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custom info to tensorboard_metrics

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initrv
2022-12-11 15:37:45 +03:00
parent 58604c747e
commit cb8fc3c8c7
5 changed files with 10 additions and 15 deletions
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6
freqtrade/freqai/prediction_models/ReinforcementLearner.py
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@@ -108,15 +108,12 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
# reward agent for entering trades
if (action == Actions.Long_enter.value
and self._position == Positions.Neutral):
self.custom_info[Actions.Long_enter.name] += 1
return 25
if (action == Actions.Short_enter.value
and self._position == Positions.Neutral):
self.custom_info[Actions.Short_enter.name] += 1
return 25
# discourage agent from not entering trades
if action == Actions.Neutral.value and self._position == Positions.Neutral:
self.custom_info[Actions.Neutral.name] += 1
return -1
max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
@@ -130,21 +127,18 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
# discourage sitting in position
if (self._position in (Positions.Short, Positions.Long) and
action == Actions.Neutral.value):
self.custom_info[Actions.Neutral.name] += 1
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)
self.custom_info[Actions.Long_exit.name] += 1
return float(pnl * 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)
self.custom_info[Actions.Short_exit.name] += 1
return float(pnl * factor)
return 0.