Merge branch 'develop' into backtest_fitlivepredictions

This commit is contained in:
Wagner Costa 2022-11-29 09:39:15 -03:00
commit df979ece33
63 changed files with 3805 additions and 1804 deletions

View File

@ -66,12 +66,6 @@ jobs:
- name: Tests - name: Tests
run: | run: |
pytest --random-order --cov=freqtrade --cov-config=.coveragerc 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 - name: Coveralls
if: (runner.os == 'Linux' && matrix.python-version == '3.10' && matrix.os == 'ubuntu-22.04') if: (runner.os == 'Linux' && matrix.python-version == '3.10' && matrix.os == 'ubuntu-22.04')
@ -310,9 +304,64 @@ jobs:
details: Freqtrade doc test failed! details: Freqtrade doc test failed!
webhookUrl: ${{ secrets.DISCORD_WEBHOOK }} 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 only once - when CI completes (and after deploy) in case it's successfull
notify-complete: 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 runs-on: ubuntu-22.04
# Discord notification can't handle schedule events # Discord notification can't handle schedule events
if: (github.event_name != 'schedule') if: (github.event_name != 'schedule')

View File

@ -7,11 +7,13 @@ export DOCKER_BUILDKIT=1
TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g") TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g")
TAG_PLOT=${TAG}_plot TAG_PLOT=${TAG}_plot
TAG_FREQAI=${TAG}_freqai TAG_FREQAI=${TAG}_freqai
TAG_FREQAI_RL=${TAG_FREQAI}rl
TAG_PI="${TAG}_pi" TAG_PI="${TAG}_pi"
TAG_ARM=${TAG}_arm TAG_ARM=${TAG}_arm
TAG_PLOT_ARM=${TAG_PLOT}_arm TAG_PLOT_ARM=${TAG_PLOT}_arm
TAG_FREQAI_ARM=${TAG_FREQAI}_arm TAG_FREQAI_ARM=${TAG_FREQAI}_arm
TAG_FREQAI_RL_ARM=${TAG_FREQAI_RL}_arm
CACHE_IMAGE=freqtradeorg/freqtrade_cache CACHE_IMAGE=freqtradeorg/freqtrade_cache
echo "Running for ${TAG}" 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_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_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_PLOT_ARM ${CACHE_IMAGE}:$TAG_PLOT_ARM
docker tag freqtrade:$TAG_FREQAI_ARM ${CACHE_IMAGE}:$TAG_FREQAI_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 # 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 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 ${IMAGE_NAME}
docker push ${CACHE_IMAGE}:$TAG_PLOT_ARM docker push ${CACHE_IMAGE}:$TAG_PLOT_ARM
docker push ${CACHE_IMAGE}:$TAG_FREQAI_ARM docker push ${CACHE_IMAGE}:$TAG_FREQAI_ARM
docker push ${CACHE_IMAGE}:$TAG_FREQAI_RL_ARM
docker push ${CACHE_IMAGE}:$TAG_ARM docker push ${CACHE_IMAGE}:$TAG_ARM
# Create multi-arch image # 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 create ${IMAGE_NAME}:${TAG_FREQAI} ${CACHE_IMAGE}:${TAG_FREQAI_ARM} ${CACHE_IMAGE}:${TAG_FREQAI}
docker manifest push -p ${IMAGE_NAME}:${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 # Tag as latest for develop builds
if [ "${TAG}" = "develop" ]; then if [ "${TAG}" = "develop" ]; then
docker manifest create ${IMAGE_NAME}:latest ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI} ${CACHE_IMAGE}:${TAG} docker manifest create ${IMAGE_NAME}:latest ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI} ${CACHE_IMAGE}:${TAG}

View File

@ -6,6 +6,7 @@
TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g") TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g")
TAG_PLOT=${TAG}_plot TAG_PLOT=${TAG}_plot
TAG_FREQAI=${TAG}_freqai TAG_FREQAI=${TAG}_freqai
TAG_FREQAI_RL=${TAG_FREQAI}rl
TAG_PI="${TAG}_pi" TAG_PI="${TAG}_pi"
PI_PLATFORM="linux/arm/v7" PI_PLATFORM="linux/arm/v7"
@ -51,9 +52,11 @@ docker tag freqtrade:$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_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:${TAG} --build-arg sourceimage=${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_PLOT ${CACHE_IMAGE}:$TAG_PLOT
docker tag freqtrade:$TAG_FREQAI ${CACHE_IMAGE}:$TAG_FREQAI docker tag freqtrade:$TAG_FREQAI ${CACHE_IMAGE}:$TAG_FREQAI
docker tag freqtrade:$TAG_FREQAI_RL ${CACHE_IMAGE}:$TAG_FREQAI_RL
# Run backtest # 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 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}
docker push ${CACHE_IMAGE}:$TAG_PLOT docker push ${CACHE_IMAGE}:$TAG_PLOT
docker push ${CACHE_IMAGE}:$TAG_FREQAI docker push ${CACHE_IMAGE}:$TAG_FREQAI
docker push ${CACHE_IMAGE}:$TAG_FREQAI_RL
docker push ${CACHE_IMAGE}:$TAG docker push ${CACHE_IMAGE}:$TAG

View File

@ -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

View File

@ -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 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). 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. `--timeframe-detail` must be smaller than the original timeframe, otherwise backtesting will fail to start.

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. 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). 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 #### 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). 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> </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 ### Create github release / tag
Once the PR against stable is merged (best right after merging): Once the PR against stable is merged (best right after merging):

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. Mandatory parameters are marked as **Required** and have to be set in one of the suggested ways.
### General configuration parameters
| Parameter | Description | | 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. | `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. | `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. | `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`. | `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`. | `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` | `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. | `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_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). | `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).
@ -38,16 +46,49 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
| `noise_standard_deviation` | If set, FreqAI adds noise to the training features with the aim of preventing overfitting. FreqAI generates random deviates from a gaussian distribution with a standard deviation of `noise_standard_deviation` and adds them to all data points. `noise_standard_deviation` should be kept relative to the normalized space, i.e., between -1 and 1. In other words, since data in FreqAI is always normalized to be between -1 and 1, `noise_standard_deviation: 0.05` would result in 32% of the data being randomly increased/decreased by more than 2.5% (i.e., the percent of data falling within the first standard deviation). <br> **Datatype:** Integer. <br> Default: `0`. | `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`. | `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). | `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. | `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. | `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`. | `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. | `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. | `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. | `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. | `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** | | **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`. | `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`.
| `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.conv_width` | The width of a convolutional neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. <br> **Datatype:** Integer. <br> Default: `2`.
| `reduce_df_footprint` | Recast all numeric columns to float32/int32, with the objective of reducing ram/disk usage and decreasing train/inference timing. This parameter is set in the main level of the Freqtrade configuration file (not inside FreqAI). <br> **Datatype:** Boolean. <br> Default: `False`. | `freqai.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`.

View File

@ -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 127.0.0.1:6060 (6060 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).

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" "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 "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 "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 "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.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.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.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 | `producers.ws_token` | **Required.** `ws_token` as configured on the producer.<br> **Datatype:** string
| | **Optional settings** | | **Optional settings**
| `wait_timeout` | Timeout until we ping again if no message is received. <br>*Defaults to `300`.*<br> **Datatype:** Integer - in seconds. | `wait_timeout` | Timeout until we ping again if no message is received. <br>*Defaults to `300`.*<br> **Datatype:** Integer - in seconds.

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 ### OpenAPI interface
To enable the builtin openAPI interface (Swagger UI), specify `"enable_openapi": true` in the api_server configuration. To enable the builtin openAPI interface (Swagger UI), specify `"enable_openapi": true` in the api_server configuration.

