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Feat/freqai (#7105)

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Vectorize weight setting, log training dates

Co-authored-by: robcaulk <rob.caulk@gmail.com>
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lolong 2022-07-19 17:49:18 +02:00 committed by GitHub
parent 714d9534b6
commit 9c051958a6
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2 changed files with 8 additions and 7 deletions
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9
freqtrade/freqai/data_kitchen.py
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@ -680,12 +680,9 @@ class FreqaiDataKitchen:
Set weights so that recent data is more heavily weighted during
training than older data.
"""
weights = np.zeros(num_weights)
for i in range(1, len(weights)):
weights[len(weights) - i] = np.exp(
-i / (self.config["freqai"]["feature_parameters"]["weight_factor"] * num_weights)
)
wfactor = self.config["freqai"]["feature_parameters"]["weight_factor"]
weights = np.exp(
- np.arange(num_weights) / (wfactor * num_weights))[::-1]
return weights
def append_predictions(self, predictions, do_predict, len_dataframe):

6
freqtrade/freqai/prediction_models/BaseRegressionModel.py
View File

@ -39,7 +39,7 @@ class BaseRegressionModel(IFreqaiModel):
:model: Trained model which can be used to inference (self.predict)
"""
logger.info("--------------------Starting training " f"{pair} --------------------")
logger.info("-------------------- Starting training " f"{pair} --------------------")
# filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features(
@ -49,6 +49,10 @@ class BaseRegressionModel(IFreqaiModel):
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.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
if not self.freqai_info.get('fit_live_predictions', 0):