From 75b26479da280fdf514c87fbbdc65cb6f41531f1 Mon Sep 17 00:00:00 2001 From: =?utf8?q?J=C3=A9r=C3=B4me=20Benoit?= Date: Tue, 25 Feb 2025 10:21:45 +0100 Subject: [PATCH] refactor(qav3): remove dead code MIME-Version: 1.0 Content-Type: text/plain; charset=utf8 Content-Transfer-Encoding: 8bit Signed-off-by: Jérôme Benoit --- .../freqaimodels/LightGBMRegressorQuickAdapterV35.py | 12 ++++++------ .../freqaimodels/XGBoostRegressorQuickAdapterV35.py | 12 ++++++------ quickadapter/user_data/strategies/QuickAdapterV3.py | 12 ------------ 3 files changed, 12 insertions(+), 24 deletions(-) diff --git a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py index 86725c9..7da3564 100644 --- 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 4654646..b2a9f7f 100644 --- 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 9b36469..a857e6c 100644 --- 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] = ( -- 2.43.0