import logging from typing import Any, Dict # Optional import torch as th import numpy as np import gym from typing import Callable from stable_baselines3.common.callbacks import EvalCallback # EvalCallback , StopTrainingOnNoModelImprovement, StopTrainingOnRewardThreshold from stable_baselines3.common.monitor import Monitor from stable_baselines3.common.vec_env import SubprocVecEnv from stable_baselines3.common.utils import set_random_seed from stable_baselines3 import DQN from freqtrade.freqai.RL.Base5ActionRLEnv import Base5ActionRLEnv, Actions, Positions from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel from freqtrade.freqai.RL.TDQNagent import TDQN from stable_baselines3.common.buffers import ReplayBuffer from freqtrade.freqai.data_kitchen import FreqaiDataKitchen logger = logging.getLogger(__name__) def make_env(env_id: str, rank: int, seed: int, train_df, price, reward_params, window_size, monitor=False) -> 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) if monitor: env = Monitor(env, ".") return env set_random_seed(seed) return _init class ReinforcementLearningTDQN_multiproc(BaseReinforcementLearningModel): """ User created Reinforcement Learning Model prediction model. """ def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen): train_df = data_dictionary["train_features"] test_df = data_dictionary["test_features"] eval_freq = self.freqai_info["rl_config"]["eval_cycles"] * len(test_df) total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df) path = dk.data_path eval_callback = EvalCallback(self.eval_env, best_model_save_path=f"{path}/", log_path=f"{path}/tdqn/logs/", eval_freq=int(eval_freq), deterministic=True, render=False) # model arch policy_kwargs = dict(activation_fn=th.nn.ReLU, net_arch=[512, 512, 512]) model = TDQN('TMultiInputPolicy', self.train_env, policy_kwargs=policy_kwargs, tensorboard_log=f"{path}/tdqn/tensorboard/", replay_buffer_class=ReplayBuffer, **self.freqai_info['model_training_parameters'] ) model.learn( total_timesteps=int(total_timesteps), callback=eval_callback ) best_model = DQN.load(dk.data_path / "best_model.zip") print('Training finished!') return best_model def set_train_and_eval_environments(self, data_dictionary, prices_train, prices_test): """ User overrides this in their prediction model if they are custom a MyRLEnv. Othwerwise leaving this will default to Base5ActEnv """ train_df = data_dictionary["train_features"] test_df = data_dictionary["test_features"] # environments if not self.train_env: env_id = "train_env" num_cpu = int(self.freqai_info["data_kitchen_thread_count"] / 2) self.train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, prices_train, self.reward_params, self.CONV_WIDTH) for i in range(num_cpu)]) eval_env_id = 'eval_env' self.eval_env = SubprocVecEnv([make_env(eval_env_id, i, 1, test_df, prices_test, self.reward_params, self.CONV_WIDTH, monitor=True) for i in range(num_cpu)]) else: self.train_env.env_method('reset_env', train_df, prices_train, self.CONV_WIDTH, self.reward_params) self.eval_env.env_method('reset_env', train_df, prices_train, self.CONV_WIDTH, self.reward_params) self.train_env.env_method('reset') self.eval_env.env_method('reset') # User can inherit and customize 5 action environment class MyRLEnv(Base5ActionRLEnv): """ User can override any function in BaseRLEnv and gym.Env. Here the user Adds 5 actions. """ def calculate_reward(self, action): if self._last_trade_tick is None: return 0. # close long if action == Actions.Long_sell.value and self._position == Positions.Long: last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open) current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open) return float(np.log(current_price) - np.log(last_trade_price)) if action == Actions.Long_sell.value and self._position == Positions.Long: if self.close_trade_profit[-1] > self.profit_aim * self.rr: last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open) current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open) return float((np.log(current_price) - np.log(last_trade_price)) * 2) # close short if action == Actions.Short_buy.value and self._position == Positions.Short: last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open) current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open) return float(np.log(last_trade_price) - np.log(current_price)) if action == Actions.Short_buy.value and self._position == Positions.Short: if self.close_trade_profit[-1] > self.profit_aim * self.rr: last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open) current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open) return float((np.log(last_trade_price) - np.log(current_price)) * 2) return 0.