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expose environment reward parameters to the user config

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robcaulk
2022-08-21 20:33:09 +02:00
parent d88a0dbf82
commit 29f0e01c4a
5 changed files with 28 additions and 32 deletions
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30
freqtrade/freqai/prediction_models/ReinforcementLearner.py
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@@ -57,26 +57,20 @@ class MyRLEnv(Base5ActionRLEnv):
# close long
if action == Actions.Long_exit.value and self._position == Positions.Long:
last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
return float(np.log(current_price) - np.log(last_trade_price))
if action == Actions.Long_exit.value and self._position == Positions.Long:
if self.close_trade_profit[-1] > self.profit_aim * self.rr:
last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
return float((np.log(current_price) - np.log(last_trade_price)) * 2)
last_trade_price = self.add_entry_fee(self.prices.iloc[self._last_trade_tick].open)
current_price = self.add_exit_fee(self.prices.iloc[self._current_tick].open)
factor = 1
if self.close_trade_profit and self.close_trade_profit[-1] > self.profit_aim * self.rr:
factor = self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
return float((np.log(current_price) - np.log(last_trade_price)) * factor)
# close short
if action == Actions.Short_exit.value and self._position == Positions.Short:
last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
return float(np.log(last_trade_price) - np.log(current_price))
if action == Actions.Short_exit.value and self._position == Positions.Short:
if self.close_trade_profit[-1] > self.profit_aim * self.rr:
last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
return float((np.log(last_trade_price) - np.log(current_price)) * 2)
last_trade_price = self.add_exit_fee(self.prices.iloc[self._last_trade_tick].open)
current_price = self.add_entry_fee(self.prices.iloc[self._current_tick].open)
factor = 1
if self.close_trade_profit and self.close_trade_profit[-1] > self.profit_aim * self.rr:
factor = self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
return float(np.log(last_trade_price) - np.log(current_price) * factor)
return 0.