"&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: