"&s-minima_threshold": -2,
       "&s-maxima_threshold": 2,
       "label_period_candles": 100,
-      "label_natr_ratio": 0.075,
+      "label_natr_ratio": 0.12125,
       "hp_rmse": -1,
       "train_rmse": -1
     },
 
                     "label_period_candles": self.ft_params.get(
                         "label_period_candles", 50
                     ),
-                    "label_natr_ratio": self.ft_params.get("label_natr_ratio", 0.075),
+                    "label_natr_ratio": self.ft_params.get("label_natr_ratio", 0.12125),
                 }
             )
         logger.info(
     df: pd.DataFrame,
     fit_live_predictions_candles: int,
     candles_step: int,
-) -> tuple[float, float]:
+) -> tuple[float, int]:
     min_label_period_candles: int = round_to_nearest(
         max(fit_live_predictions_candles // 16, 20), candles_step
     )
 
                         "label_period_candles", 50
                     ),
                     "label_natr_ratio": self.freqai_info["feature_parameters"].get(
-                        "label_natr_ratio", 0.075
+                        "label_natr_ratio", 0.12125
                     ),
                 }
             )
         label_natr_ratio = self._label_params.get(pair, {}).get("label_natr_ratio")
         if label_natr_ratio:
             return label_natr_ratio
-        return self.freqai_info["feature_parameters"].get("label_natr_ratio", 0.075)
+        return self.freqai_info["feature_parameters"].get("label_natr_ratio", 0.12125)
 
     def set_label_natr_ratio(self, pair: str, label_natr_ratio: float):
         if label_natr_ratio: