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ruff format: Update a few test files

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This commit is contained in:
Matthias
2024-05-12 15:29:14 +02:00
parent baa15f6ed6
commit 7090950db6
13 changed files with 1629 additions and 1283 deletions
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tests/freqai/test_models/ReinforcementLearner_test_4ac.py
+13 -10
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@@ -26,24 +26,25 @@ class ReinforcementLearner_test_4ac(ReinforcementLearner):
"""
def calculate_reward(self, action: int) -> float:
# 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.
factor = 100.0
# reward agent for entering trades
if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
and self._position == Positions.Neutral):
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)
max_trade_duration = self.rl_config.get("max_trade_duration_candles", 300)
trade_duration = self._current_tick - self._last_trade_tick # type: ignore
if trade_duration <= max_trade_duration:
@@ -52,20 +53,22 @@ class ReinforcementLearner_test_4ac(ReinforcementLearner):
factor *= 0.5
# discourage sitting in position
if (self._position in (Positions.Short, Positions.Long) and
action == Actions.Neutral.value):
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.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)
factor *= self.rl_config["model_reward_parameters"].get("win_reward_factor", 2)
return float(rew * factor)
# close short
if action == Actions.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)
factor *= self.rl_config["model_reward_parameters"].get("win_reward_factor", 2)
return float(rew * factor)
return 0.
return 0.0