mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-10 18:23:55 +00:00
114 lines
4.3 KiB
Python
114 lines
4.3 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 BaseRegressionModel(IFreqaiModel):
|
|
"""
|
|
User created prediction model. The class needs to override three necessary
|
|
functions, predict(), train(), fit(). The class inherits ModelHandler which
|
|
has its own DataHandler where data is held, saved, loaded, and managed.
|
|
"""
|
|
|
|
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
|
|
|
|
def predict(
|
|
self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
|
|
) -> Tuple[DataFrame, DataFrame]:
|
|
"""
|
|
Filter the prediction features data and predict with it.
|
|
:param: unfiltered_dataframe: Full dataframe for the current backtest period.
|
|
:return:
|
|
:pred_df: dataframe containing the predictions
|
|
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
|
|
data (NaNs) or felt uncertain about data (PCA and DI index)
|
|
"""
|
|
|
|
dk.find_features(unfiltered_dataframe)
|
|
filtered_dataframe, _ = dk.filter_features(
|
|
unfiltered_dataframe, dk.training_features_list, training_filter=False
|
|
)
|
|
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
|
|
dk.data_dictionary["prediction_features"] = filtered_dataframe
|
|
|
|
# optional additional data cleaning/analysis
|
|
self.data_cleaning_predict(dk, filtered_dataframe)
|
|
|
|
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
|
|
pred_df = DataFrame(predictions, columns=dk.label_list)
|
|
|
|
for label in dk.label_list:
|
|
pred_df[label] = (
|
|
(pred_df[label] + 1)
|
|
* (dk.data["labels_max"][label] - dk.data["labels_min"][label])
|
|
/ 2
|
|
) + dk.data["labels_min"][label]
|
|
|
|
return (pred_df, dk.do_predict)
|