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Merge branch 'freqtrade:develop' into develop

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Stefano Ariestasia 2022-12-08 19:35:45 +09:00 committed by GitHub
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4
.devcontainer/devcontainer.json
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@ -11,12 +11,14 @@
"mounts": [
"source=freqtrade-bashhistory,target=/home/ftuser/commandhistory,type=volume"
],
"workspaceMount": "source=${localWorkspaceFolder},target=/workspaces/freqtrade,type=bind,consistency=cached",
// Uncomment to connect as a non-root user if you've added one. See https://aka.ms/vscode-remote/containers/non-root.
"remoteUser": "ftuser",
"onCreateCommand": "pip install --user -e .",
"postCreateCommand": "freqtrade create-userdir --userdir user_data/",
"workspaceFolder": "/freqtrade/",
"workspaceFolder": "/workspaces/freqtrade",
"settings": {
"terminal.integrated.shell.linux": "/bin/bash",

67
.github/workflows/ci.yml vendored
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@ -66,12 +66,6 @@ jobs:
- name: Tests
run: |
pytest --random-order --cov=freqtrade --cov-config=.coveragerc
if: matrix.python-version != '3.9' || matrix.os != 'ubuntu-22.04'
- name: Tests incl. ccxt compatibility tests
run: |
pytest --random-order --cov=freqtrade --cov-config=.coveragerc --longrun
if: matrix.python-version == '3.9' && matrix.os == 'ubuntu-22.04'
- name: Coveralls
if: (runner.os == 'Linux' && matrix.python-version == '3.10' && matrix.os == 'ubuntu-22.04')
@ -310,9 +304,64 @@ jobs:
details: Freqtrade doc test failed!
webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
build_linux_online:
# Run pytest with "live" checks
runs-on: ubuntu-22.04
# permissions:
steps:
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: "3.9"
- name: Cache_dependencies
uses: actions/cache@v3
id: cache
with:
path: ~/dependencies/
key: ${{ runner.os }}-dependencies
- name: pip cache (linux)
uses: actions/cache@v3
if: runner.os == 'Linux'
with:
path: ~/.cache/pip
key: test-${{ matrix.os }}-${{ matrix.python-version }}-pip
- name: TA binary *nix
if: steps.cache.outputs.cache-hit != 'true'
run: |
cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
- name: Installation - *nix
if: runner.os == 'Linux'
run: |
python -m pip install --upgrade pip wheel
export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
export TA_LIBRARY_PATH=${HOME}/dependencies/lib
export TA_INCLUDE_PATH=${HOME}/dependencies/include
pip install -r requirements-dev.txt
pip install -e .
- name: Tests incl. ccxt compatibility tests
run: |
pytest --random-order --cov=freqtrade --cov-config=.coveragerc --longrun
# Notify only once - when CI completes (and after deploy) in case it's successfull
notify-complete:
needs: [ build_linux, build_macos, build_windows, docs_check, mypy_version_check, pre-commit ]
needs: [
build_linux,
build_macos,
build_windows,
docs_check,
mypy_version_check,
pre-commit,
build_linux_online
]
runs-on: ubuntu-22.04
# Discord notification can't handle schedule events
if: (github.event_name != 'schedule')
@ -361,7 +410,7 @@ jobs:
python setup.py sdist bdist_wheel
- name: Publish to PyPI (Test)
uses: pypa/gh-action-pypi-publish@v1.5.1
uses: pypa/gh-action-pypi-publish@v1.6.1
if: (github.event_name == 'release')
with:
user: __token__
@ -369,7 +418,7 @@ jobs:
repository_url: https://test.pypi.org/legacy/
- name: Publish to PyPI
uses: pypa/gh-action-pypi-publish@v1.5.1
uses: pypa/gh-action-pypi-publish@v1.6.1
if: (github.event_name == 'release')
with:
user: __token__

1
.gitignore vendored
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@ -109,7 +109,6 @@ target/
!*.gitkeep
!config_examples/config_binance.example.json
!config_examples/config_bittrex.example.json
!config_examples/config_ftx.example.json
!config_examples/config_full.example.json
!config_examples/config_kraken.example.json
!config_examples/config_freqai.example.json

4
.pre-commit-config.yaml
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@ -15,9 +15,9 @@ repos:
additional_dependencies:
- types-cachetools==5.2.1
- types-filelock==3.2.7
- types-requests==2.28.11.2
- types-requests==2.28.11.5
- types-tabulate==0.9.0.0
- types-python-dateutil==2.8.19.2
- types-python-dateutil==2.8.19.4
# stages: [push]
- repo: https://github.com/pycqa/isort

3
README.md
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@ -28,7 +28,6 @@ Please read the [exchange specific notes](docs/exchanges.md) to learn about even
- [X] [Binance](https://www.binance.com/)
- [X] [Bittrex](https://bittrex.com/)
- [X] [FTX](https://ftx.com/#a=2258149)
- [X] [Gate.io](https://www.gate.io/ref/6266643)
- [X] [Huobi](http://huobi.com/)
- [X] [Kraken](https://kraken.com/)
@ -39,7 +38,7 @@ 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] [OKX](https://okx.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.

8
build_helpers/publish_docker_arm64.sh
View File

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

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

96
config_examples/config_ftx.example.json
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@ -1,96 +0,0 @@
{
"max_open_trades": 3,
"stake_currency": "USD",
"stake_amount": 50,
"tradable_balance_ratio": 0.99,
"fiat_display_currency": "USD",
"timeframe": "5m",
"dry_run": true,
"cancel_open_orders_on_exit": false,
"unfilledtimeout": {
"entry": 10,
"exit": 10,
"exit_timeout_count": 0,
"unit": "minutes"
},
"entry_pricing": {
"price_side": "same",
"use_order_book": true,
"order_book_top": 1,
"price_last_balance": 0.0,
"check_depth_of_market": {
"enabled": false,
"bids_to_ask_delta": 1
}
},
"exit_pricing": {
"price_side": "same",
"use_order_book": true,
"order_book_top": 1
},
"exchange": {
"name": "ftx",
"key": "your_exchange_key",
"secret": "your_exchange_secret",
"ccxt_config": {},
"ccxt_async_config": {},
"pair_whitelist": [
"BTC/USD",
"ETH/USD",
"BNB/USD",
"USDT/USD",
"LTC/USD",
"SRM/USD",
"SXP/USD",
"XRP/USD",
"DOGE/USD",
"1INCH/USD",
"CHZ/USD",
"MATIC/USD",
"LINK/USD",
"OXY/USD",
"SUSHI/USD"
],
"pair_blacklist": [
"FTT/USD"
]
},
"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",
"chat_id": "your_telegram_chat_id"
},
"api_server": {
"enabled": false,
"listen_ip_address": "127.0.0.1",
"listen_port": 8080,
"verbosity": "error",
"jwt_secret_key": "somethingrandom",
"CORS_origins": [],
"username": "freqtrader",
"password": "SuperSecurePassword"
},
"bot_name": "freqtrade",
"initial_state": "running",
"force_entry_enable": false,
"internals": {
"process_throttle_secs": 5
}
}

1
config_examples/config_full.example.json
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@ -204,6 +204,7 @@
"strategy_path": "user_data/strategies/",
"recursive_strategy_search": false,
"add_config_files": [],
"reduce_df_footprint": false,
"dataformat_ohlcv": "json",
"dataformat_trades": "jsongz"
}

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

14
docs/advanced-backtesting.md
View File

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

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

17
docs/configuration.md
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@ -253,6 +253,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `add_config_files` | Additional config files. These files will be loaded and merged with the current config file. The files are resolved relative to the initial file.<br> *Defaults to `[]`*. <br> **Datatype:** List of strings
| `dataformat_ohlcv` | Data format to use to store historical candle (OHLCV) data. <br> *Defaults to `json`*. <br> **Datatype:** String
| `dataformat_trades` | Data format to use to store historical trades data. <br> *Defaults to `jsongz`*. <br> **Datatype:** String
| `reduce_df_footprint` | Recast all numeric columns to float32/int32, with the objective of reducing ram/disk usage (and decreasing train/inference timing in FreqAI). (Currently only affects FreqAI use-cases) <br> **Datatype:** Boolean. <br> Default: `False`.
### Parameters in the strategy
@ -552,7 +553,7 @@ The possible values are: `GTC` (default), `FOK` or `IOC`.
```
!!! Warning
This is ongoing work. For now, it is supported only for binance, gate, ftx and kucoin.
This is ongoing work. For now, it is supported only for binance, gate and kucoin.
Please don't change the default value unless you know what you are doing and have researched the impact of using different values for your particular exchange.
### What values can be used for fiat_display_currency?
@ -664,6 +665,7 @@ 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.
``` bash
export HTTP_PROXY="http://addr:port"
@ -671,17 +673,20 @@ export HTTPS_PROXY="http://addr:port"
freqtrade
```
#### Proxy just exchange requests
#### Proxy exchange requests
To use a proxy just for exchange connections (skips/ignores telegram and coingecko) - you can also define the proxies as part of the ccxt configuration.
To use a proxy for exchange connections - you will have to define the proxies as part of the ccxt configuration.
``` json
"ccxt_config": {
{
"exchange": {
"ccxt_config": {
"aiohttp_proxy": "http://addr:port",
"proxies": {
"http": "http://addr:port",
"https": "http://addr:port"
"http": "http://addr:port",
"https": "http://addr:port"
},
}
}
```

2
docs/data-analysis.md
View File

@ -83,7 +83,7 @@ from pathlib import Path
project_root = "somedir/freqtrade"
i=0
try:
os.chdirdir(project_root)
os.chdir(project_root)
assert Path('LICENSE').is_file()
except:
while i<4 and (not Path('LICENSE').is_file()):

6
docs/data-download.md
View File

@ -177,13 +177,13 @@ freqtrade download-data --exchange binance --pairs ETH/USDT XRP/USDT BTC/USDT --
### Data format
Freqtrade currently supports 3 data-formats for both OHLCV and trades data:
Freqtrade currently supports the following data-formats:
* `json` - plain "text" json files
* `jsongz` - a gzip-zipped version of json files
* `hdf5` - a high performance datastore
* `feather` - a dataformat based on Apache Arrow
* `parquet` - columnar datastore
* `feather` - a dataformat based on Apache Arrow (OHLCV only)
* `parquet` - columnar datastore (OHLCV only)
By default, OHLCV data is stored as `json` data, while trades data is stored as `jsongz` data.

12
docs/developer.md
View File

@ -49,6 +49,13 @@ For more information about the [Remote container extension](https://code.visuals
New code should be covered by basic unittests. Depending on the complexity of the feature, Reviewers may request more in-depth unittests.
If necessary, the Freqtrade team can assist and give guidance with writing good tests (however please don't expect anyone to write the tests for you).
#### How to run tests
Use `pytest` in root folder to run all available testcases and confirm your local environment is setup correctly
!!! Note "feature branches"
Tests are expected to pass on the `develop` and `stable` branches. Other branches may be work in progress with tests not working yet.
#### Checking log content in tests
Freqtrade uses 2 main methods to check log content in tests, `log_has()` and `log_has_re()` (to check using regex, in case of dynamic log-messages).
@ -434,6 +441,11 @@ To keep the release-log short, best wrap the full git changelog into a collapsib
</details>
```
### FreqUI release
If FreqUI has been updated substantially, make sure to create a release before merging the release branch.
Make sure that freqUI CI on the release is finished and passed before merging the release.
### Create github release / tag
Once the PR against stable is merged (best right after merging):

23
docs/exchanges.md
View File

@ -54,6 +54,9 @@ This configuration enables kraken, as well as rate-limiting to avoid bans from t
## Binance
!!! Warning "Server location and geo-ip restrictions"
Please be aware that binance restrict api access regarding the server country. The currents and non exhaustive countries blocked are United States, Malaysia (Singapour), Ontario (Canada). Please go to [binance terms > b. Eligibility](https://www.binance.com/en/terms) to find up to date list.
Binance supports [time_in_force](configuration.md#understand-order_time_in_force).
!!! Tip "Stoploss on Exchange"
@ -173,26 +176,6 @@ res = [p for p, x in lm.items() if 'US' in x['info']['prohibitedIn']]
print(res)
```
## FTX
!!! Tip "Stoploss on Exchange"
FTX supports `stoploss_on_exchange` and can use both stop-loss-market and stop-loss-limit orders. It provides great advantages, so we recommend to benefit from it.
You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type of stoploss shall be used.
### Using subaccounts
To use subaccounts with FTX, you need to edit the configuration and add the following:
``` json
"exchange": {
"ccxt_config": {
"headers": {
"FTX-SUBACCOUNT": "name"
}
},
}
```
## Kucoin
Kucoin requires a passphrase for each api key, you will therefore need to add this key into the configuration so your exchange section looks as follows:

56
docs/freqai-parameter-table.md
View File

@ -4,9 +4,11 @@ The table below will list all configuration parameters available for FreqAI. Som
Mandatory parameters are marked as **Required** and have to be set in one of the suggested ways.
### General configuration parameters
| Parameter | Description |
|------------|-------------|
| | **General configuration parameters**
| | **General configuration parameters within the `config.freqai` tree**
| `freqai` | **Required.** <br> The parent dictionary containing all the parameters for controlling FreqAI. <br> **Datatype:** Dictionary.
| `train_period_days` | **Required.** <br> Number of days to use for the training data (width of the sliding window). <br> **Datatype:** Positive integer.
| `backtest_period_days` | **Required.** <br> Number of days to inference from the trained model before sliding the `train_period_days` window defined above, and retraining the model during backtesting (more info [here](freqai-running.md#backtesting)). This can be fractional days, but beware that the provided `timerange` will be divided by this number to yield the number of trainings necessary to complete the backtest. <br> **Datatype:** Float.
@ -19,7 +21,13 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
| `follow_mode` | Use a `follower` that will look for models associated with a specific `identifier` and load those for inferencing. A `follower` will **not** train new models. <br> **Datatype:** Boolean. <br> Default: `False`.
| `continual_learning` | Use the final state of the most recently trained model as starting point for the new model, allowing for incremental learning (more information can be found [here](freqai-running.md#continual-learning)). <br> **Datatype:** Boolean. <br> Default: `False`.
| `write_metrics_to_disk` | Collect train timings, inference timings and cpu usage in json file. <br> **Datatype:** Boolean. <br> Default: `False`
| | **Feature parameters**
| `data_kitchen_thread_count` | <br> Designate the number of threads you want to use for data processing (outlier methods, normalization, etc.). This has no impact on the number of threads used for training. If user does not set it (default), FreqAI will use max number of threads - 2 (leaving 1 physical core available for Freqtrade bot and FreqUI) <br> **Datatype:** Positive integer.
### Feature parameters
| Parameter | Description |
|------------|-------------|
| | **Feature parameters within the `freqai.feature_parameters` sub dictionary**
| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples are shown [here](freqai-feature-engineering.md). <br> **Datatype:** Dictionary.
| `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for. The list is added as features to the base indicators dataset. <br> **Datatype:** List of timeframes (strings).
| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` during feature engineering (see details [here](freqai-feature-engineering.md)) will be created for each correlated coin. The correlated coins features are added to the base indicators dataset. <br> **Datatype:** List of assets (strings).
@ -29,7 +37,7 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
| `indicator_max_period_candles` | **No longer used (#7325)**. Replaced by `startup_candle_count` which is set in the [strategy](freqai-configuration.md#building-a-freqai-strategy). `startup_candle_count` is timeframe independent and defines the maximum *period* used in `populate_any_indicators()` for indicator creation. FreqAI uses this parameter together with the maximum timeframe in `include_time_frames` to calculate how many data points to download such that the first data point does not include a NaN. <br> **Datatype:** Positive integer.
| `indicator_periods_candles` | Time periods to calculate indicators for. The indicators are added to the base indicator dataset. <br> **Datatype:** List of positive integers.
| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis) <br> **Datatype:** Boolean. <br> Default: `False`.
| `plot_feature_importances` | Create a feature importance plot for each model for the top/bottom `plot_feature_importances` number of features. <br> **Datatype:** Integer. <br> Default: `0`.
| `plot_feature_importances` | Create a feature importance plot for each model for the top/bottom `plot_feature_importances` number of features. Plot is stored in `user_data/models/<identifier>/sub-train-<COIN>_<timestamp>.html`. <br> **Datatype:** Integer. <br> Default: `0`.
| `DI_threshold` | Activates the use of the Dissimilarity Index for outlier detection when set to > 0. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di). <br> **Datatype:** Positive float (typically < 1).
| `use_SVM_to_remove_outliers` | Train a support vector machine to detect and remove outliers from the training dataset, as well as from incoming data points. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Boolean.
| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Dictionary.
@ -38,15 +46,49 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
| `noise_standard_deviation` | If set, FreqAI adds noise to the training features with the aim of preventing overfitting. FreqAI generates random deviates from a gaussian distribution with a standard deviation of `noise_standard_deviation` and adds them to all data points. `noise_standard_deviation` should be kept relative to the normalized space, i.e., between -1 and 1. In other words, since data in FreqAI is always normalized to be between -1 and 1, `noise_standard_deviation: 0.05` would result in 32% of the data being randomly increased/decreased by more than 2.5% (i.e., the percent of data falling within the first standard deviation). <br> **Datatype:** Integer. <br> Default: `0`.
| `outlier_protection_percentage` | Enable to prevent outlier detection methods from discarding too much data. If more than `outlier_protection_percentage` % of points are detected as outliers by the SVM or DBSCAN, FreqAI will log a warning message and ignore outlier detection, i.e., the original dataset will be kept intact. If the outlier protection is triggered, no predictions will be made based on the training dataset. <br> **Datatype:** Float. <br> Default: `30`.
| `reverse_train_test_order` | Split the feature dataset (see below) and use the latest data split for training and test on historical split of the data. This allows the model to be trained up to the most recent data point, while avoiding overfitting. However, you should be careful to understand the unorthodox nature of this parameter before employing it. <br> **Datatype:** Boolean. <br> Default: `False` (no reversal).
| | **Data split parameters**
### Data split parameters
| Parameter | Description |
|------------|-------------|
| | **Data split parameters within the `freqai.data_split_parameters` sub dictionary**
| `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website). <br> **Datatype:** Dictionary.
| `test_size` | The fraction of data that should be used for testing instead of training. <br> **Datatype:** Positive float < 1.
| `shuffle` | Shuffle the training data points during training. Typically, to not remove the chronological order of data in time-series forecasting, this is set to `False`. <br> **Datatype:** Boolean. <br> Defaut: `False`.
| | **Model training parameters**
### Model training parameters
| Parameter | Description |
|------------|-------------|
| | **Model training parameters within the `freqai.model_training_parameters` sub dictionary**
| `model_training_parameters` | A flexible dictionary that includes all parameters available by the selected model library. For example, if you use `LightGBMRegressor`, this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html) (external website). If you select a different model, this dictionary can contain any parameter from that model. A list of the currently available models can be found [here](freqai-configuration.md#using-different-prediction-models). <br> **Datatype:** Dictionary.
| `n_estimators` | The number of boosted trees to fit in the training of the model. <br> **Datatype:** Integer.
| `learning_rate` | Boosting learning rate during training of the model. <br> **Datatype:** Float.
| `n_jobs`, `thread_count`, `task_type` | Set the number of threads for parallel processing and the `task_type` (`gpu` or `cpu`). Different model libraries use different parameter names. <br> **Datatype:** Float.
### Reinforcement Learning parameters
| Parameter | Description |
|------------|-------------|
| | **Reinforcement Learning Parameters within the `freqai.rl_config` sub dictionary**
| `rl_config` | A dictionary containing the control parameters for a Reinforcement Learning model. <br> **Datatype:** Dictionary.
| `train_cycles` | Training time steps will be set based on the `train_cycles * number of training data points. <br> **Datatype:** Integer.
| `cpu_count` | Number of processors to dedicate to the Reinforcement Learning training process. <br> **Datatype:** int.
| `max_trade_duration_candles`| Guides the agent training to keep trades below desired length. Example usage shown in `prediction_models/ReinforcementLearner.py` within the customizable `calculate_reward()` function. <br> **Datatype:** int.
| `model_type` | Model string from stable_baselines3 or SBcontrib. Available strings include: `'TRPO', 'ARS', 'RecurrentPPO', 'MaskablePPO', 'PPO', 'A2C', 'DQN'`. User should ensure that `model_training_parameters` match those available to the corresponding stable_baselines3 model by visiting their documentaiton. [PPO doc](https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html) (external website) <br> **Datatype:** string.
| `policy_type` | One of the available policy types from stable_baselines3 <br> **Datatype:** string.
| `max_training_drawdown_pct` | The maximum drawdown that the agent is allowed to experience during training. <br> **Datatype:** float. <br> Default: 0.8
| `cpu_count` | Number of threads/cpus to dedicate to the Reinforcement Learning training process (depending on if `ReinforcementLearning_multiproc` is selected or not). Recommended to leave this untouched, by default, this value is set to the total number of physical cores minus 1. <br> **Datatype:** int.
| `model_reward_parameters` | Parameters used inside the customizable `calculate_reward()` function in `ReinforcementLearner.py` <br> **Datatype:** int.
| `add_state_info` | Tell FreqAI to include state information in the feature set for training and inferencing. The current state variables include trade duration, current profit, trade position. This is only available in dry/live runs, and is automatically switched to false for backtesting. <br> **Datatype:** bool. <br> Default: `False`.
| `net_arch` | Network architecture which is well described in [`stable_baselines3` doc](https://stable-baselines3.readthedocs.io/en/master/guide/custom_policy.html#examples). In summary: `[<shared layers>, dict(vf=[<non-shared value network layers>], pi=[<non-shared policy network layers>])]`. By default this is set to `[128, 128]`, which defines 2 shared hidden layers with 128 units each.
| `randomize_starting_position` | Randomize the starting point of each episode to avoid overfitting. <br> **Datatype:** bool. <br> Default: `False`.
### Additional parameters
| Parameter | Description |
|------------|-------------|
| | **Extraneous parameters**
| `keras` | If the selected model makes use of Keras (typical for Tensorflow-based prediction models), this flag needs to be activated so that the model save/loading follows Keras standards. <br> **Datatype:** Boolean. <br> Default: `False`.
| `conv_width` | The width of a convolutional neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. <br> **Datatype:** Integer. <br> Default: `2`.
| `freqai.keras` | If the selected model makes use of Keras (typical for Tensorflow-based prediction models), this flag needs to be activated so that the model save/loading follows Keras standards. <br> **Datatype:** Boolean. <br> Default: `False`.
| `freqai.conv_width` | The width of a convolutional neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. <br> **Datatype:** Integer. <br> Default: `2`.
| `freqai.reduce_df_footprint` | Recast all numeric columns to float32/int32, with the objective of reducing ram/disk usage and decreasing train/inference timing. This parameter is set in the main level of the Freqtrade configuration file (not inside FreqAI). <br> **Datatype:** Boolean. <br> Default: `False`.

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@ -0,0 +1,260 @@
# Reinforcement Learning
!!! Note "Installation size"
Reinforcement learning dependencies include large packages such as `torch`, which should be explicitly requested during `./setup.sh -i` by answering "y" to the question "Do you also want dependencies for freqai-rl (~700mb additional space required) [y/N]?".
Users who prefer docker should ensure they use the docker image appended with `_freqairl`.
## Background and terminology
### What is RL and why does FreqAI need it?
Reinforcement learning involves two important components, the *agent* and the training *environment*. During agent training, the agent moves through historical data candle by candle, always making 1 of a set of actions: Long entry, long exit, short entry, short exit, neutral). During this training process, the environment tracks the performance of these actions and rewards the agent according to a custom user made `calculate_reward()` (here we offer a default reward for users to build on if they wish [details here](#creating-a-custom-reward-function)). The reward is used to train weights in a neural network.
A second important component of the FreqAI RL implementation is the use of *state* information. State information is fed into the network at each step, including current profit, current position, and current trade duration. These are used to train the agent in the training environment, and to reinforce the agent in dry/live (this functionality is not available in backtesting). *FreqAI + Freqtrade is a perfect match for this reinforcing mechanism since this information is readily available in live deployments.*
Reinforcement learning is a natural progression for FreqAI, since it adds a new layer of adaptivity and market reactivity that Classifiers and Regressors cannot match. However, Classifiers and Regressors have strengths that RL does not have such as robust predictions. Improperly trained RL agents may find "cheats" and "tricks" to maximize reward without actually winning any trades. For this reason, RL is more complex and demands a higher level of understanding than typical Classifiers and Regressors.
### The RL interface
With the current framework, we aim to expose the training environment via the common "prediction model" file, which is a user inherited `BaseReinforcementLearner` object (e.g. `freqai/prediction_models/ReinforcementLearner`). Inside this user class, the RL environment is available and customized via `MyRLEnv` as [shown below](#creating-a-custom-reward-function).
We envision the majority of users focusing their effort on creative design of the `calculate_reward()` function [details here](#creating-a-custom-reward-function), while leaving the rest of the environment untouched. Other users may not touch the environment at all, and they will only play with the configuration settings and the powerful feature engineering that already exists in FreqAI. Meanwhile, we enable advanced users to create their own model classes entirely.
The framework is built on stable_baselines3 (torch) and OpenAI gym for the base environment class. But generally speaking, the model class is well isolated. Thus, the addition of competing libraries can be easily integrated into the existing framework. For the environment, it is inheriting from `gym.env` which means that it is necessary to write an entirely new environment in order to switch to a different library.
### Important considerations
As explained above, the agent is "trained" in an artificial trading "environment". In our case, that environment may seem quite similar to a real Freqtrade backtesting environment, but it is *NOT*. In fact, the RL training environment is much more simplified. It does not incorporate any of the complicated strategy logic, such as callbacks like `custom_exit`, `custom_stoploss`, leverage controls, etc. The RL environment is instead a very "raw" representation of the true market, where the agent has free-will to learn the policy (read: stoploss, take profit, etc.) which is enforced by the `calculate_reward()`. Thus, it is important to consider that the agent training environment is not identical to the real world.
## Running Reinforcement Learning
Setting up and running a Reinforcement Learning model is the same as running a Regressor or Classifier. The same two flags, `--freqaimodel` and `--strategy`, must be defined on the command line:
```bash
freqtrade trade --freqaimodel ReinforcementLearner --strategy MyRLStrategy --config config.json
```
where `ReinforcementLearner` will use the templated `ReinforcementLearner` from `freqai/prediction_models/ReinforcementLearner` (or a custom user defined one located in `user_data/freqaimodels`). The strategy, on the other hand, follows the same base [feature engineering](freqai-feature-engineering.md) with `populate_any_indicators` as a typical Regressor:
```python
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
# first loop is automatically duplicating indicators for time periods
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
t = int(t)
informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, window=t)
# The following raw price values are necessary for RL models
informative[f"%-{pair}raw_close"] = informative["close"]
informative[f"%-{pair}raw_open"] = informative["open"]
informative[f"%-{pair}raw_high"] = informative["high"]
informative[f"%-{pair}raw_low"] = informative["low"]
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
]
df = df.drop(columns=skip_columns)
# Add generalized indicators here (because in live, it will call this
# function to populate indicators during training). Notice how we ensure not to
# add them multiple times
if set_generalized_indicators:
# For RL, there are no direct targets to set. This is filler (neutral)
# until the agent sends an action.
df["&-action"] = 0
return df
```
Most of the function remains the same as for typical Regressors, however, the function above shows how the strategy must pass the raw price data to the agent so that it has access to raw OHLCV in the training environment:
```python
# The following features are necessary for RL models
informative[f"%-{pair}raw_close"] = informative["close"]
informative[f"%-{pair}raw_open"] = informative["open"]
informative[f"%-{pair}raw_high"] = informative["high"]
informative[f"%-{pair}raw_low"] = informative["low"]
```
Finally, there is no explicit "label" to make - instead it is necessary to assign the `&-action` column which will contain the agent's actions when accessed in `populate_entry/exit_trends()`. In the present example, the neutral action to 0. This value should align with the environment used. FreqAI provides two environments, both use 0 as the neutral action.
After users realize there are no labels to set, they will soon understand that the agent is making its "own" entry and exit decisions. This makes strategy construction rather simple. The entry and exit signals come from the agent in the form of an integer - which are used directly to decide entries and exits in the strategy:
```python
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
enter_long_conditions = [df["do_predict"] == 1, df["&-action"] == 1]
if enter_long_conditions:
df.loc[
reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
] = (1, "long")
enter_short_conditions = [df["do_predict"] == 1, df["&-action"] == 3]
if enter_short_conditions:
df.loc[
reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
] = (1, "short")
return df
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
exit_long_conditions = [df["do_predict"] == 1, df["&-action"] == 2]
if exit_long_conditions:
df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
exit_short_conditions = [df["do_predict"] == 1, df["&-action"] == 4]
if exit_short_conditions:
df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
return df
```
It is important to consider that `&-action` depends on which environment they choose to use. The example above shows 5 actions, where 0 is neutral, 1 is enter long, 2 is exit long, 3 is enter short and 4 is exit short.
## Configuring the Reinforcement Learner
In order to configure the `Reinforcement Learner` the following dictionary must exist in the `freqai` config:
```json
"rl_config": {
"train_cycles": 25,
"add_state_info": true,
"max_trade_duration_candles": 300,
"max_training_drawdown_pct": 0.02,
"cpu_count": 8,
"model_type": "PPO",
"policy_type": "MlpPolicy",
"model_reward_parameters": {
"rr": 1,
"profit_aim": 0.025
}
}
```
Parameter details can be found [here](freqai-parameter-table.md), but in general the `train_cycles` decides how many times the agent should cycle through the candle data in its artificial environment to train weights in the model. `model_type` is a string which selects one of the available models in [stable_baselines](https://stable-baselines3.readthedocs.io/en/master/)(external link).
!!! Note
If you would like to experiment with `continual_learning`, then you should set that value to `true` in the main `freqai` configuration dictionary. This will tell the Reinforcement Learning library to continue training new models from the final state of previous models, instead of retraining new models from scratch each time a retrain is initiated.
!!! Note
Remember that the general `model_training_parameters` dictionary should contain all the model hyperparameter customizations for the particular `model_type`. For example, `PPO` parameters can be found [here](https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html).
## Creating a custom reward function
As you begin to modify the strategy and the prediction model, you will quickly realize some important differences between the Reinforcement Learner and the Regressors/Classifiers. Firstly, the strategy does not set a target value (no labels!). Instead, you set the `calculate_reward()` function inside the `MyRLEnv` class (see below). A default `calculate_reward()` is provided inside `prediction_models/ReinforcementLearner.py` to demonstrate the necessary building blocks for creating rewards, but users are encouraged to create their own custom reinforcement learning model class (see below) and save it to `user_data/freqaimodels`. It is inside the `calculate_reward()` where creative theories about the market can be expressed. For example, you can reward your agent when it makes a winning trade, and penalize the agent when it makes a losing trade. Or perhaps, you wish to reward the agent for entering trades, and penalize the agent for sitting in trades too long. Below we show examples of how these rewards are all calculated:
```python
from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
class MyCoolRLModel(ReinforcementLearner):
"""
User created RL prediction model.
Save this file to `freqtrade/user_data/freqaimodels`
then use it with:
freqtrade trade --freqaimodel MyCoolRLModel --config config.json --strategy SomeCoolStrat
Here the users can override any of the functions
available in the `IFreqaiModel` inheritance tree. Most importantly for RL, this
is where the user overrides `MyRLEnv` (see below), to define custom
`calculate_reward()` function, or to override any other parts of the environment.
This class also allows users to override any other part of the IFreqaiModel tree.
For example, the user can override `def fit()` or `def train()` or `def predict()`
to take fine-tuned control over these processes.
Another common override may be `def data_cleaning_predict()` where the user can
take fine-tuned control over the data handling pipeline.
"""
class MyRLEnv(Base5ActionRLEnv):
"""
User made custom environment. This class inherits from BaseEnvironment and gym.env.
Users can override any functions from those parent classes. Here is an example
of a user customized `calculate_reward()` function.
"""
def calculate_reward(self, action: int) -> float:
# first, penalize if the action is not valid
if not self._is_valid(action):
return -2
pnl = self.get_unrealized_profit()
factor = 100
# reward agent for entering trades
if action in (Actions.Long_enter.value, Actions.Short_enter.value) \
and self._position == Positions.Neutral:
return 25
# discourage agent from not entering trades
if action == Actions.Neutral.value and self._position == Positions.Neutral:
return -1
max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
trade_duration = self._current_tick - self._last_trade_tick
if trade_duration <= max_trade_duration:
factor *= 1.5
elif trade_duration > max_trade_duration:
factor *= 0.5
# discourage sitting in position
if self._position in (Positions.Short, Positions.Long) and \
action == Actions.Neutral.value:
return -1 * trade_duration / max_trade_duration
# close long
if action == Actions.Long_exit.value and self._position == Positions.Long:
if pnl > self.profit_aim * self.rr:
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
return float(pnl * factor)
# close short
if action == Actions.Short_exit.value and self._position == Positions.Short:
if pnl > self.profit_aim * self.rr:
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
return float(pnl * factor)
return 0.
```
### Using Tensorboard
Reinforcement Learning models benefit from tracking training metrics. FreqAI has integrated Tensorboard to allow users to track training and evaluation performance across all coins and across all retrainings. Tensorboard is activated via the following command:
```bash
cd freqtrade
tensorboard --logdir user_data/models/unique-id
```
where `unique-id` is the `identifier` set in the `freqai` configuration file. This command must be run in a separate shell to view the output in their browser at 127.0.0.1:6006 (6006 is the default port used by Tensorboard).
![tensorboard](assets/tensorboard.jpg)
### Choosing a base environment
FreqAI provides two base environments, `Base4ActionEnvironment` and `Base5ActionEnvironment`. As the names imply, the environments are customized for agents that can select from 4 or 5 actions. In the `Base4ActionEnvironment`, the agent can enter long, enter short, hold neutral, or exit position. Meanwhile, in the `Base5ActionEnvironment`, the agent has the same actions as Base4, but instead of a single exit action, it separates exit long and exit short. The main changes stemming from the environment selection include:
* the actions available in the `calculate_reward`
* the actions consumed by the user strategy
Both of the FreqAI provided environments inherit from an action/position agnostic environment object called the `BaseEnvironment`, which contains all shared logic. The architecture is designed to be easily customized. The simplest customization is the `calculate_reward()` (see details [here](#creating-a-custom-reward-function)). However, the customizations can be further extended into any of the functions inside the environment. You can do this by simply overriding those functions inside your `MyRLEnv` in the prediction model file. Or for more advanced customizations, it is encouraged to create an entirely new environment inherited from `BaseEnvironment`.
!!! Note
FreqAI does not provide by default, a long-only training environment. However, creating one should be as simple as copy-pasting one of the built in environments and removing the `short` actions (and all associated references to those).

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@ -73,12 +73,19 @@ Backtesting mode requires [downloading the necessary data](#downloading-data-to-
To allow for tweaking your strategy (**not** the features!), FreqAI will automatically save the predictions during backtesting so that they can be reused for future backtests and live runs using the same `identifier` model. This provides a performance enhancement geared towards enabling **high-level hyperopting** of entry/exit criteria.
An additional directory called `predictions`, which contains all the predictions stored in `hdf` format, will be created in the `unique-id` folder.
An additional directory called `backtesting_predictions`, which contains all the predictions stored in `hdf` format, will be created in the `unique-id` folder.
To change your **features**, you **must** set a new `identifier` in the config to signal to FreqAI to train new models.
To save the models generated during a particular backtest so that you can start a live deployment from one of them instead of training a new model, you must set `save_backtest_models` to `True` in the config.
### Backtest live collected predictions
FreqAI allow you to reuse live historic predictions through the backtest parameter `--freqai-backtest-live-models`. This can be useful when you want to reuse predictions generated in dry/run for comparison or other study.
The `--timerange` parameter must not be informed, as it will be automatically calculated through the data in the historic predictions file.
### Downloading data to cover the full backtest period
For live/dry deployments, FreqAI will download the necessary data automatically. However, to use backtesting functionality, you need to download the necessary data using `download-data` (details [here](data-download.md#data-downloading)). You need to pay careful attention to understanding how much *additional* data needs to be downloaded to ensure that there is a sufficient amount of training data *before* the start of the backtesting time range. The amount of additional data can be roughly estimated by moving the start date of the time range backwards by `train_period_days` and the `startup_candle_count` (see the [parameter table](freqai-parameter-table.md) for detailed descriptions of these parameters) from the beginning of the desired backtesting time range.

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@ -268,7 +268,7 @@ This option is disabled by default, and will only apply if set to > 0.
The `max_value` setting removes pairs where the minimum value change is above a specified value.
This is useful when an exchange has unbalanced limits. For example, if step-size = 1 (so you can only buy 1, or 2, or 3, but not 1.1 Coins) - and the price is pretty high (like 20\$) as the coin has risen sharply since the last limit adaption.
As a result of the above, you can only buy for 20\$, or 40\$ - but not for 25\$.
On exchanges that deduct fees from the receiving currency (e.g. FTX) - this can result in high value coins / amounts that are unsellable as the amount is slightly below the limit.
On exchanges that deduct fees from the receiving currency (e.g. binance) - this can result in high value coins / amounts that are unsellable as the amount is slightly below the limit.
The `low_price_ratio` setting removes pairs where a raise of 1 price unit (pip) is above the `low_price_ratio` ratio.
This option is disabled by default, and will only apply if set to > 0.

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docs/index.md
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@ -32,7 +32,7 @@ Freqtrade is a free and open source crypto trading bot written in Python. It is
- Run: Test your strategy with simulated money (Dry-Run mode) or deploy it with real money (Live-Trade mode).
- Run using Edge (optional module): The concept is to find the best historical [trade expectancy](edge.md#expectancy) by markets based on variation of the stop-loss and then allow/reject markets to trade. The sizing of the trade is based on a risk of a percentage of your capital.
- Control/Monitor: Use Telegram or a WebUI (start/stop the bot, show profit/loss, daily summary, current open trades results, etc.).
- Analyse: Further analysis can be performed on either Backtesting data or Freqtrade trading history (SQL database), including automated standard plots, and methods to load the data into [interactive environments](data-analysis.md).
- Analyze: Further analysis can be performed on either Backtesting data or Freqtrade trading history (SQL database), including automated standard plots, and methods to load the data into [interactive environments](data-analysis.md).
## Supported exchange marketplaces
@ -40,7 +40,6 @@ Please read the [exchange specific notes](exchanges.md) to learn about eventual,
- [X] [Binance](https://www.binance.com/)
- [X] [Bittrex](https://bittrex.com/)
- [X] [FTX](https://ftx.com/#a=2258149)
- [X] [Gate.io](https://www.gate.io/ref/6266643)
- [X] [Huobi](http://huobi.com/)
- [X] [Kraken](https://kraken.com/)
@ -51,7 +50,7 @@ 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] [OKX](https://okx.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.

2
docs/producer-consumer.md
View File

@ -21,6 +21,7 @@ Enable subscribing to an instance by adding the `external_message_consumer` sect
"name": "default", // This can be any name you'd like, default is "default"
"host": "127.0.0.1", // The host from your producer's api_server config
"port": 8080, // The port from your producer's api_server config
"secure": false, // Use a secure websockets connection, default false
"ws_token": "sercet_Ws_t0ken" // The ws_token from your producer's api_server config
}
],
@ -42,6 +43,7 @@ Enable subscribing to an instance by adding the `external_message_consumer` sect
| `producers.name` | **Required.** Name of this producer. This name must be used in calls to `get_producer_pairs()` and `get_producer_df()` if more than one producer is used.<br> **Datatype:** string
| `producers.host` | **Required.** The hostname or IP address from your producer.<br> **Datatype:** string
| `producers.port` | **Required.** The port matching the above host.<br> **Datatype:** string
| `producers.secure` | **Optional.** Use ssl in websockets connection. Default False.<br> **Datatype:** string
| `producers.ws_token` | **Required.** `ws_token` as configured on the producer.<br> **Datatype:** string
| | **Optional settings**
| `wait_timeout` | Timeout until we ping again if no message is received. <br>*Defaults to `300`.*<br> **Datatype:** Integer - in seconds.

6
docs/requirements-docs.txt
View File

@ -1,6 +1,6 @@
markdown==3.3.7
mkdocs==1.4.1
mkdocs-material==8.5.7
mkdocs==1.4.2
mkdocs-material==8.5.11
mdx_truly_sane_lists==1.3
pymdown-extensions==9.7
pymdown-extensions==9.9
jinja2==3.1.2

38
docs/rest-api.md
View File

@ -389,6 +389,44 @@ Now anytime those types of RPC messages are sent in the bot, you will receive th
}
```
#### Reverse Proxy setup
When using [Nginx](https://nginx.org/en/docs/), the following configuration is required for WebSockets to work (Note this configuration is incomplete, it's missing some information and can not be used as is):
Please make sure to replace `<freqtrade_listen_ip>` (and the subsequent port) with the IP and Port matching your configuration/setup.
```
http {
map $http_upgrade $connection_upgrade {
default upgrade;
'' close;
}
#...
server {
#...
location / {
proxy_http_version 1.1;
proxy_pass http://<freqtrade_listen_ip>:8080;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection $connection_upgrade;
proxy_set_header Host $host;
}
}
}
```
To properly configure your reverse proxy (securely), please consult it's documentation for proxying websockets.
- **Traefik**: Traefik supports websockets out of the box, see the [documentation](https://doc.traefik.io/traefik/)
- **Caddy**: Caddy v2 supports websockets out of the box, see the [documentation](https://caddyserver.com/docs/v2-upgrade#proxy)
!!! Tip "SSL certificates"
You can use tools like certbot to setup ssl certificates to access your bot's UI through encrypted connection by using any fo the above reverse proxies.
While this will protect your data in transit, we do not recommend to run the freqtrade API outside of your private network (VPN, SSH tunnel).
### OpenAPI interface
To enable the builtin openAPI interface (Swagger UI), specify `"enable_openapi": true` in the api_server configuration.

2
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), FTX (stop limit and stop-market) 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), Gateio (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.

14
docs/strategy-customization.md
View File

@ -446,15 +446,17 @@ A full sample can be found [in the DataProvider section](#complete-data-provider
??? Note "Alternative candle types"
Informative_pairs can also provide a 3rd tuple element defining the candle type explicitly.
Availability of alternative candle-types will depend on the trading-mode and the exchange. Details about this can be found in the exchange documentation.
Availability of alternative candle-types will depend on the trading-mode and the exchange.
In general, spot pairs cannot be used in futures markets, and futures candles can't be used as informative pairs for spot bots.
Details about this may vary, if they do, this can be found in the exchange documentation.
``` python
def informative_pairs(self):
return [
("ETH/USDT", "5m", ""), # Uses default candletype, depends on trading_mode
("ETH/USDT", "5m", "spot"), # Forces usage of spot candles
("BTC/TUSD", "15m", "futures"), # Uses futures candles
("BTC/TUSD", "15m", "mark"), # Uses mark candles
("ETH/USDT", "5m", ""), # Uses default candletype, depends on trading_mode (recommended)
("ETH/USDT", "5m", "spot"), # Forces usage of spot candles (only valid for bots running on spot markets).
("BTC/TUSD", "15m", "futures"), # Uses futures candles (only bots with `trading_mode=futures`)
("BTC/TUSD", "15m", "mark"), # Uses mark candles (only bots with `trading_mode=futures`)
]
```
***
@ -723,7 +725,7 @@ if self.dp.runmode.value in ('live', 'dry_run'):
!!! Warning
Although the ticker data structure is a part of the ccxt Unified Interface, the values returned by this method can
vary for different exchanges. For instance, many exchanges do not return `vwap` values, the FTX exchange
vary for different exchanges. For instance, many exchanges do not return `vwap` values, some exchanges
does not always fills in the `last` field (so it can be None), etc. So you need to carefully verify the ticker
data returned from the exchange and add appropriate error handling / defaults.

