180 lines
7.5 KiB
Python
180 lines
7.5 KiB
Python
import logging
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from pathlib import Path
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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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logger = logging.getLogger(__name__)
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class ReinforcementLearner(BaseReinforcementLearningModel):
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"""
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Reinforcement Learning Model prediction model.
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Users can inherit from this class to make their own RL model with custom
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environment/training controls. Define the file as follows:
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```
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from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
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class MyCoolRLModel(ReinforcementLearner):
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```
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Save the file to `user_data/freqaimodels`, then run it with:
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freqtrade trade --freqaimodel MyCoolRLModel --config config.json --strategy SomeCoolStrat
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Here the users can override any of the functions
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available in the `IFreqaiModel` inheritance tree. Most importantly for RL, this
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is where the user overrides `MyRLEnv` (see below), to define custom
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`calculate_reward()` function, or to override any other parts of the environment.
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This class also allows users to override any other part of the IFreqaiModel tree.
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For example, the user can override `def fit()` or `def train()` or `def predict()`
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to take fine-tuned control over these processes.
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Another common override may be `def data_cleaning_predict()` where the user can
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take fine-tuned control over the data handling pipeline.
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"""
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def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs):
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"""
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User customizable fit method
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:param data_dictionary: dict = common data dictionary containing all train/test
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features/labels/weights.
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:param dk: FreqaiDatakitchen = data kitchen for current pair.
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:return:
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model Any = trained model to be used for inference in dry/live/backtesting
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"""
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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=self.net_arch)
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if dk.pair not in self.dd.model_dictionary or not self.continual_learning:
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model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
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tensorboard_log=Path(
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dk.full_path / "tensorboard" / dk.pair.split('/')[0]),
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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.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, self.tensorboard_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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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 reset(self):
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# Reset custom info
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self.custom_info = {}
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self.custom_info["Invalid"] = 0
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self.custom_info["Hold"] = 0
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self.custom_info["Unknown"] = 0
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self.custom_info["pnl_factor"] = 0
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self.custom_info["duration_factor"] = 0
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self.custom_info["reward_exit"] = 0
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self.custom_info["reward_hold"] = 0
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for action in Actions:
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self.custom_info[f"{action.name}"] = 0
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return super().reset()
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def step(self, action: int):
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observation, step_reward, done, info = super().step(action)
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info = dict(
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tick=self._current_tick,
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action=action,
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total_reward=self.total_reward,
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total_profit=self._total_profit,
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position=self._position.value,
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trade_duration=self.get_trade_duration(),
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current_profit_pct=self.get_unrealized_profit()
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)
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return observation, step_reward, done, info
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def calculate_reward(self, action: int) -> float:
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"""
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An example reward function. This is the one function that users will likely
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wish to inject their own creativity into.
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:param action: int = The action made by the agent for the current candle.
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:return:
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float = the reward to give to the agent for current step (used for optimization
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of weights in NN)
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"""
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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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self.custom_info["Invalid"] += 1
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return -2
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pnl = self.get_unrealized_profit()
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factor = 100.
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# reward agent for entering trades
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if (action == Actions.Long_enter.value
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and self._position == Positions.Neutral):
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self.custom_info[f"{Actions.Long_enter.name}"] += 1
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return 25
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if (action == Actions.Short_enter.value
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and self._position == Positions.Neutral):
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self.custom_info[f"{Actions.Short_enter.name}"] += 1
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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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self.custom_info[f"{Actions.Neutral.name}"] += 1
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return -1
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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 # type: ignore
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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) and
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action == Actions.Neutral.value):
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self.custom_info["Hold"] += 1
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return -1 * 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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self.custom_info[f"{Actions.Long_exit.name}"] += 1
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return float(pnl * 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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self.custom_info[f"{Actions.Short_exit.name}"] += 1
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return float(pnl * factor)
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self.custom_info["Unknown"] += 1
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return 0.
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