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Merge branch 'freqtrade:develop' into develop

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Stefano Ariestasia 2023-03-25 13:56:43 +09:00 committed by GitHub
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42
.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
@ -90,14 +91,14 @@ jobs:
freqtrade create-userdir --userdir user_data
freqtrade hyperopt --datadir tests/testdata -e 6 --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 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
@ -186,14 +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
@ -212,7 +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
@ -248,9 +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: |
@ -321,7 +322,6 @@ jobs:
build_linux_online:
# Run pytest with "live" checks
runs-on: ubuntu-22.04
# permissions:
steps:
- uses: actions/checkout@v3
@ -425,7 +425,7 @@ jobs:
python setup.py sdist bdist_wheel
- name: Publish to PyPI (Test)
uses: pypa/gh-action-pypi-publish@v1.6.4
uses: pypa/gh-action-pypi-publish@v1.8.1
if: (github.event_name == 'release')
with:
user: __token__
@ -433,7 +433,7 @@ jobs:
repository_url: https://test.pypi.org/legacy/
- name: Publish to PyPI
uses: pypa/gh-action-pypi-publish@v1.6.4
uses: pypa/gh-action-pypi-publish@v1.8.1
if: (github.event_name == 'release')
with:
user: __token__
@ -466,12 +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]
@ -494,8 +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
View File

@ -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==2.28.11.7
- types-tabulate==0.9.0.0
- types-python-dateutil==2.8.19.5
- types-requests==2.28.11.15
- types-tabulate==0.9.0.1
- types-python-dateutil==2.8.19.10
- SQLAlchemy==2.0.7
# 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: |

9
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).
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

5
README.md
View File

@ -40,6 +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.
@ -164,6 +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
View File

@ -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_FREQAI_RL} ${GHCR_IMAGE_NAME}:${TAG_FREQAI_RL}
crane copy ${IMAGE_NAME}:${TAG_FREQAI} ${GHCR_IMAGE_NAME}:${TAG_FREQAI}
crane copy ${IMAGE_NAME}:${TAG_PLOT} ${GHCR_IMAGE_NAME}:${TAG_PLOT}
crane copy ${IMAGE_NAME}:${TAG} ${GHCR_IMAGE_NAME}:${TAG}
# 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
View File

@ -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",

10
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,

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

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:

7
docs/bot-basics.md
View File

@ -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.

6
docs/configuration.md
View 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"
@ -688,6 +689,7 @@ To use a proxy for exchange connections - you will have to define the proxies as
},
}
}
}
```
## Next step

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
View File

@ -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.

8
docs/freqai-configuration.md
View File

@ -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",
@ -165,10 +165,10 @@ Below are the values you can expect to include/use inside a typical strategy dat
## 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.
@ -205,7 +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.

45
docs/freqai-feature-engineering.md
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@ -8,7 +8,7 @@ Low level feature engineering is performed in the user strategy within a set of
|---------------|-------------|
| `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).
| `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."
@ -16,7 +16,7 @@ Meanwhile, high level feature engineering is handled within `"feature_parameters
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 feature_engineering_expand_all(self, dataframe, period, **kwargs):
def feature_engineering_expand_all(self, dataframe, period, metadata, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
@ -28,8 +28,13 @@ It is advisable to start from the template `feature_engineering_*` functions in
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)
"""
@ -62,7 +67,7 @@ It is advisable to start from the template `feature_engineering_*` functions in
return dataframe
def feature_engineering_expand_basic(self, dataframe, **kwargs):
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
@ -75,9 +80,14 @@ It is advisable to start from the template `feature_engineering_*` functions in
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)
"""
@ -86,7 +96,7 @@ It is advisable to start from the template `feature_engineering_*` functions in
dataframe["%-raw_price"] = dataframe["close"]
return dataframe
def feature_engineering_standard(self, dataframe, **kwargs):
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.
@ -98,22 +108,32 @@ It is advisable to start from the template `feature_engineering_*` functions in
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, **kwargs):
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"] = (
@ -161,6 +181,19 @@ You can ask for each of the defined features to be included also for informative
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.
@ -201,7 +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.

10
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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 all unused models during live runs (not relevant to backtesting). If set to false (not default), dry/live runs will accumulate all unused models to disk. If <br> **Datatype:** Boolean. <br> Default: `True`.
| `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.
@ -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`.

40
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View File

@ -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
@ -34,7 +34,7 @@ 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 `feature_engineering_*` as a typical Regressor. The difference lies in the creation of the targets, Reinforcement Learning doesnt require them. However, FreqAI requires a default (neutral) value to be set in the action column:
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 set_freqai_targets(self, dataframe, **kwargs):
@ -52,18 +52,18 @@ where `ReinforcementLearner` will use the templated `ReinforcementLearner` from
"""
# For RL, there are no direct targets to set. This is filler (neutral)
# until the agent sends an action.
df["&-action"] = 0
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():
def feature_engineering_standard(self, dataframe, **kwargs):
# The following features are necessary for RL models
informative[f"%-raw_close"] = informative["close"]
informative[f"%-raw_open"] = informative["open"]
informative[f"%-raw_high"] = informative["high"]
informative[f"%-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.
@ -175,10 +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
@ -235,14 +248,13 @@ FreqAI also provides a built in episodic summary logger called `self.tensorboard
"""
def calculate_reward(self, action: int) -> float:
if not self._is_valid(action):
self.tensorboard_log("is_valid")
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)` would add 0.23 to `float_metric`. In this case you can also disable incrementing using `inc=False` parameter.
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

19
docs/freqai-running.md
View File

@ -120,7 +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.
@ -165,20 +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.

15
docs/freqai.md
View File

@ -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
@ -68,13 +71,17 @@ 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

10
docs/hyperopt.md
View File

@ -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.
@ -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`).

1
docs/index.md
View File

@ -52,6 +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/requirements-docs.txt
View File

@ -1,6 +1,6 @@
markdown==3.3.7
mkdocs==1.4.2
mkdocs-material==9.0.5
mkdocs-material==9.1.3
mdx_truly_sane_lists==1.3
pymdown-extensions==9.9.1
pymdown-extensions==9.10
jinja2==3.1.2

9
docs/rest-api.md
View File

@ -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
View File

@ -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:

9
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
@ -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

23
docs/strategy-customization.md
View File

@ -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:
@ -1037,10 +1041,9 @@ 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()
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
docs/strategy_analysis_example.md
View File

@ -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})

43
docs/telegram-usage.md
View File

@ -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.

44
docs/utils.md
View File

@ -955,3 +955,47 @@ 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

2
freqtrade/__init__.py
View File

@ -1,5 +1,5 @@
""" Freqtrade bot """
__version__ = '2023.1.dev'
__version__ = '2023.3.dev'
if 'dev' in __version__:
from pathlib import Path

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

4
freqtrade/commands/analyze_commands.py Executable file → Normal file
View File

@ -40,8 +40,8 @@ def setup_analyze_configuration(args: Dict[str, Any], method: RunMode) -> Dict[s
if (not Path(signals_file).exists()):
raise OperationalException(
(f"Cannot find latest backtest signals file: {signals_file}."
"Run backtesting with `--export signals`.")
f"Cannot find latest backtest signals file: {signals_file}."
"Run backtesting with `--export signals`."
)
return config

17
freqtrade/commands/arguments.py
View File

@ -111,10 +111,13 @@ 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:
"""
@ -198,8 +201,8 @@ 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
@ -440,3 +443,11 @@ 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",

3
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',
),

22
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)
@ -20,15 +20,24 @@ 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")
@ -40,11 +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

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:

5
freqtrade/configuration/config_validation.py
View File

@ -27,10 +27,7 @@ def _extend_validator(validator_class):
if 'default' in subschema:
instance.setdefault(prop, subschema['default'])
for error in validate_properties(
validator, properties, instance, schema,
):
yield error
yield from validate_properties(validator, properties, instance, schema)
return validators.extend(
validator_class, {'properties': set_defaults}

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.

15
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'
@ -25,6 +25,7 @@ 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',
@ -229,6 +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}
@ -544,7 +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},
@ -566,7 +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", ]
},
@ -584,6 +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},
@ -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

13
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
@ -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.
@ -332,7 +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
@ -345,7 +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
@ -372,7 +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

29
freqtrade/data/dataprovider.py
View File

@ -9,7 +9,7 @@ from collections import deque
from datetime import datetime, timezone
from typing import Any, Dict, List, Optional, Tuple
from pandas import DataFrame, to_timedelta
from pandas import DataFrame, Timedelta, Timestamp, to_timedelta
from freqtrade.configuration import TimeRange
from freqtrade.constants import (FULL_DATAFRAME_THRESHOLD, Config, ListPairsWithTimeframes,
@ -18,6 +18,7 @@ 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
@ -206,9 +207,11 @@ class DataProvider:
existing_df, _ = self.__producer_pairs_df[producer_name][pair_key]
# CHECK FOR MISSING CANDLES
timeframe_delta = to_timedelta(timeframe) # Convert the timeframe to a timedelta for pandas
local_last = existing_df.iloc[-1]['date'] # We want the last date from our copy
incoming_first = dataframe.iloc[0]['date'] # We want the first date from the incoming
# 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]
@ -221,7 +224,7 @@ class DataProvider:
# we missed some candles between our data and the incoming
# so return False and candle_difference.
if candle_difference > 1:
return (False, candle_difference)
return (False, int(candle_difference))
if existing_df1.empty:
appended_df = dataframe
else:
@ -281,7 +284,7 @@ class DataProvider:
def historic_ohlcv(
self,
pair: str,
timeframe: str = None,
timeframe: Optional[str] = None,
candle_type: str = ''
) -> DataFrame:
"""
@ -333,7 +336,7 @@ class DataProvider:
def get_pair_dataframe(
self,
pair: str,
timeframe: str = None,
timeframe: Optional[str] = None,
candle_type: str = ''
) -> DataFrame:
"""
@ -415,16 +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:
@ -439,7 +440,7 @@ class DataProvider:
def ohlcv(
self,
pair: str,
timeframe: str = None,
timeframe: Optional[str] = None,
copy: bool = True,
candle_type: str = ''
) -> DataFrame:
@ -487,7 +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.

