Merge branch 'feat/freqai' of https://github.com/lolongcovas/freqtrade into feat/freqai
This commit is contained in:
longyu
112
freqtrade/freqai/prediction_models/BaseRegressionModel.py
Normal file
112
freqtrade/freqai/prediction_models/BaseRegressionModel.py
Normal file
@@ -0,0 +1,112 @@
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import logging
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from typing import Tuple
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from pandas import DataFrame
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.freqai_interface import IFreqaiModel
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logger = logging.getLogger(__name__)
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class BaseRegressionModel(IFreqaiModel):
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"""
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User created prediction model. The class needs to override three necessary
|
||||
functions, predict(), train(), fit(). The class inherits ModelHandler which
|
||||
has its own DataHandler where data is held, saved, loaded, and managed.
|
||||
"""
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def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
|
||||
"""
|
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User uses this function to add any additional return values to the dataframe.
|
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e.g.
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dataframe['volatility'] = dk.volatility_values
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"""
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return dataframe
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|
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def train(
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self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
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) -> Tuple[DataFrame, DataFrame]:
|
||||
"""
|
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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.
|
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:params:
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:unfiltered_dataframe: Full dataframe for the current training period
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:metadata: pair metadata from strategy.
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:returns:
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:model: Trained model which can be used to inference (self.predict)
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"""
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logger.info("--------------------Starting training " f"{pair} --------------------")
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# filter the features requested by user in the configuration file and elegantly handle NaNs
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features_filtered, labels_filtered = dk.filter_features(
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unfiltered_dataframe,
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dk.training_features_list,
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dk.label_list,
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training_filter=True,
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)
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# split data into train/test data.
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data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
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if not self.freqai_info.get('fit_live_predictions', 0):
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dk.fit_labels()
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# normalize all data based on train_dataset only
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data_dictionary = dk.normalize_data(data_dictionary)
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# optional additional data cleaning/analysis
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self.data_cleaning_train(dk)
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logger.info(
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f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
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)
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logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
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model = self.fit(data_dictionary)
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if pair not in self.dd.historic_predictions:
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self.set_initial_historic_predictions(
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data_dictionary['train_features'], model, dk, pair)
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elif self.freqai_info.get('fit_live_predictions_candles', 0):
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dk.fit_live_predictions()
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self.dd.save_historic_predictions_to_disk()
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logger.info(f"--------------------done training {pair}--------------------")
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return model
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def predict(
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self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
|
||||
) -> Tuple[DataFrame, DataFrame]:
|
||||
"""
|
||||
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)
|
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"""
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|
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dk.find_features(unfiltered_dataframe)
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filtered_dataframe, _ = dk.filter_features(
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unfiltered_dataframe, dk.training_features_list, training_filter=False
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)
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filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
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dk.data_dictionary["prediction_features"] = filtered_dataframe
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|
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# optional additional data cleaning/analysis
|
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self.data_cleaning_predict(dk, filtered_dataframe)
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|
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predictions = self.model.predict(dk.data_dictionary["prediction_features"])
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pred_df = DataFrame(predictions, columns=dk.label_list)
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|
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for label in dk.label_list:
|
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pred_df[label] = (
|
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(pred_df[label] + 1)
|
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* (dk.data["labels_max"][label] - dk.data["labels_min"][label])
|
||||
/ 2
|
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) + dk.data["labels_min"][label]
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return (pred_df, dk.do_predict)
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@@ -1,94 +1,21 @@
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import logging
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from typing import Any, Dict, Tuple
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from typing import Any, Dict
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|
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from catboost import CatBoostRegressor, Pool
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from pandas import DataFrame
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|
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.freqai_interface import IFreqaiModel
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from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
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logger = logging.getLogger(__name__)
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class CatboostPredictionModel(IFreqaiModel):
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class CatboostPredictionModel(BaseRegressionModel):
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"""
|
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User created prediction model. The class needs to override three necessary
|
||||
functions, predict(), train(), fit(). The class inherits ModelHandler which
|
||||
has its own DataHandler where data is held, saved, loaded, and managed.
|
||||
"""
|
||||
|
||||
def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
|
||||
"""
|
||||
User uses this function to add any additional return values to the dataframe.
|
||||
e.g.
|
||||
dataframe['volatility'] = dk.volatility_values
|
||||
"""
|
||||
|
||||
return dataframe
|
||||
|
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def make_labels(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
|
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"""
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User defines the labels here (target values).
