refactor(qav3): code formatting

Signed-off-by: Jérôme Benoit <jerome.benoit@piment-noir.org>

diff --git a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
index 561f619802a64e6019c4a6738bdffb60d85f9464..156bb91b253dfd5d9802b6d68b2e3c1d1d41c0c1 100644 (file)
--- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
@@ -145,9 +145,7 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel):
                     "label_period_candles"
                 ] = self.__optuna_period_params[dk.pair].get("label_period_candles")
 
-        model = LGBMRegressor(
-            objective="regression", **model_training_parameters
-        )
+        model = LGBMRegressor(objective="regression", **model_training_parameters)
 
         eval_set, eval_weights = self.eval_set_and_weights(X_test, y_test, test_weights)
 
@@ -575,9 +573,7 @@ def period_objective(
     test_weights = test_weights[-test_window:]
 
     # Fit the model
-    model = LGBMRegressor(
-        objective="regression", **model_training_parameters
-    )
+    model = LGBMRegressor(objective="regression", **model_training_parameters)
     model.fit(
         X=X,
         y=y,
@@ -629,9 +625,7 @@ def hp_objective(
     model_training_parameters = {**model_training_parameters, **study_parameters}
 
     # Fit the model
-    model = LGBMRegressor(
-        objective="regression", **model_training_parameters
-    )
+    model = LGBMRegressor(objective="regression", **model_training_parameters)
     model.fit(
         X=X,
         y=y,

diff --git a/quickadapter/user_data/strategies/QuickAdapterV3.py b/quickadapter/user_data/strategies/QuickAdapterV3.py
index 7c8693d0d1bda019f8767c2835c0289359f47900..28836da46fc70ba2a07a3907f589ac6579fa5b8c 100644 (file)
--- a/quickadapter/user_data/strategies/QuickAdapterV3.py
+++ b/quickadapter/user_data/strategies/QuickAdapterV3.py
@@ -204,9 +204,8 @@ class QuickAdapterV3(IStrategy):
             dataframe["vwap_upperband"],
         ) = VWAPB(dataframe, 20, 1)
         dataframe["%-vwap_width"] = (
-            (dataframe["vwap_upperband"] - dataframe["vwap_lowerband"])
-            / dataframe["vwap_middleband"]
-        )
+            dataframe["vwap_upperband"] - dataframe["vwap_lowerband"]
+        ) / dataframe["vwap_middleband"]
         dataframe["%-dist_to_vwap_upperband"] = get_distance(
             dataframe["close"], dataframe["vwap_upperband"]
         )