stable/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py
2022-07-12 19:10:09 +02:00

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