`950 / 3350`).

38
docs/strategy-customization.md
View File

@ -364,8 +364,8 @@ class AwesomeStrategy(IStrategy):
timeframe_mins = timeframe_to_minutes(timeframe)
minimal_roi = {
"0": 0.05, # 5% for the first 3 candles
str(timeframe_mins * 3)): 0.02, # 2% after 3 candles
str(timeframe_mins * 6)): 0.01, # 1% After 6 candles
str(timeframe_mins * 3): 0.02, # 2% after 3 candles
str(timeframe_mins * 6): 0.01, # 1% After 6 candles
}
```
@ -989,38 +989,18 @@ 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.is_(False),
trades = Trade.get_trades_proxy(pair=metadata['pair'],
open_date=datetime.now(timezone.utc) - timedelta(days=1),
is_open=False,
]).order_by(Trade.close_date).all()
# Summarize profit for this pair.
curdayprofit = sum(trade.close_profit for trade in trades)
# 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}
```
For a full list of available methods, please consult the [Trade object](trade-object.md) documentation.
!!! Warning
Trade history is not available during backtesting or hyperopt.
Trade history is not available in `populate_*` methods during backtesting or hyperopt, and will result in empty results.
## Prevent trades from happening for a specific pair

35
docs/strategy_analysis_example.md
View File

@ -2,12 +2,37 @@
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.
Please follow the [documentation](https://www.freqtrade.io/en/stable/data-download/) for more details.
## Setup
### Change Working directory to repository root
```python
import os
from pathlib import Path
# Change directory
# Modify this cell to insure that the output shows the correct path.
# Define all paths relative to the project root shown in the cell output
project_root = "somedir/freqtrade"
i=0
try:
os.chdirdir(project_root)
assert Path('LICENSE').is_file()
except:
while i<4 and (not Path('LICENSE').is_file()):
os.chdir(Path(Path.cwd(), '../'))
i+=1
project_root = Path.cwd()
print(Path.cwd())
```
### Configure Freqtrade environment
```python
from freqtrade.configuration import Configuration
# Customize these according to your needs.
@ -15,14 +40,14 @@ from freqtrade.configuration import Configuration
# Initialize empty configuration object
config = Configuration.from_files([])
# Optionally (recommended), use existing configuration file
# config = Configuration.from_files(["config.json"])
# config = Configuration.from_files(["user_data/config.json"])
# Define some constants
config["timeframe"] = "5m"
# Name of the strategy class
config["strategy"] = "SampleStrategy"
# Location of the data
data_location = config['datadir']
data_location = config["datadir"]
# Pair to analyze - Only use one pair here
pair = "BTC/USDT"
```
@ -36,12 +61,12 @@ from freqtrade.enums import CandleType
candles = load_pair_history(datadir=data_location,
timeframe=config["timeframe"],
pair=pair,
data_format = "hdf5",
data_format = "json", # Make sure to update this to your data
candle_type=CandleType.SPOT,
)
# Confirm success
print("Loaded " + str(len(candles)) + f" rows of data for {pair} from {data_location}")
print(f"Loaded {len(candles)} rows of data for {pair} from {data_location}")
candles.head()
```
@ -232,7 +257,7 @@ graph = generate_candlestick_graph(pair=pair,
# Show graph inline
# graph.show()
# Render graph in a seperate window
# Render graph in a separate window
graph.show(renderer="browser")
```

15
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View File

@ -11,18 +11,3 @@
.rst-versions .rst-other-versions {
color: white;
}
#widget-wrapper {
height: calc(220px * 0.5625 + 18px);
width: 220px;
margin: 0 auto 16px auto;
border-style: solid;
border-color: var(--md-code-bg-color);
border-width: 1px;
border-radius: 5px;
}
@media screen and (max-width: calc(76.25em - 1px)) {
#widget-wrapper { display: none; }
}

148
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View File

@ -0,0 +1,148 @@
# Trade Object
## Trade
A position freqtrade enters is stored in a `Trade` object - which is persisted to the database.
It's a core concept of freqtrade - and something you'll come across in many sections of the documentation, which will most likely point you to this location.
It will be passed to the strategy in many [strategy callbacks](strategy-callbacks.md). The object passed to the strategy cannot be modified directly. Indirect modifications may occur based on callback results.
## Trade - Available attributes
The following attributes / properties are available for each individual trade - and can be used with `trade.<property>` (e.g. `trade.pair`).
| Attribute | DataType | Description |
|------------|-------------|-------------|
`pair`| string | Pair of this trade
`is_open`| boolean | Is the trade currently open, or has it been concluded
`open_rate`| float | Rate this trade was entered at (Avg. entry rate in case of trade-adjustments)
`close_rate`| float | Close rate - only set when is_open = False
`stake_amount`| float | Amount in Stake (or Quote) currency.
`amount`| float | Amount in Asset / Base currency that is currently owned.
`open_date`| datetime | Timestamp when trade was opened **use `open_date_utc` instead**
`open_date_utc`| datetime | Timestamp when trade was opened - in UTC
`close_date`| datetime | Timestamp when trade was closed **use `close_date_utc` instead**
`close_date_utc`| datetime | Timestamp when trade was closed - in UTC
`close_profit`| float | Relative profit at the time of trade closure. `0.01` == 1%
`close_profit_abs`| float | Absolute profit (in stake currency) at the time of trade closure.
`leverage` | float | Leverage used for this trade - defaults to 1.0 in spot markets.
`enter_tag`| string | Tag provided on entry via the `enter_tag` column in the dataframe
`is_short` | boolean | True for short trades, False otherwise
`orders` | Order[] | List of order objects attached to this trade (includes both filled and cancelled orders)
`date_last_filled_utc` | datetime | Time of the last filled order
`entry_side` | "buy" / "sell" | Order Side the trade was entered
`exit_side` | "buy" / "sell" | Order Side that will result in a trade exit / position reduction.
`trade_direction` | "long" / "short" | Trade direction in text - long or short.
`nr_of_successful_entries` | int | Number of successful (filled) entry orders
`nr_of_successful_exits` | int | Number of successful (filled) exit orders
## Class methods
The following are class methods - which return generic information, and usually result in an explicit query against the database.
They can be used as `Trade.<method>` - e.g. `open_trades = Trade.get_open_trade_count()`
!!! Warning "Backtesting/hyperopt"
Most methods will work in both backtesting / hyperopt and live/dry modes.
During backtesting, it's limited to usage in [strategy callbacks](strategy-callbacks.md). Usage in `populate_*()` methods is not supported and will result in wrong results.
### get_trades_proxy
When your strategy needs some information on existing (open or close) trades - it's best to use `Trade.get_trades_proxy()`.
Usage:
``` python
from freqtrade.persistence import Trade
from datetime import timedelta
# ...
trade_hist = Trade.get_trades_proxy(pair='ETH/USDT', is_open=False, open_date=current_date - timedelta(days=2))
```
`get_trades_proxy()` supports the following keyword arguments. All arguments are optional - calling `get_trades_proxy()` without arguments will return a list of all trades in the database.
* `pair` e.g. `pair='ETH/USDT'`
* `is_open` e.g. `is_open=False`
* `open_date` e.g. `open_date=current_date - timedelta(days=2)`
* `close_date` e.g. `close_date=current_date - timedelta(days=5)`
### get_open_trade_count
Get the number of currently open trades
``` python
from freqtrade.persistence import Trade
# ...
open_trades = Trade.get_open_trade_count()
```
### get_total_closed_profit
Retrieve the total profit the bot has generated so far.
Aggregates `close_profit_abs` for all closed trades.
``` python
from freqtrade.persistence import Trade
# ...
profit = Trade.get_total_closed_profit()
```
### total_open_trades_stakes
Retrieve the total stake_amount that's currently in trades.
``` python
from freqtrade.persistence import Trade
# ...
profit = Trade.total_open_trades_stakes()
```
### get_overall_performance
Retrieve the overall performance - similar to the `/performance` telegram command.
``` python
from freqtrade.persistence import Trade
# ...
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}
```
## Order Object
An `Order` object represents an order on the exchange (or a simulated order in dry-run mode).
An `Order` object will always be tied to it's corresponding [`Trade`](#trade-object), and only really makes sense in the context of a trade.
### Order - Available attributes
an Order object is typically attached to a trade.
Most properties here can be None as they are dependant on the exchange response.
| Attribute | DataType | Description |
|------------|-------------|-------------|
`trade` | Trade | Trade object this order is attached to
`ft_pair` | string | Pair this order is for
`ft_is_open` | boolean | is the order filled?
`order_type` | string | Order type as defined on the exchange - usually market, limit or stoploss
`status` | string | Status as defined by ccxt. Usually open, closed, expired or canceled
`side` | string | Buy or Sell
`price` | float | Price the order was placed at
`average` | float | Average price the order filled at
`amount` | float | Amount in base currency
`filled` | float | Filled amount (in base currency)
`remaining` | float | Remaining amount
`cost` | float | Cost of the order - usually average * filled
`order_date` | datetime | Order creation date **use `order_date_utc` instead**
`order_date_utc` | datetime | Order creation date (in UTC)
`order_fill_date` | datetime | Order fill date **use `order_fill_utc` instead**
`order_fill_date_utc` | datetime | Order fill date

6
docs/updating.md
View File

@ -6,14 +6,14 @@ To update your freqtrade installation, please use one of the below methods, corr
Breaking changes / changed behavior will be documented in the changelog that is posted alongside every release.
For the develop branch, please follow PR's to avoid being surprised by changes.
## docker-compose
## docker
!!! Note "Legacy installations using the `master` image"
We're switching from master to stable for the release Images - please adjust your docker-file and replace `freqtradeorg/freqtrade:master` with `freqtradeorg/freqtrade:stable`
``` bash
docker-compose pull
docker-compose up -d
docker compose pull
docker compose up -d
```
## Installation via setup script

9
docs/utils.md
View File

@ -652,7 +652,7 @@ Common arguments:
You can also use webserver mode via docker.
Starting a one-off container requires the configuration of the port explicitly, as ports are not exposed by default.
You can use `docker-compose run --rm -p 127.0.0.1:8080:8080 freqtrade webserver` to start a one-off container that'll be removed once you stop it. This assumes that port 8080 is still available and no other bot is running on that port.
You can use `docker compose run --rm -p 127.0.0.1:8080:8080 freqtrade webserver` to start a one-off container that'll be removed once you stop it. This assumes that port 8080 is still available and no other bot is running on that port.
Alternatively, you can reconfigure the docker-compose file to have the command updated:
@ -662,7 +662,7 @@ Alternatively, you can reconfigure the docker-compose file to have the command u
--config /freqtrade/user_data/config.json
```
You can now use `docker-compose up` to start the webserver.
You can now use `docker compose up` to start the webserver.
This assumes that the configuration has a webserver enabled and configured for docker (listening port = `0.0.0.0`).
!!! Tip
@ -722,6 +722,7 @@ usage: freqtrade backtesting-analysis [-h] [-v] [--logfile FILE] [-V]
[--enter-reason-list ENTER_REASON_LIST [ENTER_REASON_LIST ...]]
[--exit-reason-list EXIT_REASON_LIST [EXIT_REASON_LIST ...]]
[--indicator-list INDICATOR_LIST [INDICATOR_LIST ...]]
[--timerange YYYYMMDD-[YYYYMMDD]]
optional arguments:
-h, --help show this help message and exit
@ -744,6 +745,10 @@ optional arguments:
--indicator-list INDICATOR_LIST [INDICATOR_LIST ...]
Comma separated list of indicators to analyse. e.g.
'close,rsi,bb_lowerband,profit_abs'
--timerange YYYYMMDD-[YYYYMMDD]
Timerange to filter trades for analysis,
start inclusive, end exclusive. e.g.
20220101-20220201
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).

2
freqtrade/__init__.py
View File

@ -1,5 +1,5 @@
""" Freqtrade bot """
__version__ = '2022.11'
__version__ = '2022.12'
if 'dev' in __version__:
try:

8
freqtrade/commands/analyze_commands.py
View File

@ -60,10 +60,4 @@ def start_analysis_entries_exits(args: Dict[str, Any]) -> None:
logger.info('Starting freqtrade in analysis mode')
process_entry_exit_reasons(config['exportfilename'],
config['exchange']['pair_whitelist'],
config['analysis_groups'],
config['enter_reason_list'],
config['exit_reason_list'],
config['indicator_list']
)
process_entry_exit_reasons(config)

2
freqtrade/commands/arguments.py
View File

@ -106,7 +106,7 @@ ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperop
"disableparamexport", "backtest_breakdown"]
ARGS_ANALYZE_ENTRIES_EXITS = ["exportfilename", "analysis_groups", "enter_reason_list",
"exit_reason_list", "indicator_list"]
"exit_reason_list", "indicator_list", "timerange"]
NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
"list-markets", "list-pairs", "list-strategies", "list-freqaimodels",

7
freqtrade/configuration/config_validation.py
View File

@ -355,6 +355,13 @@ def _validate_freqai_include_timeframes(conf: Dict[str, Any]) -> None:
f"Main timeframe of {main_tf} must be smaller or equal to FreqAI "
f"`include_timeframes`.Offending include-timeframes: {', '.join(offending_lines)}")
# Ensure that the base timeframe is included in the include_timeframes list
if main_tf not in freqai_include_timeframes:
feature_parameters = conf.get('freqai', {}).get('feature_parameters', {})
include_timeframes = [main_tf] + freqai_include_timeframes
conf.get('freqai', {}).get('feature_parameters', {}) \
.update({**feature_parameters, 'include_timeframes': include_timeframes})
def _validate_freqai_backtest(conf: Dict[str, Any]) -> None:
if conf.get('runmode', RunMode.OTHER) == RunMode.BACKTEST:

3
freqtrade/configuration/configuration.py
View File

@ -462,6 +462,9 @@ class Configuration:
self._args_to_config(config, argname='indicator_list',
logstring='Analysis indicator list: {}')
self._args_to_config(config, argname='timerange',
logstring='Filter trades by timerange: {}')
def _process_runmode(self, config: Config) -> None:
self._args_to_config(config, argname='dry_run',

26
freqtrade/constants.py
View File

@ -31,7 +31,7 @@ HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss',
'CalmarHyperOptLoss',
'MaxDrawDownHyperOptLoss', 'MaxDrawDownRelativeHyperOptLoss',
'ProfitDrawDownHyperOptLoss']
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList', 'ProducerPairList',
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList', 'ProducerPairList', 'RemotePairList',
'AgeFilter', 'OffsetFilter', 'PerformanceFilter',
'PrecisionFilter', 'PriceFilter', 'RangeStabilityFilter',
'ShuffleFilter', 'SpreadFilter', 'VolatilityFilter']
@ -61,6 +61,7 @@ USERPATH_FREQAIMODELS = 'freqaimodels'
TELEGRAM_SETTING_OPTIONS = ['on', 'off', 'silent']
WEBHOOK_FORMAT_OPTIONS = ['form', 'json', 'raw']
FULL_DATAFRAME_THRESHOLD = 100
ENV_VAR_PREFIX = 'FREQTRADE__'
@ -578,9 +579,27 @@ CONF_SCHEMA = {
},
},
"model_training_parameters": {
"type": "object"
},
"rl_config": {
"type": "object",
"properties": {
"n_estimators": {"type": "integer", "default": 1000}
"train_cycles": {"type": "integer"},
"max_trade_duration_candles": {"type": "integer"},
"add_state_info": {"type": "boolean", "default": False},
"max_training_drawdown_pct": {"type": "number", "default": 0.02},
"cpu_count": {"type": "integer", "default": 1},
"model_type": {"type": "string", "default": "PPO"},
"policy_type": {"type": "string", "default": "MlpPolicy"},
"net_arch": {"type": "array", "default": [128, 128]},
"randomize_startinng_position": {"type": "boolean", "default": False},
"model_reward_parameters": {
"type": "object",
"properties": {
"rr": {"type": "number", "default": 1},
"profit_aim": {"type": "number", "default": 0.025}
}
}
},
},
},
@ -590,8 +609,7 @@ CONF_SCHEMA = {
"backtest_period_days",
"identifier",
"feature_parameters",
"data_split_parameters",
"model_training_parameters"
"data_split_parameters"
]
},
},

50
freqtrade/data/btanalysis.py
View File

@ -20,8 +20,8 @@ from freqtrade.persistence import LocalTrade, Trade, init_db
logger = logging.getLogger(__name__)
# Newest format
BT_DATA_COLUMNS = ['pair', 'stake_amount', 'amount', 'open_date', 'close_date',
'open_rate', 'close_rate',
BT_DATA_COLUMNS = ['pair', 'stake_amount', 'max_stake_amount', 'amount',
'open_date', 'close_date', 'open_rate', 'close_rate',
'fee_open', 'fee_close', 'trade_duration',
'profit_ratio', 'profit_abs', 'exit_reason',
'initial_stop_loss_abs', 'initial_stop_loss_ratio', 'stop_loss_abs',
@ -241,6 +241,33 @@ def find_existing_backtest_stats(dirname: Union[Path, str], run_ids: Dict[str, s
return results
def _load_backtest_data_df_compatibility(df: pd.DataFrame) -> pd.DataFrame:
"""
Compatibility support for older backtest data.
"""
df['open_date'] = pd.to_datetime(df['open_date'],
utc=True,
infer_datetime_format=True
)
df['close_date'] = pd.to_datetime(df['close_date'],
utc=True,
infer_datetime_format=True
)
# Compatibility support for pre short Columns
if 'is_short' not in df.columns:
df['is_short'] = False
if 'leverage' not in df.columns:
df['leverage'] = 1.0
if 'enter_tag' not in df.columns:
df['enter_tag'] = df['buy_tag']
df = df.drop(['buy_tag'], axis=1)
if 'max_stake_amount' not in df.columns:
df['max_stake_amount'] = df['stake_amount']
if 'orders' not in df.columns:
df['orders'] = None
return df
def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = None) -> pd.DataFrame:
"""
Load backtest data file.
@ -269,24 +296,7 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
data = data['strategy'][strategy]['trades']
df = pd.DataFrame(data)
if not df.empty:
df['open_date'] = pd.to_datetime(df['open_date'],
utc=True,
infer_datetime_format=True
)
df['close_date'] = pd.to_datetime(df['close_date'],
utc=True,
infer_datetime_format=True
)
# Compatibility support for pre short Columns
if 'is_short' not in df.columns:
df['is_short'] = 0
if 'leverage' not in df.columns:
df['leverage'] = 1.0
if 'enter_tag' not in df.columns:
df['enter_tag'] = df['buy_tag']
df = df.drop(['buy_tag'], axis=1)
if 'orders' not in df.columns:
df['orders'] = None
df = _load_backtest_data_df_compatibility(df)
else:
# old format - only with lists.

100
freqtrade/data/dataprovider.py
View File

@ -9,14 +9,16 @@ from collections import deque
from datetime import datetime, timezone
from typing import Any, Dict, List, Optional, Tuple
from pandas import DataFrame
from pandas import DataFrame, to_timedelta
from freqtrade.configuration import TimeRange
from freqtrade.constants import Config, ListPairsWithTimeframes, PairWithTimeframe
from freqtrade.constants import (FULL_DATAFRAME_THRESHOLD, Config, ListPairsWithTimeframes,
PairWithTimeframe)
from freqtrade.data.history import load_pair_history
from freqtrade.enums import CandleType, RPCMessageType, RunMode
from freqtrade.exceptions import ExchangeError, OperationalException
from freqtrade.exchange import Exchange, timeframe_to_seconds
from freqtrade.misc import append_candles_to_dataframe
from freqtrade.rpc import RPCManager
from freqtrade.util import PeriodicCache
@ -104,13 +106,15 @@ class DataProvider:
def _emit_df(
self,
pair_key: PairWithTimeframe,
dataframe: DataFrame
dataframe: DataFrame,
new_candle: bool
) -> None:
"""
Send this dataframe as an ANALYZED_DF message to RPC
:param pair_key: PairWithTimeframe tuple
:param data: Tuple containing the DataFrame and the datetime it was cached
:param dataframe: Dataframe to emit
:param new_candle: This is a new candle
"""
if self.__rpc:
self.__rpc.send_msg(
@ -118,13 +122,18 @@ class DataProvider:
'type': RPCMessageType.ANALYZED_DF,
'data': {
'key': pair_key,
'df': dataframe,
'df': dataframe.tail(1),
'la': datetime.now(timezone.utc)
}
}
)
if new_candle:
self.__rpc.send_msg({
'type': RPCMessageType.NEW_CANDLE,
'data': pair_key,
})
def _add_external_df(
def _replace_external_df(
self,
pair: str,
dataframe: DataFrame,
@ -150,6 +159,85 @@ class DataProvider:
self.__producer_pairs_df[producer_name][pair_key] = (dataframe, _last_analyzed)
logger.debug(f"External DataFrame for {pair_key} from {producer_name} added.")
def _add_external_df(
self,
pair: str,
dataframe: DataFrame,
last_analyzed: datetime,
timeframe: str,
candle_type: CandleType,
producer_name: str = "default"
) -> Tuple[bool, int]:
"""
Append a candle to the existing external dataframe. The incoming dataframe
must have at least 1 candle.
:param pair: pair to get the data for
:param timeframe: Timeframe to get data for
:param candle_type: Any of the enum CandleType (must match trading mode!)
:returns: False if the candle could not be appended, or the int number of missing candles.
