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

logged dates for filtered dataframe instead of unfiltered dataframe

Browse Source
This commit is contained in:
robcaulk 2022-07-19 17:45:12 +02:00
parent 1b555ba462
commit 5ad8d08b84
1 changed files with 4 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
freqtrade/freqai/prediction_models/BaseRegressionModel.py
View File

@ -39,11 +39,7 @@ class BaseRegressionModel(IFreqaiModel):
:model: Trained model which can be used to inference (self.predict) :model: Trained model which can be used to inference (self.predict)
""" """
start_date = unfiltered_dataframe["date"].iloc[0]
end_date = unfiltered_dataframe["date"].iloc[-1]
logger.info("-------------------- Starting training " f"{pair} --------------------") logger.info("-------------------- Starting training " f"{pair} --------------------")
logger.info("-------------------- Using data "
f"from {start_date} to {end_date}--------------------")
# filter the features requested by user in the configuration file and elegantly handle NaNs # filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features( features_filtered, labels_filtered = dk.filter_features(
@ -53,6 +49,10 @@ class BaseRegressionModel(IFreqaiModel):
training_filter=True, training_filter=True,
) )
start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d")
logger.info(f"-------------------- Training on data from {start_date} to "
f"{end_date}--------------------")
# split data into train/test data. # split data into train/test data.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered) data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
if not self.freqai_info.get('fit_live_predictions', 0): if not self.freqai_info.get('fit_live_predictions', 0):