From 18e84b5285dbdd631dcf006b2036299b0526e31a Mon Sep 17 00:00:00 2001 From: =?utf8?q?J=C3=A9r=C3=B4me=20Benoit?= Date: Mon, 3 Feb 2025 16:37:58 +0100 Subject: [PATCH] fix: cut&paste typo MIME-Version: 1.0 Content-Type: text/plain; charset=utf8 Content-Transfer-Encoding: 8bit Signed-off-by: Jérôme Benoit --- .../user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py index 3b34711..642e00f 100644 --- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py +++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py @@ -150,7 +150,7 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel): pred_df_sorted[col] = pred_df_sorted[col].sort_values( ascending=False, ignore_index=True ) - frequency = num_candles / (self.self.ft_params["label_period_candles"] * 2) + frequency = num_candles / (self.ft_params["label_period_candles"] * 2) max_pred = pred_df_sorted.iloc[: int(frequency)].mean() min_pred = pred_df_sorted.iloc[-int(frequency) :].mean() dk.data["extra_returns_per_train"]["&s-maxima_sort_threshold"] = max_pred[ -- 2.43.0