freqtrade_origin/freqtrade/freqai/prediction_models/ReinforcementLearner.py

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import logging
from typing import Any, Dict
import torch as th
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
from pathlib import Path
from pandas import DataFrame
from stable_baselines3.common.callbacks import EvalCallback
from stable_baselines3.common.monitor import Monitor
import numpy as np
logger = logging.getLogger(__name__)
class ReinforcementLearner(BaseReinforcementLearningModel):
"""
User created Reinforcement Learning Model prediction model.
"""
def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen):
train_df = data_dictionary["train_features"]
total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
policy_kwargs = dict(activation_fn=th.nn.ReLU,
net_arch=[512, 512, 256])
if dk.pair not in self.dd.model_dictionary or not self.continual_learning:
model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
tensorboard_log=Path(dk.data_path / "tensorboard"),
**self.freqai_info['model_training_parameters']
)
else:
logger.info('Continual training activated - starting training from previously '
'trained agent.')
model = self.dd.model_dictionary[dk.pair]
model.tensorboard_log = Path(dk.data_path / "tensorboard")
model.set_env(self.train_env)
model.learn(
total_timesteps=int(total_timesteps),
callback=self.eval_callback
)
if Path(dk.data_path / "best_model.zip").is_file():
logger.info('Callback found a best model.')
best_model = self.MODELCLASS.load(dk.data_path / "best_model")
return best_model
logger.info('Couldnt find best model, using final model instead.')
return model
def set_train_and_eval_environments(self, data_dictionary: Dict[str, DataFrame],
prices_train: DataFrame, prices_test: DataFrame,
dk: FreqaiDataKitchen):
"""
User can override this if they are using a custom MyRLEnv
"""
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
self.train_env = MyRLEnv(df=train_df, prices=prices_train, window_size=self.CONV_WIDTH,
reward_kwargs=self.reward_params, config=self.config)
self.eval_env = Monitor(MyRLEnv(df=test_df, prices=prices_test,
window_size=self.CONV_WIDTH,
reward_kwargs=self.reward_params, config=self.config))
self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
render=False, eval_freq=len(train_df),
best_model_save_path=str(dk.data_path))
class MyRLEnv(Base5ActionRLEnv):
"""
User can override any function in BaseRLEnv and gym.Env. Here the user
sets a custom reward based on profit and trade duration.
"""
def calculate_reward(self, action):
# first, penalize if the action is not valid
if not self._is_valid(action):
return -2
pnl = self.get_unrealized_profit()
rew = np.sign(pnl) * (pnl + 1)
factor = 100
# reward agent for entering trades
if action in (Actions.Long_enter.value, Actions.Short_enter.value) \
and self._position == Positions.Neutral:
return 25
# discourage agent from not entering trades
if action == Actions.Neutral.value and self._position == Positions.Neutral:
return -1
max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
trade_duration = self._current_tick - self._last_trade_tick
if trade_duration <= max_trade_duration:
factor *= 1.5
elif trade_duration > max_trade_duration:
factor *= 0.5
# discourage sitting in position
if self._position in (Positions.Short, Positions.Long) and action == Actions.Neutral.value:
return -1 * trade_duration / max_trade_duration
# close long
if action == Actions.Long_exit.value and self._position == Positions.Long:
if pnl > self.profit_aim * self.rr:
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
return float(rew * factor)
# close short
if action == Actions.Short_exit.value and self._position == Positions.Short:
if pnl > self.profit_aim * self.rr:
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
return float(rew * factor)
return 0.