116 lines
5.0 KiB
Python
116 lines
5.0 KiB
Python
import logging
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from typing import Any, Dict # Optional
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import torch as th
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from stable_baselines3.common.callbacks import EvalCallback
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from stable_baselines3.common.monitor import Monitor
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from freqtrade.freqai.RL.Base5ActionRLEnv import Base5ActionRLEnv, 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 import DQN
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from stable_baselines3.common.buffers import ReplayBuffer
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import numpy as np
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import gc
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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logger = logging.getLogger(__name__)
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class ReinforcementLearningTDQN(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}/tdqn/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=[256, 256, 128])
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model = TDQN('TMultiInputPolicy', self.train_env,
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tensorboard_log=f"{path}/tdqn/tensorboard/",
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policy_kwargs=policy_kwargs,
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replay_buffer_class=ReplayBuffer,
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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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del model
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best_model = DQN.load(dk.data_path / "best_model")
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print('Training finished!')
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gc.collect()
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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 as shown here if they are using a custom MyRLEnv
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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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self.train_env = MyRLEnv(df=train_df, prices=prices_train, window_size=self.CONV_WIDTH,
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reward_kwargs=self.reward_params)
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self.eval_env = Monitor(MyRLEnv(df=test_df, prices=prices_test,
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window_size=self.CONV_WIDTH,
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reward_kwargs=self.reward_params), ".")
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else:
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self.train_env.reset_env(train_df, prices_train, self.CONV_WIDTH, self.reward_params)
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self.eval_env.reset_env(train_df, prices_train, self.CONV_WIDTH, self.reward_params)
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self.train_env.reset()
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self.eval_env.reset()
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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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