2
freqtrade/data/entryexitanalysis.py Executable file → Normal file
View File

@ -24,7 +24,7 @@ def _load_signal_candles(backtest_dir: Path):
scpf = Path(backtest_dir.parent / f"{backtest_dir.stem}_signals.pkl")
try:
scp = open(scpf, "rb")
with scpf.open("rb") as scp:
signal_candles = joblib.load(scp)
logger.info(f"Loaded signal candles: {str(scpf)}")
except Exception as e:

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]:
"""

8
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.
@ -418,8 +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

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()}"

2
freqtrade/exchange/__init__.py
View File

@ -17,7 +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

67
freqtrade/exchange/binance.py
View File

@ -7,7 +7,8 @@ from typing import Dict, List, Optional, Tuple
import arrow
import ccxt
from freqtrade.enums import CandleType, MarginMode, TradingMode
from freqtrade.constants import BuySell
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
@ -23,16 +24,22 @@ class Binance(Exchange):
_ft_has: Dict = {
"stoploss_on_exchange": True,
"stoploss_order_types": {"limit": "stop_loss_limit"},
"order_time_in_force": ['GTC', 'FOK', 'IOC'],
"order_time_in_force": ["GTC", "FOK", "IOC", "PO"],
"ohlcv_candle_limit": 1000,
"trades_pagination": "id",
"trades_pagination_arg": "fromId",
"l2_limit_range": [5, 10, 20, 50, 100, 500, 1000],
"ccxt_futures_name": "swap"
}
_ft_has_futures: Dict = {
"stoploss_order_types": {"limit": "stop", "market": "stop_market"},
"order_time_in_force": ["GTC", "FOK", "IOC"],
"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]] = [
@ -42,6 +49,26 @@ class Binance(Exchange):
(TradingMode.FUTURES, MarginMode.ISOLATED)
]
def _get_params(
self,
side: BuySell,
ordertype: str,
leverage: float,
reduceOnly: bool,
time_in_force: str = 'GTC',
) -> Dict:
params = super()._get_params(side, ordertype, leverage, reduceOnly, time_in_force)
if (
time_in_force == 'PO'
and ordertype != 'market'
and self.trading_mode == TradingMode.SPOT
# Only spot can do post only orders
):
params.pop('timeInForce')
params['postOnly'] = True
return params
def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Tickers:
tickers = super().get_tickers(symbols=symbols, cached=cached)
if self.trading_mode == TradingMode.FUTURES:
@ -78,33 +105,9 @@ 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
@ -150,6 +153,7 @@ 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 +163,12 @@ 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 +217,7 @@ 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:

8150
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, accept_fail: bool = False):
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',

179
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 OHLCVResponse, 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)
@ -65,7 +60,6 @@ class Exchange:
_ft_has_default: Dict = {
"stoploss_on_exchange": False,
"order_time_in_force": ["GTC"],
"time_in_force_parameter": "timeInForce",
"ohlcv_params": {},
"ohlcv_candle_limit": 500,
"ohlcv_has_history": True, # Some exchanges (Kraken) don't provide history via ohlcv
@ -74,6 +68,7 @@ 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",
@ -606,12 +601,27 @@ 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 +692,7 @@ 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 +850,7 @@ 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 +860,33 @@ 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 +908,22 @@ 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 +951,20 @@ 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 +972,8 @@ 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 +981,7 @@ 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'],
@ -992,10 +1018,10 @@ class Exchange:
# Order handling
def _lev_prep(self, pair: str, leverage: float, side: BuySell):
def _lev_prep(self, pair: str, leverage: float, side: BuySell, accept_fail: bool = False):
if self.trading_mode != TradingMode.SPOT:
self.set_margin_mode(pair, self.margin_mode)
self._set_leverage(leverage, pair)
self.set_margin_mode(pair, self.margin_mode, accept_fail)
self._set_leverage(leverage, pair, accept_fail)
def _get_params(
self,
@ -1007,8 +1033,7 @@ class Exchange:
) -> Dict:
params = self._params.copy()
if time_in_force != 'GTC' and ordertype != 'market':
param = self._ft_has.get('time_in_force_parameter', '')
params.update({param: time_in_force.upper()})
params.update({'timeInForce': time_in_force.upper()})
if reduceOnly:
params.update({'reduceOnly': True})
return params
@ -1060,7 +1085,7 @@ 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
@ -1110,8 +1135,15 @@ class Exchange:
"sell" else (stop_price >= limit_rate))
# 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')
# This can for example happen if the stop / liquidation price is set to 0
# Which is possible if a market-order closes right away.
# The InvalidOrderException will bubble up to exit_positions, where it will be
# handled gracefully.
raise InvalidOrderException(
"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:
@ -1121,7 +1153,7 @@ class Exchange:
return params
@retrier(retries=0)
def stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict,
def create_stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict,
side: BuySell, leverage: float) -> Dict:
"""
creates a stoploss order.
@ -1167,10 +1199,14 @@ 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))
self._lev_prep(pair, leverage, side)
self._lev_prep(pair, leverage, side, accept_fail=True)
order = self._api.create_order(symbol=pair, type=ordertype, side=side,
amount=amount, price=limit_rate, params=params)
self._log_exchange_response('create_stoploss_order', order)
@ -1357,7 +1393,7 @@ 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 +1533,7 @@ 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 +1576,7 @@ 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 +1614,8 @@ 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 "
@ -1801,7 +1838,7 @@ 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.
@ -1930,7 +1967,8 @@ 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)
@ -1984,9 +2022,9 @@ class Exchange:
continue
# 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
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
@ -2003,7 +2041,9 @@ 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(
@ -2491,7 +2531,7 @@ class Exchange:
self,
leverage: float,
pair: Optional[str] = None,
trading_mode: Optional[TradingMode] = None
accept_fail: bool = False,
):
"""
Set's the leverage before making a trade, in order to not
@ -2500,12 +2540,18 @@ 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, ccxt.InsufficientFunds) 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
@ -2527,7 +2573,8 @@ 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")
@ -2541,6 +2588,10 @@ 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
@ -2674,7 +2725,7 @@ 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']:
@ -2694,6 +2745,7 @@ 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
@ -2707,14 +2759,15 @@ 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,
@ -2724,16 +2777,16 @@ 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
@ -2744,6 +2797,7 @@ 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
@ -2751,7 +2805,7 @@ class Exchange:
"""
Important: Must be fetching data from cached values as this is used by backtesting!
PERPETUAL:
gateio: https://www.gate.io/help/futures/futures/27724/liquidation-price-bankruptcy-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:
@ -2765,13 +2819,14 @@ class Exchange:
: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:
"""

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")

17
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,
@ -67,8 +75,7 @@ class Gateio(Exchange):
)
if ordertype == 'market' and self.trading_mode == TradingMode.FUTURES:
params['type'] = 'market'
param = self._ft_has.get('time_in_force_parameter', '')
params.update({param: 'IOC'})
params.update({'timeInForce': 'IOC'})
return params
def get_trades_for_order(self, order_id: str, pair: str, since: datetime,
@ -77,7 +84,7 @@ 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"}
}

4
freqtrade/exchange/kraken.py
View File

@ -97,7 +97,7 @@ class Kraken(Exchange):
))
@retrier(retries=0)
def stoploss(self, pair: str, amount: float, stop_price: float,
def create_stoploss(self, pair: str, amount: float, stop_price: float,
order_types: Dict, side: BuySell, leverage: float) -> Dict:
"""
Creates a stoploss market order.
@ -158,7 +158,7 @@ class Kraken(Exchange):
self,
leverage: float,
pair: Optional[str] = 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

97
freqtrade/exchange/okx.py
View File

@ -1,13 +1,16 @@
import logging
from typing import Dict, List, Optional, Tuple
from typing import Any, Dict, List, Optional, Tuple
import ccxt
from freqtrade.constants import BuySell
from freqtrade.enums import CandleType, MarginMode, TradingMode
from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
from freqtrade.enums.pricetype import PriceType
from freqtrade.exceptions import (DDosProtection, OperationalException, RetryableOrderError,
TemporaryError)
from freqtrade.exchange import Exchange, date_minus_candles
from freqtrade.exchange.common import retrier
from freqtrade.misc import safe_value_fallback2
logger = logging.getLogger(__name__)
@ -23,10 +26,18 @@ class Okx(Exchange):
"ohlcv_candle_limit": 100, # Warning, special case with data prior to X months
"mark_ohlcv_timeframe": "4h",
"funding_fee_timeframe": "8h",
"stoploss_order_types": {"limit": "limit"},
"stoploss_on_exchange": True,
}
_ft_has_futures: Dict = {
"tickers_have_quoteVolume": False,
"fee_cost_in_contracts": True,
"stop_price_type_field": "slTriggerPxType",
"stop_price_type_value_mapping": {
PriceType.LAST: "last",
PriceType.MARK: "index",
PriceType.INDEX: "mark",
},
}
_supported_trading_mode_margin_pairs: List[Tuple[TradingMode, MarginMode]] = [
@ -114,17 +125,18 @@ class Okx(Exchange):
return params
@retrier
def _lev_prep(self, pair: str, leverage: float, side: BuySell):
def _lev_prep(self, pair: str, leverage: float, side: BuySell, accept_fail: bool = False):
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:
@ -148,3 +160,78 @@ class Okx(Exchange):
pair_tiers = self._leverage_tiers[pair]
return pair_tiers[-1]['maxNotional'] / leverage
def _get_stop_params(self, side: BuySell, ordertype: str, stop_price: float) -> Dict:
params = self._params.copy()
# Verify if stopPrice works for your exchange!
params.update({'stopLossPrice': stop_price})
if self.trading_mode == TradingMode.FUTURES and self.margin_mode:
params['tdMode'] = self.margin_mode.value
params['posSide'] = self._get_posSide(side, True)
return params
def stoploss_adjust(self, stop_loss: float, order: Dict, side: str) -> bool:
"""
OKX uses non-default stoploss price naming.