"""
pair_key = (pair, timeframe, candle_type)
if dataframe.empty:
# The incoming dataframe must have at least 1 candle
return (False, 0)
if len(dataframe) >= FULL_DATAFRAME_THRESHOLD:
# This is likely a full dataframe
# Add the dataframe to the dataprovider
self._replace_external_df(
pair,
dataframe,
last_analyzed=last_analyzed,
timeframe=timeframe,
candle_type=candle_type,
producer_name=producer_name
)
return (True, 0)
if (producer_name not in self.__producer_pairs_df
or pair_key not in self.__producer_pairs_df[producer_name]):
# We don't have data from this producer yet,
# or we don't have data for this pair_key
# return False and 1000 for the full df
return (False, 1000)
existing_df, _ = self.__producer_pairs_df[producer_name][pair_key]
# CHECK FOR MISSING CANDLES
timeframe_delta = to_timedelta(timeframe) # Convert the timeframe to a timedelta for pandas
local_last = existing_df.iloc[-1]['date'] # We want the last date from our copy
incoming_first = dataframe.iloc[0]['date'] # We want the first date from the incoming
# Remove existing candles that are newer than the incoming first candle
existing_df1 = existing_df[existing_df['date'] < incoming_first]
candle_difference = (incoming_first - local_last) / timeframe_delta
# If the difference divided by the timeframe is 1, then this
# is the candle we want and the incoming data isn't missing any.
# If the candle_difference is more than 1, that means
# we missed some candles between our data and the incoming
# so return False and candle_difference.
if candle_difference > 1:
return (False, candle_difference)
if existing_df1.empty:
appended_df = dataframe
else:
appended_df = append_candles_to_dataframe(existing_df1, dataframe)
# Everything is good, we appended
self._replace_external_df(
pair,
appended_df,
last_analyzed=last_analyzed,
timeframe=timeframe,
candle_type=candle_type,
producer_name=producer_name
)
return (True, 0)
def get_producer_df(
self,
pair: str,

92
freqtrade/data/entryexitanalysis.py
View File

@ -1,11 +1,12 @@
import logging
from pathlib import Path
from typing import List, Optional
import joblib
import pandas as pd
from tabulate import tabulate
from freqtrade.configuration import TimeRange
from freqtrade.constants import Config
from freqtrade.data.btanalysis import (get_latest_backtest_filename, load_backtest_data,
load_backtest_stats)
from freqtrade.exceptions import OperationalException
@ -152,37 +153,55 @@ def _do_group_table_output(bigdf, glist):
logger.warning("Invalid group mask specified.")
def _print_results(analysed_trades, stratname, analysis_groups,
enter_reason_list, exit_reason_list,
indicator_list, columns=None):
if columns is None:
columns = ['pair', 'open_date', 'close_date', 'profit_abs', 'enter_reason', 'exit_reason']
def _select_rows_within_dates(df, timerange=None, df_date_col: str = 'date'):
if timerange:
if timerange.starttype == 'date':
df = df.loc[(df[df_date_col] >= timerange.startdt)]
if timerange.stoptype == 'date':
df = df.loc[(df[df_date_col] < timerange.stopdt)]
return df
bigdf = pd.DataFrame()
for pair, trades in analysed_trades[stratname].items():
bigdf = pd.concat([bigdf, trades], ignore_index=True)
if bigdf.shape[0] > 0 and ('enter_reason' in bigdf.columns):
if analysis_groups:
_do_group_table_output(bigdf, analysis_groups)
def _select_rows_by_tags(df, enter_reason_list, exit_reason_list):
if enter_reason_list and "all" not in enter_reason_list:
bigdf = bigdf.loc[(bigdf['enter_reason'].isin(enter_reason_list))]
df = df.loc[(df['enter_reason'].isin(enter_reason_list))]
if exit_reason_list and "all" not in exit_reason_list:
bigdf = bigdf.loc[(bigdf['exit_reason'].isin(exit_reason_list))]
df = df.loc[(df['exit_reason'].isin(exit_reason_list))]
return df
def prepare_results(analysed_trades, stratname,
enter_reason_list, exit_reason_list,
timerange=None):
res_df = pd.DataFrame()
for pair, trades in analysed_trades[stratname].items():
res_df = pd.concat([res_df, trades], ignore_index=True)
res_df = _select_rows_within_dates(res_df, timerange)
if res_df is not None and res_df.shape[0] > 0 and ('enter_reason' in res_df.columns):
res_df = _select_rows_by_tags(res_df, enter_reason_list, exit_reason_list)
return res_df
def print_results(res_df, analysis_groups, indicator_list):
if res_df.shape[0] > 0:
if analysis_groups:
_do_group_table_output(res_df, analysis_groups)
if "all" in indicator_list:
print(bigdf)
print(res_df)
elif indicator_list is not None:
available_inds = []
for ind in indicator_list:
if ind in bigdf:
if ind in res_df:
available_inds.append(ind)
ilist = ["pair", "enter_reason", "exit_reason"] + available_inds
_print_table(bigdf[ilist], sortcols=['exit_reason'], show_index=False)
_print_table(res_df[ilist], sortcols=['exit_reason'], show_index=False)
else:
print("\\_ No trades to show")
print("\\No trades to show")
def _print_table(df, sortcols=None, show_index=False):
@ -201,26 +220,33 @@ def _print_table(df, sortcols=None, show_index=False):
)
def process_entry_exit_reasons(backtest_dir: Path,
pairlist: List[str],
analysis_groups: Optional[List[str]] = ["0", "1", "2"],
enter_reason_list: Optional[List[str]] = ["all"],
exit_reason_list: Optional[List[str]] = ["all"],
indicator_list: Optional[List[str]] = []):
def process_entry_exit_reasons(config: Config):
try:
backtest_stats = load_backtest_stats(backtest_dir)
analysis_groups = config.get('analysis_groups', [])
enter_reason_list = config.get('enter_reason_list', ["all"])
exit_reason_list = config.get('exit_reason_list', ["all"])
indicator_list = config.get('indicator_list', [])
timerange = TimeRange.parse_timerange(None if config.get(
'timerange') is None else str(config.get('timerange')))
backtest_stats = load_backtest_stats(config['exportfilename'])
for strategy_name, results in backtest_stats['strategy'].items():
trades = load_backtest_data(backtest_dir, strategy_name)
trades = load_backtest_data(config['exportfilename'], strategy_name)
if not trades.empty:
signal_candles = _load_signal_candles(backtest_dir)
analysed_trades_dict = _process_candles_and_indicators(pairlist, strategy_name,
signal_candles = _load_signal_candles(config['exportfilename'])
analysed_trades_dict = _process_candles_and_indicators(
config['exchange']['pair_whitelist'], strategy_name,
trades, signal_candles)
_print_results(analysed_trades_dict,
strategy_name,
res_df = prepare_results(analysed_trades_dict, strategy_name,
enter_reason_list, exit_reason_list,
timerange=timerange)
print_results(res_df,
analysis_groups,
enter_reason_list,
exit_reason_list,
indicator_list)
except ValueError as e:

2
freqtrade/enums/__init__.py
View File

@ -6,7 +6,7 @@ from freqtrade.enums.exittype import ExitType
from freqtrade.enums.hyperoptstate import HyperoptState
from freqtrade.enums.marginmode import MarginMode
from freqtrade.enums.ordertypevalue import OrderTypeValues
from freqtrade.enums.rpcmessagetype import RPCMessageType, RPCRequestType
from freqtrade.enums.rpcmessagetype import NO_ECHO_MESSAGES, RPCMessageType, RPCRequestType
from freqtrade.enums.runmode import NON_UTIL_MODES, OPTIMIZE_MODES, TRADING_MODES, RunMode
from freqtrade.enums.signaltype import SignalDirection, SignalTagType, SignalType
from freqtrade.enums.state import State

4
freqtrade/enums/rpcmessagetype.py
View File

@ -21,6 +21,7 @@ class RPCMessageType(str, Enum):
WHITELIST = 'whitelist'
ANALYZED_DF = 'analyzed_df'
NEW_CANDLE = 'new_candle'
def __repr__(self):
return self.value
@ -35,3 +36,6 @@ class RPCRequestType(str, Enum):
WHITELIST = 'whitelist'
ANALYZED_DF = 'analyzed_df'
NO_ECHO_MESSAGES = (RPCMessageType.ANALYZED_DF, RPCMessageType.WHITELIST, RPCMessageType.NEW_CANDLE)

1
freqtrade/exchange/__init__.py
View File

@ -3,7 +3,6 @@
from freqtrade.exchange.common import remove_credentials, MAP_EXCHANGE_CHILDCLASS
from freqtrade.exchange.exchange import Exchange
# isort: on
from freqtrade.exchange.bibox import Bibox
from freqtrade.exchange.binance import Binance
from freqtrade.exchange.bitpanda import Bitpanda
from freqtrade.exchange.bittrex import Bittrex

28
freqtrade/exchange/bibox.py
View File

@ -1,28 +0,0 @@
""" 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()
@property
def _ccxt_config(self) -> Dict:
# Parameters to add directly to ccxt sync/async initialization.
config = {"has": {"fetchCurrencies": False}}
config.update(super()._ccxt_config)
return config

2
freqtrade/exchange/binance.py
View File

@ -31,7 +31,7 @@ class Binance(Exchange):
"ccxt_futures_name": "future"
}
_ft_has_futures: Dict = {
"stoploss_order_types": {"limit": "limit", "market": "market"},
"stoploss_order_types": {"limit": "stop", "market": "stop_market"},
"tickers_have_price": False,
}

125
freqtrade/freqai/RL/Base3ActionRLEnv.py Normal file
View File

@ -0,0 +1,125 @@
import logging
from enum import Enum
from gym import spaces
from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
logger = logging.getLogger(__name__)
class Actions(Enum):
Neutral = 0
Buy = 1
Sell = 2
class Base3ActionRLEnv(BaseEnvironment):
"""
Base class for a 3 action environment
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.actions = Actions
def set_action_space(self):
self.action_space = spaces.Discrete(len(Actions))
def step(self, action: int):
"""
Logic for a single step (incrementing one candle in time)
by the agent
:param: action: int = the action type that the agent plans
to take for the current step.
:returns:
observation = current state of environment
step_reward = the reward from `calculate_reward()`
_done = if the agent "died" or if the candles finished
info = dict passed back to openai gym lib
"""
self._done = False
self._current_tick += 1
if self._current_tick == self._end_tick:
self._done = True
self._update_unrealized_total_profit()
step_reward = self.calculate_reward(action)
self.total_reward += step_reward
self.tensorboard_log(self.actions._member_names_[action])
trade_type = None
if self.is_tradesignal(action):
if action == Actions.Buy.value:
if self._position == Positions.Short:
self._update_total_profit()
self._position = Positions.Long
trade_type = "long"
self._last_trade_tick = self._current_tick
elif action == Actions.Sell.value and self.can_short:
if self._position == Positions.Long:
self._update_total_profit()
self._position = Positions.Short
trade_type = "short"
self._last_trade_tick = self._current_tick
elif action == Actions.Sell.value and not self.can_short:
self._update_total_profit()
self._position = Positions.Neutral
trade_type = "neutral"
self._last_trade_tick = None
else:
print("case not defined")
if trade_type is not None:
self.trade_history.append(
{'price': self.current_price(), 'index': self._current_tick,
'type': trade_type})
if (self._total_profit < self.max_drawdown or
self._total_unrealized_profit < self.max_drawdown):
self._done = True
self._position_history.append(self._position)
info = dict(
tick=self._current_tick,
action=action,
total_reward=self.total_reward,
total_profit=self._total_profit,
position=self._position.value,
trade_duration=self.get_trade_duration(),
current_profit_pct=self.get_unrealized_profit()
)
observation = self._get_observation()
self._update_history(info)
return observation, step_reward, self._done, info
def is_tradesignal(self, action: int) -> bool:
"""
Determine if the signal is a trade signal
e.g.: agent wants a Actions.Buy while it is in a Positions.short
"""
return (
(action == Actions.Buy.value and self._position == Positions.Neutral)
or (action == Actions.Sell.value and self._position == Positions.Long)
or (action == Actions.Sell.value and self._position == Positions.Neutral
and self.can_short)
or (action == Actions.Buy.value and self._position == Positions.Short
and self.can_short)
)
def _is_valid(self, action: int) -> bool:
"""
Determine if the signal is valid.
e.g.: agent wants a Actions.Sell while it is in a Positions.Long
"""
if self.can_short:
return action in [Actions.Buy.value, Actions.Sell.value, Actions.Neutral.value]
else:
if action == Actions.Sell.value and self._position != Positions.Long:
return False
return True

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freqtrade/freqai/RL/Base4ActionRLEnv.py Normal file
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import logging
from enum import Enum
from gym import spaces
from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
logger = logging.getLogger(__name__)
class Actions(Enum):
Neutral = 0
Exit = 1
Long_enter = 2
Short_enter = 3
class Base4ActionRLEnv(BaseEnvironment):
"""
Base class for a 4 action environment
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.actions = Actions
def set_action_space(self):
self.action_space = spaces.Discrete(len(Actions))
def step(self, action: int):
"""
Logic for a single step (incrementing one candle in time)
by the agent
:param: action: int = the action type that the agent plans
to take for the current step.
:returns:
observation = current state of environment
step_reward = the reward from `calculate_reward()`
_done = if the agent "died" or if the candles finished
info = dict passed back to openai gym lib
"""
self._done = False
self._current_tick += 1
if self._current_tick == self._end_tick:
self._done = True
self._update_unrealized_total_profit()
step_reward = self.calculate_reward(action)
self.total_reward += step_reward
self.tensorboard_log(self.actions._member_names_[action])
trade_type = None
if self.is_tradesignal(action):
"""
Action: Neutral, position: Long -> Close Long
Action: Neutral, position: Short -> Close Short
Action: Long, position: Neutral -> Open Long
Action: Long, position: Short -> Close Short and Open Long
Action: Short, position: Neutral -> Open Short
Action: Short, position: Long -> Close Long and Open Short
"""
if action == Actions.Neutral.value:
self._position = Positions.Neutral
trade_type = "neutral"
self._last_trade_tick = None
elif action == Actions.Long_enter.value:
self._position = Positions.Long
trade_type = "long"
self._last_trade_tick = self._current_tick
elif action == Actions.Short_enter.value:
self._position = Positions.Short
trade_type = "short"
self._last_trade_tick = self._current_tick
elif action == Actions.Exit.value:
self._update_total_profit()
self._position = Positions.Neutral
trade_type = "neutral"
self._last_trade_tick = None
else:
print("case not defined")
if trade_type is not None:
self.trade_history.append(
{'price': self.current_price(), 'index': self._current_tick,
'type': trade_type})
if (self._total_profit < self.max_drawdown or
self._total_unrealized_profit < self.max_drawdown):
self._done = True
self._position_history.append(self._position)
info = dict(
tick=self._current_tick,
action=action,
total_reward=self.total_reward,
total_profit=self._total_profit,
position=self._position.value,
trade_duration=self.get_trade_duration(),
current_profit_pct=self.get_unrealized_profit()
)
observation = self._get_observation()
self._update_history(info)
return observation, step_reward, self._done, info
def is_tradesignal(self, action: int) -> bool:
"""
Determine if the signal is a trade signal
e.g.: agent wants a Actions.Long_exit while it is in a Positions.short
"""
return not ((action == Actions.Neutral.value and self._position == Positions.Neutral) or
(action == Actions.Neutral.value and self._position == Positions.Short) or
(action == Actions.Neutral.value and self._position == Positions.Long) or
(action == Actions.Short_enter.value and self._position == Positions.Short) or
(action == Actions.Short_enter.value and self._position == Positions.Long) or
(action == Actions.Exit.value and self._position == Positions.Neutral) or
(action == Actions.Long_enter.value and self._position == Positions.Long) or
(action == Actions.Long_enter.value and self._position == Positions.Short))
def _is_valid(self, action: int) -> bool:
"""
Determine if the signal is valid.
e.g.: agent wants a Actions.Long_exit while it is in a Positions.short
"""
# Agent should only try to exit if it is in position
if action == Actions.Exit.value:
if self._position not in (Positions.Short, Positions.Long):
return False
# Agent should only try to enter if it is not in position
if action in (Actions.Short_enter.value, Actions.Long_enter.value):
if self._position != Positions.Neutral:
return False
return True

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freqtrade/freqai/RL/Base5ActionRLEnv.py Normal file
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import logging
from enum import Enum
from gym import spaces
from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
logger = logging.getLogger(__name__)
class Actions(Enum):
Neutral = 0
Long_enter = 1
Long_exit = 2
Short_enter = 3
Short_exit = 4
class Base5ActionRLEnv(BaseEnvironment):
"""
Base class for a 5 action environment
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.actions = Actions
def set_action_space(self):
self.action_space = spaces.Discrete(len(Actions))
def step(self, action: int):
"""
Logic for a single step (incrementing one candle in time)
by the agent
:param: action: int = the action type that the agent plans
to take for the current step.
:returns:
observation = current state of environment
step_reward = the reward from `calculate_reward()`
_done = if the agent "died" or if the candles finished
info = dict passed back to openai gym lib
"""
self._done = False
self._current_tick += 1
if self._current_tick == self._end_tick:
self._done = True
self._update_unrealized_total_profit()
step_reward = self.calculate_reward(action)
self.total_reward += step_reward
self.tensorboard_log(self.actions._member_names_[action])
trade_type = None
if self.is_tradesignal(action):
"""
Action: Neutral, position: Long -> Close Long
Action: Neutral, position: Short -> Close Short
Action: Long, position: Neutral -> Open Long
Action: Long, position: Short -> Close Short and Open Long
Action: Short, position: Neutral -> Open Short
Action: Short, position: Long -> Close Long and Open Short
"""
if action == Actions.Neutral.value:
self._position = Positions.Neutral
trade_type = "neutral"
self._last_trade_tick = None
elif action == Actions.Long_enter.value:
self._position = Positions.Long
trade_type = "long"
self._last_trade_tick = self._current_tick
elif action == Actions.Short_enter.value:
self._position = Positions.Short
trade_type = "short"
self._last_trade_tick = self._current_tick
elif action == Actions.Long_exit.value:
self._update_total_profit()
self._position = Positions.Neutral
trade_type = "neutral"
self._last_trade_tick = None
elif action == Actions.Short_exit.value:
self._update_total_profit()
self._position = Positions.Neutral
trade_type = "neutral"
self._last_trade_tick = None
else:
print("case not defined")
if trade_type is not None:
self.trade_history.append(
{'price': self.current_price(), 'index': self._current_tick,
'type': trade_type})
if (self._total_profit < self.max_drawdown or
self._total_unrealized_profit < self.max_drawdown):
self._done = True
self._position_history.append(self._position)
info = dict(
tick=self._current_tick,
action=action,
total_reward=self.total_reward,
total_profit=self._total_profit,
position=self._position.value,
trade_duration=self.get_trade_duration(),
current_profit_pct=self.get_unrealized_profit()
)
observation = self._get_observation()
self._update_history(info)
return observation, step_reward, self._done, info
def is_tradesignal(self, action: int) -> bool:
"""
Determine if the signal is a trade signal
e.g.: agent wants a Actions.Long_exit while it is in a Positions.short
"""
return not ((action == Actions.Neutral.value and self._position == Positions.Neutral) or
(action == Actions.Neutral.value and self._position == Positions.Short) or
(action == Actions.Neutral.value and self._position == Positions.Long) or
(action == Actions.Short_enter.value and self._position == Positions.Short) or
(action == Actions.Short_enter.value and self._position == Positions.Long) or
(action == Actions.Short_exit.value and self._position == Positions.Long) or
(action == Actions.Short_exit.value and self._position == Positions.Neutral) or
(action == Actions.Long_enter.value and self._position == Positions.Long) or
(action == Actions.Long_enter.value and self._position == Positions.Short) or
(action == Actions.Long_exit.value and self._position == Positions.Short) or
(action == Actions.Long_exit.value and self._position == Positions.Neutral))
def _is_valid(self, action: int) -> bool:
# trade signal
"""
Determine if the signal is valid.
e.g.: agent wants a Actions.Long_exit while it is in a Positions.short
"""
# Agent should only try to exit if it is in position
if action in (Actions.Short_exit.value, Actions.Long_exit.value):
if self._position not in (Positions.Short, Positions.Long):
return False
# Agent should only try to enter if it is not in position
if action in (Actions.Short_enter.value, Actions.Long_enter.value):
if self._position != Positions.Neutral:
return False
return True

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import logging
import random
from abc import abstractmethod
from enum import Enum
from typing import Optional, Type, Union
import gym
import numpy as np
import pandas as pd
from gym import spaces
from gym.utils import seeding
from pandas import DataFrame
logger = logging.getLogger(__name__)
class BaseActions(Enum):
"""
Default action space, mostly used for type handling.
"""
Neutral = 0
Long_enter = 1
Long_exit = 2
Short_enter = 3
Short_exit = 4
class Positions(Enum):
Short = 0
Long = 1
Neutral = 0.5
def opposite(self):
return Positions.Short if self == Positions.Long else Positions.Long
class BaseEnvironment(gym.Env):
"""
Base class for environments. This class is agnostic to action count.
Inherited classes customize this to include varying action counts/types,
See RL/Base5ActionRLEnv.py and RL/Base4ActionRLEnv.py
"""
def __init__(self, df: DataFrame = DataFrame(), prices: DataFrame = DataFrame(),
reward_kwargs: dict = {}, window_size=10, starting_point=True,
id: str = 'baseenv-1', seed: int = 1, config: dict = {}, live: bool = False,
fee: float = 0.0015, can_short: bool = False):
"""
Initializes the training/eval environment.
:param df: dataframe of features
:param prices: dataframe of prices to be used in the training environment
:param window_size: size of window (temporal) to pass to the agent
:param reward_kwargs: extra config settings assigned by user in `rl_config`
:param starting_point: start at edge of window or not
:param id: string id of the environment (used in backend for multiprocessed env)
:param seed: Sets the seed of the environment higher in the gym.Env object
:param config: Typical user configuration file
:param live: Whether or not this environment is active in dry/live/backtesting
:param fee: The fee to use for environmental interactions.
:param can_short: Whether or not the environment can short
"""
self.config = config
self.rl_config = config['freqai']['rl_config']
self.add_state_info = self.rl_config.get('add_state_info', False)
self.id = id
self.max_drawdown = 1 - self.rl_config.get('max_training_drawdown_pct', 0.8)
self.compound_trades = config['stake_amount'] == 'unlimited'
if self.config.get('fee', None) is not None:
self.fee = self.config['fee']
else:
self.fee = fee
# set here to default 5Ac, but all children envs can override this
self.actions: Type[Enum] = BaseActions
self.tensorboard_metrics: dict = {}
self.can_short = can_short
self.live = live
if not self.live and self.add_state_info:
self.add_state_info = False
logger.warning("add_state_info is not available in backtesting. Deactivating.")
self.seed(seed)
self.reset_env(df, prices, window_size, reward_kwargs, starting_point)
def reset_env(self, df: DataFrame, prices: DataFrame, window_size: int,
reward_kwargs: dict, starting_point=True):
"""
Resets the environment when the agent fails (in our case, if the drawdown
exceeds the user set max_training_drawdown_pct)
:param df: dataframe of features
:param prices: dataframe of prices to be used in the training environment
:param window_size: size of window (temporal) to pass to the agent
:param reward_kwargs: extra config settings assigned by user in `rl_config`
:param starting_point: start at edge of window or not
"""
self.df = df
self.signal_features = self.df
self.prices = prices
self.window_size = window_size
self.starting_point = starting_point
self.rr = reward_kwargs["rr"]
self.profit_aim = reward_kwargs["profit_aim"]
# # spaces
if self.add_state_info:
self.total_features = self.signal_features.shape[1] + 3
else:
self.total_features = self.signal_features.shape[1]
self.shape = (window_size, self.total_features)
self.set_action_space()
self.observation_space = spaces.Box(
low=-1, high=1, shape=self.shape, dtype=np.float32)
# episode
self._start_tick: int = self.window_size
self._end_tick: int = len(self.prices) - 1
self._done: bool = False
self._current_tick: int = self._start_tick
self._last_trade_tick: Optional[int] = None
self._position = Positions.Neutral
self._position_history: list = [None]
self.total_reward: float = 0
self._total_profit: float = 1
self._total_unrealized_profit: float = 1
self.history: dict = {}
self.trade_history: list = []
@abstractmethod
def set_action_space(self):
"""
Unique to the environment action count. Must be inherited.
"""
def seed(self, seed: int = 1):
self.np_random, seed = seeding.np_random(seed)
return [seed]
def tensorboard_log(self, metric: str, value: Union[int, float] = 1, inc: bool = True):
"""
Function builds the tensorboard_metrics dictionary
to be parsed by the TensorboardCallback. This
function is designed for tracking incremented objects,
events, actions inside the training environment.
For example, a user can call this to track the
frequency of occurence of an `is_valid` call in
their `calculate_reward()`:
def calculate_reward(self, action: int) -> float:
if not self._is_valid(action):
self.tensorboard_log("is_valid")
return -2
:param metric: metric to be tracked and incremented
:param value: value to increment `metric` by
:param inc: sets whether the `value` is incremented or not
"""
if not inc or metric not in self.tensorboard_metrics:
self.tensorboard_metrics[metric] = value
else:
self.tensorboard_metrics[metric] += value
def reset_tensorboard_log(self):
self.tensorboard_metrics = {}
def reset(self):
"""
Reset is called at the beginning of every episode
"""
self.reset_tensorboard_log()
self._done = False
if self.starting_point is True:
if self.rl_config.get('randomize_starting_position', False):
length_of_data = int(self._end_tick / 4)
start_tick = random.randint(self.window_size + 1, length_of_data)
self._start_tick = start_tick
self._position_history = (self._start_tick * [None]) + [self._position]
else:
self._position_history = (self.window_size * [None]) + [self._position]
self._current_tick = self._start_tick
self._last_trade_tick = None
self._position = Positions.Neutral
self.total_reward = 0.
self._total_profit = 1. # unit
self.history = {}
self.trade_history = []
self.portfolio_log_returns = np.zeros(len(self.prices))
self._profits = [(self._start_tick, 1)]
self.close_trade_profit = []
self._total_unrealized_profit = 1
return self._get_observation()
@abstractmethod
def step(self, action: int):
"""
Step depeneds on action types, this must be inherited.
"""
return
def _get_observation(self):
"""
This may or may not be independent of action types, user can inherit
this in their custom "MyRLEnv"
"""
features_window = self.signal_features[(
self._current_tick - self.window_size):self._current_tick]
if self.add_state_info:
features_and_state = DataFrame(np.zeros((len(features_window), 3)),
columns=['current_profit_pct',
'position',
'trade_duration'],
index=features_window.index)
features_and_state['current_profit_pct'] = self.get_unrealized_profit()
features_and_state['position'] = self._position.value
features_and_state['trade_duration'] = self.get_trade_duration()
features_and_state = pd.concat([features_window, features_and_state], axis=1)
return features_and_state
else:
return features_window
def get_trade_duration(self):
"""
Get the trade duration if the agent is in a trade
"""
if self._last_trade_tick is None:
return 0
else:
return self._current_tick - self._last_trade_tick
def get_unrealized_profit(self):
"""
Get the unrealized profit if the agent is in a trade
"""
if self._last_trade_tick is None:
return 0.
if self._position == Positions.Neutral:
return 0.
elif self._position == Positions.Short:
current_price = self.add_entry_fee(self.prices.iloc[self._current_tick].open)
last_trade_price = self.add_exit_fee(self.prices.iloc[self._last_trade_tick].open)
return (last_trade_price - current_price) / last_trade_price
elif self._position == Positions.Long:
current_price = self.add_exit_fee(self.prices.iloc[self._current_tick].open)
last_trade_price = self.add_entry_fee(self.prices.iloc[self._last_trade_tick].open)
return (current_price - last_trade_price) / last_trade_price
else:
return 0.
@abstractmethod
def is_tradesignal(self, action: int) -> bool:
"""
Determine if the signal is a trade signal. This is
unique to the actions in the environment, and therefore must be
inherited.
"""
return True
def _is_valid(self, action: int) -> bool:
"""
Determine if the signal is valid.This is
unique to the actions in the environment, and therefore must be
inherited.
"""
return True
def add_entry_fee(self, price):
return price * (1 + self.fee)
def add_exit_fee(self, price):
return price / (1 + self.fee)
def _update_history(self, info):
if not self.history:
self.history = {key: [] for key in info.keys()}
for key, value in info.items():
self.history[key].append(value)
@abstractmethod
def calculate_reward(self, action: int) -> float:
"""
An example reward function. This is the one function that users will likely
wish to inject their own creativity into.
:param action: int = The action made by the agent for the current candle.
:return:
float = the reward to give to the agent for current step (used for optimization
of weights in NN)
"""
def _update_unrealized_total_profit(self):
"""
Update the unrealized total profit incase of episode end.
"""
if self._position in (Positions.Long, Positions.Short):
pnl = self.get_unrealized_profit()
if self.compound_trades:
# assumes unit stake and compounding
unrl_profit = self._total_profit * (1 + pnl)
else:
# assumes unit stake and no compounding
unrl_profit = self._total_profit + pnl
self._total_unrealized_profit = unrl_profit
def _update_total_profit(self):
pnl = self.get_unrealized_profit()
if self.compound_trades:
# assumes unit stake and compounding
self._total_profit = self._total_profit * (1 + pnl)
else:
# assumes unit stake and no compounding
self._total_profit += pnl
def current_price(self) -> float:
return self.prices.iloc[self._current_tick].open
def get_actions(self) -> Type[Enum]:
"""
Used by SubprocVecEnv to get actions from
initialized env for tensorboard callback
"""
return self.actions
# Keeping around incase we want to start building more complex environment
# templates in the future.
# def most_recent_return(self):
# """
# Calculate the tick to tick return if in a trade.
# Return is generated from rising prices in Long
# and falling prices in Short positions.
# The actions Sell/Buy or Hold during a Long position trigger the sell/buy-fee.
# """
# # Long positions
# if self._position == Positions.Long:
# current_price = self.prices.iloc[self._current_tick].open
# previous_price = self.prices.iloc[self._current_tick - 1].open
# if (self._position_history[self._current_tick - 1] == Positions.Short
# or self._position_history[self._current_tick - 1] == Positions.Neutral):
# previous_price = self.add_entry_fee(previous_price)
# return np.log(current_price) - np.log(previous_price)
# # Short positions
# if self._position == Positions.Short:
# current_price = self.prices.iloc[self._current_tick].open
# previous_price = self.prices.iloc[self._current_tick - 1].open
# if (self._position_history[self._current_tick - 1] == Positions.Long
# or self._position_history[self._current_tick - 1] == Positions.Neutral):
# previous_price = self.add_exit_fee(previous_price)
# return np.log(previous_price) - np.log(current_price)
# return 0
# def update_portfolio_log_returns(self, action):
# self.portfolio_log_returns[self._current_tick] = self.most_recent_return(action)

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import importlib
import logging
from abc import abstractmethod
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Callable, Dict, Optional, Tuple, Type, Union
import gym
import numpy as np
import numpy.typing as npt
import pandas as pd
import torch as th
import torch.multiprocessing
from pandas import DataFrame
from stable_baselines3.common.callbacks import EvalCallback
from stable_baselines3.common.monitor import Monitor
from stable_baselines3.common.utils import set_random_seed
from stable_baselines3.common.vec_env import SubprocVecEnv
from freqtrade.exceptions import OperationalException
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.freqai_interface import IFreqaiModel
from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv
from freqtrade.freqai.RL.BaseEnvironment import BaseActions, Positions
from freqtrade.freqai.RL.TensorboardCallback import TensorboardCallback
from freqtrade.persistence import Trade
logger = logging.getLogger(__name__)
torch.multiprocessing.set_sharing_strategy('file_system')
SB3_MODELS = ['PPO', 'A2C', 'DQN']
SB3_CONTRIB_MODELS = ['TRPO', 'ARS', 'RecurrentPPO', 'MaskablePPO']
class BaseReinforcementLearningModel(IFreqaiModel):
"""
User created Reinforcement Learning Model prediction class
"""
def __init__(self, **kwargs) -> None:
super().__init__(config=kwargs['config'])
self.max_threads = min(self.freqai_info['rl_config'].get(
'cpu_count', 1), max(int(self.max_system_threads / 2), 1))
th.set_num_threads(self.max_threads)
self.reward_params = self.freqai_info['rl_config']['model_reward_parameters']
self.train_env: Union[SubprocVecEnv, Type[gym.Env]] = gym.Env()
self.eval_env: Union[SubprocVecEnv, Type[gym.Env]] = gym.Env()
self.eval_callback: Optional[EvalCallback] = None
self.model_type = self.freqai_info['rl_config']['model_type']
self.rl_config = self.freqai_info['rl_config']
self.continual_learning = self.freqai_info.get('continual_learning', False)
if self.model_type in SB3_MODELS:
import_str = 'stable_baselines3'
elif self.model_type in SB3_CONTRIB_MODELS:
import_str = 'sb3_contrib'
else:
raise OperationalException(f'{self.model_type} not available in stable_baselines3 or '
f'sb3_contrib. please choose one of {SB3_MODELS} or '
f'{SB3_CONTRIB_MODELS}')
mod = importlib.import_module(import_str, self.model_type)
self.MODELCLASS = getattr(mod, self.model_type)
self.policy_type = self.freqai_info['rl_config']['policy_type']
self.unset_outlier_removal()
self.net_arch = self.rl_config.get('net_arch', [128, 128])
self.dd.model_type = import_str
self.tensorboard_callback: TensorboardCallback = \
TensorboardCallback(verbose=1, actions=BaseActions)
def unset_outlier_removal(self):
"""
If user has activated any function that may remove training points, this
function will set them to false and warn them
"""
if self.ft_params.get('use_SVM_to_remove_outliers', False):
self.ft_params.update({'use_SVM_to_remove_outliers': False})
logger.warning('User tried to use SVM with RL. Deactivating SVM.')
if self.ft_params.get('use_DBSCAN_to_remove_outliers', False):
self.ft_params.update({'use_DBSCAN_to_remove_outliers': False})
logger.warning('User tried to use DBSCAN with RL. Deactivating DBSCAN.')
if self.freqai_info['data_split_parameters'].get('shuffle', False):
self.freqai_info['data_split_parameters'].update({'shuffle': False})
logger.warning('User tried to shuffle training data. Setting shuffle to False')
def train(
self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
) -> Any:
"""
Filter the training data and train a model to it. Train makes heavy use of the datakitchen
for storing, saving, loading, and analyzing the data.
:param unfiltered_df: Full dataframe for the current training period
:param metadata: pair metadata from strategy.
:returns:
:model: Trained model which can be used to inference (self.predict)
"""
logger.info("--------------------Starting training " f"{pair} --------------------")
features_filtered, labels_filtered = dk.filter_features(
unfiltered_df,
dk.training_features_list,
dk.label_list,
training_filter=True,
)
data_dictionary: Dict[str, Any] = dk.make_train_test_datasets(
features_filtered, labels_filtered)
dk.fit_labels() # FIXME useless for now, but just satiating append methods
# normalize all data based on train_dataset only
prices_train, prices_test = self.build_ohlc_price_dataframes(dk.data_dictionary, pair, dk)
data_dictionary = dk.normalize_data(data_dictionary)
# data cleaning/analysis
self.data_cleaning_train(dk)
logger.info(
f'Training model on {len(dk.data_dictionary["train_features"].columns)}'
f' features and {len(data_dictionary["train_features"])} data points'
)
self.set_train_and_eval_environments(data_dictionary, prices_train, prices_test, dk)
model = self.fit(data_dictionary, dk)
logger.info(f"--------------------done training {pair}--------------------")
return model
def set_train_and_eval_environments(self, data_dictionary: Dict[str, DataFrame],
prices_train: DataFrame, prices_test: DataFrame,
dk: FreqaiDataKitchen):
"""
User can override this if they are using a custom MyRLEnv
:param data_dictionary: dict = common data dictionary containing train and test
features/labels/weights.
:param prices_train/test: DataFrame = dataframe comprised of the prices to be used in the
environment during training or testing
:param dk: FreqaiDataKitchen = the datakitchen for the current pair
"""
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
env_info = self.pack_env_dict()
self.train_env = self.MyRLEnv(df=train_df,
prices=prices_train,
**env_info)
self.eval_env = Monitor(self.MyRLEnv(df=test_df,
prices=prices_test,
**env_info))
self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
render=False, eval_freq=len(train_df),
best_model_save_path=str(dk.data_path))
actions = self.train_env.get_actions()
self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)
def pack_env_dict(self) -> Dict[str, Any]:
"""
Create dictionary of environment arguments
"""
env_info = {"window_size": self.CONV_WIDTH,
"reward_kwargs": self.reward_params,
"config": self.config,
"live": self.live,
"can_short": self.can_short}
if self.data_provider:
env_info["fee"] = self.data_provider._exchange \
.get_fee(symbol=self.data_provider.current_whitelist()[0]) # type: ignore
return env_info
@abstractmethod
def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs):
"""
Agent customizations and abstract Reinforcement Learning customizations
go in here. Abstract method, so this function must be overridden by
user class.
"""
return
def get_state_info(self, pair: str) -> Tuple[float, float, int]:
"""
State info during dry/live (not backtesting) which is fed back
into the model.
:param pair: str = COIN/STAKE to get the environment information for
:return:
:market_side: float = representing short, long, or neutral for
pair
:current_profit: float = unrealized profit of the current trade
:trade_duration: int = the number of candles that the trade has
been open for
"""
open_trades = Trade.get_trades_proxy(is_open=True)
market_side = 0.5
current_profit: float = 0
trade_duration = 0
for trade in open_trades:
if trade.pair == pair:
if self.data_provider._exchange is None: # type: ignore
logger.error('No exchange available.')
return 0, 0, 0
else:
current_rate = self.data_provider._exchange.get_rate( # type: ignore
pair, refresh=False, side="exit", is_short=trade.is_short)
now = datetime.now(timezone.utc).timestamp()
trade_duration = int((now - trade.open_date_utc.timestamp()) / self.base_tf_seconds)
current_profit = trade.calc_profit_ratio(current_rate)
if trade.is_short:
market_side = 0
else:
market_side = 1
return market_side, current_profit, int(trade_duration)
def predict(
self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
"""
Filter the prediction features data and predict with it.
:param unfiltered_dataframe: Full dataframe for the current backtest period.
:return:
:pred_df: dataframe containing the predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
data (NaNs) or felt uncertain about data (PCA and DI index)
"""
dk.find_features(unfiltered_df)
filtered_dataframe, _ = dk.filter_features(
unfiltered_df, dk.training_features_list, training_filter=False
)
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
dk.data_dictionary["prediction_features"] = filtered_dataframe
# optional additional data cleaning/analysis
self.data_cleaning_predict(dk)
pred_df = self.rl_model_predict(
dk.data_dictionary["prediction_features"], dk, self.model)
pred_df.fillna(0, inplace=True)
return (pred_df, dk.do_predict)
def rl_model_predict(self, dataframe: DataFrame,
dk: FreqaiDataKitchen, model: Any) -> DataFrame:
"""
A helper function to make predictions in the Reinforcement learning module.
:param dataframe: DataFrame = the dataframe of features to make the predictions on
:param dk: FreqaiDatakitchen = data kitchen for the current pair
:param model: Any = the trained model used to inference the features.
"""
output = pd.DataFrame(np.zeros(len(dataframe)), columns=dk.label_list)
def _predict(window):
observations = dataframe.iloc[window.index]
if self.live and self.rl_config.get('add_state_info', False):
market_side, current_profit, trade_duration = self.get_state_info(dk.pair)
observations['current_profit_pct'] = current_profit
observations['position'] = market_side
observations['trade_duration'] = trade_duration
res, _ = model.predict(observations, deterministic=True)
return res
output = output.rolling(window=self.CONV_WIDTH).apply(_predict)
return output
def build_ohlc_price_dataframes(self, data_dictionary: dict,
pair: str, dk: FreqaiDataKitchen) -> Tuple[DataFrame,
DataFrame]:
"""
Builds the train prices and test prices for the environment.
"""
pair = pair.replace(':', '')
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
# price data for model training and evaluation
tf = self.config['timeframe']
ohlc_list = [f'%-{pair}raw_open_{tf}', f'%-{pair}raw_low_{tf}',
f'%-{pair}raw_high_{tf}', f'%-{pair}raw_close_{tf}']
rename_dict = {f'%-{pair}raw_open_{tf}': 'open', f'%-{pair}raw_low_{tf}': 'low',
f'%-{pair}raw_high_{tf}': ' high', f'%-{pair}raw_close_{tf}': 'close'}
prices_train = train_df.filter(ohlc_list, axis=1)
if prices_train.empty:
raise OperationalException('Reinforcement learning module didnt find the raw prices '
'assigned in populate_any_indicators. Please assign them '
'with:\n'
'informative[f"%-{pair}raw_close"] = informative["close"]\n'
'informative[f"%-{pair}raw_open"] = informative["open"]\n'
'informative[f"%-{pair}raw_high"] = informative["high"]\n'
'informative[f"%-{pair}raw_low"] = informative["low"]\n')
prices_train.rename(columns=rename_dict, inplace=True)
prices_train.reset_index(drop=True)
prices_test = test_df.filter(ohlc_list, axis=1)
prices_test.rename(columns=rename_dict, inplace=True)
prices_test.reset_index(drop=True)
return prices_train, prices_test
def load_model_from_disk(self, dk: FreqaiDataKitchen) -> Any:
"""
Can be used by user if they are trying to limit_ram_usage *and*
perform continual learning.
For now, this is unused.
"""
exists = Path(dk.data_path / f"{dk.model_filename}_model").is_file()
if exists:
model = self.MODELCLASS.load(dk.data_path / f"{dk.model_filename}_model")
else:
logger.info('No model file on disk to continue learning from.')
return model
def _on_stop(self):
"""
Hook called on bot shutdown. Close SubprocVecEnv subprocesses for clean shutdown.
"""
if self.train_env:
self.train_env.close()
if self.eval_env:
self.eval_env.close()
# Nested class which can be overridden by user to customize further
class MyRLEnv(Base5ActionRLEnv):
"""
User can override any function in BaseRLEnv and gym.Env. Here the user
sets a custom reward based on profit and trade duration.
"""
def calculate_reward(self, action: int) -> float:
"""
An example reward function. This is the one function that users will likely
wish to inject their own creativity into.
:param action: int = The action made by the agent for the current candle.
:return:
float = the reward to give to the agent for current step (used for optimization
of weights in NN)
"""
# first, penalize if the action is not valid
if not self._is_valid(action):
return -2
pnl = self.get_unrealized_profit()
factor = 100.
# reward agent for entering trades
if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
and self._position == Positions.Neutral):
return 25
# discourage agent from not entering trades
if action == Actions.Neutral.value and self._position == Positions.Neutral:
return -1
max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
if self._last_trade_tick:
trade_duration = self._current_tick - self._last_trade_tick
else:
trade_duration = 0
if trade_duration <= max_trade_duration:
factor *= 1.5
elif trade_duration > max_trade_duration:
factor *= 0.5
# discourage sitting in position
if (self._position in (Positions.Short, Positions.Long) and
action == Actions.Neutral.value):
return -1 * trade_duration / max_trade_duration
# close long
if action == Actions.Long_exit.value and self._position == Positions.Long:
if pnl > self.profit_aim * self.rr:
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
return float(pnl * factor)
# close short
if action == Actions.Short_exit.value and self._position == Positions.Short:
if pnl > self.profit_aim * self.rr:
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
return float(pnl * factor)
return 0.
def make_env(MyRLEnv: Type[gym.Env], env_id: str, rank: int,
seed: int, train_df: DataFrame, price: DataFrame,
monitor: bool = False,
env_info: Dict[str, Any] = {}) -> Callable:
"""
Utility function for multiprocessed env.
:param env_id: (str) the environment ID
:param num_env: (int) the number of environment you wish to have in subprocesses
:param seed: (int) the inital seed for RNG
:param rank: (int) index of the subprocess
:param env_info: (dict) all required arguments to instantiate the environment.
:return: (Callable)
"""
def _init() -> gym.Env:
env = MyRLEnv(df=train_df, prices=price, id=env_id, seed=seed + rank,
**env_info)
if monitor:
env = Monitor(env)
return env
set_random_seed(seed)
return _init

59
freqtrade/freqai/RL/TensorboardCallback.py Normal file
View File

@ -0,0 +1,59 @@
from enum import Enum
from typing import Any, Dict, Type, Union
from stable_baselines3.common.callbacks import BaseCallback
from stable_baselines3.common.logger import HParam
from freqtrade.freqai.RL.BaseEnvironment import BaseActions, BaseEnvironment
class TensorboardCallback(BaseCallback):
"""
Custom callback for plotting additional values in tensorboard and
episodic summary reports.
"""
def __init__(self, verbose=1, actions: Type[Enum] = BaseActions):
super(TensorboardCallback, self).__init__(verbose)
self.model: Any = None
self.logger = None # type: Any
self.training_env: BaseEnvironment = None # type: ignore
self.actions: Type[Enum] = actions
def _on_training_start(self) -> None:
hparam_dict = {
"algorithm": self.model.__class__.__name__,
"learning_rate": self.model.learning_rate,
# "gamma": self.model.gamma,
# "gae_lambda": self.model.gae_lambda,
# "batch_size": self.model.batch_size,
# "n_steps": self.model.n_steps,
}
metric_dict: Dict[str, Union[float, int]] = {
"eval/mean_reward": 0,
"rollout/ep_rew_mean": 0,
"rollout/ep_len_mean": 0,
"train/value_loss": 0,
"train/explained_variance": 0,
}
self.logger.record(
"hparams",
HParam(hparam_dict, metric_dict),
exclude=("stdout", "log", "json", "csv"),
)
def _on_step(self) -> bool:
local_info = self.locals["infos"][0]
tensorboard_metrics = self.training_env.get_attr("tensorboard_metrics")[0]
for info in local_info:
if info not in ["episode", "terminal_observation"]:
self.logger.record(f"_info/{info}", local_info[info])
for info in tensorboard_metrics:
if info in [action.name for action in self.actions]:
self.logger.record(f"_actions/{info}", tensorboard_metrics[info])
else:
self.logger.record(f"_custom/{info}", tensorboard_metrics[info])
return True

0
freqtrade/freqai/RL/__init__.py Normal file
View File

5
freqtrade/freqai/base_models/BaseClassifierModel.py
View File

@ -95,9 +95,14 @@ class BaseClassifierModel(IFreqaiModel):
self.data_cleaning_predict(dk)
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
if self.CONV_WIDTH == 1:
predictions = np.reshape(predictions, (-1, len(dk.label_list)))
pred_df = DataFrame(predictions, columns=dk.label_list)
predictions_prob = self.model.predict_proba(dk.data_dictionary["prediction_features"])
if self.CONV_WIDTH == 1:
predictions_prob = np.reshape(predictions_prob, (-1, len(self.model.classes_)))
pred_df_prob = DataFrame(predictions_prob, columns=self.model.classes_)
pred_df = pd.concat([pred_df, pred_df_prob], axis=1)

3
freqtrade/freqai/base_models/BaseRegressionModel.py
View File

@ -95,6 +95,9 @@ class BaseRegressionModel(IFreqaiModel):
self.data_cleaning_predict(dk)
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
if self.CONV_WIDTH == 1:
predictions = np.reshape(predictions, (-1, len(dk.label_list)))
pred_df = DataFrame(predictions, columns=dk.label_list)
pred_df = dk.denormalize_labels_from_metadata(pred_df)

84
freqtrade/freqai/data_drawer.py
View File

@ -1,9 +1,10 @@
import collections
import importlib
import logging
import re
import shutil
import threading
from datetime import datetime, timezone
from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Any, Dict, Tuple, TypedDict
@ -81,6 +82,7 @@ class FreqaiDataDrawer:
self.historic_predictions_bkp_path = Path(
self.full_path / "historic_predictions.backup.pkl")
self.pair_dictionary_path = Path(self.full_path / "pair_dictionary.json")
self.global_metadata_path = Path(self.full_path / "global_metadata.json")
self.metric_tracker_path = Path(self.full_path / "metric_tracker.json")
self.follow_mode = follow_mode
if follow_mode:
@ -98,6 +100,7 @@ class FreqaiDataDrawer:
self.empty_pair_dict: pair_info = {
"model_filename": "", "trained_timestamp": 0,
"data_path": "", "extras": {}}
self.model_type = self.freqai_info.get('model_save_type', 'joblib')
def update_metric_tracker(self, metric: str, value: float, pair: str) -> None:
"""
@ -125,6 +128,17 @@ class FreqaiDataDrawer:
self.update_metric_tracker('cpu_load5min', load5 / cpus, pair)
self.update_metric_tracker('cpu_load15min', load15 / cpus, pair)
def load_global_metadata_from_disk(self):
"""
Locate and load a previously saved global metadata in present model folder.
