adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

ensure ohlc is dropped from both train and predict

Browse Source
This commit is contained in:
robcaulk 2023-03-08 11:26:28 +01:00
parent d9dc831772
commit 29d337fa02
1 changed files with 18 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

23
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
View File

@ -235,6 +235,9 @@ class BaseReinforcementLearningModel(IFreqaiModel):
filtered_dataframe, _ = dk.filter_features( filtered_dataframe, _ = dk.filter_features(
unfiltered_df, dk.training_features_list, training_filter=False unfiltered_df, dk.training_features_list, training_filter=False
) )
filtered_dataframe = self.drop_ohlc_from_df(filtered_dataframe, dk)
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe) filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
dk.data_dictionary["prediction_features"] = filtered_dataframe dk.data_dictionary["prediction_features"] = filtered_dataframe
@ -314,14 +317,24 @@ class BaseReinforcementLearningModel(IFreqaiModel):
prices_test.rename(columns=rename_dict, inplace=True) prices_test.rename(columns=rename_dict, inplace=True)
prices_test.reset_index(drop=True) prices_test.reset_index(drop=True)
if self.rl_config["drop_ohlc_from_features"]: train_df = self.drop_ohlc_from_df(train_df, dk)
train_df.drop(rename_dict.keys(), axis=1, inplace=True) test_df = self.drop_ohlc_from_df(test_df, dk)
test_df.drop(rename_dict.keys(), axis=1, inplace=True)
feature_list = dk.training_features_list
feature_list = [e for e in feature_list if e not in rename_dict.keys()]
return prices_train, prices_test return prices_train, prices_test
def drop_ohlc_from_df(self, df: DataFrame, dk: FreqaiDataKitchen):
"""
Given a dataframe, drop the ohlc data
"""
drop_list = ['%-raw_open', '%-raw_low', '%-raw_high', '%-raw_close']
if self.rl_config["drop_ohlc_from_features"]:
df.drop(drop_list, axis=1, inplace=True)
feature_list = dk.training_features_list
feature_list = [e for e in feature_list if e not in drop_list]
return df
def load_model_from_disk(self, dk: FreqaiDataKitchen) -> Any: def load_model_from_disk(self, dk: FreqaiDataKitchen) -> Any:
""" """
Can be used by user if they are trying to limit_ram_usage *and* Can be used by user if they are trying to limit_ram_usage *and*