"""
if not self._ft_has.get('stoploss_on_exchange'):
raise OperationalException(f"stoploss is not implemented for {self.name}.")
return (
order.get('stopLossPrice', None) is None
or ((side == "sell" and stop_loss > float(order['stopLossPrice'])) or
(side == "buy" and stop_loss < float(order['stopLossPrice'])))
)
def fetch_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
if self._config['dry_run']:
return self.fetch_dry_run_order(order_id)
try:
params1 = {'stop': True}
order_reg = self._api.fetch_order(order_id, pair, params=params1)
self._log_exchange_response('fetch_stoploss_order', order_reg)
return order_reg
except ccxt.OrderNotFound:
pass
params2 = {'stop': True, 'ordType': 'conditional'}
for method in (self._api.fetch_open_orders, self._api.fetch_closed_orders,
self._api.fetch_canceled_orders):
try:
orders = method(pair, params=params2)
orders_f = [order for order in orders if order['id'] == order_id]
if orders_f:
order = orders_f[0]
if (order['status'] == 'closed'
and (real_order_id := order.get('info', {}).get('ordId')) is not None):
# Once a order triggered, we fetch the regular followup order.
order_reg = self.fetch_order(real_order_id, pair)
self._log_exchange_response('fetch_stoploss_order1', order_reg)
order_reg['id_stop'] = order_reg['id']
order_reg['id'] = order_id
order_reg['type'] = 'stoploss'
order_reg['status_stop'] = 'triggered'
return order_reg
order['type'] = 'stoploss'
return order
except ccxt.BaseError:
pass
raise RetryableOrderError(
f'StoplossOrder not found (pair: {pair} id: {order_id}).')
def get_order_id_conditional(self, order: Dict[str, Any]) -> str:
if order['type'] == 'stop':
return safe_value_fallback2(order, order, 'id_stop', 'id')
return order['id']
def cancel_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
params1 = {'stop': True}
# 'ordType': 'conditional'
#
return self.cancel_order(
order_id=order_id,
pair=pair,
params=params1,
)

9
freqtrade/exchange/types.py
View File

@ -15,6 +15,15 @@ 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?,

2
freqtrade/freqai/RL/Base3ActionRLEnv.py
View File

@ -47,7 +47,7 @@ class Base3ActionRLEnv(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])
self.tensorboard_log(self.actions._member_names_[action], category="actions")
trade_type = None
if self.is_tradesignal(action):

2
freqtrade/freqai/RL/Base4ActionRLEnv.py
View File

@ -48,7 +48,7 @@ class Base4ActionRLEnv(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])
self.tensorboard_log(self.actions._member_names_[action], category="actions")
trade_type = None
if self.is_tradesignal(action):

2
freqtrade/freqai/RL/Base5ActionRLEnv.py
View File

@ -49,7 +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])
self.tensorboard_log(self.actions._member_names_[action], category="actions")
trade_type = None
if self.is_tradesignal(action):

56
freqtrade/freqai/RL/BaseEnvironment.py
View File

@ -45,7 +45,8 @@ class BaseEnvironment(gym.Env):
def __init__(self, df: DataFrame = DataFrame(), prices: DataFrame = DataFrame(),
reward_kwargs: dict = {}, window_size=10, starting_point=True,
id: str = 'baseenv-1', seed: int = 1, config: dict = {}, live: bool = False,
fee: float = 0.0015, can_short: bool = False):
fee: float = 0.0015, can_short: bool = False, pair: str = "",
df_raw: DataFrame = DataFrame()):
"""
Initializes the training/eval environment.
:param df: dataframe of features
@ -60,12 +61,14 @@ class BaseEnvironment(gym.Env):
:param fee: The fee to use for environmental interactions.
:param can_short: Whether or not the environment can short
"""
self.config = config
self.rl_config = config['freqai']['rl_config']
self.add_state_info = self.rl_config.get('add_state_info', False)
self.id = id
self.max_drawdown = 1 - self.rl_config.get('max_training_drawdown_pct', 0.8)
self.compound_trades = config['stake_amount'] == 'unlimited'
self.config: dict = config
self.rl_config: dict = config['freqai']['rl_config']
self.add_state_info: bool = self.rl_config.get('add_state_info', False)
self.id: str = id
self.max_drawdown: float = 1 - self.rl_config.get('max_training_drawdown_pct', 0.8)
self.compound_trades: bool = config['stake_amount'] == 'unlimited'
self.pair: str = pair
self.raw_features: DataFrame = df_raw
if self.config.get('fee', None) is not None:
self.fee = self.config['fee']
else:
@ -74,8 +77,8 @@ class BaseEnvironment(gym.Env):
# set here to default 5Ac, but all children envs can override this
self.actions: Type[Enum] = BaseActions
self.tensorboard_metrics: dict = {}
self.can_short = can_short
self.live = live
self.can_short: bool = can_short
self.live: bool = live
if not self.live and self.add_state_info:
self.add_state_info = False
logger.warning("add_state_info is not available in backtesting. Deactivating.")
@ -93,13 +96,12 @@ class BaseEnvironment(gym.Env):
:param reward_kwargs: extra config settings assigned by user in `rl_config`
:param starting_point: start at edge of window or not
"""
self.df = df
self.signal_features = self.df
self.prices = prices
self.window_size = window_size
self.starting_point = starting_point
self.rr = reward_kwargs["rr"]
self.profit_aim = reward_kwargs["profit_aim"]
self.signal_features: DataFrame = df
self.prices: DataFrame = prices
self.window_size: int = window_size
self.starting_point: bool = starting_point
self.rr: float = reward_kwargs["rr"]
self.profit_aim: float = reward_kwargs["profit_aim"]
# # spaces
if self.add_state_info:
@ -135,7 +137,8 @@ class BaseEnvironment(gym.Env):
self.np_random, seed = seeding.np_random(seed)
return [seed]
def tensorboard_log(self, metric: str, value: Union[int, float] = 1, inc: bool = True):
def tensorboard_log(self, metric: str, value: Optional[Union[int, float]] = None,
inc: Optional[bool] = None, category: str = "custom"):
"""
Function builds the tensorboard_metrics dictionary
to be parsed by the TensorboardCallback. This
@ -147,17 +150,24 @@ class BaseEnvironment(gym.Env):
def calculate_reward(self, action: int) -> float:
if not self._is_valid(action):
self.tensorboard_log("is_valid")
self.tensorboard_log("invalid")
return -2
:param metric: metric to be tracked and incremented
:param value: value to increment `metric` by
:param inc: sets whether the `value` is incremented or not
:param value: `metric` value
:param inc: (deprecated) sets whether the `value` is incremented or not
:param category: `metric` category
"""
if not inc or metric not in self.tensorboard_metrics:
self.tensorboard_metrics[metric] = value
increment = True if value is None else False
value = 1 if increment else value
if category not in self.tensorboard_metrics:
self.tensorboard_metrics[category] = {}
if not increment or metric not in self.tensorboard_metrics[category]:
self.tensorboard_metrics[category][metric] = value
else:
self.tensorboard_metrics[metric] += value
self.tensorboard_metrics[category][metric] += value
def reset_tensorboard_log(self):
self.tensorboard_metrics = {}

53
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
View File

@ -1,3 +1,4 @@
import copy
import importlib
import logging
from abc import abstractmethod
@ -50,6 +51,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
self.eval_callback: Optional[EvalCallback] = None
self.model_type = self.freqai_info['rl_config']['model_type']
self.rl_config = self.freqai_info['rl_config']
self.df_raw: DataFrame = DataFrame()
self.continual_learning = self.freqai_info.get('continual_learning', False)
if self.model_type in SB3_MODELS:
import_str = 'stable_baselines3'
@ -107,10 +109,12 @@ class BaseReinforcementLearningModel(IFreqaiModel):
data_dictionary: Dict[str, Any] = dk.make_train_test_datasets(
features_filtered, labels_filtered)
self.df_raw = copy.deepcopy(data_dictionary["train_features"])
dk.fit_labels() # FIXME useless for now, but just satiating append methods
# normalize all data based on train_dataset only
prices_train, prices_test = self.build_ohlc_price_dataframes(dk.data_dictionary, pair, dk)
data_dictionary = dk.normalize_data(data_dictionary)
# data cleaning/analysis
@ -143,14 +147,10 @@ class BaseReinforcementLearningModel(IFreqaiModel):
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
env_info = self.pack_env_dict()
env_info = self.pack_env_dict(dk.pair)
self.train_env = self.MyRLEnv(df=train_df,
prices=prices_train,
**env_info)
self.eval_env = Monitor(self.MyRLEnv(df=test_df,
prices=prices_test,
**env_info))
self.train_env = self.MyRLEnv(df=train_df, prices=prices_train, **env_info)
self.eval_env = Monitor(self.MyRLEnv(df=test_df, prices=prices_test, **env_info))
self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
render=False, eval_freq=len(train_df),
best_model_save_path=str(dk.data_path))
@ -158,7 +158,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
actions = self.train_env.get_actions()
self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)
def pack_env_dict(self) -> Dict[str, Any]:
def pack_env_dict(self, pair: str) -> Dict[str, Any]:
"""
Create dictionary of environment arguments
"""
@ -166,7 +166,9 @@ class BaseReinforcementLearningModel(IFreqaiModel):
"reward_kwargs": self.reward_params,
"config": self.config,
"live": self.live,
"can_short": self.can_short}
"can_short": self.can_short,
"pair": pair,
"df_raw": self.df_raw}
if self.data_provider:
env_info["fee"] = self.data_provider._exchange \
.get_fee(symbol=self.data_provider.current_whitelist()[0]) # type: ignore
@ -233,6 +235,9 @@ class BaseReinforcementLearningModel(IFreqaiModel):
filtered_dataframe, _ = dk.filter_features(
unfiltered_df, dk.training_features_list, training_filter=False
)
filtered_dataframe = self.drop_ohlc_from_df(filtered_dataframe, dk)
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
dk.data_dictionary["prediction_features"] = filtered_dataframe
@ -280,7 +285,6 @@ class BaseReinforcementLearningModel(IFreqaiModel):
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
# %-raw_volume_gen_shift-2_ETH/USDT_1h
# price data for model training and evaluation
tf = self.config['timeframe']
rename_dict = {'%-raw_open': 'open', '%-raw_low': 'low',
@ -313,8 +317,24 @@ class BaseReinforcementLearningModel(IFreqaiModel):
prices_test.rename(columns=rename_dict, inplace=True)
prices_test.reset_index(drop=True)
train_df = self.drop_ohlc_from_df(train_df, dk)
test_df = self.drop_ohlc_from_df(test_df, dk)
return prices_train, prices_test
def drop_ohlc_from_df(self, df: DataFrame, dk: FreqaiDataKitchen):
"""
Given a dataframe, drop the ohlc data
"""
drop_list = ['%-raw_open', '%-raw_low', '%-raw_high', '%-raw_close']
if self.rl_config["drop_ohlc_from_features"]:
df.drop(drop_list, axis=1, inplace=True)
feature_list = dk.training_features_list
dk.training_features_list = [e for e in feature_list if e not in drop_list]
return df
def load_model_from_disk(self, dk: FreqaiDataKitchen) -> Any:
"""
Can be used by user if they are trying to limit_ram_usage *and*
@ -347,7 +367,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
sets a custom reward based on profit and trade duration.
