From: Jérôme Benoit <jerome.benoit@piment-noir.org>
Date: Thu, 13 Mar 2025 18:41:38 +0000 (+0100)
Subject: refactor(qav3): cleanup label_period_candles handling
X-Git-Url: https://git.piment-noir.org/?a=commitdiff_plain;h=d82d495c85cf50364b9454e6a88cd557d34a5e87;p=freqai-strategies.git

refactor(qav3): cleanup label_period_candles handling

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 35f40b6..291f3fe 100644
--- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
@@ -152,6 +152,11 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel):
 
         return model
 
+    def get_label_period_candles(self, pair: str) -> int:
+        if self.__optuna_period_params.get(pair, {}).get("label_period_candles"):
+            return self.__optuna_period_params[pair]["label_period_candles"]
+        return self.ft_params["label_period_candles"]
+
     def fit_live_predictions(self, dk: FreqaiDataKitchen, pair: str) -> None:
         warmed_up = True
 
@@ -176,9 +181,7 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel):
             dk.data["extra_returns_per_train"][MINIMA_THRESHOLD_COLUMN] = -2
             dk.data["extra_returns_per_train"][MAXIMA_THRESHOLD_COLUMN] = 2
         else:
-            label_period_candles = self.__optuna_period_params.get(pair, {}).get(
-                "label_period_candles", self.ft_params["label_period_candles"]
-            )
+            label_period_candles = self.get_label_period_candles(pair)
             min_pred, max_pred = self.min_max_pred(
                 pred_df_full,
                 num_candles,
@@ -217,9 +220,7 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel):
         dk.data["extra_returns_per_train"]["DI_cutoff"] = cutoff
 
         dk.data["extra_returns_per_train"]["label_period_candles"] = (
-            self.__optuna_period_params.get(pair, {}).get(
-                "label_period_candles", self.ft_params["label_period_candles"]
-            )
+            self.get_label_period_candles(pair)
         )
         dk.data["extra_returns_per_train"]["hp_rmse"] = self.__optuna_hp_rmse.get(
             pair, -1
diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
index 29a5efa..3aa9abb 100644
--- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
@@ -155,6 +155,11 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel):
 
         return model
 
+    def get_label_period_candles(self, pair: str) -> int:
+        if self.__optuna_period_params.get(pair, {}).get("label_period_candles"):
+            return self.__optuna_period_params[pair]["label_period_candles"]
+        return self.ft_params["label_period_candles"]
+
     def fit_live_predictions(self, dk: FreqaiDataKitchen, pair: str) -> None:
         warmed_up = True
 
@@ -179,9 +184,7 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel):
             dk.data["extra_returns_per_train"][MINIMA_THRESHOLD_COLUMN] = -2
             dk.data["extra_returns_per_train"][MAXIMA_THRESHOLD_COLUMN] = 2
         else:
-            label_period_candles = self.__optuna_period_params.get(pair, {}).get(
-                "label_period_candles", self.ft_params["label_period_candles"]
-            )
+            label_period_candles = self.get_label_period_candles(pair)
             min_pred, max_pred = self.min_max_pred(
                 pred_df_full,
                 num_candles,
@@ -220,9 +223,7 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel):
         dk.data["extra_returns_per_train"]["DI_cutoff"] = cutoff
 
         dk.data["extra_returns_per_train"]["label_period_candles"] = (
-            self.__optuna_period_params.get(pair, {}).get(
-                "label_period_candles", self.ft_params["label_period_candles"]
-            )
+            self.get_label_period_candles(pair)
         )
         dk.data["extra_returns_per_train"]["hp_rmse"] = self.__optuna_hp_rmse.get(
             pair, -1
diff --git a/quickadapter/user_data/strategies/QuickAdapterV3.py b/quickadapter/user_data/strategies/QuickAdapterV3.py
index eea84f8..3b2d02a 100644
--- a/quickadapter/user_data/strategies/QuickAdapterV3.py
+++ b/quickadapter/user_data/strategies/QuickAdapterV3.py
@@ -265,24 +265,13 @@ class QuickAdapterV3(IStrategy):
         dataframe["%-hour_of_day"] = (dataframe["date"].dt.hour + 1) / 25
         return dataframe
 
-    def get_label_period_candles(self, metadata, **kwargs) -> int:
-        pair = str(metadata.get("pair"))
+    def get_label_period_candles(self, pair: str) -> int:
         if self.__period_params.get(pair, {}).get("label_period_candles"):
-            label_period_candles = self.__period_params.get(pair, {}).get(
-                "label_period_candles",
-            )
-        else:
-            label_period_candles = self.freqai_info["feature_parameters"][
-                "label_period_candles"
-            ]
-        if label_period_candles < 1:
-            raise ValueError(
-                f"label_period_candles must be greater than 0, got {label_period_candles}"
-            )
-        return label_period_candles
+            return self.__period_params[pair]["label_period_candles"]
+        return self.freqai_info["feature_parameters"]["label_period_candles"]
 
     def set_freqai_targets(self, dataframe, metadata, **kwargs):
-        label_period_candles = self.get_label_period_candles(metadata, **kwargs)
+        label_period_candles = self.get_label_period_candles(str(metadata.get("pair")))
         min_peaks = argrelmin(
             dataframe["low"].values,
             order=label_period_candles,