From: Jérôme Benoit Date: Sat, 19 Apr 2025 18:07:41 +0000 (+0200) Subject: perf(qav3): fine tune predictions max/min threshold X-Git-Url: https://git.piment-noir.org/?a=commitdiff_plain;h=4754ae21748dc13ad7c78845df9736144fa5b083;p=freqai-strategies.git perf(qav3): fine tune predictions max/min threshold Signed-off-by: Jérôme Benoit --- diff --git a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py index c584bfb..8c71392 100644 --- a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py +++ b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py @@ -44,7 +44,7 @@ class QuickAdapterRegressorV3(BaseRegressionModel): https://github.com/sponsors/robcaulk """ - version = "3.7.11" + version = "3.7.12" @cached_property def _optuna_config(self) -> dict: @@ -372,7 +372,7 @@ class QuickAdapterRegressorV3(BaseRegressionModel): def min_max_pred(self, pred_df: pd.DataFrame) -> tuple[float, float]: temperature = float( - self.freqai_info.get("prediction_thresholds_temperature", 125.0) + self.freqai_info.get("prediction_thresholds_temperature", 135.0) ) min_pred = smoothed_min( pred_df[EXTREMA_COLUMN],