From 1d026313062150f19b23405084453f0fd912392f Mon Sep 17 00:00:00 2001 From: =?utf8?q?J=C3=A9r=C3=B4me=20Benoit?= Date: Fri, 28 Feb 2025 14:49:31 +0100 Subject: [PATCH] perf(qav3): fine tune long/short IQR signal for the gaussian window MIME-Version: 1.0 Content-Type: text/plain; charset=utf8 Content-Transfer-Encoding: 8bit Signed-off-by: Jérôme Benoit --- .../freqaimodels/LightGBMRegressorQuickAdapterV35.py | 4 ++-- .../user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py index d016e04..227b9af 100644 --- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py +++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py @@ -507,10 +507,10 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel): fit_live_predictions_candles / (label_period_candles * 2) ) min_pred = pred_df_sorted.iloc[-label_period_frequency:].quantile( - self.freqai_info.get("min_quantile", 0.75) + self.freqai_info.get("min_quantile", 0.67) ) max_pred = pred_df_sorted.iloc[:label_period_frequency].quantile( - self.freqai_info.get("max_quantile", 0.75) + self.freqai_info.get("max_quantile", 0.67) ) return min_pred[EXTREMA_COLUMN], max_pred[EXTREMA_COLUMN] diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py index b0df554..f0860ca 100644 --- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py +++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py @@ -508,10 +508,10 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel): fit_live_predictions_candles / (label_period_candles * 2) ) min_pred = pred_df_sorted.iloc[-label_period_frequency:].quantile( - self.freqai_info.get("min_quantile", 0.75) + self.freqai_info.get("min_quantile", 0.67) ) max_pred = pred_df_sorted.iloc[:label_period_frequency].quantile( - self.freqai_info.get("max_quantile", 0.75) + self.freqai_info.get("max_quantile", 0.67) ) return min_pred[EXTREMA_COLUMN], max_pred[EXTREMA_COLUMN] -- 2.43.0