fix(qav3): ensure extrema prediction fit into the live predictions period

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 d8ecb689705f2755bae3129ba667b222b5a64e63..b7cff11d2caeb11c88442ee63ab78224479cccf4 100644 (file)
--- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
@@ -249,8 +249,11 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel):
 def min_max_pred(
     pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int
 ) -> tuple[float, float]:
+    label_period_frequency: int = int(
+        fit_live_predictions_candles / label_period_candles
+    )
+    extrema = pred_df.tail(label_period_candles * label_period_frequency)["&s-extrema"]
     beta = 10.0
-    extrema = pred_df.tail(label_period_candles)["&s-extrema"]
     min_pred = smooth_min(extrema, beta=beta)
     max_pred = smooth_max(extrema, beta=beta)
 

diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
index 76682d0adf270eb285a007395d2cdd36d135e179..5f8922aa743e52c8713c11199f9b017137950916 100644 (file)
--- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
@@ -252,8 +252,11 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel):
 def min_max_pred(
     pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int
 ) -> tuple[float, float]:
+    label_period_frequency: int = int(
+        fit_live_predictions_candles / label_period_candles
+    )
+    extrema = pred_df.tail(label_period_candles * label_period_frequency)["&s-extrema"]
     beta = 10.0
-    extrema = pred_df.tail(label_period_candles)["&s-extrema"]
     min_pred = smooth_min(extrema, beta=beta)
     max_pred = smooth_max(extrema, beta=beta)