import logging from pathlib import Path from typing import Any, Dict, List, Optional, Type import torch as th from stable_baselines3.common.callbacks import ProgressBarCallback from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel logger = logging.getLogger(__name__) class ReinforcementLearner(BaseReinforcementLearningModel): """ Reinforcement Learning Model prediction model. Users can inherit from this class to make their own RL model with custom environment/training controls. Define the file as follows: ``` from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner class MyCoolRLModel(ReinforcementLearner): ``` Save the file to `user_data/freqaimodels`, then run it with: freqtrade trade --freqaimodel MyCoolRLModel --config config.json --strategy SomeCoolStrat Here the users can override any of the functions available in the `IFreqaiModel` inheritance tree. Most importantly for RL, this is where the user overrides `MyRLEnv` (see below), to define custom `calculate_reward()` function, or to override any other parts of the environment. This class also allows users to override any other part of the IFreqaiModel tree. For example, the user can override `def fit()` or `def train()` or `def predict()` to take fine-tuned control over these processes. Another common override may be `def data_cleaning_predict()` where the user can take fine-tuned control over the data handling pipeline. """ def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs): """ User customizable fit method :param data_dictionary: dict = common data dictionary containing all train/test features/labels/weights. :param dk: FreqaiDatakitchen = data kitchen for current pair. :return: 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=self.net_arch) if self.activate_tensorboard: tb_path = Path(dk.full_path / "tensorboard" / dk.pair.split('/')[0]) else: tb_path = None 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=tb_path, **self.freqai_info.get('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) callbacks: List[Any] = [self.eval_callback, self.tensorboard_callback] progressbar_callback: Optional[ProgressBarCallback] = None if self.rl_config.get('progress_bar', False): progressbar_callback = ProgressBarCallback() callbacks.insert(0, progressbar_callback) try: model.learn( total_timesteps=int(total_timesteps), callback=callbacks, ) finally: if progressbar_callback: progressbar_callback.on_training_end() 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("Couldn't find best model, using final model instead.") return model MyRLEnv: Type[BaseEnvironment] class MyRLEnv(Base5ActionRLEnv): # type: ignore[no-redef] """ 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: int) -> float: """ An example reward function. This is the one function that users will likely wish to inject their own creativity into. Warning! This is function is a showcase of functionality designed to show as many possible environment control features as possible. It is also designed to run quickly on small computers. This is a benchmark, it is *not* for live production. :param action: int = The action made by the agent for the current candle. :return: 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): self.tensorboard_log("invalid", category="actions") return -2 pnl = self.get_unrealized_profit() factor = 100. # reward agent for entering trades if (action == Actions.Long_enter.value and self._position == Positions.Neutral): return 25 if (action == 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 # type: ignore 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(pnl * 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(pnl * factor) return 0.