"""
def calculate_reward(self, action: int) -> float:
def calculate_reward(self, action: int) -> float: # noqa: C901
"""
An example reward function. This is the one function that users will likely
wish to inject their own creativity into.
@ -363,10 +383,19 @@ class BaseReinforcementLearningModel(IFreqaiModel):
pnl = self.get_unrealized_profit()
factor = 100.
# you can use feature values from dataframe
rsi_now = self.raw_features[f"%-rsi-period-10_shift-1_{self.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 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

16
freqtrade/freqai/RL/TensorboardCallback.py
View File

@ -13,7 +13,7 @@ class TensorboardCallback(BaseCallback):
episodic summary reports.
"""
def __init__(self, verbose=1, actions: Type[Enum] = BaseActions):
super(TensorboardCallback, self).__init__(verbose)
super().__init__(verbose)
self.model: Any = None
self.logger = None # type: Any
self.training_env: BaseEnvironment = None # type: ignore
@ -46,14 +46,12 @@ class TensorboardCallback(BaseCallback):
local_info = self.locals["infos"][0]
tensorboard_metrics = self.training_env.get_attr("tensorboard_metrics")[0]
for info in local_info:
if info not in ["episode", "terminal_observation"]:
self.logger.record(f"_info/{info}", local_info[info])
for metric in local_info:
if metric not in ["episode", "terminal_observation"]:
self.logger.record(f"info/{metric}", local_info[metric])
for info in tensorboard_metrics:
if info in [action.name for action in self.actions]:
self.logger.record(f"_actions/{info}", tensorboard_metrics[info])
else:
self.logger.record(f"_custom/{info}", tensorboard_metrics[info])
for category in tensorboard_metrics:
for metric in tensorboard_metrics[category]:
self.logger.record(f"{category}/{metric}", tensorboard_metrics[category][metric])
return True

142
freqtrade/freqai/data_drawer.py
View File

@ -59,7 +59,7 @@ class FreqaiDataDrawer:
Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert
"""
def __init__(self, full_path: Path, config: Config, follow_mode: bool = False):
def __init__(self, full_path: Path, config: Config):
self.config = config
self.freqai_info = config.get("freqai", {})
@ -72,21 +72,13 @@ class FreqaiDataDrawer:
self.model_return_values: Dict[str, DataFrame] = {}
self.historic_data: Dict[str, Dict[str, DataFrame]] = {}
self.historic_predictions: Dict[str, DataFrame] = {}
self.follower_dict: Dict[str, pair_info] = {}
self.full_path = full_path
self.follower_name: str = self.config.get("bot_name", "follower1")
self.follower_dict_path = Path(
self.full_path / f"follower_dictionary-{self.follower_name}.json"
)
self.historic_predictions_path = Path(self.full_path / "historic_predictions.pkl")
self.historic_predictions_bkp_path = Path(
self.full_path / "historic_predictions.backup.pkl")
self.pair_dictionary_path = Path(self.full_path / "pair_dictionary.json")
self.global_metadata_path = Path(self.full_path / "global_metadata.json")
self.metric_tracker_path = Path(self.full_path / "metric_tracker.json")
self.follow_mode = follow_mode
if follow_mode:
self.create_follower_dict()
self.load_drawer_from_disk()
self.load_historic_predictions_from_disk()
self.metric_tracker: Dict[str, Dict[str, Dict[str, list]]] = {}
@ -134,7 +126,7 @@ class FreqaiDataDrawer:
"""
exists = self.global_metadata_path.is_file()
if exists:
with open(self.global_metadata_path, "r") as fp:
with self.global_metadata_path.open("r") as fp:
metatada_dict = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
return metatada_dict
return {}
@ -147,15 +139,10 @@ class FreqaiDataDrawer:
"""
exists = self.pair_dictionary_path.is_file()
if exists:
with open(self.pair_dictionary_path, "r") as fp:
with self.pair_dictionary_path.open("r") as fp:
self.pair_dict = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
elif not self.follow_mode:
logger.info("Could not find existing datadrawer, starting from scratch")
else:
logger.warning(
f"Follower could not find pair_dictionary at {self.full_path} "
"sending null values back to strategy"
)
logger.info("Could not find existing datadrawer, starting from scratch")
def load_metric_tracker_from_disk(self):
"""
@ -165,7 +152,7 @@ class FreqaiDataDrawer:
if self.freqai_info.get('write_metrics_to_disk', False):
exists = self.metric_tracker_path.is_file()
if exists:
with open(self.metric_tracker_path, "r") as fp:
with self.metric_tracker_path.open("r") as fp:
self.metric_tracker = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
logger.info("Loading existing metric tracker from disk.")
else:
@ -179,7 +166,7 @@ class FreqaiDataDrawer:
exists = self.historic_predictions_path.is_file()
if exists:
try:
with open(self.historic_predictions_path, "rb") as fp:
with self.historic_predictions_path.open("rb") as fp:
self.historic_predictions = cloudpickle.load(fp)
logger.info(
f"Found existing historic predictions at {self.full_path}, but beware "
@ -189,17 +176,12 @@ class FreqaiDataDrawer:
except EOFError:
logger.warning(
'Historical prediction file was corrupted. Trying to load backup file.')
with open(self.historic_predictions_bkp_path, "rb") as fp:
with self.historic_predictions_bkp_path.open("rb") as fp:
self.historic_predictions = cloudpickle.load(fp)
logger.warning('FreqAI successfully loaded the backup historical predictions file.')
elif not self.follow_mode:
logger.info("Could not find existing historic_predictions, starting from scratch")
else:
logger.warning(
f"Follower could not find historic predictions at {self.full_path} "
"sending null values back to strategy"
)
logger.info("Could not find existing historic_predictions, starting from scratch")
return exists
@ -207,7 +189,7 @@ class FreqaiDataDrawer:
"""
Save historic predictions pickle to disk
"""
with open(self.historic_predictions_path, "wb") as fp:
with self.historic_predictions_path.open("wb") as fp:
cloudpickle.dump(self.historic_predictions, fp, protocol=cloudpickle.DEFAULT_PROTOCOL)
# create a backup
@ -218,58 +200,33 @@ class FreqaiDataDrawer:
Save metric tracker of all pair metrics collected.
"""
with self.save_lock:
with open(self.metric_tracker_path, 'w') as fp:
with self.metric_tracker_path.open('w') as fp:
rapidjson.dump(self.metric_tracker, fp, default=self.np_encoder,
number_mode=rapidjson.NM_NATIVE)
def save_drawer_to_disk(self):
def save_drawer_to_disk(self) -> None:
"""
Save data drawer full of all pair model metadata in present model folder.
"""
with self.save_lock:
with open(self.pair_dictionary_path, 'w') as fp:
with self.pair_dictionary_path.open('w') as fp:
rapidjson.dump(self.pair_dict, fp, default=self.np_encoder,
number_mode=rapidjson.NM_NATIVE)
def save_follower_dict_to_disk(self):
"""
Save follower dictionary to disk (used by strategy for persistent prediction targets)
"""
with open(self.follower_dict_path, "w") as fp:
rapidjson.dump(self.follower_dict, fp, default=self.np_encoder,
number_mode=rapidjson.NM_NATIVE)
def save_global_metadata_to_disk(self, metadata: Dict[str, Any]):
"""
Save global metadata json to disk
"""
with self.save_lock:
with open(self.global_metadata_path, 'w') as fp:
with self.global_metadata_path.open('w') as fp:
rapidjson.dump(metadata, fp, default=self.np_encoder,
number_mode=rapidjson.NM_NATIVE)
def create_follower_dict(self):
"""
Create or dictionary for each follower to maintain unique persistent prediction targets
"""
whitelist_pairs = self.config.get("exchange", {}).get("pair_whitelist")
exists = self.follower_dict_path.is_file()
if exists:
logger.info("Found an existing follower dictionary")
for pair in whitelist_pairs:
self.follower_dict[pair] = {}
self.save_follower_dict_to_disk()
def np_encoder(self, object):
if isinstance(object, np.generic):
return object.item()
def get_pair_dict_info(self, pair: str) -> Tuple[str, int, bool]:
def get_pair_dict_info(self, pair: str) -> Tuple[str, int]:
"""
Locate and load existing model metadata from persistent storage. If not located,
create a new one and append the current pair to it and prepare it for its first
@ -278,32 +235,19 @@ class FreqaiDataDrawer:
:return:
model_filename: str = unique filename used for loading persistent objects from disk
trained_timestamp: int = the last time the coin was trained
return_null_array: bool = Follower could not find pair metadata
"""
pair_dict = self.pair_dict.get(pair)
data_path_set = self.pair_dict.get(pair, self.empty_pair_dict).get("data_path", "")
return_null_array = False
if pair_dict:
model_filename = pair_dict["model_filename"]
trained_timestamp = pair_dict["trained_timestamp"]
elif not self.follow_mode:
else:
self.pair_dict[pair] = self.empty_pair_dict.copy()
model_filename = ""
trained_timestamp = 0
if not data_path_set and self.follow_mode:
logger.warning(
f"Follower could not find current pair {pair} in "
f"pair_dictionary at path {self.full_path}, sending null values "
"back to strategy."
