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Merge pull request #7367 from freqtrade/add-continual-learning

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add continual learning to catboost and friends
This commit is contained in:
Matthias
2022-09-10 20:17:28 +02:00
committed by GitHub
parent 2077f84f9b 10b6aebc5f
commit e4caccc353
16 changed files with 383 additions and 76 deletions
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20
freqtrade/freqai/freqai_interface.py
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@@ -88,6 +88,7 @@ class IFreqaiModel(ABC):
self.begin_time: float = 0
self.begin_time_train: float = 0
self.base_tf_seconds = timeframe_to_seconds(self.config['timeframe'])
self.continual_learning = self.freqai_info.get('continual_learning', False)
self._threads: List[threading.Thread] = []
self._stop_event = threading.Event()
@@ -676,21 +677,30 @@ class IFreqaiModel(ABC):
self.train_time = 0
return
def get_init_model(self, pair: str) -> Any:
if pair not in self.dd.model_dictionary or not self.continual_learning:
init_model = None
else:
init_model = self.dd.model_dictionary[pair]
return init_model
# Following methods which are overridden by user made prediction models.
# See freqai/prediction_models/CatboostPredictionModel.py for an example.
@abstractmethod
def train(self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen) -> Any:
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 datahandler
for storing, saving, loading, and analyzing the data.
:param unfiltered_dataframe: Full dataframe for the current training period
:param unfiltered_df: Full dataframe for the current training period
:param metadata: pair metadata from strategy.
:return: Trained model which can be used to inference (self.predict)
"""
@abstractmethod
def fit(self, data_dictionary: Dict[str, Any]) -> Any:
def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
Most regressors use the same function names and arguments e.g. user
can drop in LGBMRegressor in place of CatBoostRegressor and all data
@@ -703,11 +713,11 @@ class IFreqaiModel(ABC):
@abstractmethod
def predict(
self, dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = True
self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
) -> Tuple[DataFrame, NDArray[np.int_]]:
"""
Filter the prediction features data and predict with it.
:param unfiltered_dataframe: Full dataframe for the current backtest period.
:param unfiltered_df: Full dataframe for the current backtest period.
:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
:param first: boolean = whether this is the first prediction or not.
:return: