From: Jérôme Benoit <jerome.benoit@piment-noir.org>
Date: Thu, 6 Feb 2025 19:30:38 +0000 (+0100)
Subject: refactor(qav3): improve log messages
X-Git-Url: https://git.piment-noir.org/?a=commitdiff_plain;h=addb6292d4d0f248945b971e6a78431a4e8f1f40;p=freqai-strategies.git

refactor(qav3): improve log messages

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 befe599..1f3ec75 100644
--- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
@@ -90,9 +90,9 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel):
             self.__optuna_hp = study.best_params
             # log params
             for key, value in self.__optuna_hp.items():
-                logger.info(f"Optuna hyperopt {key:>20s} : {value}")
+                logger.info(f"Optuna hyperopt | {key:>20s} : {value}")
             logger.info(
-                f"Optuna hyperopt {'best objective value':>20s} : {study.best_value}"
+                f"Optuna hyperopt | {'best objective value':>20s} : {study.best_value}"
             )
 
             train_window = self.__optuna_hp.get("train_period_candles")
@@ -138,7 +138,7 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel):
             )
             if candle_diff < 0:
                 logger.warning(
-                    f"Fit live predictions not warmed up yet. Still {abs(candle_diff)} candles to go"
+                    f"{pair} fit live predictions not warmed up yet. Still {abs(candle_diff)} candles to go"
                 )
                 warmed_up = False
 
diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
index d59c289..edda07e 100644
--- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
@@ -90,9 +90,9 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel):
             self.__optuna_hp = study.best_params
             # log params
             for key, value in self.__optuna_hp.items():
-                logger.info(f"Optuna hyperopt {key:>20s} : {value}")
+                logger.info(f"Optuna hyperopt | {key:>20s} : {value}")
             logger.info(
-                f"Optuna hyperopt {'best objective value':>20s} : {study.best_value}"
+                f"Optuna hyperopt | {'best objective value':>20s} : {study.best_value}"
             )
 
             train_window = self.__optuna_hp.get("train_period_candles")
@@ -138,7 +138,7 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel):
             )
             if candle_diff < 0:
                 logger.warning(
-                    f"Fit live predictions not warmed up yet. Still {abs(candle_diff)} candles to go"
+                    f"{pair} fit live predictions not warmed up yet. Still {abs(candle_diff)} candles to go"
                 )
                 warmed_up = False