)
trained_timestamp = 0
model_filename = ''
return_null_array = True
return model_filename, trained_timestamp, return_null_array
return model_filename, trained_timestamp
def set_pair_dict_info(self, metadata: dict) -> None:
pair_in_dict = self.pair_dict.get(metadata["pair"])
@ -311,7 +255,6 @@ class FreqaiDataDrawer:
return
else:
self.pair_dict[metadata["pair"]] = self.empty_pair_dict.copy()
return
def set_initial_return_values(self, pair: str, pred_df: DataFrame) -> None:
@ -423,6 +366,12 @@ class FreqaiDataDrawer:
def purge_old_models(self) -> None:
num_keep = self.freqai_info["purge_old_models"]
if not num_keep:
return
elif type(num_keep) == bool:
num_keep = 2
model_folders = [x for x in self.full_path.iterdir() if x.is_dir()]
pattern = re.compile(r"sub-train-(\w+)_(\d{10})")
@ -445,11 +394,11 @@ class FreqaiDataDrawer:
delete_dict[coin]["timestamps"][int(timestamp)] = dir
for coin in delete_dict:
if delete_dict[coin]["num_folders"] > 2:
if delete_dict[coin]["num_folders"] > num_keep:
sorted_dict = collections.OrderedDict(
sorted(delete_dict[coin]["timestamps"].items())
)
num_delete = len(sorted_dict) - 2
num_delete = len(sorted_dict) - num_keep
deleted = 0
for k, v in sorted_dict.items():
if deleted >= num_delete:
@ -458,12 +407,6 @@ class FreqaiDataDrawer:
shutil.rmtree(v)
deleted += 1
def update_follower_metadata(self):
# follower needs to load from disk to get any changes made by leader to pair_dict
self.load_drawer_from_disk()
if self.config.get("freqai", {}).get("purge_old_models", False):
self.purge_old_models()
def save_metadata(self, dk: FreqaiDataKitchen) -> None:
"""
Saves only metadata for backtesting studies if user prefers
@ -481,7 +424,7 @@ class FreqaiDataDrawer:
dk.data["training_features_list"] = list(dk.data_dictionary["train_features"].columns)
dk.data["label_list"] = dk.label_list
with open(save_path / f"{dk.model_filename}_metadata.json", "w") as fp:
with (save_path / f"{dk.model_filename}_metadata.json").open("w") as fp:
rapidjson.dump(dk.data, fp, default=self.np_encoder, number_mode=rapidjson.NM_NATIVE)
return
@ -514,7 +457,7 @@ class FreqaiDataDrawer:
dk.data["training_features_list"] = dk.training_features_list
dk.data["label_list"] = dk.label_list
# store the metadata
with open(save_path / f"{dk.model_filename}_metadata.json", "w") as fp:
with (save_path / f"{dk.model_filename}_metadata.json").open("w") as fp:
rapidjson.dump(dk.data, fp, default=self.np_encoder, number_mode=rapidjson.NM_NATIVE)
# save the train data to file so we can check preds for area of applicability later
@ -528,7 +471,7 @@ class FreqaiDataDrawer:
if self.freqai_info["feature_parameters"].get("principal_component_analysis"):
cloudpickle.dump(
dk.pca, open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "wb")
dk.pca, (dk.data_path / f"{dk.model_filename}_pca_object.pkl").open("wb")
)
self.model_dictionary[coin] = model
@ -548,7 +491,7 @@ class FreqaiDataDrawer:
Load only metadata into datakitchen to increase performance during
presaved backtesting (prediction file loading).
"""
with open(dk.data_path / f"{dk.model_filename}_metadata.json", "r") as fp:
with (dk.data_path / f"{dk.model_filename}_metadata.json").open("r") as fp:
dk.data = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
dk.training_features_list = dk.data["training_features_list"]
dk.label_list = dk.data["label_list"]
@ -571,7 +514,7 @@ class FreqaiDataDrawer:
dk.data = self.meta_data_dictionary[coin]["meta_data"]
dk.data_dictionary["train_features"] = self.meta_data_dictionary[coin]["train_df"]
else:
with open(dk.data_path / f"{dk.model_filename}_metadata.json", "r") as fp:
with (dk.data_path / f"{dk.model_filename}_metadata.json").open("r") as fp:
dk.data = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
dk.data_dictionary["train_features"] = pd.read_pickle(
@ -609,7 +552,7 @@ class FreqaiDataDrawer:
if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]:
dk.pca = cloudpickle.load(
open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "rb")
(dk.data_path / f"{dk.model_filename}_pca_object.pkl").open("rb")
)
return model
@ -627,12 +570,12 @@ class FreqaiDataDrawer:
for pair in dk.all_pairs:
for tf in feat_params.get("include_timeframes"):
hist_df = history_data[pair][tf]
# check if newest candle is already appended
df_dp = strategy.dp.get_pair_dataframe(pair, tf)
if len(df_dp.index) == 0:
continue
if str(history_data[pair][tf].iloc[-1]["date"]) == str(
if str(hist_df.iloc[-1]["date"]) == str(
df_dp.iloc[-1:]["date"].iloc[-1]
):
continue
@ -640,21 +583,30 @@ class FreqaiDataDrawer:
try:
index = (
df_dp.loc[
df_dp["date"] == history_data[pair][tf].iloc[-1]["date"]
df_dp["date"] == hist_df.iloc[-1]["date"]
].index[0]
+ 1
)
except IndexError:
if hist_df.iloc[-1]['date'] < df_dp['date'].iloc[0]:
raise OperationalException("In memory historical data is older than "
f"oldest DataProvider candle for {pair} on "
f"timeframe {tf}")
else:
index = -1
logger.warning(
f"Unable to update pair history for {pair}. "
"If this does not resolve itself after 1 additional candle, "
"please report the error to #freqai discord channel"
f"No common dates in historical data and dataprovider for {pair}. "
f"Appending latest dataprovider candle to historical data "
"but please be aware that there is likely a gap in the historical "
"data. \n"
f"Historical data ends at {hist_df.iloc[-1]['date']} "
f"while dataprovider starts at {df_dp['date'].iloc[0]} and"
f"ends at {df_dp['date'].iloc[0]}."
)
return
history_data[pair][tf] = pd.concat(
[
history_data[pair][tf],
hist_df,
df_dp.iloc[index:],
],
ignore_index=True,

151
freqtrade/freqai/data_kitchen.py
View File

@ -1,11 +1,12 @@
import copy
import inspect
import logging
import random
import shutil
from datetime import datetime, timezone
from math import cos, sin
from pathlib import Path
from typing import Any, Dict, List, Tuple
from typing import Any, Dict, List, Optional, Tuple
import numpy as np
import numpy.typing as npt
@ -112,7 +113,7 @@ class FreqaiDataKitchen:
def set_paths(
self,
pair: str,
trained_timestamp: int = None,
trained_timestamp: Optional[int] = None,
) -> None:
"""
Set the paths to the data for the present coin/botloop
@ -170,6 +171,19 @@ class FreqaiDataKitchen:
train_labels = labels
train_weights = weights
if feat_dict["shuffle_after_split"]:
rint1 = random.randint(0, 100)
rint2 = random.randint(0, 100)
train_features = train_features.sample(
frac=1, random_state=rint1).reset_index(drop=True)
train_labels = train_labels.sample(frac=1, random_state=rint1).reset_index(drop=True)
train_weights = pd.DataFrame(train_weights).sample(
frac=1, random_state=rint1).reset_index(drop=True).to_numpy()[:, 0]
test_features = test_features.sample(frac=1, random_state=rint2).reset_index(drop=True)
test_labels = test_labels.sample(frac=1, random_state=rint2).reset_index(drop=True)
test_weights = pd.DataFrame(test_weights).sample(
frac=1, random_state=rint2).reset_index(drop=True).to_numpy()[:, 0]
# Simplest way to reverse the order of training and test data:
if self.freqai_config['feature_parameters'].get('reverse_train_test_order', False):
return self.build_data_dictionary(
@ -237,7 +251,7 @@ class FreqaiDataKitchen:
(drop_index == 0) & (drop_index_labels == 0)
]
logger.info(
f"dropped {len(unfiltered_df) - len(filtered_df)} training points"
f"{self.pair}: dropped {len(unfiltered_df) - len(filtered_df)} training points"
f" due to NaNs in populated dataset {len(unfiltered_df)}."
)
if (1 - len(filtered_df) / len(unfiltered_df)) > 0.1 and self.live:
@ -661,7 +675,7 @@ class FreqaiDataKitchen:
]
logger.info(
f"SVM tossed {len(y_pred) - kept_points.sum()}"
f"{self.pair}: SVM tossed {len(y_pred) - kept_points.sum()}"
f" test points from {len(y_pred)} total points."
)
@ -935,7 +949,7 @@ class FreqaiDataKitchen:
if (len(do_predict) - do_predict.sum()) > 0:
logger.info(
f"DI tossed {len(do_predict) - do_predict.sum()} predictions for "
f"{self.pair}: DI tossed {len(do_predict) - do_predict.sum()} predictions for "
"being too far from training data."
)
@ -1247,17 +1261,19 @@ class FreqaiDataKitchen:
tfs: List[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
for tf in tfs:
metadata = {"pair": pair, "tf": tf}
informative_df = self.get_pair_data_for_features(
pair, tf, strategy, corr_dataframes, base_dataframes, is_corr_pairs)
informative_copy = informative_df.copy()
for t in self.freqai_config["feature_parameters"]["indicator_periods_candles"]:
df_features = strategy.feature_engineering_expand_all(
informative_copy.copy(), t)
informative_copy.copy(), t, metadata=metadata)
suffix = f"{t}"
informative_df = self.merge_features(informative_df, df_features, tf, tf, suffix)
generic_df = strategy.feature_engineering_expand_basic(informative_copy.copy())
generic_df = strategy.feature_engineering_expand_basic(
informative_copy.copy(), metadata=metadata)
suffix = "gen"
informative_df = self.merge_features(informative_df, generic_df, tf, tf, suffix)
@ -1299,11 +1315,21 @@ class FreqaiDataKitchen:
dataframe: DataFrame = dataframe containing populated indicators
"""
# this is a hack to check if the user is using the populate_any_indicators function
# check if the user is using the deprecated populate_any_indicators function
new_version = inspect.getsource(strategy.populate_any_indicators) == (
inspect.getsource(IStrategy.populate_any_indicators))
if new_version:
if not new_version:
raise OperationalException(
"You are using the `populate_any_indicators()` function"
" which was deprecated on March 1, 2023. Please refer "
"to the strategy migration guide to use the new "
"feature_engineering_* methods: \n"
"https://www.freqtrade.io/en/stable/strategy_migration/#freqai-strategy \n"
"And the feature_engineering_* documentation: \n"
"https://www.freqtrade.io/en/latest/freqai-feature-engineering/"
)
tfs: List[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
pairs: List[str] = self.freqai_config["feature_parameters"].get(
"include_corr_pairlist", [])
@ -1326,8 +1352,8 @@ class FreqaiDataKitchen:
"include_corr_pairlist", [])
dataframe = self.populate_features(dataframe.copy(), pair, strategy,
corr_dataframes, base_dataframes)
dataframe = strategy.feature_engineering_standard(dataframe.copy())
metadata = {"pair": pair}
dataframe = strategy.feature_engineering_standard(dataframe.copy(), metadata=metadata)
# ensure corr pairs are always last
for corr_pair in corr_pairs:
if pair == corr_pair:
@ -1336,86 +1362,7 @@ class FreqaiDataKitchen:
dataframe = self.populate_features(dataframe.copy(), corr_pair, strategy,
corr_dataframes, base_dataframes, True)
dataframe = strategy.set_freqai_targets(dataframe.copy())
self.get_unique_classes_from_labels(dataframe)
dataframe = self.remove_special_chars_from_feature_names(dataframe)
if self.config.get('reduce_df_footprint', False):
dataframe = reduce_dataframe_footprint(dataframe)
return dataframe
else:
# the user is using the populate_any_indicators functions which is deprecated
df = self.use_strategy_to_populate_indicators_old_version(
strategy, corr_dataframes, base_dataframes, pair,
prediction_dataframe, do_corr_pairs)
return df
def use_strategy_to_populate_indicators_old_version(
self,
strategy: IStrategy,
corr_dataframes: dict = {},
base_dataframes: dict = {},
pair: str = "",
prediction_dataframe: DataFrame = pd.DataFrame(),
do_corr_pairs: bool = True,
) -> DataFrame:
"""
Use the user defined strategy for populating indicators during retrain
:param strategy: IStrategy = user defined strategy object
:param corr_dataframes: dict = dict containing the df pair dataframes
(for user defined timeframes)
:param base_dataframes: dict = dict containing the current pair dataframes
(for user defined timeframes)
:param metadata: dict = strategy furnished pair metadata
:return:
dataframe: DataFrame = dataframe containing populated indicators
"""