"""
exists = self.global_metadata_path.is_file()
if exists:
with open(self.global_metadata_path, "r") as fp:
metatada_dict = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
return metatada_dict
return {}
def load_drawer_from_disk(self):
"""
Locate and load a previously saved data drawer full of all pair model metadata in
@ -225,6 +239,15 @@ class FreqaiDataDrawer:
rapidjson.dump(self.follower_dict, fp, default=self.np_encoder,
number_mode=rapidjson.NM_NATIVE)
def save_global_metadata_to_disk(self, metadata: Dict[str, Any]):
"""
Save global metadata json to disk
"""
with self.save_lock:
with open(self.global_metadata_path, 'w') as fp:
rapidjson.dump(metadata, fp, default=self.np_encoder,
number_mode=rapidjson.NM_NATIVE)
def create_follower_dict(self):
"""
Create or dictionary for each follower to maintain unique persistent prediction targets
@ -476,10 +499,12 @@ class FreqaiDataDrawer:
save_path = Path(dk.data_path)
# Save the trained model
if not dk.keras:
if self.model_type == 'joblib':
dump(model, save_path / f"{dk.model_filename}_model.joblib")
else:
elif self.model_type == 'keras':
model.save(save_path / f"{dk.model_filename}_model.h5")
elif 'stable_baselines' in self.model_type or 'sb3_contrib' == self.model_type:
model.save(save_path / f"{dk.model_filename}_model.zip")
if dk.svm_model is not None:
dump(dk.svm_model, save_path / f"{dk.model_filename}_svm_model.joblib")
@ -506,11 +531,10 @@ class FreqaiDataDrawer:
dk.pca, open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "wb")
)
# if self.live:
# store as much in ram as possible to increase performance
self.model_dictionary[coin] = model
self.pair_dict[coin]["model_filename"] = dk.model_filename
self.pair_dict[coin]["data_path"] = str(dk.data_path)
if coin not in self.meta_data_dictionary:
self.meta_data_dictionary[coin] = {}
self.meta_data_dictionary[coin]["train_df"] = dk.data_dictionary["train_features"]
@ -542,14 +566,6 @@ class FreqaiDataDrawer:
if dk.live:
dk.model_filename = self.pair_dict[coin]["model_filename"]
dk.data_path = Path(self.pair_dict[coin]["data_path"])
if self.freqai_info.get("follow_mode", False):
# follower can be on a different system which is rsynced from the leader:
dk.data_path = Path(
self.config["user_data_dir"]
/ "models"
/ dk.data_path.parts[-2]
/ dk.data_path.parts[-1]
)
if coin in self.meta_data_dictionary:
dk.data = self.meta_data_dictionary[coin]["meta_data"]
@ -568,12 +584,16 @@ class FreqaiDataDrawer:
# try to access model in memory instead of loading object from disk to save time
if dk.live and coin in self.model_dictionary:
model = self.model_dictionary[coin]
elif not dk.keras:
elif self.model_type == 'joblib':
model = load(dk.data_path / f"{dk.model_filename}_model.joblib")
else:
elif self.model_type == 'keras':
from tensorflow import keras
model = keras.models.load_model(dk.data_path / f"{dk.model_filename}_model.h5")
elif 'stable_baselines' in self.model_type or 'sb3_contrib' == self.model_type:
mod = importlib.import_module(
self.model_type, self.freqai_info['rl_config']['model_type'])
MODELCLASS = getattr(mod, self.freqai_info['rl_config']['model_type'])
model = MODELCLASS.load(dk.data_path / f"{dk.model_filename}_model")
if Path(dk.data_path / f"{dk.model_filename}_svm_model.joblib").is_file():
dk.svm_model = load(dk.data_path / f"{dk.model_filename}_svm_model.joblib")
@ -583,6 +603,10 @@ class FreqaiDataDrawer:
f"Unable to load model, ensure model exists at " f"{dk.data_path} "
)
# load it into ram if it was loaded from disk
if coin not in self.model_dictionary:
self.model_dictionary[coin] = model
if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]:
dk.pca = cloudpickle.load(
open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "rb")
@ -693,3 +717,31 @@ class FreqaiDataDrawer:
).reset_index(drop=True)
return corr_dataframes, base_dataframes
def get_timerange_from_live_historic_predictions(self) -> TimeRange:
"""
Returns timerange information based on historic predictions file
:return: timerange calculated from saved live data
"""
if not self.historic_predictions_path.is_file():
raise OperationalException(
'Historic predictions not found. Historic predictions data is required '
'to run backtest with the freqai-backtest-live-models option '
)
self.load_historic_predictions_from_disk()
all_pairs_end_dates = []
for pair in self.historic_predictions:
pair_historic_data = self.historic_predictions[pair]
all_pairs_end_dates.append(pair_historic_data.date_pred.max())
global_metadata = self.load_global_metadata_from_disk()
start_date = datetime.fromtimestamp(int(global_metadata["start_dry_live_date"]))
end_date = max(all_pairs_end_dates)
# add 1 day to string timerange to ensure BT module will load all dataframe data
end_date = end_date + timedelta(days=1)
backtesting_timerange = TimeRange(
'date', 'date', int(start_date.timestamp()), int(end_date.timestamp())
)
return backtesting_timerange

174
freqtrade/freqai/data_kitchen.py
View File

@ -1,7 +1,7 @@
import copy
import logging
import shutil
from datetime import datetime, timedelta, timezone
from datetime import datetime, timezone
from math import cos, sin
from pathlib import Path
from typing import Any, Dict, List, Tuple
@ -9,6 +9,7 @@ from typing import Any, Dict, List, Tuple
import numpy as np
import numpy.typing as npt
import pandas as pd
import psutil
from pandas import DataFrame
from scipy import stats
from sklearn import linear_model
@ -86,12 +87,7 @@ class FreqaiDataKitchen:
if not self.live:
self.full_path = self.get_full_models_path(self.config)
if self.backtest_live_models:
if self.pair:
self.set_timerange_from_ready_models()
(self.training_timeranges,
self.backtesting_timeranges) = self.split_timerange_live_models()
else:
if not self.backtest_live_models:
self.full_timerange = self.create_fulltimerange(
self.config["timerange"], self.freqai_config.get("train_period_days", 0)
)
@ -102,7 +98,10 @@ class FreqaiDataKitchen:
)
self.data['extra_returns_per_train'] = self.freqai_config.get('extra_returns_per_train', {})
self.thread_count = self.freqai_config.get("data_kitchen_thread_count", -1)
if not self.freqai_config.get("data_kitchen_thread_count", 0):
self.thread_count = max(int(psutil.cpu_count() * 2 - 2), 1)
else:
self.thread_count = self.freqai_config["data_kitchen_thread_count"]
self.train_dates: DataFrame = pd.DataFrame()
self.unique_classes: Dict[str, list] = {}
self.unique_class_list: list = []
@ -456,29 +455,6 @@ class FreqaiDataKitchen:
# print(tr_training_list, tr_backtesting_list)
return tr_training_list_timerange, tr_backtesting_list_timerange
def split_timerange_live_models(
self
) -> Tuple[list, list]:
tr_backtesting_list_timerange = []
asset = self.pair.split("/")[0]
if asset not in self.backtest_live_models_data["assets_end_dates"]:
raise OperationalException(
f"Model not available for pair {self.pair}. "
"Please, try again after removing this pair from the configuration file."
)
asset_data = self.backtest_live_models_data["assets_end_dates"][asset]
backtesting_timerange = self.backtest_live_models_data["backtesting_timerange"]
model_end_dates = [x for x in asset_data]
model_end_dates.append(backtesting_timerange.stopts)
model_end_dates.sort()
for index, item in enumerate(model_end_dates):
if len(model_end_dates) > (index + 1):
tr_to_add = TimeRange("date", "date", item, model_end_dates[index + 1])
tr_backtesting_list_timerange.append(tr_to_add)
return tr_backtesting_list_timerange, tr_backtesting_list_timerange
def slice_dataframe(self, timerange: TimeRange, df: DataFrame) -> DataFrame:
"""
Given a full dataframe, extract the user desired window
@ -486,10 +462,10 @@ class FreqaiDataKitchen:
:param df: Dataframe containing all candles to run the entire backtest. Here
it is sliced down to just the present training period.
"""
df = df.loc[df["date"] >= timerange.startdt, :]
if not self.live:
df = df.loc[df["date"] < timerange.stopdt, :]
df = df.loc[(df["date"] >= timerange.startdt) & (df["date"] < timerange.stopdt), :]
else:
df = df.loc[df["date"] >= timerange.startdt, :]
return df
@ -974,7 +950,8 @@ class FreqaiDataKitchen:
return weights
def get_predictions_to_append(self, predictions: DataFrame,
do_predict: npt.ArrayLike) -> DataFrame:
do_predict: npt.ArrayLike,
dataframe_backtest: DataFrame) -> DataFrame:
"""
Get backtest prediction from current backtest period
"""
@ -996,7 +973,9 @@ class FreqaiDataKitchen:
if self.freqai_config["feature_parameters"].get("DI_threshold", 0) > 0:
append_df["DI_values"] = self.DI_values
return append_df
dataframe_backtest.reset_index(drop=True, inplace=True)
merged_df = pd.concat([dataframe_backtest["date"], append_df], axis=1)
return merged_df
def append_predictions(self, append_df: DataFrame) -> None:
"""
@ -1006,23 +985,18 @@ class FreqaiDataKitchen:
if self.full_df.empty:
self.full_df = append_df
else:
self.full_df = pd.concat([self.full_df, append_df], axis=0)
self.full_df = pd.concat([self.full_df, append_df], axis=0, ignore_index=True)
def fill_predictions(self, dataframe):
"""
Back fill values to before the backtesting range so that the dataframe matches size
when it goes back to the strategy. These rows are not included in the backtest.
"""
len_filler = len(dataframe) - len(self.full_df.index) # startup_candle_count
filler_df = pd.DataFrame(
np.zeros((len_filler, len(self.full_df.columns))), columns=self.full_df.columns
)
self.full_df = pd.concat([filler_df, self.full_df], axis=0, ignore_index=True)
to_keep = [col for col in dataframe.columns if not col.startswith("&")]
self.return_dataframe = pd.concat([dataframe[to_keep], self.full_df], axis=1)
self.return_dataframe = pd.merge(dataframe[to_keep],
self.full_df, how='left', on='date')
self.return_dataframe[self.full_df.columns] = (
self.return_dataframe[self.full_df.columns].fillna(value=0))
self.full_df = DataFrame()
return
@ -1319,22 +1293,22 @@ class FreqaiDataKitchen:
self, append_df: DataFrame
) -> None:
"""
Save prediction dataframe from backtesting to h5 file format
Save prediction dataframe from backtesting to feather file format
:param append_df: dataframe for backtesting period
"""
full_predictions_folder = Path(self.full_path / self.backtest_predictions_folder)
if not full_predictions_folder.is_dir():
full_predictions_folder.mkdir(parents=True, exist_ok=True)
append_df.to_hdf(self.backtesting_results_path, key='append_df', mode='w')
append_df.to_feather(self.backtesting_results_path)
def get_backtesting_prediction(
self
) -> DataFrame:
"""
Get prediction dataframe from h5 file format
Get prediction dataframe from feather file format
"""
append_df = pd.read_hdf(self.backtesting_results_path)
append_df = pd.read_feather(self.backtesting_results_path)
return append_df
def check_if_backtest_prediction_is_valid(
@ -1350,19 +1324,20 @@ class FreqaiDataKitchen:
"""
path_to_predictionfile = Path(self.full_path /
self.backtest_predictions_folder /
f"{self.model_filename}_prediction.h5")
f"{self.model_filename}_prediction.feather")
self.backtesting_results_path = path_to_predictionfile
file_exists = path_to_predictionfile.is_file()
if file_exists:
append_df = self.get_backtesting_prediction()
if len(append_df) == len_backtest_df:
if len(append_df) == len_backtest_df and 'date' in append_df:
logger.info(f"Found backtesting prediction file at {path_to_predictionfile}")
return True
else:
logger.info("A new backtesting prediction file is required. "
"(Number of predictions is different from dataframe length).")
"(Number of predictions is different from dataframe length or "
"old prediction file version).")
return False
else:
logger.info(
@ -1370,17 +1345,6 @@ class FreqaiDataKitchen:
)
return False
def set_timerange_from_ready_models(self):
backtesting_timerange, \
assets_end_dates = (
self.get_timerange_and_assets_end_dates_from_ready_models(self.full_path))
self.backtest_live_models_data = {
"backtesting_timerange": backtesting_timerange,
"assets_end_dates": assets_end_dates
}
return
def get_full_models_path(self, config: Config) -> Path:
"""
Returns default FreqAI model path
@ -1391,88 +1355,6 @@ class FreqaiDataKitchen:
config["user_data_dir"] / "models" / str(freqai_config.get("identifier"))
)
def get_timerange_and_assets_end_dates_from_ready_models(
self, models_path: Path) -> Tuple[TimeRange, Dict[str, Any]]:
"""
Returns timerange information based on a FreqAI model directory
:param models_path: FreqAI model path
:return: a Tuple with (Timerange calculated from directory and
a Dict with pair and model end training dates info)
"""
all_models_end_dates = []
assets_end_dates: Dict[str, Any] = self.get_assets_timestamps_training_from_ready_models(
models_path)
for key in assets_end_dates:
for model_end_date in assets_end_dates[key]:
if model_end_date not in all_models_end_dates:
all_models_end_dates.append(model_end_date)
if len(all_models_end_dates) == 0:
raise OperationalException(
'At least 1 saved model is required to '
'run backtest with the freqai-backtest-live-models option'
)
if len(all_models_end_dates) == 1:
logger.warning(
"Only 1 model was found. Backtesting will run with the "
"timerange from the end of the training date to the current date"
)
finish_timestamp = int(datetime.now(tz=timezone.utc).timestamp())
if len(all_models_end_dates) > 1:
# After last model end date, use the same period from previous model
# to finish the backtest
all_models_end_dates.sort(reverse=True)
finish_timestamp = all_models_end_dates[0] + \
(all_models_end_dates[0] - all_models_end_dates[1])
all_models_end_dates.append(finish_timestamp)
all_models_end_dates.sort()
start_date = (datetime(*datetime.fromtimestamp(min(all_models_end_dates),
timezone.utc).timetuple()[:3], tzinfo=timezone.utc))
end_date = (datetime(*datetime.fromtimestamp(max(all_models_end_dates),
timezone.utc).timetuple()[:3], tzinfo=timezone.utc))
# add 1 day to string timerange to ensure BT module will load all dataframe data
end_date = end_date + timedelta(days=1)
backtesting_timerange = TimeRange(
'date', 'date', int(start_date.timestamp()), int(end_date.timestamp())
)
return backtesting_timerange, assets_end_dates
def get_assets_timestamps_training_from_ready_models(
self, models_path: Path) -> Dict[str, Any]:
"""
Scan the models path and returns all assets end training dates (timestamp)
:param models_path: FreqAI model path
:return: a Dict with asset and model end training dates info
"""
assets_end_dates: Dict[str, Any] = {}
if not models_path.is_dir():
raise OperationalException(
'Model folders not found. Saved models are required '
'to run backtest with the freqai-backtest-live-models option'
)
for model_dir in models_path.iterdir():
if str(model_dir.name).startswith("sub-train"):
model_end_date = int(model_dir.name.split("_")[1])
asset = model_dir.name.split("_")[0].replace("sub-train-", "")
model_file_name = (
f"cb_{str(model_dir.name).replace('sub-train-', '').lower()}"
"_model.joblib"
)
model_path_file = Path(model_dir / model_file_name)
if model_path_file.is_file():
if asset not in assets_end_dates:
assets_end_dates[asset] = []
assets_end_dates[asset].append(model_end_date)
return assets_end_dates
def remove_special_chars_from_feature_names(self, dataframe: pd.DataFrame) -> pd.DataFrame:
"""
Remove all special characters from feature strings (:)

139
freqtrade/freqai/freqai_interface.py
View File

@ -5,15 +5,17 @@ from abc import ABC, abstractmethod
from collections import deque
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict, List, Literal, Tuple
from typing import Any, Dict, List, Literal, Optional, Tuple
import numpy as np
import pandas as pd
import psutil
from numpy.typing import NDArray
from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.constants import Config
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_seconds
@ -67,6 +69,7 @@ class IFreqaiModel(ABC):
self.save_backtest_models: bool = self.freqai_info.get("save_backtest_models", True)
if self.save_backtest_models:
logger.info('Backtesting module configured to save all models.')
self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
# set current candle to arbitrary historical date
self.current_candle: datetime = datetime.fromtimestamp(637887600, tz=timezone.utc)
@ -98,6 +101,10 @@ class IFreqaiModel(ABC):
self.get_corr_dataframes: bool = True
self._threads: List[threading.Thread] = []
self._stop_event = threading.Event()
self.metadata: Dict[str, Any] = self.dd.load_global_metadata_from_disk()
self.data_provider: Optional[DataProvider] = None
self.max_system_threads = max(int(psutil.cpu_count() * 2 - 2), 1)
self.can_short = True # overridden in start() with strategy.can_short
record_params(config, self.full_path)
@ -126,11 +133,14 @@ class IFreqaiModel(ABC):
self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
self.dd.set_pair_dict_info(metadata)
self.data_provider = strategy.dp
self.can_short = strategy.can_short
if self.live:
self.inference_timer('start')
self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
dk = self.start_live(dataframe, metadata, strategy, self.dk)
dataframe = dk.remove_features_from_df(dk.return_dataframe)
# For backtesting, each pair enters and then gets trained for each window along the
# sliding window defined by "train_period_days" (training window) and "live_retrain_hours"
@ -139,20 +149,24 @@ class IFreqaiModel(ABC):
# the concatenated results for the full backtesting period back to the strategy.
elif not self.follow_mode:
self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
if self.dk.backtest_live_models:
logger.info(
f"Backtesting {len(self.dk.backtesting_timeranges)} timeranges (live models)")
else:
logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
dataframe = self.dk.use_strategy_to_populate_indicators(
strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
)
if not self.config.get("freqai_backtest_live_models", False):
logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
dk = self.start_backtesting(dataframe, metadata, self.dk)
dataframe = dk.remove_features_from_df(dk.return_dataframe)
else:
logger.info(
"Backtesting using historic predictions (live models)")
dk = self.start_backtesting_from_historic_predictions(
dataframe, metadata, self.dk)
dataframe = dk.return_dataframe
self.clean_up()
if self.live:
self.inference_timer('stop', metadata["pair"])
return dataframe
def clean_up(self):
@ -164,6 +178,13 @@ class IFreqaiModel(ABC):
self.model = None
self.dk = None
def _on_stop(self):
"""
Callback for Subclasses to override to include logic for shutting down resources
when SIGINT is sent.
"""
return
def shutdown(self):
"""
Cleans up threads on Shutdown, set stop event. Join threads to wait
@ -172,6 +193,9 @@ class IFreqaiModel(ABC):
logger.info("Stopping FreqAI")
self._stop_event.set()
self.data_provider = None
self._on_stop()
logger.info("Waiting on Training iteration")
for _thread in self._threads:
_thread.join()
@ -260,10 +284,10 @@ class IFreqaiModel(ABC):
train_it += 1
total_trains = len(dk.backtesting_timeranges)
self.training_timerange = tr_train
dataframe_train = dk.slice_dataframe(tr_train, dataframe)
dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe)
len_backtest_df = len(dataframe.loc[(dataframe["date"] >= tr_backtest.startdt) & (
dataframe["date"] < tr_backtest.stopdt), :])
if not self.ensure_data_exists(dataframe_backtest, tr_backtest, pair):
if not self.ensure_data_exists(len_backtest_df, tr_backtest, pair):
continue
self.log_backtesting_progress(tr_train, pair, train_it, total_trains)
@ -276,13 +300,15 @@ class IFreqaiModel(ABC):
dk.set_new_model_names(pair, timestamp_model_id)
if dk.check_if_backtest_prediction_is_valid(len(dataframe_backtest)):
if dk.check_if_backtest_prediction_is_valid(len_backtest_df):
self.dd.load_metadata(dk)
dk.find_features(dataframe_train)
dk.find_features(dataframe)
self.check_if_feature_list_matches_strategy(dk)
append_df = dk.get_backtesting_prediction()
dk.append_predictions(append_df)
else:
dataframe_train = dk.slice_dataframe(tr_train, dataframe)
dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe)
if not self.model_exists(dk):
dk.find_features(dataframe_train)
dk.find_labels(dataframe_train)
@ -301,10 +327,11 @@ class IFreqaiModel(ABC):
self.model = self.dd.load_data(pair, dk)
pred_df, do_preds = self.predict(dataframe_backtest, dk)
append_df = dk.get_predictions_to_append(pred_df, do_preds)
append_df = dk.get_predictions_to_append(pred_df, do_preds, dataframe_backtest)
dk.append_predictions(append_df)
dk.save_backtesting_prediction(append_df)
self.backtesting_fit_live_predictions(dk)
dk.fill_predictions(dataframe)
return dk
@ -617,6 +644,8 @@ class IFreqaiModel(ABC):
self.dd.historic_predictions[pair] = pred_df
hist_preds_df = self.dd.historic_predictions[pair]
self.set_start_dry_live_date(strat_df)
for label in hist_preds_df.columns:
if hist_preds_df[label].dtype == object:
continue
@ -657,7 +686,8 @@ class IFreqaiModel(ABC):
for label in full_labels:
if self.dd.historic_predictions[dk.pair][label].dtype == object:
continue
f = spy.stats.norm.fit(self.dd.historic_predictions[dk.pair][label].tail(num_candles))
f = spy.stats.norm.fit(
self.dd.historic_predictions[dk.pair][label].tail(num_candles))
dk.data["labels_mean"][label], dk.data["labels_std"][label] = f[0], f[1]
return
@ -778,16 +808,16 @@ class IFreqaiModel(ABC):
self.pair_it = 1
self.current_candle = self.dd.current_candle
def ensure_data_exists(self, dataframe_backtest: DataFrame,
def ensure_data_exists(self, len_dataframe_backtest: int,
tr_backtest: TimeRange, pair: str) -> bool:
"""
Check if the dataframe is empty, if not, report useful information to user.
:param dataframe_backtest: the backtesting dataframe, maybe empty.
:param len_dataframe_backtest: the len of backtesting dataframe
:param tr_backtest: current backtesting timerange.
:param pair: current pair
:return: if the data exists or not
"""
if self.config.get("freqai_backtest_live_models", False) and len(dataframe_backtest) == 0:
if self.config.get("freqai_backtest_live_models", False) and len_dataframe_backtest == 0:
logger.info(f"No data found for pair {pair} from "
f"from { tr_backtest.start_fmt} to {tr_backtest.stop_fmt}. "
"Probably more than one training within the same candle period.")
@ -811,6 +841,81 @@ class IFreqaiModel(ABC):
f"to {tr_train.stop_fmt}, {train_it}/{total_trains} "
"trains"
)
def backtesting_fit_live_predictions(self, dk: FreqaiDataKitchen):
"""
Apply fit_live_predictions function in backtesting with a dummy historic_predictions
The loop is required to simulate dry/live operation, as it is not possible to predict
the type of logic implemented by the user.
:param dk: datakitchen object
"""
fit_live_predictions_candles = self.freqai_info.get("fit_live_predictions_candles", 0)
if fit_live_predictions_candles:
logger.info("Applying fit_live_predictions in backtesting")
label_columns = [col for col in dk.full_df.columns if (
col.startswith("&") and
not (col.startswith("&") and col.endswith("_mean")) and
not (col.startswith("&") and col.endswith("_std")) and
col not in self.dk.data["extra_returns_per_train"])
]
for index in range(len(dk.full_df)):
if index >= fit_live_predictions_candles:
self.dd.historic_predictions[self.dk.pair] = (
dk.full_df.iloc[index - fit_live_predictions_candles:index])
self.fit_live_predictions(self.dk, self.dk.pair)
for label in label_columns:
if dk.full_df[label].dtype == object:
continue
if "labels_mean" in self.dk.data:
dk.full_df.at[index, f"{label}_mean"] = (
self.dk.data["labels_mean"][label])
if "labels_std" in self.dk.data:
dk.full_df.at[index, f"{label}_std"] = self.dk.data["labels_std"][label]
for extra_col in self.dk.data["extra_returns_per_train"]:
dk.full_df.at[index, f"{extra_col}"] = (
self.dk.data["extra_returns_per_train"][extra_col])
return
def update_metadata(self, metadata: Dict[str, Any]):
"""
Update global metadata and save the updated json file
:param metadata: new global metadata dict
"""
self.dd.save_global_metadata_to_disk(metadata)
self.metadata = metadata
def set_start_dry_live_date(self, live_dataframe: DataFrame):
key_name = "start_dry_live_date"
if key_name not in self.metadata:
metadata = self.metadata
metadata[key_name] = int(
pd.to_datetime(live_dataframe.tail(1)["date"].values[0]).timestamp())
self.update_metadata(metadata)
def start_backtesting_from_historic_predictions(
self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen
) -> FreqaiDataKitchen:
"""
:param dataframe: DataFrame = strategy passed dataframe
:param metadata: Dict = pair metadata
:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
:return:
FreqaiDataKitchen = Data management/analysis tool associated to present pair only
"""
pair = metadata["pair"]
dk.return_dataframe = dataframe
saved_dataframe = self.dd.historic_predictions[pair]
columns_to_drop = list(set(saved_dataframe.columns).intersection(
dk.return_dataframe.columns))
dk.return_dataframe = dk.return_dataframe.drop(columns=list(columns_to_drop))
dk.return_dataframe = pd.merge(
dk.return_dataframe, saved_dataframe, how='left', left_on='date', right_on="date_pred")
# dk.return_dataframe = dk.return_dataframe[saved_dataframe.columns].fillna(0)
return dk
# Following methods which are overridden by user made prediction models.
# See freqai/prediction_models/CatboostPredictionModel.py for an example.

145
freqtrade/freqai/prediction_models/ReinforcementLearner.py Normal file
View File

@ -0,0 +1,145 @@
import logging
from pathlib import Path
from typing import Any, Dict
import torch as th
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
logger = logging.getLogger(__name__)
class ReinforcementLearner(BaseReinforcementLearningModel):
"""
Reinforcement Learning Model prediction model.
Users can inherit from this class to make their own RL model with custom
environment/training controls. Define the file as follows:
```
from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
class MyCoolRLModel(ReinforcementLearner):
```
Save the file to `user_data/freqaimodels`, then run it with:
freqtrade trade --freqaimodel MyCoolRLModel --config config.json --strategy SomeCoolStrat
Here the users can override any of the functions
available in the `IFreqaiModel` inheritance tree. Most importantly for RL, this
is where the user overrides `MyRLEnv` (see below), to define custom
`calculate_reward()` function, or to override any other parts of the environment.
This class also allows users to override any other part of the IFreqaiModel tree.
For example, the user can override `def fit()` or `def train()` or `def predict()`
to take fine-tuned control over these processes.
Another common override may be `def data_cleaning_predict()` where the user can
take fine-tuned control over the data handling pipeline.
"""
def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs):
"""
User customizable fit method
:param data_dictionary: dict = common data dictionary containing all train/test
features/labels/weights.
:param dk: FreqaiDatakitchen = data kitchen for current pair.
:return:
model Any = trained model to be used for inference in dry/live/backtesting
"""
train_df = data_dictionary["train_features"]
total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
policy_kwargs = dict(activation_fn=th.nn.ReLU,
net_arch=self.net_arch)
if dk.pair not in self.dd.model_dictionary or not self.continual_learning:
model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
tensorboard_log=Path(
dk.full_path / "tensorboard" / dk.pair.split('/')[0]),
**self.freqai_info.get('model_training_parameters', {})
)
else:
logger.info('Continual training activated - starting training from previously '
'trained agent.')
model = self.dd.model_dictionary[dk.pair]
model.set_env(self.train_env)
model.learn(
total_timesteps=int(total_timesteps),
callback=[self.eval_callback, self.tensorboard_callback]
)
if Path(dk.data_path / "best_model.zip").is_file():
logger.info('Callback found a best model.')
best_model = self.MODELCLASS.load(dk.data_path / "best_model")
return best_model
logger.info('Couldnt find best model, using final model instead.')
return model
class MyRLEnv(Base5ActionRLEnv):
"""
User can override any function in BaseRLEnv and gym.Env. Here the user
sets a custom reward based on profit and trade duration.
"""
def calculate_reward(self, action: int) -> float:
"""
An example reward function. This is the one function that users will likely
wish to inject their own creativity into.
:param action: int = The action made by the agent for the current candle.
:return:
float = the reward to give to the agent for current step (used for optimization
of weights in NN)
"""
# first, penalize if the action is not valid
if not self._is_valid(action):
self.tensorboard_log("is_valid")
return -2
pnl = self.get_unrealized_profit()
factor = 100.
# reward agent for entering trades
if (action == Actions.Long_enter.value
and self._position == Positions.Neutral):
return 25
if (action == Actions.Short_enter.value
and self._position == Positions.Neutral):
return 25
# discourage agent from not entering trades
if action == Actions.Neutral.value and self._position == Positions.Neutral:
return -1
max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
trade_duration = self._current_tick - self._last_trade_tick # type: ignore
if trade_duration <= max_trade_duration:
factor *= 1.5
elif trade_duration > max_trade_duration:
factor *= 0.5
# discourage sitting in position
if (self._position in (Positions.Short, Positions.Long) and
action == Actions.Neutral.value):
return -1 * trade_duration / max_trade_duration
# close long
if action == Actions.Long_exit.value and self._position == Positions.Long:
if pnl > self.profit_aim * self.rr:
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
return float(pnl * factor)
# close short
if action == Actions.Short_exit.value and self._position == Positions.Short:
if pnl > self.profit_aim * self.rr:
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
return float(pnl * factor)
return 0.

57
freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py Normal file
View File

@ -0,0 +1,57 @@
import logging
from typing import Any, Dict
from pandas import DataFrame
from stable_baselines3.common.callbacks import EvalCallback
from stable_baselines3.common.vec_env import SubprocVecEnv
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
from freqtrade.freqai.RL.BaseReinforcementLearningModel import make_env
from freqtrade.freqai.RL.TensorboardCallback import TensorboardCallback
logger = logging.getLogger(__name__)
class ReinforcementLearner_multiproc(ReinforcementLearner):
"""
Demonstration of how to build vectorized environments
"""
def set_train_and_eval_environments(self, data_dictionary: Dict[str, Any],
prices_train: DataFrame, prices_test: DataFrame,
dk: FreqaiDataKitchen):
"""
User can override this if they are using a custom MyRLEnv
:param data_dictionary: dict = common data dictionary containing train and test
features/labels/weights.
:param prices_train/test: DataFrame = dataframe comprised of the prices to be used in
the environment during training
or testing
:param dk: FreqaiDataKitchen = the datakitchen for the current pair
"""
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
env_info = self.pack_env_dict()
env_id = "train_env"
self.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1,
train_df, prices_train,
monitor=True,
env_info=env_info) for i
in range(self.max_threads)])
eval_env_id = 'eval_env'
self.eval_env = SubprocVecEnv([make_env(self.MyRLEnv, eval_env_id, i, 1,
test_df, prices_test,
monitor=True,
env_info=env_info) for i
in range(self.max_threads)])
self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
render=False, eval_freq=len(train_df),
best_model_save_path=str(dk.data_path))
actions = self.train_env.env_method("get_actions")[0]
self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)

4
freqtrade/freqai/utils.py
View File

@ -14,6 +14,7 @@ from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_seconds
from freqtrade.exchange.exchange import market_is_active
from freqtrade.freqai.data_drawer import FreqaiDataDrawer
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist
@ -229,5 +230,6 @@ def get_timerange_backtest_live_models(config: Config) -> str:
"""
dk = FreqaiDataKitchen(config)
models_path = dk.get_full_models_path(config)
timerange, _ = dk.get_timerange_and_assets_end_dates_from_ready_models(models_path)
dd = FreqaiDataDrawer(models_path, config)
timerange = dd.get_timerange_from_live_historic_predictions()
return timerange.timerange_str

12
freqtrade/freqtradebot.py
View File

@ -155,6 +155,8 @@ class FreqtradeBot(LoggingMixin):
self.cancel_all_open_orders()
self.check_for_open_trades()
except Exception as e:
logger.warning(f'Exception during cleanup: {e.__class__.__name__} {e}')
finally:
self.strategy.ft_bot_cleanup()
@ -162,8 +164,13 @@ class FreqtradeBot(LoggingMixin):
self.rpc.cleanup()
if self.emc:
self.emc.shutdown()
Trade.commit()
self.exchange.close()
try:
Trade.commit()
except Exception:
# Exeptions here will be happening if the db disappeared.
# At which point we can no longer commit anyway.
pass
def startup(self) -> None:
"""
@ -905,6 +912,7 @@ class FreqtradeBot(LoggingMixin):
stake_amount=stake_amount,
min_stake_amount=min_stake_amount,
max_stake_amount=max_stake_amount,
trade_amount=trade.stake_amount if trade else None,
)
return enter_limit_requested, stake_amount, leverage
@ -1151,7 +1159,7 @@ class FreqtradeBot(LoggingMixin):
stoploss = (
self.edge.stoploss(pair=trade.pair)
if self.edge else
self.strategy.stoploss / trade.leverage
trade.stop_loss_pct / trade.leverage
)
if trade.is_short:
stop_price = trade.open_rate * (1 - stoploss)

3
freqtrade/main.py
View File

@ -7,6 +7,8 @@ import logging
import sys
from typing import Any, List
from freqtrade.util.gc_setup import gc_set_threshold
# check min. python version
if sys.version_info < (3, 8): # pragma: no cover
@ -36,6 +38,7 @@ def main(sysargv: List[str] = None) -> None:
# Call subcommand.
if 'func' in args:
logger.info(f'freqtrade {__version__}')
gc_set_threshold()
return_code = args['func'](args)
else:
# No subcommand was issued.

18
freqtrade/misc.py
View File

@ -301,3 +301,21 @@ def remove_entry_exit_signals(dataframe: pd.DataFrame):
dataframe[SignalTagType.EXIT_TAG.value] = None
return dataframe
def append_candles_to_dataframe(left: pd.DataFrame, right: pd.DataFrame) -> pd.DataFrame:
"""
Append the `right` dataframe to the `left` dataframe
:param left: The full dataframe you want appended to
:param right: The new dataframe containing the data you want appended
:returns: The dataframe with the right data in it
"""
if left.iloc[-1]['date'] != right.iloc[-1]['date']:
left = pd.concat([left, right])
# Only keep the last 1500 candles in memory
left = left[-1500:] if len(left) > 1500 else left
left.reset_index(drop=True, inplace=True)
return left

66
freqtrade/optimize/backtesting.py
View File

@ -692,10 +692,11 @@ class Backtesting:
trade.orders.append(order)
return trade
def _get_exit_trade_entry(self, trade: LocalTrade, row: Tuple) -> Optional[LocalTrade]:
def _get_exit_trade_entry(
self, trade: LocalTrade, row: Tuple, is_first: bool) -> Optional[LocalTrade]:
exit_candle_time: datetime = row[DATE_IDX].to_pydatetime()
if self.trading_mode == TradingMode.FUTURES:
if is_first and self.trading_mode == TradingMode.FUTURES:
trade.funding_fees = self.exchange.calculate_funding_fees(
self.futures_data[trade.pair],
amount=trade.amount,
@ -704,31 +705,6 @@ class Backtesting:
close_date=exit_candle_time,
)
if self.timeframe_detail and trade.pair in self.detail_data:
exit_candle_end = exit_candle_time + timedelta(minutes=self.timeframe_min)
detail_data = self.detail_data[trade.pair]
detail_data = detail_data.loc[
(detail_data['date'] >= exit_candle_time) &
(detail_data['date'] < exit_candle_end)
].copy()
if len(detail_data) == 0:
# Fall back to "regular" data if no detail data was found for this candle
return self._get_exit_trade_entry_for_candle(trade, row)
detail_data.loc[:, 'enter_long'] = row[LONG_IDX]
detail_data.loc[:, 'exit_long'] = row[ELONG_IDX]
detail_data.loc[:, 'enter_short'] = row[SHORT_IDX]
detail_data.loc[:, 'exit_short'] = row[ESHORT_IDX]
detail_data.loc[:, 'enter_tag'] = row[ENTER_TAG_IDX]
detail_data.loc[:, 'exit_tag'] = row[EXIT_TAG_IDX]
for det_row in detail_data[HEADERS].values.tolist():
res = self._get_exit_trade_entry_for_candle(trade, det_row)
if res:
return res
return None
else:
return self._get_exit_trade_entry_for_candle(trade, row)
def get_valid_price_and_stake(
@ -793,6 +769,7 @@ class Backtesting:
stake_amount=stake_amount,
min_stake_amount=min_stake_amount,
max_stake_amount=max_stake_amount,
trade_amount=trade.stake_amount if trade else None
)
return propose_rate, stake_amount_val, leverage, min_stake_amount
@ -1074,7 +1051,7 @@ class Backtesting:
def backtest_loop(
self, row: Tuple, pair: str, current_time: datetime, end_date: datetime,
max_open_trades: int, open_trade_count_start: int) -> int:
max_open_trades: int, open_trade_count_start: int, is_first: bool = True) -> int:
"""
NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
@ -1092,9 +1069,11 @@ class Backtesting:
# without positionstacking, we can only have one open trade per pair.
# max_open_trades must be respected
# don't open on the last row
# We only open trades on the main candle, not on detail candles
trade_dir = self.check_for_trade_entry(row)
if (
(self._position_stacking or len(LocalTrade.bt_trades_open_pp[pair]) == 0)
and is_first
and self.trade_slot_available(max_open_trades, open_trade_count_start)
and current_time != end_date
and trade_dir is not None
@ -1120,7 +1099,7 @@ class Backtesting:
# 4. Create exit orders (if any)
if not trade.open_order_id:
self._get_exit_trade_entry(trade, row) # Place exit order if necessary
self._get_exit_trade_entry(trade, row, is_first) # Place exit order if necessary
# 5. Process exit orders.
order = trade.select_order(trade.exit_side, is_open=True)
@ -1171,7 +1150,6 @@ class Backtesting:
self.progress.init_step(BacktestState.BACKTEST, int(
(end_date - start_date) / timedelta(minutes=self.timeframe_min)))
# Loop timerange and get candle for each pair at that point in time
while current_time <= end_date:
open_trade_count_start = LocalTrade.bt_open_open_trade_count
@ -1185,7 +1163,35 @@ class Backtesting:
row_index += 1
indexes[pair] = row_index
self.dataprovider._set_dataframe_max_index(row_index)
current_detail_time: datetime = row[DATE_IDX].to_pydatetime()
if self.timeframe_detail and pair in self.detail_data:
exit_candle_end = current_detail_time + timedelta(minutes=self.timeframe_min)
detail_data = self.detail_data[pair]
detail_data = detail_data.loc[
(detail_data['date'] >= current_detail_time) &
(detail_data['date'] < exit_candle_end)
].copy()
if len(detail_data) == 0:
# Fall back to "regular" data if no detail data was found for this candle
open_trade_count_start = self.backtest_loop(
row, pair, current_time, end_date, max_open_trades,
open_trade_count_start)
detail_data.loc[:, 'enter_long'] = row[LONG_IDX]
detail_data.loc[:, 'exit_long'] = row[ELONG_IDX]
detail_data.loc[:, 'enter_short'] = row[SHORT_IDX]
detail_data.loc[:, 'exit_short'] = row[ESHORT_IDX]
detail_data.loc[:, 'enter_tag'] = row[ENTER_TAG_IDX]
detail_data.loc[:, 'exit_tag'] = row[EXIT_TAG_IDX]
is_first = True
current_time_det = current_time
for det_row in detail_data[HEADERS].values.tolist():
open_trade_count_start = self.backtest_loop(
det_row, pair, current_time_det, end_date, max_open_trades,
open_trade_count_start, is_first)
current_time_det += timedelta(minutes=self.timeframe_detail_min)
is_first = False
else:
open_trade_count_start = self.backtest_loop(
row, pair, current_time, end_date, max_open_trades, open_trade_count_start)

15
freqtrade/persistence/migrations.py
View File

@ -109,11 +109,10 @@ def migrate_trades_and_orders_table(
else:
is_short = get_column_def(cols, 'is_short', '0')
# Margin Properties
# Futures Properties
interest_rate = get_column_def(cols, 'interest_rate', '0.0')
# Futures properties
funding_fees = get_column_def(cols, 'funding_fees', '0.0')
max_stake_amount = get_column_def(cols, 'max_stake_amount', 'stake_amount')
# If ticker-interval existed use that, else null.
if has_column(cols, 'ticker_interval'):
@ -162,7 +161,8 @@ def migrate_trades_and_orders_table(
timeframe, open_trade_value, close_profit_abs,
trading_mode, leverage, liquidation_price, is_short,
interest_rate, funding_fees, realized_profit,
amount_precision, price_precision, precision_mode, contract_size
amount_precision, price_precision, precision_mode, contract_size,
max_stake_amount
)
select id, lower(exchange), pair, {base_currency} base_currency,
{stake_currency} stake_currency,
@ -190,7 +190,8 @@ def migrate_trades_and_orders_table(
{is_short} is_short, {interest_rate} interest_rate,
{funding_fees} funding_fees, {realized_profit} realized_profit,
{amount_precision} amount_precision, {price_precision} price_precision,
{precision_mode} precision_mode, {contract_size} contract_size
{precision_mode} precision_mode, {contract_size} contract_size,
{max_stake_amount} max_stake_amount
from {trade_back_name}
"""))
@ -310,8 +311,8 @@ def check_migrate(engine, decl_base, previous_tables) -> None:
# if ('orders' not in previous_tables
# or not has_column(cols_orders, 'funding_fee')):
migrating = False
# if not has_column(cols_trades, 'contract_size'):
if not has_column(cols_orders, 'funding_fee'):
# if not has_column(cols_orders, 'funding_fee'):
if not has_column(cols_trades, 'max_stake_amount'):
migrating = True
logger.info(f"Running database migration for trades - "
f"backup: {table_back_name}, {order_table_bak_name}")

2
freqtrade/persistence/pairlock_middleware.py
View File

@ -87,7 +87,7 @@ class PairLocks():
Get the lock that expires the latest for the pair given.
