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72 lines
2.7 KiB
72 lines
2.7 KiB
import logging
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import numpy as np
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from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
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from freqtrade.freqai.RL.Base3ActionRLEnv import Actions, Base3ActionRLEnv, Positions
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logger = logging.getLogger(__name__)
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class ReinforcementLearner_test_3ac(ReinforcementLearner):
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"""
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User created Reinforcement Learning Model prediction model.
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"""
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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. Here the user
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sets a custom reward based on profit and trade duration.
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Warning!
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This is function is a showcase of functionality designed to show as many possible
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environment control features as possible. It is also designed to run quickly
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on small computers. This is a benchmark, it is *not* for live production.
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"""
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def calculate_reward(self, action: int) -> float:
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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 -2
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pnl = self.get_unrealized_profit()
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rew = np.sign(pnl) * (pnl + 1)
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factor = 100.0
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# reward agent for entering trades
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if (
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action in (Actions.Buy.value, Actions.Sell.value)
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and self._position == Positions.Neutral
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):
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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 -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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or (action == Actions.Sell.value and self._position == Positions.Short)
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or (action == Actions.Buy.value and self._position == Positions.Long)
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):
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return -1 * trade_duration / max_trade_duration
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# close position
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if (action == Actions.Buy.value and self._position == Positions.Short) or (
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action == Actions.Sell.value and self._position == Positions.Long
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):
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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 0.0
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