124 lines
5.0 KiB
Python
124 lines
5.0 KiB
Python
import logging
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from typing import Any, Dict
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import torch as th
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
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from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
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from pathlib import Path
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from pandas import DataFrame
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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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import numpy as np
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logger = logging.getLogger(__name__)
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class ReinforcementLearner(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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total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
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policy_kwargs = dict(activation_fn=th.nn.ReLU,
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net_arch=[512, 512, 256])
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if dk.pair not in self.dd.model_dictionary or not self.continual_retraining:
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model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
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tensorboard_log=Path(dk.data_path / "tensorboard"),
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**self.freqai_info['model_training_parameters']
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)
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else:
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logger.info('Continual training activated - starting training from previously '
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'trained agent.')
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model = self.dd.model_dictionary[dk.pair]
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model.tensorboard_log = Path(dk.data_path / "tensorboard")
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model.set_env(self.train_env)
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model.learn(
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total_timesteps=int(total_timesteps),
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callback=self.eval_callback
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)
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if Path(dk.data_path / "best_model.zip").is_file():
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logger.info('Callback found a best model.')
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best_model = self.MODELCLASS.load(dk.data_path / "best_model")
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return best_model
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logger.info('Couldnt find best model, using final model instead.')
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return model
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def set_train_and_eval_environments(self, data_dictionary: Dict[str, DataFrame],
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prices_train: DataFrame, prices_test: DataFrame,
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dk: FreqaiDataKitchen):
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"""
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User can override this 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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eval_freq = self.freqai_info["rl_config"]["eval_cycles"] * len(test_df)
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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, config=self.config)
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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, config=self.config))
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self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
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render=False, eval_freq=eval_freq,
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best_model_save_path=str(dk.data_path))
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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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sets a custom reward based on profit and trade duration.
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"""
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def calculate_reward(self, action):
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# first, penalize if the action is not valid
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if not self._is_valid(action):
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return -15
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pnl = self.get_unrealized_profit()
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rew = np.sign(pnl) * (pnl + 1)
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factor = 100
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# reward agent for entering trades
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if action in (Actions.Long_enter.value, Actions.Short_enter.value):
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return 25
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# discourage agent from not entering trades
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if action == Actions.Neutral.value and self._position == Positions.Neutral:
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return -15
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max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
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trade_duration = self._current_tick - self._last_trade_tick
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if trade_duration <= max_trade_duration:
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factor *= 1.5
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elif trade_duration > max_trade_duration:
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factor *= 0.5
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# discourage sitting in position
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if self._position in (Positions.Short, Positions.Long):
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return -50 * trade_duration / max_trade_duration
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# close long
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if action == Actions.Long_exit.value and self._position == Positions.Long:
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if pnl > self.profit_aim * self.rr:
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factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
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return float(rew * factor)
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# close short
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if action == Actions.Short_exit.value and self._position == Positions.Short:
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if pnl > self.profit_aim * self.rr:
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factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
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return float(rew * factor)
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return 0.
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