stable/freqtrade/freqai/prediction_models/ReinforcementLearningModel.py

158 lines
5.9 KiB
Python
Raw Normal View History

import logging
from typing import Any, Tuple, Dict
from freqtrade.freqai.prediction_models.RL.RLPrediction_env import GymAnytrading
from freqtrade.freqai.prediction_models.RL.RLPrediction_agent import RLPrediction_agent
from pandas import DataFrame
import pandas as pd
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
import numpy as np
import numpy.typing as npt
from freqtrade.freqai.freqai_interface import IFreqaiModel
logger = logging.getLogger(__name__)
class ReinforcementLearningModel(IFreqaiModel):
"""
User created Reinforcement Learning Model prediction model.
"""
def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
) -> Any:
"""
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.
:param unfiltered_dataframe: Full dataframe for the current training period
:param 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,
)
data_dictionary: Dict[str, Any] = dk.make_train_test_datasets(
features_filtered, labels_filtered)
dk.fit_labels() # useless for now, but just satiating append methods
# 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, pair)
if pair not in self.dd.historic_predictions:
self.set_initial_historic_predictions(
data_dictionary['train_features'], model, dk, pair)
self.dd.save_historic_predictions_to_disk()
logger.info(f"--------------------done training {pair}--------------------")
return model
def fit(self, data_dictionary: Dict[str, Any], pair: str = ''):
train_df = data_dictionary["train_features"]
sep = '/'
coin = pair.split(sep, 1)[0]
price = train_df[f"%-{coin}raw_price_{self.config['timeframe']}"]
price.reset_index(inplace=True, drop=True)
model_name = 'ppo'
env_instance = GymAnytrading(train_df, price, self.CONV_WIDTH)
agent_params = self.freqai_info['model_training_parameters']
total_timesteps = agent_params.get('total_timesteps', 1000)
agent = RLPrediction_agent(env_instance)
model = agent.get_model(model_name, model_kwargs=agent_params)
trained_model = agent.train_model(model=model,
tb_log_name=model_name,
total_timesteps=total_timesteps)
print('Training finished!')
return trained_model
def predict(
self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
"""
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)
pred_df = self.rl_model_predict(dk.data_dictionary["prediction_features"], dk, self.model)
pred_df.fillna(0, inplace=True)
return (pred_df, dk.do_predict)
def rl_model_predict(self, dataframe: DataFrame,
dk: FreqaiDataKitchen, model: Any) -> DataFrame:
output = pd.DataFrame(np.full((len(dataframe), 1), 2), columns=dk.label_list)
def _predict(window):
observations = dataframe.iloc[window.index]
res, _ = model.predict(observations, deterministic=True)
return res
output = output.rolling(window=self.CONV_WIDTH).apply(_predict)
return output
def set_initial_historic_predictions(
self, df: DataFrame, model: Any, dk: FreqaiDataKitchen, pair: str
) -> None:
pred_df = self.rl_model_predict(df, dk, model)
pred_df.fillna(0, inplace=True)
self.dd.historic_predictions[pair] = pred_df
hist_preds_df = self.dd.historic_predictions[pair]
for label in hist_preds_df.columns:
if hist_preds_df[label].dtype == object:
continue
hist_preds_df[f'{label}_mean'] = 0
hist_preds_df[f'{label}_std'] = 0
hist_preds_df['do_predict'] = 0
if self.freqai_info['feature_parameters'].get('DI_threshold', 0) > 0:
hist_preds_df['DI_values'] = 0
for return_str in dk.data['extra_returns_per_train']:
hist_preds_df[return_str] = 0