"""
locks = PairLocks.get_pair_locks(pair, now, side=side)
locks = sorted(locks, key=lambda l: l.lock_end_time, reverse=True)
locks = sorted(locks, key=lambda lock: lock.lock_end_time, reverse=True)
return locks[0] if locks else None
@staticmethod

14
freqtrade/persistence/trade_model.py
View File

@ -293,6 +293,7 @@ class LocalTrade():
close_profit: Optional[float] = None
close_profit_abs: Optional[float] = None
stake_amount: float = 0.0
max_stake_amount: float = 0.0
amount: float = 0.0
amount_requested: Optional[float] = None
open_date: datetime
@ -397,12 +398,6 @@ class LocalTrade():
def close_date_utc(self):
return self.close_date.replace(tzinfo=timezone.utc)
@property
def enter_side(self) -> str:
""" DEPRECATED, please use entry_side instead"""
# TODO: Please remove me after 2022.5
return self.entry_side
@property
def entry_side(self) -> str:
if self.is_short:
@ -475,8 +470,8 @@ class LocalTrade():
'amount': round(self.amount, 8),
'amount_requested': round(self.amount_requested, 8) if self.amount_requested else None,
'stake_amount': round(self.stake_amount, 8),
'max_stake_amount': round(self.max_stake_amount, 8) if self.max_stake_amount else None,
'strategy': self.strategy,
'buy_tag': self.enter_tag,
'enter_tag': self.enter_tag,
'timeframe': self.timeframe,
@ -513,7 +508,6 @@ class LocalTrade():
'profit_pct': round(self.close_profit * 100, 2) if self.close_profit else None,
'profit_abs': self.close_profit_abs,
'sell_reason': self.exit_reason, # Deprecated
'exit_reason': self.exit_reason,
'exit_order_status': self.exit_order_status,
'stop_loss_abs': self.stop_loss,
@ -882,6 +876,7 @@ class LocalTrade():
ZERO = FtPrecise(0.0)
current_amount = FtPrecise(0.0)
current_stake = FtPrecise(0.0)
max_stake_amount = FtPrecise(0.0)
total_stake = 0.0 # Total stake after all buy orders (does not subtract!)
avg_price = FtPrecise(0.0)
close_profit = 0.0
@ -923,7 +918,9 @@ class LocalTrade():
exit_rate, amount=exit_amount, open_rate=avg_price)
else:
total_stake = total_stake + self._calc_open_trade_value(tmp_amount, price)
max_stake_amount += (tmp_amount * price)
self.funding_fees = funding_fees
self.max_stake_amount = float(max_stake_amount)
if close_profit:
self.close_profit = close_profit
@ -1175,6 +1172,7 @@ class Trade(_DECL_BASE, LocalTrade):
close_profit = Column(Float)
close_profit_abs = Column(Float)
stake_amount = Column(Float, nullable=False)
max_stake_amount = Column(Float)
amount = Column(Float)
amount_requested = Column(Float)
open_date = Column(DateTime, nullable=False, default=datetime.utcnow)

206
freqtrade/plugins/pairlist/RemotePairList.py Normal file
View File

@ -0,0 +1,206 @@
"""
Remote PairList provider
Provides pair list fetched from a remote source
"""
import json
import logging
from pathlib import Path
from typing import Any, Dict, List, Tuple
import requests
from cachetools import TTLCache
from freqtrade import __version__
from freqtrade.constants import Config
from freqtrade.exceptions import OperationalException
from freqtrade.exchange.types import Tickers
from freqtrade.plugins.pairlist.IPairList import IPairList
logger = logging.getLogger(__name__)
class RemotePairList(IPairList):
def __init__(self, exchange, pairlistmanager,
config: Config, pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
if 'number_assets' not in self._pairlistconfig:
raise OperationalException(
'`number_assets` not specified. Please check your configuration '
'for "pairlist.config.number_assets"')
if 'pairlist_url' not in self._pairlistconfig:
raise OperationalException(
'`pairlist_url` not specified. Please check your configuration '
'for "pairlist.config.pairlist_url"')
self._number_pairs = self._pairlistconfig['number_assets']
self._refresh_period: int = self._pairlistconfig.get('refresh_period', 1800)
self._keep_pairlist_on_failure = self._pairlistconfig.get('keep_pairlist_on_failure', True)
self._pair_cache: TTLCache = TTLCache(maxsize=1, ttl=self._refresh_period)
self._pairlist_url = self._pairlistconfig.get('pairlist_url', '')
self._read_timeout = self._pairlistconfig.get('read_timeout', 60)
self._bearer_token = self._pairlistconfig.get('bearer_token', '')
self._init_done = False
self._last_pairlist: List[Any] = list()
@property
def needstickers(self) -> bool:
"""
Boolean property defining if tickers are necessary.
If no Pairlist requires tickers, an empty Dict is passed
as tickers argument to filter_pairlist
"""
return False
def short_desc(self) -> str:
"""
Short whitelist method description - used for startup-messages
"""
return f"{self.name} - {self._pairlistconfig['number_assets']} pairs from RemotePairlist."
def process_json(self, jsonparse) -> List[str]:
pairlist = jsonparse.get('pairs', [])
remote_refresh_period = int(jsonparse.get('refresh_period', self._refresh_period))
if self._refresh_period < remote_refresh_period:
self.log_once(f'Refresh Period has been increased from {self._refresh_period}'
f' to minimum allowed: {remote_refresh_period} from Remote.', logger.info)
self._refresh_period = remote_refresh_period
self._pair_cache = TTLCache(maxsize=1, ttl=remote_refresh_period)
self._init_done = True
return pairlist
def return_last_pairlist(self) -> List[str]:
if self._keep_pairlist_on_failure:
pairlist = self._last_pairlist
self.log_once('Keeping last fetched pairlist', logger.info)
else:
pairlist = []
return pairlist
def fetch_pairlist(self) -> Tuple[List[str], float]:
headers = {
'User-Agent': 'Freqtrade/' + __version__ + ' Remotepairlist'
}
if self._bearer_token:
headers['Authorization'] = f'Bearer {self._bearer_token}'
try:
response = requests.get(self._pairlist_url, headers=headers,
timeout=self._read_timeout)
content_type = response.headers.get('content-type')
time_elapsed = response.elapsed.total_seconds()
if "application/json" in str(content_type):
jsonparse = response.json()
try:
pairlist = self.process_json(jsonparse)
except Exception as e:
if self._init_done:
pairlist = self.return_last_pairlist()
logger.warning(f'Error while processing JSON data: {type(e)}')
else:
raise OperationalException(f'Error while processing JSON data: {type(e)}')
else:
if self._init_done:
self.log_once(f'Error: RemotePairList is not of type JSON: '
f' {self._pairlist_url}', logger.info)
pairlist = self.return_last_pairlist()
else:
raise OperationalException('RemotePairList is not of type JSON, abort.')
except requests.exceptions.RequestException:
self.log_once(f'Was not able to fetch pairlist from:'
f' {self._pairlist_url}', logger.info)
pairlist = self.return_last_pairlist()
time_elapsed = 0
return pairlist, time_elapsed
def gen_pairlist(self, tickers: Tickers) -> List[str]:
"""
Generate the pairlist
:param tickers: Tickers (from exchange.get_tickers). May be cached.
:return: List of pairs
"""
if self._init_done:
pairlist = self._pair_cache.get('pairlist')
else:
pairlist = []
time_elapsed = 0.0
if pairlist:
# Item found - no refresh necessary
return pairlist.copy()
else:
if self._pairlist_url.startswith("file:///"):
filename = self._pairlist_url.split("file:///", 1)[1]
file_path = Path(filename)
if file_path.exists():
with open(filename) as json_file:
# Load the JSON data into a dictionary
jsonparse = json.load(json_file)
try:
pairlist = self.process_json(jsonparse)
except Exception as e:
if self._init_done:
pairlist = self.return_last_pairlist()
logger.warning(f'Error while processing JSON data: {type(e)}')
else:
raise OperationalException('Error while processing'
f'JSON data: {type(e)}')
else:
raise ValueError(f"{self._pairlist_url} does not exist.")
else:
# Fetch Pairlist from Remote URL
pairlist, time_elapsed = self.fetch_pairlist()
self.log_once(f"Fetched pairs: {pairlist}", logger.debug)
pairlist = self._whitelist_for_active_markets(pairlist)
pairlist = pairlist[:self._number_pairs]
self._pair_cache['pairlist'] = pairlist.copy()
if time_elapsed != 0.0:
self.log_once(f'Pairlist Fetched in {time_elapsed} seconds.', logger.info)
else:
self.log_once('Fetched Pairlist.', logger.info)
self._last_pairlist = list(pairlist)
return pairlist
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""
Filters and sorts pairlist and returns the whitelist again.
Called on each bot iteration - please use internal caching if necessary
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers). May be cached.
:return: new whitelist
"""
rpl_pairlist = self.gen_pairlist(tickers)
merged_list = pairlist + rpl_pairlist
merged_list = sorted(set(merged_list), key=merged_list.index)
return merged_list

4
freqtrade/plugins/pairlist/VolumePairList.py
View File

@ -135,7 +135,7 @@ class VolumePairList(IPairList):
filtered_tickers = [
v for k, v in tickers.items()
if (self._exchange.get_pair_quote_currency(k) == self._stake_currency
and (self._use_range or v[self._sort_key] is not None)
and (self._use_range or v.get(self._sort_key) is not None)
and v['symbol'] in _pairlist)]
pairlist = [s['symbol'] for s in filtered_tickers]
else:
@ -218,7 +218,7 @@ class VolumePairList(IPairList):
else:
filtered_tickers[i]['quoteVolume'] = 0
else:
# Tickers mode - filter based on incomming pairlist.
# Tickers mode - filter based on incoming pairlist.
filtered_tickers = [v for k, v in tickers.items() if k in pairlist]
if self._min_value > 0:

2
freqtrade/rpc/api_server/api_auth.py
View File

@ -81,8 +81,6 @@ async def validate_ws_token(
except HTTPException:
pass
# No checks passed, deny the connection
logger.debug("Denying websocket request.")
# If it doesn't match, close the websocket connection
await ws.close(code=status.WS_1008_POLICY_VIOLATION)

8
freqtrade/rpc/api_server/api_backtest.py
View File

@ -11,6 +11,7 @@ from freqtrade.configuration.config_validation import validate_config_consistenc
from freqtrade.data.btanalysis import get_backtest_resultlist, load_and_merge_backtest_result
from freqtrade.enums import BacktestState
from freqtrade.exceptions import DependencyException
from freqtrade.misc import deep_merge_dicts
from freqtrade.rpc.api_server.api_schemas import (BacktestHistoryEntry, BacktestRequest,
BacktestResponse)
from freqtrade.rpc.api_server.deps import get_config, is_webserver_mode
@ -37,10 +38,11 @@ async def api_start_backtest(bt_settings: BacktestRequest, background_tasks: Bac
btconfig = deepcopy(config)
settings = dict(bt_settings)
if settings.get('freqai', None) is not None:
settings['freqai'] = dict(settings['freqai'])
# Pydantic models will contain all keys, but non-provided ones are None
for setting in settings.keys():
if settings[setting] is not None:
btconfig[setting] = settings[setting]
btconfig = deep_merge_dicts(settings, btconfig, allow_null_overrides=False)
try:
btconfig['stake_amount'] = float(btconfig['stake_amount'])
except ValueError:

14
freqtrade/rpc/api_server/api_schemas.py
View File

@ -217,8 +217,8 @@ class TradeSchema(BaseModel):
amount: float
amount_requested: float
stake_amount: float
max_stake_amount: Optional[float]
strategy: str
buy_tag: Optional[str] # Deprecated
enter_tag: Optional[str]
timeframe: int
fee_open: Optional[float]
@ -243,7 +243,6 @@ class TradeSchema(BaseModel):
profit_pct: Optional[float]
profit_abs: Optional[float]
profit_fiat: Optional[float]
sell_reason: Optional[str] # Deprecated
exit_reason: Optional[str]
exit_order_status: Optional[str]
stop_loss_abs: Optional[float]
@ -372,6 +371,10 @@ class StrategyListResponse(BaseModel):
strategies: List[str]
class FreqAIModelListResponse(BaseModel):
freqaimodels: List[str]
class StrategyResponse(BaseModel):
strategy: str
code: str
@ -410,6 +413,10 @@ class PairHistory(BaseModel):
}
class BacktestFreqAIInputs(BaseModel):
identifier: str
class BacktestRequest(BaseModel):
strategy: str
timeframe: Optional[str]
@ -419,6 +426,9 @@ class BacktestRequest(BaseModel):
stake_amount: Optional[str]
enable_protections: bool
dry_run_wallet: Optional[float]
backtest_cache: Optional[str]
freqaimodel: Optional[str]
freqai: Optional[BacktestFreqAIInputs]
class BacktestResponse(BaseModel):

27
freqtrade/rpc/api_server/api_v1.py
View File

@ -13,12 +13,13 @@ from freqtrade.rpc import RPC
from freqtrade.rpc.api_server.api_schemas import (AvailablePairs, Balances, BlacklistPayload,
BlacklistResponse, Count, Daily,
DeleteLockRequest, DeleteTrade, ForceEnterPayload,
ForceEnterResponse, ForceExitPayload, Health,
Locks, Logs, OpenTradeSchema, PairHistory,
PerformanceEntry, Ping, PlotConfig, Profit,
ResultMsg, ShowConfig, Stats, StatusMsg,
StrategyListResponse, StrategyResponse, SysInfo,
Version, WhitelistResponse)
ForceEnterResponse, ForceExitPayload,
FreqAIModelListResponse, Health, Locks, Logs,
OpenTradeSchema, PairHistory, PerformanceEntry,
Ping, PlotConfig, Profit, ResultMsg, ShowConfig,
Stats, StatusMsg, StrategyListResponse,
StrategyResponse, SysInfo, Version,
WhitelistResponse)
from freqtrade.rpc.api_server.deps import get_config, get_exchange, get_rpc, get_rpc_optional
from freqtrade.rpc.rpc import RPCException
@ -37,7 +38,9 @@ logger = logging.getLogger(__name__)
# 2.16: Additional daily metrics
# 2.17: Forceentry - leverage, partial force_exit
# 2.20: Add websocket endpoints
API_VERSION = 2.20
# 2.21: Add new_candle messagetype
# 2.22: Add FreqAI to backtesting
API_VERSION = 2.22
# Public API, requires no auth.
router_public = APIRouter()
@ -278,6 +281,16 @@ def get_strategy(strategy: str, config=Depends(get_config)):
}
@router.get('/freqaimodels', response_model=FreqAIModelListResponse, tags=['freqai'])
def list_freqaimodels(config=Depends(get_config)):
from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
strategies = FreqaiModelResolver.search_all_objects(
config, False)
strategies = sorted(strategies, key=lambda x: x['name'])
return {'freqaimodels': [x['name'] for x in strategies]}
@router.get('/available_pairs', response_model=AvailablePairs, tags=['candle data'])
def list_available_pairs(timeframe: Optional[str] = None, stake_currency: Optional[str] = None,
candletype: Optional[CandleType] = None, config=Depends(get_config)):

109
freqtrade/rpc/api_server/api_ws.py
View File

@ -1,16 +1,16 @@
import logging
import time
from typing import Any, Dict
from fastapi import APIRouter, Depends, WebSocketDisconnect
from fastapi.websockets import WebSocket, WebSocketState
from fastapi import APIRouter, Depends
from fastapi.websockets import WebSocket
from pydantic import ValidationError
from websockets.exceptions import WebSocketException
from freqtrade.enums import RPCMessageType, RPCRequestType
from freqtrade.rpc.api_server.api_auth import validate_ws_token
from freqtrade.rpc.api_server.deps import get_channel_manager, get_rpc
from freqtrade.rpc.api_server.ws import WebSocketChannel
from freqtrade.rpc.api_server.ws.channel import ChannelManager
from freqtrade.rpc.api_server.deps import get_message_stream, get_rpc
from freqtrade.rpc.api_server.ws.channel import WebSocketChannel, create_channel
from freqtrade.rpc.api_server.ws.message_stream import MessageStream
from freqtrade.rpc.api_server.ws_schemas import (WSAnalyzedDFMessage, WSMessageSchema,
WSRequestSchema, WSWhitelistMessage)
from freqtrade.rpc.rpc import RPC
@ -22,23 +22,35 @@ logger = logging.getLogger(__name__)
router = APIRouter()
async def is_websocket_alive(ws: WebSocket) -> bool:
async def channel_reader(channel: WebSocketChannel, rpc: RPC):
"""
Check if a FastAPI Websocket is still open
Iterate over the messages from the channel and process the request
"""
if (
ws.application_state == WebSocketState.CONNECTED and
ws.client_state == WebSocketState.CONNECTED
):
return True
return False
async for message in channel:
await _process_consumer_request(message, channel, rpc)
async def channel_broadcaster(channel: WebSocketChannel, message_stream: MessageStream):
"""
Iterate over messages in the message stream and send them
"""
async for message, ts in message_stream:
if channel.subscribed_to(message.get('type')):
# Log a warning if this channel is behind
# on the message stream by a lot
if (time.time() - ts) > 60:
logger.warning(f"Channel {channel} is behind MessageStream by 1 minute,"
" this can cause a memory leak if you see this message"
" often, consider reducing pair list size or amount of"
" consumers.")
await channel.send(message, timeout=True)
async def _process_consumer_request(
request: Dict[str, Any],
channel: WebSocketChannel,
rpc: RPC,
channel_manager: ChannelManager
rpc: RPC
):
"""
Validate and handle a request from a websocket consumer
@ -74,65 +86,30 @@ async def _process_consumer_request(
# Format response
response = WSWhitelistMessage(data=whitelist)
# Send it back
await channel_manager.send_direct(channel, response.dict(exclude_none=True))
await channel.send(response.dict(exclude_none=True))
elif type == RPCRequestType.ANALYZED_DF:
limit = None
if data:
# Limit the amount of candles per dataframe to 'limit' or 1500
limit = max(data.get('limit', 1500), 1500)
limit = min(data.get('limit', 1500), 1500) if data else None
pair = data.get('pair', None) if data else None
# For every pair in the generator, send a separate message
for message in rpc._ws_request_analyzed_df(limit):
for message in rpc._ws_request_analyzed_df(limit, pair):
# Format response
response = WSAnalyzedDFMessage(data=message)
await channel_manager.send_direct(channel, response.dict(exclude_none=True))
await channel.send(response.dict(exclude_none=True))
@router.websocket("/message/ws")
async def message_endpoint(
ws: WebSocket,
websocket: WebSocket,
token: str = Depends(validate_ws_token),
rpc: RPC = Depends(get_rpc),
channel_manager=Depends(get_channel_manager),
token: str = Depends(validate_ws_token)
message_stream: MessageStream = Depends(get_message_stream)
):
"""
Message WebSocket endpoint, facilitates sending RPC messages
"""
try:
channel = await channel_manager.on_connect(ws)
if await is_websocket_alive(ws):
logger.info(f"Consumer connected - {channel}")
# Keep connection open until explicitly closed, and process requests
try:
while not channel.is_closed():
request = await channel.recv()
# Process the request here
await _process_consumer_request(request, channel, rpc, channel_manager)
except (WebSocketDisconnect, WebSocketException):
# Handle client disconnects
logger.info(f"Consumer disconnected - {channel}")
except RuntimeError:
# Handle cases like -
# RuntimeError('Cannot call "send" once a closed message has been sent')
pass
except Exception as e:
logger.info(f"Consumer connection failed - {channel}: {e}")
logger.debug(e, exc_info=e)
except RuntimeError:
# WebSocket was closed
# Do nothing
pass
except Exception as e:
logger.error(f"Failed to serve - {ws.client}")
# Log tracebacks to keep track of what errors are happening
logger.exception(e)
finally:
if channel:
await channel_manager.on_disconnect(ws)
if token:
async with create_channel(websocket) as channel:
await channel.run_channel_tasks(
channel_reader(channel, rpc),
channel_broadcaster(channel, message_stream)
)

4
freqtrade/rpc/api_server/deps.py
View File

@ -41,8 +41,8 @@ def get_exchange(config=Depends(get_config)):
return ApiServer._exchange
def get_channel_manager():
return ApiServer._ws_channel_manager
def get_message_stream():
return ApiServer._message_stream
def is_webserver_mode(config=Depends(get_config)):

108
freqtrade/rpc/api_server/webserver.py
View File

@ -1,22 +1,17 @@
import asyncio
import logging
from ipaddress import IPv4Address
from threading import Thread
from typing import Any, Dict, Optional
import orjson
import uvicorn
from fastapi import Depends, FastAPI
from fastapi.middleware.cors import CORSMiddleware
# Look into alternatives
from janus import Queue as ThreadedQueue
from starlette.responses import JSONResponse
from freqtrade.constants import Config
from freqtrade.exceptions import OperationalException
from freqtrade.rpc.api_server.uvicorn_threaded import UvicornServer
from freqtrade.rpc.api_server.ws import ChannelManager
from freqtrade.rpc.api_server.ws_schemas import WSMessageSchemaType
from freqtrade.rpc.api_server.ws.message_stream import MessageStream
from freqtrade.rpc.rpc import RPC, RPCException, RPCHandler
@ -50,10 +45,8 @@ class ApiServer(RPCHandler):
_config: Config = {}
# Exchange - only available in webserver mode.
_exchange = None
# websocket message queue stuff
_ws_channel_manager: ChannelManager
_ws_thread = None
_ws_loop: Optional[asyncio.AbstractEventLoop] = None
# websocket message stuff
_message_stream: Optional[MessageStream] = None
def __new__(cls, *args, **kwargs):
"""
@ -71,15 +64,11 @@ class ApiServer(RPCHandler):
return
self._standalone: bool = standalone
self._server = None
self._ws_queue: Optional[ThreadedQueue] = None
self._ws_background_task = None
ApiServer.__initialized = True
api_config = self._config['api_server']
ApiServer._ws_channel_manager = ChannelManager()
self.app = FastAPI(title="Freqtrade API",
docs_url='/docs' if api_config.get('enable_openapi', False) else None,
redoc_url=None,
@ -105,21 +94,9 @@ class ApiServer(RPCHandler):
del ApiServer._rpc
if self._server and not self._standalone:
logger.info("Stopping API Server")
# self._server.force_exit, self._server.should_exit = True, True
self._server.cleanup()
if self._ws_thread and self._ws_loop:
logger.info("Stopping API Server background tasks")
if self._ws_background_task:
# Cancel the queue task
self._ws_background_task.cancel()
self._ws_thread.join()
self._ws_thread = None
self._ws_loop = None
self._ws_background_task = None
@classmethod
def shutdown(cls):
cls.__initialized = False
@ -129,9 +106,11 @@ class ApiServer(RPCHandler):
cls._rpc = None
def send_msg(self, msg: Dict[str, Any]) -> None:
if self._ws_queue:
sync_q = self._ws_queue.sync_q
sync_q.put(msg)
"""
Publish the message to the message stream
"""
if ApiServer._message_stream:
ApiServer._message_stream.publish(msg)
def handle_rpc_exception(self, request, exc):
logger.exception(f"API Error calling: {exc}")
@ -170,54 +149,30 @@ class ApiServer(RPCHandler):
)
app.add_exception_handler(RPCException, self.handle_rpc_exception)
app.add_event_handler(
event_type="startup",
func=self._api_startup_event
)
app.add_event_handler(
event_type="shutdown",
func=self._api_shutdown_event
)
def start_message_queue(self):
if self._ws_thread:
return
async def _api_startup_event(self):
"""
Creates the MessageStream class on startup
so it has access to the same event loop
as uvicorn
"""
if not ApiServer._message_stream:
ApiServer._message_stream = MessageStream()
# Create a new loop, as it'll be just for the background thread
self._ws_loop = asyncio.new_event_loop()
# Start the thread
self._ws_thread = Thread(target=self._ws_loop.run_forever)
self._ws_thread.start()
# Finally, submit the coro to the thread
self._ws_background_task = asyncio.run_coroutine_threadsafe(
self._broadcast_queue_data(), loop=self._ws_loop)
async def _broadcast_queue_data(self) -> None:
# Instantiate the queue in this coroutine so it's attached to our loop
self._ws_queue = ThreadedQueue()
async_queue = self._ws_queue.async_q
try:
while True:
logger.debug("Getting queue messages...")
if (qsize := async_queue.qsize()) > 20:
# If the queue becomes too big for too long, this may indicate a problem.
logger.warning(f"Queue size now {qsize}")
# Get data from queue
message: WSMessageSchemaType = await async_queue.get()
logger.debug(f"Found message of type: {message.get('type')}")
async_queue.task_done()
# Broadcast it
await self._ws_channel_manager.broadcast(message)
except asyncio.CancelledError:
pass
# For testing, shouldn't happen when stable
except Exception as e:
logger.exception(f"Exception happened in background task: {e}")
finally:
# Disconnect channels and stop the loop on cancel
await self._ws_channel_manager.disconnect_all()
if self._ws_loop:
self._ws_loop.stop()
# Avoid adding more items to the queue if they aren't
# going to get broadcasted.
self._ws_queue = None
async def _api_shutdown_event(self):
"""
Removes the MessageStream class on shutdown
"""
if ApiServer._message_stream:
ApiServer._message_stream = None
def start_api(self):
"""
@ -257,7 +212,6 @@ class ApiServer(RPCHandler):
if self._standalone:
self._server.run()
else:
self.start_message_queue()
self._server.run_in_thread()
except Exception:
logger.exception("Api server failed to start.")

