From: Jérôme Benoit <jerome.benoit@piment-noir.org>
Date: Fri, 7 Mar 2025 12:45:04 +0000 (+0100)
Subject: refactor(qav3): refine type definition
X-Git-Url: https://git.piment-noir.org/?a=commitdiff_plain;h=3ad97c3d04ab5655edae68723fd0365703ba59c8;p=freqai-strategies.git

refactor(qav3): refine type definition

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 3a81851..6e30d73 100644
--- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
@@ -270,7 +270,7 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel):
         pred_df: pd.DataFrame,
         fit_live_predictions_candles: int,
         label_period_candles: int,
-    ) -> tuple[float, float]:
+    ) -> tuple[pd.Series, pd.Series]:
         prediction_thresholds_smoothing = self.freqai_info.get(
             "prediction_thresholds_smoothing", "mean"
         )
@@ -489,7 +489,7 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel):
         pred_df: pd.DataFrame,
         fit_live_predictions_candles: int,
         label_period_candles: int,
-    ) -> tuple[float, float]:
+    ) -> tuple[pd.Series, pd.Series]:
         pred_df_sorted = (
             pred_df.select_dtypes(exclude=["object"])
             .copy()
@@ -507,7 +507,7 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel):
 
 def mean_min_max_pred(
     pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int
-) -> tuple[float, float]:
+) -> tuple[pd.Series, pd.Series]:
     pred_df_sorted = (
         pred_df.select_dtypes(exclude=["object"])
         .copy()
@@ -524,7 +524,7 @@ def mean_min_max_pred(
 
 def median_min_max_pred(
     pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int
-) -> tuple[float, float]:
+) -> tuple[pd.Series, pd.Series]:
     pred_df_sorted = (
         pred_df.select_dtypes(exclude=["object"])
         .copy()
diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
index 9957a2c..5330cc9 100644
--- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
@@ -271,7 +271,7 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel):
         pred_df: pd.DataFrame,
         fit_live_predictions_candles: int,
         label_period_candles: int,
-    ) -> tuple[float, float]:
+    ) -> tuple[pd.Series, pd.Series]:
         prediction_thresholds_smoothing = self.freqai_info.get(
             "prediction_thresholds_smoothing", "mean"
         )
@@ -490,7 +490,7 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel):
         pred_df: pd.DataFrame,
         fit_live_predictions_candles: int,
         label_period_candles: int,
-    ) -> tuple[float, float]:
+    ) -> tuple[pd.Series, pd.Series]:
         pred_df_sorted = (
             pred_df.select_dtypes(exclude=["object"])
             .copy()
@@ -508,7 +508,7 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel):
 
 def mean_min_max_pred(
     pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int
-) -> tuple[float, float]:
+) -> tuple[pd.Series, pd.Series]:
     pred_df_sorted = (
         pred_df.select_dtypes(exclude=["object"])
         .copy()
@@ -525,7 +525,7 @@ def mean_min_max_pred(
 
 def median_min_max_pred(
     pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int
-) -> tuple[float, float]:
+) -> tuple[pd.Series, pd.Series]:
     pred_df_sorted = (
         pred_df.select_dtypes(exclude=["object"])
         .copy()