View File

@ -446,15 +446,17 @@ A full sample can be found [in the DataProvider section](#complete-data-provider
??? Note "Alternative candle types" ??? Note "Alternative candle types"
Informative_pairs can also provide a 3rd tuple element defining the candle type explicitly. 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 ``` python
def informative_pairs(self): def informative_pairs(self):
return [ return [
("ETH/USDT", "5m", ""), # Uses default candletype, depends on trading_mode ("ETH/USDT", "5m", ""), # Uses default candletype, depends on trading_mode (recommended)
("ETH/USDT", "5m", "spot"), # Forces usage of spot candles ("ETH/USDT", "5m", "spot"), # Forces usage of spot candles (only valid for bots running on spot markets).
("BTC/TUSD", "15m", "futures"), # Uses futures candles ("BTC/TUSD", "15m", "futures"), # Uses futures candles (only bots with `trading_mode=futures`)
("BTC/TUSD", "15m", "mark"), # Uses mark candles ("BTC/TUSD", "15m", "mark"), # Uses mark candles (only bots with `trading_mode=futures`)
] ]
``` ```
*** ***

View File

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

View File

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

View File

@ -512,6 +512,7 @@ CONF_SCHEMA = {
'minimum': 0, 'minimum': 0,
'maximum': 65535 'maximum': 65535
}, },
'secure': {'type': 'boolean', 'default': False},
'ws_token': {'type': 'string'}, 'ws_token': {'type': 'string'},
}, },
'required': ['name', 'host', 'ws_token'] 'required': ['name', 'host', 'ws_token']
@ -577,9 +578,27 @@ CONF_SCHEMA = {
}, },
}, },
"model_training_parameters": { "model_training_parameters": {
"type": "object"
},
"rl_config": {
"type": "object", "type": "object",
"properties": { "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}
}
}
}, },
}, },
}, },

File diff suppressed because it is too large Load Diff

View File

@ -20,7 +20,7 @@ class Bybit(Exchange):
""" """
_ft_has: Dict = { _ft_has: Dict = {
"ohlcv_candle_limit": 200, "ohlcv_candle_limit": 1000,
"ccxt_futures_name": "linear", "ccxt_futures_name": "linear",
"ohlcv_has_history": False, "ohlcv_has_history": False,
} }

View File

@ -218,3 +218,19 @@ class Kraken(Exchange):
fees = sum(df['open_fund'] * df['open_mark'] * amount * time_in_ratio) fees = sum(df['open_fund'] * df['open_mark'] * amount * time_in_ratio)
return fees if is_short else -fees 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

View File

@ -0,0 +1,135 @@
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 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,
total_reward=self.total_reward,
total_profit=self._total_profit,
position=self._position.value
)
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

View File

@ -0,0 +1,145 @@
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 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,
total_reward=self.total_reward,
total_profit=self._total_profit,
position=self._position.value
)
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

View File

@ -0,0 +1,307 @@
import logging
import random
from abc import abstractmethod
from enum import Enum
from typing import Optional
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
logger = logging.getLogger(__name__)
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
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):
self._done = False
if self.starting_point is True:
if self.rl_config.get('randomize_starting_position', False):
length_of_data = int(self._end_tick / 4)
start_tick = random.randint(self.window_size + 1, length_of_data)
self._start_tick = start_tick
self._position_history = (self._start_tick * [None]) + [self._position]
else:
self._position_history = (self.window_size * [None]) + [self._position]
self._current_tick = self._start_tick
self._last_trade_tick = None
self._position = Positions.Neutral
self.total_reward = 0.
self._total_profit = 1. # unit
self.history = {}
self.trade_history = []
self.portfolio_log_returns = np.zeros(len(self.prices))
self._profits = [(self._start_tick, 1)]
self.close_trade_profit = []
self._total_unrealized_profit = 1
return self._get_observation()
@abstractmethod
def step(self, action: int):
"""
Step depeneds on action types, this must be inherited.
"""
return
def _get_observation(self):
"""
This may or may not be independent of action types, user can inherit
this in their custom "MyRLEnv"
"""
features_window = self.signal_features[(
self._current_tick - self.window_size):self._current_tick]
if self.add_state_info:
features_and_state = DataFrame(np.zeros((len(features_window), 3)),
columns=['current_profit_pct',
'position',
'trade_duration'],
index=features_window.index)
features_and_state['current_profit_pct'] = self.get_unrealized_profit()
features_and_state['position'] = self._position.value
features_and_state['trade_duration'] = self.get_trade_duration()
features_and_state = pd.concat([features_window, features_and_state], axis=1)
return features_and_state
else:
return features_window
def get_trade_duration(self):
"""
Get the trade duration if the agent is in a trade
"""
if self._last_trade_tick is None:
return 0
else:
return self._current_tick - self._last_trade_tick
def get_unrealized_profit(self):
"""
Get the unrealized profit if the agent is in a trade
"""
if self._last_trade_tick is None:
return 0.
if self._position == Positions.Neutral:
return 0.
elif self._position == Positions.Short:
current_price = self.add_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 (last_trade_price - current_price) / last_trade_price
elif self._position == Positions.Long:
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 (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
# 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)

View File

@ -0,0 +1,396 @@
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 Positions
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, gym.Env] = None
self.eval_env: Union[SubprocVecEnv, gym.Env] = None
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 = "stable_baselines"
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))
@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)
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

View File

View File

@ -1,4 +1,5 @@
import collections import collections
import importlib
import logging import logging
import re import re
import shutil import shutil
@ -99,6 +100,7 @@ class FreqaiDataDrawer:
self.empty_pair_dict: pair_info = { self.empty_pair_dict: pair_info = {
"model_filename": "", "trained_timestamp": 0, "model_filename": "", "trained_timestamp": 0,
"data_path": "", "extras": {}} "data_path": "", "extras": {}}
self.model_type = self.freqai_info.get('model_save_type', 'joblib')
def update_metric_tracker(self, metric: str, value: float, pair: str) -> None: def update_metric_tracker(self, metric: str, value: float, pair: str) -> None:
""" """
@ -497,10 +499,12 @@ class FreqaiDataDrawer:
save_path = Path(dk.data_path) save_path = Path(dk.data_path)
# Save the trained model # Save the trained model
if not dk.keras: if self.model_type == 'joblib':
dump(model, save_path / f"{dk.model_filename}_model.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") model.save(save_path / f"{dk.model_filename}_model.h5")
elif 'stable_baselines' in self.model_type:
model.save(save_path / f"{dk.model_filename}_model.zip")
if dk.svm_model is not None: if dk.svm_model is not None:
dump(dk.svm_model, save_path / f"{dk.model_filename}_svm_model.joblib") dump(dk.svm_model, save_path / f"{dk.model_filename}_svm_model.joblib")
@ -527,11 +531,10 @@ class FreqaiDataDrawer:
dk.pca, open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "wb") 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.model_dictionary[coin] = model
self.pair_dict[coin]["model_filename"] = dk.model_filename self.pair_dict[coin]["model_filename"] = dk.model_filename
self.pair_dict[coin]["data_path"] = str(dk.data_path) self.pair_dict[coin]["data_path"] = str(dk.data_path)
if coin not in self.meta_data_dictionary: if coin not in self.meta_data_dictionary:
self.meta_data_dictionary[coin] = {} self.meta_data_dictionary[coin] = {}
self.meta_data_dictionary[coin]["train_df"] = dk.data_dictionary["train_features"] self.meta_data_dictionary[coin]["train_df"] = dk.data_dictionary["train_features"]
@ -563,14 +566,6 @@ class FreqaiDataDrawer:
if dk.live: if dk.live:
dk.model_filename = self.pair_dict[coin]["model_filename"] dk.model_filename = self.pair_dict[coin]["model_filename"]
dk.data_path = Path(self.pair_dict[coin]["data_path"]) 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: if coin in self.meta_data_dictionary:
dk.data = self.meta_data_dictionary[coin]["meta_data"] dk.data = self.meta_data_dictionary[coin]["meta_data"]
@ -589,12 +584,16 @@ class FreqaiDataDrawer:
# try to access model in memory instead of loading object from disk to save time # try to access model in memory instead of loading object from disk to save time
if dk.live and coin in self.model_dictionary: if dk.live and coin in self.model_dictionary:
model = self.model_dictionary[coin] 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") model = load(dk.data_path / f"{dk.model_filename}_model.joblib")
else: elif self.model_type == 'keras':
from tensorflow import keras from tensorflow import keras
model = keras.models.load_model(dk.data_path / f"{dk.model_filename}_model.h5") model = keras.models.load_model(dk.data_path / f"{dk.model_filename}_model.h5")
elif self.model_type == 'stable_baselines':
mod = importlib.import_module(
'stable_baselines3', 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(): 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") dk.svm_model = load(dk.data_path / f"{dk.model_filename}_svm_model.joblib")
@ -604,6 +603,10 @@ class FreqaiDataDrawer:
f"Unable to load model, ensure model exists at " f"{dk.data_path} " 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"]: if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]:
dk.pca = cloudpickle.load( dk.pca = cloudpickle.load(
open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "rb") open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "rb")

View File

@ -9,6 +9,7 @@ from typing import Any, Dict, List, Tuple
import numpy as np import numpy as np
import numpy.typing as npt import numpy.typing as npt
import pandas as pd import pandas as pd
import psutil
from pandas import DataFrame, HDFStore from pandas import DataFrame, HDFStore
from scipy import stats from scipy import stats
from sklearn import linear_model from sklearn import linear_model
@ -98,7 +99,10 @@ class FreqaiDataKitchen:
) )
self.data['extra_returns_per_train'] = self.freqai_config.get('extra_returns_per_train', {}) 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.train_dates: DataFrame = pd.DataFrame()
self.unique_classes: Dict[str, list] = {} self.unique_classes: Dict[str, list] = {}
self.unique_class_list: list = [] self.unique_class_list: list = []

View File

@ -5,15 +5,17 @@ from abc import ABC, abstractmethod
from collections import deque from collections import deque
from datetime import datetime, timezone from datetime import datetime, timezone
from pathlib import Path 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 numpy as np
import pandas as pd import pandas as pd
import psutil
from numpy.typing import NDArray from numpy.typing import NDArray
from pandas import DataFrame from pandas import DataFrame
from freqtrade.configuration import TimeRange from freqtrade.configuration import TimeRange
from freqtrade.constants import Config from freqtrade.constants import Config
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import RunMode from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_seconds from freqtrade.exchange import timeframe_to_seconds
@ -101,6 +103,8 @@ class IFreqaiModel(ABC):
self._threads: List[threading.Thread] = [] self._threads: List[threading.Thread] = []
self._stop_event = threading.Event() self._stop_event = threading.Event()
self.metadata = self.dd.load_global_metadata_from_disk() self.metadata = 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) record_params(config, self.full_path)
@ -129,6 +133,7 @@ class IFreqaiModel(ABC):
self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE) self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
self.dd.set_pair_dict_info(metadata) self.dd.set_pair_dict_info(metadata)
self.data_provider = strategy.dp
if self.live: if self.live:
self.inference_timer('start') self.inference_timer('start')
@ -175,6 +180,13 @@ class IFreqaiModel(ABC):
self.model = None self.model = None
self.dk = 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): def shutdown(self):
""" """
Cleans up threads on Shutdown, set stop event. Join threads to wait Cleans up threads on Shutdown, set stop event. Join threads to wait
@ -183,6 +195,9 @@ class IFreqaiModel(ABC):
logger.info("Stopping FreqAI") logger.info("Stopping FreqAI")
self._stop_event.set() self._stop_event.set()
self.data_provider = None
self._on_stop()
logger.info("Waiting on Training iteration") logger.info("Waiting on Training iteration")
for _thread in self._threads: for _thread in self._threads:
_thread.join() _thread.join()
@ -663,7 +678,7 @@ class IFreqaiModel(ABC):
hist_preds_df['DI_values'] = 0 hist_preds_df['DI_values'] = 0
for return_str in dk.data['extra_returns_per_train']: 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['close_price'] = strat_df['close']
hist_preds_df['date_pred'] = strat_df['date'] hist_preds_df['date_pred'] = strat_df['date']

View File

@ -0,0 +1,141 @@
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
)
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):
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 # type: ignore
if trade_duration <= max_trade_duration:
factor *= 1.5
elif trade_duration > max_trade_duration:
factor *= 0.5
# discourage sitting in position
if (self._position in (Positions.Short, Positions.Long) and
action == Actions.Neutral.value):
return -1 * trade_duration / max_trade_duration
# close long
if action == Actions.Long_exit.value and self._position == Positions.Long:
if pnl > self.profit_aim * self.rr:
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
return float(pnl * factor)
# close short
if action == Actions.Short_exit.value and self._position == Positions.Short:
if pnl > self.profit_aim * self.rr:
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
return float(pnl * factor)
return 0.

View File

@ -0,0 +1,51 @@
import logging
from typing import Any, Dict # , Tuple
# import numpy.typing as npt
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