# for prediction dataframe creation, we let dataprovider handle everything in the strategy
# so we create empty dictionaries, which allows us to pass None to
# `populate_any_indicators()`. Signaling we want the dp to give us the live dataframe.
tfs: List[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
pairs: List[str] = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
if not prediction_dataframe.empty:
dataframe = prediction_dataframe.copy()
for tf in tfs:
base_dataframes[tf] = None
for p in pairs:
if p not in corr_dataframes:
corr_dataframes[p] = {}
corr_dataframes[p][tf] = None
else:
dataframe = base_dataframes[self.config["timeframe"]].copy()
sgi = False
for tf in tfs:
if tf == tfs[-1]:
sgi = True # doing this last allows user to use all tf raw prices in labels
dataframe = strategy.populate_any_indicators(
pair,
dataframe.copy(),
tf,
informative=base_dataframes[tf],
set_generalized_indicators=sgi
)
# ensure corr pairs are always last
for corr_pair in pairs:
if pair == corr_pair:
continue # dont repeat anything from whitelist
for tf in tfs:
if pairs and do_corr_pairs:
dataframe = strategy.populate_any_indicators(
corr_pair,
dataframe.copy(),
tf,
informative=corr_dataframes[corr_pair][tf]
)
dataframe = strategy.set_freqai_targets(dataframe.copy(), metadata=metadata)
self.get_unique_classes_from_labels(dataframe)
@ -1546,3 +1493,25 @@ class FreqaiDataKitchen:
dataframe.columns = dataframe.columns.str.replace(c, "")
return dataframe
def buffer_timerange(self, timerange: TimeRange):
"""
Buffer the start and end of the timerange. This is used *after* the indicators
are 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.
"""
buffer = self.freqai_config["feature_parameters"]["buffer_train_data_candles"]
if buffer:
timerange.stopts -= buffer * timeframe_to_seconds(self.config["timeframe"])
timerange.startts += buffer * timeframe_to_seconds(self.config["timeframe"])
return timerange

88
freqtrade/freqai/freqai_interface.py
View File

@ -1,4 +1,3 @@
import inspect
import logging
import threading
import time
@ -66,12 +65,11 @@ class IFreqaiModel(ABC):
self.retrain = False
self.first = True
self.set_full_path()
self.follow_mode: bool = self.freqai_info.get("follow_mode", False)
self.save_backtest_models: bool = self.freqai_info.get("save_backtest_models", True)
if self.save_backtest_models:
logger.info('Backtesting module configured to save all models.')
self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
self.dd = FreqaiDataDrawer(Path(self.full_path), self.config)
# set current candle to arbitrary historical date
self.current_candle: datetime = datetime.fromtimestamp(637887600, tz=timezone.utc)
self.dd.current_candle = self.current_candle
@ -106,8 +104,7 @@ class IFreqaiModel(ABC):
self.data_provider: Optional[DataProvider] = None
self.max_system_threads = max(int(psutil.cpu_count() * 2 - 2), 1)
self.can_short = True # overridden in start() with strategy.can_short
self.warned_deprecated_populate_any_indicators = False
self.model: Any = None
record_params(config, self.full_path)
@ -139,9 +136,6 @@ class IFreqaiModel(ABC):
self.data_provider = strategy.dp
self.can_short = strategy.can_short
# check if the strategy has deprecated populate_any_indicators function
self.check_deprecated_populate_any_indicators(strategy)
if self.live:
self.inference_timer('start')
self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
@ -153,7 +147,7 @@ class IFreqaiModel(ABC):
# (backtest window, i.e. window immediately following the training window).
# FreqAI slides the window and sequentially builds the backtesting results before returning
# the concatenated results for the full backtesting period back to the strategy.
elif not self.follow_mode:
else:
self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
if not self.config.get("freqai_backtest_live_models", False):
logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
@ -228,7 +222,7 @@ class IFreqaiModel(ABC):
logger.warning(f'{pair} not in current whitelist, removing from train queue.')
continue
(_, trained_timestamp, _) = self.dd.get_pair_dict_info(pair)
(_, trained_timestamp) = self.dd.get_pair_dict_info(pair)
dk = FreqaiDataKitchen(self.config, self.live, pair)
(
@ -286,7 +280,7 @@ class IFreqaiModel(ABC):
# following tr_train. Both of these windows slide through the
# entire backtest
for tr_train, tr_backtest in zip(dk.training_timeranges, dk.backtesting_timeranges):
(_, _, _) = self.dd.get_pair_dict_info(pair)
(_, _) = self.dd.get_pair_dict_info(pair)
train_it += 1
total_trains = len(dk.backtesting_timeranges)
self.training_timerange = tr_train
@ -325,9 +319,13 @@ class IFreqaiModel(ABC):
populate_indicators = False
dataframe_base_train = dataframe.loc[dataframe["date"] < tr_train.stopdt, :]
dataframe_base_train = strategy.set_freqai_targets(dataframe_base_train)
dataframe_base_train = strategy.set_freqai_targets(
dataframe_base_train, metadata=metadata)
dataframe_base_backtest = dataframe.loc[dataframe["date"] < tr_backtest.stopdt, :]
dataframe_base_backtest = strategy.set_freqai_targets(dataframe_base_backtest)
dataframe_base_backtest = strategy.set_freqai_targets(
dataframe_base_backtest, metadata=metadata)
tr_train = dk.buffer_timerange(tr_train)
dataframe_train = dk.slice_dataframe(tr_train, dataframe_base_train)
dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe_base_backtest)
@ -341,13 +339,14 @@ class IFreqaiModel(ABC):
except Exception as msg:
logger.warning(
f"Training {pair} raised exception {msg.__class__.__name__}. "
f"Message: {msg}, skipping.")
f"Message: {msg}, skipping.", exc_info=True)
self.model = None
self.dd.pair_dict[pair]["trained_timestamp"] = int(
tr_train.stopts)
if self.plot_features:
if self.plot_features and self.model is not None:
plot_feature_importance(self.model, pair, dk, self.plot_features)
if self.save_backtest_models:
if self.save_backtest_models and self.model is not None:
logger.info('Saving backtest model to disk.')
self.dd.save_data(self.model, pair, dk)
else:
@ -379,18 +378,9 @@ class IFreqaiModel(ABC):
:returns:
dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
"""
# update follower
if self.follow_mode:
self.dd.update_follower_metadata()
# get the model metadata associated with the current pair
(_, trained_timestamp, return_null_array) = self.dd.get_pair_dict_info(metadata["pair"])
# if the metadata doesn't exist, the follower returns null arrays to strategy
if self.follow_mode and return_null_array:
logger.info("Returning null array from follower to strategy")
self.dd.return_null_values_to_strategy(dataframe, dk)
return dk
(_, trained_timestamp) = self.dd.get_pair_dict_info(metadata["pair"])
# append the historic data once per round
if self.dd.historic_data:
@ -398,8 +388,6 @@ class IFreqaiModel(ABC):
logger.debug(f'Updating historic data on pair {metadata["pair"]}')
self.track_current_candle()
if not self.follow_mode:
(_, new_trained_timerange, data_load_timerange) = dk.check_if_new_training_required(
trained_timestamp
)
@ -413,13 +401,6 @@ class IFreqaiModel(ABC):
self.scanning = True
self.start_scanning(strategy)
elif self.follow_mode:
dk.set_paths(metadata["pair"], trained_timestamp)
logger.info(
"FreqAI instance set to follow_mode, finding existing pair "
f"using { self.identifier }"
)
# load the model and associated data into the data kitchen
self.model = self.dd.load_data(metadata["pair"], dk)
@ -506,7 +487,7 @@ class IFreqaiModel(ABC):
"strategy is furnishing the same features as the pretrained"
"model. In case of --strategy-list, please be aware that FreqAI "
"requires all strategies to maintain identical "
"populate_any_indicator() functions"
"feature_engineering_* functions"
)
def data_cleaning_train(self, dk: FreqaiDataKitchen) -> None:
@ -580,7 +561,13 @@ class IFreqaiModel(ABC):
:return:
:boolean: whether the model file exists or not.
