mirror of
https://github.com/freqtrade/freqtrade.git
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133 lines
5.2 KiB
Python
133 lines
5.2 KiB
Python
import logging
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from typing import Any, Dict # , Tuple
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import numpy as np
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# import numpy.typing as npt
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import torch as th
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from stable_baselines3.common.monitor import Monitor
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from typing import Callable
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from stable_baselines3 import PPO
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from stable_baselines3.common.callbacks import EvalCallback
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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 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.data_kitchen import FreqaiDataKitchen
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import gym
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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 ReinforcementLearningPPO_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], dk: FreqaiDataKitchen):
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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 = self.freqai_info["rl_config"]["eval_cycles"] * len(test_df)
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total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
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path = dk.data_path
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eval_callback = EvalCallback(self.eval_env, best_model_save_path=f"{path}/",
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log_path=f"{path}/ppo/logs/", eval_freq=int(eval_freq),
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deterministic=True, render=False)
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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 = PPO('MlpPolicy', self.train_env, policy_kwargs=policy_kwargs,
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tensorboard_log=f"{path}/ppo/tensorboard/",
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**self.freqai_info['model_training_parameters']
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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 = PPO.load(dk.data_path / "best_model")
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print('Training finished!')
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return best_model
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def set_train_and_eval_environments(self, data_dictionary, prices_train, prices_test):
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"""
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User overrides this in their prediction model if they are custom a MyRLEnv. Othwerwise
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leaving this will default to Base5ActEnv
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"""
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train_df = data_dictionary["train_features"]
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test_df = data_dictionary["test_features"]
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# environments
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if not self.train_env:
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env_id = "train_env"
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num_cpu = int(self.freqai_info["data_kitchen_thread_count"] / 2)
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self.train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, prices_train,
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self.reward_params, self.CONV_WIDTH) for i
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in range(num_cpu)])
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eval_env_id = 'eval_env'
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self.eval_env = SubprocVecEnv([make_env(eval_env_id, i, 1, test_df, prices_test,
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self.reward_params, self.CONV_WIDTH, monitor=True) for i
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in range(num_cpu)])
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else:
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self.train_env.env_method('reset_env', train_df, prices_train,
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self.CONV_WIDTH, self.reward_params)
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self.eval_env.env_method('reset_env', train_df, prices_train,
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self.CONV_WIDTH, self.reward_params)
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self.train_env.env_method('reset')
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self.eval_env.env_method('reset')
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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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