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))

View File

@ -191,10 +191,10 @@ class FreqtradeBot(LoggingMixin):
# Check whether markets have to be reloaded and reload them when it's needed # Check whether markets have to be reloaded and reload them when it's needed
self.exchange.reload_markets() self.exchange.reload_markets()
self.update_closed_trades_without_assigned_fees() self.update_trades_without_assigned_fees()
# Query trades from persistence layer # 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) self.active_pair_whitelist = self._refresh_active_whitelist(trades)
@ -354,7 +354,7 @@ class FreqtradeBot(LoggingMixin):
if self.trading_mode == TradingMode.FUTURES: if self.trading_mode == TradingMode.FUTURES:
self._schedule.run_pending() self._schedule.run_pending()
def update_closed_trades_without_assigned_fees(self) -> None: def update_trades_without_assigned_fees(self) -> None:
""" """
Update closed trades without close fees assigned. Update closed trades without close fees assigned.
Only acts when Orders are in the database, otherwise the last order-id is unknown. Only acts when Orders are in the database, otherwise the last order-id is unknown.
@ -381,15 +381,16 @@ class FreqtradeBot(LoggingMixin):
trades = Trade.get_open_trades_without_assigned_fees() trades = Trade.get_open_trades_without_assigned_fees()
for trade in trades: for trade in trades:
if trade.is_open and not trade.fee_updated(trade.entry_side): with self._exit_lock:
order = trade.select_order(trade.entry_side, False) if trade.is_open and not trade.fee_updated(trade.entry_side):
open_order = trade.select_order(trade.entry_side, True) order = trade.select_order(trade.entry_side, False)
if order and open_order is None: open_order = trade.select_order(trade.entry_side, True)
logger.info( if order and open_order is None:
f"Updating {trade.entry_side}-fee on trade {trade}" logger.info(
f"for order {order.order_id}." 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) )
self.update_trade_state(trade, order.order_id, send_msg=False)
def handle_insufficient_funds(self, trade: Trade): def handle_insufficient_funds(self, trade: Trade):
""" """
@ -826,6 +827,8 @@ class FreqtradeBot(LoggingMixin):
co = self.exchange.cancel_stoploss_order_with_result( co = self.exchange.cancel_stoploss_order_with_result(
trade.stoploss_order_id, trade.pair, trade.amount) trade.stoploss_order_id, trade.pair, trade.amount)
trade.update_order(co) trade.update_order(co)
# Reset stoploss order id.
trade.stoploss_order_id = None
except InvalidOrderException: except InvalidOrderException:
logger.exception(f"Could not cancel stoploss order {trade.stoploss_order_id}") logger.exception(f"Could not cancel stoploss order {trade.stoploss_order_id}")
return trade return trade
@ -982,7 +985,7 @@ class FreqtradeBot(LoggingMixin):
# SELL / exit positions / close trades logic and methods # 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) Tries to execute exit orders for open trades (positions)
""" """
@ -1010,7 +1013,7 @@ class FreqtradeBot(LoggingMixin):
def handle_trade(self, trade: Trade) -> bool: 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 :return: True if trade has been sold/exited_short, False otherwise
""" """
if not trade.is_open: if not trade.is_open:
@ -1148,7 +1151,7 @@ class FreqtradeBot(LoggingMixin):
stoploss = ( stoploss = (
self.edge.stoploss(pair=trade.pair) self.edge.stoploss(pair=trade.pair)
if self.edge else if self.edge else
self.strategy.stoploss / trade.leverage trade.stop_loss_pct / trade.leverage
) )
if trade.is_short: if trade.is_short:
stop_price = trade.open_rate * (1 - stoploss) stop_price = trade.open_rate * (1 - stoploss)
@ -1167,7 +1170,6 @@ class FreqtradeBot(LoggingMixin):
if self.create_stoploss_order(trade=trade, stop_price=trade.stoploss_or_liquidation): if self.create_stoploss_order(trade=trade, stop_price=trade.stoploss_or_liquidation):
return False return False
else: else:
trade.stoploss_order_id = None
logger.warning('Stoploss order was cancelled, but unable to recreate one.') 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. # Finally we check if stoploss on exchange should be moved up because of trailing.

View File

@ -692,10 +692,11 @@ class Backtesting:
trade.orders.append(order) trade.orders.append(order)
return trade 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() 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( trade.funding_fees = self.exchange.calculate_funding_fees(
self.futures_data[trade.pair], self.futures_data[trade.pair],
amount=trade.amount, amount=trade.amount,
@ -704,32 +705,7 @@ class Backtesting:
close_date=exit_candle_time, close_date=exit_candle_time,
) )
if self.timeframe_detail and trade.pair in self.detail_data: return self._get_exit_trade_entry_for_candle(trade, row)
exit_candle_end = exit_candle_time + timedelta(minutes=self.timeframe_min)
detail_data = self.detail_data[trade.pair]
detail_data = detail_data.loc[
(detail_data['date'] >= exit_candle_time) &
(detail_data['date'] < exit_candle_end)
].copy()
if len(detail_data) == 0:
# Fall back to "regular" data if no detail data was found for this candle
return self._get_exit_trade_entry_for_candle(trade, row)
detail_data.loc[:, 'enter_long'] = row[LONG_IDX]
detail_data.loc[:, 'exit_long'] = row[ELONG_IDX]
detail_data.loc[:, 'enter_short'] = row[SHORT_IDX]
detail_data.loc[:, 'exit_short'] = row[ESHORT_IDX]
detail_data.loc[:, 'enter_tag'] = row[ENTER_TAG_IDX]
detail_data.loc[:, 'exit_tag'] = row[EXIT_TAG_IDX]
for det_row in detail_data[HEADERS].values.tolist():
res = self._get_exit_trade_entry_for_candle(trade, det_row)
if res:
return res
return None
else:
return self._get_exit_trade_entry_for_candle(trade, row)
def get_valid_price_and_stake( def get_valid_price_and_stake(
self, pair: str, row: Tuple, propose_rate: float, stake_amount: float, self, pair: str, row: Tuple, propose_rate: float, stake_amount: float,
@ -1074,7 +1050,7 @@ class Backtesting:
def backtest_loop( def backtest_loop(
self, row: Tuple, pair: str, current_time: datetime, end_date: datetime, 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. NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
@ -1092,9 +1068,11 @@ class Backtesting:
# without positionstacking, we can only have one open trade per pair. # without positionstacking, we can only have one open trade per pair.
# max_open_trades must be respected # max_open_trades must be respected
# don't open on the last row # 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) trade_dir = self.check_for_trade_entry(row)
if ( if (
(self._position_stacking or len(LocalTrade.bt_trades_open_pp[pair]) == 0) (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 self.trade_slot_available(max_open_trades, open_trade_count_start)
and current_time != end_date and current_time != end_date
and trade_dir is not None and trade_dir is not None
@ -1120,7 +1098,7 @@ class Backtesting:
# 4. Create exit orders (if any) # 4. Create exit orders (if any)
if not trade.open_order_id: 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. # 5. Process exit orders.
order = trade.select_order(trade.exit_side, is_open=True) order = trade.select_order(trade.exit_side, is_open=True)
@ -1171,7 +1149,6 @@ class Backtesting:
self.progress.init_step(BacktestState.BACKTEST, int( self.progress.init_step(BacktestState.BACKTEST, int(
(end_date - start_date) / timedelta(minutes=self.timeframe_min))) (end_date - start_date) / timedelta(minutes=self.timeframe_min)))
# Loop timerange and get candle for each pair at that point in time # Loop timerange and get candle for each pair at that point in time
while current_time <= end_date: while current_time <= end_date:
open_trade_count_start = LocalTrade.bt_open_open_trade_count open_trade_count_start = LocalTrade.bt_open_open_trade_count
@ -1185,9 +1162,37 @@ class Backtesting:
row_index += 1 row_index += 1
indexes[pair] = row_index indexes[pair] = row_index
self.dataprovider._set_dataframe_max_index(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( detail_data = self.detail_data[pair]
row, pair, current_time, end_date, max_open_trades, open_trade_count_start) 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. # Move time one configured time_interval ahead.
self.progress.increment() self.progress.increment()

View File

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

View File

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

View File

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

View File

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

View File

@ -1,16 +1,16 @@
import logging import logging
import time
from typing import Any, Dict from typing import Any, Dict
from fastapi import APIRouter, Depends, WebSocketDisconnect from fastapi import APIRouter, Depends
from fastapi.websockets import WebSocket, WebSocketState from fastapi.websockets import WebSocket
from pydantic import ValidationError from pydantic import ValidationError
from websockets.exceptions import WebSocketException
from freqtrade.enums import RPCMessageType, RPCRequestType from freqtrade.enums import RPCMessageType, RPCRequestType
from freqtrade.rpc.api_server.api_auth import validate_ws_token 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.deps import get_message_stream, get_rpc
from freqtrade.rpc.api_server.ws import WebSocketChannel from freqtrade.rpc.api_server.ws.channel import WebSocketChannel, create_channel
from freqtrade.rpc.api_server.ws.channel import ChannelManager from freqtrade.rpc.api_server.ws.message_stream import MessageStream
from freqtrade.rpc.api_server.ws_schemas import (WSAnalyzedDFMessage, WSMessageSchema, from freqtrade.rpc.api_server.ws_schemas import (WSAnalyzedDFMessage, WSMessageSchema,
WSRequestSchema, WSWhitelistMessage) WSRequestSchema, WSWhitelistMessage)
from freqtrade.rpc.rpc import RPC from freqtrade.rpc.rpc import RPC
@ -22,23 +22,35 @@ logger = logging.getLogger(__name__)
router = APIRouter() 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 ( async for message in channel:
ws.application_state == WebSocketState.CONNECTED and await _process_consumer_request(message, channel, rpc)
ws.client_state == WebSocketState.CONNECTED
):
return True async def channel_broadcaster(channel: WebSocketChannel, message_stream: MessageStream):
return False """
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( async def _process_consumer_request(
request: Dict[str, Any], request: Dict[str, Any],
channel: WebSocketChannel, channel: WebSocketChannel,
rpc: RPC, rpc: RPC
channel_manager: ChannelManager
): ):
""" """
Validate and handle a request from a websocket consumer Validate and handle a request from a websocket consumer
@ -74,65 +86,29 @@ async def _process_consumer_request(
# Format response # Format response
response = WSWhitelistMessage(data=whitelist) response = WSWhitelistMessage(data=whitelist)
# Send it back await channel.send(response.dict(exclude_none=True))
await channel_manager.send_direct(channel, response.dict(exclude_none=True))
elif type == RPCRequestType.ANALYZED_DF: 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)
# For every pair in the generator, send a separate message # For every pair in the generator, send a separate message
for message in rpc._ws_request_analyzed_df(limit): for message in rpc._ws_request_analyzed_df(limit):
# Format response
response = WSAnalyzedDFMessage(data=message) 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") @router.websocket("/message/ws")
async def message_endpoint( async def message_endpoint(
ws: WebSocket, websocket: WebSocket,
token: str = Depends(validate_ws_token),
rpc: RPC = Depends(get_rpc), rpc: RPC = Depends(get_rpc),
channel_manager=Depends(get_channel_manager), message_stream: MessageStream = Depends(get_message_stream)
token: str = Depends(validate_ws_token)
): ):
""" if token:
Message WebSocket endpoint, facilitates sending RPC messages async with create_channel(websocket) as channel:
""" await channel.run_channel_tasks(
try: channel_reader(channel, rpc),
channel = await channel_manager.on_connect(ws) channel_broadcaster(channel, message_stream)
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)

View File

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

View File

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

View File

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

View File

@ -1,11 +1,13 @@
import asyncio import asyncio
import logging import logging
import time import time
from threading import RLock from collections import deque
from typing import Any, Dict, List, Optional, Type, Union from contextlib import asynccontextmanager
from typing import Any, AsyncIterator, Deque, Dict, List, Optional, Type, Union
from uuid import uuid4 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.proxy import WebSocketProxy
from freqtrade.rpc.api_server.ws.serializer import (HybridJSONWebSocketSerializer, from freqtrade.rpc.api_server.ws.serializer import (HybridJSONWebSocketSerializer,
@ -21,31 +23,27 @@ class WebSocketChannel:
""" """
Object to help facilitate managing a websocket connection Object to help facilitate managing a websocket connection
""" """
def __init__( def __init__(
self, self,
websocket: WebSocketType, websocket: WebSocketType,
channel_id: Optional[str] = None, channel_id: Optional[str] = None,
drain_timeout: int = 3,
throttle: float = 0.01,
serializer_cls: Type[WebSocketSerializer] = HybridJSONWebSocketSerializer serializer_cls: Type[WebSocketSerializer] = HybridJSONWebSocketSerializer
): ):
self.channel_id = channel_id if channel_id else uuid4().hex[:8] self.channel_id = channel_id if channel_id else uuid4().hex[:8]
# The WebSocket object
self._websocket = WebSocketProxy(websocket) self._websocket = WebSocketProxy(websocket)
self.drain_timeout = drain_timeout
self.throttle = throttle
self._subscriptions: List[str] = []
# 32 is the size of the receiving queue in websockets package
self.queue: asyncio.Queue[Dict[str, Any]] = asyncio.Queue(maxsize=32)
self._relay_task = asyncio.create_task(self.relay())
# Internal event to signify a closed websocket # Internal event to signify a closed websocket
self._closed = asyncio.Event() 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 # Wrap the WebSocket in the Serializing class
self._wrapped_ws = serializer_cls(self._websocket) self._wrapped_ws = serializer_cls(self._websocket)
@ -61,43 +59,58 @@ class WebSocketChannel:
def remote_addr(self): def remote_addr(self):
return self._websocket.remote_addr return self._websocket.remote_addr
async def _send(self, data): @property