"""
path_to_modelfile = Path(dk.data_path / f"{dk.model_filename}_model.joblib")
if self.dd.model_type == 'joblib':
file_type = ".joblib"
elif self.dd.model_type == 'keras':
file_type = ".h5"
elif 'stable_baselines' in self.dd.model_type or 'sb3_contrib' == self.dd.model_type:
file_type = ".zip"
path_to_modelfile = Path(dk.data_path / f"{dk.model_filename}_model{file_type}")
file_exists = path_to_modelfile.is_file()
if file_exists:
logger.info("Found model at %s", dk.data_path / dk.model_filename)
@ -612,7 +599,7 @@ class IFreqaiModel(ABC):
:param strategy: IStrategy = user defined strategy object
:param dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
:param data_load_timerange: TimeRange = the amount of data to be loaded
for populate_any_indicators
for populating indicators
(larger than new_trained_timerange so that
new_trained_timerange does not contain any NaNs)
"""
@ -625,6 +612,8 @@ class IFreqaiModel(ABC):
strategy, corr_dataframes, base_dataframes, pair
)
new_trained_timerange = dk.buffer_timerange(new_trained_timerange)
unfiltered_dataframe = dk.slice_dataframe(new_trained_timerange, unfiltered_dataframe)
# find the features indicated by strategy and store in datakitchen
@ -640,7 +629,6 @@ class IFreqaiModel(ABC):
if self.plot_features:
plot_feature_importance(model, pair, dk, self.plot_features)
if self.freqai_info.get("purge_old_models", False):
self.dd.purge_old_models()
def set_initial_historic_predictions(
@ -817,7 +805,7 @@ class IFreqaiModel(ABC):
logger.warning("Couldn't cache corr_pair dataframes for improved performance. "
"Consider ensuring that the full coin/stake, e.g. XYZ/USD, "
"is included in the column names when you are creating features "
"in `populate_any_indicators()`.")
"in `feature_engineering_*` functions.")
self.get_corr_dataframes = not bool(self.corr_dataframes)
elif self.corr_dataframes:
dataframe = dk.attach_corr_pair_columns(
@ -944,26 +932,6 @@ class IFreqaiModel(ABC):
dk.return_dataframe, saved_dataframe, how='left', left_on='date', right_on="date_pred")
return dk
def check_deprecated_populate_any_indicators(self, strategy: IStrategy):
"""
Check and warn if the deprecated populate_any_indicators function is used.
:param strategy: strategy object
"""
if not self.warned_deprecated_populate_any_indicators:
self.warned_deprecated_populate_any_indicators = True
old_version = inspect.getsource(strategy.populate_any_indicators) != (
inspect.getsource(IStrategy.populate_any_indicators))
if old_version:
logger.warning("DEPRECATION WARNING: "
"You are using the deprecated populate_any_indicators function. "
"This function will raise an error on March 1 2023. "
"Please update your strategy by using "
"the new feature_engineering functions. See \n"
"https://www.freqtrade.io/en/latest/freqai-feature-engineering/"
"for details.")
# Following methods which are overridden by user made prediction models.
# See freqai/prediction_models/CatboostPredictionModel.py for an example.

2
freqtrade/freqai/prediction_models/ReinforcementLearner.py
View File

@ -100,7 +100,7 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
"""
# first, penalize if the action is not valid
if not self._is_valid(action):
self.tensorboard_log("is_valid")
self.tensorboard_log("invalid", category="actions")
return -2
pnl = self.get_unrealized_profit()

7
freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py
View File

@ -34,7 +34,12 @@ class ReinforcementLearner_multiproc(ReinforcementLearner):
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
env_info = self.pack_env_dict()
if self.train_env:
self.train_env.close()
if self.eval_env:
self.eval_env.close()
env_info = self.pack_env_dict(dk.pair)
env_id = "train_env"
self.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1,

2
freqtrade/freqai/utils.py
View File

@ -211,7 +211,7 @@ def record_params(config: Dict[str, Any], full_path: Path) -> None:
"pairs": config.get('exchange', {}).get('pair_whitelist')
}
with open(params_record_path, "w") as handle:
with params_record_path.open("w") as handle:
rapidjson.dump(
run_params,
handle,

141
freqtrade/freqtradebot.py
View File

@ -127,19 +127,19 @@ class FreqtradeBot(LoggingMixin):
for minutes in [0, 15, 30, 45]:
t = str(time(time_slot, minutes, 2))
self._schedule.every().day.at(t).do(update)
self.last_process = datetime(1970, 1, 1, tzinfo=timezone.utc)
self.last_process: Optional[datetime] = None
self.strategy.ft_bot_start()
# Initialize protections AFTER bot start - otherwise parameters are not loaded.
self.protections = ProtectionManager(self.config, self.strategy.protections)
def notify_status(self, msg: str) -> None:
def notify_status(self, msg: str, msg_type=RPCMessageType.STATUS) -> None:
"""
Public method for users of this class (worker, etc.) to send notifications
via RPC about changes in the bot status.
"""
self.rpc.send_msg({
'type': RPCMessageType.STATUS,
'type': msg_type,
'status': msg
})
@ -344,7 +344,15 @@ class FreqtradeBot(LoggingMixin):
try:
fo = self.exchange.fetch_order_or_stoploss_order(order.order_id, order.ft_pair,
order.ft_order_side == 'stoploss')
if not order.trade:
# This should not happen, but it does if trades were deleted manually.
# This can only incur on sqlite, which doesn't enforce foreign constraints.
logger.warning(
f"Order {order.order_id} has no trade attached. "
"This may suggest a database corruption. "
f"The expected trade ID is {order.ft_trade_id}. Ignoring this order."
)
continue
self.update_trade_state(order.trade, order.order_id, fo,
stoploss_order=(order.ft_order_side == 'stoploss'))
@ -355,7 +363,7 @@ class FreqtradeBot(LoggingMixin):
"Order is older than 5 days. Assuming order was fully cancelled.")
fo = order.to_ccxt_object()
fo['status'] = 'canceled'
self.handle_timedout_order(fo, order.trade)
self.handle_cancel_order(fo, order.trade, constants.CANCEL_REASON['TIMEOUT'])
except ExchangeError as e:
@ -578,7 +586,7 @@ class FreqtradeBot(LoggingMixin):
min_entry_stake = self.exchange.get_min_pair_stake_amount(trade.pair,
current_entry_rate,
self.strategy.stoploss)
0.0)
min_exit_stake = self.exchange.get_min_pair_stake_amount(trade.pair,
current_exit_rate,
self.strategy.stoploss)
@ -586,7 +594,7 @@ class FreqtradeBot(LoggingMixin):
stake_available = self.wallets.get_available_stake_amount()
logger.debug(f"Calling adjust_trade_position for pair {trade.pair}")
stake_amount = strategy_safe_wrapper(self.strategy.adjust_trade_position,
default_retval=None)(
default_retval=None, supress_error=True)(
trade=trade,
current_time=datetime.now(timezone.utc), current_rate=current_entry_rate,
current_profit=current_entry_profit, min_stake=min_entry_stake,
@ -625,7 +633,7 @@ class FreqtradeBot(LoggingMixin):
return
remaining = (trade.amount - amount) * current_exit_rate
if remaining < min_exit_stake:
if min_exit_stake and remaining < min_exit_stake:
logger.info(f"Remaining amount of {remaining} would be smaller "
f"than the minimum of {min_exit_stake}.")
return
@ -692,7 +700,8 @@ class FreqtradeBot(LoggingMixin):
pos_adjust = trade is not None
enter_limit_requested, stake_amount, leverage = self.get_valid_enter_price_and_stake(
pair, price, stake_amount, trade_side, enter_tag, trade, order_adjust, leverage_)
pair, price, stake_amount, trade_side, enter_tag, trade, order_adjust, leverage_,
pos_adjust)
if not stake_amount:
return False
@ -750,13 +759,15 @@ class FreqtradeBot(LoggingMixin):
self.exchange.name, order['filled'], order['amount'],
order['remaining']
)
amount = safe_value_fallback(order, 'filled', 'amount')
enter_limit_filled_price = safe_value_fallback(order, 'average', 'price')
amount = safe_value_fallback(order, 'filled', 'amount', amount)
enter_limit_filled_price = safe_value_fallback(
order, 'average', 'price', enter_limit_filled_price)
# in case of FOK the order may be filled immediately and fully
elif order_status == 'closed':
amount = safe_value_fallback(order, 'filled', 'amount')
enter_limit_filled_price = safe_value_fallback(order, 'average', 'price')
amount = safe_value_fallback(order, 'filled', 'amount', amount)
enter_limit_filled_price = safe_value_fallback(
order, 'average', 'price', enter_limit_requested)
# Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
fee = self.exchange.get_fee(symbol=pair, taker_or_maker='maker')
@ -799,6 +810,9 @@ class FreqtradeBot(LoggingMixin):
precision_mode=self.exchange.precisionMode,
contract_size=self.exchange.get_contract_size(pair),
)
stoploss = self.strategy.stoploss if not self.edge else self.edge.get_stoploss(pair)
trade.adjust_stop_loss(trade.open_rate, stoploss, initial=True)
else:
# This is additional buy, we reset fee_open_currency so timeout checking can work
trade.is_open = True
@ -808,7 +822,7 @@ class FreqtradeBot(LoggingMixin):
trade.orders.append(order_obj)
trade.recalc_trade_from_orders()
Trade.query.session.add(trade)
Trade.session.add(trade)
Trade.commit()
# Updating wallets
@ -831,7 +845,7 @@ class FreqtradeBot(LoggingMixin):
def cancel_stoploss_on_exchange(self, trade: Trade) -> Trade:
# First cancelling stoploss on exchange ...
if self.strategy.order_types.get('stoploss_on_exchange') and trade.stoploss_order_id:
if trade.stoploss_order_id:
try:
logger.info(f"Canceling stoploss on exchange for {trade}")
co = self.exchange.cancel_stoploss_order_with_result(
@ -850,7 +864,12 @@ class FreqtradeBot(LoggingMixin):
trade: Optional[Trade],
order_adjust: bool,
leverage_: Optional[float],
pos_adjust: bool,
) -> Tuple[float, float, float]:
"""
Validate and eventually adjust (within limits) limit, amount and leverage
:return: Tuple with (price, amount, leverage)
"""
if price:
enter_limit_requested = price
@ -896,7 +915,9 @@ class FreqtradeBot(LoggingMixin):
# We do however also need min-stake to determine leverage, therefore this is ignored as
# edge-case for now.
min_stake_amount = self.exchange.get_min_pair_stake_amount(
pair, enter_limit_requested, self.strategy.stoploss, leverage)
pair, enter_limit_requested,
self.strategy.stoploss if not pos_adjust else 0.0,
leverage)
max_stake_amount = self.exchange.get_max_pair_stake_amount(
pair, enter_limit_requested, leverage)
@ -1003,12 +1024,16 @@ class FreqtradeBot(LoggingMixin):
trades_closed = 0
for trade in trades:
try:
try:
if (self.strategy.order_types.get('stoploss_on_exchange') and
self.handle_stoploss_on_exchange(trade)):
trades_closed += 1
Trade.commit()
continue
except InvalidOrderException as exception:
logger.warning(
f'Unable to handle stoploss on exchange for {trade.pair}: {exception}')
# Check if we can sell our current pair
if trade.open_order_id is None and trade.is_open and self.handle_trade(trade):
trades_closed += 1
@ -1068,7 +1093,7 @@ class FreqtradeBot(LoggingMixin):
datetime.now(timezone.utc),
enter=enter,
exit_=exit_,
force_stoploss=self.edge.stoploss(trade.pair) if self.edge else 0
force_stoploss=self.edge.get_stoploss(trade.pair) if self.edge else 0
)
for should_exit in exits:
if should_exit.exit_flag:
@ -1088,7 +1113,7 @@ class FreqtradeBot(LoggingMixin):
:return: True if the order succeeded, and False in case of problems.