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:params:
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:dataframe: the full dataframe for the present training period
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"""
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dataframe["s"] = (
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dataframe["close"]
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.shift(-self.feature_parameters["period"])
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.rolling(self.feature_parameters["period"])
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.mean()
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/ dataframe["close"]
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- 1
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)
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return dataframe["s"]
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def train(
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self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
|
||||
) -> Tuple[DataFrame, DataFrame]:
|
||||
"""
|
||||
Filter the training data and train a model to it. Train makes heavy use of the datahkitchen
|
||||
for storing, saving, loading, and analyzing the data.
|
||||
:params:
|
||||
:unfiltered_dataframe: Full dataframe for the current training period
|
||||
:metadata: pair metadata from strategy.
|
||||
:returns:
|
||||
:model: Trained model which can be used to inference (self.predict)
|
||||
"""
|
||||
|
||||
logger.info("--------------------Starting training " f"{pair} --------------------")
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||||
|
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# unfiltered_labels = self.make_labels(unfiltered_dataframe, dk)
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# filter the features requested by user in the configuration file and elegantly handle NaNs
|
||||
features_filtered, labels_filtered = dk.filter_features(
|
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unfiltered_dataframe,
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dk.training_features_list,
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||||
dk.label_list,
|
||||
training_filter=True,
|
||||
)
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|
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# split data into train/test data.
|
||||
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
|
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dk.fit_labels() # fit labels to a cauchy distribution so we know what to expect in strategy
|
||||
# normalize all data based on train_dataset only
|
||||
data_dictionary = dk.normalize_data(data_dictionary)
|
||||
|
||||
# optional additional data cleaning/analysis
|
||||
self.data_cleaning_train(dk)
|
||||
|
||||
logger.info(
|
||||
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
|
||||
)
|
||||
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
|
||||
|
||||
model = self.fit(data_dictionary)
|
||||
|
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logger.info(f"--------------------done training {pair}--------------------")
|
||||
|
||||
return model
|
||||
|
||||
def fit(self, data_dictionary: Dict) -> Any:
|
||||
"""
|
||||
User sets up the training and test data to fit their desired model here
|
||||
@@ -118,37 +45,3 @@ class CatboostPredictionModel(IFreqaiModel):
|
||||
model.fit(X=train_data, eval_set=test_data)
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return model
|
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|
||||
def predict(
|
||||
self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
|
||||
) -> Tuple[DataFrame, DataFrame]:
|
||||
"""
|
||||
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_dataframe)
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||||
filtered_dataframe, _ = dk.filter_features(
|
||||
unfiltered_dataframe, dk.training_features_list, training_filter=False
|
||||
)
|
||||
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
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||||
dk.data_dictionary["prediction_features"] = filtered_dataframe
|
||||
|
||||
# optional additional data cleaning/analysis
|
||||
self.data_cleaning_predict(dk, filtered_dataframe)
|
||||
|
||||
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
|
||||
pred_df = DataFrame(predictions, columns=dk.label_list)
|
||||
|
||||
for label in dk.label_list:
|
||||
pred_df[label] = (
|
||||
(pred_df[label] + 1)
|
||||
* (dk.data["labels_max"][label] - dk.data["labels_min"][label])
|
||||
/ 2
|
||||
) + dk.data["labels_min"][label]
|
||||
|
||||
return (pred_df, dk.do_predict)
|
||||
|
||||
@@ -1,77 +1,22 @@
|
||||
import logging
|
||||
from typing import Any, Dict, Tuple
|
||||
from typing import Any, Dict
|
||||
|
||||
from catboost import CatBoostRegressor # , Pool
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||||
from pandas import DataFrame
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||||
from sklearn.multioutput import MultiOutputRegressor
|
||||
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
from freqtrade.freqai.freqai_interface import IFreqaiModel
|
||||
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class CatboostPredictionMultiModel(IFreqaiModel):
|
||||
class CatboostPredictionMultiModel(BaseRegressionModel):
|
||||
"""
|
||||
User created prediction model. The class needs to override three necessary
|
||||
functions, predict(), train(), fit(). The class inherits ModelHandler which
|
||||
has its own DataHandler where data is held, saved, loaded, and managed.