""" def avg_send_time(self):
Send data on the wrapped websocket return sum(self._send_times) / len(self._send_times)
"""
await self._wrapped_ws.send(data)
async def send(self, data) -> bool: def _calc_send_limit(self):
""" """
Add the data to the queue to be sent. Calculate the send high limit for this channel
:returns: True if data added to queue, False otherwise
""" """
# This block only runs if the queue is full, it will wait # Only update if we have enough data
# until self.drain_timeout for the relay to drain the outgoing queue if len(self._send_times) == self._send_times.maxlen:
# We can't use asyncio.wait_for here because the queue may have been created with a # At least 1s or twice the average of send times, with a
# different eventloop # maximum of 3 seconds per message
if not self.is_closed(): self._send_high_limit = min(max(self.avg_send_time * 2, 1), 3)
start = time.time()
while self.queue.full():
await asyncio.sleep(1)
if (time.time() - start) > self.drain_timeout:
return False
# If for some reason the queue is still full, just return False async def send(
try: self,
self.queue.put_nowait(data) message: Union[WSMessageSchemaType, Dict[str, Any]],
except asyncio.QueueFull: timeout: bool = False
return False ):
"""
Send a message on the wrapped websocket. If the sending
takes too long, it will raise a TimeoutError and
disconnect the connection.
# If we got here everything is ok :param message: The message to send
return True :param timeout: Enforce send high limit, defaults to False
else: """
return False try:
_ = 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)
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): async def recv(self):
""" """
Receive data on the wrapped websocket Receive a message on the wrapped websocket
""" """
return await self._wrapped_ws.recv() return await self._wrapped_ws.recv()
@ -107,17 +120,27 @@ class WebSocketChannel:
""" """
return await self._websocket.ping() 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): async def close(self):
""" """
Close the WebSocketChannel Close the WebSocketChannel
""" """
self._closed.set() self._closed.set()
self._relay_task.cancel()
try: try:
await self.raw_websocket.close() await self._websocket.close()
except Exception: except RuntimeError:
pass pass
def is_closed(self) -> bool: def is_closed(self) -> bool:
@ -142,99 +165,76 @@ class WebSocketChannel:
""" """
return message_type in self._subscriptions 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 Create and await on the channel tasks unless an exception
as a task. 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: try:
await self._send(message) return await asyncio.gather(*self._channel_tasks, **kwargs)
self.queue.task_done() except Exception:
# If an exception occurred, cancel the rest of the tasks
await self.cancel_channel_tasks()
# Limit messages per sec. async def cancel_channel_tasks(self):
# 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):
""" """
Wrap websocket connection into Channel and add to list Cancel and wait on all channel tasks
:param websocket: The WebSocket object to attach to the Channel
""" """
if isinstance(websocket, FastAPIWebSocket): for task in self._channel_tasks:
task.cancel()
# Wait for tasks to finish cancelling
try: try:
await websocket.accept() await task
except RuntimeError: except (
# The connection was closed before we could accept it asyncio.CancelledError,
return 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: async def __aiter__(self):
self.channels[websocket] = ws_channel
return ws_channel
async def on_disconnect(self, websocket: WebSocketType):
""" """
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): yield channel
""" finally:
Disconnect all Channels await channel.close()
""" logger.info(f"Disconnected from channel - {channel}")
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

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

View File

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

View File

@ -31,6 +31,7 @@ class Producer(TypedDict):
name: str name: str
host: str host: str
port: int port: int
secure: bool
ws_token: str ws_token: str
@ -180,7 +181,8 @@ class ExternalMessageConsumer:
host, port = producer['host'], producer['port'] host, port = producer['host'], producer['port']
token = producer['ws_token'] token = producer['ws_token']
name = producer['name'] 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 # This will raise InvalidURI if the url is bad
async with websockets.connect( async with websockets.connect(

View File

@ -789,17 +789,18 @@ class RPC:
if not order_type: if not order_type:
order_type = self._freqtrade.strategy.order_types.get( order_type = self._freqtrade.strategy.order_types.get(
'force_entry', self._freqtrade.strategy.order_types['entry']) 'force_entry', self._freqtrade.strategy.order_types['entry'])
if self._freqtrade.execute_entry(pair, stake_amount, price, with self._freqtrade._exit_lock:
ordertype=order_type, trade=trade, if self._freqtrade.execute_entry(pair, stake_amount, price,
is_short=is_short, ordertype=order_type, trade=trade,
enter_tag=enter_tag, is_short=is_short,
leverage_=leverage, enter_tag=enter_tag,
): leverage_=leverage,
Trade.commit() ):
trade = Trade.get_trades([Trade.is_open.is_(True), Trade.pair == pair]).first() Trade.commit()
return trade trade = Trade.get_trades([Trade.is_open.is_(True), Trade.pair == pair]).first()
else: return trade
raise RPCException(f'Failed to enter position for {pair}.') else:
raise RPCException(f'Failed to enter position for {pair}.')
def _rpc_delete(self, trade_id: int) -> Dict[str, Union[str, int]]: def _rpc_delete(self, trade_id: int) -> Dict[str, Union[str, int]]:
""" """

View File

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

View File

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

View File

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

View File

@ -3,10 +3,11 @@
-r requirements-plot.txt -r requirements-plot.txt
-r requirements-hyperopt.txt -r requirements-hyperopt.txt
-r requirements-freqai.txt -r requirements-freqai.txt
-r requirements-freqai-rl.txt
-r docs/requirements-docs.txt -r docs/requirements-docs.txt
coveralls==3.3.1 coveralls==3.3.1
flake8==5.0.4 flake8==6.0.0
flake8-tidy-imports==4.8.0 flake8-tidy-imports==4.8.0
mypy==0.991 mypy==0.991
pre-commit==2.20.0 pre-commit==2.20.0

View File

@ -0,0 +1,9 @@
# Include all requirements to run the bot.
-r requirements-freqai.txt
# Required for freqai-rl
torch==1.12.1
stable-baselines3==1.6.2
sb3-contrib==1.6.2
# Gym is forced to this version by stable-baselines3.
gym==0.21

View File

@ -2,18 +2,18 @@ numpy==1.23.5
pandas==1.5.1 pandas==1.5.1
pandas-ta==0.3.14b pandas-ta==0.3.14b
ccxt==2.1.96 ccxt==2.2.36
# Pin cryptography for now due to rust build errors with piwheels # Pin cryptography for now due to rust build errors with piwheels
cryptography==38.0.1; platform_machine == 'armv7l' cryptography==38.0.1; platform_machine == 'armv7l'
cryptography==38.0.3; platform_machine != 'armv7l' cryptography==38.0.4; platform_machine != 'armv7l'
aiohttp==3.8.3 aiohttp==3.8.3
SQLAlchemy==1.4.44 SQLAlchemy==1.4.44
python-telegram-bot==13.14 python-telegram-bot==13.14
arrow==1.2.3 arrow==1.2.3
cachetools==4.2.2 cachetools==4.2.2
requests==2.28.1 requests==2.28.1
urllib3==1.26.12 urllib3==1.26.13
jsonschema==4.17.0 jsonschema==4.17.1
TA-Lib==0.4.25 TA-Lib==0.4.25
technical==1.3.0 technical==1.3.0
tabulate==0.9.0 tabulate==0.9.0
@ -22,7 +22,7 @@ jinja2==3.1.2
tables==3.7.0 tables==3.7.0
blosc==1.10.6 blosc==1.10.6
joblib==1.2.0 joblib==1.2.0
pyarrow==10.0.0; platform_machine != 'armv7l' pyarrow==10.0.1; platform_machine != 'armv7l'
# find first, C search in arrays # find first, C search in arrays
py_find_1st==1.1.5 py_find_1st==1.1.5
@ -47,7 +47,7 @@ psutil==5.9.4
colorama==0.4.6 colorama==0.4.6
# Building config files interactively # Building config files interactively
questionary==1.10.0 questionary==1.10.0
prompt-toolkit==3.0.32 prompt-toolkit==3.0.33
# Extensions to datetime library # Extensions to datetime library
python-dateutil==2.8.2 python-dateutil==2.8.2

View File

@ -199,6 +199,7 @@ async def create_client(
host, host,
port, port,
token, token,
scheme='ws',
name='default', name='default',
protocol=ClientProtocol(), protocol=ClientProtocol(),
sleep_time=10, sleep_time=10,
@ -211,13 +212,14 @@ async def create_client(
:param host: The host :param host: The host
:param port: The port :param port: The port
:param token: The websocket auth token :param token: The websocket auth token
:param scheme: `ws` for most connections, `wss` for ssl
:param name: The name of the producer :param name: The name of the producer
:param **kwargs: Any extra kwargs passed to websockets.connect :param **kwargs: Any extra kwargs passed to websockets.connect
""" """
while 1: while 1:
try: try:
websocket_url = f"ws://{host}:{port}/api/v1/message/ws?token={token}" websocket_url = f"{scheme}://{host}:{port}/api/v1/message/ws?token={token}"
logger.info(f"Attempting to connect to {name} @ {host}:{port}") logger.info(f"Attempting to connect to {name} @ {host}:{port}")
async with websockets.connect(websocket_url, **kwargs) as ws: async with websockets.connect(websocket_url, **kwargs) as ws:
@ -304,6 +306,7 @@ async def _main(args):
producer['host'], producer['host'],
producer['port'], producer['port'],
producer['ws_token'], producer['ws_token'],
'wss' if producer.get('secure', False) else 'ws',
producer['name'], producer['name'],
sleep_time=sleep_time, sleep_time=sleep_time,
ping_timeout=ping_timeout, ping_timeout=ping_timeout,

View File

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

View File

@ -78,14 +78,21 @@ function updateenv() {
fi fi
REQUIREMENTS_FREQAI="" REQUIREMENTS_FREQAI=""
REQUIREMENTS_FREQAI_RL=""
read -p "Do you want to install dependencies for freqai [y/N]? " read -p "Do you want to install dependencies for freqai [y/N]? "
dev=$REPLY dev=$REPLY
if [[ $REPLY =~ ^[Yy]$ ]] if [[ $REPLY =~ ^[Yy]$ ]]
then then
REQUIREMENTS_FREQAI="-r requirements-freqai.txt" REQUIREMENTS_FREQAI="-r requirements-freqai.txt --use-pep517"
read -p "Do you also want dependencies for freqai-rl (~700mb additional space required) [y/N]? "
dev=$REPLY
if [[ $REPLY =~ ^[Yy]$ ]]
then
REQUIREMENTS_FREQAI="-r requirements-freqai-rl.txt"
fi
fi fi
${PYTHON} -m pip install --upgrade -r ${REQUIREMENTS} ${REQUIREMENTS_HYPEROPT} ${REQUIREMENTS_PLOT} ${REQUIREMENTS_FREQAI} ${PYTHON} -m pip install --upgrade -r ${REQUIREMENTS} ${REQUIREMENTS_HYPEROPT} ${REQUIREMENTS_PLOT} ${REQUIREMENTS_FREQAI} ${REQUIREMENTS_FREQAI_RL}
if [ $? -ne 0 ]; then if [ $? -ne 0 ]; then
echo "Failed installing dependencies" echo "Failed installing dependencies"
exit 1 exit 1

View File

@ -1271,7 +1271,7 @@ def test_hyperopt_list(mocker, capsys, caplog, saved_hyperopt_results, tmpdir):
assert csv_file.is_file() assert csv_file.is_file()
line = csv_file.read_text() line = csv_file.read_text()
assert ('Best,1,2,-1.25%,-1.2222,-0.00125625,,-2.51,"3,930.0 m",0.43662' in line assert ('Best,1,2,-1.25%,-1.2222,-0.00125625,,-2.51,"3,930.0 m",0.43662' in line
or "Best,1,2,-1.25%,-1.2222,-0.00125625,,-2.51,2 days 17:30:00,0.43662" in line) or "Best,1,2,-1.25%,-1.2222,-0.00125625,,-2.51,2 days 17:30:00,2,0,0.43662" in line)
csv_file.unlink() csv_file.unlink()

View File

@ -2679,7 +2679,7 @@ def saved_hyperopt_results():
'params_dict': { 'params_dict': {
'mfi-value': 15, 'fastd-value': 20, 'adx-value': 25, 'rsi-value': 28, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 88, 'sell-fastd-value': 97, 'sell-adx-value': 51, 'sell-rsi-value': 67, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1190, 'roi_t2': 541, 'roi_t3': 408, 'roi_p1': 0.026035863879169705, 'roi_p2': 0.12508730043628782, 'roi_p3': 0.27766427921605896, 'stoploss': -0.2562930402099556}, # noqa: E501 'mfi-value': 15, 'fastd-value': 20, 'adx-value': 25, 'rsi-value': 28, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 88, 'sell-fastd-value': 97, 'sell-adx-value': 51, 'sell-rsi-value': 67, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1190, 'roi_t2': 541, 'roi_t3': 408, 'roi_p1': 0.026035863879169705, 'roi_p2': 0.12508730043628782, 'roi_p3': 0.27766427921605896, 'stoploss': -0.2562930402099556}, # noqa: E501
'params_details': {'buy': {'mfi-value': 15, 'fastd-value': 20, 'adx-value': 25, 'rsi-value': 28, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 88, 'sell-fastd-value': 97, 'sell-adx-value': 51, 'sell-rsi-value': 67, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.4287874435315165, 408: 0.15112316431545753, 949: 0.026035863879169705, 2139: 0}, 'stoploss': {'stoploss': -0.2562930402099556}}, # noqa: E501 'params_details': {'buy': {'mfi-value': 15, 'fastd-value': 20, 'adx-value': 25, 'rsi-value': 28, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 88, 'sell-fastd-value': 97, 'sell-adx-value': 51, 'sell-rsi-value': 67, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.4287874435315165, 408: 0.15112316431545753, 949: 0.026035863879169705, 2139: 0}, 'stoploss': {'stoploss': -0.2562930402099556}}, # noqa: E501
'results_metrics': {'total_trades': 2, 'wins': 0, 'draws': 0, 'losses': 2, 'profit_mean': -0.01254995, 'profit_median': -0.012222, 'profit_total': -0.00125625, 'profit_total_abs': -2.50999, 'max_drawdown': 0.23, 'max_drawdown_abs': -0.00125625, 'holding_avg': timedelta(minutes=3930.0), 'stake_currency': 'BTC', 'strategy_name': 'SampleStrategy'}, # noqa: E501 'results_metrics': {'total_trades': 2, 'trade_count_long': 2, 'trade_count_short': 0, 'wins': 0, 'draws': 0, 'losses': 2, 'profit_mean': -0.01254995, 'profit_median': -0.012222, 'profit_total': -0.00125625, 'profit_total_abs': -2.50999, 'max_drawdown': 0.23, 'max_drawdown_abs': -0.00125625, 'holding_avg': timedelta(minutes=3930.0), 'stake_currency': 'BTC', 'strategy_name': 'SampleStrategy'}, # noqa: E501
'results_explanation': ' 2 trades. Avg profit -1.25%. Total profit -0.00125625 BTC ( -2.51Σ%). Avg duration 3930.0 min.', # noqa: E501 'results_explanation': ' 2 trades. Avg profit -1.25%. Total profit -0.00125625 BTC ( -2.51Σ%). Avg duration 3930.0 min.', # noqa: E501
'total_profit': -0.00125625, 'total_profit': -0.00125625,
'current_epoch': 1, 'current_epoch': 1,
@ -2696,7 +2696,7 @@ def saved_hyperopt_results():
'sell': {'sell-mfi-value': 96, 'sell-fastd-value': 68, 'sell-adx-value': 63, 'sell-rsi-value': 81, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, # noqa: E501 'sell': {'sell-mfi-value': 96, 'sell-fastd-value': 68, 'sell-adx-value': 63, 'sell-rsi-value': 81, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, # noqa: E501
