refactor(qav3): remove dead code

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 86725c902949f77c8c87e583e2affc9528ad132e..7da35646fbcececef275a9d993b9cc5fdaa66899 100644 (file)
--- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
@@ -71,9 +71,9 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel):
             )
             self.freqai_info["feature_parameters"][pair] = {}
             self.freqai_info["feature_parameters"][pair]["label_period_candles"] = (
-                self.__optuna_period_params[
-                    pair
-                ].get("label_period_candles", self.ft_params["label_period_candles"])
+                self.__optuna_period_params[pair].get(
+                    "label_period_candles", self.ft_params["label_period_candles"]
+                )
             )
 
     def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
@@ -230,9 +230,9 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel):
         dk.data["extra_returns_per_train"]["DI_cutoff"] = cutoff
 
         dk.data["extra_returns_per_train"]["label_period_candles"] = (
-            self.__optuna_period_params.get(
-                pair, {}
-            ).get("label_period_candles", self.ft_params["label_period_candles"])
+            self.__optuna_period_params.get(pair, {}).get(
+                "label_period_candles", self.ft_params["label_period_candles"]
+            )
         )
         dk.data["extra_returns_per_train"]["hp_rmse"] = self.__optuna_hp_rmse.get(
             pair, -1

diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
index 46546463c542645ab88a79eaf339d3e15628af24..b2a9f7f18609301fd0fcab22b62cdb577c749b54 100644 (file)
--- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
@@ -71,9 +71,9 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel):
             )
             self.freqai_info["feature_parameters"][pair] = {}
             self.freqai_info["feature_parameters"][pair]["label_period_candles"] = (
-                self.__optuna_period_params[
-                    pair
-                ].get("label_period_candles", self.ft_params["label_period_candles"])
+                self.__optuna_period_params[pair].get(
+                    "label_period_candles", self.ft_params["label_period_candles"]
+                )
             )
 
     def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
@@ -231,9 +231,9 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel):
         dk.data["extra_returns_per_train"]["DI_cutoff"] = cutoff
 
         dk.data["extra_returns_per_train"]["label_period_candles"] = (
-            self.__optuna_period_params.get(
-                pair, {}
-            ).get("label_period_candles", self.ft_params["label_period_candles"])
+            self.__optuna_period_params.get(pair, {}).get(
+                "label_period_candles", self.ft_params["label_period_candles"]
+            )
         )
         dk.data["extra_returns_per_train"]["hp_rmse"] = self.__optuna_hp_rmse.get(
             pair, -1

diff --git a/quickadapter/user_data/strategies/QuickAdapterV3.py b/quickadapter/user_data/strategies/QuickAdapterV3.py
index 9b36469a9b1c428d717dc6b958bcc4e3043735e0..a857e6cc22e6e3f8579d617739a8b361d5747edd 100644 (file)
--- a/quickadapter/user_data/strategies/QuickAdapterV3.py
+++ b/quickadapter/user_data/strategies/QuickAdapterV3.py
@@ -256,23 +256,11 @@ class QuickAdapterV3(IStrategy):
             dataframe["high"].values,
             order=label_period_candles,
         )
-        # min_peaks, _ = find_peaks(
-        #     -dataframe["low"].values,
-        #     distance=label_period_candles,
-        # )
-        # max_peaks, _ = find_peaks(
-        #     dataframe["high"].values,
-        #     distance=label_period_candles,
-        # )
         dataframe[EXTREMA_COLUMN] = 0
         for mp in min_peaks[0]:
             dataframe.at[mp, EXTREMA_COLUMN] = -1
         for mp in max_peaks[0]:
             dataframe.at[mp, EXTREMA_COLUMN] = 1
-        # for mp in min_peaks:
-        #     dataframe.at[mp, EXTREMA_COLUMN] = -1
-        # for mp in max_peaks:
-        #     dataframe.at[mp, EXTREMA_COLUMN] = 1
         dataframe["minima"] = np.where(dataframe[EXTREMA_COLUMN] == -1, -1, 0)
         dataframe["maxima"] = np.where(dataframe[EXTREMA_COLUMN] == 1, 1, 0)
         dataframe[EXTREMA_COLUMN] = (