2
docs/strategy_analysis_example.md
View File

@ -232,7 +232,7 @@ graph = generate_candlestick_graph(pair=pair,
# Show graph inline
# graph.show()
# Render graph in a seperate window
# Render graph in a separate window
graph.show(renderer="browser")
```

7
docs/utils.md
View File

@ -263,7 +263,6 @@ equos True missing opt: fetchTicker, fetchTickers
eterbase True
fcoin True missing opt: fetchMyTrades, fetchTickers
fcoinjp True missing opt: fetchMyTrades, fetchTickers
ftx True
gateio True
gemini True
gopax True
@ -369,7 +368,6 @@ fcoin True missing opt: fetchMyTrades, fetchTickers
fcoinjp True missing opt: fetchMyTrades, fetchTickers
flowbtc False missing: fetchOrder, fetchOHLCV
foxbit False missing: fetchOrder, fetchOHLCV
ftx True
gateio True
gemini True
gopax True
@ -724,6 +722,7 @@ usage: freqtrade backtesting-analysis [-h] [-v] [--logfile FILE] [-V]
[--enter-reason-list ENTER_REASON_LIST [ENTER_REASON_LIST ...]]
[--exit-reason-list EXIT_REASON_LIST [EXIT_REASON_LIST ...]]
[--indicator-list INDICATOR_LIST [INDICATOR_LIST ...]]
[--timerange YYYYMMDD-[YYYYMMDD]]
optional arguments:
-h, --help show this help message and exit
@ -746,6 +745,10 @@ optional arguments:
--indicator-list INDICATOR_LIST [INDICATOR_LIST ...]
Comma separated list of indicators to analyse. e.g.
'close,rsi,bb_lowerband,profit_abs'
--timerange YYYYMMDD-[YYYYMMDD]
Timerange to filter trades for analysis,
start inclusive, end exclusive. e.g.
20220101-20220201
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).

2
freqtrade/__init__.py
View File

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

8
freqtrade/commands/analyze_commands.py
View File

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

5
freqtrade/commands/arguments.py
View File

@ -25,7 +25,8 @@ ARGS_COMMON_OPTIMIZE = ["timeframe", "timerange", "dataformat_ohlcv",
ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + ["position_stacking", "use_max_market_positions",
"enable_protections", "dry_run_wallet", "timeframe_detail",
"strategy_list", "export", "exportfilename",
"backtest_breakdown", "backtest_cache"]
"backtest_breakdown", "backtest_cache",
"freqai_backtest_live_models"]
ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
"position_stacking", "use_max_market_positions",
@ -105,7 +106,7 @@ ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperop
"disableparamexport", "backtest_breakdown"]
ARGS_ANALYZE_ENTRIES_EXITS = ["exportfilename", "analysis_groups", "enter_reason_list",
"exit_reason_list", "indicator_list"]
"exit_reason_list", "indicator_list", "timerange"]
NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
"list-markets", "list-pairs", "list-strategies", "list-freqaimodels",

1
freqtrade/commands/build_config_commands.py
View File

@ -108,7 +108,6 @@ def ask_user_config() -> Dict[str, Any]:
"binance",
"binanceus",
"bittrex",
"ftx",
"gateio",
"huobi",
"kraken",

5
freqtrade/commands/cli_options.py
View File

@ -668,4 +668,9 @@ AVAILABLE_CLI_OPTIONS = {
help='Specify additional lookup path for freqaimodels.',
metavar='PATH',
),
"freqai_backtest_live_models": Arg(
'--freqai-backtest-live-models',
help='Run backtest with ready models.',
action='store_true'
),
}

21
freqtrade/configuration/config_validation.py
View File

@ -86,6 +86,7 @@ def validate_config_consistency(conf: Dict[str, Any], preliminary: bool = False)
_validate_unlimited_amount(conf)
_validate_ask_orderbook(conf)
_validate_freqai_hyperopt(conf)
_validate_freqai_backtest(conf)
_validate_freqai_include_timeframes(conf)
_validate_consumers(conf)
validate_migrated_strategy_settings(conf)
@ -355,6 +356,26 @@ def _validate_freqai_include_timeframes(conf: Dict[str, Any]) -> None:
f"`include_timeframes`.Offending include-timeframes: {', '.join(offending_lines)}")
def _validate_freqai_backtest(conf: Dict[str, Any]) -> None:
if conf.get('runmode', RunMode.OTHER) == RunMode.BACKTEST:
freqai_enabled = conf.get('freqai', {}).get('enabled', False)
timerange = conf.get('timerange')
freqai_backtest_live_models = conf.get('freqai_backtest_live_models', False)
if freqai_backtest_live_models and freqai_enabled and timerange:
raise OperationalException(
'Using timerange parameter is not supported with '
'--freqai-backtest-live-models parameter.')
if freqai_backtest_live_models and not freqai_enabled:
raise OperationalException(
'Using --freqai-backtest-live-models parameter is only '
'supported with a FreqAI strategy.')
if freqai_enabled and not freqai_backtest_live_models and not timerange:
raise OperationalException(
'Please pass --timerange if you intend to use FreqAI for backtesting.')
def _validate_consumers(conf: Dict[str, Any]) -> None:
emc_conf = conf.get('external_message_consumer', {})
if emc_conf.get('enabled', False):

6
freqtrade/configuration/configuration.py
View File

@ -279,6 +279,9 @@ class Configuration:
self._args_to_config(config, argname='disableparamexport',
logstring='Parameter --disableparamexport detected: {} ...')
self._args_to_config(config, argname='freqai_backtest_live_models',
logstring='Parameter --freqai-backtest-live-models detected ...')
# Edge section:
if 'stoploss_range' in self.args and self.args["stoploss_range"]:
txt_range = eval(self.args["stoploss_range"])
@ -459,6 +462,9 @@ class Configuration:
self._args_to_config(config, argname='indicator_list',
logstring='Analysis indicator list: {}')
self._args_to_config(config, argname='timerange',
logstring='Filter trades by timerange: {}')
def _process_runmode(self, config: Config) -> None:
self._args_to_config(config, argname='dry_run',

49
freqtrade/configuration/timerange.py
View File

@ -3,11 +3,12 @@ This module contains the argument manager class
"""
import logging
import re
from datetime import datetime
from datetime import datetime, timezone
from typing import Optional
import arrow
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.exceptions import OperationalException
@ -29,6 +30,52 @@ class TimeRange:
self.startts: int = startts
self.stopts: int = stopts
@property
def startdt(self) -> Optional[datetime]:
if self.startts:
return datetime.fromtimestamp(self.startts, tz=timezone.utc)
return None
@property
def stopdt(self) -> Optional[datetime]:
if self.stopts:
return datetime.fromtimestamp(self.stopts, tz=timezone.utc)
return None
@property
def timerange_str(self) -> str:
"""
Returns a string representation of the timerange as used by parse_timerange.
Follows the format yyyymmdd-yyyymmdd - leaving out the parts that are not set.
"""
start = ''
stop = ''
if startdt := self.startdt:
start = startdt.strftime('%Y%m%d')
if stopdt := self.stopdt:
stop = stopdt.strftime('%Y%m%d')
return f"{start}-{stop}"
@property
def start_fmt(self) -> str:
"""
Returns a string representation of the start date
"""
val = 'unbounded'
if (startdt := self.startdt) is not None:
val = startdt.strftime(DATETIME_PRINT_FORMAT)
return val
@property
def stop_fmt(self) -> str:
"""
Returns a string representation of the stop date
"""
val = 'unbounded'
if (stopdt := self.stopdt) is not None:
val = stopdt.strftime(DATETIME_PRINT_FORMAT)
return val
def __eq__(self, other):
"""Override the default Equals behavior"""
return (self.starttype == other.starttype and self.stoptype == other.stoptype

24
freqtrade/constants.py
View File

@ -159,6 +159,7 @@ CONF_SCHEMA = {
'ignore_buying_expired_candle_after': {'type': 'number'},
'trading_mode': {'type': 'string', 'enum': TRADING_MODES},
'margin_mode': {'type': 'string', 'enum': MARGIN_MODES},
'reduce_df_footprint': {'type': 'boolean', 'default': False},
'liquidation_buffer': {'type': 'number', 'minimum': 0.0, 'maximum': 0.99},
'backtest_breakdown': {
'type': 'array',
@ -511,6 +512,7 @@ CONF_SCHEMA = {
'minimum': 0,
'maximum': 65535
},
'secure': {'type': 'boolean', 'default': False},
'ws_token': {'type': 'string'},
},
'required': ['name', 'host', 'ws_token']
@ -542,7 +544,7 @@ CONF_SCHEMA = {
"keras": {"type": "boolean", "default": False},
"write_metrics_to_disk": {"type": "boolean", "default": False},
"purge_old_models": {"type": "boolean", "default": True},
"conv_width": {"type": "integer", "default": 2},
"conv_width": {"type": "integer", "default": 1},
"train_period_days": {"type": "integer", "default": 0},
"backtest_period_days": {"type": "number", "default": 7},
"identifier": {"type": "string", "default": "example"},
@ -576,9 +578,27 @@ CONF_SCHEMA = {
},
},
"model_training_parameters": {
"type": "object"
},
"rl_config": {
"type": "object",
"properties": {
"n_estimators": {"type": "integer", "default": 1000}
"train_cycles": {"type": "integer"},
"max_trade_duration_candles": {"type": "integer"},
"add_state_info": {"type": "boolean", "default": False},
"max_training_drawdown_pct": {"type": "number", "default": 0.02},
"cpu_count": {"type": "integer", "default": 1},
"model_type": {"type": "string", "default": "PPO"},
"policy_type": {"type": "string", "default": "MlpPolicy"},
"net_arch": {"type": "array", "default": [128, 128]},
"randomize_startinng_position": {"type": "boolean", "default": False},
"model_reward_parameters": {
"type": "object",
"properties": {
"rr": {"type": "number", "default": 1},
"profit_aim": {"type": "number", "default": 0.025}
}
}
},
},
},

4
freqtrade/data/btanalysis.py
View File

@ -26,7 +26,7 @@ BT_DATA_COLUMNS = ['pair', 'stake_amount', 'amount', 'open_date', 'close_date',
'profit_ratio', 'profit_abs', 'exit_reason',
'initial_stop_loss_abs', 'initial_stop_loss_ratio', 'stop_loss_abs',
'stop_loss_ratio', 'min_rate', 'max_rate', 'is_open', 'enter_tag',
'is_short', 'open_timestamp', 'close_timestamp', 'orders'
'leverage', 'is_short', 'open_timestamp', 'close_timestamp', 'orders'
]
@ -280,6 +280,8 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
# Compatibility support for pre short Columns
if 'is_short' not in df.columns:
df['is_short'] = 0
if 'leverage' not in df.columns:
df['leverage'] = 1.0
if 'enter_tag' not in df.columns:
df['enter_tag'] = df['buy_tag']
df = df.drop(['buy_tag'], axis=1)

34
freqtrade/data/converter.py
View File

@ -3,10 +3,10 @@ Functions to convert data from one format to another
"""
import itertools
import logging
from datetime import datetime, timezone
from operator import itemgetter
from typing import Dict, List
import numpy as np
import pandas as pd
from pandas import DataFrame, to_datetime
@ -176,11 +176,9 @@ def trim_dataframe(df: DataFrame, timerange, df_date_col: str = 'date',
df = df.iloc[startup_candles:, :]
else:
if timerange.starttype == 'date':
start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
df = df.loc[df[df_date_col] >= start, :]
df = df.loc[df[df_date_col] >= timerange.startdt, :]
if timerange.stoptype == 'date':
stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
df = df.loc[df[df_date_col] <= stop, :]
df = df.loc[df[df_date_col] <= timerange.stopdt, :]
return df
@ -356,3 +354,29 @@ def convert_ohlcv_format(
if erase and convert_from != convert_to:
logger.info(f"Deleting source data for {pair} / {timeframe}")
src.ohlcv_purge(pair=pair, timeframe=timeframe, candle_type=candle_type)
def reduce_dataframe_footprint(df: DataFrame) -> DataFrame:
"""
Ensure all values are float32 in the incoming dataframe.
:param df: Dataframe to be converted to float/int 32s
:return: Dataframe converted to float/int 32s
"""
logger.debug(f"Memory usage of dataframe is "
f"{df.memory_usage().sum() / 1024**2:.2f} MB")
df_dtypes = df.dtypes
for column, dtype in df_dtypes.items():
if column in ['open', 'high', 'low', 'close', 'volume']:
continue
if dtype == np.float64:
df_dtypes[column] = np.float32
elif dtype == np.int64:
df_dtypes[column] = np.int32
df = df.astype(df_dtypes)
logger.debug(f"Memory usage after optimization is: "
f"{df.memory_usage().sum() / 1024**2:.2f} MB")
return df

11
freqtrade/data/dataprovider.py
View File

@ -104,13 +104,15 @@ class DataProvider:
def _emit_df(
self,
pair_key: PairWithTimeframe,
dataframe: DataFrame
dataframe: DataFrame,
new_candle: bool
) -> None:
"""
Send this dataframe as an ANALYZED_DF message to RPC
:param pair_key: PairWithTimeframe tuple
:param data: Tuple containing the DataFrame and the datetime it was cached
:param dataframe: Dataframe to emit
:param new_candle: This is a new candle
"""
if self.__rpc:
self.__rpc.send_msg(
@ -123,6 +125,11 @@ class DataProvider:
}
}
)
if new_candle:
self.__rpc.send_msg({
'type': RPCMessageType.NEW_CANDLE,
'data': pair_key,
})
def _add_external_df(
self,

100
freqtrade/data/entryexitanalysis.py
View File

@ -1,11 +1,12 @@
import logging
from pathlib import Path
from typing import List, Optional
import joblib
import pandas as pd
from tabulate import tabulate
from freqtrade.configuration import TimeRange
from freqtrade.constants import Config
from freqtrade.data.btanalysis import (get_latest_backtest_filename, load_backtest_data,
load_backtest_stats)
from freqtrade.exceptions import OperationalException
@ -152,37 +153,55 @@ def _do_group_table_output(bigdf, glist):
logger.warning("Invalid group mask specified.")
def _print_results(analysed_trades, stratname, analysis_groups,
enter_reason_list, exit_reason_list,
indicator_list, columns=None):
if columns is None:
columns = ['pair', 'open_date', 'close_date', 'profit_abs', 'enter_reason', 'exit_reason']
def _select_rows_within_dates(df, timerange=None, df_date_col: str = 'date'):
if timerange:
if timerange.starttype == 'date':
df = df.loc[(df[df_date_col] >= timerange.startdt)]
if timerange.stoptype == 'date':
df = df.loc[(df[df_date_col] < timerange.stopdt)]
return df
bigdf = pd.DataFrame()
def _select_rows_by_tags(df, enter_reason_list, exit_reason_list):
if enter_reason_list and "all" not in enter_reason_list:
df = df.loc[(df['enter_reason'].isin(enter_reason_list))]
if exit_reason_list and "all" not in exit_reason_list:
df = df.loc[(df['exit_reason'].isin(exit_reason_list))]
return df
def prepare_results(analysed_trades, stratname,
enter_reason_list, exit_reason_list,
timerange=None):
res_df = pd.DataFrame()
for pair, trades in analysed_trades[stratname].items():
bigdf = pd.concat([bigdf, trades], ignore_index=True)
res_df = pd.concat([res_df, trades], ignore_index=True)
if bigdf.shape[0] > 0 and ('enter_reason' in bigdf.columns):
res_df = _select_rows_within_dates(res_df, timerange)
if res_df is not None and res_df.shape[0] > 0 and ('enter_reason' in res_df.columns):
res_df = _select_rows_by_tags(res_df, enter_reason_list, exit_reason_list)
return res_df
def print_results(res_df, analysis_groups, indicator_list):
if res_df.shape[0] > 0:
if analysis_groups:
_do_group_table_output(bigdf, analysis_groups)
if enter_reason_list and "all" not in enter_reason_list:
bigdf = bigdf.loc[(bigdf['enter_reason'].isin(enter_reason_list))]
if exit_reason_list and "all" not in exit_reason_list:
bigdf = bigdf.loc[(bigdf['exit_reason'].isin(exit_reason_list))]
_do_group_table_output(res_df, analysis_groups)
if "all" in indicator_list:
print(bigdf)
print(res_df)
elif indicator_list is not None:
available_inds = []
for ind in indicator_list:
if ind in bigdf:
if ind in res_df:
available_inds.append(ind)
ilist = ["pair", "enter_reason", "exit_reason"] + available_inds
_print_table(bigdf[ilist], sortcols=['exit_reason'], show_index=False)
_print_table(res_df[ilist], sortcols=['exit_reason'], show_index=False)
else:
print("\\_ No trades to show")
print("\\No trades to show")
def _print_table(df, sortcols=None, show_index=False):
@ -201,27 +220,34 @@ def _print_table(df, sortcols=None, show_index=False):
)
def process_entry_exit_reasons(backtest_dir: Path,
pairlist: List[str],
analysis_groups: Optional[List[str]] = ["0", "1", "2"],
enter_reason_list: Optional[List[str]] = ["all"],
exit_reason_list: Optional[List[str]] = ["all"],
indicator_list: Optional[List[str]] = []):
def process_entry_exit_reasons(config: Config):
try:
backtest_stats = load_backtest_stats(backtest_dir)
analysis_groups = config.get('analysis_groups', [])
enter_reason_list = config.get('enter_reason_list', ["all"])
exit_reason_list = config.get('exit_reason_list', ["all"])
indicator_list = config.get('indicator_list', [])
timerange = TimeRange.parse_timerange(None if config.get(
'timerange') is None else str(config.get('timerange')))
backtest_stats = load_backtest_stats(config['exportfilename'])
for strategy_name, results in backtest_stats['strategy'].items():
trades = load_backtest_data(backtest_dir, strategy_name)
trades = load_backtest_data(config['exportfilename'], strategy_name)
if not trades.empty:
signal_candles = _load_signal_candles(backtest_dir)
analysed_trades_dict = _process_candles_and_indicators(pairlist, strategy_name,
trades, signal_candles)
_print_results(analysed_trades_dict,
strategy_name,
analysis_groups,
enter_reason_list,
exit_reason_list,
indicator_list)
signal_candles = _load_signal_candles(config['exportfilename'])
analysed_trades_dict = _process_candles_and_indicators(
config['exchange']['pair_whitelist'], strategy_name,
trades, signal_candles)
res_df = prepare_results(analysed_trades_dict, strategy_name,
enter_reason_list, exit_reason_list,
timerange=timerange)
print_results(res_df,
analysis_groups,
indicator_list)
except ValueError as e:
raise OperationalException(e) from e

6
freqtrade/data/history/history_utils.py
View File

@ -1,6 +1,6 @@
import logging
import operator
from datetime import datetime, timezone
from datetime import datetime
from pathlib import Path
from typing import Dict, List, Optional, Tuple
@ -160,9 +160,9 @@ def _load_cached_data_for_updating(
end = None
if timerange:
if timerange.starttype == 'date':
start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
start = timerange.startdt
if timerange.stoptype == 'date':
end = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
end = timerange.stopdt
# Intentionally don't pass timerange in - since we need to load the full dataset.
data = data_handler.ohlcv_load(pair, timeframe=timeframe,

11
freqtrade/data/history/idatahandler.py
View File

@ -102,6 +102,11 @@ class IDataHandler(ABC):
:return: (min, max)
"""
data = self._ohlcv_load(pair, timeframe, None, candle_type)
if data.empty:
return (
datetime.fromtimestamp(0, tz=timezone.utc),
datetime.fromtimestamp(0, tz=timezone.utc)
)
return data.iloc[0]['date'].to_pydatetime(), data.iloc[-1]['date'].to_pydatetime()
@abstractmethod
@ -361,13 +366,11 @@ class IDataHandler(ABC):
"""
if timerange.starttype == 'date':
start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
if pairdata.iloc[0]['date'] > start:
if pairdata.iloc[0]['date'] > timerange.startdt:
logger.warning(f"{pair}, {candle_type}, {timeframe}, "
f"data starts at {pairdata.iloc[0]['date']:%Y-%m-%d %H:%M:%S}")
if timerange.stoptype == 'date':
stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
if pairdata.iloc[-1]['date'] < stop:
if pairdata.iloc[-1]['date'] < timerange.stopdt:
logger.warning(f"{pair}, {candle_type}, {timeframe}, "
f"data ends at {pairdata.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}")

4
freqtrade/edge/edge_positioning.py
View File

@ -392,7 +392,7 @@ class Edge:
# Returning a list of pairs in order of "expectancy"
return final
def _find_trades_for_stoploss_range(self, df, pair, stoploss_range):
def _find_trades_for_stoploss_range(self, df, pair: str, stoploss_range) -> list:
buy_column = df['enter_long'].values
sell_column = df['exit_long'].values
date_column = df['date'].values
@ -407,7 +407,7 @@ class Edge:
return result
def _detect_next_stop_or_sell_point(self, buy_column, sell_column, date_column,
ohlc_columns, stoploss, pair):
ohlc_columns, stoploss, pair: str):
"""
Iterate through ohlc_columns in order to find the next trade
Next trade opens from the first buy signal noticed to

2
freqtrade/enums/__init__.py
View File

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

4
freqtrade/enums/rpcmessagetype.py
View File

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

1
freqtrade/exchange/__init__.py
View File

@ -18,7 +18,6 @@ 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.ftx import Ftx
from freqtrade.exchange.gateio import Gateio
from freqtrade.exchange.hitbtc import Hitbtc
from freqtrade.exchange.huobi import Huobi

2582
freqtrade/exchange/binance_leverage_tiers.json
View File

File diff suppressed because it is too large Load Diff

8
freqtrade/exchange/bybit.py
View File

@ -20,8 +20,12 @@ class Bybit(Exchange):
"""
_ft_has: Dict = {
"ohlcv_candle_limit": 200,
"ccxt_futures_name": "linear"
"ohlcv_candle_limit": 1000,
"ccxt_futures_name": "linear",
"ohlcv_has_history": False,
}
_ft_has_futures: Dict = {
"ohlcv_has_history": True,
}
_supported_trading_mode_margin_pairs: List[Tuple[TradingMode, MarginMode]] = [

1
freqtrade/exchange/common.py
View File

@ -52,7 +52,6 @@ MAP_EXCHANGE_CHILDCLASS = {
SUPPORTED_EXCHANGES = [
'binance',
'bittrex',
'ftx',
'gateio',
'huobi',
'kraken',

11
freqtrade/exchange/exchange.py
View File

@ -1689,6 +1689,17 @@ class Exchange:
@retrier
def get_fee(self, symbol: str, type: str = '', side: str = '', amount: float = 1,
price: float = 1, taker_or_maker: MakerTaker = 'maker') -> float:
"""
Retrieve fee from exchange
:param symbol: Pair
:param type: Type of order (market, limit, ...)
:param side: Side of order (buy, sell)
:param amount: Amount of order
:param price: Price of order
:param taker_or_maker: 'maker' or 'taker' (ignored if "type" is provided)
"""
if type and type == 'market':
taker_or_maker = 'taker'
try:
if self._config['dry_run'] and self._config.get('fee', None) is not None:
return self._config['fee']

178
freqtrade/exchange/ftx.py
View File

@ -1,178 +0,0 @@
""" FTX exchange subclass """
import logging
from typing import Any, Dict, List, Optional, Tuple
import ccxt
from freqtrade.constants import BuySell
from freqtrade.enums import MarginMode, TradingMode
from freqtrade.exceptions import (DDosProtection, InsufficientFundsError, InvalidOrderException,
OperationalException, TemporaryError)
from freqtrade.exchange import Exchange
from freqtrade.exchange.common import API_FETCH_ORDER_RETRY_COUNT, retrier
from freqtrade.misc import safe_value_fallback2
logger = logging.getLogger(__name__)
class Ftx(Exchange):
_ft_has: Dict = {
"order_time_in_force": ['GTC', 'IOC', 'PO'],
"stoploss_on_exchange": True,
"ohlcv_candle_limit": 1500,
"ohlcv_require_since": True,
"ohlcv_volume_currency": "quote",
"mark_ohlcv_price": "index",
"mark_ohlcv_timeframe": "1h",
}
_supported_trading_mode_margin_pairs: List[Tuple[TradingMode, MarginMode]] = [
# TradingMode.SPOT always supported and not required in this list
# (TradingMode.MARGIN, MarginMode.CROSS),
# (TradingMode.FUTURES, MarginMode.CROSS)
]
def stoploss_adjust(self, stop_loss: float, order: Dict, side: str) -> bool:
"""
Verify stop_loss against stoploss-order value (limit or price)
Returns True if adjustment is necessary.
"""
return order['type'] == 'stop' and (
side == "sell" and stop_loss > float(order['price']) or
side == "buy" and stop_loss < float(order['price'])
)
@retrier(retries=0)
def stoploss(self, pair: str, amount: float, stop_price: float,
order_types: Dict, side: BuySell, leverage: float) -> Dict:
"""
Creates a stoploss order.
depending on order_types.stoploss configuration, uses 'market' or limit order.
Limit orders are defined by having orderPrice set, otherwise a market order is used.
"""
limit_price_pct = order_types.get('stoploss_on_exchange_limit_ratio', 0.99)
if side == "sell":
limit_rate = stop_price * limit_price_pct
else:
limit_rate = stop_price * (2 - limit_price_pct)
ordertype = "stop"
stop_price = self.price_to_precision(pair, stop_price)
if self._config['dry_run']:
dry_order = self.create_dry_run_order(
pair, ordertype, side, amount, stop_price, leverage, stop_loss=True)
return dry_order
try:
params = self._params.copy()
if order_types.get('stoploss', 'market') == 'limit':
# set orderPrice to place limit order, otherwise it's a market order
params['orderPrice'] = limit_rate
if self.trading_mode == TradingMode.FUTURES:
params.update({'reduceOnly': True})
params['stopPrice'] = stop_price
amount = self.amount_to_precision(pair, amount)
self._lev_prep(pair, leverage, side)
order = self._api.create_order(symbol=pair, type=ordertype, side=side,
amount=amount, params=params)
self._log_exchange_response('create_stoploss_order', order)
logger.info('stoploss order added for %s. '
'stop price: %s.', pair, stop_price)
return order
except ccxt.InsufficientFunds as e:
raise InsufficientFundsError(
f'Insufficient funds to create {ordertype} {side} order on market {pair}. '
f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
f'Message: {e}') from e
except ccxt.InvalidOrder as e:
raise InvalidOrderException(
f'Could not create {ordertype} {side} order on market {pair}. '
f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
f'Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not place {side} order due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
@retrier(retries=API_FETCH_ORDER_RETRY_COUNT)
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:
orders = self._api.fetch_orders(pair, None, params={'type': 'stop'})
order = [order for order in orders if order['id'] == order_id]
self._log_exchange_response('fetch_stoploss_order', order)
if len(order) == 1:
if order[0].get('status') == 'closed':
# Trigger order was triggered ...
real_order_id: Optional[str] = order[0].get('info', {}).get('orderId')
# OrderId may be None for stoploss-market orders
# So we need to get it through the endpoint
# /conditional_orders/{conditional_order_id}/triggers
if not real_order_id:
res = self._api.privateGetConditionalOrdersConditionalOrderIdTriggers(
params={'conditional_order_id': order_id})
self._log_exchange_response('fetch_stoploss_order2', res)
real_order_id = res['result'][0]['orderId'] if res.get(
'result', []) else None
if real_order_id:
order1 = self._api.fetch_order(real_order_id, pair)
self._log_exchange_response('fetch_stoploss_order1', order1)
# Fake type to stop - as this was really a stop order.
order1['id_stop'] = order1['id']
order1['id'] = order_id
order1['type'] = 'stop'
order1['status_stop'] = 'triggered'
return order1
return order[0]
else:
raise InvalidOrderException(f"Could not get stoploss order for id {order_id}")
except ccxt.InvalidOrder as e:
raise InvalidOrderException(
f'Tried to get an invalid order (id: {order_id}). Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not get order due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
@retrier
def cancel_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
if self._config['dry_run']:
return {}
try:
order = self._api.cancel_order(order_id, pair, params={'type': 'stop'})
self._log_exchange_response('cancel_stoploss_order', order)
return order
except ccxt.InvalidOrder as e:
raise InvalidOrderException(
f'Could not cancel order. Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not cancel order due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
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']

16
freqtrade/exchange/kraken.py
View File

@ -218,3 +218,19 @@ class Kraken(Exchange):
fees = sum(df['open_fund'] * df['open_mark'] * amount * time_in_ratio)
return fees if is_short else -fees
def _trades_contracts_to_amount(self, trades: List) -> List:
"""
Fix "last" id issue for kraken data downloads
This whole override can probably be removed once the following
issue is closed in ccxt: https://github.com/ccxt/ccxt/issues/15827
"""
super()._trades_contracts_to_amount(trades)
if (
len(trades) > 0
and isinstance(trades[-1].get('info'), list)
and len(trades[-1].get('info', [])) > 7
):
trades[-1]['id'] = trades[-1].get('info', [])[-1]
return trades

141
freqtrade/freqai/RL/Base4ActionRLEnv.py Normal file
View File

@ -0,0 +1,141 @@
import logging
from enum import Enum
from gym import spaces
from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
logger = logging.getLogger(__name__)
class Actions(Enum):
Neutral = 0
Exit = 1
Long_enter = 2
Short_enter = 3
class Base4ActionRLEnv(BaseEnvironment):
"""
Base class for a 4 action environment
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.actions = Actions
def set_action_space(self):
self.action_space = spaces.Discrete(len(Actions))
def step(self, action: int):
"""
Logic for a single step (incrementing one candle in time)
by the agent
:param: action: int = the action type that the agent plans
to take for the current step.
:returns:
observation = current state of environment
step_reward = the reward from `calculate_reward()`
_done = if the agent "died" or if the candles finished
info = dict passed back to openai gym lib
"""
self._done = False
self._current_tick += 1
if self._current_tick == self._end_tick:
self._done = True
self._update_unrealized_total_profit()
step_reward = self.calculate_reward(action)
self.total_reward += step_reward
trade_type = None
if self.is_tradesignal(action):
"""
Action: Neutral, position: Long -> Close Long
Action: Neutral, position: Short -> Close Short
Action: Long, position: Neutral -> Open Long
Action: Long, position: Short -> Close Short and Open Long
Action: Short, position: Neutral -> Open Short
Action: Short, position: Long -> Close Long and Open Short
"""
if action == Actions.Neutral.value:
self._position = Positions.Neutral
trade_type = "neutral"
self._last_trade_tick = None
elif action == Actions.Long_enter.value:
self._position = Positions.Long
trade_type = "long"
self._last_trade_tick = self._current_tick
elif action == Actions.Short_enter.value:
self._position = Positions.Short
trade_type = "short"
self._last_trade_tick = self._current_tick
elif action == Actions.Exit.value:
self._update_total_profit()
self._position = Positions.Neutral
trade_type = "neutral"
self._last_trade_tick = None
else:
print("case not defined")
if trade_type is not None:
self.trade_history.append(
{'price': self.current_price(), 'index': self._current_tick,
'type': trade_type})
if self._total_profit < 1 - self.rl_config.get('max_training_drawdown_pct', 0.8):
self._done = True
self._position_history.append(self._position)
info = dict(
tick=self._current_tick,
action=action,
total_reward=self.total_reward,
total_profit=self._total_profit,
position=self._position.value,
trade_duration=self.get_trade_duration(),
current_profit_pct=self.get_unrealized_profit()
)
observation = self._get_observation()
self._update_history(info)
return observation, step_reward, self._done, info
def is_tradesignal(self, action: int) -> bool:
"""
Determine if the signal is a trade signal
e.g.: agent wants a Actions.Long_exit while it is in a Positions.short
"""
return not ((action == Actions.Neutral.value and self._position == Positions.Neutral) or
(action == Actions.Neutral.value and self._position == Positions.Short) or
(action == Actions.Neutral.value and self._position == Positions.Long) or
(action == Actions.Short_enter.value and self._position == Positions.Short) or
(action == Actions.Short_enter.value and self._position == Positions.Long) or
(action == Actions.Exit.value and self._position == Positions.Neutral) or
(action == Actions.Long_enter.value and self._position == Positions.Long) or
(action == Actions.Long_enter.value and self._position == Positions.Short))
def _is_valid(self, action: int) -> bool:
"""
Determine if the signal is valid.
e.g.: agent wants a Actions.Long_exit while it is in a Positions.short
"""
# Agent should only try to exit if it is in position
if action == Actions.Exit.value:
if self._position not in (Positions.Short, Positions.Long):
return False
# Agent should only try to enter if it is not in position
if action in (Actions.Short_enter.value, Actions.Long_enter.value):
if self._position != Positions.Neutral:
return False
return True

151
freqtrade/freqai/RL/Base5ActionRLEnv.py Normal file
View File

@ -0,0 +1,151 @@
import logging
from enum import Enum
from gym import spaces
from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
logger = logging.getLogger(__name__)
class Actions(Enum):
Neutral = 0
Long_enter = 1
Long_exit = 2
Short_enter = 3
Short_exit = 4
class Base5ActionRLEnv(BaseEnvironment):
"""
Base class for a 5 action environment
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.actions = Actions
def set_action_space(self):
self.action_space = spaces.Discrete(len(Actions))
def step(self, action: int):
"""
Logic for a single step (incrementing one candle in time)
by the agent
:param: action: int = the action type that the agent plans
to take for the current step.
:returns:
observation = current state of environment
step_reward = the reward from `calculate_reward()`
_done = if the agent "died" or if the candles finished
info = dict passed back to openai gym lib
"""
self._done = False
self._current_tick += 1
if self._current_tick == self._end_tick:
self._done = True
self._update_unrealized_total_profit()
step_reward = self.calculate_reward(action)
self.total_reward += step_reward
trade_type = None
if self.is_tradesignal(action):
"""
Action: Neutral, position: Long -> Close Long
Action: Neutral, position: Short -> Close Short
Action: Long, position: Neutral -> Open Long
Action: Long, position: Short -> Close Short and Open Long
Action: Short, position: Neutral -> Open Short
Action: Short, position: Long -> Close Long and Open Short
"""
if action == Actions.Neutral.value:
self._position = Positions.Neutral
trade_type = "neutral"
self._last_trade_tick = None
elif action == Actions.Long_enter.value:
self._position = Positions.Long
trade_type = "long"
self._last_trade_tick = self._current_tick
elif action == Actions.Short_enter.value:
self._position = Positions.Short
trade_type = "short"
self._last_trade_tick = self._current_tick
elif action == Actions.Long_exit.value:
self._update_total_profit()
self._position = Positions.Neutral
trade_type = "neutral"
self._last_trade_tick = None
elif action == Actions.Short_exit.value:
self._update_total_profit()
self._position = Positions.Neutral
trade_type = "neutral"
self._last_trade_tick = None
else:
print("case not defined")
if trade_type is not None:
self.trade_history.append(
{'price': self.current_price(), 'index': self._current_tick,
'type': trade_type})
if (self._total_profit < self.max_drawdown or
self._total_unrealized_profit < self.max_drawdown):
self._done = True
self._position_history.append(self._position)
info = dict(
tick=self._current_tick,
action=action,
total_reward=self.total_reward,
total_profit=self._total_profit,
position=self._position.value,
trade_duration=self.get_trade_duration(),
current_profit_pct=self.get_unrealized_profit()
)
observation = self._get_observation()
self._update_history(info)
return observation, step_reward, self._done, info
def is_tradesignal(self, action: int) -> bool:
"""
Determine if the signal is a trade signal
e.g.: agent wants a Actions.Long_exit while it is in a Positions.short
"""
return not ((action == Actions.Neutral.value and self._position == Positions.Neutral) or
(action == Actions.Neutral.value and self._position == Positions.Short) or
(action == Actions.Neutral.value and self._position == Positions.Long) or
(action == Actions.Short_enter.value and self._position == Positions.Short) or
(action == Actions.Short_enter.value and self._position == Positions.Long) or
(action == Actions.Short_exit.value and self._position == Positions.Long) or
(action == Actions.Short_exit.value and self._position == Positions.Neutral) or
(action == Actions.Long_enter.value and self._position == Positions.Long) or
(action == Actions.Long_enter.value and self._position == Positions.Short) or
(action == Actions.Long_exit.value and self._position == Positions.Short) or
(action == Actions.Long_exit.value and self._position == Positions.Neutral))
def _is_valid(self, action: int) -> bool:
# trade signal
"""
Determine if the signal is valid.
e.g.: agent wants a Actions.Long_exit while it is in a Positions.short
"""
# Agent should only try to exit if it is in position
if action in (Actions.Short_exit.value, Actions.Long_exit.value):
if self._position not in (Positions.Short, Positions.Long):
return False
# Agent should only try to enter if it is not in position
if action in (Actions.Short_enter.value, Actions.Long_enter.value):
if self._position != Positions.Neutral:
return False
return True

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

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

60
freqtrade/freqai/RL/TensorboardCallback.py Normal file
View File

@ -0,0 +1,60 @@
from enum import Enum
from typing import Any, Dict, Type, Union
from stable_baselines3.common.callbacks import BaseCallback
from stable_baselines3.common.logger import HParam
from freqtrade.freqai.RL.BaseEnvironment import BaseActions, BaseEnvironment
class TensorboardCallback(BaseCallback):
"""
Custom callback for plotting additional values in tensorboard and
episodic summary reports.
"""
def __init__(self, verbose=1, actions: Type[Enum] = BaseActions):
super(TensorboardCallback, self).__init__(verbose)
self.model: Any = None
self.logger = None # type: Any
self.training_env: BaseEnvironment = None # type: ignore
self.actions: Type[Enum] = actions
def _on_training_start(self) -> None:
hparam_dict = {
"algorithm": self.model.__class__.__name__,
"learning_rate": self.model.learning_rate,
# "gamma": self.model.gamma,
# "gae_lambda": self.model.gae_lambda,
# "batch_size": self.model.batch_size,
# "n_steps": self.model.n_steps,
}
metric_dict: Dict[str, Union[float, int]] = {
"eval/mean_reward": 0,
"rollout/ep_rew_mean": 0,
"rollout/ep_len_mean": 0,
"train/value_loss": 0,
"train/explained_variance": 0,
}
self.logger.record(
"hparams",
HParam(hparam_dict, metric_dict),
exclude=("stdout", "log", "json", "csv"),
)
def _on_step(self) -> bool:
custom_info = self.training_env.get_attr("custom_info")[0]
self.logger.record("_state/position", self.locals["infos"][0]["position"])
self.logger.record("_state/trade_duration", self.locals["infos"][0]["trade_duration"])
self.logger.record("_state/current_profit_pct", self.locals["infos"]
[0]["current_profit_pct"])
self.logger.record("_reward/total_profit", self.locals["infos"][0]["total_profit"])
self.logger.record("_reward/total_reward", self.locals["infos"][0]["total_reward"])
self.logger.record_mean("_reward/mean_trade_duration", self.locals["infos"]
[0]["trade_duration"])
self.logger.record("_actions/action", self.locals["infos"][0]["action"])
self.logger.record("_actions/_Invalid", custom_info["Invalid"])
self.logger.record("_actions/_Unknown", custom_info["Unknown"])
self.logger.record("_actions/Hold", custom_info["Hold"])
for action in self.actions:
self.logger.record(f"_actions/{action.name}", custom_info[action.name])
return True

0
freqtrade/freqai/RL/__init__.py Normal file
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93
freqtrade/freqai/base_models/FreqaiMultiOutputClassifier.py Normal file
View File

@ -0,0 +1,93 @@
import numpy as np
from joblib import Parallel
from sklearn.base import is_classifier
from sklearn.multioutput import MultiOutputClassifier, _fit_estimator
from sklearn.utils.fixes import delayed
from sklearn.utils.multiclass import check_classification_targets
from sklearn.utils.validation import has_fit_parameter
from freqtrade.exceptions import OperationalException
class FreqaiMultiOutputClassifier(MultiOutputClassifier):
def fit(self, X, y, sample_weight=None, fit_params=None):
"""Fit the model to data, separately for each output variable.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
Multi-output targets. An indicator matrix turns on multilabel
estimation.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If `None`, then samples are equally weighted.
Only supported if the underlying classifier supports sample
weights.
fit_params : A list of dicts for the fit_params
Parameters passed to the ``estimator.fit`` method of each step.
Each dict may contain same or different values (e.g. different
eval_sets or init_models)
.. versionadded:: 0.23
Returns
-------
self : object
Returns a fitted instance.
"""
if not hasattr(self.estimator, "fit"):
raise ValueError("The base estimator should implement a fit method")
y = self._validate_data(X="no_validation", y=y, multi_output=True)
if is_classifier(self):
check_classification_targets(y)
if y.ndim == 1:
raise ValueError(
"y must have at least two dimensions for "
"multi-output regression but has only one."
)
if sample_weight is not None and not has_fit_parameter(
self.estimator, "sample_weight"
):
raise ValueError("Underlying estimator does not support sample weights.")
if not fit_params:
fit_params = [None] * y.shape[1]
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
delayed(_fit_estimator)(
self.estimator, X, y[:, i], sample_weight, **fit_params[i]
)
for i in range(y.shape[1])
)
self.classes_ = []
for estimator in self.estimators_:
self.classes_.extend(estimator.classes_)
if len(set(self.classes_)) != len(self.classes_):
raise OperationalException(f"Class labels must be unique across targets: "
f"{self.classes_}")
if hasattr(self.estimators_[0], "n_features_in_"):
self.n_features_in_ = self.estimators_[0].n_features_in_
if hasattr(self.estimators_[0], "feature_names_in_"):
self.feature_names_in_ = self.estimators_[0].feature_names_in_
return self
def predict_proba(self, X):
"""
Get predict_proba and stack arrays horizontally
"""
results = np.hstack(super().predict_proba(X))
return np.squeeze(results)
def predict(self, X):
"""
Get predict and squeeze into 2D array
"""
results = super().predict(X)
return np.squeeze(results)

89
freqtrade/freqai/data_drawer.py
View File

@ -1,9 +1,10 @@
import collections
import importlib
import logging
import re
import shutil
import threading
from datetime import datetime, timezone
from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Any, Dict, Tuple, TypedDict
@ -81,12 +82,14 @@ class FreqaiDataDrawer:
self.historic_predictions_bkp_path = Path(
self.full_path / "historic_predictions.backup.pkl")
self.pair_dictionary_path = Path(self.full_path / "pair_dictionary.json")
self.global_metadata_path = Path(self.full_path / "global_metadata.json")
self.metric_tracker_path = Path(self.full_path / "metric_tracker.json")
self.follow_mode = follow_mode
if follow_mode:
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]]] = {}
self.load_metric_tracker_from_disk()
self.training_queue: Dict[str, int] = {}
self.history_lock = threading.Lock()
@ -97,7 +100,7 @@ class FreqaiDataDrawer:
self.empty_pair_dict: pair_info = {
"model_filename": "", "trained_timestamp": 0,
"data_path": "", "extras": {}}
self.metric_tracker: Dict[str, Dict[str, Dict[str, list]]] = {}
self.model_type = self.freqai_info.get('model_save_type', 'joblib')
def update_metric_tracker(self, metric: str, value: float, pair: str) -> None:
"""
@ -125,6 +128,17 @@ class FreqaiDataDrawer:
self.update_metric_tracker('cpu_load5min', load5 / cpus, pair)
self.update_metric_tracker('cpu_load15min', load15 / cpus, pair)
def load_global_metadata_from_disk(self):
"""
Locate and load a previously saved global metadata in present model folder.
"""
exists = self.global_metadata_path.is_file()
if exists:
with open(self.global_metadata_path, "r") as fp:
metatada_dict = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
return metatada_dict
return {}
def load_drawer_from_disk(self):
"""
Locate and load a previously saved data drawer full of all pair model metadata in
@ -153,6 +167,7 @@ class FreqaiDataDrawer:
if exists:
with open(self.metric_tracker_path, "r") as fp:
self.metric_tracker = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
logger.info("Loading existing metric tracker from disk.")
else:
logger.info("Could not find existing metric tracker, starting from scratch")
@ -224,6 +239,15 @@ class FreqaiDataDrawer:
rapidjson.dump(self.follower_dict, fp, default=self.np_encoder,
number_mode=rapidjson.NM_NATIVE)
def save_global_metadata_to_disk(self, metadata: Dict[str, Any]):
"""
Save global metadata json to disk
"""
with self.save_lock:
with open(self.global_metadata_path, 'w') as fp:
rapidjson.dump(metadata, fp, default=self.np_encoder,
number_mode=rapidjson.NM_NATIVE)
def create_follower_dict(self):
"""
Create or dictionary for each follower to maintain unique persistent prediction targets
@ -475,10 +499,12 @@ class FreqaiDataDrawer:
save_path = Path(dk.data_path)
# Save the trained model
if not dk.keras:
if self.model_type == 'joblib':
dump(model, save_path / f"{dk.model_filename}_model.joblib")
else:
elif self.model_type == 'keras':
model.save(save_path / f"{dk.model_filename}_model.h5")
elif 'stable_baselines' in self.model_type or 'sb3_contrib' == self.model_type:
model.save(save_path / f"{dk.model_filename}_model.zip")
if dk.svm_model is not None:
dump(dk.svm_model, save_path / f"{dk.model_filename}_svm_model.joblib")
@ -505,11 +531,10 @@ class FreqaiDataDrawer:
dk.pca, open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "wb")
)
# if self.live:
# store as much in ram as possible to increase performance
self.model_dictionary[coin] = model
self.pair_dict[coin]["model_filename"] = dk.model_filename
self.pair_dict[coin]["data_path"] = str(dk.data_path)
if coin not in self.meta_data_dictionary:
self.meta_data_dictionary[coin] = {}
self.meta_data_dictionary[coin]["train_df"] = dk.data_dictionary["train_features"]
@ -541,14 +566,6 @@ class FreqaiDataDrawer:
if dk.live:
dk.model_filename = self.pair_dict[coin]["model_filename"]
dk.data_path = Path(self.pair_dict[coin]["data_path"])
if self.freqai_info.get("follow_mode", False):
# follower can be on a different system which is rsynced from the leader:
dk.data_path = Path(
self.config["user_data_dir"]
/ "models"
/ dk.data_path.parts[-2]
/ dk.data_path.parts[-1]
)
if coin in self.meta_data_dictionary:
dk.data = self.meta_data_dictionary[coin]["meta_data"]
@ -567,12 +584,16 @@ class FreqaiDataDrawer:
# try to access model in memory instead of loading object from disk to save time
if dk.live and coin in self.model_dictionary:
model = self.model_dictionary[coin]
elif not dk.keras:
elif self.model_type == 'joblib':
model = load(dk.data_path / f"{dk.model_filename}_model.joblib")
else:
elif self.model_type == 'keras':
from tensorflow import keras
model = keras.models.load_model(dk.data_path / f"{dk.model_filename}_model.h5")
elif 'stable_baselines' in self.model_type or 'sb3_contrib' == self.model_type:
mod = importlib.import_module(
self.model_type, self.freqai_info['rl_config']['model_type'])
MODELCLASS = getattr(mod, self.freqai_info['rl_config']['model_type'])
model = MODELCLASS.load(dk.data_path / f"{dk.model_filename}_model")
if Path(dk.data_path / f"{dk.model_filename}_svm_model.joblib").is_file():
dk.svm_model = load(dk.data_path / f"{dk.model_filename}_svm_model.joblib")
@ -582,6 +603,10 @@ class FreqaiDataDrawer:
f"Unable to load model, ensure model exists at " f"{dk.data_path} "
)
# load it into ram if it was loaded from disk
if coin not in self.model_dictionary:
self.model_dictionary[coin] = model
if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]:
dk.pca = cloudpickle.load(
open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "rb")
@ -636,6 +661,8 @@ class FreqaiDataDrawer:
axis=0,
)
self.current_candle = history_data[dk.pair][self.config['timeframe']].iloc[-1]['date']
def load_all_pair_histories(self, timerange: TimeRange, dk: FreqaiDataKitchen) -> None:
"""
Load pair histories for all whitelist and corr_pairlist pairs.
@ -690,3 +717,31 @@ class FreqaiDataDrawer:
).reset_index(drop=True)
return corr_dataframes, base_dataframes
def get_timerange_from_live_historic_predictions(self) -> TimeRange:
"""
Returns timerange information based on historic predictions file
:return: timerange calculated from saved live data
"""
if not self.historic_predictions_path.is_file():
raise OperationalException(
'Historic predictions not found. Historic predictions data is required '
'to run backtest with the freqai-backtest-live-models option '
)
self.load_historic_predictions_from_disk()
all_pairs_end_dates = []
for pair in self.historic_predictions:
pair_historic_data = self.historic_predictions[pair]
all_pairs_end_dates.append(pair_historic_data.date_pred.max())
global_metadata = self.load_global_metadata_from_disk()
start_date = datetime.fromtimestamp(int(global_metadata["start_dry_live_date"]))
end_date = max(all_pairs_end_dates)
# add 1 day to string timerange to ensure BT module will load all dataframe data
end_date = end_date + timedelta(days=1)
backtesting_timerange = TimeRange(
'date', 'date', int(start_date.timestamp()), int(end_date.timestamp())
)
return backtesting_timerange

155
freqtrade/freqai/data_kitchen.py
View File

@ -9,6 +9,7 @@ from typing import Any, Dict, List, Tuple
import numpy as np
import numpy.typing as npt
import pandas as pd
import psutil
from pandas import DataFrame
from scipy import stats
from sklearn import linear_model
@ -19,6 +20,7 @@ from sklearn.neighbors import NearestNeighbors
from freqtrade.configuration import TimeRange
from freqtrade.constants import Config
from freqtrade.data.converter import reduce_dataframe_footprint
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_seconds
from freqtrade.strategy.interface import IStrategy
@ -80,25 +82,30 @@ class FreqaiDataKitchen:
self.svm_model: linear_model.SGDOneClassSVM = None
self.keras: bool = self.freqai_config.get("keras", False)
self.set_all_pairs()
if not self.live:
if not self.config["timerange"]:
raise OperationalException(
'Please pass --timerange if you intend to use FreqAI for backtesting.')
self.full_timerange = self.create_fulltimerange(
self.config["timerange"], self.freqai_config.get("train_period_days", 0)
)
self.backtest_live_models = config.get("freqai_backtest_live_models", False)
(self.training_timeranges, self.backtesting_timeranges) = self.split_timerange(
self.full_timerange,
config["freqai"]["train_period_days"],
config["freqai"]["backtest_period_days"],
)
if not self.live:
self.full_path = self.get_full_models_path(self.config)
if not self.backtest_live_models:
self.full_timerange = self.create_fulltimerange(
self.config["timerange"], self.freqai_config.get("train_period_days", 0)
)
(self.training_timeranges, self.backtesting_timeranges) = self.split_timerange(
self.full_timerange,
config["freqai"]["train_period_days"],
config["freqai"]["backtest_period_days"],
)
self.data['extra_returns_per_train'] = self.freqai_config.get('extra_returns_per_train', {})
self.thread_count = self.freqai_config.get("data_kitchen_thread_count", -1)
if not self.freqai_config.get("data_kitchen_thread_count", 0):
self.thread_count = max(int(psutil.cpu_count() * 2 - 2), 1)
else:
self.thread_count = self.freqai_config["data_kitchen_thread_count"]
self.train_dates: DataFrame = pd.DataFrame()
self.unique_classes: Dict[str, list] = {}
self.unique_class_list: list = []
self.backtest_live_models_data: Dict[str, Any] = {}
def set_paths(
self,
@ -110,10 +117,7 @@ class FreqaiDataKitchen:
:param metadata: dict = strategy furnished pair metadata
:param trained_timestamp: int = timestamp of most recent training
"""
self.full_path = Path(
self.config["user_data_dir"] / "models" / str(self.freqai_config.get("identifier"))
)
self.full_path = self.get_full_models_path(self.config)
self.data_path = Path(
self.full_path
/ f"sub-train-{pair.split('/')[0]}_{trained_timestamp}"
@ -244,7 +248,7 @@ class FreqaiDataKitchen:
self.data["filter_drop_index_training"] = drop_index
else:
if len(self.data['constant_features_list']):
if 'constant_features_list' in self.data and len(self.data['constant_features_list']):
filtered_df = self.check_pred_labels(filtered_df)
# we are backtesting so we need to preserve row number to send back to strategy,
# so now we use do_predict to avoid any prediction based on a NaN
@ -428,9 +432,7 @@ class FreqaiDataKitchen:
timerange_train.stopts = timerange_train.startts + train_period_days
first = False
start = datetime.fromtimestamp(timerange_train.startts, tz=timezone.utc)
stop = datetime.fromtimestamp(timerange_train.stopts, tz=timezone.utc)
tr_training_list.append(start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d"))
tr_training_list.append(timerange_train.timerange_str)
tr_training_list_timerange.append(copy.deepcopy(timerange_train))
# associated backtest period
@ -442,9 +444,7 @@ class FreqaiDataKitchen:
if timerange_backtest.stopts > config_timerange.stopts:
timerange_backtest.stopts = config_timerange.stopts
start = datetime.fromtimestamp(timerange_backtest.startts, tz=timezone.utc)
stop = datetime.fromtimestamp(timerange_backtest.stopts, tz=timezone.utc)
tr_backtesting_list.append(start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d"))
tr_backtesting_list.append(timerange_backtest.timerange_str)
tr_backtesting_list_timerange.append(copy.deepcopy(timerange_backtest))
# ensure we are predicting on exactly same amount of data as requested by user defined
@ -462,12 +462,10 @@ class FreqaiDataKitchen:
:param df: Dataframe containing all candles to run the entire backtest. Here
it is sliced down to just the present training period.
"""
start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
df = df.loc[df["date"] >= start, :]
if not self.live:
df = df.loc[df["date"] < stop, :]
df = df.loc[(df["date"] >= timerange.startdt) & (df["date"] < timerange.stopdt), :]
else:
df = df.loc[df["date"] >= timerange.startdt, :]
return df
@ -952,7 +950,8 @@ class FreqaiDataKitchen:
return weights
def get_predictions_to_append(self, predictions: DataFrame,
do_predict: npt.ArrayLike) -> DataFrame:
do_predict: npt.ArrayLike,
dataframe_backtest: DataFrame) -> DataFrame:
"""
Get backtest prediction from current backtest period
"""
@ -962,8 +961,10 @@ class FreqaiDataKitchen:
append_df[label] = predictions[label]
if append_df[label].dtype == object:
continue
append_df[f"{label}_mean"] = self.data["labels_mean"][label]
append_df[f"{label}_std"] = self.data["labels_std"][label]
if "labels_mean" in self.data:
append_df[f"{label}_mean"] = self.data["labels_mean"][label]
if "labels_std" in self.data:
append_df[f"{label}_std"] = self.data["labels_std"][label]
for extra_col in self.data["extra_returns_per_train"]:
append_df[f"{extra_col}"] = self.data["extra_returns_per_train"][extra_col]
@ -972,7 +973,9 @@ class FreqaiDataKitchen:
if self.freqai_config["feature_parameters"].get("DI_threshold", 0) > 0:
append_df["DI_values"] = self.DI_values
return append_df
dataframe_backtest.reset_index(drop=True, inplace=True)
merged_df = pd.concat([dataframe_backtest["date"], append_df], axis=1)
return merged_df
def append_predictions(self, append_df: DataFrame) -> None:
"""
@ -982,23 +985,18 @@ class FreqaiDataKitchen:
if self.full_df.empty:
self.full_df = append_df
else:
self.full_df = pd.concat([self.full_df, append_df], axis=0)
self.full_df = pd.concat([self.full_df, append_df], axis=0, ignore_index=True)
def fill_predictions(self, dataframe):
"""
Back fill values to before the backtesting range so that the dataframe matches size
when it goes back to the strategy. These rows are not included in the backtest.
"""
len_filler = len(dataframe) - len(self.full_df.index) # startup_candle_count
filler_df = pd.DataFrame(
np.zeros((len_filler, len(self.full_df.columns))), columns=self.full_df.columns
)
self.full_df = pd.concat([filler_df, self.full_df], axis=0, ignore_index=True)
to_keep = [col for col in dataframe.columns if not col.startswith("&")]
self.return_dataframe = pd.concat([dataframe[to_keep], self.full_df], axis=1)
self.return_dataframe = pd.merge(dataframe[to_keep],
self.full_df, how='left', on='date')
self.return_dataframe[self.full_df.columns] = (
self.return_dataframe[self.full_df.columns].fillna(value=0))
self.full_df = DataFrame()
return
@ -1028,14 +1026,7 @@ class FreqaiDataKitchen:
backtest_timerange.startts = (
backtest_timerange.startts - backtest_period_days * SECONDS_IN_DAY
)
start = datetime.fromtimestamp(backtest_timerange.startts, tz=timezone.utc)
stop = datetime.fromtimestamp(backtest_timerange.stopts, tz=timezone.utc)
full_timerange = start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d")
self.full_path = Path(
self.config["user_data_dir"] / "models" / f"{self.freqai_config['identifier']}"
)
full_timerange = backtest_timerange.timerange_str
config_path = Path(self.config["config_files"][0])
if not self.full_path.is_dir():
@ -1118,15 +1109,15 @@ class FreqaiDataKitchen:
return retrain, trained_timerange, data_load_timerange
def set_new_model_names(self, pair: str, trained_timerange: TimeRange):
def set_new_model_names(self, pair: str, timestamp_id: int):
coin, _ = pair.split("/")
self.data_path = Path(
self.full_path
/ f"sub-train-{pair.split('/')[0]}_{int(trained_timerange.stopts)}"
/ f"sub-train-{pair.split('/')[0]}_{timestamp_id}"
)
self.model_filename = f"cb_{coin.lower()}_{int(trained_timerange.stopts)}"
self.model_filename = f"cb_{coin.lower()}_{timestamp_id}"
def set_all_pairs(self) -> None:
@ -1153,11 +1144,13 @@ class FreqaiDataKitchen:
pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
for pair in pairs:
pair = pair.replace(':', '') # lightgbm doesnt like colons
valid_strs = [f"%-{pair}", f"%{pair}", f"%_{pair}"]
pair_cols = [col for col in dataframe.columns if
any(substr in col for substr in valid_strs)]
pair_cols.insert(0, 'date')
corr_dataframes[pair] = dataframe.filter(pair_cols, axis=1)
if pair_cols:
pair_cols.insert(0, 'date')
corr_dataframes[pair] = dataframe.filter(pair_cols, axis=1)
return corr_dataframes
@ -1175,8 +1168,9 @@ class FreqaiDataKitchen:
ready for training
"""
pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
current_pair = current_pair.replace(':', '')
for pair in pairs:
pair = pair.replace(':', '') # lightgbm doesnt work with colons
if current_pair != pair:
dataframe = dataframe.merge(corr_dataframes[pair], how='left', on='date')
@ -1246,6 +1240,11 @@ class FreqaiDataKitchen:
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
def fit_labels(self) -> None:
@ -1294,53 +1293,77 @@ class FreqaiDataKitchen:
self, append_df: DataFrame
) -> None:
"""
Save prediction dataframe from backtesting to h5 file format
Save prediction dataframe from backtesting to feather file format
:param append_df: dataframe for backtesting period
"""
full_predictions_folder = Path(self.full_path / self.backtest_predictions_folder)
if not full_predictions_folder.is_dir():
full_predictions_folder.mkdir(parents=True, exist_ok=True)
append_df.to_hdf(self.backtesting_results_path, key='append_df', mode='w')
append_df.to_feather(self.backtesting_results_path)
def get_backtesting_prediction(
self
) -> DataFrame:
"""
Get prediction dataframe from h5 file format
Get prediction dataframe from feather file format
"""
append_df = pd.read_hdf(self.backtesting_results_path)
append_df = pd.read_feather(self.backtesting_results_path)
return append_df
def check_if_backtest_prediction_is_valid(
self,
length_backtesting_dataframe: int
len_backtest_df: int
) -> bool:
"""
Check if a backtesting prediction already exists and if the predictions
to append has the same size of backtesting dataframe slice
to append have the same size as the backtesting dataframe slice
:param length_backtesting_dataframe: Length of backtesting dataframe slice
:return:
:boolean: whether the prediction file is valid.
"""
path_to_predictionfile = Path(self.full_path /
self.backtest_predictions_folder /
f"{self.model_filename}_prediction.h5")
f"{self.model_filename}_prediction.feather")
self.backtesting_results_path = path_to_predictionfile
file_exists = path_to_predictionfile.is_file()
if file_exists:
append_df = self.get_backtesting_prediction()
if len(append_df) == length_backtesting_dataframe:
if len(append_df) == len_backtest_df and 'date' in append_df:
logger.info(f"Found backtesting prediction file at {path_to_predictionfile}")
return True
else:
logger.info("A new backtesting prediction file is required. "
"(Number of predictions is different from dataframe length).")
"(Number of predictions is different from dataframe length or "
"old prediction file version).")
return False
else:
logger.info(
f"Could not find backtesting prediction file at {path_to_predictionfile}"
)
return False
def get_full_models_path(self, config: Config) -> Path:
"""
Returns default FreqAI model path
:param config: Configuration dictionary
"""
freqai_config: Dict[str, Any] = config["freqai"]
return Path(
config["user_data_dir"] / "models" / str(freqai_config.get("identifier"))
)
def remove_special_chars_from_feature_names(self, dataframe: pd.DataFrame) -> pd.DataFrame:
"""
Remove all special characters from feature strings (:)
:param dataframe: the dataframe that just finished indicator population. (unfiltered)
:return: dataframe with cleaned featrue names
"""
spec_chars = [':']
for c in spec_chars:
dataframe.columns = dataframe.columns.str.replace(c, "")
return dataframe

216
freqtrade/freqai/freqai_interface.py
View File

@ -5,15 +5,17 @@ from abc import ABC, abstractmethod
from collections import deque
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict, List, Literal, Tuple
from typing import Any, Dict, List, Literal, Optional, Tuple
import numpy as np
import pandas as pd
import psutil
from numpy.typing import NDArray
from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.constants import DATETIME_PRINT_FORMAT, Config
from freqtrade.constants import Config
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_seconds
@ -67,7 +69,11 @@ class IFreqaiModel(ABC):
self.save_backtest_models: bool = self.freqai_info.get("save_backtest_models", True)
if self.save_backtest_models:
logger.info('Backtesting module configured to save all models.')
self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
# set current candle to arbitrary historical date
self.current_candle: datetime = datetime.fromtimestamp(637887600, tz=timezone.utc)
self.dd.current_candle = self.current_candle
self.scanning = False
self.ft_params = self.freqai_info["feature_parameters"]
self.corr_pairlist: List[str] = self.ft_params.get("include_corr_pairlist", [])
@ -75,7 +81,7 @@ class IFreqaiModel(ABC):
if self.keras and self.ft_params.get("DI_threshold", 0):
self.ft_params["DI_threshold"] = 0
logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
self.CONV_WIDTH = self.freqai_info.get("conv_width", 2)
self.CONV_WIDTH = self.freqai_info.get('conv_width', 1)
if self.ft_params.get("inlier_metric_window", 0):
self.CONV_WIDTH = self.ft_params.get("inlier_metric_window", 0) * 2
self.pair_it = 0
@ -93,9 +99,11 @@ class IFreqaiModel(ABC):
# get_corr_dataframes is controlling the caching of corr_dataframes
# for improved performance. Careful with this boolean.
self.get_corr_dataframes: bool = True
self._threads: List[threading.Thread] = []
self._stop_event = threading.Event()
self.metadata: Dict[str, Any] = self.dd.load_global_metadata_from_disk()
self.data_provider: Optional[DataProvider] = None
self.max_system_threads = max(int(psutil.cpu_count() * 2 - 2), 1)
record_params(config, self.full_path)
@ -124,11 +132,13 @@ class IFreqaiModel(ABC):
self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
self.dd.set_pair_dict_info(metadata)
self.data_provider = strategy.dp
if self.live:
self.inference_timer('start')
self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
dk = self.start_live(dataframe, metadata, strategy, self.dk)
dataframe = dk.remove_features_from_df(dk.return_dataframe)
# For backtesting, each pair enters and then gets trained for each window along the
# sliding window defined by "train_period_days" (training window) and "live_retrain_hours"
@ -137,16 +147,24 @@ class IFreqaiModel(ABC):
# the concatenated results for the full backtesting period back to the strategy.
elif not self.follow_mode:
self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
dataframe = self.dk.use_strategy_to_populate_indicators(
strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
)
dk = self.start_backtesting(dataframe, metadata, self.dk)
if not self.config.get("freqai_backtest_live_models", False):
logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
dk = self.start_backtesting(dataframe, metadata, self.dk)
dataframe = dk.remove_features_from_df(dk.return_dataframe)
else:
logger.info(
"Backtesting using historic predictions (live models)")
dk = self.start_backtesting_from_historic_predictions(
dataframe, metadata, self.dk)
dataframe = dk.return_dataframe
dataframe = dk.remove_features_from_df(dk.return_dataframe)
self.clean_up()
if self.live:
self.inference_timer('stop', metadata["pair"])
return dataframe
def clean_up(self):
@ -158,6 +176,13 @@ class IFreqaiModel(ABC):
self.model = None
self.dk = None
def _on_stop(self):
"""
Callback for Subclasses to override to include logic for shutting down resources
when SIGINT is sent.
"""
return
def shutdown(self):
"""
Cleans up threads on Shutdown, set stop event. Join threads to wait
@ -166,6 +191,9 @@ class IFreqaiModel(ABC):
logger.info("Stopping FreqAI")
self._stop_event.set()
self.data_provider = None
self._on_stop()
logger.info("Waiting on Training iteration")
for _thread in self._threads:
_thread.join()
@ -254,40 +282,37 @@ class IFreqaiModel(ABC):
train_it += 1
total_trains = len(dk.backtesting_timeranges)
self.training_timerange = tr_train
dataframe_train = dk.slice_dataframe(tr_train, dataframe)
dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe)
len_backtest_df = len(dataframe.loc[(dataframe["date"] >= tr_backtest.startdt) & (
dataframe["date"] < tr_backtest.stopdt), :])
trained_timestamp = tr_train
tr_train_startts_str = datetime.fromtimestamp(
tr_train.startts,
tz=timezone.utc).strftime(DATETIME_PRINT_FORMAT)
tr_train_stopts_str = datetime.fromtimestamp(
tr_train.stopts,
tz=timezone.utc).strftime(DATETIME_PRINT_FORMAT)
logger.info(
f"Training {pair}, {self.pair_it}/{self.total_pairs} pairs"
f" from {tr_train_startts_str} to {tr_train_stopts_str}, {train_it}/{total_trains} "
"trains"
)
if not self.ensure_data_exists(len_backtest_df, tr_backtest, pair):
continue
trained_timestamp_int = int(trained_timestamp.stopts)
dk.set_paths(pair, trained_timestamp_int)
self.log_backtesting_progress(tr_train, pair, train_it, total_trains)
dk.set_new_model_names(pair, trained_timestamp)
timestamp_model_id = int(tr_train.stopts)
if dk.backtest_live_models:
timestamp_model_id = int(tr_backtest.startts)
if dk.check_if_backtest_prediction_is_valid(len(dataframe_backtest)):
dk.set_paths(pair, timestamp_model_id)
dk.set_new_model_names(pair, timestamp_model_id)
if dk.check_if_backtest_prediction_is_valid(len_backtest_df):
self.dd.load_metadata(dk)
dk.find_features(dataframe_train)
dk.find_features(dataframe)
self.check_if_feature_list_matches_strategy(dk)
append_df = dk.get_backtesting_prediction()
dk.append_predictions(append_df)
else:
dataframe_train = dk.slice_dataframe(tr_train, dataframe)
dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe)
if not self.model_exists(dk):
dk.find_features(dataframe_train)
dk.find_labels(dataframe_train)
self.model = self.train(dataframe_train, pair, dk)
self.dd.pair_dict[pair]["trained_timestamp"] = int(
trained_timestamp.stopts)
tr_train.stopts)
if self.plot_features:
plot_feature_importance(self.model, pair, dk, self.plot_features)
if self.save_backtest_models:
@ -300,10 +325,11 @@ class IFreqaiModel(ABC):
self.model = self.dd.load_data(pair, dk)
pred_df, do_preds = self.predict(dataframe_backtest, dk)
append_df = dk.get_predictions_to_append(pred_df, do_preds)
append_df = dk.get_predictions_to_append(pred_df, do_preds, dataframe_backtest)
dk.append_predictions(append_df)
dk.save_backtesting_prediction(append_df)
self.backtesting_fit_live_predictions(dk)
dk.fill_predictions(dataframe)
return dk
@ -339,6 +365,7 @@ class IFreqaiModel(ABC):
if self.dd.historic_data:
self.dd.update_historic_data(strategy, dk)
logger.debug(f'Updating historic data on pair {metadata["pair"]}')
self.track_current_candle()
if not self.follow_mode:
@ -576,7 +603,7 @@ class IFreqaiModel(ABC):
model = self.train(unfiltered_dataframe, pair, dk)
self.dd.pair_dict[pair]["trained_timestamp"] = new_trained_timerange.stopts
dk.set_new_model_names(pair, new_trained_timerange)
dk.set_new_model_names(pair, new_trained_timerange.stopts)
self.dd.save_data(model, pair, dk)
if self.plot_features:
@ -615,6 +642,8 @@ class IFreqaiModel(ABC):
self.dd.historic_predictions[pair] = pred_df
hist_preds_df = self.dd.historic_predictions[pair]
self.set_start_dry_live_date(strat_df)
for label in hist_preds_df.columns:
if hist_preds_df[label].dtype == object:
continue
@ -627,7 +656,7 @@ class IFreqaiModel(ABC):
hist_preds_df['DI_values'] = 0
for return_str in dk.data['extra_returns_per_train']:
hist_preds_df[return_str] = 0
hist_preds_df[return_str] = dk.data['extra_returns_per_train'][return_str]
hist_preds_df['close_price'] = strat_df['close']
hist_preds_df['date_pred'] = strat_df['date']
@ -655,7 +684,8 @@ class IFreqaiModel(ABC):
for label in full_labels:
if self.dd.historic_predictions[dk.pair][label].dtype == object:
continue
f = spy.stats.norm.fit(self.dd.historic_predictions[dk.pair][label].tail(num_candles))
f = spy.stats.norm.fit(
self.dd.historic_predictions[dk.pair][label].tail(num_candles))
dk.data["labels_mean"][label], dk.data["labels_std"][label] = f[0], f[1]
return
@ -683,8 +713,6 @@ class IFreqaiModel(ABC):
" avoid blinding open trades and degrading performance.")
self.pair_it = 0
self.inference_time = 0
if self.corr_pairlist:
self.get_corr_dataframes = True
return
def train_timer(self, do: Literal['start', 'stop'] = 'start', pair: str = ''):
@ -760,12 +788,132 @@ class IFreqaiModel(ABC):
"is included in the column names when you are creating features "
"in `populate_any_indicators()`.")
self.get_corr_dataframes = not bool(self.corr_dataframes)
else:
elif self.corr_dataframes:
dataframe = dk.attach_corr_pair_columns(
dataframe, self.corr_dataframes, dk.pair)
return dataframe
def track_current_candle(self):
"""
Checks if the latest candle appended by the datadrawer is
equivalent to the latest candle seen by FreqAI. If not, it
asks to refresh the cached corr_dfs, and resets the pair
counter.
"""
if self.dd.current_candle > self.current_candle:
self.get_corr_dataframes = True
self.pair_it = 1
self.current_candle = self.dd.current_candle
def ensure_data_exists(self, len_dataframe_backtest: int,
tr_backtest: TimeRange, pair: str) -> bool:
"""
Check if the dataframe is empty, if not, report useful information to user.
:param len_dataframe_backtest: the len of backtesting dataframe
:param tr_backtest: current backtesting timerange.
:param pair: current pair
:return: if the data exists or not
"""
if self.config.get("freqai_backtest_live_models", False) and len_dataframe_backtest == 0:
logger.info(f"No data found for pair {pair} from "
f"from { tr_backtest.start_fmt} to {tr_backtest.stop_fmt}. "
"Probably more than one training within the same candle period.")
return False
return True
def log_backtesting_progress(self, tr_train: TimeRange, pair: str,
train_it: int, total_trains: int):
"""
Log the backtesting progress so user knows how many pairs have been trained and
how many more pairs/trains remain.
:param tr_train: the training timerange
:param train_it: the train iteration for the current pair (the sliding window progress)
:param pair: the current pair
:param total_trains: total trains (total number of slides for the sliding window)
"""
if not self.config.get("freqai_backtest_live_models", False):
logger.info(
f"Training {pair}, {self.pair_it}/{self.total_pairs} pairs"
f" from {tr_train.start_fmt} "
f"to {tr_train.stop_fmt}, {train_it}/{total_trains} "
"trains"
)
def backtesting_fit_live_predictions(self, dk: FreqaiDataKitchen):
"""
Apply fit_live_predictions function in backtesting with a dummy historic_predictions
The loop is required to simulate dry/live operation, as it is not possible to predict
the type of logic implemented by the user.
:param dk: datakitchen object
"""
fit_live_predictions_candles = self.freqai_info.get("fit_live_predictions_candles", 0)
if fit_live_predictions_candles:
logger.info("Applying fit_live_predictions in backtesting")
label_columns = [col for col in dk.full_df.columns if (
col.startswith("&") and
not (col.startswith("&") and col.endswith("_mean")) and
not (col.startswith("&") and col.endswith("_std")) and
col not in self.dk.data["extra_returns_per_train"])
]
for index in range(len(dk.full_df)):
if index >= fit_live_predictions_candles:
self.dd.historic_predictions[self.dk.pair] = (
dk.full_df.iloc[index - fit_live_predictions_candles:index])
self.fit_live_predictions(self.dk, self.dk.pair)
for label in label_columns:
if dk.full_df[label].dtype == object:
continue
if "labels_mean" in self.dk.data:
dk.full_df.at[index, f"{label}_mean"] = (
self.dk.data["labels_mean"][label])
if "labels_std" in self.dk.data:
dk.full_df.at[index, f"{label}_std"] = self.dk.data["labels_std"][label]
for extra_col in self.dk.data["extra_returns_per_train"]:
dk.full_df.at[index, f"{extra_col}"] = (
self.dk.data["extra_returns_per_train"][extra_col])
return
def update_metadata(self, metadata: Dict[str, Any]):
"""
Update global metadata and save the updated json file
:param metadata: new global metadata dict
"""
self.dd.save_global_metadata_to_disk(metadata)
self.metadata = metadata
def set_start_dry_live_date(self, live_dataframe: DataFrame):
key_name = "start_dry_live_date"
if key_name not in self.metadata:
metadata = self.metadata
metadata[key_name] = int(
pd.to_datetime(live_dataframe.tail(1)["date"].values[0]).timestamp())
self.update_metadata(metadata)
def start_backtesting_from_historic_predictions(
self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen
) -> FreqaiDataKitchen:
"""
:param dataframe: DataFrame = strategy passed dataframe
:param metadata: Dict = pair metadata
:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
:return:
FreqaiDataKitchen = Data management/analysis tool associated to present pair only
"""
pair = metadata["pair"]
dk.return_dataframe = dataframe
saved_dataframe = self.dd.historic_predictions[pair]
columns_to_drop = list(set(saved_dataframe.columns).intersection(
dk.return_dataframe.columns))
dk.return_dataframe = dk.return_dataframe.drop(columns=list(columns_to_drop))
dk.return_dataframe = pd.merge(
dk.return_dataframe, saved_dataframe, how='left', left_on='date', right_on="date_pred")
# dk.return_dataframe = dk.return_dataframe[saved_dataframe.columns].fillna(0)
return dk
# Following methods which are overridden by user made prediction models.
# See freqai/prediction_models/CatboostPredictionModel.py for an example.

74
freqtrade/freqai/prediction_models/CatboostClassifierMultiTarget.py Normal file
View File

@ -0,0 +1,74 @@
import logging
import sys
from pathlib import Path
from typing import Any, Dict
from catboost import CatBoostClassifier, Pool
from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
from freqtrade.freqai.base_models.FreqaiMultiOutputClassifier import FreqaiMultiOutputClassifier
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
class CatboostClassifierMultiTarget(BaseClassifierModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:param data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
cbc = CatBoostClassifier(
allow_writing_files=True,
loss_function='MultiClass',
train_dir=Path(dk.data_path),
**self.model_training_parameters,
)
X = data_dictionary["train_features"]
y = data_dictionary["train_labels"]
sample_weight = data_dictionary["train_weights"]
eval_sets = [None] * y.shape[1]
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
eval_sets = [None] * data_dictionary['test_labels'].shape[1]
for i in range(data_dictionary['test_labels'].shape[1]):
eval_sets[i] = Pool(
data=data_dictionary["test_features"],
label=data_dictionary["test_labels"].iloc[:, i],
weight=data_dictionary["test_weights"],
)
init_model = self.get_init_model(dk.pair)
if init_model:
init_models = init_model.estimators_
else:
init_models = [None] * y.shape[1]
fit_params = []
for i in range(len(eval_sets)):
fit_params.append({
'eval_set': eval_sets[i], 'init_model': init_models[i],
'log_cout': sys.stdout, 'log_cerr': sys.stderr,
})
model = FreqaiMultiOutputClassifier(estimator=cbc)
thread_training = self.freqai_info.get('multitarget_parallel_training', False)
if thread_training:
model.n_jobs = y.shape[1]
model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
return model

64
freqtrade/freqai/prediction_models/LightGBMClassifierMultiTarget.py Normal file
View File

@ -0,0 +1,64 @@
import logging
from typing import Any, Dict
from lightgbm import LGBMClassifier
from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
from freqtrade.freqai.base_models.FreqaiMultiOutputClassifier import FreqaiMultiOutputClassifier
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
class LightGBMClassifierMultiTarget(BaseClassifierModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:param data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
lgb = LGBMClassifier(**self.model_training_parameters)
X = data_dictionary["train_features"]
y = data_dictionary["train_labels"]
sample_weight = data_dictionary["train_weights"]
eval_weights = None
eval_sets = [None] * y.shape[1]
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
eval_weights = [data_dictionary["test_weights"]]
eval_sets = [(None, None)] * data_dictionary['test_labels'].shape[1] # type: ignore
for i in range(data_dictionary['test_labels'].shape[1]):
eval_sets[i] = ( # type: ignore
data_dictionary["test_features"],
data_dictionary["test_labels"].iloc[:, i]
)
init_model = self.get_init_model(dk.pair)
if init_model:
init_models = init_model.estimators_
else:
init_models = [None] * y.shape[1]
fit_params = []
for i in range(len(eval_sets)):
fit_params.append(
{'eval_set': eval_sets[i], 'eval_sample_weight': eval_weights,
'init_model': init_models[i]})
model = FreqaiMultiOutputClassifier(estimator=lgb)
thread_training = self.freqai_info.get('multitarget_parallel_training', False)
if thread_training:
model.n_jobs = y.shape[1]
model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
return model

152
freqtrade/freqai/prediction_models/ReinforcementLearner.py Normal file
View File

@ -0,0 +1,152 @@
import logging
from pathlib import Path
from typing import Any, Dict
import torch as th
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
logger = logging.getLogger(__name__)
class ReinforcementLearner(BaseReinforcementLearningModel):
"""
Reinforcement Learning Model prediction model.
Users can inherit from this class to make their own RL model with custom
environment/training controls. Define the file as follows:
```
from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
class MyCoolRLModel(ReinforcementLearner):
```
Save the file to `user_data/freqaimodels`, then run it with:
freqtrade trade --freqaimodel MyCoolRLModel --config config.json --strategy SomeCoolStrat
Here the users can override any of the functions
available in the `IFreqaiModel` inheritance tree. Most importantly for RL, this
is where the user overrides `MyRLEnv` (see below), to define custom
`calculate_reward()` function, or to override any other parts of the environment.
This class also allows users to override any other part of the IFreqaiModel tree.
For example, the user can override `def fit()` or `def train()` or `def predict()`
to take fine-tuned control over these processes.
Another common override may be `def data_cleaning_predict()` where the user can
take fine-tuned control over the data handling pipeline.
"""
def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs):
"""
User customizable fit method
:param data_dictionary: dict = common data dictionary containing all train/test
features/labels/weights.
:param dk: FreqaiDatakitchen = data kitchen for current pair.
:return:
model Any = trained model to be used for inference in dry/live/backtesting
"""
train_df = data_dictionary["train_features"]
total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
policy_kwargs = dict(activation_fn=th.nn.ReLU,
net_arch=self.net_arch)
if dk.pair not in self.dd.model_dictionary or not self.continual_learning:
model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
tensorboard_log=Path(
dk.full_path / "tensorboard" / dk.pair.split('/')[0]),
**self.freqai_info['model_training_parameters']
)
else:
logger.info('Continual training activated - starting training from previously '
'trained agent.')
model = self.dd.model_dictionary[dk.pair]
model.set_env(self.train_env)
model.learn(
total_timesteps=int(total_timesteps),
callback=[self.eval_callback, self.tensorboard_callback]
)
if Path(dk.data_path / "best_model.zip").is_file():
logger.info('Callback found a best model.')
best_model = self.MODELCLASS.load(dk.data_path / "best_model")
return best_model
logger.info('Couldnt find best model, using final model instead.')
return model
class MyRLEnv(Base5ActionRLEnv):
"""
User can override any function in BaseRLEnv and gym.Env. Here the user
sets a custom reward based on profit and trade duration.
"""
def calculate_reward(self, action: int) -> float:
"""
An example reward function. This is the one function that users will likely
wish to inject their own creativity into.
:param action: int = The action made by the agent for the current candle.
:return:
float = the reward to give to the agent for current step (used for optimization
of weights in NN)
"""
# first, penalize if the action is not valid
if not self._is_valid(action):
self.custom_info["Invalid"] += 1
return -2
pnl = self.get_unrealized_profit()
factor = 100.
# reward agent for entering trades
if (action == Actions.Long_enter.value
and self._position == Positions.Neutral):
self.custom_info[f"{Actions.Long_enter.name}"] += 1
return 25
if (action == Actions.Short_enter.value
and self._position == Positions.Neutral):
self.custom_info[f"{Actions.Short_enter.name}"] += 1
return 25
# discourage agent from not entering trades
if action == Actions.Neutral.value and self._position == Positions.Neutral:
self.custom_info[f"{Actions.Neutral.name}"] += 1
return -1
max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
trade_duration = self._current_tick - self._last_trade_tick # type: ignore
if trade_duration <= max_trade_duration:
factor *= 1.5
elif trade_duration > max_trade_duration:
factor *= 0.5
# discourage sitting in position
if (self._position in (Positions.Short, Positions.Long) and
action == Actions.Neutral.value):
self.custom_info["Hold"] += 1
return -1 * trade_duration / max_trade_duration
# close long
if action == Actions.Long_exit.value and self._position == Positions.Long:
if pnl > self.profit_aim * self.rr:
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
self.custom_info[f"{Actions.Long_exit.name}"] += 1
return float(pnl * factor)
# close short
if action == Actions.Short_exit.value and self._position == Positions.Short:
if pnl > self.profit_aim * self.rr:
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
self.custom_info[f"{Actions.Short_exit.name}"] += 1
return float(pnl * factor)
self.custom_info["Unknown"] += 1
return 0.

54
freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py Normal file
View File

@ -0,0 +1,54 @@
import logging
from typing import Any, Dict
from pandas import DataFrame
from stable_baselines3.common.callbacks import EvalCallback
from stable_baselines3.common.vec_env import SubprocVecEnv
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
from freqtrade.freqai.RL.BaseReinforcementLearningModel import make_env
from freqtrade.freqai.RL.TensorboardCallback import TensorboardCallback
logger = logging.getLogger(__name__)
class ReinforcementLearner_multiproc(ReinforcementLearner):
"""
Demonstration of how to build vectorized environments
"""
def set_train_and_eval_environments(self, data_dictionary: Dict[str, Any],
prices_train: DataFrame, prices_test: DataFrame,
dk: FreqaiDataKitchen):
"""
User can override this if they are using a custom MyRLEnv
:param data_dictionary: dict = common data dictionary containing train and test
features/labels/weights.
:param prices_train/test: DataFrame = dataframe comprised of the prices to be used in
the environment during training
or testing
:param dk: FreqaiDataKitchen = the datakitchen for the current pair
"""
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
env_id = "train_env"
self.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1, train_df, prices_train,
self.reward_params, self.CONV_WIDTH, monitor=True,
config=self.config) for i
in range(self.max_threads)])
eval_env_id = 'eval_env'
self.eval_env = SubprocVecEnv([make_env(self.MyRLEnv, eval_env_id, i, 1,
test_df, prices_test,
self.reward_params, self.CONV_WIDTH, monitor=True,
config=self.config) for i
in range(self.max_threads)])
self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
render=False, eval_freq=len(train_df),
best_model_save_path=str(dk.data_path))
actions = self.train_env.env_method("get_actions")[0]
self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)

15
freqtrade/freqai/utils.py
View File

@ -14,6 +14,7 @@ from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_seconds
from freqtrade.exchange.exchange import market_is_active
from freqtrade.freqai.data_drawer import FreqaiDataDrawer
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist
@ -218,3 +219,17 @@ def record_params(config: Dict[str, Any], full_path: Path) -> None:
default=str,
number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN
)
def get_timerange_backtest_live_models(config: Config) -> str:
"""
Returns a formated timerange for backtest live/ready models
:param config: Configuration dictionary
:return: a string timerange (format example: '20220801-20220822')
"""
dk = FreqaiDataKitchen(config)
models_path = dk.get_full_models_path(config)
dd = FreqaiDataDrawer(models_path, config)
timerange = dd.get_timerange_from_live_historic_predictions()
return timerange.timerange_str

47
freqtrade/freqtradebot.py
View File

@ -191,10 +191,10 @@ class FreqtradeBot(LoggingMixin):
# Check whether markets have to be reloaded and reload them when it's needed
self.exchange.reload_markets()
self.update_closed_trades_without_assigned_fees()
self.update_trades_without_assigned_fees()
# Query trades from persistence layer
trades = Trade.get_open_trades()
trades: List[Trade] = Trade.get_open_trades()
self.active_pair_whitelist = self._refresh_active_whitelist(trades)
@ -354,7 +354,7 @@ class FreqtradeBot(LoggingMixin):
if self.trading_mode == TradingMode.FUTURES:
self._schedule.run_pending()
def update_closed_trades_without_assigned_fees(self):
def update_trades_without_assigned_fees(self) -> None:
"""
Update closed trades without close fees assigned.
Only acts when Orders are in the database, otherwise the last order-id is unknown.
@ -379,17 +379,18 @@ class FreqtradeBot(LoggingMixin):
stoploss_order=order.ft_order_side == 'stoploss',
send_msg=False)
trades: List[Trade] = Trade.get_open_trades_without_assigned_fees()
trades = Trade.get_open_trades_without_assigned_fees()
for trade in trades:
if trade.is_open and not trade.fee_updated(trade.entry_side):
order = trade.select_order(trade.entry_side, False)
open_order = trade.select_order(trade.entry_side, True)
if order and open_order is None:
logger.info(
f"Updating {trade.entry_side}-fee on trade {trade}"
f"for order {order.order_id}."
)
self.update_trade_state(trade, order.order_id, send_msg=False)
with self._exit_lock:
if trade.is_open and not trade.fee_updated(trade.entry_side):
order = trade.select_order(trade.entry_side, False)
open_order = trade.select_order(trade.entry_side, True)
if order and open_order is None:
logger.info(
f"Updating {trade.entry_side}-fee on trade {trade}"
f"for order {order.order_id}."
)
self.update_trade_state(trade, order.order_id, send_msg=False)
def handle_insufficient_funds(self, trade: Trade):
"""
@ -826,6 +827,8 @@ class FreqtradeBot(LoggingMixin):
co = self.exchange.cancel_stoploss_order_with_result(
trade.stoploss_order_id, trade.pair, trade.amount)
trade.update_order(co)
# Reset stoploss order id.
trade.stoploss_order_id = None
except InvalidOrderException:
logger.exception(f"Could not cancel stoploss order {trade.stoploss_order_id}")
return trade
@ -982,7 +985,7 @@ class FreqtradeBot(LoggingMixin):
# SELL / exit positions / close trades logic and methods
#
def exit_positions(self, trades: List[Any]) -> int:
def exit_positions(self, trades: List[Trade]) -> int:
"""
Tries to execute exit orders for open trades (positions)
"""
@ -1010,7 +1013,7 @@ class FreqtradeBot(LoggingMixin):
def handle_trade(self, trade: Trade) -> bool:
"""
Sells/exits_short the current pair if the threshold is reached and updates the trade record.
Exits the current pair if the threshold is reached and updates the trade record.
:return: True if trade has been sold/exited_short, False otherwise
"""
if not trade.is_open:
@ -1133,10 +1136,8 @@ class FreqtradeBot(LoggingMixin):
trade.exit_reason = ExitType.STOPLOSS_ON_EXCHANGE.value
self.update_trade_state(trade, trade.stoploss_order_id, stoploss_order,
stoploss_order=True)
# Lock pair for one candle to prevent immediate rebuys
self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc),
reason='Auto lock')
self._notify_exit(trade, "stoploss", True)
self.handle_protections(trade.pair, trade.trade_direction)
return True
if trade.open_order_id or not trade.is_open:
@ -1150,7 +1151,7 @@ class FreqtradeBot(LoggingMixin):
stoploss = (
self.edge.stoploss(pair=trade.pair)
if self.edge else
self.strategy.stoploss / trade.leverage
trade.stop_loss_pct / trade.leverage
)
if trade.is_short:
stop_price = trade.open_rate * (1 - stoploss)
@ -1169,7 +1170,6 @@ class FreqtradeBot(LoggingMixin):
if self.create_stoploss_order(trade=trade, stop_price=trade.stoploss_or_liquidation):
return False
else:
trade.stoploss_order_id = None
logger.warning('Stoploss order was cancelled, but unable to recreate one.')
# Finally we check if stoploss on exchange should be moved up because of trailing.
@ -1595,11 +1595,6 @@ class FreqtradeBot(LoggingMixin):
trade.close_rate_requested = limit
trade.exit_reason = exit_reason
if not sub_trade_amt:
# Lock pair for one candle to prevent immediate re-trading
self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc),
reason='Auto lock')
self._notify_exit(trade, order_type, sub_trade=bool(sub_trade_amt), order=order_obj)
# In case of market sell orders the order can be closed immediately
if order.get('status', 'unknown') in ('closed', 'expired'):
@ -1809,6 +1804,8 @@ class FreqtradeBot(LoggingMixin):
self._notify_enter(trade, order, fill=True, sub_trade=sub_trade)
def handle_protections(self, pair: str, side: LongShort) -> None:
# Lock pair for one candle to prevent immediate rebuys
self.strategy.lock_pair(pair, datetime.now(timezone.utc), reason='Auto lock')
prot_trig = self.protections.stop_per_pair(pair, side=side)
if prot_trig:
msg = {'type': RPCMessageType.PROTECTION_TRIGGER, }

4
freqtrade/leverage/interest.py
View File

@ -35,9 +35,5 @@ def interest(
elif exchange_name == "kraken":
# Rounded based on https://kraken-fees-calculator.github.io/
return borrowed * rate * (one + FtPrecise(ceil(hours / four)))
elif exchange_name == "ftx":
# As Explained under #Interest rates section in
# https://help.ftx.com/hc/en-us/articles/360053007671-Spot-Margin-Trading-Explainer
return borrowed * rate * FtPrecise(ceil(hours)) / twenty_four
else:
raise OperationalException(f"Leverage not available on {exchange_name} with freqtrade")

3
freqtrade/main.py
View File

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

23
freqtrade/misc.py
View File

@ -10,7 +10,8 @@ from typing import Any, Dict, Iterator, List, Mapping, Union
from typing.io import IO
from urllib.parse import urlparse
import pandas
import orjson
import pandas as pd
import rapidjson
from freqtrade.constants import DECIMAL_PER_COIN_FALLBACK, DECIMALS_PER_COIN
@ -256,29 +257,37 @@ def parse_db_uri_for_logging(uri: str):
return parsed_db_uri.geturl().replace(f':{pwd}@', ':*****@')
def dataframe_to_json(dataframe: pandas.DataFrame) -> str:
def dataframe_to_json(dataframe: pd.DataFrame) -> str:
"""
Serialize a DataFrame for transmission over the wire using JSON
:param dataframe: A pandas DataFrame
:returns: A JSON string of the pandas DataFrame
"""
return dataframe.to_json(orient='split')
# https://github.com/pandas-dev/pandas/issues/24889
# https://github.com/pandas-dev/pandas/issues/40443
# We need to convert to a dict to avoid mem leak
def default(z):
if isinstance(z, pd.Timestamp):
return z.timestamp() * 1e3
raise TypeError
return str(orjson.dumps(dataframe.to_dict(orient='split'), default=default), 'utf-8')
def json_to_dataframe(data: str) -> pandas.DataFrame:
def json_to_dataframe(data: str) -> pd.DataFrame:
"""
Deserialize JSON into a DataFrame
:param data: A JSON string
:returns: A pandas DataFrame from the JSON string
"""
dataframe = pandas.read_json(data, orient='split')
dataframe = pd.read_json(data, orient='split')
if 'date' in dataframe.columns:
dataframe['date'] = pandas.to_datetime(dataframe['date'], unit='ms', utc=True)
dataframe['date'] = pd.to_datetime(dataframe['date'], unit='ms', utc=True)
return dataframe
def remove_entry_exit_signals(dataframe: pandas.DataFrame):
def remove_entry_exit_signals(dataframe: pd.DataFrame):
"""
Remove Entry and Exit signals from a DataFrame

80
freqtrade/optimize/backtesting.py
View File

@ -134,6 +134,10 @@ class Backtesting:
self.fee = self.exchange.get_fee(symbol=self.pairlists.whitelist[0])
self.precision_mode = self.exchange.precisionMode
if self.config.get('freqai_backtest_live_models', False):
from freqtrade.freqai.utils import get_timerange_backtest_live_models
self.config['timerange'] = get_timerange_backtest_live_models(self.config)
self.timerange = TimeRange.parse_timerange(
None if self.config.get('timerange') is None else str(self.config.get('timerange')))
@ -162,7 +166,7 @@ class Backtesting:
PairLocks.use_db = True
Trade.use_db = True
def init_backtest_detail(self):
def init_backtest_detail(self) -> None:
# Load detail timeframe if specified
self.timeframe_detail = str(self.config.get('timeframe_detail', ''))
if self.timeframe_detail:
@ -688,10 +692,11 @@ class Backtesting:
trade.orders.append(order)
return trade
def _get_exit_trade_entry(self, trade: LocalTrade, row: Tuple) -> Optional[LocalTrade]:
def _get_exit_trade_entry(
self, trade: LocalTrade, row: Tuple, is_first: bool) -> Optional[LocalTrade]:
exit_candle_time: datetime = row[DATE_IDX].to_pydatetime()
if self.trading_mode == TradingMode.FUTURES:
if is_first and self.trading_mode == TradingMode.FUTURES:
trade.funding_fees = self.exchange.calculate_funding_fees(
self.futures_data[trade.pair],
amount=trade.amount,
@ -700,32 +705,7 @@ class Backtesting:
close_date=exit_candle_time,
)
if self.timeframe_detail and trade.pair in self.detail_data:
exit_candle_end = exit_candle_time + timedelta(minutes=self.timeframe_min)
detail_data = self.detail_data[trade.pair]
detail_data = detail_data.loc[
(detail_data['date'] >= exit_candle_time) &
(detail_data['date'] < exit_candle_end)
].copy()
if len(detail_data) == 0:
# Fall back to "regular" data if no detail data was found for this candle
return self._get_exit_trade_entry_for_candle(trade, row)
detail_data.loc[:, 'enter_long'] = row[LONG_IDX]
detail_data.loc[:, 'exit_long'] = row[ELONG_IDX]
detail_data.loc[:, 'enter_short'] = row[SHORT_IDX]
detail_data.loc[:, 'exit_short'] = row[ESHORT_IDX]
detail_data.loc[:, 'enter_tag'] = row[ENTER_TAG_IDX]
detail_data.loc[:, 'exit_tag'] = row[EXIT_TAG_IDX]
for det_row in detail_data[HEADERS].values.tolist():
res = self._get_exit_trade_entry_for_candle(trade, det_row)
if res:
return res
return None
else:
return self._get_exit_trade_entry_for_candle(trade, row)
return self._get_exit_trade_entry_for_candle(trade, row)
def get_valid_price_and_stake(
self, pair: str, row: Tuple, propose_rate: float, stake_amount: float,
@ -1070,7 +1050,7 @@ class Backtesting:
def backtest_loop(
self, row: Tuple, pair: str, current_time: datetime, end_date: datetime,
max_open_trades: int, open_trade_count_start: int) -> int:
max_open_trades: int, open_trade_count_start: int, is_first: bool = True) -> int:
"""
NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
@ -1088,9 +1068,11 @@ class Backtesting:
# without positionstacking, we can only have one open trade per pair.
# max_open_trades must be respected
# don't open on the last row
# We only open trades on the main candle, not on detail candles
trade_dir = self.check_for_trade_entry(row)
if (
(self._position_stacking or len(LocalTrade.bt_trades_open_pp[pair]) == 0)
and is_first
and self.trade_slot_available(max_open_trades, open_trade_count_start)
and current_time != end_date
and trade_dir is not None
@ -1116,7 +1098,7 @@ class Backtesting:
# 4. Create exit orders (if any)
if not trade.open_order_id:
self._get_exit_trade_entry(trade, row) # Place exit order if necessary
self._get_exit_trade_entry(trade, row, is_first) # Place exit order if necessary
# 5. Process exit orders.
order = trade.select_order(trade.exit_side, is_open=True)
@ -1167,7 +1149,6 @@ class Backtesting:
self.progress.init_step(BacktestState.BACKTEST, int(
(end_date - start_date) / timedelta(minutes=self.timeframe_min)))
# Loop timerange and get candle for each pair at that point in time
while current_time <= end_date:
open_trade_count_start = LocalTrade.bt_open_open_trade_count
@ -1181,9 +1162,37 @@ class Backtesting:
row_index += 1
indexes[pair] = row_index
self.dataprovider._set_dataframe_max_index(row_index)
current_detail_time: datetime = row[DATE_IDX].to_pydatetime()
if self.timeframe_detail and pair in self.detail_data:
exit_candle_end = current_detail_time + timedelta(minutes=self.timeframe_min)
open_trade_count_start = self.backtest_loop(
row, pair, current_time, end_date, max_open_trades, open_trade_count_start)
detail_data = self.detail_data[pair]
detail_data = detail_data.loc[
(detail_data['date'] >= current_detail_time) &
(detail_data['date'] < exit_candle_end)
].copy()
if len(detail_data) == 0:
# Fall back to "regular" data if no detail data was found for this candle
open_trade_count_start = self.backtest_loop(
row, pair, current_time, end_date, max_open_trades,
open_trade_count_start)
detail_data.loc[:, 'enter_long'] = row[LONG_IDX]
detail_data.loc[:, 'exit_long'] = row[ELONG_IDX]
detail_data.loc[:, 'enter_short'] = row[SHORT_IDX]
detail_data.loc[:, 'exit_short'] = row[ESHORT_IDX]
detail_data.loc[:, 'enter_tag'] = row[ENTER_TAG_IDX]
detail_data.loc[:, 'exit_tag'] = row[EXIT_TAG_IDX]
is_first = True
current_time_det = current_time
for det_row in detail_data[HEADERS].values.tolist():
open_trade_count_start = self.backtest_loop(
det_row, pair, current_time_det, end_date, max_open_trades,
open_trade_count_start, is_first)
current_time_det += timedelta(minutes=self.timeframe_detail_min)
is_first = False
else:
open_trade_count_start = self.backtest_loop(
row, pair, current_time, end_date, max_open_trades, open_trade_count_start)
# Move time one configured time_interval ahead.
self.progress.increment()
@ -1282,8 +1291,7 @@ class Backtesting:
def _get_min_cached_backtest_date(self):
min_backtest_date = None
backtest_cache_age = self.config.get('backtest_cache', constants.BACKTEST_CACHE_DEFAULT)
if self.timerange.stopts == 0 or datetime.fromtimestamp(
self.timerange.stopts, tz=timezone.utc) > datetime.now(tz=timezone.utc):
if self.timerange.stopts == 0 or self.timerange.stopdt > datetime.now(tz=timezone.utc):
logger.warning('Backtest result caching disabled due to use of open-ended timerange.')
elif backtest_cache_age == 'day':
min_backtest_date = datetime.now(tz=timezone.utc) - timedelta(days=1)

17
freqtrade/optimize/hyperopt_tools.py
View File

@ -17,6 +17,7 @@ from freqtrade.enums import HyperoptState
from freqtrade.exceptions import OperationalException
from freqtrade.misc import deep_merge_dicts, round_coin_value, round_dict, safe_value_fallback2
from freqtrade.optimize.hyperopt_epoch_filters import hyperopt_filter_epochs
from freqtrade.optimize.optimize_reports import generate_wins_draws_losses
logger = logging.getLogger(__name__)
@ -325,8 +326,10 @@ class HyperoptTools():
# New mode, using backtest result for metrics
trials['results_metrics.winsdrawslosses'] = trials.apply(
lambda x: f"{x['results_metrics.wins']} {x['results_metrics.draws']:>4} "
f"{x['results_metrics.losses']:>4}", axis=1)
lambda x: generate_wins_draws_losses(
x['results_metrics.wins'], x['results_metrics.draws'],
x['results_metrics.losses']
), axis=1)
trials = trials[['Best', 'current_epoch', 'results_metrics.total_trades',
'results_metrics.winsdrawslosses',
@ -337,7 +340,7 @@ class HyperoptTools():
'loss', 'is_initial_point', 'is_random', 'is_best']]
trials.columns = [
'Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
'Best', 'Epoch', 'Trades', ' Win Draw Loss Win%', 'Avg profit',
'Total profit', 'Profit', 'Avg duration', 'max_drawdown', 'max_drawdown_account',
'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_random', 'is_best'
]
@ -467,9 +470,9 @@ class HyperoptTools():
base_metrics = ['Best', 'current_epoch', 'results_metrics.total_trades',
'results_metrics.profit_mean', 'results_metrics.profit_median',
'results_metrics.profit_total',
'Stake currency',
'results_metrics.profit_total', 'Stake currency',
'results_metrics.profit_total_abs', 'results_metrics.holding_avg',
'results_metrics.trade_count_long', 'results_metrics.trade_count_short',
'loss', 'is_initial_point', 'is_best']
perc_multi = 100
@ -477,7 +480,9 @@ class HyperoptTools():
trials = trials[base_metrics + param_metrics]
base_columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Median profit', 'Total profit',
'Stake currency', 'Profit', 'Avg duration', 'Objective',
'Stake currency', 'Profit', 'Avg duration',
'Trade count long', 'Trade count short',
'Objective',
'is_initial_point', 'is_best']
param_columns = list(results[0]['params_dict'].keys())
trials.columns = base_columns + param_columns

10
freqtrade/optimize/optimize_reports.py
View File

@ -86,7 +86,7 @@ def _get_line_header(first_column: str, stake_currency: str,
'Win Draw Loss Win%']
def _generate_wins_draws_losses(wins, draws, losses):
def generate_wins_draws_losses(wins, draws, losses):
if wins > 0 and losses == 0:
wl_ratio = '100'
elif wins == 0:
@ -600,7 +600,7 @@ def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: st
output = [[
t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
t['profit_total_pct'], t['duration_avg'],
_generate_wins_draws_losses(t['wins'], t['draws'], t['losses'])
generate_wins_draws_losses(t['wins'], t['draws'], t['losses'])
] for t in pair_results]
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(output, headers=headers,
@ -626,7 +626,7 @@ def text_table_exit_reason(exit_reason_stats: List[Dict[str, Any]], stake_curren
output = [[
t.get('exit_reason', t.get('sell_reason')), t['trades'],
_generate_wins_draws_losses(t['wins'], t['draws'], t['losses']),
generate_wins_draws_losses(t['wins'], t['draws'], t['losses']),
t['profit_mean_pct'], t['profit_sum_pct'],
round_coin_value(t['profit_total_abs'], stake_currency, False),
t['profit_total_pct'],
@ -656,7 +656,7 @@ def text_table_tags(tag_type: str, tag_results: List[Dict[str, Any]], stake_curr
t['profit_total_abs'],
t['profit_total_pct'],
t['duration_avg'],
_generate_wins_draws_losses(
generate_wins_draws_losses(
t['wins'],
t['draws'],
t['losses'])] for t in tag_results]
@ -715,7 +715,7 @@ def text_table_strategy(strategy_results, stake_currency: str) -> str:
output = [[
t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
t['profit_total_pct'], t['duration_avg'],
_generate_wins_draws_losses(t['wins'], t['draws'], t['losses']), drawdown]
generate_wins_draws_losses(t['wins'], t['draws'], t['losses']), drawdown]
for t, drawdown in zip(strategy_results, drawdown)]
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(output, headers=headers,

8
freqtrade/persistence/migrations.py
View File

@ -1,5 +1,5 @@
import logging
from typing import List
from typing import List, Optional
from sqlalchemy import inspect, select, text, tuple_, update
@ -31,9 +31,9 @@ def get_backup_name(tabs: List[str], backup_prefix: str):
return table_back_name
def get_last_sequence_ids(engine, trade_back_name, order_back_name):
order_id: int = None
trade_id: int = None
def get_last_sequence_ids(engine, trade_back_name: str, order_back_name: str):
order_id: Optional[int] = None
trade_id: Optional[int] = None
if engine.name == 'postgresql':
with engine.begin() as connection:

2
freqtrade/persistence/pairlock_middleware.py
View File

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

12
freqtrade/persistence/trade_model.py
View File

@ -90,6 +90,13 @@ class Order(_DECL_BASE):
def safe_filled(self) -> float:
return self.filled if self.filled is not None else self.amount or 0.0
@property
def safe_remaining(self) -> float:
return (
self.remaining if self.remaining is not None else
self.amount - (self.filled or 0.0)
)
@property
def safe_fee_base(self) -> float:
return self.ft_fee_base or 0.0
@ -667,7 +674,7 @@ class LocalTrade():
self.close(order.safe_price)
else:
self.recalc_trade_from_orders()
elif order.ft_order_side == 'stoploss':
elif order.ft_order_side == 'stoploss' and order.status not in ('canceled', 'open'):
self.stoploss_order_id = None
self.close_rate_requested = self.stop_loss
self.exit_reason = ExitType.STOPLOSS_ON_EXCHANGE.value
@ -1144,7 +1151,8 @@ class Trade(_DECL_BASE, LocalTrade):
id = Column(Integer, primary_key=True)
orders = relationship("Order", order_by="Order.id", cascade="all, delete-orphan", lazy="joined")
orders = relationship("Order", order_by="Order.id", cascade="all, delete-orphan",
lazy="selectin", innerjoin=True)
exchange = Column(String(25), nullable=False)
pair = Column(String(25), nullable=False, index=True)

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

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

3
freqtrade/rpc/api_server/api_v1.py
View File

@ -37,7 +37,8 @@ logger = logging.getLogger(__name__)
# 2.16: Additional daily metrics
# 2.17: Forceentry - leverage, partial force_exit
# 2.20: Add websocket endpoints
API_VERSION = 2.20
# 2.21: Add new_candle messagetype
API_VERSION = 2.21
# Public API, requires no auth.
router_public = APIRouter()

115
freqtrade/rpc/api_server/api_ws.py
View File

@ -1,16 +1,16 @@
import logging
import time
from typing import Any, Dict
from fastapi import APIRouter, Depends, WebSocketDisconnect
from fastapi.websockets import WebSocket, WebSocketState
from fastapi import APIRouter, Depends
from fastapi.websockets import WebSocket
from pydantic import ValidationError
from websockets.exceptions import WebSocketException
from freqtrade.enums import RPCMessageType, RPCRequestType
from freqtrade.rpc.api_server.api_auth import validate_ws_token
from freqtrade.rpc.api_server.deps import get_channel_manager, get_rpc
from freqtrade.rpc.api_server.ws import WebSocketChannel
from freqtrade.rpc.api_server.ws.channel import ChannelManager
from freqtrade.rpc.api_server.deps import get_message_stream, get_rpc
from freqtrade.rpc.api_server.ws.channel import WebSocketChannel, create_channel
from freqtrade.rpc.api_server.ws.message_stream import MessageStream
from freqtrade.rpc.api_server.ws_schemas import (WSAnalyzedDFMessage, WSMessageSchema,
WSRequestSchema, WSWhitelistMessage)
from freqtrade.rpc.rpc import RPC
@ -22,23 +22,35 @@ logger = logging.getLogger(__name__)
router = APIRouter()
async def is_websocket_alive(ws: WebSocket) -> bool:
async def channel_reader(channel: WebSocketChannel, rpc: RPC):
"""
Check if a FastAPI Websocket is still open
Iterate over the messages from the channel and process the request
"""
if (
ws.application_state == WebSocketState.CONNECTED and
ws.client_state == WebSocketState.CONNECTED
):
return True
return False
async for message in channel:
await _process_consumer_request(message, channel, rpc)
async def channel_broadcaster(channel: WebSocketChannel, message_stream: MessageStream):
"""
Iterate over messages in the message stream and send them
"""
async for message, ts in message_stream:
if channel.subscribed_to(message.get('type')):
# Log a warning if this channel is behind
# on the message stream by a lot
if (time.time() - ts) > 60:
logger.warning(f"Channel {channel} is behind MessageStream by 1 minute,"
" this can cause a memory leak if you see this message"
" often, consider reducing pair list size or amount of"
" consumers.")
await channel.send(message, timeout=True)
async def _process_consumer_request(
request: Dict[str, Any],
channel: WebSocketChannel,
rpc: RPC,
channel_manager: ChannelManager
rpc: RPC
):
"""
Validate and handle a request from a websocket consumer
@ -74,68 +86,29 @@ async def _process_consumer_request(