3
freqtrade/rpc/api_server/ws/__init__.py
View File

@ -3,4 +3,5 @@
from freqtrade.rpc.api_server.ws.types import WebSocketType
from freqtrade.rpc.api_server.ws.proxy import WebSocketProxy
from freqtrade.rpc.api_server.ws.serializer import HybridJSONWebSocketSerializer
from freqtrade.rpc.api_server.ws.channel import ChannelManager, WebSocketChannel
from freqtrade.rpc.api_server.ws.channel import WebSocketChannel
from freqtrade.rpc.api_server.ws.message_stream import MessageStream

267
freqtrade/rpc/api_server/ws/channel.py
View File

@ -1,11 +1,13 @@
import asyncio
import logging
import time
from threading import RLock
from typing import Any, Dict, List, Optional, Type, Union
from collections import deque
from contextlib import asynccontextmanager
from typing import Any, AsyncIterator, Deque, Dict, List, Optional, Type, Union
from uuid import uuid4
from fastapi import WebSocket as FastAPIWebSocket
from fastapi import WebSocketDisconnect
from websockets.exceptions import ConnectionClosed
from freqtrade.rpc.api_server.ws.proxy import WebSocketProxy
from freqtrade.rpc.api_server.ws.serializer import (HybridJSONWebSocketSerializer,
@ -21,31 +23,29 @@ class WebSocketChannel:
"""
Object to help facilitate managing a websocket connection
"""
def __init__(
self,
websocket: WebSocketType,
channel_id: Optional[str] = None,
drain_timeout: int = 3,
throttle: float = 0.01,
serializer_cls: Type[WebSocketSerializer] = HybridJSONWebSocketSerializer
serializer_cls: Type[WebSocketSerializer] = HybridJSONWebSocketSerializer,
send_throttle: float = 0.01
):
self.channel_id = channel_id if channel_id else uuid4().hex[:8]
# The WebSocket object
self._websocket = WebSocketProxy(websocket)
self.drain_timeout = drain_timeout
self.throttle = throttle
self._subscriptions: List[str] = []
# 32 is the size of the receiving queue in websockets package
self.queue: asyncio.Queue[Dict[str, Any]] = asyncio.Queue(maxsize=32)
self._relay_task = asyncio.create_task(self.relay())
# Internal event to signify a closed websocket
self._closed = asyncio.Event()
# The async tasks created for the channel
self._channel_tasks: List[asyncio.Task] = []
# Deque for average send times
self._send_times: Deque[float] = deque([], maxlen=10)
# High limit defaults to 3 to start
self._send_high_limit = 3
self._send_throttle = send_throttle
# The subscribed message types
self._subscriptions: List[str] = []
# Wrap the WebSocket in the Serializing class
self._wrapped_ws = serializer_cls(self._websocket)
@ -61,43 +61,59 @@ class WebSocketChannel:
def remote_addr(self):
return self._websocket.remote_addr
async def _send(self, data):
"""
Send data on the wrapped websocket
"""
await self._wrapped_ws.send(data)
@property
def avg_send_time(self):
return sum(self._send_times) / len(self._send_times)
async def send(self, data) -> bool:
def _calc_send_limit(self):
"""
Add the data to the queue to be sent.
:returns: True if data added to queue, False otherwise
Calculate the send high limit for this channel
"""
# This block only runs if the queue is full, it will wait
# until self.drain_timeout for the relay to drain the outgoing queue
# We can't use asyncio.wait_for here because the queue may have been created with a
# different eventloop
if not self.is_closed():
start = time.time()
while self.queue.full():
await asyncio.sleep(1)
if (time.time() - start) > self.drain_timeout:
return False
# Only update if we have enough data
if len(self._send_times) == self._send_times.maxlen:
# At least 1s or twice the average of send times, with a
# maximum of 3 seconds per message
self._send_high_limit = min(max(self.avg_send_time * 2, 1), 3)
# If for some reason the queue is still full, just return False
async def send(
self,
message: Union[WSMessageSchemaType, Dict[str, Any]],
timeout: bool = False
):
"""
Send a message on the wrapped websocket. If the sending
takes too long, it will raise a TimeoutError and
disconnect the connection.
:param message: The message to send
:param timeout: Enforce send high limit, defaults to False
"""
try:
self.queue.put_nowait(data)
except asyncio.QueueFull:
return False
_ = time.time()
# If the send times out, it will raise
# a TimeoutError and bubble up to the
# message_endpoint to close the connection
await asyncio.wait_for(
self._wrapped_ws.send(message),
timeout=self._send_high_limit if timeout else None
)
total_time = time.time() - _
self._send_times.append(total_time)
# If we got here everything is ok
return True
else:
return False
self._calc_send_limit()
except asyncio.TimeoutError:
logger.info(f"Connection for {self} timed out, disconnecting")
raise
# Explicitly give control back to event loop as
# websockets.send does not
# Also throttles how fast we send
await asyncio.sleep(self._send_throttle)
async def recv(self):
"""
Receive data on the wrapped websocket
Receive a message on the wrapped websocket
"""
return await self._wrapped_ws.recv()
@ -107,17 +123,27 @@ class WebSocketChannel:
"""
return await self._websocket.ping()
async def accept(self):
"""
Accept the underlying websocket connection,
if the connection has been closed before we can
accept, just close the channel.
"""
try:
return await self._websocket.accept()
except RuntimeError:
await self.close()
async def close(self):
"""
Close the WebSocketChannel
"""
self._closed.set()
self._relay_task.cancel()
try:
await self.raw_websocket.close()
except Exception:
await self._websocket.close()
except RuntimeError:
pass
def is_closed(self) -> bool:
@ -142,99 +168,76 @@ class WebSocketChannel:
"""
return message_type in self._subscriptions
async def relay(self):
async def run_channel_tasks(self, *tasks, **kwargs):
"""
Relay messages from the channel's queue and send them out. This is started
as a task.
Create and await on the channel tasks unless an exception
was raised, then cancel them all.
:params *tasks: All coros or tasks to be run concurrently
:param **kwargs: Any extra kwargs to pass to gather
"""
while not self._closed.is_set():
message = await self.queue.get()
if not self.is_closed():
# Wrap the coros into tasks if they aren't already
self._channel_tasks = [
task if isinstance(task, asyncio.Task) else asyncio.create_task(task)
for task in tasks
]
try:
await self._send(message)
self.queue.task_done()
return await asyncio.gather(*self._channel_tasks, **kwargs)
except Exception:
# If an exception occurred, cancel the rest of the tasks
await self.cancel_channel_tasks()
# Limit messages per sec.
# Could cause problems with queue size if too low, and
# problems with network traffik if too high.
# 0.01 = 100/s
await asyncio.sleep(self.throttle)
except RuntimeError:
# The connection was closed, just exit the task
return
class ChannelManager:
def __init__(self):
self.channels = dict()
self._lock = RLock() # Re-entrant Lock
async def on_connect(self, websocket: WebSocketType):
async def cancel_channel_tasks(self):
"""
Wrap websocket connection into Channel and add to list
:param websocket: The WebSocket object to attach to the Channel
Cancel and wait on all channel tasks
"""
if isinstance(websocket, FastAPIWebSocket):
for task in self._channel_tasks:
task.cancel()
# Wait for tasks to finish cancelling
try:
await websocket.accept()
except RuntimeError:
# The connection was closed before we could accept it
return
await task
except (
asyncio.CancelledError,
asyncio.TimeoutError,
WebSocketDisconnect,
ConnectionClosed,
RuntimeError
):
pass
except Exception as e:
logger.info(f"Encountered unknown exception: {e}", exc_info=e)
ws_channel = WebSocketChannel(websocket)
self._channel_tasks = []
with self._lock:
self.channels[websocket] = ws_channel
return ws_channel
async def on_disconnect(self, websocket: WebSocketType):
async def __aiter__(self):
"""
Call close on the channel if it's not, and remove from channel list
:param websocket: The WebSocket objet attached to the Channel
Generator for received messages
"""
with self._lock:
channel = self.channels.get(websocket)
if channel:
logger.info(f"Disconnecting channel {channel}")
if not channel.is_closed():
# We can not catch any errors here as websocket.recv is
# the first to catch any disconnects and bubble it up
# so the connection is garbage collected right away
while not self.is_closed():
yield await self.recv()
@asynccontextmanager
async def create_channel(
websocket: WebSocketType,
**kwargs
) -> AsyncIterator[WebSocketChannel]:
"""
Context manager for safely opening and closing a WebSocketChannel
"""
channel = WebSocketChannel(websocket, **kwargs)
try:
await channel.accept()
logger.info(f"Connected to channel - {channel}")
yield channel
finally:
await channel.close()
del self.channels[websocket]
async def disconnect_all(self):
"""
Disconnect all Channels
"""
with self._lock:
for websocket in self.channels.copy().keys():
await self.on_disconnect(websocket)
async def broadcast(self, message: WSMessageSchemaType):
"""
Broadcast a message on all Channels
:param message: The message to send
"""
with self._lock:
for channel in self.channels.copy().values():
if channel.subscribed_to(message.get('type')):
await self.send_direct(channel, message)
async def send_direct(
self, channel: WebSocketChannel, message: Union[WSMessageSchemaType, Dict[str, Any]]):
"""
Send a message directly through direct_channel only
:param direct_channel: The WebSocketChannel object to send the message through
:param message: The message to send
"""
if not await channel.send(message):
await self.on_disconnect(channel.raw_websocket)
def has_channels(self):
"""
Flag for more than 0 channels
"""
return len(self.channels) > 0
logger.info(f"Disconnected from channel - {channel}")

31
freqtrade/rpc/api_server/ws/message_stream.py Normal file
View File

@ -0,0 +1,31 @@
import asyncio
import time
class MessageStream:
"""
A message stream for consumers to subscribe to,
and for producers to publish to.
"""
def __init__(self):
self._loop = asyncio.get_running_loop()
self._waiter = self._loop.create_future()
def publish(self, message):
"""
Publish a message to this MessageStream
:param message: The message to publish
"""
waiter, self._waiter = self._waiter, self._loop.create_future()
waiter.set_result((message, time.time(), self._waiter))
async def __aiter__(self):
"""
Iterate over the messages in the message stream
"""
waiter = self._waiter
while True:
# Shield the future from being cancelled by a task waiting on it
message, ts, waiter = await asyncio.shield(waiter)
yield message, ts

8
freqtrade/rpc/api_server/ws/serializer.py
View File

@ -1,5 +1,6 @@
import logging
from abc import ABC, abstractmethod
from typing import Any, Dict, Union
import orjson
import rapidjson
@ -7,6 +8,7 @@ from pandas import DataFrame
from freqtrade.misc import dataframe_to_json, json_to_dataframe
from freqtrade.rpc.api_server.ws.proxy import WebSocketProxy
from freqtrade.rpc.api_server.ws_schemas import WSMessageSchemaType
logger = logging.getLogger(__name__)
@ -24,17 +26,13 @@ class WebSocketSerializer(ABC):
def _deserialize(self, data):
raise NotImplementedError()
async def send(self, data: bytes):
async def send(self, data: Union[WSMessageSchemaType, Dict[str, Any]]):
await self._websocket.send(self._serialize(data))
async def recv(self) -> bytes:
data = await self._websocket.recv()
return self._deserialize(data)
async def close(self, code: int = 1000):
await self._websocket.close(code)
class HybridJSONWebSocketSerializer(WebSocketSerializer):
def _serialize(self, data) -> str:

2
freqtrade/rpc/api_server/ws_schemas.py
View File

@ -47,7 +47,7 @@ class WSWhitelistRequest(WSRequestSchema):
class WSAnalyzedDFRequest(WSRequestSchema):
type: RPCRequestType = RPCRequestType.ANALYZED_DF
data: Dict[str, Any] = {"limit": 1500}
data: Dict[str, Any] = {"limit": 1500, "pair": None}
# ------------------------------ MESSAGE SCHEMAS ----------------------------

114
freqtrade/rpc/external_message_consumer.py
View File

@ -8,15 +8,17 @@ import asyncio
import logging
import socket
from threading import Thread
from typing import TYPE_CHECKING, Any, Callable, Dict, List, TypedDict
from typing import TYPE_CHECKING, Any, Callable, Dict, List, TypedDict, Union
import websockets
from pydantic import ValidationError
from freqtrade.constants import FULL_DATAFRAME_THRESHOLD
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import RPCMessageType
from freqtrade.misc import remove_entry_exit_signals
from freqtrade.rpc.api_server.ws import WebSocketChannel
from freqtrade.rpc.api_server.ws.channel import WebSocketChannel, create_channel
from freqtrade.rpc.api_server.ws.message_stream import MessageStream
from freqtrade.rpc.api_server.ws_schemas import (WSAnalyzedDFMessage, WSAnalyzedDFRequest,
WSMessageSchema, WSRequestSchema,
WSSubscribeRequest, WSWhitelistMessage,
@ -38,6 +40,10 @@ class Producer(TypedDict):
logger = logging.getLogger(__name__)
def schema_to_dict(schema: Union[WSMessageSchema, WSRequestSchema]):
return schema.dict(exclude_none=True)
class ExternalMessageConsumer:
"""
The main controller class for consuming external messages from
@ -92,6 +98,8 @@ class ExternalMessageConsumer:
RPCMessageType.ANALYZED_DF: self._consume_analyzed_df_message,
}
self._channel_streams: Dict[str, MessageStream] = {}
self.start()
def start(self):
@ -118,6 +126,8 @@ class ExternalMessageConsumer:
logger.info("Stopping ExternalMessageConsumer")
self._running = False
self._channel_streams = {}
if self._sub_tasks:
# Cancel sub tasks
for task in self._sub_tasks:
@ -175,7 +185,6 @@ class ExternalMessageConsumer:
:param producer: Dictionary containing producer info
:param lock: An asyncio Lock
"""
channel = None
while self._running:
try:
host, port = producer['host'], producer['port']
@ -190,19 +199,21 @@ class ExternalMessageConsumer:
max_size=self.message_size_limit,
ping_interval=None
) as ws:
channel = WebSocketChannel(ws, channel_id=name)
async with create_channel(
ws,
channel_id=name,
send_throttle=0.5
) as channel:
logger.info(f"Producer connection success - {channel}")
# Create the message stream for this channel
self._channel_streams[name] = MessageStream()
# Now request the initial data from this Producer
for request in self._initial_requests:
await channel.send(
request.dict(exclude_none=True)
# Run the channel tasks while connected
await channel.run_channel_tasks(
self._receive_messages(channel, producer, lock),
self._send_requests(channel, self._channel_streams[name])
)
# Now receive data, if none is within the time limit, ping
await self._receive_messages(channel, producer, lock)
except (websockets.exceptions.InvalidURI, ValueError) as e:
logger.error(f"{ws_url} is an invalid WebSocket URL - {e}")
break
@ -229,11 +240,19 @@ class ExternalMessageConsumer:
# An unforseen error has occurred, log and continue
logger.error("Unexpected error has occurred:")
logger.exception(e)
await asyncio.sleep(self.sleep_time)
continue
finally:
if channel:
await channel.close()
async def _send_requests(self, channel: WebSocketChannel, channel_stream: MessageStream):
# Send the initial requests
for init_request in self._initial_requests:
await channel.send(schema_to_dict(init_request))
# Now send any subsequent requests published to
# this channel's stream
async for request, _ in channel_stream:
logger.debug(f"Sending request to channel - {channel} - {request}")
await channel.send(request)
async def _receive_messages(
self,
@ -270,19 +289,31 @@ class ExternalMessageConsumer:
latency = (await asyncio.wait_for(pong, timeout=self.ping_timeout) * 1000)
logger.info(f"Connection to {channel} still alive, latency: {latency}ms")
continue
except (websockets.exceptions.ConnectionClosed):
# Just eat the error and continue reconnecting
logger.warning(f"Disconnection in {channel} - retrying in {self.sleep_time}s")
await asyncio.sleep(self.sleep_time)
break
except Exception as e:
# Just eat the error and continue reconnecting
logger.warning(f"Ping error {channel} - {e} - retrying in {self.sleep_time}s")
logger.debug(e, exc_info=e)
await asyncio.sleep(self.sleep_time)
raise
break
def send_producer_request(
self,
producer_name: str,
request: Union[WSRequestSchema, Dict[str, Any]]
):
"""
Publish a message to the producer's message stream to be
sent by the channel task.
:param producer_name: The name of the producer to publish the message to
:param request: The request to send to the producer
"""
if isinstance(request, WSRequestSchema):
request = schema_to_dict(request)
if channel_stream := self._channel_streams.get(producer_name):
channel_stream.publish(request)
def handle_producer_message(self, producer: Producer, message: Dict[str, Any]):
"""
@ -336,16 +367,45 @@ class ExternalMessageConsumer:
pair, timeframe, candle_type = key
if df.empty:
logger.debug(f"Received Empty Dataframe for {key}")
return
# If set, remove the Entry and Exit signals from the Producer
if self._emc_config.get('remove_entry_exit_signals', False):
df = remove_entry_exit_signals(df)
# Add the dataframe to the dataprovider
self._dp._add_external_df(pair, df,
logger.debug(f"Received {len(df)} candle(s) for {key}")
did_append, n_missing = self._dp._add_external_df(
pair,
df,
last_analyzed=la,
timeframe=timeframe,
candle_type=candle_type,
producer_name=producer_name)
producer_name=producer_name
)
if not did_append:
# We want an overlap in candles incase some data has changed
n_missing += 1
# Set to None for all candles if we missed a full df's worth of candles
n_missing = n_missing if n_missing < FULL_DATAFRAME_THRESHOLD else 1500
logger.warning(f"Holes in data or no existing df, requesting {n_missing} candles "
f"for {key} from `{producer_name}`")
self.send_producer_request(
producer_name,
WSAnalyzedDFRequest(
data={
"limit": n_missing,
"pair": pair
}
)
)
return
logger.debug(
f"Consumed message from `{producer_name}` of type `RPCMessageType.ANALYZED_DF`")
f"Consumed message from `{producer_name}` "
f"of type `RPCMessageType.ANALYZED_DF` for {key}")

88
freqtrade/rpc/rpc.py
View File

@ -167,6 +167,7 @@ class RPC:
results = []
for trade in trades:
order: Optional[Order] = None
current_profit_fiat: Optional[float] = None
if trade.open_order_id:
order = trade.select_order_by_order_id(trade.open_order_id)
# calculate profit and send message to user
@ -176,23 +177,26 @@ class RPC:
trade.pair, side='exit', is_short=trade.is_short, refresh=False)
except (ExchangeError, PricingError):
current_rate = NAN
else:
current_rate = trade.close_rate
if len(trade.select_filled_orders(trade.entry_side)) > 0:
current_profit = trade.calc_profit_ratio(
current_rate) if not isnan(current_rate) else NAN
current_profit_abs = trade.calc_profit(
current_rate) if not isnan(current_rate) else NAN
current_profit_fiat: Optional[float] = None
else:
current_profit = current_profit_abs = current_profit_fiat = 0.0
else:
# Closed trade ...
current_rate = trade.close_rate
current_profit = trade.close_profit
current_profit_abs = trade.close_profit_abs
# Calculate fiat profit
if self._fiat_converter:
if not isnan(current_profit_abs) and self._fiat_converter:
current_profit_fiat = self._fiat_converter.convert_amount(
current_profit_abs,
self._freqtrade.config['stake_currency'],
self._freqtrade.config['fiat_display_currency']
)
else:
current_profit = current_profit_abs = current_profit_fiat = 0.0
# Calculate guaranteed profit (in case of trailing stop)
stoploss_entry_dist = trade.calc_profit(trade.stop_loss)
@ -740,6 +744,24 @@ class RPC:
self._freqtrade.wallets.update()
return {'result': f'Created sell order for trade {trade_id}.'}
def _force_entry_validations(self, pair: str, order_side: SignalDirection):
if not self._freqtrade.config.get('force_entry_enable', False):
raise RPCException('Force_entry not enabled.')
if self._freqtrade.state != State.RUNNING:
raise RPCException('trader is not running')
if order_side == SignalDirection.SHORT and self._freqtrade.trading_mode == TradingMode.SPOT:
raise RPCException("Can't go short on Spot markets.")
if pair not in self._freqtrade.exchange.get_markets(tradable_only=True):
raise RPCException('Symbol does not exist or market is not active.')
# Check if pair quote currency equals to the stake currency.
stake_currency = self._freqtrade.config.get('stake_currency')
if not self._freqtrade.exchange.get_pair_quote_currency(pair) == stake_currency:
raise RPCException(
f'Wrong pair selected. Only pairs with stake-currency {stake_currency} allowed.')
def _rpc_force_entry(self, pair: str, price: Optional[float], *,
order_type: Optional[str] = None,
order_side: SignalDirection = SignalDirection.LONG,
@ -750,21 +772,8 @@ class RPC:
Handler for forcebuy <asset> <price>
Buys a pair trade at the given or current price
"""
self._force_entry_validations(pair, order_side)
if not self._freqtrade.config.get('force_entry_enable', False):
raise RPCException('Force_entry not enabled.')
if self._freqtrade.state != State.RUNNING:
raise RPCException('trader is not running')
if order_side == SignalDirection.SHORT and self._freqtrade.trading_mode == TradingMode.SPOT:
raise RPCException("Can't go short on Spot markets.")
# Check if pair quote currency equals to the stake currency.
stake_currency = self._freqtrade.config.get('stake_currency')
if not self._freqtrade.exchange.get_pair_quote_currency(pair) == stake_currency:
raise RPCException(
f'Wrong pair selected. Only pairs with stake-currency {stake_currency} allowed.')
