79 lines
2.7 KiB
Python
79 lines
2.7 KiB
Python
import logging
|
|
from typing import Tuple
|
|
|
|
from pandas import DataFrame
|
|
|
|
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
|
from freqtrade.freqai.freqai_interface import IFreqaiModel
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class BaseTensorFlowModel(IFreqaiModel):
|
|
"""
|
|
Base class for TensorFlow type models.
|
|
User *must* inherit from this class and set fit() and predict().
|
|
"""
|
|
|
|
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 datakitchen
|
|
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} --------------------")
|
|
|
|
# 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)
|
|
if not self.freqai_info.get('fit_live_predictions', 0):
|
|
dk.fit_labels()
|
|
# 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)
|
|
|
|
if pair not in self.dd.historic_predictions:
|
|
self.set_initial_historic_predictions(
|
|
data_dictionary['train_features'], model, dk, pair)
|
|
elif self.freqai_info.get('fit_live_predictions_candles', 0):
|
|
dk.fit_live_predictions()
|
|
|
|
self.dd.save_historic_predictions_to_disk()
|
|
|
|
logger.info(f"--------------------done training {pair}--------------------")
|
|
|
|
return model
|