# Format response
response = WSWhitelistMessage(data=whitelist)
# Send it back
await channel_manager.send_direct(channel, response.dict(exclude_none=True))
await channel.send(response.dict(exclude_none=True))
elif type == RPCRequestType.ANALYZED_DF:
limit = None
# Limit the amount of candles per dataframe to 'limit' or 1500
limit = min(data.get('limit', 1500), 1500) if data else None
if data:
# Limit the amount of candles per dataframe to 'limit' or 1500
limit = max(data.get('limit', 1500), 1500)
# They requested the full historical analyzed dataframes
analyzed_df = rpc._ws_request_analyzed_df(limit)
# For every dataframe, send as a separate message
for _, message in analyzed_df.items():
# For every pair in the generator, send a separate message
for message in rpc._ws_request_analyzed_df(limit):
# Format response
response = WSAnalyzedDFMessage(data=message)
await channel_manager.send_direct(channel, response.dict(exclude_none=True))
await channel.send(response.dict(exclude_none=True))
@router.websocket("/message/ws")
async def message_endpoint(
ws: WebSocket,
websocket: WebSocket,
token: str = Depends(validate_ws_token),
rpc: RPC = Depends(get_rpc),
channel_manager=Depends(get_channel_manager),
token: str = Depends(validate_ws_token)
message_stream: MessageStream = Depends(get_message_stream)
):
"""
Message WebSocket endpoint, facilitates sending RPC messages
"""
try:
channel = await channel_manager.on_connect(ws)
if await is_websocket_alive(ws):
logger.info(f"Consumer connected - {channel}")
# Keep connection open until explicitly closed, and process requests
try:
while not channel.is_closed():
request = await channel.recv()
# Process the request here
await _process_consumer_request(request, channel, rpc, channel_manager)
except (WebSocketDisconnect, WebSocketException):
# Handle client disconnects
logger.info(f"Consumer disconnected - {channel}")
except RuntimeError:
# Handle cases like -
# RuntimeError('Cannot call "send" once a closed message has been sent')
pass
except Exception as e:
logger.info(f"Consumer connection failed - {channel}: {e}")
logger.debug(e, exc_info=e)
except RuntimeError:
# WebSocket was closed
# Do nothing
pass
except Exception as e:
logger.error(f"Failed to serve - {ws.client}")
# Log tracebacks to keep track of what errors are happening
logger.exception(e)
finally:
if channel:
await channel_manager.on_disconnect(ws)
if token:
async with create_channel(websocket) as channel:
await channel.run_channel_tasks(
channel_reader(channel, rpc),
channel_broadcaster(channel, message_stream)
)

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

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

106
freqtrade/rpc/api_server/webserver.py
View File

@ -1,22 +1,17 @@
import asyncio
import logging
from ipaddress import IPv4Address
from threading import Thread
from typing import Any, Dict
from typing import Any, Dict, Optional
import orjson
import uvicorn
from fastapi import Depends, FastAPI
from fastapi.middleware.cors import CORSMiddleware
# Look into alternatives
from janus import Queue as ThreadedQueue
from starlette.responses import JSONResponse
from freqtrade.constants import Config
from freqtrade.exceptions import OperationalException
from freqtrade.rpc.api_server.uvicorn_threaded import UvicornServer
from freqtrade.rpc.api_server.ws import ChannelManager
from freqtrade.rpc.api_server.ws_schemas import WSMessageSchemaType
from freqtrade.rpc.api_server.ws.message_stream import MessageStream
from freqtrade.rpc.rpc import RPC, RPCException, RPCHandler
@ -50,10 +45,8 @@ class ApiServer(RPCHandler):
_config: Config = {}
# Exchange - only available in webserver mode.
_exchange = None
# websocket message queue stuff
_ws_channel_manager = None
_ws_thread = None
_ws_loop = None
# websocket message stuff
_message_stream: Optional[MessageStream] = None
def __new__(cls, *args, **kwargs):
"""
@ -71,15 +64,11 @@ class ApiServer(RPCHandler):
return
self._standalone: bool = standalone
self._server = None
self._ws_queue = None
self._ws_background_task = None
ApiServer.__initialized = True
api_config = self._config['api_server']
ApiServer._ws_channel_manager = ChannelManager()
self.app = FastAPI(title="Freqtrade API",
docs_url='/docs' if api_config.get('enable_openapi', False) else None,
redoc_url=None,
@ -105,21 +94,9 @@ class ApiServer(RPCHandler):
del ApiServer._rpc
if self._server and not self._standalone:
logger.info("Stopping API Server")
# self._server.force_exit, self._server.should_exit = True, True
self._server.cleanup()
if self._ws_thread and self._ws_loop:
logger.info("Stopping API Server background tasks")
if self._ws_background_task:
# Cancel the queue task
self._ws_background_task.cancel()
self._ws_thread.join()
self._ws_thread = None
self._ws_loop = None
self._ws_background_task = None
@classmethod
def shutdown(cls):
cls.__initialized = False
@ -129,9 +106,11 @@ class ApiServer(RPCHandler):
cls._rpc = None
def send_msg(self, msg: Dict[str, Any]) -> None:
if self._ws_queue:
sync_q = self._ws_queue.sync_q
sync_q.put(msg)
"""
Publish the message to the message stream
"""
if ApiServer._message_stream:
ApiServer._message_stream.publish(msg)
def handle_rpc_exception(self, request, exc):
logger.exception(f"API Error calling: {exc}")
@ -170,50 +149,30 @@ class ApiServer(RPCHandler):
)
app.add_exception_handler(RPCException, self.handle_rpc_exception)
app.add_event_handler(
event_type="startup",
func=self._api_startup_event
)
app.add_event_handler(
event_type="shutdown",
func=self._api_shutdown_event
)
def start_message_queue(self):
if self._ws_thread:
return
async def _api_startup_event(self):
"""
Creates the MessageStream class on startup
so it has access to the same event loop
as uvicorn
"""
if not ApiServer._message_stream:
ApiServer._message_stream = MessageStream()
# Create a new loop, as it'll be just for the background thread
self._ws_loop = asyncio.new_event_loop()
# Start the thread
self._ws_thread = Thread(target=self._ws_loop.run_forever)
self._ws_thread.start()
# Finally, submit the coro to the thread
self._ws_background_task = asyncio.run_coroutine_threadsafe(
self._broadcast_queue_data(), loop=self._ws_loop)
async def _broadcast_queue_data(self):
# Instantiate the queue in this coroutine so it's attached to our loop
self._ws_queue = ThreadedQueue()
async_queue = self._ws_queue.async_q
try:
while True:
logger.debug("Getting queue messages...")
# Get data from queue
message: WSMessageSchemaType = await async_queue.get()
logger.debug(f"Found message of type: {message.get('type')}")
async_queue.task_done()
# Broadcast it
await self._ws_channel_manager.broadcast(message)
except asyncio.CancelledError:
pass
# For testing, shouldn't happen when stable
except Exception as e:
logger.exception(f"Exception happened in background task: {e}")
finally:
# Disconnect channels and stop the loop on cancel
await self._ws_channel_manager.disconnect_all()
self._ws_loop.stop()
# Avoid adding more items to the queue if they aren't
# going to get broadcasted.
self._ws_queue = None
async def _api_shutdown_event(self):
"""
Removes the MessageStream class on shutdown
"""
if ApiServer._message_stream:
ApiServer._message_stream = None
def start_api(self):
"""
@ -253,7 +212,6 @@ class ApiServer(RPCHandler):
if self._standalone:
self._server.run()
else:
self.start_message_queue()
self._server.run_in_thread()
except Exception:
logger.exception("Api server failed to start.")

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

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

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

@ -1,11 +1,13 @@
import asyncio
import logging
import time
from threading import RLock
from typing import Any, Dict, List, Optional, Type, Union
from collections import deque
from contextlib import asynccontextmanager
from typing import Any, AsyncIterator, Deque, Dict, List, Optional, Type, Union
from uuid import uuid4
from fastapi import WebSocket as FastAPIWebSocket
from fastapi import WebSocketDisconnect
from websockets.exceptions import ConnectionClosed
from freqtrade.rpc.api_server.ws.proxy import WebSocketProxy
from freqtrade.rpc.api_server.ws.serializer import (HybridJSONWebSocketSerializer,
@ -21,36 +23,30 @@ class WebSocketChannel:
"""
Object to help facilitate managing a websocket connection
"""
def __init__(
self,
websocket: WebSocketType,
channel_id: Optional[str] = None,
drain_timeout: int = 3,
throttle: float = 0.01,
serializer_cls: Type[WebSocketSerializer] = HybridJSONWebSocketSerializer
):
self.channel_id = channel_id if channel_id else uuid4().hex[:8]
# The WebSocket object
self._websocket = WebSocketProxy(websocket)
# The Serializing class for the WebSocket object
self._serializer_cls = serializer_cls
self.drain_timeout = drain_timeout
self.throttle = throttle
self._subscriptions: List[str] = []
# 32 is the size of the receiving queue in websockets package
self.queue: asyncio.Queue[Dict[str, Any]] = asyncio.Queue(maxsize=32)
self._relay_task = asyncio.create_task(self.relay())
# Internal event to signify a closed websocket
self._closed = asyncio.Event()
# The async tasks created for the channel
self._channel_tasks: List[asyncio.Task] = []
# Deque for average send times
self._send_times: Deque[float] = deque([], maxlen=10)
# High limit defaults to 3 to start
self._send_high_limit = 3
# The subscribed message types
self._subscriptions: List[str] = []
# Wrap the WebSocket in the Serializing class
self._wrapped_ws = self._serializer_cls(self._websocket)
self._wrapped_ws = serializer_cls(self._websocket)
def __repr__(self):
return f"WebSocketChannel({self.channel_id}, {self.remote_addr})"
@ -63,40 +59,58 @@ class WebSocketChannel:
def remote_addr(self):
return self._websocket.remote_addr
async def _send(self, data):
"""
Send data on the wrapped websocket
"""
await self._wrapped_ws.send(data)
@property
def avg_send_time(self):
return sum(self._send_times) / len(self._send_times)
async def send(self, data) -> bool:
def _calc_send_limit(self):
"""
Add the data to the queue to be sent.
:returns: True if data added to queue, False otherwise
Calculate the send high limit for this channel
"""
# This block only runs if the queue is full, it will wait
# until self.drain_timeout for the relay to drain the outgoing queue
# We can't use asyncio.wait_for here because the queue may have been created with a
# different eventloop
start = time.time()
while self.queue.full():
await asyncio.sleep(1)
if (time.time() - start) > self.drain_timeout:
return False
# Only update if we have enough data
if len(self._send_times) == self._send_times.maxlen:
# At least 1s or twice the average of send times, with a
# maximum of 3 seconds per message
self._send_high_limit = min(max(self.avg_send_time * 2, 1), 3)
# If for some reason the queue is still full, just return False
async def send(
self,
message: Union[WSMessageSchemaType, Dict[str, Any]],
timeout: bool = False
):
"""
Send a message on the wrapped websocket. If the sending
takes too long, it will raise a TimeoutError and
disconnect the connection.
:param message: The message to send
:param timeout: Enforce send high limit, defaults to False
"""
try:
self.queue.put_nowait(data)
except asyncio.QueueFull:
return False
_ = time.time()
# If the send times out, it will raise
# a TimeoutError and bubble up to the
# message_endpoint to close the connection
await asyncio.wait_for(
self._wrapped_ws.send(message),
timeout=self._send_high_limit if timeout else None
)
total_time = time.time() - _
self._send_times.append(total_time)
# If we got here everything is ok
return True
self._calc_send_limit()
except asyncio.TimeoutError:
logger.info(f"Connection for {self} timed out, disconnecting")
raise
# Explicitly give control back to event loop as
# websockets.send does not
await asyncio.sleep(0.01)
async def recv(self):
"""
Receive data on the wrapped websocket
Receive a message on the wrapped websocket
"""
return await self._wrapped_ws.recv()
@ -106,18 +120,28 @@ class WebSocketChannel:
"""
return await self._websocket.ping()
async def accept(self):
"""
Accept the underlying websocket connection,
if the connection has been closed before we can
accept, just close the channel.
"""
try:
return await self._websocket.accept()
except RuntimeError:
await self.close()
async def close(self):
"""
Close the WebSocketChannel
"""
try:
await self.raw_websocket.close()
except Exception:
pass
self._closed.set()
self._relay_task.cancel()
try:
await self._websocket.close()
except RuntimeError:
pass
def is_closed(self) -> bool:
"""
@ -141,99 +165,76 @@ class WebSocketChannel:
"""
return message_type in self._subscriptions
async def relay(self):
async def run_channel_tasks(self, *tasks, **kwargs):
"""
Relay messages from the channel's queue and send them out. This is started
as a task.
Create and await on the channel tasks unless an exception
was raised, then cancel them all.
:params *tasks: All coros or tasks to be run concurrently
:param **kwargs: Any extra kwargs to pass to gather
"""
while not self._closed.is_set():
message = await self.queue.get()
if not self.is_closed():
# Wrap the coros into tasks if they aren't already
self._channel_tasks = [
task if isinstance(task, asyncio.Task) else asyncio.create_task(task)
for task in tasks
]
try:
await self._send(message)
self.queue.task_done()
return await asyncio.gather(*self._channel_tasks, **kwargs)
except Exception:
# If an exception occurred, cancel the rest of the tasks
await self.cancel_channel_tasks()
# Limit messages per sec.
# Could cause problems with queue size if too low, and
# problems with network traffik if too high.
# 0.01 = 100/s
await asyncio.sleep(self.throttle)
except RuntimeError:
# The connection was closed, just exit the task
return
class ChannelManager:
def __init__(self):
self.channels = dict()
self._lock = RLock() # Re-entrant Lock
async def on_connect(self, websocket: WebSocketType):
async def cancel_channel_tasks(self):
"""
Wrap websocket connection into Channel and add to list
:param websocket: The WebSocket object to attach to the Channel
Cancel and wait on all channel tasks
"""
if isinstance(websocket, FastAPIWebSocket):
for task in self._channel_tasks:
task.cancel()
# Wait for tasks to finish cancelling
try:
await websocket.accept()
except RuntimeError:
# The connection was closed before we could accept it
return
await task
except (
asyncio.CancelledError,
asyncio.TimeoutError,
WebSocketDisconnect,
ConnectionClosed,
RuntimeError
):
pass
except Exception as e:
logger.info(f"Encountered unknown exception: {e}", exc_info=e)
ws_channel = WebSocketChannel(websocket)
self._channel_tasks = []
with self._lock:
self.channels[websocket] = ws_channel
return ws_channel
async def on_disconnect(self, websocket: WebSocketType):
async def __aiter__(self):
"""
Call close on the channel if it's not, and remove from channel list
Generator for received messages
"""
# We can not catch any errors here as websocket.recv is
# the first to catch any disconnects and bubble it up
# so the connection is garbage collected right away
while not self.is_closed():
yield await self.recv()
:param websocket: The WebSocket objet attached to the Channel
"""
with self._lock:
channel = self.channels.get(websocket)
if channel:
logger.info(f"Disconnecting channel {channel}")
if not channel.is_closed():
await channel.close()
del self.channels[websocket]
@asynccontextmanager
async def create_channel(
websocket: WebSocketType,
**kwargs
) -> AsyncIterator[WebSocketChannel]:
"""
Context manager for safely opening and closing a WebSocketChannel
"""
channel = WebSocketChannel(websocket, **kwargs)
try:
await channel.accept()
logger.info(f"Connected to channel - {channel}")
async def disconnect_all(self):
"""
Disconnect all Channels
"""
with self._lock:
for websocket in self.channels.copy().keys():
await self.on_disconnect(websocket)
async def broadcast(self, message: WSMessageSchemaType):
"""
Broadcast a message on all Channels
:param message: The message to send
"""
with self._lock:
for channel in self.channels.copy().values():
if channel.subscribed_to(message.get('type')):
await self.send_direct(channel, message)
async def send_direct(
self, channel: WebSocketChannel, message: Union[WSMessageSchemaType, Dict[str, Any]]):
"""
Send a message directly through direct_channel only
:param direct_channel: The WebSocketChannel object to send the message through
:param message: The message to send
"""
if not await channel.send(message):
await self.on_disconnect(channel.raw_websocket)
def has_channels(self):
"""
Flag for more than 0 channels
"""
return len(self.channels) > 0
yield channel
finally:
await channel.close()
logger.info(f"Disconnected from channel - {channel}")

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

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

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

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

4
freqtrade/rpc/external_message_consumer.py
View File

@ -31,6 +31,7 @@ class Producer(TypedDict):
name: str
host: str
port: int
secure: bool
ws_token: str
@ -180,7 +181,8 @@ class ExternalMessageConsumer:
host, port = producer['host'], producer['port']
token = producer['ws_token']
name = producer['name']
ws_url = f"ws://{host}:{port}/api/v1/message/ws?token={token}"
scheme = 'wss' if producer.get('secure', False) else 'ws'
ws_url = f"{scheme}://{host}:{port}/api/v1/message/ws?token={token}"
# This will raise InvalidURI if the url is bad
async with websockets.connect(

75
freqtrade/rpc/rpc.py
View File

@ -5,7 +5,7 @@ import logging
from abc import abstractmethod
from datetime import date, datetime, timedelta, timezone
from math import isnan
from typing import Any, Dict, List, Optional, Tuple, Union
from typing import Any, Dict, Generator, List, Optional, Tuple, Union
import arrow
import psutil
@ -218,9 +218,10 @@ class RPC:
stoploss_current_dist_pct=round(stoploss_current_dist_ratio * 100, 2),
stoploss_entry_dist=stoploss_entry_dist,
stoploss_entry_dist_ratio=round(stoploss_entry_dist_ratio, 8),
open_order='({} {} rem={:.8f})'.format(
order.order_type, order.side, order.remaining
) if order else None,
open_order=(
f'({order.order_type} {order.side} rem={order.safe_remaining:.8f})' if
order else None
),
))
results.append(trade_dict)
return results
@ -739,6 +740,24 @@ class RPC:
self._freqtrade.wallets.update()
return {'result': f'Created sell order for trade {trade_id}.'}
def _force_entry_validations(self, pair: str, order_side: SignalDirection):
if not self._freqtrade.config.get('force_entry_enable', False):
raise RPCException('Force_entry not enabled.')
if self._freqtrade.state != State.RUNNING:
raise RPCException('trader is not running')
if order_side == SignalDirection.SHORT and self._freqtrade.trading_mode == TradingMode.SPOT:
raise RPCException("Can't go short on Spot markets.")
if pair not in self._freqtrade.exchange.get_markets(tradable_only=True):
raise RPCException('Symbol does not exist or market is not active.')
# Check if pair quote currency equals to the stake currency.
stake_currency = self._freqtrade.config.get('stake_currency')
if not self._freqtrade.exchange.get_pair_quote_currency(pair) == stake_currency:
raise RPCException(
f'Wrong pair selected. Only pairs with stake-currency {stake_currency} allowed.')
def _rpc_force_entry(self, pair: str, price: Optional[float], *,
order_type: Optional[str] = None,
order_side: SignalDirection = SignalDirection.LONG,
@ -749,21 +768,8 @@ class RPC:
Handler for forcebuy <asset> <price>
Buys a pair trade at the given or current price
"""
self._force_entry_validations(pair, order_side)
if not self._freqtrade.config.get('force_entry_enable', False):
raise RPCException('Force_entry not enabled.')
if self._freqtrade.state != State.RUNNING:
raise RPCException('trader is not running')
if order_side == SignalDirection.SHORT and self._freqtrade.trading_mode == TradingMode.SPOT:
raise RPCException("Can't go short on Spot markets.")
# Check if pair quote currency equals to the stake currency.
stake_currency = self._freqtrade.config.get('stake_currency')
if not self._freqtrade.exchange.get_pair_quote_currency(pair) == stake_currency:
raise RPCException(
f'Wrong pair selected. Only pairs with stake-currency {stake_currency} allowed.')
# check if valid pair
# check if pair already has an open pair
@ -773,6 +779,9 @@ class RPC:
is_short = trade.is_short
if not self._freqtrade.strategy.position_adjustment_enable:
raise RPCException(f'position for {pair} already open - id: {trade.id}')
if trade.open_order_id is not None:
raise RPCException(f'position for {pair} already open - id: {trade.id} '
f'and has open order {trade.open_order_id}')
else:
if Trade.get_open_trade_count() >= self._config['max_open_trades']:
raise RPCException("Maximum number of trades is reached.")
@ -785,17 +794,18 @@ class RPC:
if not order_type:
order_type = self._freqtrade.strategy.order_types.get(
'force_entry', self._freqtrade.strategy.order_types['entry'])
if self._freqtrade.execute_entry(pair, stake_amount, price,
ordertype=order_type, trade=trade,
is_short=is_short,
enter_tag=enter_tag,
leverage_=leverage,
):
Trade.commit()
trade = Trade.get_trades([Trade.is_open.is_(True), Trade.pair == pair]).first()
return trade
else:
raise RPCException(f'Failed to enter position for {pair}.')
with self._freqtrade._exit_lock:
if self._freqtrade.execute_entry(pair, stake_amount, price,
ordertype=order_type, trade=trade,
is_short=is_short,
enter_tag=enter_tag,
leverage_=leverage,
):
Trade.commit()
trade = Trade.get_trades([Trade.is_open.is_(True), Trade.pair == pair]).first()
return trade
else:
raise RPCException(f'Failed to enter position for {pair}.')
def _rpc_delete(self, trade_id: int) -> Dict[str, Union[str, int]]:
"""
@ -1063,23 +1073,20 @@ class RPC:
self,
pairlist: List[str],
limit: Optional[int]
) -> Dict[str, Any]:
) -> Generator[Dict[str, Any], None, None]:
""" Get the analysed dataframes of each pair in the pairlist """
timeframe = self._freqtrade.config['timeframe']
candle_type = self._freqtrade.config.get('candle_type_def', CandleType.SPOT)
_data = {}
for pair in pairlist:
dataframe, last_analyzed = self.__rpc_analysed_dataframe_raw(pair, timeframe, limit)
_data[pair] = {
yield {
"key": (pair, timeframe, candle_type),
"df": dataframe,
"la": last_analyzed
}
return _data
def _ws_request_analyzed_df(self, limit: Optional[int]):
""" Historical Analyzed Dataframes for WebSocket """
whitelist = self._freqtrade.active_pair_whitelist

4
freqtrade/rpc/rpc_manager.py
View File

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

694
freqtrade/rpc/telegram.py
View File

@ -79,6 +79,8 @@ def authorized_only(command_handler: Callable[..., None]) -> Callable[..., Any]:
)
try:
return command_handler(self, *args, **kwargs)
except RPCException as e:
self._send_msg(str(e))
except BaseException:
logger.exception('Exception occurred within Telegram module')
@ -538,72 +540,67 @@ class Telegram(RPCHandler):
handler for `/status` and `/status <id>`.
"""
try:
# Check if there's at least one numerical ID provided.
# If so, try to get only these trades.
trade_ids = []
if context.args and len(context.args) > 0:
trade_ids = [int(i) for i in context.args if i.isnumeric()]
# Check if there's at least one numerical ID provided.
# If so, try to get only these trades.
trade_ids = []
if context.args and len(context.args) > 0:
trade_ids = [int(i) for i in context.args if i.isnumeric()]
results = self._rpc._rpc_trade_status(trade_ids=trade_ids)
position_adjust = self._config.get('position_adjustment_enable', False)
max_entries = self._config.get('max_entry_position_adjustment', -1)
for r in results:
r['open_date_hum'] = arrow.get(r['open_date']).humanize()
r['num_entries'] = len([o for o in r['orders'] if o['ft_is_entry']])
r['exit_reason'] = r.get('exit_reason', "")
lines = [
"*Trade ID:* `{trade_id}`" +
(" `(since {open_date_hum})`" if r['is_open'] else ""),
"*Current Pair:* {pair}",
"*Direction:* " + ("`Short`" if r.get('is_short') else "`Long`"),
"*Leverage:* `{leverage}`" if r.get('leverage') else "",
"*Amount:* `{amount} ({stake_amount} {quote_currency})`",
"*Enter Tag:* `{enter_tag}`" if r['enter_tag'] else "",
"*Exit Reason:* `{exit_reason}`" if r['exit_reason'] else "",
]
results = self._rpc._rpc_trade_status(trade_ids=trade_ids)
position_adjust = self._config.get('position_adjustment_enable', False)
max_entries = self._config.get('max_entry_position_adjustment', -1)
for r in results:
r['open_date_hum'] = arrow.get(r['open_date']).humanize()
r['num_entries'] = len([o for o in r['orders'] if o['ft_is_entry']])
r['exit_reason'] = r.get('exit_reason', "")
lines = [
"*Trade ID:* `{trade_id}`" +
(" `(since {open_date_hum})`" if r['is_open'] else ""),
"*Current Pair:* {pair}",
"*Direction:* " + ("`Short`" if r.get('is_short') else "`Long`"),
"*Leverage:* `{leverage}`" if r.get('leverage') else "",
"*Amount:* `{amount} ({stake_amount} {quote_currency})`",
"*Enter Tag:* `{enter_tag}`" if r['enter_tag'] else "",
"*Exit Reason:* `{exit_reason}`" if r['exit_reason'] else "",
]
if position_adjust:
max_buy_str = (f"/{max_entries + 1}" if (max_entries > 0) else "")
lines.append("*Number of Entries:* `{num_entries}`" + max_buy_str)
if position_adjust:
max_buy_str = (f"/{max_entries + 1}" if (max_entries > 0) else "")
lines.append("*Number of Entries:* `{num_entries}`" + max_buy_str)
lines.extend([
"*Open Rate:* `{open_rate:.8f}`",
"*Close Rate:* `{close_rate:.8f}`" if r['close_rate'] else "",
"*Open Date:* `{open_date}`",
"*Close Date:* `{close_date}`" if r['close_date'] else "",
"*Current Rate:* `{current_rate:.8f}`" if r['is_open'] else "",
("*Current Profit:* " if r['is_open'] else "*Close Profit: *")
+ "`{profit_ratio:.2%}`",
])
lines.extend([
"*Open Rate:* `{open_rate:.8f}`",
"*Close Rate:* `{close_rate:.8f}`" if r['close_rate'] else "",
"*Open Date:* `{open_date}`",
"*Close Date:* `{close_date}`" if r['close_date'] else "",
"*Current Rate:* `{current_rate:.8f}`" if r['is_open'] else "",
("*Current Profit:* " if r['is_open'] else "*Close Profit: *")
+ "`{profit_ratio:.2%}`",
])
if r['is_open']:
if r.get('realized_profit'):
lines.append("*Realized Profit:* `{realized_profit:.8f}`")
if (r['stop_loss_abs'] != r['initial_stop_loss_abs']
and r['initial_stop_loss_ratio'] is not None):
# Adding initial stoploss only if it is different from stoploss
lines.append("*Initial Stoploss:* `{initial_stop_loss_abs:.8f}` "
"`({initial_stop_loss_ratio:.2%})`")
if r['is_open']:
if r.get('realized_profit'):
lines.append("*Realized Profit:* `{realized_profit:.8f}`")
if (r['stop_loss_abs'] != r['initial_stop_loss_abs']
and r['initial_stop_loss_ratio'] is not None):
# Adding initial stoploss only if it is different from stoploss
lines.append("*Initial Stoploss:* `{initial_stop_loss_abs:.8f}` "
"`({initial_stop_loss_ratio:.2%})`")
# Adding stoploss and stoploss percentage only if it is not None
lines.append("*Stoploss:* `{stop_loss_abs:.8f}` " +
("`({stop_loss_ratio:.2%})`" if r['stop_loss_ratio'] else ""))
lines.append("*Stoploss distance:* `{stoploss_current_dist:.8f}` "
"`({stoploss_current_dist_ratio:.2%})`")
