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Merge branch 'develop' into pr/froggleston/7861

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Matthias 2023-03-19 15:00:20 +01:00
commit cd9c2c4c23
259 changed files with 23487 additions and 14834 deletions
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59
.github/workflows/ci.yml vendored
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@ -16,15 +16,16 @@ on:
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: true
permissions:
repository-projects: read
jobs:
build_linux:
runs-on: ${{ matrix.os }}
strategy:
matrix:
os: [ ubuntu-18.04, ubuntu-20.04, ubuntu-22.04 ]
python-version: ["3.8", "3.9", "3.10"]
os: [ ubuntu-20.04, ubuntu-22.04 ]
python-version: ["3.8", "3.9", "3.10", "3.11"]
steps:
- uses: actions/checkout@v3
@ -88,16 +89,16 @@ jobs:
run: |
cp config_examples/config_bittrex.example.json config.json
freqtrade create-userdir --userdir user_data
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
- name: Flake8
run: |
flake8
freqtrade hyperopt --datadir tests/testdata -e 6 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
- name: Sort imports (isort)
run: |
isort --check .
- name: Run Ruff
run: |
ruff check --format=github .
- name: Mypy
run: |
mypy freqtrade scripts tests
@ -115,7 +116,7 @@ jobs:
strategy:
matrix:
os: [ macos-latest ]
python-version: ["3.8", "3.9", "3.10"]
python-version: ["3.8", "3.9", "3.10", "3.11"]
steps:
- uses: actions/checkout@v3
@ -148,6 +149,19 @@ jobs:
if: runner.os == 'macOS'
run: |
brew update
# homebrew fails to update python due to unlinking failures
# https://github.com/actions/runner-images/issues/6817
rm /usr/local/bin/2to3 || true
rm /usr/local/bin/2to3-3.11 || true
rm /usr/local/bin/idle3 || true
rm /usr/local/bin/idle3.11 || true
rm /usr/local/bin/pydoc3 || true
rm /usr/local/bin/pydoc3.11 || true
rm /usr/local/bin/python3 || true
rm /usr/local/bin/python3.11 || true
rm /usr/local/bin/python3-config || true
rm /usr/local/bin/python3.11-config || true
brew install hdf5 c-blosc
python -m pip install --upgrade pip wheel
export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
@ -173,14 +187,14 @@ jobs:
freqtrade create-userdir --userdir user_data
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
- name: Flake8
run: |
flake8
- name: Sort imports (isort)
run: |
isort --check .
- name: Run Ruff
run: |
ruff check --format=github .
- name: Mypy
run: |
mypy freqtrade scripts
@ -199,7 +213,7 @@ jobs:
strategy:
matrix:
os: [ windows-latest ]
python-version: ["3.8", "3.9", "3.10"]
python-version: ["3.8", "3.9", "3.10", "3.11"]
steps:
- uses: actions/checkout@v3
@ -235,9 +249,9 @@ jobs:
freqtrade create-userdir --userdir user_data
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
- name: Flake8
- name: Run Ruff
run: |
flake8
ruff check --format=github .
- name: Mypy
run: |
@ -308,7 +322,6 @@ jobs:
build_linux_online:
# Run pytest with "live" checks
runs-on: ubuntu-22.04
# permissions:
steps:
- uses: actions/checkout@v3
@ -347,6 +360,8 @@ jobs:
pip install -e .
- name: Tests incl. ccxt compatibility tests
env:
CI_WEB_PROXY: http://152.67.78.211:13128
run: |
pytest --random-order --cov=freqtrade --cov-config=.coveragerc --longrun
@ -410,7 +425,7 @@ jobs:
python setup.py sdist bdist_wheel
- name: Publish to PyPI (Test)
uses: pypa/gh-action-pypi-publish@v1.6.1
uses: pypa/gh-action-pypi-publish@v1.7.1
if: (github.event_name == 'release')
with:
user: __token__
@ -418,7 +433,7 @@ jobs:
repository_url: https://test.pypi.org/legacy/
- name: Publish to PyPI
uses: pypa/gh-action-pypi-publish@v1.6.1
uses: pypa/gh-action-pypi-publish@v1.7.1
if: (github.event_name == 'release')
with:
user: __token__
@ -451,12 +466,13 @@ jobs:
- name: Build and test and push docker images
env:
IMAGE_NAME: freqtradeorg/freqtrade
BRANCH_NAME: ${{ steps.extract_branch.outputs.branch }}
run: |
build_helpers/publish_docker_multi.sh
deploy_arm:
permissions:
packages: write
needs: [ deploy ]
# Only run on 64bit machines
runs-on: [self-hosted, linux, ARM64]
@ -479,8 +495,9 @@ jobs:
- name: Build and test and push docker images
env:
IMAGE_NAME: freqtradeorg/freqtrade
BRANCH_NAME: ${{ steps.extract_branch.outputs.branch }}
GHCR_USERNAME: ${{ github.actor }}
GHCR_TOKEN: ${{ secrets.GITHUB_TOKEN }}
run: |
build_helpers/publish_docker_arm64.sh

23
.pre-commit-config.yaml
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@ -2,33 +2,40 @@
# See https://pre-commit.com/hooks.html for more hooks
repos:
- repo: https://github.com/pycqa/flake8
rev: "4.0.1"
rev: "6.0.0"
hooks:
- id: flake8
# stages: [push]
- repo: https://github.com/pre-commit/mirrors-mypy
rev: "v0.942"
rev: "v1.0.1"
hooks:
- id: mypy
exclude: build_helpers
additional_dependencies:
- types-cachetools==5.2.1
- types-cachetools==5.3.0.4
- types-filelock==3.2.7
- types-requests==2.28.11.5
- types-tabulate==0.9.0.0
- types-python-dateutil==2.8.19.4
- types-requests==2.28.11.15
- types-tabulate==0.9.0.1
- types-python-dateutil==2.8.19.10
- SQLAlchemy==2.0.5.post1
# stages: [push]
- repo: https://github.com/pycqa/isort
rev: "5.10.1"
rev: "5.12.0"
hooks:
- id: isort
name: isort (python)
# stages: [push]
- repo: https://github.com/charliermarsh/ruff-pre-commit
# Ruff version.
rev: 'v0.0.255'
hooks:
- id: ruff
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v2.4.0
rev: v4.4.0
hooks:
- id: end-of-file-fixer
exclude: |

11
CONTRIBUTING.md
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@ -45,16 +45,17 @@ pytest tests/test_<file_name>.py::test_<method_name>
### 2. Test if your code is PEP8 compliant
#### Run Flake8
#### Run Ruff
```bash
flake8 freqtrade tests scripts
ruff .
```
We receive a lot of code that fails the `flake8` checks.
We receive a lot of code that fails the `ruff` checks.
To help with that, we encourage you to install the git pre-commit
hook that will warn you when you try to commit code that fails these checks.
Guide for installing them is [here](http://flake8.pycqa.org/en/latest/user/using-hooks.html).
hook that will warn you when you try to commit code that fails these checks.
you can manually run pre-commit with `pre-commit run -a`.
##### Additional styles applied

2
Dockerfile
View File

@ -1,4 +1,4 @@
FROM python:3.10.7-slim-bullseye as base
FROM python:3.10.10-slim-bullseye as base
# Setup env
ENV LANG C.UTF-8

6
README.md
View File

@ -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)
@ -39,6 +40,7 @@ Please read the [exchange specific notes](docs/exchanges.md) to learn about even
- [X] [Binance](https://www.binance.com/)
- [X] [Gate.io](https://www.gate.io/ref/6266643)
- [X] [OKX](https://okx.com/)
- [X] [Bybit](https://bybit.com/)
Please make sure to read the [exchange specific notes](docs/exchanges.md), as well as the [trading with leverage](docs/leverage.md) documentation before diving in.
@ -163,6 +165,10 @@ first. If it hasn't been reported, please
ensure you follow the template guide so that the team can assist you as
quickly as possible.
For every [issue](https://github.com/freqtrade/freqtrade/issues/new/choose) created, kindly follow up and mark satisfaction or reminder to close issue when equilibrium ground is reached.
--Maintain github's [community policy](https://docs.github.com/en/site-policy/github-terms/github-community-code-of-conduct)--
### [Feature Requests](https://github.com/freqtrade/freqtrade/labels/enhancement)
Have you a great idea to improve the bot you want to share? Please,

BIN
build_helpers/TA_Lib-0.4.25-cp311-cp311-win_amd64.whl Normal file
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3
build_helpers/install_windows.ps1
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@ -14,5 +14,8 @@ if ($pyv -eq '3.9') {
if ($pyv -eq '3.10') {
pip install build_helpers\TA_Lib-0.4.25-cp310-cp310-win_amd64.whl
}
if ($pyv -eq '3.11') {
pip install build_helpers\TA_Lib-0.4.25-cp311-cp311-win_amd64.whl
}
pip install -r requirements-dev.txt
pip install -e .

7
build_helpers/pre_commit_update.py
View File

@ -8,12 +8,17 @@ import yaml
pre_commit_file = Path('.pre-commit-config.yaml')
require_dev = Path('requirements-dev.txt')
require = Path('requirements.txt')
with require_dev.open('r') as rfile:
requirements = rfile.readlines()
with require.open('r') as rfile:
requirements.extend(rfile.readlines())
# Extract types only
type_reqs = [r.strip('\n') for r in requirements if r.startswith('types-')]
type_reqs = [r.strip('\n') for r in requirements if r.startswith(
'types-') or r.startswith('SQLAlchemy')]
with pre_commit_file.open('r') as file:
f = yaml.load(file, Loader=yaml.FullLoader)

35
build_helpers/publish_docker_arm64.sh
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@ -3,6 +3,10 @@
# Use BuildKit, otherwise building on ARM fails
export DOCKER_BUILDKIT=1
IMAGE_NAME=freqtradeorg/freqtrade
CACHE_IMAGE=freqtradeorg/freqtrade_cache
GHCR_IMAGE_NAME=ghcr.io/freqtrade/freqtrade
# Replace / with _ to create a valid tag
TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g")
TAG_PLOT=${TAG}_plot
@ -14,7 +18,6 @@ TAG_ARM=${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}"
@ -38,13 +41,13 @@ if [ $? -ne 0 ]; then
echo "failed building multiarch images"
return 1
fi
# Tag image for upload and next build step
docker tag freqtrade:$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 .
# Tag image for upload and next build step
docker tag freqtrade:$TAG_ARM ${CACHE_IMAGE}:$TAG_ARM
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
@ -59,7 +62,6 @@ fi
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
@ -70,25 +72,42 @@ docker push ${CACHE_IMAGE}:$TAG_ARM
# Otherwise installation might fail.
echo "create manifests"
docker manifest create --amend ${IMAGE_NAME}:${TAG} ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI} ${CACHE_IMAGE}:${TAG}
docker manifest create ${IMAGE_NAME}:${TAG} ${CACHE_IMAGE}:${TAG} ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI}
docker manifest push -p ${IMAGE_NAME}:${TAG}
docker manifest create ${IMAGE_NAME}:${TAG_PLOT} ${CACHE_IMAGE}:${TAG_PLOT_ARM} ${CACHE_IMAGE}:${TAG_PLOT}
docker manifest create ${IMAGE_NAME}:${TAG_PLOT} ${CACHE_IMAGE}:${TAG_PLOT} ${CACHE_IMAGE}:${TAG_PLOT_ARM}
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 create ${IMAGE_NAME}:${TAG_FREQAI} ${CACHE_IMAGE}:${TAG_FREQAI} ${CACHE_IMAGE}:${TAG_FREQAI_ARM}
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 create ${IMAGE_NAME}:${TAG_FREQAI_RL} ${CACHE_IMAGE}:${TAG_FREQAI_RL} ${CACHE_IMAGE}:${TAG_FREQAI_RL_ARM}
docker manifest push -p ${IMAGE_NAME}:${TAG_FREQAI_RL}
# copy images to ghcr.io
alias crane="docker run --rm -i -v $(pwd)/.crane:/home/nonroot/.docker/ gcr.io/go-containerregistry/crane"
mkdir .crane
chmod a+rwx .crane
echo "${GHCR_TOKEN}" | crane auth login ghcr.io -u "${GHCR_USERNAME}" --password-stdin
crane copy ${IMAGE_NAME}:${TAG} ${GHCR_IMAGE_NAME}:${TAG}
crane copy ${IMAGE_NAME}:${TAG_PLOT} ${GHCR_IMAGE_NAME}:${TAG_PLOT}
crane copy ${IMAGE_NAME}:${TAG_FREQAI} ${GHCR_IMAGE_NAME}:${TAG_FREQAI}
crane copy ${IMAGE_NAME}:${TAG_FREQAI_RL} ${GHCR_IMAGE_NAME}:${TAG_FREQAI_RL}
# Tag as latest for develop builds
if [ "${TAG}" = "develop" ]; then
echo 'Tagging image as latest'
docker manifest create ${IMAGE_NAME}:latest ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI} ${CACHE_IMAGE}:${TAG}
docker manifest push -p ${IMAGE_NAME}:latest
crane copy ${IMAGE_NAME}:latest ${GHCR_IMAGE_NAME}:latest
fi
docker images
rm -rf .crane
# Cleanup old images from arm64 node.
docker image prune -a --force --filter "until=24h"

18
build_helpers/publish_docker_multi.sh
View File

@ -2,6 +2,8 @@
# The below assumes a correctly setup docker buildx environment
IMAGE_NAME=freqtradeorg/freqtrade
CACHE_IMAGE=freqtradeorg/freqtrade_cache
# Replace / with _ to create a valid tag
TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g")
TAG_PLOT=${TAG}_plot
@ -11,7 +13,6 @@ TAG_PI="${TAG}_pi"
PI_PLATFORM="linux/arm/v7"
echo "Running for ${TAG}"
CACHE_IMAGE=freqtradeorg/freqtrade_cache
CACHE_TAG=${CACHE_IMAGE}:${TAG_PI}_cache
# Add commit and commit_message to docker container
@ -26,7 +27,10 @@ if [ "${GITHUB_EVENT_NAME}" = "schedule" ]; then
--cache-to=type=registry,ref=${CACHE_TAG} \
-f docker/Dockerfile.armhf \
--platform ${PI_PLATFORM} \
-t ${IMAGE_NAME}:${TAG_PI} --push .
-t ${IMAGE_NAME}:${TAG_PI} \
--push \
--provenance=false \
.
else
echo "event ${GITHUB_EVENT_NAME}: building with cache"
# Build regular image
@ -35,12 +39,16 @@ else
# Pull last build to avoid rebuilding the whole image
# docker pull --platform ${PI_PLATFORM} ${IMAGE_NAME}:${TAG}
# disable provenance due to https://github.com/docker/buildx/issues/1509
docker buildx build \
--cache-from=type=registry,ref=${CACHE_TAG} \
--cache-to=type=registry,ref=${CACHE_TAG} \
-f docker/Dockerfile.armhf \
--platform ${PI_PLATFORM} \
-t ${IMAGE_NAME}:${TAG_PI} --push .
-t ${IMAGE_NAME}:${TAG_PI} \
--push \
--provenance=false \
.
fi
if [ $? -ne 0 ]; then
@ -68,12 +76,10 @@ fi
docker images
docker push ${CACHE_IMAGE}
docker push ${CACHE_IMAGE}:$TAG
docker push ${CACHE_IMAGE}:$TAG_PLOT
docker push ${CACHE_IMAGE}:$TAG_FREQAI
docker push ${CACHE_IMAGE}:$TAG_FREQAI_RL
docker push ${CACHE_IMAGE}:$TAG
docker images

BIN
build_helpers/pyarrow-10.0.0-cp39-cp39-linux_armv7l.whl → build_helpers/pyarrow-11.0.0-cp39-cp39-linux_armv7l.whl
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14
config_examples/config_binance.example.json
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@ -59,20 +59,6 @@
"pairlists": [
{"method": "StaticPairList"}
],
"edge": {
"enabled": false,
"process_throttle_secs": 3600,
"calculate_since_number_of_days": 7,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,
"stoploss_range_step": -0.01,
"minimum_winrate": 0.60,
"minimum_expectancy": 0.20,
"min_trade_number": 10,
"max_trade_duration_minute": 1440,
"remove_pumps": false
},
"telegram": {
"enabled": false,
"token": "your_telegram_token",

14
config_examples/config_bittrex.example.json
View File

@ -56,20 +56,6 @@
"pairlists": [
{"method": "StaticPairList"}
],
"edge": {
"enabled": false,
"process_throttle_secs": 3600,
"calculate_since_number_of_days": 7,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,
"stoploss_range_step": -0.01,
"minimum_winrate": 0.60,
"minimum_expectancy": 0.20,
"min_trade_number": 10,
"max_trade_duration_minute": 1440,
"remove_pumps": false
},
"telegram": {
"enabled": false,
"token": "your_telegram_token",

14
config_examples/config_freqai.example.json
View File

@ -21,8 +21,8 @@
"ccxt_config": {},
"ccxt_async_config": {},
"pair_whitelist": [
"1INCH/USDT",
"ALGO/USDT"
"1INCH/USDT:USDT",
"ALGO/USDT:USDT"
],
"pair_blacklist": []
},
@ -48,7 +48,7 @@
],
"freqai": {
"enabled": true,
"purge_old_models": true,
"purge_old_models": 2,
"train_period_days": 15,
"backtest_period_days": 7,
"live_retrain_hours": 0,
@ -60,8 +60,8 @@
"1h"
],
"include_corr_pairlist": [
"BTC/USDT",
"ETH/USDT"
"BTC/USDT:USDT",
"ETH/USDT:USDT"
],
"label_period_candles": 20,
"include_shifted_candles": 2,
@ -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,

1
config_examples/config_full.example.json
View File

@ -60,6 +60,7 @@
"force_entry": "market",
"stoploss": "market",
"stoploss_on_exchange": false,
"stoploss_price_type": "last",
"stoploss_on_exchange_interval": 60,
"stoploss_on_exchange_limit_ratio": 0.99
},

14
config_examples/config_kraken.example.json
View File

@ -64,20 +64,6 @@
"pairlists": [
{"method": "StaticPairList"}
],
"edge": {
"enabled": false,
"process_throttle_secs": 3600,
"calculate_since_number_of_days": 7,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,
"stoploss_range_step": -0.01,
"minimum_winrate": 0.60,
"minimum_expectancy": 0.20,
"min_trade_number": 10,
"max_trade_duration_minute": 1440,
"remove_pumps": false
},
"telegram": {
"enabled": false,
"token": "your_telegram_token",

2
docker/Dockerfile.armhf
View File

@ -1,4 +1,4 @@
FROM python:3.9.12-slim-bullseye as base
FROM python:3.9.16-slim-bullseye as base
# Setup env
ENV LANG C.UTF-8

3
docs/advanced-backtesting.md
View File

@ -32,7 +32,7 @@ To analyze the entry/exit tags, we now need to use the `freqtrade backtesting-an
with `--analysis-groups` option provided with space-separated arguments:
``` bash
freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 1 2 3 4
freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 1 2 3 4 5
```
This command will read from the last backtesting results. The `--analysis-groups` option is
@ -44,6 +44,7 @@ ranging from the simplest (0) to the most detailed per pair, per buy and per sel
* 2: profit summaries grouped by enter_tag and exit_tag
* 3: profit summaries grouped by pair and enter_tag
* 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
* 5: profit summaries grouped by exit_tag
More options are available by running with the `-h` option.

8
docs/advanced-hyperopt.md
View File

@ -75,7 +75,7 @@ This function needs to return a floating point number (`float`). Smaller numbers
## Overriding pre-defined spaces
To override a pre-defined space (`roi_space`, `generate_roi_table`, `stoploss_space`, `trailing_space`), define a nested class called Hyperopt and define the required spaces as follows:
To override a pre-defined space (`roi_space`, `generate_roi_table`, `stoploss_space`, `trailing_space`, `max_open_trades_space`), define a nested class called Hyperopt and define the required spaces as follows:
```python
from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal
@ -123,6 +123,12 @@ class MyAwesomeStrategy(IStrategy):
Categorical([True, False], name='trailing_only_offset_is_reached'),
]
# Define a custom max_open_trades space
def max_open_trades_space(self) -> List[Dimension]:
return [
Integer(-1, 10, name='max_open_trades'),
]
```
!!! Note

2
docs/advanced-setup.md
View File

@ -192,7 +192,7 @@ $RepeatedMsgReduction on
### Logging to journald
This needs the `systemd` python package installed as the dependency, which is not available on Windows. Hence, the whole journald logging functionality is not available for a bot running on Windows.
This needs the `cysystemd` python package installed as dependency (`pip install cysystemd`), which is not available on Windows. Hence, the whole journald logging functionality is not available for a bot running on Windows.
To send Freqtrade log messages to `journald` system service use the `--logfile` command line option with the value in the following format:

11
docs/backtesting.md
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@ -300,7 +300,11 @@ A backtesting result will look like that:
| Absolute profit | 0.00762792 BTC |
| Total profit % | 76.2% |
| CAGR % | 460.87% |
| Sortino | 1.88 |
| Sharpe | 2.97 |
| Calmar | 6.29 |
| Profit factor | 1.11 |
| Expectancy | -0.15 |
| Avg. stake amount | 0.001 BTC |
| Total trade volume | 0.429 BTC |
| | |
@ -400,7 +404,11 @@ It contains some useful key metrics about performance of your strategy on backte
| Absolute profit | 0.00762792 BTC |
| Total profit % | 76.2% |
| CAGR % | 460.87% |
| Sortino | 1.88 |
| Sharpe | 2.97 |
| Calmar | 6.29 |
| Profit factor | 1.11 |
| Expectancy | -0.15 |
| Avg. stake amount | 0.001 BTC |
| Total trade volume | 0.429 BTC |
| | |
@ -447,6 +455,9 @@ It contains some useful key metrics about performance of your strategy on backte
- `Absolute profit`: Profit made in stake currency.
- `Total profit %`: Total profit. Aligned to the `TOTAL` row's `Tot Profit %` from the first table. Calculated as `(End capital − Starting capital) / Starting capital`.
- `CAGR %`: Compound annual growth rate.
- `Sortino`: Annualized Sortino ratio.
- `Sharpe`: Annualized Sharpe ratio.
- `Calmar`: Annualized Calmar ratio.
- `Profit factor`: profit / loss.
- `Avg. stake amount`: Average stake amount, either `stake_amount` or the average when using dynamic stake amount.
- `Total trade volume`: Volume generated on the exchange to reach the above profit.

