165 lines
7.3 KiB
Python
165 lines
7.3 KiB
Python
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import logging
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from typing import Any, Dict # Optional
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import torch as th
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import numpy as np
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import gym
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from typing import Callable
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from stable_baselines3.common.callbacks import EvalCallback, StopTrainingOnNoModelImprovement, StopTrainingOnRewardThreshold
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from stable_baselines3.common.monitor import Monitor
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from stable_baselines3.common.vec_env import SubprocVecEnv
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from stable_baselines3.common.utils import set_random_seed
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from stable_baselines3 import DQN
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from freqtrade.freqai.RL.Base3ActionRLEnv import Base3ActionRLEnv, Actions, Positions
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from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
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from freqtrade.freqai.RL.TDQNagent import TDQN
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from stable_baselines3.common.buffers import ReplayBuffer
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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logger = logging.getLogger(__name__)
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def make_env(env_id: str, rank: int, seed: int, train_df, price,
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reward_params, window_size, monitor=False) -> Callable:
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"""
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Utility function for multiprocessed env.
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:param env_id: (str) the environment ID
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:param num_env: (int) the number of environment you wish to have in subprocesses
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:param seed: (int) the inital seed for RNG
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:param rank: (int) index of the subprocess
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:return: (Callable)
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"""
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def _init() -> gym.Env:
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env = MyRLEnv(df=train_df, prices=price, window_size=window_size,
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reward_kwargs=reward_params, id=env_id, seed=seed + rank)
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if monitor:
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env = Monitor(env, ".")
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return env
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set_random_seed(seed)
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return _init
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class ReinforcementLearningTDQN_multiproc(BaseReinforcementLearningModel):
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"""
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User created Reinforcement Learning Model prediction model.
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"""
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def fit_rl(self, data_dictionary: Dict[str, Any], pair: str, dk: FreqaiDataKitchen):
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agent_params = self.freqai_info['model_training_parameters']
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reward_params = self.freqai_info['model_reward_parameters']
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train_df = data_dictionary["train_features"]
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test_df = data_dictionary["test_features"]
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eval_freq = agent_params["eval_cycles"] * len(test_df)
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total_timesteps = agent_params["train_cycles"] * len(train_df)
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learning_rate = agent_params["learning_rate"]
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# price data for model training and evaluation
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price = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(len(train_df.index))
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price_test = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(
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len(test_df.index))
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env_id = "train_env"
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num_cpu = int(dk.thread_count / 2)
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train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, price, reward_params,
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self.CONV_WIDTH) for i in range(num_cpu)])
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eval_env_id = 'eval_env'
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eval_env = SubprocVecEnv([make_env(eval_env_id, i, 1, test_df, price_test, reward_params,
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self.CONV_WIDTH, monitor=True) for i in range(num_cpu)])
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path = dk.data_path
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stop_train_callback = StopTrainingOnNoModelImprovement(max_no_improvement_evals=5, min_evals=10, verbose=2)
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callback_on_best = StopTrainingOnRewardThreshold(reward_threshold=-200, verbose=2)
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eval_callback = EvalCallback(eval_env, best_model_save_path=f"{path}/",
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log_path=f"{path}/tdqn/logs/", eval_freq=int(eval_freq),
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deterministic=True, render=True, callback_after_eval=stop_train_callback, callback_on_new_best=callback_on_best, verbose=2)
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# model arch
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policy_kwargs = dict(activation_fn=th.nn.ReLU,
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net_arch=[512, 512, 512])
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model = TDQN('TMultiInputPolicy', train_env,
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policy_kwargs=policy_kwargs,
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tensorboard_log=f"{path}/tdqn/tensorboard/",
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learning_rate=learning_rate, gamma=0.9,
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target_update_interval=5000, buffer_size=50000,
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exploration_initial_eps=1, exploration_final_eps=0.1,
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replay_buffer_class=ReplayBuffer
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)
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model.learn(
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total_timesteps=int(total_timesteps),
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callback=eval_callback
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)
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best_model = DQN.load(dk.data_path / "best_model.zip")
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print('Training finished!')
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eval_env.close()
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return best_model
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class MyRLEnv(Base3ActionRLEnv):
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"""
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User can override any function in BaseRLEnv and gym.Env
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"""
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def calculate_reward(self, action):
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if self._last_trade_tick is None:
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return 0.
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# close long
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if (action == Actions.Short.value or
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action == Actions.Neutral.value) and self._position == Positions.Long:
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last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
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current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
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return float(np.log(current_price) - np.log(last_trade_price))
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# close short
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if (action == Actions.Long.value or
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action == Actions.Neutral.value) and self._position == Positions.Short:
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last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
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current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
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return float(np.log(last_trade_price) - np.log(current_price))
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return 0.
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# User can inherit and customize 5 action environment
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# class MyRLEnv(Base5ActionRLEnv):
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# """
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# User can override any function in BaseRLEnv and gym.Env. Here the user
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# Adds 5 actions.
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# """
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# def calculate_reward(self, action):
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# if self._last_trade_tick is None:
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# return 0.
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# # close long
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# if action == Actions.Long_sell.value and self._position == Positions.Long:
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# last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
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# current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
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# return float(np.log(current_price) - np.log(last_trade_price))
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# if action == Actions.Long_sell.value and self._position == Positions.Long:
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# if self.close_trade_profit[-1] > self.profit_aim * self.rr:
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# last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
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# current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
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# return float((np.log(current_price) - np.log(last_trade_price)) * 2)
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# # close short
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# if action == Actions.Short_buy.value and self._position == Positions.Short:
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# last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
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# current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
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# return float(np.log(last_trade_price) - np.log(current_price))
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# if action == Actions.Short_buy.value and self._position == Positions.Short:
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# if self.close_trade_profit[-1] > self.profit_aim * self.rr:
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# last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
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# current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
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# return float((np.log(last_trade_price) - np.log(current_price)) * 2)
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# return 0.
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