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Merge branch 'develop' into freqai_bt_from_predictions_improvement

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Wagner Costa
2022-12-05 18:00:55 -03:00
parent 77dc2c92a7 b0f430b5ac
commit c81b00fb37
14 changed files with 54 additions and 30 deletions
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8
freqtrade/freqai/RL/BaseEnvironment.py
View File

@@ -194,12 +194,12 @@ class BaseEnvironment(gym.Env):
if self._position == Positions.Neutral:
return 0.
elif self._position == Positions.Short:
current_price = self.add_exit_fee(self.prices.iloc[self._current_tick].open)
last_trade_price = self.add_entry_fee(self.prices.iloc[self._last_trade_tick].open)
return (last_trade_price - current_price) / last_trade_price
elif self._position == Positions.Long:
current_price = self.add_entry_fee(self.prices.iloc[self._current_tick].open)
last_trade_price = self.add_exit_fee(self.prices.iloc[self._last_trade_tick].open)
return (last_trade_price - current_price) / last_trade_price
elif self._position == Positions.Long:
current_price = self.add_exit_fee(self.prices.iloc[self._current_tick].open)
last_trade_price = self.add_entry_fee(self.prices.iloc[self._last_trade_tick].open)
return (current_price - last_trade_price) / last_trade_price
else:
return 0.

2
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
View File

@@ -64,7 +64,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
self.policy_type = self.freqai_info['rl_config']['policy_type']
self.unset_outlier_removal()
self.net_arch = self.rl_config.get('net_arch', [128, 128])
self.dd.model_type = "stable_baselines"
self.dd.model_type = import_str
def unset_outlier_removal(self):
"""

6
freqtrade/freqai/data_drawer.py
View File

@@ -503,7 +503,7 @@ class FreqaiDataDrawer:
dump(model, save_path / f"{dk.model_filename}_model.joblib")
elif self.model_type == 'keras':
model.save(save_path / f"{dk.model_filename}_model.h5")
elif 'stable_baselines' in self.model_type:
elif 'stable_baselines' in self.model_type or 'sb3_contrib' == self.model_type:
model.save(save_path / f"{dk.model_filename}_model.zip")
if dk.svm_model is not None:
@@ -589,9 +589,9 @@ class FreqaiDataDrawer:
elif self.model_type == 'keras':
from tensorflow import keras
model = keras.models.load_model(dk.data_path / f"{dk.model_filename}_model.h5")
elif self.model_type == 'stable_baselines':
elif 'stable_baselines' in self.model_type or 'sb3_contrib' == self.model_type:
mod = importlib.import_module(
'stable_baselines3', self.freqai_info['rl_config']['model_type'])
self.model_type, self.freqai_info['rl_config']['model_type'])
MODELCLASS = getattr(mod, self.freqai_info['rl_config']['model_type'])
model = MODELCLASS.load(dk.data_path / f"{dk.model_filename}_model")