import logging from typing import Any, Dict, Tuple import numpy as np import numpy.typing as npt import pandas as pd from pandas import DataFrame from abc import abstractmethod from freqtrade.exceptions import OperationalException from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from freqtrade.freqai.freqai_interface import IFreqaiModel from freqtrade.freqai.RL.Base5ActionRLEnv import Base5ActionRLEnv, Actions, Positions from freqtrade.persistence import Trade import torch.multiprocessing from stable_baselines3.common.callbacks import EvalCallback from stable_baselines3.common.monitor import Monitor import torch as th from typing import Callable from datetime import datetime, timezone from stable_baselines3.common.utils import set_random_seed import gym logger = logging.getLogger(__name__) torch.multiprocessing.set_sharing_strategy('file_system') SB3_MODELS = ['PPO', 'A2C', 'DQN'] SB3_CONTRIB_MODELS = ['TRPO', 'ARS', 'RecurrentPPO', 'MaskablePPO'] class BaseReinforcementLearningModel(IFreqaiModel): """ User created Reinforcement Learning Model prediction model. """ def __init__(self, **kwargs): super().__init__(config=kwargs['config']) th.set_num_threads(self.freqai_info['rl_config'].get('thread_count', 4)) self.reward_params = self.freqai_info['rl_config']['model_reward_parameters'] self.train_env: Base5ActionRLEnv = None self.eval_env: Base5ActionRLEnv = None self.eval_callback: EvalCallback = None self.model_type = self.freqai_info['rl_config']['model_type'] self.rl_config = self.freqai_info['rl_config'] self.continual_learning = self.rl_config.get('continual_learning', False) if self.model_type in SB3_MODELS: import_str = 'stable_baselines3' elif self.model_type in SB3_CONTRIB_MODELS: import_str = 'sb3_contrib' else: raise OperationalException(f'{self.model_type} not available in stable_baselines3 or ' f'sb3_contrib. please choose one of {SB3_MODELS} or ' f'{SB3_CONTRIB_MODELS}') mod = __import__(import_str, fromlist=[ self.model_type]) self.MODELCLASS = getattr(mod, self.model_type) self.policy_type = self.freqai_info['rl_config']['policy_type'] 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} --------------------") 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() # FIXME useless for now, but just satiating append methods # normalize all data based on train_dataset only prices_train, prices_test = self.build_ohlc_price_dataframes(dk.data_dictionary, pair, dk) data_dictionary = dk.normalize_data(data_dictionary) # data cleaning/analysis self.data_cleaning_train(dk) logger.info( f'Training model on {len(dk.data_dictionary["train_features"].columns)}' f' features and {len(data_dictionary["train_features"])} data points' ) self.set_train_and_eval_environments(data_dictionary, prices_train, prices_test, dk) model = self.fit_rl(data_dictionary, dk) logger.info(f"--------------------done training {pair}--------------------") return model def set_train_and_eval_environments(self, data_dictionary: Dict[str, DataFrame], prices_train: DataFrame, prices_test: DataFrame, dk: FreqaiDataKitchen): """ User can override this if they are using a custom MyRLEnv """ train_df = data_dictionary["train_features"] test_df = data_dictionary["test_features"] self.train_env = MyRLEnv(df=train_df, prices=prices_train, window_size=self.CONV_WIDTH, reward_kwargs=self.reward_params, config=self.config) self.eval_env = Monitor(MyRLEnv(df=test_df, prices=prices_test, window_size=self.CONV_WIDTH, reward_kwargs=self.reward_params, config=self.config)) self.eval_callback = EvalCallback(self.eval_env, deterministic=True, render=False, eval_freq=len(train_df), best_model_save_path=str(dk.data_path)) @abstractmethod def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen): """ Agent customizations and abstract Reinforcement Learning customizations go in here. Abstract method, so this function must be overridden by user class. """ return def get_state_info(self, pair: str): open_trades = Trade.get_trades_proxy(is_open=True) market_side = 0.5 current_profit: float = 0 trade_duration = 0 for trade in open_trades: if trade.pair == pair: # FIXME: mypy typing doesnt like that strategy may be "None" (it never will be) # FIXME: get_rate and trade_udration shouldn't work with backtesting, # we need to use candle dates and prices to compute that. current_value = self.strategy.dp._exchange.get_rate( pair, refresh=False, side="exit", is_short=trade.is_short) openrate = trade.open_rate now = datetime.now(timezone.utc).timestamp() trade_duration = int((now - trade.open_date.timestamp()) / self.base_tf_seconds) if 'long' in str(trade.enter_tag): market_side = 1 current_profit = (current_value - openrate) / openrate else: market_side = 0 current_profit = (openrate - current_value) / openrate # total_profit = 0 # closed_trades = Trade.get_trades_proxy(pair=pair, is_open=False) # for trade in closed_trades: # total_profit += trade.close_profit return market_side, current_profit, int(trade_duration) 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.zeros(len(dataframe)), columns=dk.label_list) def _predict(window): market_side, current_profit, trade_duration = self.get_state_info(dk.pair) observations = dataframe.iloc[window.index] observations['current_profit'] = current_profit observations['position'] = market_side observations['trade_duration'] = trade_duration res, _ = model.predict(observations, deterministic=True) return res output = output.rolling(window=self.CONV_WIDTH).apply(_predict) return output def build_ohlc_price_dataframes(self, data_dictionary: dict, pair: str, dk: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]: """ Builds the train prices and test prices for the environment. """ coin = pair.split('/')[0] train_df = data_dictionary["train_features"] test_df = data_dictionary["test_features"] # price data for model training and evaluation tf = self.config['timeframe'] ohlc_list = [f'%-{coin}raw_open_{tf}', f'%-{coin}raw_low_{tf}', f'%-{coin}raw_high_{tf}', f'%-{coin}raw_close_{tf}'] rename_dict = {f'%-{coin}raw_open_{tf}': 'open', f'%-{coin}raw_low_{tf}': 'low', f'%-{coin}raw_high_{tf}': ' high', f'%-{coin}raw_close_{tf}': 'close'} prices_train = train_df.filter(ohlc_list, axis=1) prices_train.rename(columns=rename_dict, inplace=True) prices_train.reset_index(drop=True) prices_test = test_df.filter(ohlc_list, axis=1) prices_test.rename(columns=rename_dict, inplace=True) prices_test.reset_index(drop=True) return prices_train, prices_test # TODO take care of this appendage. Right now it needs to be called because FreqAI enforces it. # But FreqaiRL needs more objects passed to fit() (like DK) and we dont want to go refactor # all the other existing fit() functions to include dk argument. For now we instantiate and # leave it. def fit(self, data_dictionary: Dict[str, Any], pair: str = '') -> Any: return def make_env(env_id: str, rank: int, seed: int, train_df: DataFrame, price: DataFrame, reward_params: Dict[str, int], window_size: int, monitor: bool = False, config: Dict[str, Any] = {}) -> Callable: """ Utility function for multiprocessed env. :param env_id: (str) the environment ID :param num_env: (int) the number of environment you wish to have in subprocesses :param seed: (int) the inital seed for RNG :param rank: (int) index of the subprocess :return: (Callable) """ def _init() -> gym.Env: env = MyRLEnv(df=train_df, prices=price, window_size=window_size, reward_kwargs=reward_params, id=env_id, seed=seed + rank, config=config) if monitor: env = Monitor(env) return env set_random_seed(seed) return _init class MyRLEnv(Base5ActionRLEnv): """ User can override any function in BaseRLEnv and gym.Env. Here the user sets a custom reward based on profit and trade duration. """ def calculate_reward(self, action): # first, penalize if the action is not valid if not self._is_valid(action): return -2 pnl = self.get_unrealized_profit() rew = np.sign(pnl) * (pnl + 1) factor = 100 # reward agent for entering trades if action in (Actions.Long_enter.value, Actions.Short_enter.value) \ and self._position == Positions.Neutral: return 25 # discourage agent from not entering trades if action == Actions.Neutral.value and self._position == Positions.Neutral: return -1 max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300) trade_duration = self._current_tick - self._last_trade_tick if trade_duration <= max_trade_duration: factor *= 1.5 elif trade_duration > max_trade_duration: factor *= 0.5 # discourage sitting in position if self._position in (Positions.Short, Positions.Long) and action == Actions.Neutral.value: return -1 * trade_duration / max_trade_duration # close long if action == Actions.Long_exit.value and self._position == Positions.Long: if pnl > self.profit_aim * self.rr: factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2) return float(rew * factor) # close short if action == Actions.Short_exit.value and self._position == Positions.Short: if pnl > self.profit_aim * self.rr: factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2) return float(rew * factor) return 0.