import logging from pathlib import Path from typing import Any, Dict import numpy as np import torch as th from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel logger = logging.getLogger(__name__) class ReinforcementLearner(BaseReinforcementLearningModel): """ User created Reinforcement Learning Model prediction model. """ def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs): """ User customizable fit method :params: data_dictionary: dict = common data dictionary containing all train/test features/labels/weights. dk: FreqaiDatakitchen = data kitchen for current pair. :returns: model: Any = trained model to be used for inference in dry/live/backtesting """ train_df = data_dictionary["train_features"] total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df) policy_kwargs = dict(activation_fn=th.nn.ReLU, net_arch=[128, 128]) if dk.pair not in self.dd.model_dictionary or not self.continual_learning: model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs, tensorboard_log=Path( dk.full_path / "tensorboard" / dk.pair.split('/')[0]), **self.freqai_info['model_training_parameters'] ) else: logger.info('Continual training activated - starting training from previously ' 'trained agent.') model = self.dd.model_dictionary[dk.pair] model.set_env(self.train_env) model.learn( total_timesteps=int(total_timesteps), callback=self.eval_callback ) if Path(dk.data_path / "best_model.zip").is_file(): logger.info('Callback found a best model.') best_model = self.MODELCLASS.load(dk.data_path / "best_model") return best_model logger.info('Couldnt find best model, using final model instead.') return model 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): """ An example reward function. This is the one function that users will likely wish to inject their own creativity into. :params: action: int = The action made by the agent for the current candle. :returns: float = the reward to give to the agent for current step (used for optimization of weights in NN) """ # 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.