'roi': {0: 0.4449309386008759, 140: 0.11955965746663, 823: 0.06403981740598495, 1157: 0}, # noqa: E501 'roi': {0: 0.4449309386008759, 140: 0.11955965746663, 823: 0.06403981740598495, 1157: 0}, # noqa: E501
'stoploss': {'stoploss': -0.338070047333259}}, 'stoploss': {'stoploss': -0.338070047333259}},
'results_metrics': {'total_trades': 1, 'wins': 0, 'draws': 0, 'losses': 1, 'profit_mean': 0.012357, 'profit_median': -0.012222, 'profit_total': 6.185e-05, 'profit_total_abs': 0.12357, 'max_drawdown': 0.23, 'max_drawdown_abs': -0.00125625, 'holding_avg': timedelta(minutes=1200.0)}, # noqa: E501 'results_metrics': {'total_trades': 1, 'trade_count_long': 1, 'trade_count_short': 0, 'wins': 0, 'draws': 0, 'losses': 1, 'profit_mean': 0.012357, 'profit_median': -0.012222, 'profit_total': 6.185e-05, 'profit_total_abs': 0.12357, 'max_drawdown': 0.23, 'max_drawdown_abs': -0.00125625, 'holding_avg': timedelta(minutes=1200.0)}, # noqa: E501
'results_explanation': ' 1 trades. Avg profit 0.12%. Total profit 0.00006185 BTC ( 0.12Σ%). Avg duration 1200.0 min.', # noqa: E501 'results_explanation': ' 1 trades. Avg profit 0.12%. Total profit 0.00006185 BTC ( 0.12Σ%). Avg duration 1200.0 min.', # noqa: E501
'total_profit': 6.185e-05, 'total_profit': 6.185e-05,
'current_epoch': 2, 'current_epoch': 2,
@ -2707,7 +2707,7 @@ def saved_hyperopt_results():
'loss': 14.241196856510731, 'loss': 14.241196856510731,
'params_dict': {'mfi-value': 25, 'fastd-value': 16, 'adx-value': 29, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 98, 'sell-fastd-value': 72, 'sell-adx-value': 51, 'sell-rsi-value': 82, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 889, 'roi_t2': 533, 'roi_t3': 263, 'roi_p1': 0.04759065393663096, 'roi_p2': 0.1488819964638463, 'roi_p3': 0.4102801822104605, 'stoploss': -0.05394588767607611}, # noqa: E501 'params_dict': {'mfi-value': 25, 'fastd-value': 16, 'adx-value': 29, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 98, 'sell-fastd-value': 72, 'sell-adx-value': 51, 'sell-rsi-value': 82, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 889, 'roi_t2': 533, 'roi_t3': 263, 'roi_p1': 0.04759065393663096, 'roi_p2': 0.1488819964638463, 'roi_p3': 0.4102801822104605, 'stoploss': -0.05394588767607611}, # noqa: E501
'params_details': {'buy': {'mfi-value': 25, 'fastd-value': 16, 'adx-value': 29, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 98, 'sell-fastd-value': 72, 'sell-adx-value': 51, 'sell-rsi-value': 82, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.6067528326109377, 263: 0.19647265040047726, 796: 0.04759065393663096, 1685: 0}, 'stoploss': {'stoploss': -0.05394588767607611}}, # noqa: E501 'params_details': {'buy': {'mfi-value': 25, 'fastd-value': 16, 'adx-value': 29, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 98, 'sell-fastd-value': 72, 'sell-adx-value': 51, 'sell-rsi-value': 82, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.6067528326109377, 263: 0.19647265040047726, 796: 0.04759065393663096, 1685: 0}, 'stoploss': {'stoploss': -0.05394588767607611}}, # noqa: E501
'results_metrics': {'total_trades': 621, 'wins': 320, 'draws': 0, 'losses': 301, 'profit_mean': -0.043883302093397747, 'profit_median': -0.012222, 'profit_total': -0.13639474, 'profit_total_abs': -272.515306, 'max_drawdown': 0.25, 'max_drawdown_abs': -272.515306, 'holding_avg': timedelta(minutes=1691.207729468599)}, # noqa: E501 'results_metrics': {'total_trades': 621, 'trade_count_long': 621, 'trade_count_short': 0, 'wins': 320, 'draws': 0, 'losses': 301, 'profit_mean': -0.043883302093397747, 'profit_median': -0.012222, 'profit_total': -0.13639474, 'profit_total_abs': -272.515306, 'max_drawdown': 0.25, 'max_drawdown_abs': -272.515306, 'holding_avg': timedelta(minutes=1691.207729468599)}, # noqa: E501
'results_explanation': ' 621 trades. Avg profit -0.44%. Total profit -0.13639474 BTC (-272.52Σ%). Avg duration 1691.2 min.', # noqa: E501 'results_explanation': ' 621 trades. Avg profit -0.44%. Total profit -0.13639474 BTC (-272.52Σ%). Avg duration 1691.2 min.', # noqa: E501
'total_profit': -0.13639474, 'total_profit': -0.13639474,
'current_epoch': 3, 'current_epoch': 3,
@ -2718,14 +2718,14 @@ def saved_hyperopt_results():
'loss': 100000, 'loss': 100000,
'params_dict': {'mfi-value': 13, 'fastd-value': 35, 'adx-value': 39, 'rsi-value': 29, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 87, 'sell-fastd-value': 54, 'sell-adx-value': 63, 'sell-rsi-value': 93, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1402, 'roi_t2': 676, 'roi_t3': 215, 'roi_p1': 0.06264755784937427, 'roi_p2': 0.14258587851894644, 'roi_p3': 0.20671291201040828, 'stoploss': -0.11818343570194478}, # noqa: E501 'params_dict': {'mfi-value': 13, 'fastd-value': 35, 'adx-value': 39, 'rsi-value': 29, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 87, 'sell-fastd-value': 54, 'sell-adx-value': 63, 'sell-rsi-value': 93, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1402, 'roi_t2': 676, 'roi_t3': 215, 'roi_p1': 0.06264755784937427, 'roi_p2': 0.14258587851894644, 'roi_p3': 0.20671291201040828, 'stoploss': -0.11818343570194478}, # noqa: E501
'params_details': {'buy': {'mfi-value': 13, 'fastd-value': 35, 'adx-value': 39, 'rsi-value': 29, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 54, 'sell-adx-value': 63, 'sell-rsi-value': 93, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.411946348378729, 215: 0.2052334363683207, 891: 0.06264755784937427, 2293: 0}, 'stoploss': {'stoploss': -0.11818343570194478}}, # noqa: E501 'params_details': {'buy': {'mfi-value': 13, 'fastd-value': 35, 'adx-value': 39, 'rsi-value': 29, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 54, 'sell-adx-value': 63, 'sell-rsi-value': 93, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.411946348378729, 215: 0.2052334363683207, 891: 0.06264755784937427, 2293: 0}, 'stoploss': {'stoploss': -0.11818343570194478}}, # noqa: E501
'results_metrics': {'total_trades': 0, 'wins': 0, 'draws': 0, 'losses': 0, 'profit_mean': None, 'profit_median': None, 'profit_total': 0, 'profit': 0.0, 'holding_avg': timedelta()}, # noqa: E501 'results_metrics': {'total_trades': 0, 'trade_count_long': 0, 'trade_count_short': 0, 'wins': 0, 'draws': 0, 'losses': 0, 'profit_mean': None, 'profit_median': None, 'profit_total': 0, 'profit': 0.0, 'holding_avg': timedelta()}, # noqa: E501
'results_explanation': ' 0 trades. Avg profit nan%. Total profit 0.00000000 BTC ( 0.00Σ%). Avg duration nan min.', # noqa: E501 'results_explanation': ' 0 trades. Avg profit nan%. Total profit 0.00000000 BTC ( 0.00Σ%). Avg duration nan min.', # noqa: E501
'total_profit': 0, 'current_epoch': 4, 'is_initial_point': True, 'is_random': False, 'is_best': False # noqa: E501 'total_profit': 0, 'current_epoch': 4, 'is_initial_point': True, 'is_random': False, 'is_best': False # noqa: E501
}, { }, {
'loss': 0.22195522184191518, 'loss': 0.22195522184191518,
'params_dict': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 1269, 'roi_t2': 601, 'roi_t3': 444, 'roi_p1': 0.07280999507931168, 'roi_p2': 0.08946698095898986, 'roi_p3': 0.1454876733325284, 'stoploss': -0.18181041180901014}, # noqa: E501 'params_dict': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 1269, 'roi_t2': 601, 'roi_t3': 444, 'roi_p1': 0.07280999507931168, 'roi_p2': 0.08946698095898986, 'roi_p3': 0.1454876733325284, 'stoploss': -0.18181041180901014}, # noqa: E501
'params_details': {'buy': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.3077646493708299, 444: 0.16227697603830155, 1045: 0.07280999507931168, 2314: 0}, 'stoploss': {'stoploss': -0.18181041180901014}}, # noqa: E501 'params_details': {'buy': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.3077646493708299, 444: 0.16227697603830155, 1045: 0.07280999507931168, 2314: 0}, 'stoploss': {'stoploss': -0.18181041180901014}}, # noqa: E501
'results_metrics': {'total_trades': 14, 'wins': 6, 'draws': 0, 'losses': 8, 'profit_mean': -0.003539515, 'profit_median': -0.012222, 'profit_total': -0.002480140000000001, 'profit_total_abs': -4.955321, 'max_drawdown': 0.34, 'max_drawdown_abs': -4.955321, 'holding_avg': timedelta(minutes=3402.8571428571427)}, # noqa: E501 'results_metrics': {'total_trades': 14, 'trade_count_long': 14, 'trade_count_short': 0, 'wins': 6, 'draws': 0, 'losses': 8, 'profit_mean': -0.003539515, 'profit_median': -0.012222, 'profit_total': -0.002480140000000001, 'profit_total_abs': -4.955321, 'max_drawdown': 0.34, 'max_drawdown_abs': -4.955321, 'holding_avg': timedelta(minutes=3402.8571428571427)}, # noqa: E501
'results_explanation': ' 14 trades. Avg profit -0.35%. Total profit -0.00248014 BTC ( -4.96Σ%). Avg duration 3402.9 min.', # noqa: E501 'results_explanation': ' 14 trades. Avg profit -0.35%. Total profit -0.00248014 BTC ( -4.96Σ%). Avg duration 3402.9 min.', # noqa: E501
'total_profit': -0.002480140000000001, 'total_profit': -0.002480140000000001,
'current_epoch': 5, 'current_epoch': 5,
@ -2736,7 +2736,7 @@ def saved_hyperopt_results():
'loss': 0.545315889154162, 'loss': 0.545315889154162,
'params_dict': {'mfi-value': 22, 'fastd-value': 43, 'adx-value': 46, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'bb_lower', 'sell-mfi-value': 87, 'sell-fastd-value': 65, 'sell-adx-value': 94, 'sell-rsi-value': 63, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 319, 'roi_t2': 556, 'roi_t3': 216, 'roi_p1': 0.06251955472249589, 'roi_p2': 0.11659519602202795, 'roi_p3': 0.0953744132197762, 'stoploss': -0.024551752215582423}, # noqa: E501 'params_dict': {'mfi-value': 22, 'fastd-value': 43, 'adx-value': 46, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'bb_lower', 'sell-mfi-value': 87, 'sell-fastd-value': 65, 'sell-adx-value': 94, 'sell-rsi-value': 63, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 319, 'roi_t2': 556, 'roi_t3': 216, 'roi_p1': 0.06251955472249589, 'roi_p2': 0.11659519602202795, 'roi_p3': 0.0953744132197762, 'stoploss': -0.024551752215582423}, # noqa: E501
'params_details': {'buy': {'mfi-value': 22, 'fastd-value': 43, 'adx-value': 46, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 65, 'sell-adx-value': 94, 'sell-rsi-value': 63, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.2744891639643, 216: 0.17911475074452382, 772: 0.06251955472249589, 1091: 0}, 'stoploss': {'stoploss': -0.024551752215582423}}, # noqa: E501 'params_details': {'buy': {'mfi-value': 22, 'fastd-value': 43, 'adx-value': 46, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 65, 'sell-adx-value': 94, 'sell-rsi-value': 63, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.2744891639643, 216: 0.17911475074452382, 772: 0.06251955472249589, 1091: 0}, 'stoploss': {'stoploss': -0.024551752215582423}}, # noqa: E501
'results_metrics': {'total_trades': 39, 'wins': 20, 'draws': 0, 'losses': 19, 'profit_mean': -0.0021400679487179478, 'profit_median': -0.012222, 'profit_total': -0.0041773, 'profit_total_abs': -8.346264999999997, 'max_drawdown': 0.45, 'max_drawdown_abs': -4.955321, 'holding_avg': timedelta(minutes=636.9230769230769)}, # noqa: E501 'results_metrics': {'total_trades': 39, 'trade_count_long': 39, 'trade_count_short': 0, 'wins': 20, 'draws': 0, 'losses': 19, 'profit_mean': -0.0021400679487179478, 'profit_median': -0.012222, 'profit_total': -0.0041773, 'profit_total_abs': -8.346264999999997, 'max_drawdown': 0.45, 'max_drawdown_abs': -4.955321, 'holding_avg': timedelta(minutes=636.9230769230769)}, # noqa: E501
'results_explanation': ' 39 trades. Avg profit -0.21%. Total profit -0.00417730 BTC ( -8.35Σ%). Avg duration 636.9 min.', # noqa: E501 'results_explanation': ' 39 trades. Avg profit -0.21%. Total profit -0.00417730 BTC ( -8.35Σ%). Avg duration 636.9 min.', # noqa: E501
'total_profit': -0.0041773, 'total_profit': -0.0041773,
'current_epoch': 6, 'current_epoch': 6,
@ -2749,7 +2749,7 @@ def saved_hyperopt_results():
'params_details': { 'params_details': {
'buy': {'mfi-value': 13, 'fastd-value': 41, 'adx-value': 21, 'rsi-value': 29, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 99, 'sell-fastd-value': 60, 'sell-adx-value': 81, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.4837436938134452, 145: 0.10853310701097472, 765: 0.0586919200378493, 1536: 0}, # noqa: E501 'buy': {'mfi-value': 13, 'fastd-value': 41, 'adx-value': 21, 'rsi-value': 29, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 99, 'sell-fastd-value': 60, 'sell-adx-value': 81, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.4837436938134452, 145: 0.10853310701097472, 765: 0.0586919200378493, 1536: 0}, # noqa: E501
'stoploss': {'stoploss': -0.14613268022709905}}, # noqa: E501 'stoploss': {'stoploss': -0.14613268022709905}}, # noqa: E501
'results_metrics': {'total_trades': 318, 'wins': 100, 'draws': 0, 'losses': 218, 'profit_mean': -0.0039833954716981146, 'profit_median': -0.012222, 'profit_total': -0.06339929, 'profit_total_abs': -126.67197600000004, 'max_drawdown': 0.50, 'max_drawdown_abs': -200.955321, 'holding_avg': timedelta(minutes=3140.377358490566)}, # noqa: E501 'results_metrics': {'total_trades': 318, 'trade_count_long': 318, 'trade_count_short': 0, 'wins': 100, 'draws': 0, 'losses': 218, 'profit_mean': -0.0039833954716981146, 'profit_median': -0.012222, 'profit_total': -0.06339929, 'profit_total_abs': -126.67197600000004, 'max_drawdown': 0.50, 'max_drawdown_abs': -200.955321, 'holding_avg': timedelta(minutes=3140.377358490566)}, # noqa: E501
'results_explanation': ' 318 trades. Avg profit -0.40%. Total profit -0.06339929 BTC (-126.67Σ%). Avg duration 3140.4 min.', # noqa: E501 'results_explanation': ' 318 trades. Avg profit -0.40%. Total profit -0.06339929 BTC (-126.67Σ%). Avg duration 3140.4 min.', # noqa: E501
'total_profit': -0.06339929, 'total_profit': -0.06339929,
'current_epoch': 7, 'current_epoch': 7,
@ -2760,7 +2760,7 @@ def saved_hyperopt_results():
'loss': 20.0, # noqa: E501 'loss': 20.0, # noqa: E501
'params_dict': {'mfi-value': 24, 'fastd-value': 43, 'adx-value': 33, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'sar_reversal', 'sell-mfi-value': 89, 'sell-fastd-value': 74, 'sell-adx-value': 70, 'sell-rsi-value': 70, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': False, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 1149, 'roi_t2': 375, 'roi_t3': 289, 'roi_p1': 0.05571820757172588, 'roi_p2': 0.0606240398618907, 'roi_p3': 0.1729012220156157, 'stoploss': -0.1588514289110401}, # noqa: E501 'params_dict': {'mfi-value': 24, 'fastd-value': 43, 'adx-value': 33, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'sar_reversal', 'sell-mfi-value': 89, 'sell-fastd-value': 74, 'sell-adx-value': 70, 'sell-rsi-value': 70, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': False, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 1149, 'roi_t2': 375, 'roi_t3': 289, 'roi_p1': 0.05571820757172588, 'roi_p2': 0.0606240398618907, 'roi_p3': 0.1729012220156157, 'stoploss': -0.1588514289110401}, # noqa: E501
