From 22243fe918c04368ccb6b7cedbd22fe5438cbb8f Mon Sep 17 00:00:00 2001 From: =?utf8?q?J=C3=A9r=C3=B4me=20Benoit?= Date: Tue, 1 Apr 2025 14:24:21 +0200 Subject: [PATCH] chore(qav3): more sensible defaults MIME-Version: 1.0 Content-Type: text/plain; charset=utf8 Content-Transfer-Encoding: 8bit Signed-off-by: Jérôme Benoit --- .../freqaimodels/LightGBMRegressorQuickAdapterV3.py | 6 +++--- .../freqaimodels/XGBoostRegressorQuickAdapterV3.py | 6 +++--- quickadapter/user_data/strategies/QuickAdapterV3.py | 6 +++--- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV3.py b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV3.py index c1d1ba2..cffc45e 100644 --- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV3.py +++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV3.py @@ -43,7 +43,7 @@ class LightGBMRegressorQuickAdapterV3(BaseRegressionModel): https://github.com/sponsors/robcaulk """ - version = "3.6.2" + version = "3.6.3" def __init__(self, **kwargs): super().__init__(**kwargs) @@ -268,7 +268,7 @@ class LightGBMRegressorQuickAdapterV3(BaseRegressionModel): label_period_candles: int, ) -> tuple[pd.Series, pd.Series]: prediction_thresholds_smoothing = self.freqai_info.get( - "prediction_thresholds_smoothing", "mean" + "prediction_thresholds_smoothing", "quantile" ) smoothing_methods: dict = { "quantile": self.quantile_min_max_pred, @@ -276,7 +276,7 @@ class LightGBMRegressorQuickAdapterV3(BaseRegressionModel): "median": LightGBMRegressorQuickAdapterV3.median_min_max_pred, } return smoothing_methods.get( - prediction_thresholds_smoothing, smoothing_methods["mean"] + prediction_thresholds_smoothing, smoothing_methods["quantile"] )(pred_df, fit_live_predictions_candles, label_period_candles) def optuna_hp_enqueue_previous_best_trial( diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV3.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV3.py index 3b5e5f6..16a1b4e 100644 --- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV3.py +++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV3.py @@ -43,7 +43,7 @@ class XGBoostRegressorQuickAdapterV3(BaseRegressionModel): https://github.com/sponsors/robcaulk """ - version = "3.6.2" + version = "3.6.3" def __init__(self, **kwargs): super().__init__(**kwargs) @@ -271,7 +271,7 @@ class XGBoostRegressorQuickAdapterV3(BaseRegressionModel): label_period_candles: int, ) -> tuple[pd.Series, pd.Series]: prediction_thresholds_smoothing = self.freqai_info.get( - "prediction_thresholds_smoothing", "mean" + "prediction_thresholds_smoothing", "quantile" ) smoothing_methods: dict = { "quantile": self.quantile_min_max_pred, @@ -279,7 +279,7 @@ class XGBoostRegressorQuickAdapterV3(BaseRegressionModel): "median": XGBoostRegressorQuickAdapterV3.median_min_max_pred, } return smoothing_methods.get( - prediction_thresholds_smoothing, smoothing_methods["mean"] + prediction_thresholds_smoothing, smoothing_methods["quantile"] )(pred_df, fit_live_predictions_candles, label_period_candles) def optuna_hp_enqueue_previous_best_trial( diff --git a/quickadapter/user_data/strategies/QuickAdapterV3.py b/quickadapter/user_data/strategies/QuickAdapterV3.py index af818d7..f2a39f5 100644 --- a/quickadapter/user_data/strategies/QuickAdapterV3.py +++ b/quickadapter/user_data/strategies/QuickAdapterV3.py @@ -59,7 +59,7 @@ class QuickAdapterV3(IStrategy): INTERFACE_VERSION = 3 def version(self) -> str: - return "3.2.5" + return "3.2.6" timeframe = "5m" @@ -136,11 +136,11 @@ class QuickAdapterV3(IStrategy): "fit_live_predictions_candles", 100 ) return [ - {"method": "CooldownPeriod", "stop_duration_candles": 4}, + {"method": "CooldownPeriod", "stop_duration_candles": 2}, { "method": "MaxDrawdown", "lookback_period_candles": fit_live_predictions_candles, - "trade_limit": 20, + "trade_limit": self.config.get("max_open_trades"), "stop_duration_candles": fit_live_predictions_candles, "max_allowed_drawdown": 0.2, }, -- 2.43.0