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improve data persistence/mapping for live/dry. This accommodates quick reloads after crash and handles multi-pair cleanly

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robcaulk
2022-05-23 21:05:05 +02:00
parent e1c068ca66
commit b0d2d13eb1
4 changed files with 199 additions and 131 deletions
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46
freqtrade/freqai/prediction_models/CatboostPredictionModel.py
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@@ -140,49 +140,3 @@ class CatboostPredictionModel(IFreqaiModel):
# logger.info("--------------------Finished prediction--------------------")
return (self.dh.predictions, self.dh.do_predict)
def data_cleaning_train(self) -> None:
"""
User can add data analysis and cleaning here.
Any function inside this method should drop training data points from the filtered_dataframe
based on user decided logic. See FreqaiDataKitchen::remove_outliers() for an example
of how outlier data points are dropped from the dataframe used for training.
"""
if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'):
self.dh.principal_component_analysis()
# if self.feature_parameters["determine_statistical_distributions"]:
# self.dh.determine_statistical_distributions()
# if self.feature_parameters["remove_outliers"]:
# self.dh.remove_outliers(predict=False)
if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'):
self.dh.use_SVM_to_remove_outliers(predict=False)
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'):
self.dh.data["avg_mean_dist"] = self.dh.compute_distances()
def data_cleaning_predict(self, filtered_dataframe: DataFrame) -> None:
"""
User can add data analysis and cleaning here.
These functions each modify self.dh.do_predict, which is a dataframe with equal length
to the number of candles coming from and returning to the strategy. Inside do_predict,
1 allows prediction and < 0 signals to the strategy that the model is not confident in
the prediction.
See FreqaiDataKitchen::remove_outliers() for an example
of how the do_predict vector is modified. do_predict is ultimately passed back to strategy
for buy signals.
"""
if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'):
self.dh.pca_transform()
# if self.feature_parameters["determine_statistical_distributions"]:
# self.dh.determine_statistical_distributions()
# if self.feature_parameters["remove_outliers"]:
# self.dh.remove_outliers(predict=True) # creates dropped index
if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'):
self.dh.use_SVM_to_remove_outliers(predict=True)
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'):
self.dh.check_if_pred_in_training_spaces() # sets do_predict