# check if valid pair
# check if pair already has an open pair
@ -1053,15 +1062,26 @@ class RPC:
return self._convert_dataframe_to_dict(self._freqtrade.config['strategy'],
pair, timeframe, _data, last_analyzed)
def __rpc_analysed_dataframe_raw(self, pair: str, timeframe: str,
limit: Optional[int]) -> Tuple[DataFrame, datetime]:
""" Get the dataframe and last analyze from the dataprovider """
def __rpc_analysed_dataframe_raw(
self,
pair: str,
timeframe: str,
limit: Optional[int]
) -> Tuple[DataFrame, datetime]:
"""
Get the dataframe and last analyze from the dataprovider
:param pair: The pair to get
:param timeframe: The timeframe of data to get
:param limit: The amount of candles in the dataframe
"""
_data, last_analyzed = self._freqtrade.dataprovider.get_analyzed_dataframe(
pair, timeframe)
_data = _data.copy()
if limit:
_data = _data.iloc[-limit:]
return _data, last_analyzed
def _ws_all_analysed_dataframes(
@ -1069,7 +1089,16 @@ class RPC:
pairlist: List[str],
limit: Optional[int]
) -> Generator[Dict[str, Any], None, None]:
""" Get the analysed dataframes of each pair in the pairlist """
"""
Get the analysed dataframes of each pair in the pairlist.
If specified, only return the most recent `limit` candles for
each dataframe.
:param pairlist: A list of pairs to get
:param limit: If an integer, limits the size of dataframe
If a list of string date times, only returns those candles
:returns: A generator of dictionaries with the key, dataframe, and last analyzed timestamp
"""
timeframe = self._freqtrade.config['timeframe']
candle_type = self._freqtrade.config.get('candle_type_def', CandleType.SPOT)
@ -1082,10 +1111,15 @@ class RPC:
"la": last_analyzed
}
def _ws_request_analyzed_df(self, limit: Optional[int]):
def _ws_request_analyzed_df(
self,
limit: Optional[int] = None,
pair: Optional[str] = None
):
""" Historical Analyzed Dataframes for WebSocket """
whitelist = self._freqtrade.active_pair_whitelist
return self._ws_all_analysed_dataframes(whitelist, limit)
pairlist = [pair] if pair else self._freqtrade.active_pair_whitelist
return self._ws_all_analysed_dataframes(pairlist, limit)
def _ws_request_whitelist(self):
""" Whitelist data for WebSocket """

4
freqtrade/rpc/rpc_manager.py
View File

@ -6,7 +6,7 @@ from collections import deque
from typing import Any, Dict, List
from freqtrade.constants import Config
from freqtrade.enums import RPCMessageType
from freqtrade.enums import NO_ECHO_MESSAGES, RPCMessageType
from freqtrade.rpc import RPC, RPCHandler
@ -67,7 +67,7 @@ class RPCManager:
'status': 'stopping bot'
}
"""
if msg.get('type') not in (RPCMessageType.ANALYZED_DF, RPCMessageType.WHITELIST):
if msg.get('type') not in NO_ECHO_MESSAGES:
logger.info('Sending rpc message: %s', msg)
if 'pair' in msg:
msg.update({

51
freqtrade/rpc/telegram.py
View File

@ -79,6 +79,8 @@ def authorized_only(command_handler: Callable[..., None]) -> Callable[..., Any]:
)
try:
return command_handler(self, *args, **kwargs)
except RPCException as e:
self._send_msg(str(e))
except BaseException:
logger.exception('Exception occurred within Telegram module')
@ -538,8 +540,6 @@ class Telegram(RPCHandler):
handler for `/status` and `/status <id>`.
"""
try:
# Check if there's at least one numerical ID provided.
# If so, try to get only these trades.
trade_ids = []
@ -602,9 +602,6 @@ class Telegram(RPCHandler):
lines.extend(lines_detail if lines_detail else "")
self.__send_status_msg(lines, r)
except RPCException as e:
self._send_msg(str(e))
def __send_status_msg(self, lines: List[str], r: Dict[str, Any]) -> None:
"""
Send status message.
@ -630,7 +627,6 @@ class Telegram(RPCHandler):
:param update: message update
:return: None
"""
try:
fiat_currency = self._config.get('fiat_display_currency', '')
statlist, head, fiat_profit_sum = self._rpc._rpc_status_table(
self._config['stake_currency'], fiat_currency)
@ -659,8 +655,6 @@ class Telegram(RPCHandler):
self._send_msg(f"<pre>{message}</pre>", parse_mode=ParseMode.HTML,
reload_able=True, callback_path="update_status_table",
query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _timeunit_stats(self, update: Update, context: CallbackContext, unit: str) -> None:
@ -686,7 +680,6 @@ class Telegram(RPCHandler):
timescale = int(context.args[0]) if context.args else val.default
except (TypeError, ValueError, IndexError):
timescale = val.default
try:
stats = self._rpc._rpc_timeunit_profit(
timescale,
stake_cur,
@ -713,8 +706,6 @@ class Telegram(RPCHandler):
)
self._send_msg(message, parse_mode=ParseMode.HTML, reload_able=True,
callback_path=val.callback, query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _daily(self, update: Update, context: CallbackContext) -> None:
@ -878,7 +869,6 @@ class Telegram(RPCHandler):
@authorized_only
def _balance(self, update: Update, context: CallbackContext) -> None:
""" Handler for /balance """
try:
result = self._rpc._rpc_balance(self._config['stake_currency'],
self._config.get('fiat_display_currency', ''))
@ -949,8 +939,6 @@ class Telegram(RPCHandler):
f"{fiat_val}\n")
self._send_msg(output, reload_able=True, callback_path="update_balance",
query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _start(self, update: Update, context: CallbackContext) -> None:
@ -1125,7 +1113,6 @@ class Telegram(RPCHandler):
nrecent = int(context.args[0]) if context.args else 10
except (TypeError, ValueError, IndexError):
nrecent = 10
try:
trades = self._rpc._rpc_trade_history(
nrecent
)
@ -1143,8 +1130,6 @@ class Telegram(RPCHandler):
message = (f"<b>{min(trades['trades_count'], nrecent)} recent trades</b>:\n"
+ (f"<pre>{trades_tab}</pre>" if trades['trades_count'] > 0 else ''))
self._send_msg(message, parse_mode=ParseMode.HTML)
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _delete_trade(self, update: Update, context: CallbackContext) -> None:
@ -1155,7 +1140,6 @@ class Telegram(RPCHandler):
:param update: message update
:return: None
"""
try:
if not context.args or len(context.args) == 0:
raise RPCException("Trade-id not set.")
trade_id = int(context.args[0])
@ -1165,9 +1149,6 @@ class Telegram(RPCHandler):
'Please make sure to take care of this asset on the exchange manually.'
))
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _performance(self, update: Update, context: CallbackContext) -> None:
"""
@ -1177,7 +1158,6 @@ class Telegram(RPCHandler):
:param update: message update
:return: None
"""
try:
trades = self._rpc._rpc_performance()
output = "<b>Performance:</b>\n"
for i, trade in enumerate(trades):
@ -1196,8 +1176,6 @@ class Telegram(RPCHandler):
self._send_msg(output, parse_mode=ParseMode.HTML,
reload_able=True, callback_path="update_performance",
query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _enter_tag_performance(self, update: Update, context: CallbackContext) -> None:
@ -1208,7 +1186,6 @@ class Telegram(RPCHandler):
:param update: message update
:return: None
"""
try:
pair = None
if context.args and isinstance(context.args[0], str):
pair = context.args[0]
@ -1231,8 +1208,6 @@ class Telegram(RPCHandler):
self._send_msg(output, parse_mode=ParseMode.HTML,
reload_able=True, callback_path="update_enter_tag_performance",
query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _exit_reason_performance(self, update: Update, context: CallbackContext) -> None:
@ -1243,7 +1218,6 @@ class Telegram(RPCHandler):
:param update: message update
:return: None
"""
try:
pair = None
if context.args and isinstance(context.args[0], str):
pair = context.args[0]
@ -1266,8 +1240,6 @@ class Telegram(RPCHandler):
self._send_msg(output, parse_mode=ParseMode.HTML,
reload_able=True, callback_path="update_exit_reason_performance",
query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _mix_tag_performance(self, update: Update, context: CallbackContext) -> None:
@ -1278,7 +1250,6 @@ class Telegram(RPCHandler):
:param update: message update
:return: None
"""
try:
pair = None
if context.args and isinstance(context.args[0], str):
pair = context.args[0]
@ -1301,8 +1272,6 @@ class Telegram(RPCHandler):
self._send_msg(output, parse_mode=ParseMode.HTML,
reload_able=True, callback_path="update_mix_tag_performance",
query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _count(self, update: Update, context: CallbackContext) -> None:
@ -1313,7 +1282,6 @@ class Telegram(RPCHandler):
:param update: message update
:return: None
"""
try:
counts = self._rpc._rpc_count()
message = tabulate({k: [v] for k, v in counts.items()},
headers=['current', 'max', 'total stake'],
@ -1323,8 +1291,6 @@ class Telegram(RPCHandler):
self._send_msg(message, parse_mode=ParseMode.HTML,
reload_able=True, callback_path="update_count",
query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _locks(self, update: Update, context: CallbackContext) -> None:
@ -1372,7 +1338,6 @@ class Telegram(RPCHandler):
Handler for /whitelist
Shows the currently active whitelist
"""
try:
whitelist = self._rpc._rpc_whitelist()
if context.args:
@ -1386,8 +1351,6 @@ class Telegram(RPCHandler):
logger.debug(message)
self._send_msg(message)
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _blacklist(self, update: Update, context: CallbackContext) -> None:
@ -1424,7 +1387,6 @@ class Telegram(RPCHandler):
Handler for /logs
Shows the latest logs
"""
try:
try:
limit = int(context.args[0]) if context.args else 10
except (TypeError, ValueError, IndexError):
@ -1447,8 +1409,6 @@ class Telegram(RPCHandler):
if msgs:
self._send_msg(msgs, parse_mode=ParseMode.MARKDOWN_V2)
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _edge(self, update: Update, context: CallbackContext) -> None:
@ -1456,7 +1416,6 @@ class Telegram(RPCHandler):
Handler for /edge
Shows information related to Edge
"""
try:
edge_pairs = self._rpc._rpc_edge()
if not edge_pairs:
message = '<b>Edge only validated following pairs:</b>'
@ -1469,9 +1428,6 @@ class Telegram(RPCHandler):
self._send_msg(message, parse_mode=ParseMode.HTML)
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _help(self, update: Update, context: CallbackContext) -> None:
"""
@ -1551,12 +1507,9 @@ class Telegram(RPCHandler):
Handler for /health
Shows the last process timestamp
"""
try:
health = self._rpc._health()
message = f"Last process: `{health['last_process_loc']}`"
self._send_msg(message)
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _version(self, update: Update, context: CallbackContext) -> None:

1
freqtrade/rpc/webhook.py
View File

@ -68,6 +68,7 @@ class Webhook(RPCHandler):
RPCMessageType.PROTECTION_TRIGGER_GLOBAL,
RPCMessageType.WHITELIST,
RPCMessageType.ANALYZED_DF,
RPCMessageType.NEW_CANDLE,
RPCMessageType.STRATEGY_MSG):
# Don't fail for non-implemented types
return None

6
freqtrade/strategy/interface.py
View File

@ -739,10 +739,10 @@ class IStrategy(ABC, HyperStrategyMixin):
"""
pair = str(metadata.get('pair'))
new_candle = self._last_candle_seen_per_pair.get(pair, None) != dataframe.iloc[-1]['date']
# Test if seen this pair and last candle before.
# always run if process_only_new_candles is set to false
if (not self.process_only_new_candles or
self._last_candle_seen_per_pair.get(pair, None) != dataframe.iloc[-1]['date']):
if not self.process_only_new_candles or new_candle:
# Defs that only make change on new candle data.
dataframe = self.analyze_ticker(dataframe, metadata)
@ -751,7 +751,7 @@ class IStrategy(ABC, HyperStrategyMixin):
candle_type = self.config.get('candle_type_def', CandleType.SPOT)
self.dp._set_cached_df(pair, self.timeframe, dataframe, candle_type=candle_type)
self.dp._emit_df((pair, self.timeframe, candle_type), dataframe)
self.dp._emit_df((pair, self.timeframe, candle_type), dataframe, new_candle)
else:
logger.debug("Skipping TA Analysis for already analyzed candle")

4
freqtrade/templates/FreqaiExampleHybridStrategy.py
View File

@ -19,7 +19,7 @@ class FreqaiExampleHybridStrategy(IStrategy):
Launching this strategy would be:
freqtrade trade --strategy FreqaiExampleHyridStrategy --strategy-path freqtrade/templates
freqtrade trade --strategy FreqaiExampleHybridStrategy --strategy-path freqtrade/templates
--freqaimodel CatboostClassifier --config config_examples/config_freqai.example.json
or the user simply adds this to their config:
@ -86,7 +86,7 @@ class FreqaiExampleHybridStrategy(IStrategy):
process_only_new_candles = True
stoploss = -0.05
use_exit_signal = True
startup_candle_count: int = 300
startup_candle_count: int = 30
can_short = True
# Hyperoptable parameters

50
freqtrade/templates/strategy_analysis_example.ipynb
View File

@ -7,14 +7,17 @@
"# Strategy analysis example\n",
"\n",
"Debugging a strategy can be time-consuming. Freqtrade offers helper functions to visualize raw data.\n",
"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."
"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.\n",
"Please follow the [documentation](https://www.freqtrade.io/en/stable/data-download/) for more details."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup"
"## Setup\n",
"\n",
"### Change Working directory to repository root"
]
},
{
@ -23,7 +26,38 @@
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from pathlib import Path\n",
"\n",
"# Change directory\n",
"# Modify this cell to insure that the output shows the correct path.\n",
"# Define all paths relative to the project root shown in the cell output\n",
"project_root = \"somedir/freqtrade\"\n",
"i=0\n",
"try:\n",
" os.chdirdir(project_root)\n",
" assert Path('LICENSE').is_file()\n",
"except:\n",
" while i<4 and (not Path('LICENSE').is_file()):\n",
" os.chdir(Path(Path.cwd(), '../'))\n",
" i+=1\n",
" project_root = Path.cwd()\n",
"print(Path.cwd())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Configure Freqtrade environment"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from freqtrade.configuration import Configuration\n",
"\n",
"# Customize these according to your needs.\n",
@ -31,14 +65,14 @@
"# Initialize empty configuration object\n",
"config = Configuration.from_files([])\n",
"# Optionally (recommended), use existing configuration file\n",
"# config = Configuration.from_files([\"config.json\"])\n",
"# config = Configuration.from_files([\"user_data/config.json\"])\n",
"\n",
"# Define some constants\n",
"config[\"timeframe\"] = \"5m\"\n",
"# Name of the strategy class\n",
"config[\"strategy\"] = \"SampleStrategy\"\n",
"# Location of the data\n",
"data_location = config['datadir']\n",
"data_location = config[\"datadir\"]\n",
"# Pair to analyze - Only use one pair here\n",
"pair = \"BTC/USDT\""
]
@ -56,12 +90,12 @@
"candles = load_pair_history(datadir=data_location,\n",
" timeframe=config[\"timeframe\"],\n",
" pair=pair,\n",
" data_format = \"hdf5\",\n",
" data_format = \"json\", # Make sure to update this to your data\n",
" candle_type=CandleType.SPOT,\n",
" )\n",
"\n",
"# Confirm success\n",
"print(\"Loaded \" + str(len(candles)) + f\" rows of data for {pair} from {data_location}\")\n",
"print(f\"Loaded {len(candles)} rows of data for {pair} from {data_location}\")\n",
"candles.head()"
]
},
@ -328,7 +362,7 @@
"# Show graph inline\n",
"# graph.show()\n",
"\n",
"# Render graph in a seperate window\n",
"# Render graph in a separate window\n",
"graph.show(renderer=\"browser\")\n"
]
},
@ -365,7 +399,7 @@
"metadata": {
"file_extension": ".py",
"kernelspec": {
"display_name": "Python 3.9.7 64-bit ('trade_397')",
"display_name": "Python 3.9.7 64-bit",
"language": "python",
"name": "python3"
},

18
freqtrade/util/gc_setup.py Normal file
View File

@ -0,0 +1,18 @@
import gc
import logging
import platform
logger = logging.getLogger(__name__)
def gc_set_threshold():
"""
Reduce number of GC runs to improve performance (explanation video)
https://www.youtube.com/watch?v=p4Sn6UcFTOU
"""
if platform.python_implementation() == "CPython":
# allocs, g1, g2 = gc.get_threshold()
gc.set_threshold(50_000, 500, 1000)
logger.debug("Adjusting python allocations to reduce GC runs")

7
freqtrade/wallets.py
View File

@ -291,12 +291,17 @@ class Wallets:
return self._check_available_stake_amount(stake_amount, available_amount)
def validate_stake_amount(self, pair: str, stake_amount: Optional[float],
min_stake_amount: Optional[float], max_stake_amount: float):
min_stake_amount: Optional[float], max_stake_amount: float,
trade_amount: Optional[float]):
if not stake_amount:
logger.debug(f"Stake amount is {stake_amount}, ignoring possible trade for {pair}.")
return 0
max_stake_amount = min(max_stake_amount, self.get_available_stake_amount())
if trade_amount:
# if in a trade, then the resulting trade size cannot go beyond the max stake
# Otherwise we could no longer exit.
max_stake_amount = min(max_stake_amount, max_stake_amount - trade_amount)
if min_stake_amount is not None and min_stake_amount > max_stake_amount:
if self._log:

2
mkdocs.yml
View File

@ -29,6 +29,7 @@ nav:
- Parameter table: freqai-parameter-table.md
- Feature engineering: freqai-feature-engineering.md
- Running FreqAI: freqai-running.md
- Reinforcement Learning: freqai-reinforcement-learning.md
- Developer guide: freqai-developers.md
- Short / Leverage: leverage.md
- Utility Sub-commands: utils.md
@ -40,6 +41,7 @@ nav:
- Backtest analysis: advanced-backtesting.md
- Advanced Topics:
- Advanced Post-installation Tasks: advanced-setup.md
- Trade Object: trade-object.md
- Advanced Strategy: strategy-advanced.md
- Advanced Hyperopt: advanced-hyperopt.md
- Producer/Consumer mode: producer-consumer.md

17
requirements-dev.txt
View File

@ -3,30 +3,31 @@
-r requirements-plot.txt
-r requirements-hyperopt.txt
-r requirements-freqai.txt
-r requirements-freqai-rl.txt
-r docs/requirements-docs.txt
coveralls==3.3.1
flake8==5.0.4
flake8==6.0.0
flake8-tidy-imports==4.8.0
mypy==0.991
pre-commit==2.20.0
pre-commit==2.21.0
pytest==7.2.0
pytest-asyncio==0.20.2
pytest-asyncio==0.20.3
pytest-cov==4.0.0
pytest-mock==3.10.0
pytest-random-order==1.0.4
isort==5.10.1
pytest-random-order==1.1.0
isort==5.11.4
# For datetime mocking
time-machine==2.8.2
# fastapi testing
httpx==0.23.1
# Convert jupyter notebooks to markdown documents
nbconvert==7.2.5
nbconvert==7.2.7
# mypy types
types-cachetools==5.2.1
types-filelock==3.2.7
types-requests==2.28.11.5
types-requests==2.28.11.7
types-tabulate==0.9.0.0
types-python-dateutil==2.8.19.4
types-python-dateutil==2.8.19.5

9
requirements-freqai-rl.txt Normal file
View File

@ -0,0 +1,9 @@
# Include all requirements to run the bot.
-r requirements-freqai.txt
# Required for freqai-rl
torch==1.13.1
stable-baselines3==1.6.2
sb3-contrib==1.6.2
# Gym is forced to this version by stable-baselines3.
gym==0.21

2
requirements-freqai.txt
View File

@ -7,5 +7,5 @@ scikit-learn==1.1.3
joblib==1.2.0
catboost==1.1.1; platform_machine != 'aarch64'
lightgbm==3.3.3
xgboost==1.7.1
xgboost==1.7.2
tensorboard==2.11.0

2
requirements-hyperopt.txt
View File

@ -5,5 +5,5 @@
scipy==1.9.3
scikit-learn==1.1.3
scikit-optimize==0.9.0
filelock==3.8.0
filelock==3.8.2
progressbar2==4.2.0

26
requirements.txt
View File

@ -1,28 +1,28 @@
numpy==1.23.5
pandas==1.5.1
numpy==1.24.1
pandas==1.5.2
pandas-ta==0.3.14b
ccxt==2.1.96
ccxt==2.4.60
# Pin cryptography for now due to rust build errors with piwheels
cryptography==38.0.1; platform_machine == 'armv7l'
cryptography==38.0.3; platform_machine != 'armv7l'
cryptography==38.0.4; platform_machine != 'armv7l'
aiohttp==3.8.3
SQLAlchemy==1.4.44
python-telegram-bot==13.14
SQLAlchemy==1.4.45
python-telegram-bot==13.15
arrow==1.2.3
cachetools==4.2.2
requests==2.28.1
urllib3==1.26.12
jsonschema==4.17.0
urllib3==1.26.13
jsonschema==4.17.3
TA-Lib==0.4.25
technical==1.3.0
tabulate==0.9.0
pycoingecko==3.1.0
jinja2==3.1.2
tables==3.7.0
blosc==1.10.6
blosc==1.11.1
joblib==1.2.0
pyarrow==10.0.0; platform_machine != 'armv7l'
pyarrow==10.0.1; platform_machine != 'armv7l'
# find first, C search in arrays
py_find_1st==1.1.5
@ -30,13 +30,13 @@ py_find_1st==1.1.5
# Load ticker files 30% faster
python-rapidjson==1.9
# Properly format api responses
orjson==3.8.2
orjson==3.8.3
# Notify systemd
sdnotify==0.3.2
# API Server
fastapi==0.87.0
fastapi==0.88.0
pydantic==1.10.2
uvicorn==0.20.0
pyjwt==2.6.0
@ -47,7 +47,7 @@ psutil==5.9.4
colorama==0.4.6
# Building config files interactively
questionary==1.10.0
prompt-toolkit==3.0.32
prompt-toolkit==3.0.36
# Extensions to datetime library
python-dateutil==2.8.2

11
setup.py
View File

@ -15,6 +15,14 @@ freqai = [
'scikit-learn',
'catboost; platform_machine != "aarch64"',
'lightgbm',
'xgboost'
]
freqai_rl = [
'torch',
'stable-baselines3',
'gym==0.21',
'sb3-contrib'
]
develop = [
@ -36,7 +44,7 @@ jupyter = [
'nbconvert',
]
all_extra = plot + develop + jupyter + hyperopt + freqai
all_extra = plot + develop + jupyter + hyperopt + freqai + freqai_rl
setup(
tests_require=[
@ -90,6 +98,7 @@ setup(
'jupyter': jupyter,
'hyperopt': hyperopt,
'freqai': freqai,
'freqai_rl': freqai_rl,
'all': all_extra,
},
)

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