|
||||
"""
|
||||
|
||||
def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
|
||||
"""
|
||||
User uses this function to add any additional return values to the dataframe.
|
||||
e.g.
|
||||
dataframe['volatility'] = dk.volatility_values
|
||||
"""
|
||||
|
||||
return dataframe
|
||||
|
||||
def train(
|
||||
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
|
||||
) -> Tuple[DataFrame, DataFrame]:
|
||||
"""
|
||||
Filter the training data and train a model to it. Train makes heavy use of the datahkitchen
|
||||
for storing, saving, loading, and analyzing the data.
|
||||
:params:
|
||||
:unfiltered_dataframe: Full dataframe for the current training period
|
||||
:metadata: pair metadata from strategy.
|
||||
:returns:
|
||||
:model: Trained model which can be used to inference (self.predict)
|
||||
"""
|
||||
|
||||
logger.info("--------------------Starting training " f"{pair} --------------------")
|
||||
|
||||
# unfiltered_labels = self.make_labels(unfiltered_dataframe, dk)
|
||||
# filter the features requested by user in the configuration file and elegantly handle NaNs
|
||||
features_filtered, labels_filtered = dk.filter_features(
|
||||
unfiltered_dataframe,
|
||||
dk.training_features_list,
|
||||
dk.label_list,
|
||||
training_filter=True,
|
||||
)
|
||||
|
||||
# split data into train/test data.
|
||||
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
|
||||
dk.fit_labels() # fit labels to a cauchy distribution so we know what to expect in strategy
|
||||
# normalize all data based on train_dataset only
|
||||
data_dictionary = dk.normalize_data(data_dictionary)
|
||||
|
||||
# optional additional data cleaning/analysis
|
||||
self.data_cleaning_train(dk)
|
||||
|
||||
logger.info(
|
||||
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
|
||||
)
|
||||
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
|
||||
|
||||
model = self.fit(data_dictionary)
|
||||
|
||||
logger.info(f"--------------------done training {pair}--------------------")
|
||||
|
||||
return model
|
||||
|
||||
def fit(self, data_dictionary: Dict) -> Any:
|
||||
"""
|
||||
User sets up the training and test data to fit their desired model here
|
||||
@@ -99,37 +44,3 @@ class CatboostPredictionMultiModel(IFreqaiModel):
|
||||
test_score = model.score(*eval_set)
|
||||
logger.info(f"Train score {train_score}, Test score {test_score}")
|
||||
return model
|
||||
|
||||
def predict(
|
||||
self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
|
||||
) -> Tuple[DataFrame, DataFrame]:
|
||||
"""
|
||||
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_dataframe)
|
||||
filtered_dataframe, _ = dk.filter_features(
|
||||
unfiltered_dataframe, 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, filtered_dataframe)
|
||||
|
||||
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
|
||||
pred_df = DataFrame(predictions, columns=dk.label_list)
|
||||
|
||||
for label in dk.label_list:
|
||||
pred_df[label] = (
|
||||
(pred_df[label] + 1)
|
||||
* (dk.data["labels_max"][label] - dk.data["labels_min"][label])
|
||||
/ 2
|
||||
) + dk.data["labels_min"][label]
|
||||
|
||||
return (pred_df, dk.do_predict)
|
||||
|
||||
@@ -1,76 +1,21 @@
|
||||
import logging
|
||||
from typing import Any, Dict, Tuple
|
||||
from typing import Any, Dict
|
||||
|
||||
from lightgbm import LGBMRegressor
|
||||
from pandas import DataFrame
|
||||
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
from freqtrade.freqai.freqai_interface import IFreqaiModel
|
||||
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class LightGBMPredictionModel(IFreqaiModel):
|
||||
class LightGBMPredictionModel(BaseRegressionModel):
|
||||
"""
|
||||
User created prediction model. The class needs to override three necessary
|
||||
functions, predict(), train(), fit(). The class inherits ModelHandler which
|
||||
has its own DataHandler where data is held, saved, loaded, and managed.
|
||||
"""
|
||||
|
||||
def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
|
||||
"""
|
||||
User uses this function to add any additional return values to the dataframe.
|
||||
e.g.
|
||||
dataframe['volatility'] = dk.volatility_values
|
||||
"""
|
||||
|
||||
return dataframe
|
||||
|
||||
def train(
|
||||
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
|
||||
) -> Tuple[DataFrame, DataFrame]:
|
||||
"""
|
||||
Filter the training data and train a model to it. Train makes heavy use of the datahkitchen
|
||||
for storing, saving, loading, and analyzing the data.
|
||||
:params:
|
||||
:unfiltered_dataframe: Full dataframe for the current training period
|
||||
:metadata: pair metadata from strategy.
|
||||
:returns:
|
||||
:model: Trained model which can be used to inference (self.predict)
|
||||
"""
|
||||
|
||||
logger.info("--------------------Starting training " f"{pair} --------------------")
|
||||
|
||||
# unfiltered_labels = self.make_labels(unfiltered_dataframe, dk)
|
||||
# filter the features requested by user in the configuration file and elegantly handle NaNs
|
||||
features_filtered, labels_filtered = dk.filter_features(
|
||||
unfiltered_dataframe,
|
||||
dk.training_features_list,
|
||||
dk.label_list,
|
||||
training_filter=True,
|
||||
)
|
||||
|
||||
# split data into train/test data.
|
||||
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
|
||||
dk.fit_labels() # fit labels to a cauchy distribution so we know what to expect in strategy
|
||||
# normalize all data based on train_dataset only
|
||||
data_dictionary = dk.normalize_data(data_dictionary)
|
||||
|
||||
# optional additional data cleaning/analysis
|
||||
self.data_cleaning_train(dk)
|
||||
|
||||
logger.info(
|
||||
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
|
||||
)
|
||||
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
|
||||
|
||||
model = self.fit(data_dictionary)
|
||||
|
||||
logger.info(f"--------------------done training {pair}--------------------")
|
||||
|
||||
return model
|
||||
|
||||
def fit(self, data_dictionary: Dict) -> Any:
|
||||
"""
|
||||
Most regressors use the same function names and arguments e.g. user
|
||||
@@ -89,39 +34,3 @@ class LightGBMPredictionModel(IFreqaiModel):
|
||||
model.fit(X=X, y=y, eval_set=eval_set)
|
||||
|
||||
return model
|
||||
|
||||
def predict(
|
||||
self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen
|
||||
) -> Tuple[DataFrame, DataFrame]:
|
||||
"""
|
||||
Filter the prediction features data and predict with it.
|
||||
:param: unfiltered_dataframe: Full dataframe for the current backtest period.
|
||||
:return:
|
||||
:predictions: np.array of 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)
|
||||
"""
|
||||
|
||||
# logger.info("--------------------Starting prediction--------------------")
|
||||
|
||||
dk.find_features(unfiltered_dataframe)
|
||||
filtered_dataframe, _ = dk.filter_features(
|
||||
unfiltered_dataframe, 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, filtered_dataframe)
|
||||
|
||||
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
|
||||
pred_df = DataFrame(predictions, columns=dk.label_list)
|
||||
|
||||
for label in dk.label_list:
|
||||
pred_df[label] = (
|
||||
(pred_df[label] + 1)
|
||||
* (dk.data["labels_max"][label] - dk.data["labels_min"][label])
|
||||
/ 2
|
||||
) + dk.data["labels_min"][label]
|
||||
|
||||
return (pred_df, dk.do_predict)
|
||||
|
||||
Reference in New Issue
Block a user