'params_details': {'buy': {'mfi-value': 24, 'fastd-value': 43, 'adx-value': 33, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 89, 'sell-fastd-value': 74, 'sell-adx-value': 70, 'sell-rsi-value': 70, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': False, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, 'roi': {0: 0.2892434694492323, 289: 0.11634224743361658, 664: 0.05571820757172588, 1813: 0}, 'stoploss': {'stoploss': -0.1588514289110401}}, # noqa: E501 'params_details': {'buy': {'mfi-value': 24, 'fastd-value': 43, 'adx-value': 33, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 89, 'sell-fastd-value': 74, 'sell-adx-value': 70, 'sell-rsi-value': 70, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': False, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, 'roi': {0: 0.2892434694492323, 289: 0.11634224743361658, 664: 0.05571820757172588, 1813: 0}, 'stoploss': {'stoploss': -0.1588514289110401}}, # noqa: E501
'results_metrics': {'total_trades': 1, 'wins': 0, 'draws': 1, 'losses': 0, 'profit_mean': 0.0, 'profit_median': 0.0, 'profit_total': 0.0, 'profit_total_abs': 0.0, 'max_drawdown': 0.0, 'max_drawdown_abs': 0.52, 'holding_avg': timedelta(minutes=5340.0)}, # noqa: E501 'results_metrics': {'total_trades': 1, 'trade_count_long': 1, 'trade_count_short': 0, 'wins': 0, 'draws': 1, 'losses': 0, 'profit_mean': 0.0, 'profit_median': 0.0, 'profit_total': 0.0, 'profit_total_abs': 0.0, 'max_drawdown': 0.0, 'max_drawdown_abs': 0.52, 'holding_avg': timedelta(minutes=5340.0)}, # noqa: E501
'results_explanation': ' 1 trades. Avg profit 0.00%. Total profit 0.00000000 BTC ( 0.00Σ%). Avg duration 5340.0 min.', # noqa: E501 'results_explanation': ' 1 trades. Avg profit 0.00%. Total profit 0.00000000 BTC ( 0.00Σ%). Avg duration 5340.0 min.', # noqa: E501
'total_profit': 0.0, 'total_profit': 0.0,
'current_epoch': 8, 'current_epoch': 8,
@ -2771,7 +2771,7 @@ def saved_hyperopt_results():
'loss': 2.4731817780991223, 'loss': 2.4731817780991223,
'params_dict': {'mfi-value': 22, 'fastd-value': 20, 'adx-value': 29, 'rsi-value': 40, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'sar_reversal', 'sell-mfi-value': 97, 'sell-fastd-value': 65, 'sell-adx-value': 81, 'sell-rsi-value': 64, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1012, 'roi_t2': 584, 'roi_t3': 422, 'roi_p1': 0.036764323603472565, 'roi_p2': 0.10335480573205287, 'roi_p3': 0.10322347377503042, 'stoploss': -0.2780610808108503}, # noqa: E501 'params_dict': {'mfi-value': 22, 'fastd-value': 20, 'adx-value': 29, 'rsi-value': 40, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'sar_reversal', 'sell-mfi-value': 97, 'sell-fastd-value': 65, 'sell-adx-value': 81, 'sell-rsi-value': 64, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1012, 'roi_t2': 584, 'roi_t3': 422, 'roi_p1': 0.036764323603472565, 'roi_p2': 0.10335480573205287, 'roi_p3': 0.10322347377503042, 'stoploss': -0.2780610808108503}, # noqa: E501
'params_details': {'buy': {'mfi-value': 22, 'fastd-value': 20, 'adx-value': 29, 'rsi-value': 40, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 97, 'sell-fastd-value': 65, 'sell-adx-value': 81, 'sell-rsi-value': 64, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.2433426031105559, 422: 0.14011912933552545, 1006: 0.036764323603472565, 2018: 0}, 'stoploss': {'stoploss': -0.2780610808108503}}, # noqa: E501 'params_details': {'buy': {'mfi-value': 22, 'fastd-value': 20, 'adx-value': 29, 'rsi-value': 40, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 97, 'sell-fastd-value': 65, 'sell-adx-value': 81, 'sell-rsi-value': 64, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.2433426031105559, 422: 0.14011912933552545, 1006: 0.036764323603472565, 2018: 0}, 'stoploss': {'stoploss': -0.2780610808108503}}, # noqa: E501
'results_metrics': {'total_trades': 229, 'wins': 150, 'draws': 0, 'losses': 79, 'profit_mean': -0.0038433433624454144, 'profit_median': -0.012222, 'profit_total': -0.044050070000000004, 'profit_total_abs': -88.01256299999999, 'max_drawdown': 0.41, 'max_drawdown_abs': -150.955321, 'holding_avg': timedelta(minutes=6505.676855895196)}, # noqa: E501 'results_metrics': {'total_trades': 229, 'trade_count_long': 229, 'trade_count_short': 0, 'wins': 150, 'draws': 0, 'losses': 79, 'profit_mean': -0.0038433433624454144, 'profit_median': -0.012222, 'profit_total': -0.044050070000000004, 'profit_total_abs': -88.01256299999999, 'max_drawdown': 0.41, 'max_drawdown_abs': -150.955321, 'holding_avg': timedelta(minutes=6505.676855895196)}, # noqa: E501
'results_explanation': ' 229 trades. Avg profit -0.38%. Total profit -0.04405007 BTC ( -88.01Σ%). Avg duration 6505.7 min.', # noqa: E501 'results_explanation': ' 229 trades. Avg profit -0.38%. Total profit -0.04405007 BTC ( -88.01Σ%). Avg duration 6505.7 min.', # noqa: E501
'total_profit': -0.044050070000000004, # noqa: E501 'total_profit': -0.044050070000000004, # noqa: E501
'current_epoch': 9, 'current_epoch': 9,
@ -2782,7 +2782,7 @@ def saved_hyperopt_results():
'loss': -0.2604606005845212, # noqa: E501 'loss': -0.2604606005845212, # noqa: E501
'params_dict': {'mfi-value': 23, 'fastd-value': 24, 'adx-value': 22, 'rsi-value': 24, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 97, 'sell-fastd-value': 70, 'sell-adx-value': 64, 'sell-rsi-value': 80, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 792, 'roi_t2': 464, 'roi_t3': 215, 'roi_p1': 0.04594053535385903, 'roi_p2': 0.09623192684243963, 'roi_p3': 0.04428219070850663, 'stoploss': -0.16992287161634415}, # noqa: E501 'params_dict': {'mfi-value': 23, 'fastd-value': 24, 'adx-value': 22, 'rsi-value': 24, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 97, 'sell-fastd-value': 70, 'sell-adx-value': 64, 'sell-rsi-value': 80, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 792, 'roi_t2': 464, 'roi_t3': 215, 'roi_p1': 0.04594053535385903, 'roi_p2': 0.09623192684243963, 'roi_p3': 0.04428219070850663, 'stoploss': -0.16992287161634415}, # noqa: E501
'params_details': {'buy': {'mfi-value': 23, 'fastd-value': 24, 'adx-value': 22, 'rsi-value': 24, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 97, 'sell-fastd-value': 70, 'sell-adx-value': 64, 'sell-rsi-value': 80, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, 'roi': {0: 0.18645465290480528, 215: 0.14217246219629864, 679: 0.04594053535385903, 1471: 0}, 'stoploss': {'stoploss': -0.16992287161634415}}, # noqa: E501 'params_details': {'buy': {'mfi-value': 23, 'fastd-value': 24, 'adx-value': 22, 'rsi-value': 24, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 97, 'sell-fastd-value': 70, 'sell-adx-value': 64, 'sell-rsi-value': 80, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, 'roi': {0: 0.18645465290480528, 215: 0.14217246219629864, 679: 0.04594053535385903, 1471: 0}, 'stoploss': {'stoploss': -0.16992287161634415}}, # noqa: E501
'results_metrics': {'total_trades': 4, 'wins': 0, 'draws': 0, 'losses': 4, 'profit_mean': 0.001080385, 'profit_median': -0.012222, 'profit_total': 0.00021629, 'profit_total_abs': 0.432154, 'max_drawdown': 0.13, 'max_drawdown_abs': -4.955321, 'holding_avg': timedelta(minutes=2850.0)}, # noqa: E501 'results_metrics': {'total_trades': 4, 'trade_count_long': 4, 'trade_count_short': 0, 'wins': 0, 'draws': 0, 'losses': 4, 'profit_mean': 0.001080385, 'profit_median': -0.012222, 'profit_total': 0.00021629, 'profit_total_abs': 0.432154, 'max_drawdown': 0.13, 'max_drawdown_abs': -4.955321, 'holding_avg': timedelta(minutes=2850.0)}, # noqa: E501
'results_explanation': ' 4 trades. Avg profit 0.11%. Total profit 0.00021629 BTC ( 0.43Σ%). Avg duration 2850.0 min.', # noqa: E501 'results_explanation': ' 4 trades. Avg profit 0.11%. Total profit 0.00021629 BTC ( 0.43Σ%). Avg duration 2850.0 min.', # noqa: E501
'total_profit': 0.00021629, 'total_profit': 0.00021629,
'current_epoch': 10, 'current_epoch': 10,
@ -2794,7 +2794,7 @@ def saved_hyperopt_results():
'params_dict': {'mfi-value': 20, 'fastd-value': 32, 'adx-value': 49, 'rsi-value': 23, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower', 'sell-mfi-value': 75, 'sell-fastd-value': 56, 'sell-adx-value': 61, 'sell-rsi-value': 62, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 579, 'roi_t2': 614, 'roi_t3': 273, 'roi_p1': 0.05307643172744114, 'roi_p2': 0.1352282078262871, 'roi_p3': 0.1913307406325751, 'stoploss': -0.25728526022513887}, # noqa: E501 'params_dict': {'mfi-value': 20, 'fastd-value': 32, 'adx-value': 49, 'rsi-value': 23, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower', 'sell-mfi-value': 75, 'sell-fastd-value': 56, 'sell-adx-value': 61, 'sell-rsi-value': 62, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 579, 'roi_t2': 614, 'roi_t3': 273, 'roi_p1': 0.05307643172744114, 'roi_p2': 0.1352282078262871, 'roi_p3': 0.1913307406325751, 'stoploss': -0.25728526022513887}, # noqa: E501
'params_details': {'buy': {'mfi-value': 20, 'fastd-value': 32, 'adx-value': 49, 'rsi-value': 23, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 75, 'sell-fastd-value': 56, 'sell-adx-value': 61, 'sell-rsi-value': 62, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.3796353801863034, 273: 0.18830463955372825, 887: 0.05307643172744114, 1466: 0}, 'stoploss': {'stoploss': -0.25728526022513887}}, # noqa: E501 'params_details': {'buy': {'mfi-value': 20, 'fastd-value': 32, 'adx-value': 49, 'rsi-value': 23, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 75, 'sell-fastd-value': 56, 'sell-adx-value': 61, 'sell-rsi-value': 62, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.3796353801863034, 273: 0.18830463955372825, 887: 0.05307643172744114, 1466: 0}, 'stoploss': {'stoploss': -0.25728526022513887}}, # noqa: E501
# New Hyperopt mode! # New Hyperopt mode!
'results_metrics': {'total_trades': 117, 'wins': 67, 'draws': 0, 'losses': 50, 'profit_mean': -0.012698609145299145, 'profit_median': -0.012222, 'profit_total': -0.07436117, 'profit_total_abs': -148.573727, 'max_drawdown': 0.52, 'max_drawdown_abs': -224.955321, 'holding_avg': timedelta(minutes=4282.5641025641025)}, # noqa: E501 'results_metrics': {'total_trades': 117, 'trade_count_long': 117, 'trade_count_short': 0, 'wins': 67, 'draws': 0, 'losses': 50, 'profit_mean': -0.012698609145299145, 'profit_median': -0.012222, 'profit_total': -0.07436117, 'profit_total_abs': -148.573727, 'max_drawdown': 0.52, 'max_drawdown_abs': -224.955321, 'holding_avg': timedelta(minutes=4282.5641025641025)}, # noqa: E501
'results_explanation': ' 117 trades. Avg profit -1.27%. Total profit -0.07436117 BTC (-148.57Σ%). Avg duration 4282.6 min.', # noqa: E501 'results_explanation': ' 117 trades. Avg profit -1.27%. Total profit -0.07436117 BTC (-148.57Σ%). Avg duration 4282.6 min.', # noqa: E501
'total_profit': -0.07436117, 'total_profit': -0.07436117,
'current_epoch': 11, 'current_epoch': 11,
@ -2805,7 +2805,7 @@ def saved_hyperopt_results():
'loss': 100000, 'loss': 100000,
'params_dict': {'mfi-value': 10, 'fastd-value': 36, 'adx-value': 31, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'sar_reversal', 'sell-mfi-value': 80, 'sell-fastd-value': 71, 'sell-adx-value': 60, 'sell-rsi-value': 85, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1156, 'roi_t2': 581, 'roi_t3': 408, 'roi_p1': 0.06860454019988212, 'roi_p2': 0.12473718444931989, 'roi_p3': 0.2896360635226823, 'stoploss': -0.30889015124682806}, # noqa: E501 'params_dict': {'mfi-value': 10, 'fastd-value': 36, 'adx-value': 31, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'sar_reversal', 'sell-mfi-value': 80, 'sell-fastd-value': 71, 'sell-adx-value': 60, 'sell-rsi-value': 85, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1156, 'roi_t2': 581, 'roi_t3': 408, 'roi_p1': 0.06860454019988212, 'roi_p2': 0.12473718444931989, 'roi_p3': 0.2896360635226823, 'stoploss': -0.30889015124682806}, # noqa: E501
'params_details': {'buy': {'mfi-value': 10, 'fastd-value': 36, 'adx-value': 31, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 80, 'sell-fastd-value': 71, 'sell-adx-value': 60, 'sell-rsi-value': 85, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.4829777881718843, 408: 0.19334172464920202, 989: 0.06860454019988212, 2145: 0}, 'stoploss': {'stoploss': -0.30889015124682806}}, # noqa: E501 'params_details': {'buy': {'mfi-value': 10, 'fastd-value': 36, 'adx-value': 31, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 80, 'sell-fastd-value': 71, 'sell-adx-value': 60, 'sell-rsi-value': 85, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.4829777881718843, 408: 0.19334172464920202, 989: 0.06860454019988212, 2145: 0}, 'stoploss': {'stoploss': -0.30889015124682806}}, # noqa: E501
'results_metrics': {'total_trades': 0, 'wins': 0, 'draws': 0, 'losses': 0, 'profit_mean': None, 'profit_median': None, 'profit_total': 0, 'profit_total_abs': 0.0, 'max_drawdown': 0.0, 'max_drawdown_abs': 0.0, 'holding_avg': timedelta()}, # noqa: E501 'results_metrics': {'total_trades': 0, 'trade_count_long': 0, 'trade_count_short': 0, 'wins': 0, 'draws': 0, 'losses': 0, 'profit_mean': None, 'profit_median': None, 'profit_total': 0, 'profit_total_abs': 0.0, 'max_drawdown': 0.0, 'max_drawdown_abs': 0.0, 'holding_avg': timedelta()}, # noqa: E501
'results_explanation': ' 0 trades. Avg profit nan%. Total profit 0.00000000 BTC ( 0.00Σ%). Avg duration nan min.', # noqa: E501 'results_explanation': ' 0 trades. Avg profit nan%. Total profit 0.00000000 BTC ( 0.00Σ%). Avg duration nan min.', # noqa: E501
'total_profit': 0, 'total_profit': 0,
'current_epoch': 12, 'current_epoch': 12,

View File

@ -1207,12 +1207,17 @@ def test_create_dry_run_order_fees(
assert order1['fee']['rate'] == fee assert order1['fee']['rate'] == fee
@pytest.mark.parametrize("side,startprice,endprice", [ @pytest.mark.parametrize("side,price,filled", [
("buy", 25.563, 25.566), # order_book_l2_usd spread:
("sell", 25.566, 25.563) # best ask: 25.566
# best bid: 25.563
("buy", 25.563, False),
("buy", 25.566, True),
("sell", 25.566, False),
("sell", 25.563, True),
]) ])
@pytest.mark.parametrize("exchange_name", EXCHANGES) @pytest.mark.parametrize("exchange_name", EXCHANGES)
def test_create_dry_run_order_limit_fill(default_conf, mocker, side, startprice, endprice, def test_create_dry_run_order_limit_fill(default_conf, mocker, side, price, filled,
exchange_name, order_book_l2_usd): exchange_name, order_book_l2_usd):
default_conf['dry_run'] = True default_conf['dry_run'] = True