"""
try:
stoploss_order = self.exchange.stoploss(
stoploss_order = self.exchange.create_stoploss(
pair=trade.pair,
amount=trade.amount,
stop_price=stop_price,
@ -1112,8 +1137,7 @@ class FreqtradeBot(LoggingMixin):
trade.stoploss_order_id = None
logger.error(f'Unable to place a stoploss order on exchange. {e}')
logger.warning('Exiting the trade forcefully')
self.execute_trade_exit(trade, stop_price, exit_check=ExitCheckTuple(
exit_type=ExitType.EMERGENCY_EXIT))
self.emergency_exit(trade, stop_price)
except ExchangeError:
trade.stoploss_order_id = None
@ -1160,15 +1184,13 @@ class FreqtradeBot(LoggingMixin):
# If enter order is fulfilled but there is no stoploss, we add a stoploss on exchange
if not stoploss_order:
stoploss = (
self.edge.stoploss(pair=trade.pair)
if self.edge else
trade.stop_loss_pct / trade.leverage
stop_price = trade.stoploss_or_liquidation
if self.edge:
stoploss = self.edge.get_stoploss(pair=trade.pair)
stop_price = (
trade.open_rate * (1 - stoploss) if trade.is_short
else trade.open_rate * (1 + stoploss)
)
if trade.is_short:
stop_price = trade.open_rate * (1 - stoploss)
else:
stop_price = trade.open_rate * (1 + stoploss)
if self.create_stoploss_order(trade=trade, stop_price=stop_price):
# The above will return False if the placement failed and the trade was force-sold.
@ -1253,11 +1275,11 @@ class FreqtradeBot(LoggingMixin):
if not_closed:
if fully_cancelled or (order_obj and self.strategy.ft_check_timed_out(
trade, order_obj, datetime.now(timezone.utc))):
self.handle_timedout_order(order, trade)
self.handle_cancel_order(order, trade, constants.CANCEL_REASON['TIMEOUT'])
else:
self.replace_order(order, order_obj, trade)
def handle_timedout_order(self, order: Dict, trade: Trade) -> None:
def handle_cancel_order(self, order: Dict, trade: Trade, reason: str) -> None:
"""
Check if current analyzed order timed out and cancel if necessary.
:param order: Order dict grabbed with exchange.fetch_order()
@ -1265,22 +1287,24 @@ class FreqtradeBot(LoggingMixin):
:return: None
"""
if order['side'] == trade.entry_side:
self.handle_cancel_enter(trade, order, constants.CANCEL_REASON['TIMEOUT'])
self.handle_cancel_enter(trade, order, reason)
else:
canceled = self.handle_cancel_exit(
trade, order, constants.CANCEL_REASON['TIMEOUT'])
canceled = self.handle_cancel_exit(trade, order, reason)
canceled_count = trade.get_exit_order_count()
max_timeouts = self.config.get('unfilledtimeout', {}).get('exit_timeout_count', 0)
if canceled and max_timeouts > 0 and canceled_count >= max_timeouts:
logger.warning(f'Emergency exiting trade {trade}, as the exit order '
f'timed out {max_timeouts} times.')
self.emergency_exit(trade, order['price'])
def emergency_exit(self, trade: Trade, price: float) -> None:
try:
self.execute_trade_exit(
trade, order['price'],
trade, price,
exit_check=ExitCheckTuple(exit_type=ExitType.EMERGENCY_EXIT))
except DependencyException as exception:
logger.warning(
f'Unable to emergency sell trade {trade.pair}: {exception}')
f'Unable to emergency exit trade {trade.pair}: {exception}')
def replace_order(self, order: Dict, order_obj: Optional[Order], trade: Trade) -> None:
"""
@ -1307,7 +1331,7 @@ class FreqtradeBot(LoggingMixin):
default_retval=order_obj.price)(
trade=trade, order=order_obj, pair=trade.pair,
current_time=datetime.now(timezone.utc), proposed_rate=proposed_rate,
current_order_rate=order_obj.price, entry_tag=trade.enter_tag,
current_order_rate=order_obj.safe_price, entry_tag=trade.enter_tag,
side=trade.entry_side)
replacing = True
@ -1323,7 +1347,8 @@ class FreqtradeBot(LoggingMixin):
# place new order only if new price is supplied
self.execute_entry(
pair=trade.pair,
stake_amount=(order_obj.remaining * order_obj.price / trade.leverage),
stake_amount=(
order_obj.safe_remaining * order_obj.safe_price / trade.leverage),
price=adjusted_entry_price,
trade=trade,
is_short=trade.is_short,
@ -1337,6 +1362,8 @@ class FreqtradeBot(LoggingMixin):
"""
for trade in Trade.get_open_order_trades():
if not trade.open_order_id:
continue
try:
order = self.exchange.fetch_order(trade.open_order_id, trade.pair)
except (ExchangeError):
@ -1361,6 +1388,9 @@ class FreqtradeBot(LoggingMixin):
"""
was_trade_fully_canceled = False
side = trade.entry_side.capitalize()
if not trade.open_order_id:
logger.warning(f"No open order for {trade}.")
return False
# Cancelled orders may have the status of 'canceled' or 'closed'
if order['status'] not in constants.NON_OPEN_EXCHANGE_STATES:
@ -1447,34 +1477,32 @@ class FreqtradeBot(LoggingMixin):
return False
try:
co = self.exchange.cancel_order_with_result(trade.open_order_id, trade.pair,
order = self.exchange.cancel_order_with_result(order['id'], trade.pair,
trade.amount)
except InvalidOrderException:
logger.exception(
f"Could not cancel {trade.exit_side} order {trade.open_order_id}")
return False
trade.close_rate = None
trade.close_rate_requested = None
trade.close_profit = None
trade.close_profit_abs = None
# Set exit_reason for fill message
exit_reason_prev = trade.exit_reason
trade.exit_reason = trade.exit_reason + f", {reason}" if trade.exit_reason else reason
self.update_trade_state(trade, trade.open_order_id, co)
# Order might be filled above in odd timing issues.
if co.get('status') in ('canceled', 'cancelled'):
if order.get('status') in ('canceled', 'cancelled'):
trade.exit_reason = None
trade.open_order_id = None
else:
trade.exit_reason = exit_reason_prev
logger.info(f'{trade.exit_side.capitalize()} order {reason} for {trade}.')
cancelled = True
else:
reason = constants.CANCEL_REASON['CANCELLED_ON_EXCHANGE']
logger.info(f'{trade.exit_side.capitalize()} order {reason} for {trade}.')
trade.exit_reason = None
self.update_trade_state(trade, trade.open_order_id, order)
logger.info(f'{trade.exit_side.capitalize()} order {reason} for {trade}.')
trade.open_order_id = None
trade.close_rate = None
trade.close_rate_requested = None
self._notify_exit_cancel(
trade,
@ -1522,7 +1550,7 @@ class FreqtradeBot(LoggingMixin):
*,
exit_tag: Optional[str] = None,
ordertype: Optional[str] = None,
sub_trade_amt: float = None,
sub_trade_amt: Optional[float] = None,
) -> bool:
"""
Executes a trade exit for the given trade and limit
@ -1616,7 +1644,7 @@ class FreqtradeBot(LoggingMixin):
return True
def _notify_exit(self, trade: Trade, order_type: str, fill: bool = False,
sub_trade: bool = False, order: Order = None) -> None:
sub_trade: bool = False, order: Optional[Order] = None) -> None:
"""
Sends rpc notification when a sell occurred.
"""
@ -1626,13 +1654,13 @@ class FreqtradeBot(LoggingMixin):
# second condition is for mypy only; order will always be passed during sub trade
if sub_trade and order is not None:
amount = order.safe_filled if fill else order.amount
amount = order.safe_filled if fill else order.safe_amount
order_rate: float = order.safe_price
profit = trade.calc_profit(rate=order_rate, amount=amount, open_rate=trade.open_rate)
profit_ratio = trade.calc_profit_ratio(order_rate, amount, trade.open_rate)
else:
order_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
order_rate = trade.safe_close_rate
profit = trade.calc_profit(rate=order_rate) + (0.0 if fill else trade.realized_profit)
profit_ratio = trade.calc_profit_ratio(order_rate)
amount = trade.amount
@ -1687,7 +1715,7 @@ class FreqtradeBot(LoggingMixin):
raise DependencyException(
f"Order_obj not found for {order_id}. This should not have happened.")
profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
profit_rate: float = trade.safe_close_rate
profit_trade = trade.calc_profit(rate=profit_rate)
current_rate = self.exchange.get_rate(
trade.pair, side='exit', is_short=trade.is_short, refresh=False)
@ -1729,7 +1757,9 @@ class FreqtradeBot(LoggingMixin):
# Common update trade state methods
#
def update_trade_state(self, trade: Trade, order_id: str, action_order: Dict[str, Any] = None,
def update_trade_state(
self, trade: Trade, order_id: Optional[str],
action_order: Optional[Dict[str, Any]] = None,
stoploss_order: bool = False, send_msg: bool = True) -> bool:
"""
Checks trades with open orders and updates the amount if necessary
@ -1788,6 +1818,7 @@ class FreqtradeBot(LoggingMixin):
is_short=trade.is_short,
amount=trade.amount,
stake_amount=trade.stake_amount,
leverage=trade.leverage,
wallet_balance=trade.stake_amount,
))

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