7
docs/bot-basics.md
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@ -12,6 +12,9 @@ This page provides you some basic concepts on how Freqtrade works and operates.
* **Indicators**: Technical indicators (SMA, EMA, RSI, ...).
* **Limit order**: Limit orders which execute at the defined limit price or better.
* **Market order**: Guaranteed to fill, may move price depending on the order size.
* **Current Profit**: Currently pending (unrealized) profit for this trade. This is mainly used throughout the bot and UI.
* **Realized Profit**: Already realized profit. Only relevant in combination with [partial exits](strategy-callbacks.md#adjust-trade-position) - which also explains the calculation logic for this.
* **Total Profit**: Combined realized and unrealized profit. The relative number (%) is calculated against the total investment in this trade.
## Fee handling
@ -75,3 +78,7 @@ This loop will be repeated again and again until the bot is stopped.
!!! Note
Both Backtesting and Hyperopt include exchange default Fees in the calculation. Custom fees can be passed to backtesting / hyperopt by specifying the `--fee` argument.
!!! Warning "Callback call frequency"
Backtesting will call each callback at max. once per candle (`--timeframe-detail` modifies this behavior to once per detailed candle).
Most callbacks will be called once per iteration in live (usually every ~5s) - which can cause backtesting mismatches.

18
docs/configuration.md
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@ -11,7 +11,7 @@ Per default, the bot loads the configuration from the `config.json` file, locate
You can specify a different configuration file used by the bot with the `-c/--config` command-line option.
If you used the [Quick start](installation.md/#quick-start) method for installing
If you used the [Quick start](docker_quickstart.md#docker-quick-start) method for installing
the bot, the installation script should have already created the default configuration file (`config.json`) for you.
If the default configuration file is not created we recommend to use `freqtrade new-config --config config.json` to generate a basic configuration file.
@ -134,7 +134,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| Parameter | Description |
|------------|-------------|
| `max_open_trades` | **Required.** Number of open trades your bot is allowed to have. Only one open trade per pair is possible, so the length of your pairlist is another limitation that can apply. If -1 then it is ignored (i.e. potentially unlimited open trades, limited by the pairlist). [More information below](#configuring-amount-per-trade).<br> **Datatype:** Positive integer or -1.
| `max_open_trades` | **Required.** Number of open trades your bot is allowed to have. Only one open trade per pair is possible, so the length of your pairlist is another limitation that can apply. If -1 then it is ignored (i.e. potentially unlimited open trades, limited by the pairlist). [More information below](#configuring-amount-per-trade). [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Positive integer or -1.
| `stake_currency` | **Required.** Crypto-currency used for trading. <br> **Datatype:** String
| `stake_amount` | **Required.** Amount of crypto-currency your bot will use for each trade. Set it to `"unlimited"` to allow the bot to use all available balance. [More information below](#configuring-amount-per-trade). <br> **Datatype:** Positive float or `"unlimited"`.
| `tradable_balance_ratio` | Ratio of the total account balance the bot is allowed to trade. [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.99` 99%).*<br> **Datatype:** Positive float between `0.1` and `1.0`.
@ -263,6 +263,7 @@ Values set in the configuration file always overwrite values set in the strategy
* `minimal_roi`
* `timeframe`
* `stoploss`
* `max_open_trades`
* `trailing_stop`
* `trailing_stop_positive`
* `trailing_stop_positive_offset`
@ -665,7 +666,7 @@ You should also make sure to read the [Exchanges](exchanges.md) section of the d
### Using proxy with Freqtrade
To use a proxy with freqtrade, export your proxy settings using the variables `"HTTP_PROXY"` and `"HTTPS_PROXY"` set to the appropriate values.
This will have the proxy settings applied to everything (telegram, coingecko, ...) except exchange requests.
This will have the proxy settings applied to everything (telegram, coingecko, ...) **except** for exchange requests.
``` bash
export HTTP_PROXY="http://addr:port"
@ -681,11 +682,12 @@ To use a proxy for exchange connections - you will have to define the proxies as
{
"exchange": {
"ccxt_config": {
"aiohttp_proxy": "http://addr:port",
"proxies": {
"http": "http://addr:port",
"https": "http://addr:port"
},
"aiohttp_proxy": "http://addr:port",
"proxies": {
"http": "http://addr:port",
"https": "http://addr:port"
},
}
}
}
```

5
docs/deprecated.md
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@ -74,3 +74,8 @@ Webhook terminology changed from "sell" to "exit", and from "buy" to "entry", re
* `webhooksell`, `webhookexit` -> `exit`
* `webhooksellfill`, `webhookexitfill` -> `exit_fill`
* `webhooksellcancel`, `webhookexitcancel` -> `exit_cancel`
## Removal of `populate_any_indicators`
version 2023.3 saw the removal of `populate_any_indicators` in favor of split methods for feature engineering and targets. Please read the [migration document](strategy_migration.md#freqai-strategy) for full details.

4
docs/developer.md
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@ -24,7 +24,7 @@ This will spin up a local server (usually on port 8000) so you can see if everyt
To configure a development environment, you can either use the provided [DevContainer](#devcontainer-setup), or use the `setup.sh` script and answer "y" when asked "Do you want to install dependencies for dev [y/N]? ".
Alternatively (e.g. if your system is not supported by the setup.sh script), follow the manual installation process and run `pip3 install -e .[all]`.
This will install all required tools for development, including `pytest`, `flake8`, `mypy`, and `coveralls`.
This will install all required tools for development, including `pytest`, `ruff`, `mypy`, and `coveralls`.
Then install the git hook scripts by running `pre-commit install`, so your changes will be verified locally before committing.
This avoids a lot of waiting for CI already, as some basic formatting checks are done locally on your machine.
@ -363,7 +363,7 @@ from pathlib import Path
exchange = ccxt.binance({
'apiKey': '<apikey>',
'secret': '<secret>'
'options': {'defaultType': 'future'}
'options': {'defaultType': 'swap'}
})
_ = exchange.load_markets()

35
docs/exchanges.md
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@ -75,6 +75,25 @@ Binance has been split into 2, and users must use the correct ccxt exchange ID f
* [binance.com](https://www.binance.com/) - International users. Use exchange id: `binance`.
* [binance.us](https://www.binance.us/) - US based users. Use exchange id: `binanceus`.
### Binance RSA keys
Freqtrade supports binance RSA API keys.
We recommend to use them as environment variable.
``` bash
export FREQTRADE__EXCHANGE__SECRET="$(cat ./rsa_binance.private)"
```
They can however also be configured via configuration file. Since json doesn't support multi-line strings, you'll have to replace all newlines with `\n` to have a valid json file.
``` json
// ...
"key": "<someapikey>",
"secret": "-----BEGIN PRIVATE KEY-----\nMIIEvQIBABACAFQA<...>s8KX8=\n-----END PRIVATE KEY-----"
// ...
```
### Binance Futures
Binance has specific (unfortunately complex) [Futures Trading Quantitative Rules](https://www.binance.com/en/support/faq/4f462ebe6ff445d4a170be7d9e897272) which need to be followed, and which prohibit a too low stake-amount (among others) for too many orders.
@ -224,8 +243,8 @@ OKX requires a passphrase for each api key, you will therefore need to add this
OKX only provides 100 candles per api call. Therefore, the strategy will only have a pretty low amount of data available in backtesting mode.
!!! Warning "Futures"
OKX Futures has the concept of "position mode" - which can be Net or long/short (hedge mode).
Freqtrade supports both modes (we recommend to use net mode) - but changing the mode mid-trading is not supported and will lead to exceptions and failures to place trades.
OKX Futures has the concept of "position mode" - which can be "Buy/Sell" or long/short (hedge mode).
Freqtrade supports both modes (we recommend to use Buy/Sell mode) - but changing the mode mid-trading is not supported and will lead to exceptions and failures to place trades.
OKX also only provides MARK candles for the past ~3 months. Backtesting futures prior to that date will therefore lead to slight deviations, as funding-fees cannot be calculated correctly without this data.
## Gate.io
@ -236,6 +255,18 @@ OKX requires a passphrase for each api key, you will therefore need to add this
Gate.io allows the use of `POINT` to pay for fees. As this is not a tradable currency (no regular market available), automatic fee calculations will fail (and default to a fee of 0).
The configuration parameter `exchange.unknown_fee_rate` can be used to specify the exchange rate between Point and the stake currency. Obviously, changing the stake-currency will also require changes to this value.
## Bybit
Futures trading on bybit is currently supported for USDT markets, and will use isolated futures mode.
Users with unified accounts (there's no way back) can create a Sub-account which will start as "non-unified", and can therefore use isolated futures.
On startup, freqtrade will set the position mode to "One-way Mode" for the whole (sub)account. This avoids making this call over and over again (slowing down bot operations), but means that changes to this setting may result in exceptions and errors.
As bybit doesn't provide funding rate history, the dry-run calculation is used for live trades as well.
!!! Tip "Stoploss on Exchange"
Bybit (futures only) supports `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange.
On futures, Bybit supports both `stop-limit` as well as `stop-market` orders. You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type to use.
## All exchanges
Should you experience constant errors with Nonce (like `InvalidNonce`), it is best to regenerate the API keys. Resetting Nonce is difficult and it's usually easier to regenerate the API keys.

24
docs/faq.md
View File

@ -2,7 +2,7 @@
## Supported Markets
Freqtrade supports spot trading only.
Freqtrade supports spot trading, as well as (isolated) futures trading for some selected exchanges. Please refer to the [documentation start page](index.md#supported-futures-exchanges-experimental) for an uptodate list of supported exchanges.
### Can my bot open short positions?
@ -248,8 +248,26 @@ The Edge module is mostly a result of brainstorming of [@mishaker](https://githu
You can find further info on expectancy, win rate, risk management and position size in the following sources:
- https://www.tradeciety.com/ultimate-math-guide-for-traders/
- http://www.vantharp.com/tharp-concepts/expectancy.asp
- https://samuraitradingacademy.com/trading-expectancy/
- https://www.learningmarkets.com/determining-expectancy-in-your-trading/
- http://www.lonestocktrader.com/make-money-trading-positive-expectancy/
- https://www.lonestocktrader.com/make-money-trading-positive-expectancy/
- https://www.babypips.com/trading/trade-expectancy-matter
## Official channels
Freqtrade is using exclusively the following official channels:
* [Freqtrade discord server](https://discord.gg/p7nuUNVfP7)
* [Freqtrade documentation (https://freqtrade.io)](https://freqtrade.io)
* [Freqtrade github organization](https://github.com/freqtrade)
Nobody affiliated with the freqtrade project will ask you about your exchange keys or anything else exposing your funds to exploitation.
Should you be asked to expose your exchange keys or send funds to some random wallet, then please don't follow these instructions.
Failing to follow these guidelines will not be responsibility of freqtrade.
## "Freqtrade token"
Freqtrade does not have a Crypto token offering.
Token offerings you find on the internet referring Freqtrade, FreqAI or freqUI must be considered to be a scam, trying to exploit freqtrade's popularity for their own, nefarious gains.

192
docs/freqai-configuration.md
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@ -9,7 +9,7 @@ FreqAI is configured through the typical [Freqtrade config file](configuration.m
```json
"freqai": {
"enabled": true,
"purge_old_models": true,
"purge_old_models": 2,
"train_period_days": 30,
"backtest_period_days": 7,
"identifier" : "unique-id",
@ -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
},
}
}
```
@ -46,116 +43,113 @@ The FreqAI strategy requires including the following lines of code in the standa
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# the model will return all labels created by user in `populate_any_indicators`
# the model will return all labels created by user in `set_freqai_labels()`
# (& appended targets), an indication of whether or not the prediction should be accepted,
# the target mean/std values for each of the labels created by user in
# `populate_any_indicators()` for each training period.
# `feature_engineering_*` for each training period.
dataframe = self.freqai.start(dataframe, metadata, self)
return dataframe
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
def feature_engineering_expand_all(self, dataframe, period, **kwargs):
"""
Function designed to automatically generate, name and merge features
from user indicated timeframes in the configuration file. User controls the indicators
passed to the training/prediction by prepending indicators with `'%-' + pair `
(see convention below). I.e. user should not prepend any supporting metrics
(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
model.
:param pair: pair to be used as informative
:param df: strategy dataframe which will receive merges from informatives
:param tf: timeframe of the dataframe which will modify the feature names
:param informative: the dataframe associated with the informative pair
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
`include_corr_pairs`. In other words, a single feature defined in this function
will automatically expand to a total of
`indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
`include_corr_pairs` numbers of features added to the model.
All features must be prepended with `%` to be recognized by FreqAI internals.
:param df: strategy dataframe which will receive the features
:param period: period of the indicator - usage example:
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
"""
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
# 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)
return dataframe
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)
def feature_engineering_expand_basic(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
In other words, a single feature defined in this function
will automatically expand to a total of
`include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
numbers of features added to the model.
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)
Features defined here will *not* be automatically duplicated on user defined
`indicator_periods_candles`
# 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:
All features must be prepended with `%` to be recognized by FreqAI internals.
# user adds targets here by prepending them with &- (see convention below)
# If user wishes to use multiple targets, a multioutput prediction model
# needs to be used such as templates/CatboostPredictionMultiModel.py
df["&-s_close"] = (
df["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ df["close"]
- 1
:param df: strategy dataframe which will receive the features
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
"""
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-raw_volume"] = dataframe["volume"]
dataframe["%-raw_price"] = dataframe["close"]
return dataframe
def feature_engineering_standard(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This optional function will be called once with the dataframe of the base timeframe.
This is the final function to be called, which means that the dataframe entering this
function will contain all the features and columns created by all other
freqai_feature_engineering_* functions.
This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
This function is a good place for any feature that should not be auto-expanded upon
(e.g. day of the week).
All features must be prepended with `%` to be recognized by FreqAI internals.
:param df: strategy dataframe which will receive the features
usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
"""
dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
dataframe["%-hour_of_day"] = (dataframe["date"].dt.hour + 1) / 25
return dataframe
def set_freqai_targets(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
Required function to set the targets for the model.
All targets must be prepended with `&` to be recognized by the FreqAI internals.
:param df: strategy dataframe which will receive the targets
usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
"""
dataframe["&-s_close"] = (
dataframe["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ dataframe["close"]
- 1
)
return df
```
Notice how the `populate_any_indicators()` is where [features](freqai-feature-engineering.md#feature-engineering) and labels/targets are added. A full example strategy is available in `templates/FreqaiExampleStrategy.py`.
Notice also the location of the labels under `if set_generalized_indicators:` at the bottom of the example. This is where single features and labels/targets should be added to the feature set to avoid duplication of them from various configuration parameters that multiply the feature set, such as `include_timeframes`.
Notice how the `feature_engineering_*()` is where [features](freqai-feature-engineering.md#feature-engineering) are added. Meanwhile `set_freqai_targets()` adds the labels/targets. A full example strategy is available in `templates/FreqaiExampleStrategy.py`.
!!! Note
The `self.freqai.start()` function cannot be called outside the `populate_indicators()`.
!!! Note
Features **must** be defined in `populate_any_indicators()`. Defining FreqAI features in `populate_indicators()`
will cause the algorithm to fail in live/dry mode. In order to add generalized features that are not associated with a specific pair or timeframe, the following structure inside `populate_any_indicators()` should be used
(as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`):
```python
def populate_any_indicators(self, pair, df, tf, informative=None, set_generalized_indicators=False):
...
# Add generalized indicators here (because in live, it will call only this function to populate
# indicators for retraining). Notice how we ensure not to add them multiple times by associating
# these generalized indicators to the basepair/timeframe
if set_generalized_indicators:
df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7
df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25
# user adds targets here by prepending them with &- (see convention below)
# If user wishes to use multiple targets, a multioutput prediction model
# needs to be used such as templates/CatboostPredictionMultiModel.py
df["&-s_close"] = (
df["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ df["close"]
- 1
)
```
Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`.
Features **must** be defined in `feature_engineering_*()`. Defining FreqAI features in `populate_indicators()`
will cause the algorithm to fail in live/dry mode. In order to add generalized features that are not associated with a specific pair or timeframe, you should use `feature_engineering_standard()`
(as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`).
## Important dataframe key patterns
@ -163,18 +157,18 @@ Below are the values you can expect to include/use inside a typical strategy dat
| DataFrame Key | Description |
|------------|-------------|
| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). For example, to predict the close price 40 candles into the future, you would set `df['&-s_close'] = df['close'].shift(-self.freqai_info["feature_parameters"]["label_period_candles"])` with `"label_period_candles": 40` in the config. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`. <br> **Datatype:** Depends on the output of the model.
| `df['&*']` | Any dataframe column prepended with `&` in `set_freqai_targets()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). For example, to predict the close price 40 candles into the future, you would set `df['&-s_close'] = df['close'].shift(-self.freqai_info["feature_parameters"]["label_period_candles"])` with `"label_period_candles": 40` in the config. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`. <br> **Datatype:** Depends on the output of the model.
| `df['&*_std/mean']` | Standard deviation and mean values of the defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` and explained [here](#creating-a-dynamic-target-threshold) to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`). <br> **Datatype:** Float.
| `df['do_predict']` | Indication of an outlier data point. The return value is integer between -2 and 2, which lets you know if the prediction is trustworthy or not. `do_predict==1` means that the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di)) of the input data point is above the threshold defined in the config, FreqAI will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers()` is active, the Support Vector Machine (SVM, see details [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm)) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, or vice versa, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. As with the SVM, if `use_DBSCAN_to_remove_outliers` is active, DBSCAN (see details [here](freqai-feature-engineering.md#identifying-outliers-with-dbscan)) may also detect outliers and subtract 1 from `do_predict`. Hence, if both the SVM and DBSCAN are active and identify a datapoint that was above the DI threshold as an outlier, the result will be `do_predict==-2`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`. <br> **Datatype:** Integer between -2 and 2.
| `df['DI_values']` | Dissimilarity Index (DI) values are proxies for the level of confidence FreqAI has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence. See details about the DI [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di). <br> **Datatype:** Float.
| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md). <br> **Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features are easily engineered using the multiplictative functionality of, e.g., `include_shifted_candles` and `include_timeframes` as described in the [parameter table](freqai-parameter-table.md)), these features are removed from the dataframe that is returned from FreqAI to the strategy. To keep a particular type of feature for plotting purposes, you would prepend it with `%%`. <br> **Datatype:** Depends on the output of the model.
| `df['%*']` | Any dataframe column prepended with `%` in `feature_engineering_*()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md). <br> **Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features are easily engineered using the multiplictative functionality of, e.g., `include_shifted_candles` and `include_timeframes` as described in the [parameter table](freqai-parameter-table.md)), these features are removed from the dataframe that is returned from FreqAI to the strategy. To keep a particular type of feature for plotting purposes, you would prepend it with `%%`. <br> **Datatype:** Depends on the output of the model.
## Setting the `startup_candle_count`
The `startup_candle_count` in the FreqAI strategy needs to be set up in the same way as in the standard Freqtrade strategy (see details [here](strategy-customization.md#strategy-startup-period)). This value is used by Freqtrade to ensure that a sufficient amount of data is provided when calling the `dataprovider`, to avoid any NaNs at the beginning of the first training. You can easily set this value by identifying the longest period (in candle units) which is passed to the indicator creation functions (e.g., Ta-Lib functions). In the presented example, `startup_candle_count` is 20 since this is the maximum value in `indicators_periods_candles`.
The `startup_candle_count` in the FreqAI strategy needs to be set up in the same way as in the standard Freqtrade strategy (see details [here](strategy-customization.md#strategy-startup-period)). This value is used by Freqtrade to ensure that a sufficient amount of data is provided when calling the `dataprovider`, to avoid any NaNs at the beginning of the first training. You can easily set this value by identifying the longest period (in candle units) which is passed to the indicator creation functions (e.g., TA-Lib functions). In the presented example, `startup_candle_count` is 20 since this is the maximum value in `indicators_periods_candles`.
!!! Note
There are instances where the Ta-Lib functions actually require more data than just the passed `period` or else the feature dataset gets populated with NaNs. Anecdotally, multiplying the `startup_candle_count` by 2 always leads to a fully NaN free training dataset. Hence, it is typically safest to multiply the expected `startup_candle_count` by 2. Look out for this log message to confirm that the data is clean:
There are instances where the TA-Lib functions actually require more data than just the passed `period` or else the feature dataset gets populated with NaNs. Anecdotally, multiplying the `startup_candle_count` by 2 always leads to a fully NaN free training dataset. Hence, it is typically safest to multiply the expected `startup_candle_count` by 2. Look out for this log message to confirm that the data is clean:
```
2022-08-31 15:14:04 - freqtrade.freqai.data_kitchen - INFO - dropped 0 training points due to NaNs in populated dataset 4319.
@ -182,7 +176,7 @@ The `startup_candle_count` in the FreqAI strategy needs to be set up in the same
## Creating a dynamic target threshold
Deciding when to enter or exit a trade can be done in a dynamic way to reflect current market conditions. FreqAI allows you to return additional information from the training of a model (more info [here](freqai-feature-engineering.md#returning-additional-info-from-training)). For example, the `&*_std/mean` return values describe the statistical distribution of the target/label *during the most recent training*. Comparing a given prediction to these values allows you to know the rarity of the prediction. In `templates/FreqaiExampleStrategy.py`, the `target_roi` and `sell_roi` are defined to be 1.25 z-scores away from the mean which causes predictions that are closer to the mean to be filtered out.
Deciding when to enter or exit a trade can be done in a dynamic way to reflect current market conditions. FreqAI allows you to return additional information from the training of a model (more info [here](freqai-feature-engineering.md#returning-additional-info-from-training)). For example, the `&*_std/mean` return values describe the statistical distribution of the target/label *during the most recent training*. Comparing a given prediction to these values allows you to know the rarity of the prediction. In `templates/FreqaiExampleStrategy.py`, the `target_roi` and `sell_roi` are defined to be 1.25 z-scores away from the mean which causes predictions that are closer to the mean to be filtered out.
```python
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
@ -211,7 +205,7 @@ All of the aforementioned model libraries implement gradient boosted decision tr
* LightGBM: https://lightgbm.readthedocs.io/en/v3.3.2/#
* XGBoost: https://xgboost.readthedocs.io/en/stable/#
There are also numerous online articles describing and comparing the algorithms. Some relatively light-weight examples would be [CatBoost vs. LightGBM vs. XGBoost — Which is the best algorithm?](https://towardsdatascience.com/catboost-vs-lightgbm-vs-xgboost-c80f40662924#:~:text=In%20CatBoost%2C%20symmetric%20trees%2C%20or,the%20same%20depth%20can%20differ.) and [XGBoost, LightGBM or CatBoost — which boosting algorithm should I use?](https://medium.com/riskified-technology/xgboost-lightgbm-or-catboost-which-boosting-algorithm-should-i-use-e7fda7bb36bc). Keep in mind that the performance of each model is highly dependent on the application and so any reported metrics might not be true for your particular use of the model.
There are also numerous online articles describing and comparing the algorithms. Some relatively lightweight examples would be [CatBoost vs. LightGBM vs. XGBoost — Which is the best algorithm?](https://towardsdatascience.com/catboost-vs-lightgbm-vs-xgboost-c80f40662924#:~:text=In%20CatBoost%2C%20symmetric%20trees%2C%20or,the%20same%20depth%20can%20differ.) and [XGBoost, LightGBM or CatBoost — which boosting algorithm should I use?](https://medium.com/riskified-technology/xgboost-lightgbm-or-catboost-which-boosting-algorithm-should-i-use-e7fda7bb36bc). Keep in mind that the performance of each model is highly dependent on the application and so any reported metrics might not be true for your particular use of the model.
Apart from the models already available in FreqAI, it is also possible to customize and create your own prediction models using the `IFreqaiModel` class. You are encouraged to inherit `fit()`, `train()`, and `predict()` to customize various aspects of the training procedures. You can place custom FreqAI models in `user_data/freqaimodels` - and freqtrade will pick them up from there based on the provided `--freqaimodel` name - which has to correspond to the class name of your custom model.
Make sure to use unique names to avoid overriding built-in models.
@ -230,7 +224,7 @@ If you want to predict multiple targets, you need to define multiple labels usin
#### Classifiers
If you are using a classifier, you need to specify a target that has discrete values. FreqAI includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If you elects to use a classifier, the classes need to be set using strings. For example, if you want to predict if the price 100 candles into the future goes up or down you would set
If you are using a classifier, you need to specify a target that has discrete values. FreqAI includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If you elects to use a classifier, the classes need to be set using strings. For example, if you want to predict if the price 100 candles into the future goes up or down you would set
```python
df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')