if r['open_order']:
lines.append(
"*Open Order:* `{open_order}`"
+ "- `{exit_order_status}`" if r['exit_order_status'] else "")
# Adding stoploss and stoploss percentage only if it is not None
lines.append("*Stoploss:* `{stop_loss_abs:.8f}` " +
("`({stop_loss_ratio:.2%})`" if r['stop_loss_ratio'] else ""))
lines.append("*Stoploss distance:* `{stoploss_current_dist:.8f}` "
"`({stoploss_current_dist_ratio:.2%})`")
if r['open_order']:
lines.append(
"*Open Order:* `{open_order}`"
+ "- `{exit_order_status}`" if r['exit_order_status'] else "")
lines_detail = self._prepare_order_details(
r['orders'], r['quote_currency'], r['is_open'])
lines.extend(lines_detail if lines_detail else "")
self.__send_status_msg(lines, r)
except RPCException as e:
self._send_msg(str(e))
lines_detail = self._prepare_order_details(
r['orders'], r['quote_currency'], r['is_open'])
lines.extend(lines_detail if lines_detail else "")
self.__send_status_msg(lines, r)
def __send_status_msg(self, lines: List[str], r: Dict[str, Any]) -> None:
"""
@ -630,37 +627,34 @@ class Telegram(RPCHandler):
:param update: message update
:return: None
"""
try:
fiat_currency = self._config.get('fiat_display_currency', '')
statlist, head, fiat_profit_sum = self._rpc._rpc_status_table(
self._config['stake_currency'], fiat_currency)
fiat_currency = self._config.get('fiat_display_currency', '')
statlist, head, fiat_profit_sum = self._rpc._rpc_status_table(
self._config['stake_currency'], fiat_currency)
show_total = not isnan(fiat_profit_sum) and len(statlist) > 1
max_trades_per_msg = 50
"""
Calculate the number of messages of 50 trades per message
0.99 is used to make sure that there are no extra (empty) messages
As an example with 50 trades, there will be int(50/50 + 0.99) = 1 message
"""
messages_count = max(int(len(statlist) / max_trades_per_msg + 0.99), 1)
for i in range(0, messages_count):
trades = statlist[i * max_trades_per_msg:(i + 1) * max_trades_per_msg]
if show_total and i == messages_count - 1:
# append total line
trades.append(["Total", "", "", f"{fiat_profit_sum:.2f} {fiat_currency}"])
show_total = not isnan(fiat_profit_sum) and len(statlist) > 1
max_trades_per_msg = 50
"""
Calculate the number of messages of 50 trades per message
0.99 is used to make sure that there are no extra (empty) messages
As an example with 50 trades, there will be int(50/50 + 0.99) = 1 message
"""
messages_count = max(int(len(statlist) / max_trades_per_msg + 0.99), 1)
for i in range(0, messages_count):
trades = statlist[i * max_trades_per_msg:(i + 1) * max_trades_per_msg]
if show_total and i == messages_count - 1:
# append total line
trades.append(["Total", "", "", f"{fiat_profit_sum:.2f} {fiat_currency}"])
message = tabulate(trades,
headers=head,
tablefmt='simple')
if show_total and i == messages_count - 1:
# insert separators line between Total
lines = message.split("\n")
message = "\n".join(lines[:-1] + [lines[1]] + [lines[-1]])
self._send_msg(f"<pre>{message}</pre>", parse_mode=ParseMode.HTML,
reload_able=True, callback_path="update_status_table",
query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
message = tabulate(trades,
headers=head,
tablefmt='simple')
if show_total and i == messages_count - 1:
# insert separators line between Total
lines = message.split("\n")
message = "\n".join(lines[:-1] + [lines[1]] + [lines[-1]])
self._send_msg(f"<pre>{message}</pre>", parse_mode=ParseMode.HTML,
reload_able=True, callback_path="update_status_table",
query=update.callback_query)
@authorized_only
def _timeunit_stats(self, update: Update, context: CallbackContext, unit: str) -> None:
@ -686,35 +680,32 @@ class Telegram(RPCHandler):
timescale = int(context.args[0]) if context.args else val.default
except (TypeError, ValueError, IndexError):
timescale = val.default
try:
stats = self._rpc._rpc_timeunit_profit(
timescale,
stake_cur,
fiat_disp_cur,
unit
)
stats_tab = tabulate(
[[f"{period['date']} ({period['trade_count']})",
f"{round_coin_value(period['abs_profit'], stats['stake_currency'])}",
f"{period['fiat_value']:.2f} {stats['fiat_display_currency']}",
f"{period['rel_profit']:.2%}",
] for period in stats['data']],
headers=[
f"{val.header} (count)",
f'{stake_cur}',
f'{fiat_disp_cur}',
'Profit %',
'Trades',
],
tablefmt='simple')
message = (
f'<b>{val.message} Profit over the last {timescale} {val.message2}</b>:\n'
f'<pre>{stats_tab}</pre>'
)
self._send_msg(message, parse_mode=ParseMode.HTML, reload_able=True,
callback_path=val.callback, query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
stats = self._rpc._rpc_timeunit_profit(
timescale,
stake_cur,
fiat_disp_cur,
unit
)
stats_tab = tabulate(
[[f"{period['date']} ({period['trade_count']})",
f"{round_coin_value(period['abs_profit'], stats['stake_currency'])}",
f"{period['fiat_value']:.2f} {stats['fiat_display_currency']}",
f"{period['rel_profit']:.2%}",
] for period in stats['data']],
headers=[
f"{val.header} (count)",
f'{stake_cur}',
f'{fiat_disp_cur}',
'Profit %',
'Trades',
],
tablefmt='simple')
message = (
f'<b>{val.message} Profit over the last {timescale} {val.message2}</b>:\n'
f'<pre>{stats_tab}</pre>'
)
self._send_msg(message, parse_mode=ParseMode.HTML, reload_able=True,
callback_path=val.callback, query=update.callback_query)
@authorized_only
def _daily(self, update: Update, context: CallbackContext) -> None:
@ -878,79 +869,76 @@ class Telegram(RPCHandler):
@authorized_only
def _balance(self, update: Update, context: CallbackContext) -> None:
""" Handler for /balance """
try:
result = self._rpc._rpc_balance(self._config['stake_currency'],
self._config.get('fiat_display_currency', ''))
result = self._rpc._rpc_balance(self._config['stake_currency'],
self._config.get('fiat_display_currency', ''))
balance_dust_level = self._config['telegram'].get('balance_dust_level', 0.0)
if not balance_dust_level:
balance_dust_level = DUST_PER_COIN.get(self._config['stake_currency'], 1.0)
balance_dust_level = self._config['telegram'].get('balance_dust_level', 0.0)
if not balance_dust_level:
balance_dust_level = DUST_PER_COIN.get(self._config['stake_currency'], 1.0)
output = ''
if self._config['dry_run']:
output += "*Warning:* Simulated balances in Dry Mode.\n"
starting_cap = round_coin_value(
result['starting_capital'], self._config['stake_currency'])
output += f"Starting capital: `{starting_cap}`"
starting_cap_fiat = round_coin_value(
result['starting_capital_fiat'], self._config['fiat_display_currency']
) if result['starting_capital_fiat'] > 0 else ''
output += (f" `, {starting_cap_fiat}`.\n"
) if result['starting_capital_fiat'] > 0 else '.\n'
output = ''
if self._config['dry_run']:
output += "*Warning:* Simulated balances in Dry Mode.\n"
starting_cap = round_coin_value(
result['starting_capital'], self._config['stake_currency'])
output += f"Starting capital: `{starting_cap}`"
starting_cap_fiat = round_coin_value(
result['starting_capital_fiat'], self._config['fiat_display_currency']
) if result['starting_capital_fiat'] > 0 else ''
output += (f" `, {starting_cap_fiat}`.\n"
) if result['starting_capital_fiat'] > 0 else '.\n'
total_dust_balance = 0
total_dust_currencies = 0
for curr in result['currencies']:
curr_output = ''
if curr['est_stake'] > balance_dust_level:
if curr['is_position']:
curr_output = (
f"*{curr['currency']}:*\n"
f"\t`{curr['side']}: {curr['position']:.8f}`\n"
f"\t`Leverage: {curr['leverage']:.1f}`\n"
f"\t`Est. {curr['stake']}: "
f"{round_coin_value(curr['est_stake'], curr['stake'], False)}`\n")
else:
curr_output = (
f"*{curr['currency']}:*\n"
f"\t`Available: {curr['free']:.8f}`\n"
f"\t`Balance: {curr['balance']:.8f}`\n"
f"\t`Pending: {curr['used']:.8f}`\n"
f"\t`Est. {curr['stake']}: "
f"{round_coin_value(curr['est_stake'], curr['stake'], False)}`\n")
elif curr['est_stake'] <= balance_dust_level:
total_dust_balance += curr['est_stake']
total_dust_currencies += 1
# Handle overflowing message length
if len(output + curr_output) >= MAX_MESSAGE_LENGTH:
self._send_msg(output)
output = curr_output
total_dust_balance = 0
total_dust_currencies = 0
for curr in result['currencies']:
curr_output = ''
if curr['est_stake'] > balance_dust_level:
if curr['is_position']:
curr_output = (
f"*{curr['currency']}:*\n"
f"\t`{curr['side']}: {curr['position']:.8f}`\n"
f"\t`Leverage: {curr['leverage']:.1f}`\n"
f"\t`Est. {curr['stake']}: "
f"{round_coin_value(curr['est_stake'], curr['stake'], False)}`\n")
else:
output += curr_output
curr_output = (
f"*{curr['currency']}:*\n"
f"\t`Available: {curr['free']:.8f}`\n"
f"\t`Balance: {curr['balance']:.8f}`\n"
f"\t`Pending: {curr['used']:.8f}`\n"
f"\t`Est. {curr['stake']}: "
f"{round_coin_value(curr['est_stake'], curr['stake'], False)}`\n")
elif curr['est_stake'] <= balance_dust_level:
total_dust_balance += curr['est_stake']
total_dust_currencies += 1
if total_dust_balance > 0:
output += (
f"*{total_dust_currencies} Other "
f"{plural(total_dust_currencies, 'Currency', 'Currencies')} "
f"(< {balance_dust_level} {result['stake']}):*\n"
f"\t`Est. {result['stake']}: "
f"{round_coin_value(total_dust_balance, result['stake'], False)}`\n")
tc = result['trade_count'] > 0
stake_improve = f" `({result['starting_capital_ratio']:.2%})`" if tc else ''
fiat_val = f" `({result['starting_capital_fiat_ratio']:.2%})`" if tc else ''
# Handle overflowing message length
if len(output + curr_output) >= MAX_MESSAGE_LENGTH:
self._send_msg(output)
output = curr_output
else:
output += curr_output
output += ("\n*Estimated Value*:\n"
f"\t`{result['stake']}: "
f"{round_coin_value(result['total'], result['stake'], False)}`"
f"{stake_improve}\n"
f"\t`{result['symbol']}: "
f"{round_coin_value(result['value'], result['symbol'], False)}`"
f"{fiat_val}\n")
self._send_msg(output, reload_able=True, callback_path="update_balance",
query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
if total_dust_balance > 0:
output += (
f"*{total_dust_currencies} Other "
f"{plural(total_dust_currencies, 'Currency', 'Currencies')} "
f"(< {balance_dust_level} {result['stake']}):*\n"
f"\t`Est. {result['stake']}: "
f"{round_coin_value(total_dust_balance, result['stake'], False)}`\n")
tc = result['trade_count'] > 0
stake_improve = f" `({result['starting_capital_ratio']:.2%})`" if tc else ''
fiat_val = f" `({result['starting_capital_fiat_ratio']:.2%})`" if tc else ''
output += ("\n*Estimated Value*:\n"
f"\t`{result['stake']}: "
f"{round_coin_value(result['total'], result['stake'], False)}`"
f"{stake_improve}\n"
f"\t`{result['symbol']}: "
f"{round_coin_value(result['value'], result['symbol'], False)}`"
f"{fiat_val}\n")
self._send_msg(output, reload_able=True, callback_path="update_balance",
query=update.callback_query)
@authorized_only
def _start(self, update: Update, context: CallbackContext) -> None:
@ -1061,7 +1049,8 @@ class Telegram(RPCHandler):
try:
self._rpc._rpc_force_entry(pair, price, order_side=order_side)
except RPCException as e:
self._send_msg(str(e))
logger.exception("Forcebuy error!")
self._send_msg(str(e), ParseMode.HTML)
def _force_enter_inline(self, update: Update, _: CallbackContext) -> None:
if update.callback_query:
@ -1124,26 +1113,23 @@ class Telegram(RPCHandler):
nrecent = int(context.args[0]) if context.args else 10
except (TypeError, ValueError, IndexError):
nrecent = 10
try:
trades = self._rpc._rpc_trade_history(
nrecent
)
trades_tab = tabulate(
[[arrow.get(trade['close_date']).humanize(),
trade['pair'] + " (#" + str(trade['trade_id']) + ")",
f"{(trade['close_profit']):.2%} ({trade['close_profit_abs']})"]
for trade in trades['trades']],
headers=[
'Close Date',
'Pair (ID)',
f'Profit ({stake_cur})',
],
tablefmt='simple')
message = (f"<b>{min(trades['trades_count'], nrecent)} recent trades</b>:\n"
+ (f"<pre>{trades_tab}</pre>" if trades['trades_count'] > 0 else ''))
self._send_msg(message, parse_mode=ParseMode.HTML)
except RPCException as e:
self._send_msg(str(e))
trades = self._rpc._rpc_trade_history(
nrecent
)
trades_tab = tabulate(
[[arrow.get(trade['close_date']).humanize(),
trade['pair'] + " (#" + str(trade['trade_id']) + ")",
f"{(trade['close_profit']):.2%} ({trade['close_profit_abs']})"]
for trade in trades['trades']],
headers=[
'Close Date',
'Pair (ID)',
f'Profit ({stake_cur})',
],
tablefmt='simple')
message = (f"<b>{min(trades['trades_count'], nrecent)} recent trades</b>:\n"
+ (f"<pre>{trades_tab}</pre>" if trades['trades_count'] > 0 else ''))
self._send_msg(message, parse_mode=ParseMode.HTML)
@authorized_only
def _delete_trade(self, update: Update, context: CallbackContext) -> None:
@ -1154,18 +1140,14 @@ class Telegram(RPCHandler):
:param update: message update
:return: None
"""
try:
if not context.args or len(context.args) == 0:
raise RPCException("Trade-id not set.")
trade_id = int(context.args[0])
msg = self._rpc._rpc_delete(trade_id)
self._send_msg((
f"`{msg['result_msg']}`\n"
'Please make sure to take care of this asset on the exchange manually.'
))
except RPCException as e:
self._send_msg(str(e))
if not context.args or len(context.args) == 0:
raise RPCException("Trade-id not set.")
trade_id = int(context.args[0])
msg = self._rpc._rpc_delete(trade_id)
self._send_msg((
f"`{msg['result_msg']}`\n"
'Please make sure to take care of this asset on the exchange manually.'
))
@authorized_only
def _performance(self, update: Update, context: CallbackContext) -> None:
@ -1176,27 +1158,24 @@ class Telegram(RPCHandler):
:param update: message update
:return: None
"""
try:
trades = self._rpc._rpc_performance()
output = "<b>Performance:</b>\n"
for i, trade in enumerate(trades):
stat_line = (
f"{i+1}.\t <code>{trade['pair']}\t"
f"{round_coin_value(trade['profit_abs'], self._config['stake_currency'])} "
f"({trade['profit_ratio']:.2%}) "
f"({trade['count']})</code>\n")
trades = self._rpc._rpc_performance()
output = "<b>Performance:</b>\n"
for i, trade in enumerate(trades):
stat_line = (
f"{i+1}.\t <code>{trade['pair']}\t"
f"{round_coin_value(trade['profit_abs'], self._config['stake_currency'])} "
f"({trade['profit_ratio']:.2%}) "
f"({trade['count']})</code>\n")
if len(output + stat_line) >= MAX_MESSAGE_LENGTH:
self._send_msg(output, parse_mode=ParseMode.HTML)
output = stat_line
else:
output += stat_line
if len(output + stat_line) >= MAX_MESSAGE_LENGTH:
self._send_msg(output, parse_mode=ParseMode.HTML)
output = stat_line
else:
output += stat_line
self._send_msg(output, parse_mode=ParseMode.HTML,
reload_able=True, callback_path="update_performance",
query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
self._send_msg(output, parse_mode=ParseMode.HTML,
reload_able=True, callback_path="update_performance",
query=update.callback_query)
@authorized_only
def _enter_tag_performance(self, update: Update, context: CallbackContext) -> None:
@ -1207,31 +1186,28 @@ class Telegram(RPCHandler):
:param update: message update
:return: None
"""
try:
pair = None
if context.args and isinstance(context.args[0], str):
pair = context.args[0]
pair = None
if context.args and isinstance(context.args[0], str):
pair = context.args[0]
trades = self._rpc._rpc_enter_tag_performance(pair)
output = "<b>Entry Tag Performance:</b>\n"
for i, trade in enumerate(trades):
stat_line = (
f"{i+1}.\t <code>{trade['enter_tag']}\t"
f"{round_coin_value(trade['profit_abs'], self._config['stake_currency'])} "
f"({trade['profit_ratio']:.2%}) "
f"({trade['count']})</code>\n")
trades = self._rpc._rpc_enter_tag_performance(pair)
output = "<b>Entry Tag Performance:</b>\n"
for i, trade in enumerate(trades):
stat_line = (
f"{i+1}.\t <code>{trade['enter_tag']}\t"
f"{round_coin_value(trade['profit_abs'], self._config['stake_currency'])} "
f"({trade['profit_ratio']:.2%}) "
f"({trade['count']})</code>\n")
if len(output + stat_line) >= MAX_MESSAGE_LENGTH:
self._send_msg(output, parse_mode=ParseMode.HTML)
output = stat_line
else:
output += stat_line
if len(output + stat_line) >= MAX_MESSAGE_LENGTH:
self._send_msg(output, parse_mode=ParseMode.HTML)
output = stat_line
else:
output += stat_line
self._send_msg(output, parse_mode=ParseMode.HTML,
reload_able=True, callback_path="update_enter_tag_performance",
query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
self._send_msg(output, parse_mode=ParseMode.HTML,
reload_able=True, callback_path="update_enter_tag_performance",
query=update.callback_query)
@authorized_only
def _exit_reason_performance(self, update: Update, context: CallbackContext) -> None:
@ -1242,31 +1218,28 @@ class Telegram(RPCHandler):
:param update: message update
:return: None
"""
try:
pair = None
if context.args and isinstance(context.args[0], str):
pair = context.args[0]
pair = None
if context.args and isinstance(context.args[0], str):
pair = context.args[0]
trades = self._rpc._rpc_exit_reason_performance(pair)
output = "<b>Exit Reason Performance:</b>\n"
for i, trade in enumerate(trades):
stat_line = (
f"{i+1}.\t <code>{trade['exit_reason']}\t"
f"{round_coin_value(trade['profit_abs'], self._config['stake_currency'])} "
f"({trade['profit_ratio']:.2%}) "
f"({trade['count']})</code>\n")
trades = self._rpc._rpc_exit_reason_performance(pair)
output = "<b>Exit Reason Performance:</b>\n"
for i, trade in enumerate(trades):
stat_line = (
f"{i+1}.\t <code>{trade['exit_reason']}\t"
f"{round_coin_value(trade['profit_abs'], self._config['stake_currency'])} "
f"({trade['profit_ratio']:.2%}) "
f"({trade['count']})</code>\n")
if len(output + stat_line) >= MAX_MESSAGE_LENGTH:
self._send_msg(output, parse_mode=ParseMode.HTML)
output = stat_line
else:
output += stat_line
if len(output + stat_line) >= MAX_MESSAGE_LENGTH:
self._send_msg(output, parse_mode=ParseMode.HTML)
output = stat_line
else:
output += stat_line
self._send_msg(output, parse_mode=ParseMode.HTML,
reload_able=True, callback_path="update_exit_reason_performance",
query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
self._send_msg(output, parse_mode=ParseMode.HTML,
reload_able=True, callback_path="update_exit_reason_performance",
query=update.callback_query)
@authorized_only
def _mix_tag_performance(self, update: Update, context: CallbackContext) -> None:
@ -1277,31 +1250,28 @@ class Telegram(RPCHandler):
:param update: message update
:return: None
"""
try:
pair = None
if context.args and isinstance(context.args[0], str):
pair = context.args[0]
pair = None
if context.args and isinstance(context.args[0], str):
pair = context.args[0]
trades = self._rpc._rpc_mix_tag_performance(pair)
output = "<b>Mix Tag Performance:</b>\n"
for i, trade in enumerate(trades):
stat_line = (
f"{i+1}.\t <code>{trade['mix_tag']}\t"
f"{round_coin_value(trade['profit_abs'], self._config['stake_currency'])} "
f"({trade['profit']:.2%}) "
f"({trade['count']})</code>\n")
trades = self._rpc._rpc_mix_tag_performance(pair)
output = "<b>Mix Tag Performance:</b>\n"
for i, trade in enumerate(trades):
stat_line = (
f"{i+1}.\t <code>{trade['mix_tag']}\t"
f"{round_coin_value(trade['profit_abs'], self._config['stake_currency'])} "
f"({trade['profit']:.2%}) "
f"({trade['count']})</code>\n")
if len(output + stat_line) >= MAX_MESSAGE_LENGTH:
self._send_msg(output, parse_mode=ParseMode.HTML)
output = stat_line
else:
output += stat_line
if len(output + stat_line) >= MAX_MESSAGE_LENGTH:
self._send_msg(output, parse_mode=ParseMode.HTML)
output = stat_line
else:
output += stat_line
self._send_msg(output, parse_mode=ParseMode.HTML,
reload_able=True, callback_path="update_mix_tag_performance",
query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
self._send_msg(output, parse_mode=ParseMode.HTML,
reload_able=True, callback_path="update_mix_tag_performance",
query=update.callback_query)
@authorized_only
def _count(self, update: Update, context: CallbackContext) -> None:
@ -1312,18 +1282,15 @@ class Telegram(RPCHandler):
:param update: message update
:return: None
"""
try:
counts = self._rpc._rpc_count()
message = tabulate({k: [v] for k, v in counts.items()},
headers=['current', 'max', 'total stake'],
tablefmt='simple')
message = "<pre>{}</pre>".format(message)
logger.debug(message)
self._send_msg(message, parse_mode=ParseMode.HTML,
reload_able=True, callback_path="update_count",
query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
counts = self._rpc._rpc_count()
message = tabulate({k: [v] for k, v in counts.items()},
headers=['current', 'max', 'total stake'],
tablefmt='simple')
message = "<pre>{}</pre>".format(message)
logger.debug(message)
self._send_msg(message, parse_mode=ParseMode.HTML,
reload_able=True, callback_path="update_count",
query=update.callback_query)
@authorized_only
def _locks(self, update: Update, context: CallbackContext) -> None:
@ -1371,22 +1338,19 @@ class Telegram(RPCHandler):
Handler for /whitelist
Shows the currently active whitelist
"""
try:
whitelist = self._rpc._rpc_whitelist()
whitelist = self._rpc._rpc_whitelist()
if context.args:
if "sorted" in context.args:
whitelist['whitelist'] = sorted(whitelist['whitelist'])
if "baseonly" in context.args:
whitelist['whitelist'] = [pair.split("/")[0] for pair in whitelist['whitelist']]
if context.args:
if "sorted" in context.args:
whitelist['whitelist'] = sorted(whitelist['whitelist'])
if "baseonly" in context.args:
whitelist['whitelist'] = [pair.split("/")[0] for pair in whitelist['whitelist']]
message = f"Using whitelist `{whitelist['method']}` with {whitelist['length']} pairs\n"
message += f"`{', '.join(whitelist['whitelist'])}`"
message = f"Using whitelist `{whitelist['method']}` with {whitelist['length']} pairs\n"
message += f"`{', '.join(whitelist['whitelist'])}`"
logger.debug(message)
self._send_msg(message)
except RPCException as e:
self._send_msg(str(e))
logger.debug(message)
self._send_msg(message)
@authorized_only
def _blacklist(self, update: Update, context: CallbackContext) -> None:
@ -1424,30 +1388,27 @@ class Telegram(RPCHandler):
Shows the latest logs
"""
try:
try:
limit = int(context.args[0]) if context.args else 10
except (TypeError, ValueError, IndexError):
limit = 10
logs = RPC._rpc_get_logs(limit)['logs']
msgs = ''
msg_template = "*{}* {}: {} \\- `{}`"
for logrec in logs:
msg = msg_template.format(escape_markdown(logrec[0], version=2),
escape_markdown(logrec[2], version=2),
escape_markdown(logrec[3], version=2),
escape_markdown(logrec[4], version=2))
if len(msgs + msg) + 10 >= MAX_MESSAGE_LENGTH:
# Send message immediately if it would become too long
self._send_msg(msgs, parse_mode=ParseMode.MARKDOWN_V2)
msgs = msg + '\n'
else:
# Append message to messages to send
msgs += msg + '\n'
if msgs:
limit = int(context.args[0]) if context.args else 10
except (TypeError, ValueError, IndexError):
limit = 10
logs = RPC._rpc_get_logs(limit)['logs']
msgs = ''
msg_template = "*{}* {}: {} \\- `{}`"
for logrec in logs:
msg = msg_template.format(escape_markdown(logrec[0], version=2),
escape_markdown(logrec[2], version=2),
escape_markdown(logrec[3], version=2),
escape_markdown(logrec[4], version=2))
if len(msgs + msg) + 10 >= MAX_MESSAGE_LENGTH:
# Send message immediately if it would become too long
self._send_msg(msgs, parse_mode=ParseMode.MARKDOWN_V2)
except RPCException as e:
self._send_msg(str(e))
msgs = msg + '\n'
else:
# Append message to messages to send
msgs += msg + '\n'
if msgs:
self._send_msg(msgs, parse_mode=ParseMode.MARKDOWN_V2)
@authorized_only
def _edge(self, update: Update, context: CallbackContext) -> None:
@ -1455,21 +1416,17 @@ class Telegram(RPCHandler):
Handler for /edge
Shows information related to Edge
"""
try:
edge_pairs = self._rpc._rpc_edge()
if not edge_pairs:
message = '<b>Edge only validated following pairs:</b>'
self._send_msg(message, parse_mode=ParseMode.HTML)
edge_pairs = self._rpc._rpc_edge()
if not edge_pairs:
message = '<b>Edge only validated following pairs:</b>'
self._send_msg(message, parse_mode=ParseMode.HTML)
for chunk in chunks(edge_pairs, 25):
edge_pairs_tab = tabulate(chunk, headers='keys', tablefmt='simple')
message = (f'<b>Edge only validated following pairs:</b>\n'
f'<pre>{edge_pairs_tab}</pre>')
for chunk in chunks(edge_pairs, 25):
edge_pairs_tab = tabulate(chunk, headers='keys', tablefmt='simple')
message = (f'<b>Edge only validated following pairs:</b>\n'
f'<pre>{edge_pairs_tab}</pre>')
self._send_msg(message, parse_mode=ParseMode.HTML)
except RPCException as e:
self._send_msg(str(e))
self._send_msg(message, parse_mode=ParseMode.HTML)
@authorized_only
def _help(self, update: Update, context: CallbackContext) -> None:
@ -1550,12 +1507,9 @@ class Telegram(RPCHandler):
Handler for /health
Shows the last process timestamp
"""
try:
health = self._rpc._health()
message = f"Last process: `{health['last_process_loc']}`"
self._send_msg(message)
except RPCException as e:
self._send_msg(str(e))
health = self._rpc._health()
message = f"Last process: `{health['last_process_loc']}`"
self._send_msg(message)
@authorized_only
def _version(self, update: Update, context: CallbackContext) -> None:

1
freqtrade/rpc/webhook.py
View File

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

6
freqtrade/strategy/interface.py
View File

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

4
freqtrade/templates/FreqaiExampleHybridStrategy.py
View File

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

2
freqtrade/templates/strategy_analysis_example.ipynb
View File

@ -328,7 +328,7 @@
"# Show graph inline\n",
"# graph.show()\n",
"\n",
"# Render graph in a seperate window\n",
"# Render graph in a separate window\n",
"graph.show(renderer=\"browser\")\n"
]
},

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

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

12
freqtrade/worker.py
View File

@ -150,14 +150,20 @@ class Worker:
if timeframe:
next_tf = timeframe_to_next_date(timeframe)
# Maximum throttling should be until new candle arrives
# Offset of 0.2s is added to ensure a new candle has been issued.
next_tf_with_offset = next_tf.timestamp() - time.time() + timeframe_offset
# Offset is added to ensure a new candle has been issued.
next_tft = next_tf.timestamp() - time.time()
next_tf_with_offset = next_tft + timeframe_offset
if next_tft < sleep_duration and sleep_duration < next_tf_with_offset:
# Avoid hitting a new loop between the new candle and the candle with offset
sleep_duration = next_tf_with_offset
sleep_duration = min(sleep_duration, next_tf_with_offset)
sleep_duration = max(sleep_duration, 0.0)
# next_iter = datetime.now(timezone.utc) + timedelta(seconds=sleep_duration)
logger.debug(f"Throttling with '{func.__name__}()': sleep for {sleep_duration:.2f} s, "
f"last iteration took {time_passed:.2f} s.")
f"last iteration took {time_passed:.2f} s."
# f"next: {next_iter}"
)
self._sleep(sleep_duration)
return result

1
mkdocs.yml
View File

@ -29,6 +29,7 @@ nav:
- Parameter table: freqai-parameter-table.md
- Feature engineering: freqai-feature-engineering.md
- Running FreqAI: freqai-running.md
- Reinforcement Learning: freqai-reinforcement-learning.md
- Developer guide: freqai-developers.md
- Short / Leverage: leverage.md
- Utility Sub-commands: utils.md

2
pyproject.toml
View File

@ -30,6 +30,8 @@ asyncio_mode = "auto"
[tool.mypy]
ignore_missing_imports = true
namespace_packages = false
implicit_optional = true
warn_unused_ignores = true
exclude = [
'^build_helpers\.py$'

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