exchange = get_patched_exchange(mocker, default_conf, id=exchange_name) exchange = get_patched_exchange(mocker, default_conf, id=exchange_name)
@ -1226,7 +1231,7 @@ def test_create_dry_run_order_limit_fill(default_conf, mocker, side, startprice,
ordertype='limit', ordertype='limit',
side=side, side=side,
amount=1, amount=1,
rate=startprice, rate=price,
leverage=1.0 leverage=1.0
) )
assert order_book_l2_usd.call_count == 1 assert order_book_l2_usd.call_count == 1
@ -1235,22 +1240,17 @@ def test_create_dry_run_order_limit_fill(default_conf, mocker, side, startprice,
assert order["side"] == side assert order["side"] == side
assert order["type"] == "limit" assert order["type"] == "limit"
assert order["symbol"] == "LTC/USDT" assert order["symbol"] == "LTC/USDT"
assert order["average"] == price
assert order['status'] == 'open' if not filled else 'closed'
order_book_l2_usd.reset_mock() order_book_l2_usd.reset_mock()
# fetch order again...
order_closed = exchange.fetch_dry_run_order(order['id']) order_closed = exchange.fetch_dry_run_order(order['id'])
assert order_book_l2_usd.call_count == 1 assert order_book_l2_usd.call_count == (1 if not filled else 0)
assert order_closed['status'] == 'open' assert order_closed['status'] == ('open' if not filled else 'closed')
assert not order['fee'] assert order_closed['filled'] == (0 if not filled else 1)
assert order_closed['filled'] == 0
order_book_l2_usd.reset_mock() order_book_l2_usd.reset_mock()
order_closed['price'] = endprice
order_closed = exchange.fetch_dry_run_order(order['id'])
assert order_closed['status'] == 'closed'
assert order['fee']
assert order_closed['filled'] == 1
assert order_closed['filled'] == order_closed['amount']
# Empty orderbook test # Empty orderbook test
mocker.patch('freqtrade.exchange.Exchange.fetch_l2_order_book', mocker.patch('freqtrade.exchange.Exchange.fetch_l2_order_book',

View File

@ -27,10 +27,9 @@ def freqai_conf(default_conf, tmpdir):
"timerange": "20180110-20180115", "timerange": "20180110-20180115",
"freqai": { "freqai": {
"enabled": True, "enabled": True,
"startup_candles": 10000,
"purge_old_models": True, "purge_old_models": True,
"train_period_days": 2, "train_period_days": 2,
"backtest_period_days": 2, "backtest_period_days": 10,
"live_retrain_hours": 0, "live_retrain_hours": 0,
"expiration_hours": 1, "expiration_hours": 1,
"identifier": "uniqe-id100", "identifier": "uniqe-id100",
@ -58,6 +57,30 @@ def freqai_conf(default_conf, tmpdir):
return freqaiconf return freqaiconf
def make_rl_config(conf):
conf.update({"strategy": "freqai_rl_test_strat"})
conf["freqai"].update({"model_training_parameters": {
"learning_rate": 0.00025,
"gamma": 0.9,
"verbose": 1
}})
conf["freqai"]["rl_config"] = {
"train_cycles": 1,
"thread_count": 2,
"max_trade_duration_candles": 300,
"model_type": "PPO",
"policy_type": "MlpPolicy",
"max_training_drawdown_pct": 0.5,
"net_arch": [32, 32],
"model_reward_parameters": {
"rr": 1,
"profit_aim": 0.02,
"win_reward_factor": 2
}}
return conf
def get_patched_data_kitchen(mocker, freqaiconf): def get_patched_data_kitchen(mocker, freqaiconf):
dk = FreqaiDataKitchen(freqaiconf) dk = FreqaiDataKitchen(freqaiconf)
return dk return dk

View File

@ -13,8 +13,8 @@ from freqtrade.freqai.utils import download_all_data_for_training, get_required_
from freqtrade.optimize.backtesting import Backtesting from freqtrade.optimize.backtesting import Backtesting
from freqtrade.persistence import Trade from freqtrade.persistence import Trade
from freqtrade.plugins.pairlistmanager import PairListManager from freqtrade.plugins.pairlistmanager import PairListManager
from tests.conftest import get_patched_exchange, log_has_re from tests.conftest import create_mock_trades, get_patched_exchange, log_has_re
from tests.freqai.conftest import get_patched_freqai_strategy from tests.freqai.conftest import get_patched_freqai_strategy, make_rl_config
def is_arm() -> bool: def is_arm() -> bool:
@ -32,11 +32,17 @@ def is_mac() -> bool:
('XGBoostRegressor', False, True, False), ('XGBoostRegressor', False, True, False),
('XGBoostRFRegressor', False, False, False), ('XGBoostRFRegressor', False, False, False),
('CatboostRegressor', False, False, False), ('CatboostRegressor', False, False, False),
('ReinforcementLearner', False, True, False),
('ReinforcementLearner_multiproc', False, False, False),
('ReinforcementLearner_test_4ac', False, False, False)
]) ])
def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca, dbscan, float32): def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca, dbscan, float32):
if is_arm() and model == 'CatboostRegressor': if is_arm() and model == 'CatboostRegressor':
pytest.skip("CatBoost is not supported on ARM") pytest.skip("CatBoost is not supported on ARM")
if is_mac() and 'Reinforcement' in model:
pytest.skip("Reinforcement learning module not available on intel based Mac OS")
model_save_ext = 'joblib' model_save_ext = 'joblib'
freqai_conf.update({"freqaimodel": model}) freqai_conf.update({"freqaimodel": model})
freqai_conf.update({"timerange": "20180110-20180130"}) freqai_conf.update({"timerange": "20180110-20180130"})
@ -45,6 +51,26 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
freqai_conf['freqai']['feature_parameters'].update({"use_DBSCAN_to_remove_outliers": dbscan}) freqai_conf['freqai']['feature_parameters'].update({"use_DBSCAN_to_remove_outliers": dbscan})
freqai_conf.update({"reduce_df_footprint": float32}) freqai_conf.update({"reduce_df_footprint": float32})
if 'ReinforcementLearner' in model:
model_save_ext = 'zip'
freqai_conf = make_rl_config(freqai_conf)
# test the RL guardrails
freqai_conf['freqai']['feature_parameters'].update({"use_SVM_to_remove_outliers": True})
freqai_conf['freqai']['data_split_parameters'].update({'shuffle': True})
if 'test_4ac' in model:
freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
if 'ReinforcementLearner' in model:
model_save_ext = 'zip'
freqai_conf = make_rl_config(freqai_conf)
# test the RL guardrails
freqai_conf['freqai']['feature_parameters'].update({"use_SVM_to_remove_outliers": True})
freqai_conf['freqai']['data_split_parameters'].update({'shuffle': True})
if 'test_4ac' in model:
freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
strategy = get_patched_freqai_strategy(mocker, freqai_conf) strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf) exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange) strategy.dp = DataProvider(freqai_conf, exchange)
@ -52,6 +78,7 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
freqai = strategy.freqai freqai = strategy.freqai
freqai.live = True freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf) freqai.dk = FreqaiDataKitchen(freqai_conf)
freqai.dk.set_paths('ADA/BTC', 10000)
timerange = TimeRange.parse_timerange("20180110-20180130") timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk) freqai.dd.load_all_pair_histories(timerange, freqai.dk)
@ -165,25 +192,35 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
@pytest.mark.parametrize( @pytest.mark.parametrize(
"model, num_files, strat", "model, num_files, strat",
[ [
("LightGBMRegressor", 6, "freqai_test_strat"), ("LightGBMRegressor", 2, "freqai_test_strat"),
("XGBoostRegressor", 6, "freqai_test_strat"), ("XGBoostRegressor", 2, "freqai_test_strat"),
("CatboostRegressor", 6, "freqai_test_strat"), ("CatboostRegressor", 2, "freqai_test_strat"),
("XGBoostClassifier", 6, "freqai_test_classifier"), ("ReinforcementLearner", 3, "freqai_rl_test_strat"),
("LightGBMClassifier", 6, "freqai_test_classifier"), ("XGBoostClassifier", 2, "freqai_test_classifier"),
("CatboostClassifier", 6, "freqai_test_classifier") ("LightGBMClassifier", 2, "freqai_test_classifier"),
("CatboostClassifier", 2, "freqai_test_classifier")
], ],
) )
def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog): def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog):
freqai_conf.get("freqai", {}).update({"save_backtest_models": True}) freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
freqai_conf['runmode'] = RunMode.BACKTEST freqai_conf['runmode'] = RunMode.BACKTEST
Trade.use_db = False
if is_arm() and "Catboost" in model: if is_arm() and "Catboost" in model:
pytest.skip("CatBoost is not supported on ARM") pytest.skip("CatBoost is not supported on ARM")
if is_mac() and 'Reinforcement' in model:
pytest.skip("Reinforcement learning module not available on intel based Mac OS")
Trade.use_db = False
freqai_conf.update({"freqaimodel": model}) freqai_conf.update({"freqaimodel": model})
freqai_conf.update({"timerange": "20180120-20180130"}) freqai_conf.update({"timerange": "20180120-20180130"})
freqai_conf.update({"strategy": strat}) freqai_conf.update({"strategy": strat})
if 'ReinforcementLearner' in model:
freqai_conf = make_rl_config(freqai_conf)
if 'test_4ac' in model:
freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
strategy = get_patched_freqai_strategy(mocker, freqai_conf) strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf) exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange) strategy.dp = DataProvider(freqai_conf, exchange)
@ -207,6 +244,7 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog)
model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()] model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
assert len(model_folders) == num_files assert len(model_folders) == num_files
Trade.use_db = True
assert log_has_re( assert log_has_re(
"Removed features ", "Removed features ",
caplog, caplog,
@ -269,7 +307,7 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
freqai.start_backtesting(df, metadata, freqai.dk) freqai.start_backtesting(df, metadata, freqai.dk)
model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()] model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
assert len(model_folders) == 6 assert len(model_folders) == 2
# without deleting the existing folder structure, re-run # without deleting the existing folder structure, re-run
@ -507,3 +545,43 @@ def test_download_all_data_for_training(mocker, freqai_conf, caplog, tmpdir):
"Downloading", "Downloading",
caplog, caplog,
) )
@pytest.mark.usefixtures("init_persistence")
@pytest.mark.parametrize('dp_exists', [(False), (True)])
def test_get_state_info(mocker, freqai_conf, dp_exists, caplog, tickers):
if is_mac():
pytest.skip("Reinforcement learning module not available on intel based Mac OS")
freqai_conf.update({"freqaimodel": "ReinforcementLearner"})
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"strategy": "freqai_rl_test_strat"})
freqai_conf = make_rl_config(freqai_conf)
freqai_conf['entry_pricing']['price_side'] = 'same'
freqai_conf['exit_pricing']['price_side'] = 'same'
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
ticker_mock = MagicMock(return_value=tickers()['ETH/BTC'])
mocker.patch("freqtrade.exchange.Exchange.fetch_ticker", ticker_mock)
strategy.dp = DataProvider(freqai_conf, exchange)
if not dp_exists:
strategy.dp._exchange = None
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.data_provider = strategy.dp
freqai.live = True
Trade.use_db = True
create_mock_trades(MagicMock(return_value=0.0025), False, True)
freqai.get_state_info("ADA/BTC")
freqai.get_state_info("ETH/BTC")
if not dp_exists:
assert log_has_re(
"No exchange available",
caplog,
)

View File

@ -0,0 +1,66 @@
import logging
import numpy as np
from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
from freqtrade.freqai.RL.Base4ActionRLEnv import Actions, Base4ActionRLEnv, Positions
logger = logging.getLogger(__name__)
class ReinforcementLearner_test_4ac(ReinforcementLearner):
"""
User created Reinforcement Learning Model prediction model.
"""
class MyRLEnv(Base4ActionRLEnv):
"""
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:
# first, penalize if the action is not valid
if not self._is_valid(action):
return -2
pnl = self.get_unrealized_profit()
rew = np.sign(pnl) * (pnl + 1)
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 # type: ignore
if trade_duration <= max_trade_duration:
factor *= 1.5
elif trade_duration > max_trade_duration:
factor *= 0.5
# discourage sitting in position
if (self._position in (Positions.Short, Positions.Long) and
action == Actions.Neutral.value):
return -1 * trade_duration / max_trade_duration
# close long
if action == Actions.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(rew * factor)
# close short
if action == Actions.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(rew * factor)
return 0.

View File

@ -663,30 +663,9 @@ def test_backtest__get_sell_trade_entry(default_conf, fee, mocker) -> None:
'', # Exit Signal Name '', # Exit Signal Name
] ]
row_detail = pd.DataFrame(
[
[
pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=0, tzinfo=timezone.utc),
200, 200.1, 197, 199, 1, 0, 0, 0, '', '', '',
], [
pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=1, tzinfo=timezone.utc),
199, 199.7, 199, 199.5, 0, 0, 0, 0, '', '', '',
], [
pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=2, tzinfo=timezone.utc),
199.5, 200.8, 199, 200.9, 0, 0, 0, 0, '', '', '',
], [
pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=3, tzinfo=timezone.utc),
200.5, 210.5, 193, 210.5, 0, 0, 0, 0, '', '', '', # ROI sell (?)
], [
pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=4, tzinfo=timezone.utc),
200, 200.1, 193, 199, 0, 0, 0, 0, '', '', '',
],
], columns=['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long',
'enter_short', 'exit_short', 'long_tag', 'short_tag', 'exit_tag']
)
# No data available. # No data available.
res = backtesting._get_exit_trade_entry(trade, row_sell) res = backtesting._get_exit_trade_entry(trade, row_sell, True)
assert res is not None assert res is not None
assert res.exit_reason == ExitType.ROI.value assert res.exit_reason == ExitType.ROI.value
assert res.close_date_utc == datetime(2020, 1, 1, 5, 0, tzinfo=timezone.utc) assert res.close_date_utc == datetime(2020, 1, 1, 5, 0, tzinfo=timezone.utc)
@ -699,20 +678,9 @@ def test_backtest__get_sell_trade_entry(default_conf, fee, mocker) -> None:
[], columns=['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long', [], columns=['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long',
'enter_short', 'exit_short', 'long_tag', 'short_tag', 'exit_tag']) 'enter_short', 'exit_short', 'long_tag', 'short_tag', 'exit_tag'])
res = backtesting._get_exit_trade_entry(trade, row) res = backtesting._get_exit_trade_entry(trade, row, True)
assert res is None assert res is None
# Assign backtest-detail data
backtesting.detail_data[pair] = row_detail
res = backtesting._get_exit_trade_entry(trade, row_sell)
assert res is not None
assert res.exit_reason == ExitType.ROI.value
# Sell at minute 3 (not available above!)
assert res.close_date_utc == datetime(2020, 1, 1, 5, 3, tzinfo=timezone.utc)
sell_order = res.select_order('sell', True)
assert sell_order is not None
def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None: def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
default_conf['use_exit_signal'] = False default_conf['use_exit_signal'] = False
@ -788,17 +756,98 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
for _, t in results.iterrows(): for _, t in results.iterrows():
assert len(t['orders']) == 2 assert len(t['orders']) == 2
ln = data_pair.loc[data_pair["date"] == t["open_date"]] ln = data_pair.loc[data_pair["date"] == t["open_date"]]
# Check open trade rate alignes to open rate # Check open trade rate aligns to open rate
assert not ln.empty assert not ln.empty
assert round(ln.iloc[0]["open"], 6) == round(t["open_rate"], 6) assert round(ln.iloc[0]["open"], 6) == round(t["open_rate"], 6)
# check close trade rate alignes to close rate or is between high and low # check close trade rate aligns to close rate or is between high and low
ln1 = data_pair.loc[data_pair["date"] == t["close_date"]] ln1 = data_pair.loc[data_pair["date"] == t["close_date"]]
assert not ln1.empty
assert (round(ln1.iloc[0]["open"], 6) == round(t["close_rate"], 6) or assert (round(ln1.iloc[0]["open"], 6) == round(t["close_rate"], 6) or
round(ln1.iloc[0]["low"], 6) < round( round(ln1.iloc[0]["low"], 6) < round(
t["close_rate"], 6) < round(ln1.iloc[0]["high"], 6)) t["close_rate"], 6) < round(ln1.iloc[0]["high"], 6))
@pytest.mark.parametrize('use_detail', [True, False])
def test_backtest_one_detail(default_conf_usdt, fee, mocker, testdatadir, use_detail) -> None:
default_conf_usdt['use_exit_signal'] = False
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001)
mocker.patch("freqtrade.exchange.Exchange.get_max_pair_stake_amount", return_value=float('inf'))
if use_detail:
default_conf_usdt['timeframe_detail'] = '1m'
patch_exchange(mocker)
def advise_entry(df, *args, **kwargs):
# Mock function to force several entries
df.loc[(df['rsi'] < 40), 'enter_long'] = 1
return df
def custom_entry_price(proposed_rate, **kwargs):
return proposed_rate * 0.997
backtesting = Backtesting(default_conf_usdt)
backtesting._set_strategy(backtesting.strategylist[0])
backtesting.strategy.populate_entry_trend = advise_entry
backtesting.strategy.custom_entry_price = custom_entry_price
pair = 'XRP/ETH'
# Pick a timerange adapted to the pair we use to test
timerange = TimeRange.parse_timerange('20191010-20191013')
data = history.load_data(datadir=testdatadir, timeframe='5m', pairs=['XRP/ETH'],
timerange=timerange)
if use_detail:
data_1m = history.load_data(datadir=testdatadir, timeframe='1m', pairs=['XRP/ETH'],
timerange=timerange)
backtesting.detail_data = data_1m
processed = backtesting.strategy.advise_all_indicators(data)
min_date, max_date = get_timerange(processed)
result = backtesting.backtest(
processed=deepcopy(processed),
start_date=min_date,
end_date=max_date,
max_open_trades=10,
)
results = result['results']
assert not results.empty
# Timeout settings from default_conf = entry: 10, exit: 30
assert len(results) == (2 if use_detail else 3)
assert 'orders' in results.columns
data_pair = processed[pair]
data_1m_pair = data_1m[pair] if use_detail else pd.DataFrame()
late_entry = 0
for _, t in results.iterrows():
assert len(t['orders']) == 2
entryo = t['orders'][0]
entry_ts = datetime.fromtimestamp(entryo['order_filled_timestamp'] // 1000, tz=timezone.utc)
if entry_ts > t['open_date']:
late_entry += 1
# Get "entry fill" candle
ln = (data_1m_pair.loc[data_1m_pair["date"] == entry_ts]
if use_detail else data_pair.loc[data_pair["date"] == entry_ts])
# Check open trade rate aligns to open rate
assert not ln.empty
# assert round(ln.iloc[0]["open"], 6) == round(t["open_rate"], 6)
assert round(ln.iloc[0]["low"], 6) <= round(
t["open_rate"], 6) <= round(ln.iloc[0]["high"], 6)
# check close trade rate aligns to close rate or is between high and low
ln1 = data_pair.loc[data_pair["date"] == t["close_date"]]
if use_detail:
ln1_1m = data_1m_pair.loc[data_1m_pair["date"] == t["close_date"]]
assert not ln1.empty or not ln1_1m.empty
else:
assert not ln1.empty
ln2 = ln1_1m if ln1.empty else ln1
assert (round(ln2.iloc[0]["low"], 6) <= round(
t["close_rate"], 6) <= round(ln2.iloc[0]["high"], 6))
assert late_entry > 0
def test_backtest_timedout_entry_orders(default_conf, fee, mocker, testdatadir) -> None: def test_backtest_timedout_entry_orders(default_conf, fee, mocker, testdatadir) -> None:
# This strategy intentionally places unfillable orders. # This strategy intentionally places unfillable orders.
default_conf['strategy'] = 'StrategyTestV3CustomEntryPrice' default_conf['strategy'] = 'StrategyTestV3CustomEntryPrice'

View File

@ -57,7 +57,10 @@ def botclient(default_conf, mocker):
try: try:
apiserver = ApiServer(default_conf) apiserver = ApiServer(default_conf)
apiserver.add_rpc_handler(rpc) apiserver.add_rpc_handler(rpc)
yield ftbot, TestClient(apiserver.app) # We need to use the TestClient as a context manager to
# handle lifespan events correctly
with TestClient(apiserver.app) as client:
yield ftbot, client
# Cleanup ... ? # Cleanup ... ?
finally: finally:
if apiserver: if apiserver:
@ -438,7 +441,6 @@ def test_api_cleanup(default_conf, mocker, caplog):
apiserver.cleanup() apiserver.cleanup()
assert apiserver._server.cleanup.call_count == 1 assert apiserver._server.cleanup.call_count == 1
assert log_has("Stopping API Server", caplog) assert log_has("Stopping API Server", caplog)
assert log_has("Stopping API Server background tasks", caplog)
ApiServer.shutdown() ApiServer.shutdown()
@ -1459,6 +1461,7 @@ def test_api_strategies(botclient, tmpdir):
'StrategyTestV3', 'StrategyTestV3',
'StrategyTestV3CustomEntryPrice', 'StrategyTestV3CustomEntryPrice',
'StrategyTestV3Futures', 'StrategyTestV3Futures',
'freqai_rl_test_strat',
'freqai_test_classifier', 'freqai_test_classifier',
'freqai_test_multimodel_classifier_strat', 'freqai_test_multimodel_classifier_strat',
'freqai_test_multimodel_strat', 'freqai_test_multimodel_strat',
@ -1714,12 +1717,14 @@ def test_api_ws_subscribe(botclient, mocker):
with client.websocket_connect(ws_url) as ws: with client.websocket_connect(ws_url) as ws:
ws.send_json({'type': 'subscribe', 'data': ['whitelist']}) ws.send_json({'type': 'subscribe', 'data': ['whitelist']})
time.sleep(1)
# Check call count is now 1 as we sent a valid subscribe request # Check call count is now 1 as we sent a valid subscribe request
assert sub_mock.call_count == 1 assert sub_mock.call_count == 1
with client.websocket_connect(ws_url) as ws: with client.websocket_connect(ws_url) as ws:
ws.send_json({'type': 'subscribe', 'data': 'whitelist'}) ws.send_json({'type': 'subscribe', 'data': 'whitelist'})
time.sleep(1)
# Call count hasn't changed as the subscribe request was invalid # Call count hasn't changed as the subscribe request was invalid
assert sub_mock.call_count == 1 assert sub_mock.call_count == 1
@ -1773,24 +1778,18 @@ def test_api_ws_send_msg(default_conf, mocker, caplog):
mocker.patch('freqtrade.rpc.api_server.ApiServer.start_api') mocker.patch('freqtrade.rpc.api_server.ApiServer.start_api')
apiserver = ApiServer(default_conf) apiserver = ApiServer(default_conf)
apiserver.add_rpc_handler(RPC(get_patched_freqtradebot(mocker, default_conf))) apiserver.add_rpc_handler(RPC(get_patched_freqtradebot(mocker, default_conf)))
apiserver.start_message_queue()
# Give the queue thread time to start
time.sleep(0.2)
# Test message_queue coro receives the message # Start test client context manager to run lifespan events
test_message = {"type": "status", "data": "test"} with TestClient(apiserver.app):
apiserver.send_msg(test_message) # Test message is published on the Message Stream
time.sleep(0.1) # Not sure how else to wait for the coro to receive the data test_message = {"type": "status", "data": "test"}
assert log_has("Found message of type: status", caplog) first_waiter = apiserver._message_stream._waiter
apiserver.send_msg(test_message)
assert first_waiter.result()[0] == test_message
# Test if exception logged when error occurs in sending second_waiter = apiserver._message_stream._waiter
mocker.patch('freqtrade.rpc.api_server.ws.channel.ChannelManager.broadcast', apiserver.send_msg(test_message)
side_effect=Exception) assert first_waiter != second_waiter
apiserver.send_msg(test_message)
time.sleep(0.1) # Not sure how else to wait for the coro to receive the data
assert log_has_re(r"Exception happened in background task.*", caplog)
finally: finally:
apiserver.cleanup()
ApiServer.shutdown() ApiServer.shutdown()

View File

@ -0,0 +1,105 @@
import logging
from functools import reduce
import pandas as pd
import talib.abstract as ta
from pandas import DataFrame
from freqtrade.strategy import IStrategy, merge_informative_pair
logger = logging.getLogger(__name__)
class freqai_rl_test_strat(IStrategy):
"""
Test strategy - used for testing freqAI functionalities.
DO not use in production.
"""
minimal_roi = {"0": 0.1, "240": -1}
process_only_new_candles = True
stoploss = -0.05
use_exit_signal = True
startup_candle_count: int = 30
can_short = False
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)
# The following columns 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
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe = self.freqai.start(dataframe, metadata, self)
return dataframe
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

View File

@ -34,7 +34,7 @@ def test_search_all_strategies_no_failed():
directory = Path(__file__).parent / "strats" directory = Path(__file__).parent / "strats"
strategies = StrategyResolver._search_all_objects(directory, enum_failed=False) strategies = StrategyResolver._search_all_objects(directory, enum_failed=False)
assert isinstance(strategies, list) assert isinstance(strategies, list)
assert len(strategies) == 11 assert len(strategies) == 12
assert isinstance(strategies[0], dict) assert isinstance(strategies[0], dict)
@ -42,10 +42,10 @@ def test_search_all_strategies_with_failed():
directory = Path(__file__).parent / "strats" directory = Path(__file__).parent / "strats"
strategies = StrategyResolver._search_all_objects(directory, enum_failed=True) strategies = StrategyResolver._search_all_objects(directory, enum_failed=True)
assert isinstance(strategies, list) assert isinstance(strategies, list)
assert len(strategies) == 12 assert len(strategies) == 13
# with enum_failed=True search_all_objects() shall find 2 good strategies # with enum_failed=True search_all_objects() shall find 2 good strategies
# and 1 which fails to load # and 1 which fails to load
assert len([x for x in strategies if x['class'] is not None]) == 11 assert len([x for x in strategies if x['class'] is not None]) == 12
assert len([x for x in strategies if x['class'] is None]) == 1 assert len([x for x in strategies if x['class'] is None]) == 1

View File

@ -1498,6 +1498,7 @@ def test_handle_stoploss_on_exchange_trailing(
}) })
) )
assert freqtrade.handle_trade(trade) is True assert freqtrade.handle_trade(trade) is True
assert trade.stoploss_order_id is None
@pytest.mark.parametrize("is_short", [False, True]) @pytest.mark.parametrize("is_short", [False, True])
@ -5046,7 +5047,7 @@ def test_startup_backpopulate_precision(mocker, default_conf_usdt, fee, caplog):
@pytest.mark.usefixtures("init_persistence") @pytest.mark.usefixtures("init_persistence")
@pytest.mark.parametrize("is_short", [False, True]) @pytest.mark.parametrize("is_short", [False, True])
def test_update_closed_trades_without_assigned_fees(mocker, default_conf_usdt, fee, is_short): def test_update_trades_without_assigned_fees(mocker, default_conf_usdt, fee, is_short):
freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt) freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
def patch_with_fee(order): def patch_with_fee(order):
@ -5075,7 +5076,7 @@ def test_update_closed_trades_without_assigned_fees(mocker, default_conf_usdt, f
assert trade.fee_close_cost is None assert trade.fee_close_cost is None
assert trade.fee_close_currency is None assert trade.fee_close_currency is None
freqtrade.update_closed_trades_without_assigned_fees() freqtrade.update_trades_without_assigned_fees()
# Does nothing for dry-run # Does nothing for dry-run
trades = Trade.get_trades().all() trades = Trade.get_trades().all()
@ -5088,7 +5089,7 @@ def test_update_closed_trades_without_assigned_fees(mocker, default_conf_usdt, f
freqtrade.config['dry_run'] = False freqtrade.config['dry_run'] = False
freqtrade.update_closed_trades_without_assigned_fees() freqtrade.update_trades_without_assigned_fees()
trades = Trade.get_trades().all() trades = Trade.get_trades().all()
assert len(trades) == MOCK_TRADE_COUNT assert len(trades) == MOCK_TRADE_COUNT
@ -5551,7 +5552,7 @@ def test_position_adjust(mocker, default_conf_usdt, fee) -> None:
assert trade.stake_amount == 110 assert trade.stake_amount == 110
# Assume it does nothing since order is closed and trade is open # Assume it does nothing since order is closed and trade is open
freqtrade.update_closed_trades_without_assigned_fees() freqtrade.update_trades_without_assigned_fees()
trade = Trade.query.first() trade = Trade.query.first()
assert trade assert trade
@ -5622,7 +5623,7 @@ def test_position_adjust(mocker, default_conf_usdt, fee) -> None:
mocker.patch('freqtrade.exchange.Exchange.create_order', fetch_order_mm) mocker.patch('freqtrade.exchange.Exchange.create_order', fetch_order_mm)
mocker.patch('freqtrade.exchange.Exchange.fetch_order', fetch_order_mm) mocker.patch('freqtrade.exchange.Exchange.fetch_order', fetch_order_mm)
mocker.patch('freqtrade.exchange.Exchange.fetch_order_or_stoploss_order', fetch_order_mm) mocker.patch('freqtrade.exchange.Exchange.fetch_order_or_stoploss_order', fetch_order_mm)
freqtrade.update_closed_trades_without_assigned_fees() freqtrade.update_trades_without_assigned_fees()
orders = Order.query.all() orders = Order.query.all()
assert orders assert orders
@ -5839,7 +5840,7 @@ def test_position_adjust2(mocker, default_conf_usdt, fee) -> None:
assert trade.stake_amount == bid * amount assert trade.stake_amount == bid * amount
# Assume it does nothing since order is closed and trade is open # Assume it does nothing since order is closed and trade is open
freqtrade.update_closed_trades_without_assigned_fees() freqtrade.update_trades_without_assigned_fees()
trade = Trade.query.first() trade = Trade.query.first()
assert trade assert trade

File diff suppressed because one or more lines are too long