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@ -2,96 +2,150 @@
## Defining the features
Low level feature engineering is performed in the user strategy within a function called `populate_any_indicators()`. That function sets the `base features` such as, `RSI`, `MFI`, `EMA`, `SMA`, time of day, volume, etc. The `base features` can be custom indicators or they can be imported from any technical-analysis library that you can find. One important syntax rule is that all `base features` string names are prepended with `%-{pair}`, while labels/targets are prepended with `&`.
Low level feature engineering is performed in the user strategy within a set of functions called `feature_engineering_*`. These function set the `base features` such as, `RSI`, `MFI`, `EMA`, `SMA`, time of day, volume, etc. The `base features` can be custom indicators or they can be imported from any technical-analysis library that you can find. FreqAI is equipped with a set of functions to simplify rapid large-scale feature engineering:
!!! Note
Adding the full pair string, e.g. XYZ/USD, in the feature name enables improved performance for dataframe caching on the backend. If you decide *not* to add the full pair string in the feature string, FreqAI will operate in a reduced performance mode.
| Function | Description |
|---------------|-------------|
| `feature_engineering__expand_all()` | This optional function will automatically expand the defined features on the config defined `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
| `feature_engineering__expand_basic()` | This optional function will automatically expand the defined features on the config defined `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`. Note: this function does *not* expand across `include_periods_candles`.
| `feature_engineering_standard()` | This optional function will be called once with the dataframe of the base timeframe. This is the final function to be called, which means that the dataframe entering this function will contain all the features and columns from the base asset created by the other `feature_engineering_expand` functions. This function is a good place to do custom exotic feature extractions (e.g. tsfresh). This function is also a good place for any feature that should not be auto-expanded upon (e.g., day of the week).
| `set_freqai_targets()` | Required function to set the targets for the model. All targets must be prepended with `&` to be recognized by the FreqAI internals.
Meanwhile, high level feature engineering is handled within `"feature_parameters":{}` in the FreqAI config. Within this file, it is possible to decide large scale feature expansions on top of the `base_features` such as "including correlated pairs" or "including informative timeframes" or even "including recent candles."
It is advisable to start from the template `populate_any_indicators()` in the source provided example strategy (found in `templates/FreqaiExampleStrategy.py`) to ensure that the feature definitions are following the correct conventions. Here is an example of how to set the indicators and labels in the strategy:
It is advisable to start from the template `feature_engineering_*` functions in the source provided example strategy (found in `templates/FreqaiExampleStrategy.py`) to ensure that the feature definitions are following the correct conventions. Here is an example of how to set the indicators and labels in the strategy:
```python
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
def feature_engineering_expand_all(self, dataframe, period, metadata, **kwargs):
"""
Function designed to automatically generate, name, and merge features
from user-indicated timeframes in the configuration file. The user controls the indicators
passed to the training/prediction by prepending indicators with `'%-' + pair `
(see convention below). I.e., the user should not prepend any supporting metrics
(e.g., bb_lowerband below) with % unless they explicitly want to pass that metric to the
model.
:param pair: pair to be used as informative
:param df: strategy dataframe which will receive merges from informatives
:param tf: timeframe of the dataframe which will modify the feature names
:param informative: the dataframe associated with the informative pair
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
`include_corr_pairs`. In other words, a single feature defined in this function
will automatically expand to a total of
`indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
`include_corr_pairs` numbers of features added to the model.
All features must be prepended with `%` to be recognized by FreqAI internals.
Access metadata such as the current pair/timeframe/period with:
`metadata["pair"]` `metadata["tf"]` `metadata["period"]`
:param df: strategy dataframe which will receive the features
:param period: period of the indicator - usage example:
:param metadata: metadata of current pair
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
"""
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
# 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)
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(dataframe), window=period, stds=2.2
)
dataframe["bb_lowerband-period"] = bollinger["lower"]
dataframe["bb_middleband-period"] = bollinger["mid"]
dataframe["bb_upperband-period"] = bollinger["upper"]
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(informative), window=t, stds=2.2
dataframe["%-bb_width-period"] = (
dataframe["bb_upperband-period"]
- dataframe["bb_lowerband-period"]
) / dataframe["bb_middleband-period"]
dataframe["%-close-bb_lower-period"] = (
dataframe["close"] / dataframe["bb_lowerband-period"]
)
dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
dataframe["%-relative_volume-period"] = (
dataframe["volume"] / dataframe["volume"].rolling(period).mean()
)
return dataframe
def feature_engineering_expand_basic(self, dataframe, metadata, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
In other words, a single feature defined in this function
will automatically expand to a total of
`include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
numbers of features added to the model.
Features defined here will *not* be automatically duplicated on user defined
`indicator_periods_candles`
Access metadata such as the current pair/timeframe with:
`metadata["pair"]` `metadata["tf"]`
All features must be prepended with `%` to be recognized by FreqAI internals.
:param df: strategy dataframe which will receive the features
:param metadata: metadata of current pair
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
"""
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-raw_volume"] = dataframe["volume"]
dataframe["%-raw_price"] = dataframe["close"]
return dataframe
def feature_engineering_standard(self, dataframe, metadata, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This optional function will be called once with the dataframe of the base timeframe.
This is the final function to be called, which means that the dataframe entering this
function will contain all the features and columns created by all other
freqai_feature_engineering_* functions.
This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
This function is a good place for any feature that should not be auto-expanded upon
(e.g. day of the week).
Access metadata such as the current pair with:
`metadata["pair"]`
All features must be prepended with `%` to be recognized by FreqAI internals.
:param df: strategy dataframe which will receive the features
:param metadata: metadata of current pair
usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
"""
dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
dataframe["%-hour_of_day"] = (dataframe["date"].dt.hour + 1) / 25
return dataframe
def set_freqai_targets(self, dataframe, metadata, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
Required function to set the targets for the model.
All targets must be prepended with `&` to be recognized by the FreqAI internals.
Access metadata such as the current pair with:
`metadata["pair"]`
:param df: strategy dataframe which will receive the targets
:param metadata: metadata of current pair
usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
"""
dataframe["&-s_close"] = (
dataframe["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ dataframe["close"]
- 1
)
informative[f"{pair}bb_lowerband-period_{t}"] = bollinger["lower"]
informative[f"{pair}bb_middleband-period_{t}"] = bollinger["mid"]
informative[f"{pair}bb_upperband-period_{t}"] = bollinger["upper"]
informative[f"%-{pair}bb_width-period_{t}"] = (
informative[f"{pair}bb_upperband-period_{t}"]
- informative[f"{pair}bb_lowerband-period_{t}"]
) / informative[f"{pair}bb_middleband-period_{t}"]
informative[f"%-{pair}close-bb_lower-period_{t}"] = (
informative["close"] / informative[f"{pair}bb_lowerband-period_{t}"]
)
informative[f"%-{pair}relative_volume-period_{t}"] = (
informative["volume"] / informative["volume"].rolling(t).mean()
)
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
]
df = df.drop(columns=skip_columns)
# Add generalized indicators here (because in live, it will call this
# function to populate indicators during training). Notice how we ensure not to
# add them multiple times
if set_generalized_indicators:
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
# user adds targets here by prepending them with &- (see convention below)
# If user wishes to use multiple targets, a multioutput prediction model
# needs to be used such as templates/CatboostPredictionMultiModel.py
df["&-s_close"] = (
df["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ df["close"]
- 1
)
return df
return dataframe
```
In the presented example, the user does not wish to pass the `bb_lowerband` as a feature to the model,
@ -118,15 +172,28 @@ After having defined the `base features`, the next step is to expand upon them u
}
```
The `include_timeframes` in the config above are the timeframes (`tf`) of each call to `populate_any_indicators()` in the strategy. In the presented case, the user is asking for the `5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set.
The `include_timeframes` in the config above are the timeframes (`tf`) of each call to `feature_engineering_expand_*()` in the strategy. In the presented case, the user is asking for the `5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set.
You can ask for each of the defined features to be included also for informative pairs using the `include_corr_pairlist`. This means that the feature set will include all the features from `populate_any_indicators` on all the `include_timeframes` for each of the correlated pairs defined in the config (`ETH/USD`, `LINK/USD`, and `BNB/USD` in the presented example).
You can ask for each of the defined features to be included also for informative pairs using the `include_corr_pairlist`. This means that the feature set will include all the features from `feature_engineering_expand_*()` on all the `include_timeframes` for each of the correlated pairs defined in the config (`ETH/USD`, `LINK/USD`, and `BNB/USD` in the presented example).
`include_shifted_candles` indicates the number of previous candles to include in the feature set. For example, `include_shifted_candles: 2` tells FreqAI to include the past 2 candles for each of the features in the feature set.
In total, the number of features the user of the presented example strat has created is: length of `include_timeframes` * no. features in `populate_any_indicators()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles`
In total, the number of features the user of the presented example strat has created is: length of `include_timeframes` * no. features in `feature_engineering_expand_*()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles`
$= 3 * 3 * 3 * 2 * 2 = 108$.
### Gain finer control over `feature_engineering_*` functions with `metadata`
All `feature_engineering_*` and `set_freqai_targets()` functions are passed a `metadata` dictionary which contains information about the `pair`, `tf` (timeframe), and `period` that FreqAI is automating for feature building. As such, a user can use `metadata` inside `feature_engineering_*` functions as criteria for blocking/reserving features for certain timeframes, periods, pairs etc.
```py
def feature_engineering_expand_all(self, dataframe, period, metadata, **kwargs):
if metadata["tf"] == "1h":
dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
```
This will block `ta.ROC()` from being added to any timeframes other than `"1h"`.
### Returning additional info from training
Important metrics can be returned to the strategy at the end of each model training by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside the custom prediction model class.
@ -167,7 +234,7 @@ This will perform PCA on the features and reduce their dimensionality so that th
## Inlier metric
The `inlier_metric` is a metric aimed at quantifying how similar a the features of a data point are to the most recent historic data points.
The `inlier_metric` is a metric aimed at quantifying how similar the features of a data point are to the most recent historical data points.
You define the lookback window by setting `inlier_metric_window` and FreqAI computes the distance between the present time point and each of the previous `inlier_metric_window` lookback points. A Weibull function is fit to each of the lookback distributions and its cumulative distribution function (CDF) is used to produce a quantile for each lookback point. The `inlier_metric` is then computed for each time point as the average of the corresponding lookback quantiles. The figure below explains the concept for an `inlier_metric_window` of 5.

18
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View File

@ -15,10 +15,9 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
| `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` | Number of models to keep on disk (not relevant to backtesting). Default is 2, which means that dry/live runs will keep the latest 2 models on disk. Setting to 0 keeps all models. This parameter also accepts a boolean to maintain backwards compatibility. <br> **Datatype:** Integer. <br> Default: `2`.
| `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`
| `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.
@ -29,12 +28,12 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
|------------|-------------|
| | **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).
| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators` (see `templates/FreqaiExampleStrategy.py` for detailed usage). You can create custom labels and choose whether to make use of this parameter or not. <br> **Datatype:** Positive integer.
| `include_timeframes` | A list of timeframes that all indicators in `feature_engineering_expand_*()` 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 `feature_engineering_expand_*()` 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).
| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `feature_engineering_expand_all()` (see `templates/FreqaiExampleStrategy.py` for detailed usage). You can create custom labels and choose whether to make use of this parameter or not. <br> **Datatype:** Positive integer.
| `include_shifted_candles` | Add features from previous candles to subsequent candles with the intent of adding historical information. If used, FreqAI will duplicate and shift all features from the `include_shifted_candles` previous candles so that the information is available for the subsequent candle. <br> **Datatype:** Positive integer.
| `weight_factor` | Weight training data points according to their recency (see details [here](freqai-feature-engineering.md#weighting-features-for-temporal-importance)). <br> **Datatype:** Positive float (typically < 1).
| `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_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 `feature_engineering_*()` 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. Plot is stored in `user_data/models/<identifier>/sub-train-<COIN>_<timestamp>.html`. <br> **Datatype:** Integer. <br> Default: `0`.
@ -46,13 +45,15 @@ 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).
| `shuffle_after_split` | Split the data into train and test sets, and then shuffle both sets individually. <br> **Datatype:** Boolean. <br> Default: `False`.
| `buffer_train_data_candles` | Cut `buffer_train_data_candles` off the beginning and end of the training data *after* the indicators were populated. The main example use is when predicting maxima and minima, the argrelextrema function cannot know the maxima/minima at the edges of the timerange. To improve model accuracy, it is best to compute argrelextrema on the full timerange and then use this function to cut off the edges (buffer) by the kernel. In another case, if the targets are set to a shifted price movement, this buffer is unnecessary because the shifted candles at the end of the timerange will be NaN and FreqAI will automatically cut those off of the training dataset.<br> **Datatype:** Boolean. <br> Default: `False`.
### 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.
| `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`.
@ -83,12 +84,13 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
| `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`.
| `drop_ohlc_from_features` | Do not include the normalized ohlc data in the feature set passed to the agent during training (ohlc will still be used for driving the environment in all cases) <br> **Datatype:** Boolean. <br> **Default:** `False`
### Additional parameters
| Parameter | Description |
|------------|-------------|
| | **Extraneous parameters**
| `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.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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@ -24,7 +24,7 @@ The framework is built on stable_baselines3 (torch) and OpenAI gym for the base
### 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.
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
@ -34,65 +34,36 @@ Setting up and running a Reinforcement Learning model is the same as running a R
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:
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 `feature_engineering_*` as a typical Regressor. The difference lies in the creation of the targets, Reinforcement Learning doesn't require them. However, FreqAI requires a default (neutral) value to be set in the action column:
```python
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
def set_freqai_targets(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
Required function to set the targets for the model.
All targets must be prepended with `&` to be recognized by the FreqAI internals.
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
More details about feature engineering available:
# first loop is automatically duplicating indicators for time periods
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
https://www.freqtrade.io/en/latest/freqai-feature-engineering
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
:param df: strategy dataframe which will receive the targets
usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
"""
# For RL, there are no direct targets to set. This is filler (neutral)
# until the agent sends an action.
dataframe["&-action"] = 0
```
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
def feature_engineering_standard(self, dataframe, **kwargs):
# 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"]
dataframe[f"%-raw_close"] = dataframe["close"]
dataframe[f"%-raw_open"] = dataframe["open"]
dataframe[f"%-raw_high"] = dataframe["high"]
dataframe[f"%-raw_low"] = dataframe["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.
@ -204,10 +175,23 @@ As you begin to modify the strategy and the prediction model, you will quickly r
pnl = self.get_unrealized_profit()
factor = 100
pair = self.pair.replace(':', '')
# you can use feature values from dataframe
# Assumes the shifted RSI indicator has been generated in the strategy.
rsi_now = self.raw_features[f"%-rsi-period-10_shift-1_{pair}_"
f"{self.config['timeframe']}"].iloc[self._current_tick]
# reward agent for entering trades
if action in (Actions.Long_enter.value, Actions.Short_enter.value) \
and self._position == Positions.Neutral:
return 25
if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
and self._position == Positions.Neutral):
if rsi_now < 40:
factor = 40 / rsi_now
else:
factor = 1
return 25 * factor
# discourage agent from not entering trades
if action == Actions.Neutral.value and self._position == Positions.Neutral:
return -1
@ -247,14 +231,39 @@ where `unique-id` is the `identifier` set in the `freqai` configuration file. Th
![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("invalid")
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)`. In this case the metric values are not incremented.
### Choosing a base environment
FreqAI provides two base environments, `Base4ActionEnvironment` and `Base5ActionEnvironment`. As the names imply, the environments are customized for agents that can select from 4 or 5 actions. In the `Base4ActionEnvironment`, the agent can enter long, enter short, hold neutral, or exit position. Meanwhile, 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:
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
Both 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`.
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
FreqAI does not provide by default, a long-only training environment. However, creating one should be as simple as copy-pasting one of the built in environments and removing the `short` actions (and all associated references to those).
Only the `Base3ActionRLEnv` can do long-only training/trading (set the user strategy attribute `can_short = False`).

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@ -67,6 +67,10 @@ Backtesting mode requires [downloading the necessary data](#downloading-data-to-
*want* to retrain a new model with the same config file, you should simply change the `identifier`.
This way, you can return to using any model you wish by simply specifying the `identifier`.
!!! Note
Backtesting calls `set_freqai_targets()` one time for each backtest window (where the number of windows is the full backtest timerange divided by the `backtest_period_days` parameter). Doing this means that the targets simulate dry/live behavior without look ahead bias. However, the definition of the features in `feature_engineering_*()` is performed once on the entire backtest timerange. This means that you should be sure that features do look-ahead into the future.
More details about look-ahead bias can be found in [Common Mistakes](strategy-customization.md#common-mistakes-when-developing-strategies).
---
### Saving prediction data
@ -116,7 +120,7 @@ In the presented example config, the user will only allow predictions on models
Model training parameters are unique to the selected machine learning library. FreqAI allows you to set any parameter for any library using the `model_training_parameters` dictionary in the config. The example config (found in `config_examples/config_freqai.example.json`) shows some of the example parameters associated with `Catboost` and `LightGBM`, but you can add any parameters available in those libraries or any other machine learning library you choose to implement.
Data split parameters are defined in `data_split_parameters` which can be any parameters associated with Scikit-learn's `train_test_split()` function. `train_test_split()` has a parameters called `shuffle` which allows to shuffle the data or keep it unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data. More details about these parameters can be found the [Scikit-learn website](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website).
Data split parameters are defined in `data_split_parameters` which can be any parameters associated with scikit-learn's `train_test_split()` function. `train_test_split()` has a parameters called `shuffle` which allows to shuffle the data or keep it unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data. More details about these parameters can be found the [scikit-learn website](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website).
The FreqAI specific parameter `label_period_candles` defines the offset (number of candles into the future) used for the `labels`. In the presented [example config](freqai-configuration.md#setting-up-the-configuration-file), the user is asking for `labels` that are 24 candles in the future.
@ -135,7 +139,7 @@ freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --strategy FreqaiExampleSt
`hyperopt` requires you to have the data pre-downloaded in the same fashion as if you were doing [backtesting](#backtesting). In addition, you must consider some restrictions when trying to hyperopt FreqAI strategies:
- The `--analyze-per-epoch` hyperopt parameter is not compatible with FreqAI.
- It's not possible to hyperopt indicators in the `populate_any_indicators()` function. This means that you cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space).
- It's not possible to hyperopt indicators in the `feature_engineering_*()` and `set_freqai_targets()` functions. This means that you cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space).
- The backtesting instructions also apply to hyperopt.
The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. You need to focus on hyperopting parameters that are not used in your features. For example, you should not try to hyperopt rolling window lengths in the feature creation, or any part of the FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only.
@ -161,20 +165,3 @@ 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 if you wish to view the output in your browser at 127.0.0.1:6060 (6060 is the default port used by Tensorboard).
![tensorboard](assets/tensorboard.jpg)
## Setting up a follower
You can indicate to the bot that it should not train models, but instead should look for models trained by a leader with a specific `identifier` by defining:
```json
"freqai": {
"enabled": true,
"follow_mode": true,
"identifier": "example",
"feature_parameters": {
// leader bots feature_parameters inserted here
},
}
```
In this example, the user has a leader bot with the `"identifier": "example"`. The leader bot is already running or is launched simultaneously with the follower. The follower will load models created by the leader and inference them to obtain predictions instead of training its own models. The user will also need to duplicate the `feature_parameters` parameters from from the leaders freqai configuration file into the freqai section of the followers config.

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@ -4,7 +4,10 @@
## Introduction
FreqAI is a software designed to automate a variety of tasks associated with training a predictive machine learning model to generate market forecasts given a set of input signals. In general, the FreqAI aims to be a sand-box for easily deploying robust machine-learning libraries on real-time data ([details])(#freqai-position-in-open-source-machine-learning-landscape).
FreqAI is a software designed to automate a variety of tasks associated with training a predictive machine learning model to generate market forecasts given a set of input signals. In general, FreqAI aims to be a sandbox for easily deploying robust machine learning libraries on real-time data ([details](#freqai-position-in-open-source-machine-learning-landscape)).
!!! Note
FreqAI is, and always will be, a not-for-profit, open-source project. FreqAI does *not* have a crypto token, FreqAI does *not* sell signals, and FreqAI does not have a domain besides the present [freqtrade documentation](https://www.freqtrade.io/en/latest/freqai/).
Features include:
@ -19,7 +22,7 @@ Features include:
* **Automatic data download** - Compute timeranges for data downloads and update historic data (in live deployments)
* **Cleaning of incoming data** - Handle NaNs safely before training and model inferencing
* **Dimensionality reduction** - Reduce the size of the training data via [Principal Component Analysis](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis)
* **Deploying bot fleets** - Set one bot to train models while a fleet of [follower bots](freqai-running.md#setting-up-a-follower) inference the models and handle trades
* **Deploying bot fleets** - Set one bot to train models while a fleet of [consumers](producer-consumer.md) use signals.
## Quick start
@ -68,14 +71,32 @@ pip install -r requirements-freqai.txt
!!! Note
Catboost will not be installed on arm devices (raspberry, Mac M1, ARM based VPS, ...), since it does not provide wheels for this platform.
!!! Note "python 3.11"
Some dependencies (Catboost, Torch) currently don't support python 3.11. Freqtrade therefore only supports python 3.10 for these models/dependencies.
Tests involving these dependencies are skipped on 3.11.
### Usage with docker
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.
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.
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
@ -99,6 +120,8 @@ 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

14
docs/hyperopt.md
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@ -50,7 +50,7 @@ usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--eps] [--dmmp] [--enable-protections]
[--dry-run-wallet DRY_RUN_WALLET]
[--timeframe-detail TIMEFRAME_DETAIL] [-e INT]
[--spaces {all,buy,sell,roi,stoploss,trailing,protection,default} [{all,buy,sell,roi,stoploss,trailing,protection,default} ...]]
[--spaces {all,buy,sell,roi,stoploss,trailing,protection,trades,default} [{all,buy,sell,roi,stoploss,trailing,protection,trades,default} ...]]
[--print-all] [--no-color] [--print-json] [-j JOBS]
[--random-state INT] [--min-trades INT]
[--hyperopt-loss NAME] [--disable-param-export]
@ -96,7 +96,7 @@ optional arguments:
Specify detail timeframe for backtesting (`1m`, `5m`,
`30m`, `1h`, `1d`).
-e INT, --epochs INT Specify number of epochs (default: 100).
--spaces {all,buy,sell,roi,stoploss,trailing,protection,default} [{all,buy,sell,roi,stoploss,trailing,protection,default} ...]
--spaces {all,buy,sell,roi,stoploss,trailing,protection,trades,default} [{all,buy,sell,roi,stoploss,trailing,protection,trades,default} ...]
Specify which parameters to hyperopt. Space-separated
list.
--print-all Print all results, not only the best ones.
@ -180,6 +180,7 @@ Rarely you may also need to create a [nested class](advanced-hyperopt.md#overrid
* `generate_roi_table` - for custom ROI optimization (if you need the ranges for the values in the ROI table that differ from default or the number of entries (steps) in the ROI table which differs from the default 4 steps)
* `stoploss_space` - for custom stoploss optimization (if you need the range for the stoploss parameter in the optimization hyperspace that differs from default)
* `trailing_space` - for custom trailing stop optimization (if you need the ranges for the trailing stop parameters in the optimization hyperspace that differ from default)
* `max_open_trades_space` - for custom max_open_trades optimization (if you need the ranges for the max_open_trades parameter in the optimization hyperspace that differ from default)
!!! Tip "Quickly optimize ROI, stoploss and trailing stoploss"
You can quickly optimize the spaces `roi`, `stoploss` and `trailing` without changing anything in your strategy.
@ -365,7 +366,7 @@ class MyAwesomeStrategy(IStrategy):
timeframe = '15m'
minimal_roi = {
"0": 0.10
},
}
# Define the parameter spaces
buy_ema_short = IntParameter(3, 50, default=5)
buy_ema_long = IntParameter(15, 200, default=50)
@ -400,7 +401,7 @@ class MyAwesomeStrategy(IStrategy):
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
conditions = []
conditions.append(qtpylib.crossed_above(
dataframe[f'ema_long_{self.buy_ema_long.value}'], dataframe[f'ema_short_{self.buy_ema_short.value}']
))
@ -643,6 +644,7 @@ Legal values are:
* `roi`: just optimize the minimal profit table for your strategy
* `stoploss`: search for the best stoploss value
* `trailing`: search for the best trailing stop values
* `trades`: search for the best max open trades values
* `protection`: search for the best protection parameters (read the [protections section](#optimizing-protections) on how to properly define these)
* `default`: `all` except `trailing` and `protection`
* space-separated list of any of the above values for example `--spaces roi stoploss`
@ -916,5 +918,5 @@ Once the optimized strategy has been implemented into your strategy, you should
To achieve same the results (number of trades, their durations, profit, etc.) as during Hyperopt, please use the same configuration and parameters (timerange, timeframe, ...) used for hyperopt `--dmmp`/`--disable-max-market-positions` and `--eps`/`--enable-position-stacking` for Backtesting.
Should results not match, please double-check to make sure you transferred all conditions correctly.
Pay special care to the stoploss (and trailing stoploss) parameters, as these are often set in configuration files, which override changes to the strategy.
You should also carefully review the log of your backtest to ensure that there were no parameters inadvertently set by the configuration (like `stoploss` or `trailing_stop`).
Pay special care to the stoploss, max_open_trades and trailing stoploss parameters, as these are often set in configuration files, which override changes to the strategy.
You should also carefully review the log of your backtest to ensure that there were no parameters inadvertently set by the configuration (like `stoploss`, `max_open_trades` or `trailing_stop`).

43
docs/includes/pairlists.md
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@ -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).

2
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)
@ -51,6 +52,7 @@ Please read the [exchange specific notes](exchanges.md) to learn about eventual,
- [X] [Binance](https://www.binance.com/)
- [X] [Gate.io](https://www.gate.io/ref/6266643)
- [X] [OKX](https://okx.com/)
- [X] [Bybit](https://bybit.com/)
Please make sure to read the [exchange specific notes](exchanges.md), as well as the [trading with leverage](leverage.md) documentation before diving in.

24
docs/installation.md
View File

@ -30,6 +30,12 @@ The easiest way to install and run Freqtrade is to clone the bot Github reposito
!!! Warning "Up-to-date clock"
The clock on the system running the bot must be accurate, synchronized to a NTP server frequently enough to avoid problems with communication to the exchanges.
!!! Error "Running setup.py install for gym did not run successfully."
If you get an error related with gym we suggest you to downgrade setuptools it to version 65.5.0 you can do it with the following command:
```bash
pip install setuptools==65.5.0
```
------
## Requirements
@ -284,10 +290,8 @@ cd freqtrade
#### Freqtrade install: Conda Environment
Prepare conda-freqtrade environment, using file `environment.yml`, which exist in main freqtrade directory
```bash
conda env create -n freqtrade-conda -f environment.yml
conda create --name freqtrade python=3.10
```
!!! Note "Creating Conda Environment"
@ -296,12 +300,9 @@ conda env create -n freqtrade-conda -f environment.yml
```bash
# choose your own packages
conda env create -n [name of the environment] [python version] [packages]
# point to file with packages
conda env create -n [name of the environment] -f [file]
```
#### Enter/exit freqtrade-conda environment
#### Enter/exit freqtrade environment
To check available environments, type
@ -313,7 +314,7 @@ Enter installed environment
```bash
# enter conda environment
conda activate freqtrade-conda
conda activate freqtrade
# exit conda environment - don't do it now
conda deactivate
@ -323,6 +324,7 @@ Install last python dependencies with pip
```bash
python3 -m pip install --upgrade pip
python3 -m pip install -r requirements.txt
python3 -m pip install -e .
```
@ -330,7 +332,7 @@ Patch conda libta-lib (Linux only)
```bash
# Ensure that the environment is active!
conda activate freqtrade-conda
conda activate freqtrade
cd build_helpers
bash install_ta-lib.sh ${CONDA_PREFIX} nosudo
@ -349,8 +351,8 @@ conda env list
# activate base environment
conda activate
# activate freqtrade-conda environment
conda activate freqtrade-conda
# activate freqtrade environment
conda activate freqtrade
#deactivate any conda environments
conda deactivate

4
docs/leverage.md
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@ -67,8 +67,6 @@ You will also have to pick a "margin mode" (explanation below) - with freqtrade
Freqtrade follows the [ccxt naming conventions for futures](https://docs.ccxt.com/en/latest/manual.html?#perpetual-swap-perpetual-future).
A futures pair will therefore have the naming of `base/quote:settle` (e.g. `ETH/USDT:USDT`).
Binance is currently still an exception to this naming scheme, where pairs are named `ETH/USDT` also for futures markets, but will be aligned as soon as CCXT is ready.
### Margin mode
On top of `trading_mode` - you will also have to configure your `margin_mode`.
@ -92,6 +90,8 @@ One account is used to share collateral between markets (trading pairs). Margin
"margin_mode": "cross"
```
Please read the [exchange specific notes](exchanges.md) for exchanges that support this mode and how they differ.
## Set leverage to use
Different strategies and risk profiles will require different levels of leverage.

