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, 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.Base3ActionRLEnv import Base3ActionRLEnv, 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], pair: str, dk: FreqaiDataKitchen): agent_params = self.freqai_info['model_training_parameters'] reward_params = self.freqai_info['model_reward_parameters'] train_df = data_dictionary["train_features"] test_df = data_dictionary["test_features"] eval_freq = agent_params["eval_cycles"] * len(test_df) total_timesteps = agent_params["train_cycles"] * len(train_df) learning_rate = agent_params["learning_rate"] # price data for model training and evaluation price = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(len(train_df.index)) price_test = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail( len(test_df.index)) env_id = "train_env" num_cpu = int(dk.thread_count / 2) train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, price, reward_params, self.CONV_WIDTH) for i in range(num_cpu)]) eval_env_id = 'eval_env' eval_env = SubprocVecEnv([make_env(eval_env_id, i, 1, test_df, price_test, reward_params, self.CONV_WIDTH, monitor=True) for i in range(num_cpu)]) path = dk.data_path stop_train_callback = StopTrainingOnNoModelImprovement(max_no_improvement_evals=5, min_evals=10, verbose=2) callback_on_best = StopTrainingOnRewardThreshold(reward_threshold=-200, verbose=2) eval_callback = EvalCallback(eval_env, best_model_save_path=f"{path}/", log_path=f"{path}/tdqn/logs/", eval_freq=int(eval_freq), deterministic=True, render=True, callback_after_eval=stop_train_callback, callback_on_new_best=callback_on_best, verbose=2) # model arch policy_kwargs = dict(activation_fn=th.nn.ReLU, net_arch=[512, 512, 512]) model = TDQN('TMultiInputPolicy', train_env, policy_kwargs=policy_kwargs, tensorboard_log=f"{path}/tdqn/tensorboard/", learning_rate=learning_rate, gamma=0.9, target_update_interval=5000, buffer_size=50000, exploration_initial_eps=1, exploration_final_eps=0.1, replay_buffer_class=ReplayBuffer ) model.learn( total_timesteps=int(total_timesteps), callback=eval_callback ) best_model = DQN.load(dk.data_path / "best_model.zip") print('Training finished!') eval_env.close() return best_model class MyRLEnv(Base3ActionRLEnv): """ User can override any function in BaseRLEnv and gym.Env """ def calculate_reward(self, action): if self._last_trade_tick is None: return 0. # close long if (action == Actions.Short.value or action == Actions.Neutral.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)) # close short if (action == Actions.Long.value or action == Actions.Neutral.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)) return 0. # 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.