perf(qav3): fine tune long/short IQR signal for the gaussian window

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 d016e0463ceb5f1f799014d81c81668102411529..227b9af0b88fdb2845d15f29f96392d164821186 100644 (file)
--- 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 b0df5548f19b6552b0af706c7a5fb21067551164..f0860ca958f9da98917def928d477d372839305d 100644 (file)
--- 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]