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==8.5.11
mkdocs-material==9.1.2
mdx_truly_sane_lists==1.3
pymdown-extensions==9.9
pymdown-extensions==9.10
jinja2==3.1.2

9
docs/rest-api.md
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@ -163,7 +163,7 @@ python3 scripts/rest_client.py --config rest_config.json <command> [optional par
| `strategy <strategy>` | Get specific Strategy content. **Alpha**
| `available_pairs` | List available backtest data. **Alpha**
| `version` | Show version.
| `sysinfo` | Show informations about the system load.
| `sysinfo` | Show information about the system load.
| `health` | Show bot health (last bot loop).
!!! Warning "Alpha status"
@ -192,6 +192,11 @@ blacklist
:param add: List of coins to add (example: "BNB/BTC")
cancel_open_order
Cancel open order for trade.
:param trade_id: Cancels open orders for this trade.
count
Return the amount of open trades.
@ -274,7 +279,6 @@ reload_config
Reload configuration.
show_config
Returns part of the configuration, relevant for trading operations.
start
@ -320,6 +324,7 @@ version
whitelist
Show the current whitelist.
```
### Message WebSocket

14
docs/stoploss.md
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@ -24,7 +24,7 @@ These modes can be configured with these values:
```
!!! Note
Stoploss on exchange is only supported for Binance (stop-loss-limit), Huobi (stop-limit), Kraken (stop-loss-market, stop-loss-limit), Gateio (stop-limit), and Kucoin (stop-limit and stop-market) as of now.
Stoploss on exchange is only supported for Binance (stop-loss-limit), Huobi (stop-limit), Kraken (stop-loss-market, stop-loss-limit), Gate (stop-limit), and Kucoin (stop-limit and stop-market) as of now.
<ins>Do not set too low/tight stoploss value if using stop loss on exchange!</ins>
If set to low/tight then you have greater risk of missing fill on the order and stoploss will not work.
@ -52,6 +52,18 @@ The bot cannot do these every 5 seconds (at each iteration), otherwise it would
So this parameter will tell the bot how often it should update the stoploss order. The default value is 60 (1 minute).
This same logic will reapply a stoploss order on the exchange should you cancel it accidentally.
### stoploss_price_type
!!! Warning "Only applies to futures"
`stoploss_price_type` only applies to futures markets (on exchanges where it's available).
Freqtrade will perform a validation of this setting on startup, failing to start if an invalid setting for your exchange has been selected.
Supported price types are gonna differs between each exchanges. Please check with your exchange on which price types it supports.
Stoploss on exchange on futures markets can trigger on different price types.
The naming for these prices in exchange terminology often varies, but is usually something around "last" (or "contract price" ), "mark" and "index".
Acceptable values for this setting are `"last"`, `"mark"` and `"index"` - which freqtrade will transfer automatically to the corresponding API type, and place the [stoploss on exchange](#stoploss_on_exchange-and-stoploss_on_exchange_limit_ratio) order correspondingly.
### force_exit
`force_exit` is an optional value, which defaults to the same value as `exit` and is used when sending a `/forceexit` command from Telegram or from the Rest API.

2
docs/strategy-advanced.md
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@ -80,7 +80,7 @@ class AwesomeStrategy(IStrategy):
## Enter Tag
When your strategy has multiple buy signals, you can name the signal that triggered.
Then you can access you buy signal on `custom_exit`
Then you can access your buy signal on `custom_exit`
```python
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

11
docs/strategy-callbacks.md
View File

@ -316,11 +316,11 @@ class AwesomeStrategy(IStrategy):
# evaluate highest to lowest, so that highest possible stop is used
if current_profit > 0.40:
return stoploss_from_open(0.25, current_profit, is_short=trade.is_short)
return stoploss_from_open(0.25, current_profit, is_short=trade.is_short, leverage=trade.leverage)
elif current_profit > 0.25:
return stoploss_from_open(0.15, current_profit, is_short=trade.is_short)
return stoploss_from_open(0.15, current_profit, is_short=trade.is_short, leverage=trade.leverage)
elif current_profit > 0.20:
return stoploss_from_open(0.07, current_profit, is_short=trade.is_short)
return stoploss_from_open(0.07, current_profit, is_short=trade.is_short, leverage=trade.leverage)
# return maximum stoploss value, keeping current stoploss price unchanged
return 1
@ -659,6 +659,7 @@ Position adjustments will always be applied in the direction of the trade, so a
!!! Warning "Backtesting"
During backtesting this callback is called for each candle in `timeframe` or `timeframe_detail`, so run-time performance will be affected.
This can also cause deviating results between live and backtesting, since backtesting can adjust the trade only once per candle, whereas live could adjust the trade multiple times per candle.
``` python
from freqtrade.persistence import Trade
@ -773,7 +774,7 @@ 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`).
@ -827,7 +828,7 @@ class AwesomeStrategy(IStrategy):
"""
# Limit orders to use and follow SMA200 as price target for the first 10 minutes since entry trigger for BTC/USDT pair.
if pair == 'BTC/USDT' and entry_tag == 'long_sma200' and side == 'long' and (current_time - timedelta(minutes=10) > trade.open_date_utc:
if pair == 'BTC/USDT' and entry_tag == 'long_sma200' and side == 'long' and (current_time - timedelta(minutes=10)) > trade.open_date_utc:
# just cancel the order if it has been filled more than half of the amount
if order.filled > order.remaining:
return None

67
docs/strategy-customization.md
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@ -363,9 +363,9 @@ class AwesomeStrategy(IStrategy):
timeframe = "1d"
timeframe_mins = timeframe_to_minutes(timeframe)
minimal_roi = {
"0": 0.05, # 5% for the first 3 candles
str(timeframe_mins * 3)): 0.02, # 2% after 3 candles
str(timeframe_mins * 6)): 0.01, # 1% After 6 candles
"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
}
```
@ -881,7 +881,7 @@ All columns of the informative dataframe will be available on the returning data
### *stoploss_from_open()*
Stoploss values returned from `custom_stoploss` must specify a percentage relative to `current_rate`, but sometimes you may want to specify a stoploss relative to the open price instead. `stoploss_from_open()` is a helper function to calculate a stoploss value that can be returned from `custom_stoploss` which will be equivalent to the desired percentage above the open price.
Stoploss values returned from `custom_stoploss` must specify a percentage relative to `current_rate`, but sometimes you may want to specify a stoploss relative to the entry point instead. `stoploss_from_open()` is a helper function to calculate a stoploss value that can be returned from `custom_stoploss` which will be equivalent to the desired trade profit above the entry point.
??? Example "Returning a stoploss relative to the open price from the custom stoploss function"
@ -889,6 +889,8 @@ Stoploss values returned from `custom_stoploss` must specify a percentage relati
If we want a stop price at 7% above the open price we can call `stoploss_from_open(0.07, current_profit, False)` which will return `0.1157024793`. 11.57% below $121 is $107, which is the same as 7% above $100.
This function will consider leverage - so at 10x leverage, the actual stoploss would be 0.7% above $100 (0.7% * 10x = 7%).
``` python
@ -907,7 +909,7 @@ Stoploss values returned from `custom_stoploss` must specify a percentage relati
# once the profit has risen above 10%, keep the stoploss at 7% above the open price
if current_profit > 0.10:
return stoploss_from_open(0.07, current_profit, is_short=trade.is_short)
return stoploss_from_open(0.07, current_profit, is_short=trade.is_short, leverage=trade.leverage)
return 1
@ -954,12 +956,14 @@ In some situations it may be confusing to deal with stops relative to current ra
## Additional data (Wallets)
The strategy provides access to the `Wallets` object. This contains the current balances on the exchange.
The strategy provides access to the `wallets` object. This contains the current balances on the exchange.
!!! Note
Wallets is not available during backtesting / hyperopt.
!!! Note "Backtesting / Hyperopt"
Wallets behaves differently depending on the function it's called.
Within `populate_*()` methods, it'll return the full wallet as configured.
Within [callbacks](strategy-callbacks.md), you'll get the wallet state corresponding to the actual simulated wallet at that point in the simulation process.
Please always check if `Wallets` is available to avoid failures during backtesting.
Please always check if `wallets` is available to avoid failures during backtesting.
``` python
if self.wallets:
@ -989,38 +993,18 @@ 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),
]).order_by(Trade.close_date).all()
# Summarize profit for this pair.
curdayprofit = sum(trade.close_profit for trade in trades)
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)
```
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
@ -1056,11 +1040,10 @@ from datetime import timedelta, datetime, timezone
# Within populate indicators (or populate_buy):
if self.config['runmode'].value in ('live', 'dry_run'):
# fetch closed trades for the last 2 days
trades = Trade.get_trades([Trade.pair == metadata['pair'],
Trade.open_date > datetime.utcnow() - timedelta(days=2),
Trade.is_open.is_(False),
]).all()
# fetch closed trades for the last 2 days
trades = Trade.get_trades_proxy(
pair=metadata['pair'], is_open=False,
open_date=datetime.now(timezone.utc) - timedelta(days=2))
# Analyze the conditions you'd like to lock the pair .... will probably be different for every strategy
sumprofit = sum(trade.close_profit for trade in trades)
if sumprofit < 0:

1
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@ -80,6 +80,7 @@ from freqtrade.resolvers import StrategyResolver
from freqtrade.data.dataprovider import DataProvider
strategy = StrategyResolver.load_strategy(config)
strategy.dp = DataProvider(config, None, None)
strategy.ft_bot_start()
# Generate buy/sell signals using strategy
df = strategy.analyze_ticker(candles, {'pair': pair})

251
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@ -477,3 +477,254 @@ after:
"ignore_buying_expired_candle_after": 120
}
```
## FreqAI strategy
The `populate_any_indicators()` method has been split into `feature_engineering_expand_all()`, `feature_engineering_expand_basic()`, `feature_engineering_standard()` and`set_freqai_targets()`.
For each new function, the pair (and timeframe where necessary) will be automatically added to the column.
As such, the definition of features becomes much simpler with the new logic.
For a full explanation of each method, please go to the corresponding [freqAI documentation page](freqai-feature-engineering.md#defining-the-features)
``` python linenums="1" hl_lines="12-37 39-42 63-65 67-75"
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, timeperiod=t)
informative[f"%-{pair}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
informative[f"%-{pair}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(informative), window=t, stds=2.2
)
informative[f"{pair}bb_lowerband-period_{t}"] = bollinger["lower"]
informative[f"{pair}bb_middleband-period_{t}"] = bollinger["mid"]
informative[f"{pair}bb_upperband-period_{t}"] = bollinger["upper"]
informative[f"%-{pair}bb_width-period_{t}"] = (
informative[f"{pair}bb_upperband-period_{t}"]
- informative[f"{pair}bb_lowerband-period_{t}"]
) / informative[f"{pair}bb_middleband-period_{t}"]
informative[f"%-{pair}close-bb_lower-period_{t}"] = (
informative["close"] / informative[f"{pair}bb_lowerband-period_{t}"]
)
informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
informative[f"%-{pair}relative_volume-period_{t}"] = (
informative["volume"] / informative["volume"].rolling(t).mean()
) # (1)
informative[f"%-{pair}pct-change"] = informative["close"].pct_change()
informative[f"%-{pair}raw_volume"] = informative["volume"]
informative[f"%-{pair}raw_price"] = informative["close"]
# (2)
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
]
df = df.drop(columns=skip_columns)
# Add generalized indicators here (because in live, it will call this
# function to populate indicators during training). Notice how we ensure not to
# add them multiple times
if set_generalized_indicators:
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
# (3)
# user adds targets here by prepending them with &- (see convention below)
df["&-s_close"] = (
df["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ df["close"]
- 1
) # (4)
return df
```
1. Features - Move to `feature_engineering_expand_all`
2. Basic features, not expanded across `include_periods_candles` - move to`feature_engineering_expand_basic()`.
3. Standard features which should not be expanded - move to `feature_engineering_standard()`.
4. Targets - Move this part to `set_freqai_targets()`.
### freqai - feature engineering expand all
Features will now expand automatically. As such, the expansion loops, as well as the `{pair}` / `{timeframe}` parts will need to be removed.
``` python linenums="1"
def feature_engineering_expand_all(self, dataframe, period, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
`include_corr_pairs`. In other words, a single feature defined in this function
will automatically expand to a total of
`indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
`include_corr_pairs` numbers of features added to the model.
All features must be prepended with `%` to be recognized by FreqAI internals.
More details on how these config defined parameters accelerate feature engineering
in the documentation at:
https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
:param df: strategy dataframe which will receive the features
:param period: period of the indicator - usage example:
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
"""
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(dataframe), window=period, stds=2.2
)
dataframe["bb_lowerband-period"] = bollinger["lower"]
dataframe["bb_middleband-period"] = bollinger["mid"]
dataframe["bb_upperband-period"] = bollinger["upper"]
dataframe["%-bb_width-period"] = (
dataframe["bb_upperband-period"]
- dataframe["bb_lowerband-period"]
) / dataframe["bb_middleband-period"]
dataframe["%-close-bb_lower-period"] = (
dataframe["close"] / dataframe["bb_lowerband-period"]
)
dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
dataframe["%-relative_volume-period"] = (
dataframe["volume"] / dataframe["volume"].rolling(period).mean()
)
return dataframe
```
### Freqai - feature engineering basic
Basic features. Make sure to remove the `{pair}` part from your features.
``` python linenums="1"
def feature_engineering_expand_basic(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
In other words, a single feature defined in this function
will automatically expand to a total of
`include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
numbers of features added to the model.
Features defined here will *not* be automatically duplicated on user defined
`indicator_periods_candles`
All features must be prepended with `%` to be recognized by FreqAI internals.
More details on how these config defined parameters accelerate feature engineering
in the documentation at:
https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
:param df: strategy dataframe which will receive the features
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
"""
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-raw_volume"] = dataframe["volume"]
dataframe["%-raw_price"] = dataframe["close"]
return dataframe
```
### FreqAI - feature engineering standard
``` python linenums="1"
def feature_engineering_standard(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This optional function will be called once with the dataframe of the base timeframe.
This is the final function to be called, which means that the dataframe entering this
function will contain all the features and columns created by all other
freqai_feature_engineering_* functions.
This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
This function is a good place for any feature that should not be auto-expanded upon
(e.g. day of the week).
All features must be prepended with `%` to be recognized by FreqAI internals.
More details about feature engineering available:
https://www.freqtrade.io/en/latest/freqai-feature-engineering
:param df: strategy dataframe which will receive the features
usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
"""
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
return dataframe
```
### FreqAI - set Targets
Targets now get their own, dedicated method.
``` python linenums="1"
def set_freqai_targets(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
Required function to set the targets for the model.
All targets must be prepended with `&` to be recognized by the FreqAI internals.
More details about feature engineering available:
https://www.freqtrade.io/en/latest/freqai-feature-engineering
:param df: strategy dataframe which will receive the targets
usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
"""
dataframe["&-s_close"] = (
dataframe["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ dataframe["close"]
- 1
)
return dataframe
```

15
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@ -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; }
}

43
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@ -152,7 +152,7 @@ You can create your own keyboard in `config.json`:
!!! Note "Supported Commands"
Only the following commands are allowed. Command arguments are not supported!
`/start`, `/stop`, `/status`, `/status table`, `/trades`, `/profit`, `/performance`, `/daily`, `/stats`, `/count`, `/locks`, `/balance`, `/stopentry`, `/reload_config`, `/show_config`, `/logs`, `/whitelist`, `/blacklist`, `/edge`, `/help`, `/version`
`/start`, `/stop`, `/status`, `/status table`, `/trades`, `/profit`, `/performance`, `/daily`, `/stats`, `/count`, `/locks`, `/balance`, `/stopentry`, `/reload_config`, `/show_config`, `/logs`, `/whitelist`, `/blacklist`, `/edge`, `/help`, `/version`, `/marketdir`
## Telegram commands
@ -162,26 +162,34 @@ official commands. You can ask at any moment for help with `/help`.
| Command | Description |
|----------|-------------|
| **System commands**
| `/start` | Starts the trader
| `/stop` | Stops the trader
| `/stopbuy | /stopentry` | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
| `/reload_config` | Reloads the configuration file
| `/show_config` | Shows part of the current configuration with relevant settings to operation
| `/logs [limit]` | Show last log messages.
| `/help` | Show help message
| `/version` | Show version
| **Status** |
| `/status` | Lists all open trades
| `/status <trade_id>` | Lists one or more specific trade. Separate multiple <trade_id> with a blank space.
| `/status table` | List all open trades in a table format. Pending buy orders are marked with an asterisk (*) Pending sell orders are marked with a double asterisk (**)
| `/trades [limit]` | List all recently closed trades in a table format.
| `/delete <trade_id>` | Delete a specific trade from the Database. Tries to close open orders. Requires manual handling of this trade on the exchange.
| `/count` | Displays number of trades used and available
| `/locks` | Show currently locked pairs.
| `/unlock <pair or lock_id>` | Remove the lock for this pair (or for this lock id).
| `/profit [<n>]` | Display a summary of your profit/loss from close trades and some stats about your performance, over the last n days (all trades by default)
| `/marketdir [long | short | even | none]` | Updates the user managed variable that represents the current market direction. If no direction is provided, the currently set direction will be displayed.
| **Modify Trade states** |
| `/forceexit <trade_id> | /fx <tradeid>` | Instantly exits the given trade (Ignoring `minimum_roi`).
| `/forceexit all | /fx all` | Instantly exits all open trades (Ignoring `minimum_roi`).
| `/fx` | alias for `/forceexit`
| `/forcelong <pair> [rate]` | Instantly buys the given pair. Rate is optional and only applies to limit orders. (`force_entry_enable` must be set to True)
| `/forceshort <pair> [rate]` | Instantly shorts the given pair. Rate is optional and only applies to limit orders. This will only work on non-spot markets. (`force_entry_enable` must be set to True)
| `/delete <trade_id>` | Delete a specific trade from the Database. Tries to close open orders. Requires manual handling of this trade on the exchange.
| `/cancel_open_order <trade_id> | /coo <trade_id>` | Cancel an open order for a trade.
| **Metrics** |
| `/profit [<n>]` | Display a summary of your profit/loss from close trades and some stats about your performance, over the last n days (all trades by default)
| `/performance` | Show performance of each finished trade grouped by pair
| `/balance` | Show account balance per currency
| `/daily <n>` | Shows profit or loss per day, over the last n days (n defaults to 7)
@ -193,8 +201,7 @@ official commands. You can ask at any moment for help with `/help`.
| `/whitelist [sorted] [baseonly]` | Show the current whitelist. Optionally display in alphabetical order and/or with just the base currency of each pairing.
| `/blacklist [pair]` | Show the current blacklist, or adds a pair to the blacklist.
| `/edge` | Show validated pairs by Edge if it is enabled.
| `/help` | Show help message
| `/version` | Show version
## Telegram commands in action
@ -236,7 +243,7 @@ Enter Tag is configurable via Strategy.
> **Enter Tag:** Awesome Long Signal
> **Open Rate:** `0.00007489`
> **Current Rate:** `0.00007489`
> **Current Profit:** `12.95%`
> **Unrealized Profit:** `12.95%`
> **Stoploss:** `0.00007389 (-0.02%)`
### /status table
@ -410,3 +417,27 @@ ARDR/ETH 0.366667 0.143059 -0.01
### /version
> **Version:** `0.14.3`
### /marketdir
If a market direction is provided the command updates the user managed variable that represents the current market direction.
This variable is not set to any valid market direction on bot startup and must be set by the user. The example below is for `/marketdir long`:
```
Successfully updated marketdirection from none to long.
```
If no market direction is provided the command outputs the currently set market directions. The example below is for `/marketdir`:
```
Currently set marketdirection: even
```
You can use the market direction in your strategy via `self.market_direction`.
!!! Warning "Bot restarts"
Please note that the market direction is not persisted, and will be reset after a bot restart/reload.
!!! Danger "Backtesting"
As this value/variable is intended to be changed manually in dry/live trading.
Strategies using `market_direction` will probably not produce reliable, reproducible results (changes to this variable will not be reflected for backtesting). Use at your own risk.

148
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@ -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

44
docs/utils.md
View File

@ -966,3 +966,47 @@ Print trades with id 2 and 3 as json
``` bash
freqtrade show-trades --db-url sqlite:///tradesv3.sqlite --trade-ids 2 3 --print-json
```
### Strategy-Updater
Updates listed strategies or all strategies within the strategies folder to be v3 compliant.
If the command runs without --strategy-list then all strategies inside the strategies folder will be converted.
Your original strategy will remain available in the `user_data/strategies_orig_updater/` directory.
!!! Warning "Conversion results"
Strategy updater will work on a "best effort" approach. Please do your due diligence and verify the results of the conversion.
We also recommend to run a python formatter (e.g. `black`) to format results in a sane manner.
```
usage: freqtrade strategy-updater [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH]
[--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
options:
-h, --help show this help message and exit
--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]
Provide a space-separated list of strategies to
backtest. Please note that timeframe needs to be set
either in config or via command line. When using this
together with `--export trades`, the strategy-name is
injected into the filename (so `backtest-data.json`
becomes `backtest-data-SampleStrategy.json`
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE, --log-file FILE
Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH, --data-dir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```

2
docs/windows_installation.md
View File

@ -26,7 +26,7 @@ Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7
As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial pre-compiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which need to be downloaded and installed using `pip install TA_Lib-0.4.25-cp38-cp38-win_amd64.whl` (make sure to use the version matching your python version).
Freqtrade provides these dependencies for the latest 3 Python versions (3.8, 3.9 and 3.10) and for 64bit Windows.
Freqtrade provides these dependencies for the latest 3 Python versions (3.8, 3.9, 3.10 and 3.11) and for 64bit Windows.
Other versions must be downloaded from the above link.
``` powershell

75
environment.yml
View File

@ -1,75 +0,0 @@
name: freqtrade
channels:
- conda-forge
# - defaults
dependencies:
# 1/4 req main
- python>=3.8,<=3.10
- numpy
- pandas
- pip
- py-find-1st
- aiohttp
- SQLAlchemy
- python-telegram-bot
- arrow
- cachetools
- requests
- urllib3
- jsonschema
- TA-Lib
- tabulate
- jinja2
- blosc
- sdnotify
- fastapi
- uvicorn
- pyjwt
- aiofiles
- psutil
- colorama
- questionary
- prompt-toolkit
- schedule
- python-dateutil
- joblib
- pyarrow
# ============================
# 2/4 req dev
- coveralls
- flake8
- mypy
- pytest
- pytest-asyncio
- pytest-cov
- pytest-mock
- isort
- nbconvert
# ============================
# 3/4 req hyperopt
- scipy
- scikit-learn
- filelock
- scikit-optimize
- progressbar2
# ============================
# 4/4 req plot
- plotly
- jupyter
- pip:
- pycoingecko
# - py_find_1st
- tables
- pytest-random-order
- ccxt
- flake8-tidy-imports
- -e .
# - python-rapidjso

7
freqtrade/__init__.py
View File

@ -1,19 +1,20 @@
""" Freqtrade bot """
__version__ = '2022.12.dev'
__version__ = '2023.3.dev'
if 'dev' in __version__:
from pathlib import Path
try:
import subprocess
freqtrade_basedir = Path(__file__).parent
__version__ = __version__ + '-' + subprocess.check_output(
['git', 'log', '--format="%h"', '-n 1'],
stderr=subprocess.DEVNULL).decode("utf-8").rstrip().strip('"')
stderr=subprocess.DEVNULL, cwd=freqtrade_basedir).decode("utf-8").rstrip().strip('"')
except Exception: # pragma: no cover
# git not available, ignore
try:
# Try Fallback to freqtrade_commit file (created by CI while building docker image)
from pathlib import Path
versionfile = Path('./freqtrade_commit')
if versionfile.is_file():
__version__ = f"docker-{__version__}-{versionfile.read_text()[:8]}"

0
freqtrade/__main__.py Normal file → Executable file
View File

1
freqtrade/commands/__init__.py
View File

@ -22,5 +22,6 @@ from freqtrade.commands.optimize_commands import (start_backtesting, start_backt
start_edge, start_hyperopt)
from freqtrade.commands.pairlist_commands import start_test_pairlist
from freqtrade.commands.plot_commands import start_plot_dataframe, start_plot_profit
from freqtrade.commands.strategy_utils_commands import start_strategy_update
from freqtrade.commands.trade_commands import start_trading
from freqtrade.commands.webserver_commands import start_webserver

0
freqtrade/commands/analyze_commands.py Executable file → Normal file
View File

17
freqtrade/commands/arguments.py
View File

@ -112,10 +112,13 @@ ARGS_ANALYZE_ENTRIES_EXITS = ["exportfilename", "analysis_groups", "enter_reason
NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
"list-markets", "list-pairs", "list-strategies", "list-freqaimodels",
"list-data", "hyperopt-list", "hyperopt-show", "backtest-filter",
"plot-dataframe", "plot-profit", "show-trades", "trades-to-ohlcv"]
"plot-dataframe", "plot-profit", "show-trades", "trades-to-ohlcv",
"strategy-updater"]
NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-strategy"]
ARGS_STRATEGY_UTILS = ["strategy_list", "strategy_path", "recursive_strategy_search"]
class Arguments:
"""
@ -199,8 +202,8 @@ class Arguments:
start_list_freqAI_models, start_list_markets,
start_list_strategies, start_list_timeframes,
start_new_config, start_new_strategy, start_plot_dataframe,
start_plot_profit, start_show_trades, start_test_pairlist,
start_trading, start_webserver)
start_plot_profit, start_show_trades, start_strategy_update,
start_test_pairlist, start_trading, start_webserver)
subparsers = self.parser.add_subparsers(dest='command',
# Use custom message when no subhandler is added
@ -441,3 +444,11 @@ class Arguments:
parents=[_common_parser])
webserver_cmd.set_defaults(func=start_webserver)
self._build_args(optionlist=ARGS_WEBSERVER, parser=webserver_cmd)
# Add strategy_updater subcommand
strategy_updater_cmd = subparsers.add_parser('strategy-updater',
help='updates outdated strategy'
'files to the current version',
parents=[_common_parser])
strategy_updater_cmd.set_defaults(func=start_strategy_update)
self._build_args(optionlist=ARGS_STRATEGY_UTILS, parser=strategy_updater_cmd)

4
freqtrade/commands/build_config_commands.py
View File

@ -108,7 +108,7 @@ def ask_user_config() -> Dict[str, Any]:
"binance",
"binanceus",
"bittrex",
"gateio",
"gate",
"huobi",
"kraken",
"kucoin",
@ -123,7 +123,7 @@ def ask_user_config() -> Dict[str, Any]:
"message": "Do you want to trade Perpetual Swaps (perpetual futures)?",
"default": False,
"filter": lambda val: 'futures' if val else 'spot',
"when": lambda x: x["exchange_name"] in ['binance', 'gateio', 'okx'],
"when": lambda x: x["exchange_name"] in ['binance', 'gate', 'okx'],
},
{
"type": "autocomplete",

8
freqtrade/commands/cli_options.py
View File

@ -251,7 +251,8 @@ AVAILABLE_CLI_OPTIONS = {
"spaces": Arg(
'--spaces',
help='Specify which parameters to hyperopt. Space-separated list.',
choices=['all', 'buy', 'sell', 'roi', 'stoploss', 'trailing', 'protection', 'default'],
choices=['all', 'buy', 'sell', 'roi', 'stoploss',
'trailing', 'protection', 'trades', 'default'],
nargs='+',
default='default',
),
@ -632,10 +633,11 @@ AVAILABLE_CLI_OPTIONS = {
"1: by enter_tag, "
"2: by enter_tag and exit_tag, "
"3: by pair and enter_tag, "
"4: by pair, enter_ and exit_tag (this can get quite large)"),
"4: by pair, enter_ and exit_tag (this can get quite large), "
"5: by exit_tag"),
nargs='+',
default=[],
choices=['0', '1', '2', '3', '4'],
choices=['0', '1', '2', '3', '4', '5'],
),
"enter_reason_list": Arg(
"--enter-reason-list",

25
freqtrade/commands/data_commands.py
View File

@ -5,7 +5,7 @@ from datetime import datetime, timedelta
from typing import Any, Dict, List
from freqtrade.configuration import TimeRange, setup_utils_configuration
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.constants import DATETIME_PRINT_FORMAT, Config
from freqtrade.data.converter import convert_ohlcv_format, convert_trades_format
from freqtrade.data.history import (convert_trades_to_ohlcv, refresh_backtest_ohlcv_data,
refresh_backtest_trades_data)
@ -14,20 +14,30 @@ from freqtrade.exceptions import OperationalException
from freqtrade.exchange import market_is_active, timeframe_to_minutes
from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist, expand_pairlist
from freqtrade.resolvers import ExchangeResolver
from freqtrade.util.binance_mig import migrate_binance_futures_data
logger = logging.getLogger(__name__)
def _data_download_sanity(config: Config) -> None:
if 'days' in config and 'timerange' in config:
raise OperationalException("--days and --timerange are mutually exclusive. "
"You can only specify one or the other.")
if 'pairs' not in config:
raise OperationalException(
"Downloading data requires a list of pairs. "
"Please check the documentation on how to configure this.")
def start_download_data(args: Dict[str, Any]) -> None:
"""
Download data (former download_backtest_data.py script)
"""
config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
if 'days' in config and 'timerange' in config:
raise OperationalException("--days and --timerange are mutually exclusive. "
"You can only specify one or the other.")
_data_download_sanity(config)
timerange = TimeRange()
if 'days' in config:
time_since = (datetime.now() - timedelta(days=config['days'])).strftime("%Y%m%d")
@ -39,11 +49,6 @@ def start_download_data(args: Dict[str, Any]) -> None:
# Remove stake-currency to skip checks which are not relevant for datadownload
config['stake_currency'] = ''
if 'pairs' not in config:
raise OperationalException(
"Downloading data requires a list of pairs. "
"Please check the documentation on how to configure this.")
pairs_not_available: List[str] = []
# Init exchange
@ -86,6 +91,7 @@ def start_download_data(args: Dict[str, Any]) -> None:
"Please use `--dl-trades` instead for this exchange "
"(will unfortunately take a long time)."
)
migrate_binance_futures_data(config)
pairs_not_available = refresh_backtest_ohlcv_data(
exchange, pairs=expanded_pairs, timeframes=config['timeframes'],
datadir=config['datadir'], timerange=timerange,
@ -145,6 +151,7 @@ def start_convert_data(args: Dict[str, Any], ohlcv: bool = True) -> None:
"""
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
if ohlcv:
migrate_binance_futures_data(config)
candle_types = [CandleType.from_string(ct) for ct in config.get('candle_types', ['spot'])]
for candle_type in candle_types:
convert_ohlcv_format(config,

12
freqtrade/commands/db_commands.py
View File

@ -1,7 +1,7 @@
import logging
from typing import Any, Dict
from sqlalchemy import func
from sqlalchemy import func, select
from freqtrade.configuration.config_setup import setup_utils_configuration
from freqtrade.enums import RunMode
@ -20,7 +20,7 @@ def start_convert_db(args: Dict[str, Any]) -> None:
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
init_db(config['db_url'])
session_target = Trade._session
session_target = Trade.session
init_db(config['db_url_from'])
logger.info("Starting db migration.")
@ -36,16 +36,16 @@ def start_convert_db(args: Dict[str, Any]) -> None:
session_target.commit()
for pairlock in PairLock.query:
for pairlock in PairLock.get_all_locks():
pairlock_count += 1
make_transient(pairlock)
session_target.add(pairlock)
session_target.commit()
# Update sequences
max_trade_id = session_target.query(func.max(Trade.id)).scalar()
max_order_id = session_target.query(func.max(Order.id)).scalar()
max_pairlock_id = session_target.query(func.max(PairLock.id)).scalar()
max_trade_id = session_target.scalar(select(func.max(Trade.id)))
max_order_id = session_target.scalar(select(func.max(Order.id)))
max_pairlock_id = session_target.scalar(select(func.max(PairLock.id)))
set_sequence_ids(session_target.get_bind(),
trade_id=max_trade_id,

0
freqtrade/commands/hyperopt_commands.py Executable file → Normal file
View File

55
freqtrade/commands/strategy_utils_commands.py Normal file
View File

@ -0,0 +1,55 @@
import logging
import sys
import time
from pathlib import Path
from typing import Any, Dict
from freqtrade.configuration import setup_utils_configuration
from freqtrade.enums import RunMode
from freqtrade.resolvers import StrategyResolver
from freqtrade.strategy.strategyupdater import StrategyUpdater
logger = logging.getLogger(__name__)
def start_strategy_update(args: Dict[str, Any]) -> None:
"""
Start the strategy updating script
:param args: Cli args from Arguments()
:return: None
"""
if sys.version_info == (3, 8): # pragma: no cover
sys.exit("Freqtrade strategy updater requires Python version >= 3.9")
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
strategy_objs = StrategyResolver.search_all_objects(
config, enum_failed=False, recursive=config.get('recursive_strategy_search', False))
filtered_strategy_objs = []
if args['strategy_list']:
filtered_strategy_objs = [
strategy_obj for strategy_obj in strategy_objs
if strategy_obj['name'] in args['strategy_list']
]
else:
# Use all available entries.
filtered_strategy_objs = strategy_objs
processed_locations = set()
for strategy_obj in filtered_strategy_objs:
if strategy_obj['location'] not in processed_locations:
processed_locations.add(strategy_obj['location'])
start_conversion(strategy_obj, config)
def start_conversion(strategy_obj, config):
print(f"Conversion of {Path(strategy_obj['location']).name} started.")
instance_strategy_updater = StrategyUpdater()
start = time.perf_counter()
instance_strategy_updater.start(config, strategy_obj)
elapsed = time.perf_counter() - start
print(f"Conversion of {Path(strategy_obj['location']).name} took {elapsed:.1f} seconds.")

8
freqtrade/commands/trade_commands.py
View File

@ -1,4 +1,5 @@
import logging
import signal
from typing import Any, Dict
@ -12,15 +13,20 @@ def start_trading(args: Dict[str, Any]) -> int:
# Import here to avoid loading worker module when it's not used
from freqtrade.worker import Worker
def term_handler(signum, frame):
# Raise KeyboardInterrupt - so we can handle it in the same way as Ctrl-C
raise KeyboardInterrupt()
# Create and run worker
worker = None
try:
signal.signal(signal.SIGTERM, term_handler)
worker = Worker(args)
worker.run()
except Exception as e:
logger.error(str(e))
logger.exception("Fatal exception!")
except KeyboardInterrupt:
except (KeyboardInterrupt):
logger.info('SIGINT received, aborting ...')
finally:
if worker:

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:

2
freqtrade/configuration/configuration.py
View File

@ -28,7 +28,7 @@ class Configuration:
Reuse this class for the bot, backtesting, hyperopt and every script that required configuration
"""
def __init__(self, args: Dict[str, Any], runmode: RunMode = None) -> None:
def __init__(self, args: Dict[str, Any], runmode: Optional[RunMode] = None) -> None:
self.args = args
self.config: Optional[Config] = None
self.runmode = runmode

2
freqtrade/configuration/environment_vars.py
View File

@ -32,7 +32,7 @@ def flat_vars_to_nested_dict(env_dict: Dict[str, Any], prefix: str) -> Dict[str,
:param prefix: Prefix to consider (usually FREQTRADE__)
:return: Nested dict based on available and relevant variables.
"""
no_convert = ['CHAT_ID']
no_convert = ['CHAT_ID', 'PASSWORD']
relevant_vars: Dict[str, Any] = {}
for env_var, val in sorted(env_dict.items()):

7
freqtrade/configuration/load_config.py
View File

@ -6,7 +6,7 @@ import re
import sys
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict, List
from typing import Any, Dict, List, Optional
import rapidjson
@ -58,7 +58,7 @@ def load_config_file(path: str) -> Dict[str, Any]:
"""
try:
# Read config from stdin if requested in the options
with open(path) if path != '-' else sys.stdin as file:
with Path(path).open() if path != '-' else sys.stdin as file:
config = rapidjson.load(file, parse_mode=CONFIG_PARSE_MODE)
except FileNotFoundError:
raise OperationalException(
@ -75,7 +75,8 @@ def load_config_file(path: str) -> Dict[str, Any]:
return config
def load_from_files(files: List[str], base_path: Path = None, level: int = 0) -> Dict[str, Any]:
def load_from_files(
files: List[str], base_path: Optional[Path] = None, level: int = 0) -> Dict[str, Any]:
"""
Recursively load configuration files if specified.
Sub-files are assumed to be relative to the initial config.

23
freqtrade/constants.py
View File

@ -5,7 +5,7 @@ bot constants
"""
from typing import Any, Dict, List, Literal, Tuple
from freqtrade.enums import CandleType, RPCMessageType
from freqtrade.enums import CandleType, PriceType, RPCMessageType
DEFAULT_CONFIG = 'config.json'
@ -25,13 +25,14 @@ PRICING_SIDES = ['ask', 'bid', 'same', 'other']
ORDERTYPE_POSSIBILITIES = ['limit', 'market']
_ORDERTIF_POSSIBILITIES = ['GTC', 'FOK', 'IOC', 'PO']
ORDERTIF_POSSIBILITIES = _ORDERTIF_POSSIBILITIES + [t.lower() for t in _ORDERTIF_POSSIBILITIES]
STOPLOSS_PRICE_TYPES = [p for p in PriceType]
HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss',
'SharpeHyperOptLoss', 'SharpeHyperOptLossDaily',
'SortinoHyperOptLoss', 'SortinoHyperOptLossDaily',
'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 +62,7 @@ USERPATH_FREQAIMODELS = 'freqaimodels'
TELEGRAM_SETTING_OPTIONS = ['on', 'off', 'silent']
WEBHOOK_FORMAT_OPTIONS = ['form', 'json', 'raw']
FULL_DATAFRAME_THRESHOLD = 100
ENV_VAR_PREFIX = 'FREQTRADE__'
@ -228,6 +230,7 @@ CONF_SCHEMA = {
'default': 'market'},
'stoploss': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'stoploss_on_exchange': {'type': 'boolean'},
'stoploss_price_type': {'type': 'string', 'enum': STOPLOSS_PRICE_TYPES},
'stoploss_on_exchange_interval': {'type': 'number'},
'stoploss_on_exchange_limit_ratio': {'type': 'number', 'minimum': 0.0,
'maximum': 1.0}
@ -543,7 +546,7 @@ CONF_SCHEMA = {
"enabled": {"type": "boolean", "default": False},
"keras": {"type": "boolean", "default": False},
"write_metrics_to_disk": {"type": "boolean", "default": False},
"purge_old_models": {"type": "boolean", "default": True},
"purge_old_models": {"type": ["boolean", "number"], "default": 2},
"conv_width": {"type": "integer", "default": 1},
"train_period_days": {"type": "integer", "default": 0},
"backtest_period_days": {"type": "number", "default": 7},
@ -565,7 +568,9 @@ CONF_SCHEMA = {
"shuffle": {"type": "boolean", "default": False},
"nu": {"type": "number", "default": 0.1}
},
}
},
"shuffle_after_split": {"type": "boolean", "default": False},
"buffer_train_data_candles": {"type": "integer", "default": 0}
},
"required": ["include_timeframes", "include_corr_pairlist", ]
},
@ -583,6 +588,7 @@ CONF_SCHEMA = {
"rl_config": {
"type": "object",
"properties": {
"drop_ohlc_from_features": {"type": "boolean", "default": False},
"train_cycles": {"type": "integer"},
"max_trade_duration_candles": {"type": "integer"},
"add_state_info": {"type": "boolean", "default": False},
@ -608,9 +614,8 @@ CONF_SCHEMA = {
"backtest_period_days",
"identifier",
"feature_parameters",
"data_split_parameters",
"model_training_parameters"
]
"data_split_parameters"
]
},
},
}
@ -636,7 +641,6 @@ SCHEMA_TRADE_REQUIRED = [
SCHEMA_BACKTEST_REQUIRED = [
'exchange',
'max_open_trades',
'stake_currency',
'stake_amount',
'dry_run_wallet',
@ -646,6 +650,7 @@ SCHEMA_BACKTEST_REQUIRED = [
SCHEMA_BACKTEST_REQUIRED_FINAL = SCHEMA_BACKTEST_REQUIRED + [
'stoploss',
'minimal_roi',
'max_open_trades'
]
SCHEMA_MINIMAL_REQUIRED = [
@ -679,5 +684,7 @@ EntryExit = Literal['entry', 'exit']
BuySell = Literal['buy', 'sell']
MakerTaker = Literal['maker', 'taker']
BidAsk = Literal['bid', 'ask']
OBLiteral = Literal['asks', 'bids']
Config = Dict[str, Any]
IntOrInf = float

63
freqtrade/data/btanalysis.py
View File

@ -10,7 +10,7 @@ from typing import Any, Dict, List, Optional, Union
import numpy as np
import pandas as pd
from freqtrade.constants import LAST_BT_RESULT_FN
from freqtrade.constants import LAST_BT_RESULT_FN, IntOrInf
from freqtrade.exceptions import OperationalException
from freqtrade.misc import json_load
from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename
@ -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',
@ -90,7 +90,8 @@ def get_latest_hyperopt_filename(directory: Union[Path, str]) -> str:
return 'hyperopt_results.pickle'
def get_latest_hyperopt_file(directory: Union[Path, str], predef_filename: str = None) -> Path:
def get_latest_hyperopt_file(
directory: Union[Path, str], predef_filename: Optional[str] = None) -> Path:
"""
Get latest hyperopt export based on '.last_result.json'.
:param directory: Directory to search for last result
@ -193,7 +194,7 @@ def get_backtest_resultlist(dirname: Path):
def find_existing_backtest_stats(dirname: Union[Path, str], run_ids: Dict[str, str],
min_backtest_date: datetime = None) -> Dict[str, Any]:
min_backtest_date: Optional[datetime] = None) -> Dict[str, Any]:
"""
Find existing backtest stats that match specified run IDs and load them.
:param dirname: pathlib.Path object, or string pointing to the file.
@ -241,6 +242,33 @@ 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 +297,7 @@ 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.
@ -322,7 +333,7 @@ def analyze_trade_parallelism(results: pd.DataFrame, timeframe: str) -> pd.DataF
def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
max_open_trades: int) -> pd.DataFrame:
max_open_trades: IntOrInf) -> pd.DataFrame:
"""
Find overlapping trades by expanding each trade once per period it was open
and then counting overlaps
