From: Jérôme Benoit <jerome.benoit@piment-noir.org>
Date: Mon, 10 Feb 2025 21:01:56 +0000 (+0100)
Subject: refactor(qav3): code cleanups
X-Git-Url: https://git.piment-noir.org/?a=commitdiff_plain;h=a044e51965c66c71d339dc204e04ec76b22ed1f7;p=freqai-strategies.git

refactor(qav3): code cleanups

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 cdf4a34..a02cb20 100644
--- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
@@ -242,11 +242,12 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel):
 def min_max_pred(
     pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int
 ):
+    beta = 10.0
     min_pred = pred_df.tail(label_period_candles).apply(
-        lambda col: smooth_min(col, beta=10.0)
+        lambda col: smooth_min(col, beta=beta)
     )
     max_pred = pred_df.tail(label_period_candles).apply(
-        lambda col: smooth_max(col, beta=10.0)
+        lambda col: smooth_max(col, beta=beta)
     )
 
     return min_pred, max_pred
diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
index 9b8d629..22c7776 100644
--- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
@@ -242,11 +242,12 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel):
 def min_max_pred(
     pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int
 ):
+    beta = 10.0
     min_pred = pred_df.tail(label_period_candles).apply(
-        lambda col: smooth_min(col, beta=10.0)
+        lambda col: smooth_min(col, beta=beta)
     )
     max_pred = pred_df.tail(label_period_candles).apply(
-        lambda col: smooth_max(col, beta=10.0)
+        lambda col: smooth_max(col, beta=beta)
     )
 
     return min_pred, max_pred
@@ -325,7 +326,9 @@ def objective(
     min_label_period_candles = int(fit_live_predictions_candles / 10)
     max_label_period_candles = fit_live_predictions_candles
     label_period_candles = trial.suggest_int(
-        "label_period_candles", min_label_period_candles, max_label_period_candles
+        "label_period_candles",
+        min_label_period_candles,
+        max_label_period_candles,
     )
     y_test = y_test.tail(label_period_candles)
     y_pred = y_pred[-label_period_candles:]