@ -335,7 +346,7 @@ def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
return df_final[df_final['open_trades'] > max_open_trades]
def trade_list_to_dataframe(trades: List[LocalTrade]) -> pd.DataFrame:
def trade_list_to_dataframe(trades: Union[List[Trade], List[LocalTrade]]) -> pd.DataFrame:
"""
Convert list of Trade objects to pandas Dataframe
:param trades: List of trade objects
@ -362,7 +373,7 @@ def load_trades_from_db(db_url: str, strategy: Optional[str] = None) -> pd.DataF
filters = []
if strategy:
filters.append(Trade.strategy == strategy)
trades = trade_list_to_dataframe(Trade.get_trades(filters).all())
trades = trade_list_to_dataframe(list(Trade.get_trades(filters).all()))
return trades

108
freqtrade/data/dataprovider.py
View File

@ -9,14 +9,17 @@ 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, Timedelta, Timestamp, 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.exchange.types import OrderBook
from freqtrade.misc import append_candles_to_dataframe
from freqtrade.rpc import RPCManager
from freqtrade.util import PeriodicCache
@ -120,7 +123,7 @@ class DataProvider:
'type': RPCMessageType.ANALYZED_DF,
'data': {
'key': pair_key,
'df': dataframe,
'df': dataframe.tail(1),
'la': datetime.now(timezone.utc)
}
}
@ -131,7 +134,7 @@ class DataProvider:
'data': pair_key,
})
def _add_external_df(
def _replace_external_df(
self,
pair: str,
dataframe: DataFrame,
@ -157,6 +160,87 @@ 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
# Convert the timeframe to a timedelta for pandas
timeframe_delta: Timedelta = to_timedelta(timeframe)
local_last: Timestamp = existing_df.iloc[-1]['date'] # We want the last date from our copy
# We want the first date from the incoming
incoming_first: Timestamp = dataframe.iloc[0]['date']
# 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, int(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,
@ -200,7 +284,7 @@ class DataProvider:
def historic_ohlcv(
self,
pair: str,
timeframe: str = None,
timeframe: Optional[str] = None,
candle_type: str = ''
) -> DataFrame:
"""
@ -252,7 +336,7 @@ class DataProvider:
def get_pair_dataframe(
self,
pair: str,
timeframe: str = None,
timeframe: Optional[str] = None,
candle_type: str = ''
) -> DataFrame:
"""
@ -334,16 +418,14 @@ class DataProvider:
def refresh(self,
pairlist: ListPairsWithTimeframes,
helping_pairs: ListPairsWithTimeframes = None) -> None:
helping_pairs: Optional[ListPairsWithTimeframes] = None) -> None:
"""
Refresh data, called with each cycle
"""
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
if helping_pairs:
self._exchange.refresh_latest_ohlcv(pairlist + helping_pairs)
else:
self._exchange.refresh_latest_ohlcv(pairlist)
final_pairs = (pairlist + helping_pairs) if helping_pairs else pairlist
self._exchange.refresh_latest_ohlcv(final_pairs)
@property
def available_pairs(self) -> ListPairsWithTimeframes:
@ -358,7 +440,7 @@ class DataProvider:
def ohlcv(
self,
pair: str,
timeframe: str = None,
timeframe: Optional[str] = None,
copy: bool = True,
candle_type: str = ''
) -> DataFrame:
@ -406,7 +488,7 @@ class DataProvider:
except ExchangeError:
return {}
def orderbook(self, pair: str, maximum: int) -> Dict[str, List]:
def orderbook(self, pair: str, maximum: int) -> OrderBook:
"""
Fetch latest l2 orderbook data
Warning: Does a network request - so use with common sense.

17
freqtrade/data/entryexitanalysis.py Executable file → Normal file
View File

@ -24,11 +24,12 @@ def _load_backtest_analysis_data(backtest_dir: Path, name: str):
scpf = Path(backtest_dir.parent / f"{backtest_dir.stem}_{name}.pkl")
try:
scp = open(scpf, "rb")
loaded_data = joblib.load(scp)
logger.info(f"Loaded {name} data: {str(scpf)}")
with scpf.open("rb") as scp:
loaded_data = joblib.load(scp)
logger.info(f"Loaded {name} candles: {str(scpf)}")
except Exception as e:
logger.error(f"Cannot load {name} data from pickled results: ", e)
return None
return loaded_data
@ -60,7 +61,7 @@ def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_cand
return analysed_trades_dict
def _analyze_candles_and_indicators(pair, trades, signal_candles):
def _analyze_candles_and_indicators(pair, trades: pd.DataFrame, signal_candles: pd.DataFrame):
buyf = signal_candles
if len(buyf) > 0:
@ -129,7 +130,7 @@ def _do_group_table_output(bigdf, glist, to_csv=False, csv_path=None):
else:
agg_mask = {'profit_abs': ['count', 'sum', 'median', 'mean'],
'profit_ratio': ['sum', 'median', 'mean']}
'profit_ratio': ['median', 'mean', 'sum']}
agg_cols = ['num_buys', 'profit_abs_sum', 'profit_abs_median',
'profit_abs_mean', 'median_profit_pct', 'mean_profit_pct',
'total_profit_pct']
@ -150,6 +151,12 @@ def _do_group_table_output(bigdf, glist, to_csv=False, csv_path=None):
# 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
if g == "4":
group_mask = ['pair', 'enter_reason', 'exit_reason']
# 5: profit summaries grouped by exit_tag
if g == "5":
group_mask = ['exit_reason']
sortcols = ['exit_reason']
if group_mask:
new = bigdf.groupby(group_mask).agg(agg_mask).reset_index()
new.columns = group_mask + agg_cols

12
freqtrade/data/history/history_utils.py
View File

@ -28,8 +28,8 @@ def load_pair_history(pair: str,
fill_up_missing: bool = True,
drop_incomplete: bool = False,
startup_candles: int = 0,
data_format: str = None,
data_handler: IDataHandler = None,
data_format: Optional[str] = None,
data_handler: Optional[IDataHandler] = None,
candle_type: CandleType = CandleType.SPOT
) -> DataFrame:
"""
@ -69,7 +69,7 @@ def load_data(datadir: Path,
fail_without_data: bool = False,
data_format: str = 'json',
candle_type: CandleType = CandleType.SPOT,
user_futures_funding_rate: int = None,
user_futures_funding_rate: Optional[int] = None,
) -> Dict[str, DataFrame]:
"""
Load ohlcv history data for a list of pairs.
@ -116,7 +116,7 @@ def refresh_data(*, datadir: Path,
timeframe: str,
pairs: List[str],
exchange: Exchange,
data_format: str = None,
data_format: Optional[str] = None,
timerange: Optional[TimeRange] = None,
candle_type: CandleType,
) -> None:
@ -189,7 +189,7 @@ def _download_pair_history(pair: str, *,
timeframe: str = '5m',
process: str = '',
new_pairs_days: int = 30,
data_handler: IDataHandler = None,
data_handler: Optional[IDataHandler] = None,
timerange: Optional[TimeRange] = None,
candle_type: CandleType,
erase: bool = False,
@ -272,7 +272,7 @@ def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes
datadir: Path, trading_mode: str,
timerange: Optional[TimeRange] = None,
new_pairs_days: int = 30, erase: bool = False,
data_format: str = None,
data_format: Optional[str] = None,
prepend: bool = False,
) -> List[str]:
"""

23
freqtrade/data/history/idatahandler.py
View File

@ -308,7 +308,7 @@ class IDataHandler(ABC):
timerange=timerange_startup,
candle_type=candle_type
)
if self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data, True):
if self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data):
return pairdf
else:
enddate = pairdf.iloc[-1]['date']
@ -316,7 +316,7 @@ class IDataHandler(ABC):
if timerange_startup:
self._validate_pairdata(pair, pairdf, timeframe, candle_type, timerange_startup)
pairdf = trim_dataframe(pairdf, timerange_startup)
if self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data):
if self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data, True):
return pairdf
# incomplete candles should only be dropped if we didn't trim the end beforehand.
@ -374,6 +374,21 @@ class IDataHandler(ABC):
logger.warning(f"{pair}, {candle_type}, {timeframe}, "
f"data ends at {pairdata.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}")
def rename_futures_data(
self, pair: str, new_pair: str, timeframe: str, candle_type: CandleType):
"""
Temporary method to migrate data from old naming to new naming (BTC/USDT -> BTC/USDT:USDT)
Only used for binance to support the binance futures naming unification.
"""
file_old = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
file_new = self._pair_data_filename(self._datadir, new_pair, timeframe, candle_type)
# print(file_old, file_new)
if file_new.exists():
logger.warning(f"{file_new} exists already, can't migrate {pair}.")
return
file_old.rename(file_new)
def get_datahandlerclass(datatype: str) -> Type[IDataHandler]:
"""
@ -403,8 +418,8 @@ def get_datahandlerclass(datatype: str) -> Type[IDataHandler]:
raise ValueError(f"No datahandler for datatype {datatype} available.")
def get_datahandler(datadir: Path, data_format: str = None,
data_handler: IDataHandler = None) -> IDataHandler:
def get_datahandler(datadir: Path, data_format: Optional[str] = None,
data_handler: Optional[IDataHandler] = None) -> IDataHandler:
"""
:param datadir: Folder to save data
:param data_format: dataformat to use

118
freqtrade/data/metrics.py
View File

@ -1,4 +1,6 @@
import logging
import math
from datetime import datetime
from typing import Dict, Tuple
import numpy as np
@ -190,3 +192,119 @@ def calculate_cagr(days_passed: int, starting_balance: float, final_balance: flo
:return: CAGR
"""
return (final_balance / starting_balance) ** (1 / (days_passed / 365)) - 1
def calculate_expectancy(trades: pd.DataFrame) -> float:
"""
Calculate expectancy
:param trades: DataFrame containing trades (requires columns close_date and profit_abs)
:return: expectancy
"""
if len(trades) == 0:
return 0
expectancy = 1
profit_sum = trades.loc[trades['profit_abs'] > 0, 'profit_abs'].sum()
loss_sum = abs(trades.loc[trades['profit_abs'] < 0, 'profit_abs'].sum())
nb_win_trades = len(trades.loc[trades['profit_abs'] > 0])
nb_loss_trades = len(trades.loc[trades['profit_abs'] < 0])
if (nb_win_trades > 0) and (nb_loss_trades > 0):
average_win = profit_sum / nb_win_trades
average_loss = loss_sum / nb_loss_trades
risk_reward_ratio = average_win / average_loss
winrate = nb_win_trades / len(trades)
expectancy = ((1 + risk_reward_ratio) * winrate) - 1
elif nb_win_trades == 0:
expectancy = 0
return expectancy
def calculate_sortino(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
starting_balance: float) -> float:
"""
Calculate sortino
:param trades: DataFrame containing trades (requires columns profit_abs)
:return: sortino
"""
if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
return 0
total_profit = trades['profit_abs'] / starting_balance
days_period = max(1, (max_date - min_date).days)
expected_returns_mean = total_profit.sum() / days_period
down_stdev = np.std(trades.loc[trades['profit_abs'] < 0, 'profit_abs'] / starting_balance)
if down_stdev != 0 and not np.isnan(down_stdev):
sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
else:
# Define high (negative) sortino ratio to be clear that this is NOT optimal.
sortino_ratio = -100
# print(expected_returns_mean, down_stdev, sortino_ratio)
return sortino_ratio
def calculate_sharpe(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
starting_balance: float) -> float:
"""
Calculate sharpe
:param trades: DataFrame containing trades (requires column profit_abs)
:return: sharpe
"""
if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
return 0
total_profit = trades['profit_abs'] / starting_balance
days_period = max(1, (max_date - min_date).days)
expected_returns_mean = total_profit.sum() / days_period
up_stdev = np.std(total_profit)
if up_stdev != 0:
sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
else:
# Define high (negative) sharpe ratio to be clear that this is NOT optimal.
sharp_ratio = -100
# print(expected_returns_mean, up_stdev, sharp_ratio)
return sharp_ratio
def calculate_calmar(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
starting_balance: float) -> float:
"""
Calculate calmar
:param trades: DataFrame containing trades (requires columns close_date and profit_abs)
:return: calmar
"""
if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
return 0
total_profit = trades['profit_abs'].sum() / starting_balance
days_period = max(1, (max_date - min_date).days)
# adding slippage of 0.1% per trade
# total_profit = total_profit - 0.0005
expected_returns_mean = total_profit / days_period * 100
# calculate max drawdown
try:
_, _, _, _, _, max_drawdown = calculate_max_drawdown(
trades, value_col="profit_abs", starting_balance=starting_balance
)
except ValueError:
max_drawdown = 0
if max_drawdown != 0:
calmar_ratio = expected_returns_mean / max_drawdown * math.sqrt(365)
else:
# Define high (negative) calmar ratio to be clear that this is NOT optimal.
calmar_ratio = -100
# print(expected_returns_mean, max_drawdown, calmar_ratio)
return calmar_ratio

4
freqtrade/edge/edge_positioning.py
View File

@ -195,7 +195,7 @@ class Edge:
def stake_amount(self, pair: str, free_capital: float,
total_capital: float, capital_in_trade: float) -> float:
stoploss = self.stoploss(pair)
stoploss = self.get_stoploss(pair)
available_capital = (total_capital + capital_in_trade) * self._capital_ratio
allowed_capital_at_risk = available_capital * self._allowed_risk
max_position_size = abs(allowed_capital_at_risk / stoploss)
@ -214,7 +214,7 @@ class Edge:
)
return round(position_size, 15)
def stoploss(self, pair: str) -> float:
def get_stoploss(self, pair: str) -> float:
if pair in self._cached_pairs:
return self._cached_pairs[pair].stoploss
else:

2
freqtrade/enums/__init__.py
View File

@ -5,7 +5,9 @@ from freqtrade.enums.exitchecktuple import ExitCheckTuple
from freqtrade.enums.exittype import ExitType
from freqtrade.enums.hyperoptstate import HyperoptState
from freqtrade.enums.marginmode import MarginMode
from freqtrade.enums.marketstatetype import MarketDirection
from freqtrade.enums.ordertypevalue import OrderTypeValues
from freqtrade.enums.pricetype import PriceType
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

3
freqtrade/enums/candletype.py
View File

@ -13,6 +13,9 @@ class CandleType(str, Enum):
FUNDING_RATE = "funding_rate"
# BORROW_RATE = "borrow_rate" # * unimplemented
def __str__(self):
return f"{self.name.lower()}"
@staticmethod
def from_string(value: str) -> 'CandleType':
if not value:

15
freqtrade/enums/marketstatetype.py Normal file
View File

@ -0,0 +1,15 @@
from enum import Enum
class MarketDirection(Enum):
"""
Enum for various market directions.
"""
LONG = "long"
SHORT = "short"
EVEN = "even"
NONE = "none"
def __str__(self):
# convert to string
return self.value

8
freqtrade/enums/pricetype.py Normal file
View File

@ -0,0 +1,8 @@
from enum import Enum
class PriceType(str, Enum):
"""Enum to distinguish possible trigger prices for stoplosses"""
LAST = "last"
MARK = "mark"
INDEX = "index"

4
freqtrade/enums/rpcmessagetype.py
View File

@ -4,6 +4,7 @@ from enum import Enum
class RPCMessageType(str, Enum):
STATUS = 'status'
WARNING = 'warning'
EXCEPTION = 'exception'
STARTUP = 'startup'
ENTRY = 'entry'
@ -37,5 +38,8 @@ class RPCRequestType(str, Enum):
WHITELIST = 'whitelist'
ANALYZED_DF = 'analyzed_df'
def __str__(self):
return self.value
NO_ECHO_MESSAGES = (RPCMessageType.ANALYZED_DF, RPCMessageType.WHITELIST, RPCMessageType.NEW_CANDLE)

9
freqtrade/enums/signaltype.py
View File

@ -10,6 +10,9 @@ class SignalType(Enum):
ENTER_SHORT = "enter_short"
EXIT_SHORT = "exit_short"
def __str__(self):
return f"{self.name.lower()}"
class SignalTagType(Enum):
"""
@ -18,7 +21,13 @@ class SignalTagType(Enum):
ENTER_TAG = "enter_tag"
EXIT_TAG = "exit_tag"
def __str__(self):
return f"{self.name.lower()}"
class SignalDirection(str, Enum):
LONG = 'long'
SHORT = 'short'
def __str__(self):
return f"{self.name.lower()}"

3
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
@ -18,7 +17,7 @@ from freqtrade.exchange.exchange_utils import (amount_to_contract_precision, amo
timeframe_to_next_date, timeframe_to_prev_date,
timeframe_to_seconds, validate_exchange,
validate_exchanges)
from freqtrade.exchange.gateio import Gateio
from freqtrade.exchange.gate import Gate
from freqtrade.exchange.hitbtc import Hitbtc
from freqtrade.exchange.huobi import Huobi
from freqtrade.exchange.kraken import Kraken

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

49
freqtrade/exchange/binance.py
View File

@ -7,11 +7,11 @@ from typing import Dict, List, Optional, Tuple
import arrow
import ccxt
from freqtrade.enums import CandleType, MarginMode, TradingMode
from freqtrade.enums import CandleType, MarginMode, PriceType, TradingMode
from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
from freqtrade.exchange import Exchange
from freqtrade.exchange.common import retrier
from freqtrade.exchange.types import Tickers
from freqtrade.exchange.types import OHLCVResponse, Tickers
from freqtrade.misc import deep_merge_dicts, json_load
@ -28,11 +28,16 @@ class Binance(Exchange):
"trades_pagination": "id",
"trades_pagination_arg": "fromId",
"l2_limit_range": [5, 10, 20, 50, 100, 500, 1000],
"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,
"floor_leverage": True,
"stop_price_type_field": "workingType",
"stop_price_type_value_mapping": {
PriceType.LAST: "CONTRACT_PRICE",
PriceType.MARK: "MARK_PRICE",
},
}
_supported_trading_mode_margin_pairs: List[Tuple[TradingMode, MarginMode]] = [
@ -78,33 +83,9 @@ class Binance(Exchange):
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not set leverage due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
f'Error in additional_exchange_init due to {e.__class__.__name__}. Message: {e}'
) from e
@retrier
def _set_leverage(
self,
leverage: float,
pair: Optional[str] = None,
trading_mode: Optional[TradingMode] = None
):
"""
Set's the leverage before making a trade, in order to not
have the same leverage on every trade
"""
trading_mode = trading_mode or self.trading_mode
if self._config['dry_run'] or trading_mode != TradingMode.FUTURES:
return
try:
self._api.set_leverage(symbol=pair, leverage=round(leverage))
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not set leverage due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
@ -112,7 +93,7 @@ class Binance(Exchange):
since_ms: int, candle_type: CandleType,
is_new_pair: bool = False, raise_: bool = False,
until_ms: Optional[int] = None
) -> Tuple[str, str, str, List]:
) -> OHLCVResponse:
"""
Overwrite to introduce "fast new pair" functionality by detecting the pair's listing date
Does not work for other exchanges, which don't return the earliest data when called with "0"
@ -150,6 +131,7 @@ class Binance(Exchange):
is_short: bool,
amount: float,
stake_amount: float,
leverage: float,
wallet_balance: float, # Or margin balance
mm_ex_1: float = 0.0, # (Binance) Cross only
upnl_ex_1: float = 0.0, # (Binance) Cross only
@ -159,11 +141,12 @@ class Binance(Exchange):
MARGIN: https://www.binance.com/en/support/faq/f6b010588e55413aa58b7d63ee0125ed
PERPETUAL: https://www.binance.com/en/support/faq/b3c689c1f50a44cabb3a84e663b81d93
:param exchange_name:
:param pair: Pair to calculate liquidation price for
:param open_rate: Entry price of position
:param is_short: True if the trade is a short, false otherwise
:param amount: Absolute value of position size incl. leverage (in base currency)
:param stake_amount: Stake amount - Collateral in settle currency.
:param leverage: Leverage used for this position.
:param trading_mode: SPOT, MARGIN, FUTURES, etc.
:param margin_mode: Either ISOLATED or CROSS
:param wallet_balance: Amount of margin_mode in the wallet being used to trade
@ -212,7 +195,7 @@ class Binance(Exchange):
leverage_tiers_path = (
Path(__file__).parent / 'binance_leverage_tiers.json'
)
with open(leverage_tiers_path) as json_file:
with leverage_tiers_path.open() as json_file:
return json_load(json_file)
else:
try:

24524
freqtrade/exchange/binance_leverage_tiers.json
View File

File diff suppressed because it is too large Load Diff

181
freqtrade/exchange/bybit.py
View File

@ -1,9 +1,16 @@
""" Bybit exchange subclass """
import logging
from typing import Dict, List, Tuple
from datetime import datetime
from typing import Any, Dict, List, Optional, Tuple
from freqtrade.enums import MarginMode, TradingMode
import ccxt
from freqtrade.constants import BuySell
from freqtrade.enums import MarginMode, PriceType, TradingMode
from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
from freqtrade.exchange import Exchange
from freqtrade.exchange.common import retrier
from freqtrade.exchange.exchange_utils import timeframe_to_msecs
logger = logging.getLogger(__name__)
@ -20,18 +27,27 @@ class Bybit(Exchange):
"""
_ft_has: Dict = {
"ohlcv_candle_limit": 1000,
"ccxt_futures_name": "linear",
"ohlcv_candle_limit": 200,
"ohlcv_has_history": False,
}
_ft_has_futures: Dict = {
"ohlcv_has_history": True,
"mark_ohlcv_timeframe": "4h",
"funding_fee_timeframe": "8h",
"stoploss_on_exchange": True,
"stoploss_order_types": {"limit": "limit", "market": "market"},
"stop_price_type_field": "triggerBy",
"stop_price_type_value_mapping": {
PriceType.LAST: "LastPrice",
PriceType.MARK: "MarkPrice",
PriceType.INDEX: "IndexPrice",
},
}
_supported_trading_mode_margin_pairs: List[Tuple[TradingMode, MarginMode]] = [
# TradingMode.SPOT always supported and not required in this list
# (TradingMode.FUTURES, MarginMode.CROSS),
# (TradingMode.FUTURES, MarginMode.ISOLATED)
(TradingMode.FUTURES, MarginMode.ISOLATED)
]
@property
@ -47,3 +63,158 @@ class Bybit(Exchange):
})
config.update(super()._ccxt_config)
return config
def market_is_future(self, market: Dict[str, Any]) -> bool:
main = super().market_is_future(market)
# For ByBit, we'll only support USDT markets for now.
return (
main and market['settle'] == 'USDT'
)
@retrier
def additional_exchange_init(self) -> None:
"""
Additional exchange initialization logic.
.api will be available at this point.
Must be overridden in child methods if required.
"""
try:
if self.trading_mode == TradingMode.FUTURES and not self._config['dry_run']:
position_mode = self._api.set_position_mode(False)
self._log_exchange_response('set_position_mode', position_mode)
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Error in additional_exchange_init due to {e.__class__.__name__}. Message: {e}'
) from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
async def _fetch_funding_rate_history(
self,
pair: str,
timeframe: str,
limit: int,
since_ms: Optional[int] = None,
) -> List[List]:
"""
Fetch funding rate history
Necessary workaround until https://github.com/ccxt/ccxt/issues/15990 is fixed.
"""
params = {}
if since_ms:
until = since_ms + (timeframe_to_msecs(timeframe) * self._ft_has['ohlcv_candle_limit'])
params.update({'until': until})
# Funding rate
data = await self._api_async.fetch_funding_rate_history(
pair, since=since_ms,
params=params)
# Convert funding rate to candle pattern
data = [[x['timestamp'], x['fundingRate'], 0, 0, 0, 0] for x in data]
return data
def _lev_prep(self, pair: str, leverage: float, side: BuySell):
if self.trading_mode != TradingMode.SPOT:
params = {'leverage': leverage}
self.set_margin_mode(pair, self.margin_mode, accept_fail=True, params=params)
self._set_leverage(leverage, pair, accept_fail=True)
def _get_params(
self,
side: BuySell,
ordertype: str,
leverage: float,
reduceOnly: bool,
time_in_force: str = 'GTC',
) -> Dict:
params = super()._get_params(
side=side,
ordertype=ordertype,
leverage=leverage,
reduceOnly=reduceOnly,
time_in_force=time_in_force,
)
if self.trading_mode == TradingMode.FUTURES and self.margin_mode:
params['position_idx'] = 0
return params
def dry_run_liquidation_price(
self,
pair: str,
open_rate: float, # Entry price of position
is_short: bool,
amount: float,
stake_amount: float,
leverage: float,
wallet_balance: float, # Or margin balance
mm_ex_1: float = 0.0, # (Binance) Cross only
upnl_ex_1: float = 0.0, # (Binance) Cross only
) -> Optional[float]:
"""
Important: Must be fetching data from cached values as this is used by backtesting!
PERPETUAL:
bybit:
https://www.bybithelp.com/HelpCenterKnowledge/bybitHC_Article?language=en_US&id=000001067
Long:
Liquidation Price = (
Entry Price * (1 - Initial Margin Rate + Maintenance Margin Rate)
- Extra Margin Added/ Contract)
Short:
Liquidation Price = (
Entry Price * (1 + Initial Margin Rate - Maintenance Margin Rate)
+ Extra Margin Added/ Contract)
Implementation Note: Extra margin is currently not used.
:param pair: Pair to calculate liquidation price for
:param open_rate: Entry price of position
:param is_short: True if the trade is a short, false otherwise
:param amount: Absolute value of position size incl. leverage (in base currency)
:param stake_amount: Stake amount - Collateral in settle currency.
:param leverage: Leverage used for this position.
:param trading_mode: SPOT, MARGIN, FUTURES, etc.
:param margin_mode: Either ISOLATED or CROSS
:param wallet_balance: Amount of margin_mode in the wallet being used to trade
Cross-Margin Mode: crossWalletBalance
Isolated-Margin Mode: isolatedWalletBalance
"""
market = self.markets[pair]
mm_ratio, _ = self.get_maintenance_ratio_and_amt(pair, stake_amount)
if self.trading_mode == TradingMode.FUTURES and self.margin_mode == MarginMode.ISOLATED:
if market['inverse']:
raise OperationalException(
"Freqtrade does not yet support inverse contracts")
initial_margin_rate = 1 / leverage
# See docstring - ignores extra margin!
if is_short:
return open_rate * (1 + initial_margin_rate - mm_ratio)
else:
return open_rate * (1 - initial_margin_rate + mm_ratio)
else:
raise OperationalException(
"Freqtrade only supports isolated futures for leverage trading")
def get_funding_fees(
self, pair: str, amount: float, is_short: bool, open_date: datetime) -> float:
"""
Fetch funding fees, either from the exchange (live) or calculates them
based on funding rate/mark price history
:param pair: The quote/base pair of the trade
:param is_short: trade direction
:param amount: Trade amount
:param open_date: Open date of the trade
:return: funding fee since open_date
:raises: ExchangeError if something goes wrong.
"""
# Bybit does not provide "applied" funding fees per position.
if self.trading_mode == TradingMode.FUTURES:
return self._fetch_and_calculate_funding_fees(
pair, amount, is_short, open_date)
return 0.0

4
freqtrade/exchange/common.py
View File

@ -46,13 +46,13 @@ MAP_EXCHANGE_CHILDCLASS = {
'binanceje': 'binance',
'binanceusdm': 'binance',
'okex': 'okx',
'gate': 'gateio',
'gateio': 'gate',
}
SUPPORTED_EXCHANGES = [
'binance',
'bittrex',
'gateio',
'gate',
'huobi',
'kraken',
'okx',

253
freqtrade/exchange/exchange.py
View File

@ -3,11 +3,11 @@
Cryptocurrency Exchanges support
"""
import asyncio
import http
import inspect
import logging
from copy import deepcopy
from datetime import datetime, timedelta, timezone
from math import floor
from threading import Lock
from typing import Any, Coroutine, Dict, List, Literal, Optional, Tuple, Union
@ -21,9 +21,10 @@ from pandas import DataFrame, concat
from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHANGE_STATES, BidAsk,
BuySell, Config, EntryExit, ListPairsWithTimeframes, MakerTaker,
PairWithTimeframe)
OBLiteral, PairWithTimeframe)
from freqtrade.data.converter import clean_ohlcv_dataframe, ohlcv_to_dataframe, trades_dict_to_list
from freqtrade.enums import OPTIMIZE_MODES, CandleType, MarginMode, TradingMode
from freqtrade.enums.pricetype import PriceType
from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFundsError,
InvalidOrderException, OperationalException, PricingError,
RetryableOrderError, TemporaryError)
@ -36,7 +37,7 @@ from freqtrade.exchange.exchange_utils import (CcxtModuleType, amount_to_contrac
price_to_precision, timeframe_to_minutes,
timeframe_to_msecs, timeframe_to_next_date,
timeframe_to_prev_date, timeframe_to_seconds)
from freqtrade.exchange.types import Ticker, Tickers
from freqtrade.exchange.types import OHLCVResponse, OrderBook, Ticker, Tickers
from freqtrade.misc import (chunks, deep_merge_dicts, file_dump_json, file_load_json,
safe_value_fallback2)
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
@ -45,12 +46,6 @@ from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
logger = logging.getLogger(__name__)
# Workaround for adding samesite support to pre 3.8 python
# Only applies to python3.7, and only on certain exchanges (kraken)
# Replicates the fix from starlette (which is actually causing this problem)
http.cookies.Morsel._reserved["samesite"] = "SameSite" # type: ignore
class Exchange:
# Parameters to add directly to buy/sell calls (like agreeing to trading agreement)
@ -74,6 +69,7 @@ class Exchange:
# Check https://github.com/ccxt/ccxt/issues/10767 for removal of ohlcv_volume_currency
"ohlcv_volume_currency": "base", # "base" or "quote"
"tickers_have_quoteVolume": True,
"tickers_have_bid_ask": True, # bid / ask empty for fetch_tickers
"tickers_have_price": True,
"trades_pagination": "time", # Possible are "time" or "id"
"trades_pagination_arg": "since",
@ -474,7 +470,7 @@ class Exchange:
try:
if self._api_async:
self.loop.run_until_complete(
self._api_async.load_markets(reload=reload))
self._api_async.load_markets(reload=reload, params={}))
except (asyncio.TimeoutError, ccxt.BaseError) as e:
logger.warning('Could not load async markets. Reason: %s', e)
@ -483,7 +479,7 @@ class Exchange:
def _load_markets(self) -> None:
""" Initialize markets both sync and async """
try:
self._markets = self._api.load_markets()
self._markets = self._api.load_markets(params={})
self._load_async_markets()
self._last_markets_refresh = arrow.utcnow().int_timestamp
if self._ft_has['needs_trading_fees']:
@ -501,7 +497,7 @@ class Exchange:
return None
logger.debug("Performing scheduled market reload..")
try:
self._markets = self._api.load_markets(reload=True)
self._markets = self._api.load_markets(reload=True, params={})
# Also reload async markets to avoid issues with newly listed pairs
self._load_async_markets(reload=True)
self._last_markets_refresh = arrow.utcnow().int_timestamp
@ -606,12 +602,27 @@ class Exchange:
if not self.exchange_has('createMarketOrder'):
raise OperationalException(
f'Exchange {self.name} does not support market orders.')
self.validate_stop_ordertypes(order_types)
def validate_stop_ordertypes(self, order_types: Dict) -> None:
"""
Validate stoploss order types
"""
if (order_types.get("stoploss_on_exchange")
and not self._ft_has.get("stoploss_on_exchange", False)):
raise OperationalException(
f'On exchange stoploss is not supported for {self.name}.'
)
if self.trading_mode == TradingMode.FUTURES:
price_mapping = self._ft_has.get('stop_price_type_value_mapping', {}).keys()
if (
order_types.get("stoploss_on_exchange", False) is True
and 'stoploss_price_type' in order_types
and order_types['stoploss_price_type'] not in price_mapping
):
raise OperationalException(
f'On exchange stoploss price type is not supported for {self.name}.'
)
def validate_pricing(self, pricing: Dict) -> None:
if pricing.get('use_order_book', False) and not self.exchange_has('fetchL2OrderBook'):
@ -682,7 +693,7 @@ class Exchange:
f"Freqtrade does not support {mm_value} {trading_mode.value} on {self.name}"
)
def get_option(self, param: str, default: Any = None) -> Any:
def get_option(self, param: str, default: Optional[Any] = None) -> Any:
"""
Get parameter value from _ft_has
"""
@ -840,7 +851,7 @@ class Exchange:
'remaining': _amount,
'datetime': arrow.utcnow().strftime('%Y-%m-%dT%H:%M:%S.%fZ'),
'timestamp': arrow.utcnow().int_timestamp * 1000,
'status': "closed" if ordertype == "market" and not stop_loss else "open",
'status': "open",
'fee': None,
'info': {},
'leverage': leverage
@ -850,20 +861,33 @@ class Exchange:
dry_order["stopPrice"] = dry_order["price"]
# Workaround to avoid filling stoploss orders immediately
dry_order["ft_order_type"] = "stoploss"
orderbook: Optional[OrderBook] = None
if self.exchange_has('fetchL2OrderBook'):
orderbook = self.fetch_l2_order_book(pair, 20)
if ordertype == "limit" and orderbook:
# Allow a 3% price difference
allowed_diff = 0.03
if self._dry_is_price_crossed(pair, side, rate, orderbook, allowed_diff):
logger.info(
f"Converted order {pair} to market order due to price {rate} crossing spread "
f"by more than {allowed_diff:.2%}.")
dry_order["type"] = "market"
if dry_order["type"] == "market" and not dry_order.get("ft_order_type"):
# Update market order pricing
average = self.get_dry_market_fill_price(pair, side, amount, rate)
average = self.get_dry_market_fill_price(pair, side, amount, rate, orderbook)
dry_order.update({
'average': average,
'filled': _amount,
'remaining': 0.0,
'status': "closed",
'cost': (dry_order['amount'] * average) / leverage
})
# market orders will always incurr taker fees
dry_order = self.add_dry_order_fee(pair, dry_order, 'taker')
dry_order = self.check_dry_limit_order_filled(dry_order, immediate=True)
dry_order = self.check_dry_limit_order_filled(
dry_order, immediate=True, orderbook=orderbook)
self._dry_run_open_orders[dry_order["id"]] = dry_order
# Copy order and close it - so the returned order is open unless it's a market order
@ -885,20 +909,22 @@ class Exchange:
})
return dry_order
def get_dry_market_fill_price(self, pair: str, side: str, amount: float, rate: float) -> float:
def get_dry_market_fill_price(self, pair: str, side: str, amount: float, rate: float,
orderbook: Optional[OrderBook]) -> float:
"""
Get the market order fill price based on orderbook interpolation
"""
if self.exchange_has('fetchL2OrderBook'):
ob = self.fetch_l2_order_book(pair, 20)
ob_type = 'asks' if side == 'buy' else 'bids'
if not orderbook:
orderbook = self.fetch_l2_order_book(pair, 20)
ob_type: OBLiteral = 'asks' if side == 'buy' else 'bids'
slippage = 0.05
max_slippage_val = rate * ((1 + slippage) if side == 'buy' else (1 - slippage))
remaining_amount = amount
filled_amount = 0.0
book_entry_price = 0.0
for book_entry in ob[ob_type]:
for book_entry in orderbook[ob_type]:
book_entry_price = book_entry[0]
book_entry_coin_volume = book_entry[1]
if remaining_amount > 0:
@ -926,20 +952,20 @@ class Exchange:
return rate
def _is_dry_limit_order_filled(self, pair: str, side: str, limit: float) -> bool:
def _dry_is_price_crossed(self, pair: str, side: str, limit: float,
orderbook: Optional[OrderBook] = None, offset: float = 0.0) -> bool:
if not self.exchange_has('fetchL2OrderBook'):
return True
ob = self.fetch_l2_order_book(pair, 1)
if not orderbook:
orderbook = self.fetch_l2_order_book(pair, 1)
try:
if side == 'buy':
price = ob['asks'][0][0]
logger.debug(f"{pair} checking dry buy-order: price={price}, limit={limit}")
if limit >= price:
price = orderbook['asks'][0][0]
if limit * (1 - offset) >= price:
return True
else:
price = ob['bids'][0][0]
logger.debug(f"{pair} checking dry sell-order: price={price}, limit={limit}")
if limit <= price:
price = orderbook['bids'][0][0]
if limit * (1 + offset) <= price:
return True
except IndexError:
# Ignore empty orderbooks when filling - can be filled with the next iteration.
@ -947,7 +973,8 @@ class Exchange:
return False
def check_dry_limit_order_filled(
self, order: Dict[str, Any], immediate: bool = False) -> Dict[str, Any]:
self, order: Dict[str, Any], immediate: bool = False,
orderbook: Optional[OrderBook] = None) -> Dict[str, Any]:
"""
Check dry-run limit order fill and update fee (if it filled).
"""
@ -955,7 +982,7 @@ class Exchange:
and order['type'] in ["limit"]
and not order.get('ft_order_type')):
pair = order['symbol']
if self._is_dry_limit_order_filled(pair, order['side'], order['price']):
if self._dry_is_price_crossed(pair, order['side'], order['price'], orderbook):
order.update({
'status': 'closed',
'filled': order['amount'],
@ -1060,7 +1087,7 @@ class Exchange:
f'Tried to {side} amount {amount} at rate {rate}.'
f'Message: {e}') from e
except ccxt.InvalidOrder as e:
raise ExchangeError(
raise InvalidOrderException(
f'Could not create {ordertype} {side} order on market {pair}. '
f'Tried to {side} amount {amount} at rate {rate}. '
f'Message: {e}') from e
@ -1111,7 +1138,10 @@ class Exchange:
# Ensure rate is less than stop price
if bad_stop_price:
raise OperationalException(
'In stoploss limit order, stop price should be more than limit price')
"In stoploss limit order, stop price should be more than limit price. "
f"Stop price: {stop_price}, Limit price: {limit_rate}, "
f"Limit Price pct: {limit_price_pct}"
)
return limit_rate
def _get_stop_params(self, side: BuySell, ordertype: str, stop_price: float) -> Dict:
@ -1121,8 +1151,8 @@ class Exchange:
return params
@retrier(retries=0)
def stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict,
side: BuySell, leverage: float) -> Dict:
def create_stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict,
side: BuySell, leverage: float) -> Dict:
"""
creates a stoploss order.
requires `_ft_has['stoploss_order_types']` to be set as a dict mapping limit and market
@ -1167,6 +1197,10 @@ class Exchange:
stop_price=stop_price_norm)
if self.trading_mode == TradingMode.FUTURES:
params['reduceOnly'] = True
if 'stoploss_price_type' in order_types and 'stop_price_type_field' in self._ft_has:
price_type = self._ft_has['stop_price_type_value_mapping'][
order_types.get('stoploss_price_type', PriceType.LAST)]
params[self._ft_has['stop_price_type_field']] = price_type
amount = self.amount_to_precision(pair, self._amount_to_contracts(pair, amount))
@ -1357,7 +1391,7 @@ class Exchange:
raise OperationalException(e) from e
@retrier
def fetch_positions(self, pair: str = None) -> List[Dict]:
def fetch_positions(self, pair: Optional[str] = None) -> List[Dict]:
"""
Fetch positions from the exchange.
If no pair is given, all positions are returned.
@ -1497,7 +1531,7 @@ class Exchange:
return result
@retrier
def fetch_l2_order_book(self, pair: str, limit: int = 100) -> dict:
def fetch_l2_order_book(self, pair: str, limit: int = 100) -> OrderBook:
"""
Get L2 order book from exchange.
Can be limited to a certain amount (if supported).
@ -1540,7 +1574,7 @@ class Exchange:
def get_rate(self, pair: str, refresh: bool,
side: EntryExit, is_short: bool,
order_book: Optional[dict] = None, ticker: Optional[Ticker] = None) -> float:
order_book: Optional[OrderBook] = None, ticker: Optional[Ticker] = None) -> float:
"""
Calculates bid/ask target
bid rate - between current ask price and last price
@ -1578,7 +1612,8 @@ class Exchange:
logger.debug('order_book %s', order_book)
# top 1 = index 0
try:
rate = order_book[f"{price_side}s"][order_book_top - 1][0]
obside: OBLiteral = 'bids' if price_side == 'bid' else 'asks'
rate = order_book[obside][order_book_top - 1][0]
except (IndexError, KeyError) as e:
logger.warning(
f"{pair} - {name} Price at location {order_book_top} from orderbook "
@ -1705,7 +1740,7 @@ class Exchange:
return self._config['fee']
# validate that markets are loaded before trying to get fee
if self._api.markets is None or len(self._api.markets) == 0:
self._api.load_markets()
self._api.load_markets(params={})
return self._api.calculate_fee(symbol=symbol, type=type, side=side, amount=amount,
price=price, takerOrMaker=taker_or_maker)['rate']
@ -1801,7 +1836,7 @@ class Exchange:
def get_historic_ohlcv(self, pair: str, timeframe: str,
since_ms: int, candle_type: CandleType,
is_new_pair: bool = False,
until_ms: int = None) -> List:
until_ms: Optional[int] = None) -> List:
"""
Get candle history using asyncio and returns the list of candles.
Handles all async work for this.
@ -1813,32 +1848,18 @@ class Exchange:
:param candle_type: '', mark, index, premiumIndex, or funding_rate
:return: List with candle (OHLCV) data
"""
pair, _, _, data = self.loop.run_until_complete(
pair, _, _, data, _ = self.loop.run_until_complete(
self._async_get_historic_ohlcv(pair=pair, timeframe=timeframe,
since_ms=since_ms, until_ms=until_ms,
is_new_pair=is_new_pair, candle_type=candle_type))
logger.info(f"Downloaded data for {pair} with length {len(data)}.")
return data
def get_historic_ohlcv_as_df(self, pair: str, timeframe: str,
since_ms: int, candle_type: CandleType) -> DataFrame:
"""
Minimal wrapper around get_historic_ohlcv - converting the result into a dataframe
:param pair: Pair to download
:param timeframe: Timeframe to get data for
:param since_ms: Timestamp in milliseconds to get history from
:param candle_type: Any of the enum CandleType (must match trading mode!)
:return: OHLCV DataFrame
"""
ticks = self.get_historic_ohlcv(pair, timeframe, since_ms=since_ms, candle_type=candle_type)
return ohlcv_to_dataframe(ticks, timeframe, pair=pair, fill_missing=True,
drop_incomplete=self._ohlcv_partial_candle)
async def _async_get_historic_ohlcv(self, pair: str, timeframe: str,
since_ms: int, candle_type: CandleType,
is_new_pair: bool = False, raise_: bool = False,
until_ms: Optional[int] = None
) -> Tuple[str, str, str, List]:
) -> OHLCVResponse:
"""
Download historic ohlcv
:param is_new_pair: used by binance subclass to allow "fast" new pair downloading
@ -1869,15 +1890,16 @@ class Exchange:
continue
else:
# Deconstruct tuple if it's not an exception
p, _, c, new_data = res
p, _, c, new_data, _ = res
if p == pair and c == candle_type:
data.extend(new_data)
# Sort data again after extending the result - above calls return in "async order"
data = sorted(data, key=lambda x: x[0])
return pair, timeframe, candle_type, data
return pair, timeframe, candle_type, data, self._ohlcv_partial_candle
def _build_coroutine(self, pair: str, timeframe: str, candle_type: CandleType,
since_ms: Optional[int], cache: bool) -> Coroutine:
def _build_coroutine(
self, pair: str, timeframe: str, candle_type: CandleType,
since_ms: Optional[int], cache: bool) -> Coroutine[Any, Any, OHLCVResponse]:
not_all_data = cache and self.required_candle_call_count > 1
if cache and (pair, timeframe, candle_type) in self._klines:
candle_limit = self.ohlcv_candle_limit(timeframe, candle_type)
@ -1914,7 +1936,7 @@ class Exchange:
"""
Build Coroutines to execute as part of refresh_latest_ohlcv
"""
input_coroutines = []
input_coroutines: List[Coroutine[Any, Any, OHLCVResponse]] = []
cached_pairs = []
for pair, timeframe, candle_type in set(pair_list):
if (timeframe not in self.timeframes
@ -1943,7 +1965,8 @@ class Exchange:
cache: bool, drop_incomplete: bool) -> DataFrame:
# keeping last candle time as last refreshed time of the pair
if ticks and cache:
self._pairs_last_refresh_time[(pair, timeframe, c_type)] = ticks[-1][0] // 1000
idx = -2 if drop_incomplete and len(ticks) > 1 else -1
self._pairs_last_refresh_time[(pair, timeframe, c_type)] = ticks[idx][0] // 1000
# keeping parsed dataframe in cache
ohlcv_df = ohlcv_to_dataframe(ticks, timeframe, pair=pair, fill_missing=True,
drop_incomplete=drop_incomplete)
@ -1978,7 +2001,6 @@ class Exchange:
:return: Dict of [{(pair, timeframe): Dataframe}]
"""
logger.debug("Refreshing candle (OHLCV) data for %d pairs", len(pair_list))
drop_incomplete = self._ohlcv_partial_candle if drop_incomplete is None else drop_incomplete
# Gather coroutines to run
input_coroutines, cached_pairs = self._build_ohlcv_dl_jobs(pair_list, since_ms, cache)
@ -1996,10 +2018,11 @@ class Exchange:
if isinstance(res, Exception):
logger.warning(f"Async code raised an exception: {repr(res)}")
continue
# Deconstruct tuple (has 4 elements)
pair, timeframe, c_type, ticks = res
# Deconstruct tuple (has 5 elements)
pair, timeframe, c_type, ticks, drop_hint = res
drop_incomplete_ = drop_hint if drop_incomplete is None else drop_incomplete
ohlcv_df = self._process_ohlcv_df(
pair, timeframe, c_type, ticks, cache, drop_incomplete)
pair, timeframe, c_type, ticks, cache, drop_incomplete_)
results_df[(pair, timeframe, c_type)] = ohlcv_df
@ -2016,7 +2039,9 @@ class Exchange:
# Timeframe in seconds
interval_in_sec = timeframe_to_seconds(timeframe)
plr = self._pairs_last_refresh_time.get((pair, timeframe, candle_type), 0) + interval_in_sec
return plr < arrow.utcnow().int_timestamp
# current,active candle open date
now = int(timeframe_to_prev_date(timeframe).timestamp())
return plr < now
@retrier_async
async def _async_get_candle_history(
@ -2025,7 +2050,7 @@ class Exchange:
timeframe: str,
candle_type: CandleType,
since_ms: Optional[int] = None,
) -> Tuple[str, str, str, List]:
) -> OHLCVResponse:
"""
Asynchronously get candle history data using fetch_ohlcv
:param candle_type: '', mark, index, premiumIndex, or funding_rate
@ -2035,8 +2060,8 @@ class Exchange:
# Fetch OHLCV asynchronously
s = '(' + arrow.get(since_ms // 1000).isoformat() + ') ' if since_ms is not None else ''
logger.debug(
"Fetching pair %s, interval %s, since %s %s...",
pair, timeframe, since_ms, s
"Fetching pair %s, %s, interval %s, since %s %s...",
pair, candle_type, timeframe, since_ms, s
)
params = deepcopy(self._ft_has.get('ohlcv_params', {}))
candle_limit = self.ohlcv_candle_limit(
@ -2050,11 +2075,12 @@ class Exchange:
limit=candle_limit, params=params)
else:
# Funding rate
data = await self._api_async.fetch_funding_rate_history(
pair, since=since_ms,
limit=candle_limit)
# Convert funding rate to candle pattern
data = [[x['timestamp'], x['fundingRate'], 0, 0, 0, 0] for x in data]
data = await self._fetch_funding_rate_history(
pair=pair,
timeframe=timeframe,
limit=candle_limit,
since_ms=since_ms,
)
# Some exchanges sort OHLCV in ASC order and others in DESC.
# Ex: Bittrex returns the list of OHLCV in ASC order (oldest first, newest last)
# while GDAX returns the list of OHLCV in DESC order (newest first, oldest last)
@ -2064,9 +2090,9 @@ class Exchange:
data = sorted(data, key=lambda x: x[0])
except IndexError:
logger.exception("Error loading %s. Result was %s.", pair, data)
return pair, timeframe, candle_type, []
return pair, timeframe, candle_type, [], self._ohlcv_partial_candle
logger.debug("Done fetching pair %s, interval %s ...", pair, timeframe)
return pair, timeframe, candle_type, data
return pair, timeframe, candle_type, data, self._ohlcv_partial_candle
except ccxt.NotSupported as e:
raise OperationalException(
@ -2082,6 +2108,24 @@ class Exchange:
raise OperationalException(f'Could not fetch historical candle (OHLCV) data '
f'for pair {pair}. Message: {e}') from e
async def _fetch_funding_rate_history(
self,
pair: str,
timeframe: str,
limit: int,
since_ms: Optional[int] = None,
) -> List[List]:
"""
Fetch funding rate history - used to selectively override this by subclasses.
"""
# Funding rate
data = await self._api_async.fetch_funding_rate_history(
pair, since=since_ms,
limit=limit)
# Convert funding rate to candle pattern
data = [[x['timestamp'], x['fundingRate'], 0, 0, 0, 0] for x in data]
return data
# Fetch historic trades
@retrier_async
@ -2485,7 +2529,8 @@ class Exchange:
self,
leverage: float,
pair: Optional[str] = None,
trading_mode: Optional[TradingMode] = None
trading_mode: Optional[TradingMode] = None,
accept_fail: bool = False,
):
"""
Set's the leverage before making a trade, in order to not
@ -2494,12 +2539,18 @@ class Exchange:
if self._config['dry_run'] or not self.exchange_has("setLeverage"):
# Some exchanges only support one margin_mode type
return
if self._ft_has.get('floor_leverage', False) is True:
# Rounding for binance ...
leverage = floor(leverage)
try:
res = self._api.set_leverage(symbol=pair, leverage=leverage)
self._log_exchange_response('set_leverage', res)
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except ccxt.BadRequest as e:
if not accept_fail:
raise TemporaryError(
f'Could not set leverage due to {e.__class__.__name__}. Message: {e}') from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not set leverage due to {e.__class__.__name__}. Message: {e}') from e
@ -2521,7 +2572,8 @@ class Exchange:
return open_date.minute > 0 or open_date.second > 0
@retrier
def set_margin_mode(self, pair: str, margin_mode: MarginMode, params: dict = {}):
def set_margin_mode(self, pair: str, margin_mode: MarginMode, accept_fail: bool = False,
params: dict = {}):
"""
Set's the margin mode on the exchange to cross or isolated for a specific pair
:param pair: base/quote currency pair (e.g. "ADA/USDT")
@ -2535,6 +2587,10 @@ class Exchange:
self._log_exchange_response('set_margin_mode', res)
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except ccxt.BadRequest as e:
if not accept_fail:
raise TemporaryError(
f'Could not set margin mode due to {e.__class__.__name__}. Message: {e}') from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not set margin mode due to {e.__class__.__name__}. Message: {e}') from e
@ -2668,7 +2724,7 @@ class Exchange:
:param amount: Trade amount
:param open_date: Open date of the trade
:return: funding fee since open_date
:raies: ExchangeError if something goes wrong.
:raises: ExchangeError if something goes wrong.
"""
if self.trading_mode == TradingMode.FUTURES:
if self._config['dry_run']:
@ -2688,6 +2744,7 @@ class Exchange:
is_short: bool,
amount: float, # Absolute value of position size
stake_amount: float,
leverage: float,
wallet_balance: float,
mm_ex_1: float = 0.0, # (Binance) Cross only
upnl_ex_1: float = 0.0, # (Binance) Cross only
@ -2701,14 +2758,15 @@ class Exchange:
raise OperationalException(
f"{self.name} does not support {self.margin_mode} {self.trading_mode}")
isolated_liq = None
liquidation_price = None
if self._config['dry_run'] or not self.exchange_has("fetchPositions"):
isolated_liq = self.dry_run_liquidation_price(
liquidation_price = self.dry_run_liquidation_price(
pair=pair,
open_rate=open_rate,
is_short=is_short,
amount=amount,
leverage=leverage,
stake_amount=stake_amount,
wallet_balance=wallet_balance,
mm_ex_1=mm_ex_1,
@ -2718,16 +2776,16 @@ class Exchange:
positions = self.fetch_positions(pair)
if len(positions) > 0:
pos = positions[0]
isolated_liq = pos['liquidationPrice']
liquidation_price = pos['liquidationPrice']
if isolated_liq:
buffer_amount = abs(open_rate - isolated_liq) * self.liquidation_buffer
isolated_liq = (
isolated_liq - buffer_amount
if liquidation_price is not None:
buffer_amount = abs(open_rate - liquidation_price) * self.liquidation_buffer
liquidation_price_buffer = (
liquidation_price - buffer_amount
if is_short else
isolated_liq + buffer_amount
liquidation_price + buffer_amount
)
return isolated_liq
return max(liquidation_price_buffer, 0.0)
else:
return None
@ -2738,6 +2796,7 @@ class Exchange:
is_short: bool,
amount: float,
stake_amount: float,
leverage: float,
wallet_balance: float, # Or margin balance
mm_ex_1: float = 0.0, # (Binance) Cross only
upnl_ex_1: float = 0.0, # (Binance) Cross only
@ -2745,22 +2804,28 @@ class Exchange:
"""
Important: Must be fetching data from cached values as this is used by backtesting!
PERPETUAL:
gateio: https://www.gate.io/help/futures/perpetual/22160/calculation-of-liquidation-price
gate: https://www.gate.io/help/futures/futures/27724/liquidation-price-bankruptcy-price
> Liquidation Price = (Entry Price ± Margin / Contract Multiplier / Size) /
[ 1 ± (Maintenance Margin Ratio + Taker Rate)]
Wherein, "+" or "-" depends on whether the contract goes long or short:
"-" for long, and "+" for short.
okex: https://www.okex.com/support/hc/en-us/articles/
360053909592-VI-Introduction-to-the-isolated-mode-of-Single-Multi-currency-Portfolio-margin
:param exchange_name:
:param pair: Pair to calculate liquidation price for
:param open_rate: Entry price of position
:param is_short: True if the trade is a short, false otherwise
:param amount: Absolute value of position size incl. leverage (in base currency)
:param stake_amount: Stake amount - Collateral in settle currency.
:param leverage: Leverage used for this position.
:param trading_mode: SPOT, MARGIN, FUTURES, etc.
:param margin_mode: Either ISOLATED or CROSS
:param wallet_balance: Amount of margin_mode in the wallet being used to trade
Cross-Margin Mode: crossWalletBalance
Isolated-Margin Mode: isolatedWalletBalance
# * Not required by Gateio or OKX
# * Not required by Gate or OKX
:param mm_ex_1:
:param upnl_ex_1:
"""
@ -2789,7 +2854,7 @@ class Exchange:
def get_maintenance_ratio_and_amt(
self,
pair: str,
nominal_value: float = 0.0,
nominal_value: float,
) -> Tuple[float, Optional[float]]:
"""
Important: Must be fetching data from cached values as this is used by backtesting!

11
freqtrade/exchange/exchange_utils.py
View File

@ -15,18 +15,19 @@ from freqtrade.util import FtPrecise
CcxtModuleType = Any
def is_exchange_known_ccxt(exchange_name: str, ccxt_module: CcxtModuleType = None) -> bool:
def is_exchange_known_ccxt(
exchange_name: str, ccxt_module: Optional[CcxtModuleType] = None) -> bool:
return exchange_name in ccxt_exchanges(ccxt_module)
def ccxt_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:
def ccxt_exchanges(ccxt_module: Optional[CcxtModuleType] = None) -> List[str]:
"""
Return the list of all exchanges known to ccxt
"""
return ccxt_module.exchanges if ccxt_module is not None else ccxt.exchanges
def available_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:
def available_exchanges(ccxt_module: Optional[CcxtModuleType] = None) -> List[str]:
"""
Return exchanges available to the bot, i.e. non-bad exchanges in the ccxt list
"""
@ -86,7 +87,7 @@ def timeframe_to_msecs(timeframe: str) -> int:
return ccxt.Exchange.parse_timeframe(timeframe) * 1000
def timeframe_to_prev_date(timeframe: str, date: datetime = None) -> datetime:
def timeframe_to_prev_date(timeframe: str, date: Optional[datetime] = None) -> datetime:
"""
Use Timeframe and determine the candle start date for this date.
Does not round when given a candle start date.
@ -102,7 +103,7 @@ def timeframe_to_prev_date(timeframe: str, date: datetime = None) -> datetime:
return datetime.fromtimestamp(new_timestamp, tz=timezone.utc)
def timeframe_to_next_date(timeframe: str, date: datetime = None) -> datetime:
def timeframe_to_next_date(timeframe: str, date: Optional[datetime] = None) -> datetime:
"""
Use Timeframe and determine next candle.
:param timeframe: timeframe in string format (e.g. "5m")

14
freqtrade/exchange/gateio.py → freqtrade/exchange/gate.py
View File

@ -4,7 +4,7 @@ from datetime import datetime
from typing import Any, Dict, List, Optional, Tuple
from freqtrade.constants import BuySell
from freqtrade.enums import MarginMode, TradingMode
from freqtrade.enums import MarginMode, PriceType, TradingMode
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import Exchange
from freqtrade.misc import safe_value_fallback2
@ -13,7 +13,7 @@ from freqtrade.misc import safe_value_fallback2
logger = logging.getLogger(__name__)
class Gateio(Exchange):
class Gate(Exchange):
"""
Gate.io exchange class. Contains adjustments needed for Freqtrade to work
with this exchange.
@ -32,8 +32,15 @@ class Gateio(Exchange):
_ft_has_futures: Dict = {
"needs_trading_fees": True,
"tickers_have_bid_ask": False,
"fee_cost_in_contracts": False, # Set explicitly to false for clarity
"order_props_in_contracts": ['amount', 'filled', 'remaining'],
"stop_price_type_field": "price_type",
"stop_price_type_value_mapping": {
PriceType.LAST: 0,
PriceType.MARK: 1,
PriceType.INDEX: 2,
},
}
_supported_trading_mode_margin_pairs: List[Tuple[TradingMode, MarginMode]] = [
@ -49,6 +56,7 @@ class Gateio(Exchange):
if any(v == 'market' for k, v in order_types.items()):
raise OperationalException(
f'Exchange {self.name} does not support market orders.')
super().validate_stop_ordertypes(order_types)
def _get_params(
self,
@ -77,7 +85,7 @@ class Gateio(Exchange):
if self.trading_mode == TradingMode.FUTURES:
# Futures usually don't contain fees in the response.
# As such, futures orders on gateio will not contain a fee, which causes
# As such, futures orders on gate will not contain a fee, which causes
# a repeated "update fee" cycle and wrong calculations.
# Therefore we patch the response with fees if it's not available.
# An alternative also contianing fees would be

1
freqtrade/exchange/hitbtc.py
View File

@ -19,5 +19,4 @@ class Hitbtc(Exchange):
_ft_has: Dict = {
"ohlcv_candle_limit": 1000,
"ohlcv_params": {"sort": "DESC"}
}

7
freqtrade/exchange/kraken.py
View File

@ -97,8 +97,8 @@ class Kraken(Exchange):
))
@retrier(retries=0)
def stoploss(self, pair: str, amount: float, stop_price: float,
order_types: Dict, side: BuySell, leverage: float) -> Dict:
def create_stoploss(self, pair: str, amount: float, stop_price: float,
order_types: Dict, side: BuySell, leverage: float) -> Dict:
"""
Creates a stoploss market order.
Stoploss market orders is the only stoploss type supported by kraken.
@ -158,7 +158,8 @@ class Kraken(Exchange):
self,
leverage: float,
pair: Optional[str] = None,
trading_mode: Optional[TradingMode] = None
trading_mode: Optional[TradingMode] = None,
accept_fail: bool = False,
):
"""
Kraken set's the leverage as an option in the order object, so we need to

32
freqtrade/exchange/kucoin.py
View File

@ -36,3 +36,35 @@ class Kucoin(Exchange):
'stop': 'loss'
})
return params
def create_order(
self,
*,
pair: str,
ordertype: str,
side: BuySell,
amount: float,
rate: float,
leverage: float,
reduceOnly: bool = False,
time_in_force: str = 'GTC',
) -> Dict:
res = super().create_order(
pair=pair,
ordertype=ordertype,
side=side,
amount=amount,
rate=rate,
leverage=leverage,
reduceOnly=reduceOnly,
time_in_force=time_in_force,
)
# Kucoin returns only the order-id.
# ccxt returns status = 'closed' at the moment - which is information ccxt invented.
# Since we rely on status heavily, we must set it to 'open' here.
# ref: https://github.com/ccxt/ccxt/pull/16674, (https://github.com/ccxt/ccxt/pull/16553)
if not self._config['dry_run']:
res['type'] = ordertype
res['status'] = 'open'
return res

11
freqtrade/exchange/okx.py
View File

@ -5,6 +5,7 @@ import ccxt
from freqtrade.constants import BuySell
from freqtrade.enums import CandleType, MarginMode, TradingMode
from freqtrade.enums.pricetype import PriceType
from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
from freqtrade.exchange import Exchange, date_minus_candles
from freqtrade.exchange.common import retrier
@ -27,6 +28,12 @@ class Okx(Exchange):
_ft_has_futures: Dict = {
"tickers_have_quoteVolume": False,
"fee_cost_in_contracts": True,
"stop_price_type_field": "tpTriggerPxType",
"stop_price_type_value_mapping": {
PriceType.LAST: "last",
PriceType.MARK: "index",
PriceType.INDEX: "mark",
},
}
_supported_trading_mode_margin_pairs: List[Tuple[TradingMode, MarginMode]] = [
@ -118,13 +125,15 @@ class Okx(Exchange):
if self.trading_mode != TradingMode.SPOT and self.margin_mode is not None:
try:
# TODO-lev: Test me properly (check mgnMode passed)
self._api.set_leverage(
res = self._api.set_leverage(
leverage=leverage,
symbol=pair,
params={
"mgnMode": self.margin_mode.value,
"posSide": self._get_posSide(side, False),
})
self._log_exchange_response('set_leverage', res)
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:

16
freqtrade/exchange/types.py
View File

@ -1,4 +1,6 @@
from typing import Dict, Optional, TypedDict
from typing import Dict, List, Optional, Tuple, TypedDict
from freqtrade.enums import CandleType
class Ticker(TypedDict):
@ -13,4 +15,16 @@ class Ticker(TypedDict):
# Several more - only listing required.
class OrderBook(TypedDict):
symbol: str
bids: List[Tuple[float, float]]
asks: List[Tuple[float, float]]
timestamp: Optional[int]
datetime: Optional[str]
nonce: Optional[int]
Tickers = Dict[str, Ticker]
# pair, timeframe, candleType, OHLCV, drop last?,
OHLCVResponse = Tuple[str, str, CandleType, List, bool]

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], category="actions")
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

5
freqtrade/freqai/RL/Base4ActionRLEnv.py
View File

@ -46,9 +46,9 @@ class Base4ActionRLEnv(BaseEnvironment):
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], category="actions")
trade_type = None
if self.is_tradesignal(action):
@ -88,7 +88,8 @@ class Base4ActionRLEnv(BaseEnvironment):
{'price': self.current_price(), 'index': self._current_tick,
'type': trade_type})
if self._total_profit < 1 - self.rl_config.get('max_training_drawdown_pct', 0.8):
if (self._total_profit < self.max_drawdown or
self._total_unrealized_profit < self.max_drawdown):
self._done = True
self._position_history.append(self._position)

1
freqtrade/freqai/RL/Base5ActionRLEnv.py
View File

@ -49,6 +49,7 @@ class Base5ActionRLEnv(BaseEnvironment):
self._update_unrealized_total_profit()
step_reward = self.calculate_reward(action)
self.total_reward += step_reward
self.tensorboard_log(self.actions._member_names_[action], category="actions")
trade_type = None
if self.is_tradesignal(action):

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