From 9a51ea092d804441c7d7358d3ff91937db7a4610 Mon Sep 17 00:00:00 2001 From: =?utf8?q?J=C3=A9r=C3=B4me=20Benoit?= Date: Tue, 11 Mar 2025 11:38:12 +0100 Subject: [PATCH] refactor(qav3)!: refine features list MIME-Version: 1.0 Content-Type: text/plain; charset=utf8 Content-Transfer-Encoding: 8bit Signed-off-by: Jérôme Benoit --- .../freqaimodels/LightGBMRegressorQuickAdapterV35.py | 6 +++--- quickadapter/user_data/strategies/QuickAdapterV3.py | 10 +++++----- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py index 3107ae7..561f619 100644 --- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py +++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py @@ -146,7 +146,7 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel): ] = self.__optuna_period_params[dk.pair].get("label_period_candles") model = LGBMRegressor( - objective="regression", metric="rmse", **model_training_parameters + objective="regression", **model_training_parameters ) eval_set, eval_weights = self.eval_set_and_weights(X_test, y_test, test_weights) @@ -576,7 +576,7 @@ def period_objective( # Fit the model model = LGBMRegressor( - objective="regression", metric="rmse", **model_training_parameters + objective="regression", **model_training_parameters ) model.fit( X=X, @@ -630,7 +630,7 @@ def hp_objective( # Fit the model model = LGBMRegressor( - objective="regression", metric="rmse", **model_training_parameters + objective="regression", **model_training_parameters ) model.fit( X=X, diff --git a/quickadapter/user_data/strategies/QuickAdapterV3.py b/quickadapter/user_data/strategies/QuickAdapterV3.py index 4e8d66b..7c8693d 100644 --- a/quickadapter/user_data/strategies/QuickAdapterV3.py +++ b/quickadapter/user_data/strategies/QuickAdapterV3.py @@ -124,6 +124,7 @@ class QuickAdapterV3(IStrategy): def feature_engineering_expand_all(self, dataframe, period, **kwargs): dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period) + dataframe["%-aroonosc-period"] = ta.AROONOSC(dataframe, timeperiod=period) dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period) dataframe["%-adx-period"] = ta.ADX(dataframe, window=period) dataframe["%-cci-period"] = ta.CCI(dataframe, timeperiod=period) @@ -136,19 +137,18 @@ class QuickAdapterV3(IStrategy): dataframe["%-tcp-period"] = top_percent_change(dataframe, period=period) dataframe["%-cti-period"] = pta.cti(dataframe["close"], length=period) dataframe["%-chop-period"] = qtpylib.chopiness(dataframe, period) - dataframe["%-linear-period"] = ta.LINEARREG_ANGLE( + dataframe["%-linearreg-angle-period"] = ta.LINEARREG_ANGLE( dataframe["close"], timeperiod=period ) dataframe["%-atr-period"] = ta.ATR(dataframe, timeperiod=period) - dataframe["%-atr-periodp"] = ta.NATR(dataframe, timeperiod=period) + dataframe["%-natr-period"] = ta.NATR(dataframe, timeperiod=period) return dataframe def feature_engineering_expand_basic(self, dataframe, **kwargs): dataframe["%-pct-change"] = dataframe["close"].pct_change() dataframe["%-raw_volume"] = dataframe["volume"] dataframe["%-obv"] = ta.OBV(dataframe) - # Added - # dataframe["%-ewo"] = EWO(dataframe=dataframe, mode="zlewma", normalize=True) + dataframe["%-ewo"] = EWO(dataframe=dataframe, mode="zlewma", normalize=True) psar = ta.SAR( dataframe["high"], dataframe["low"], acceleration=0.02, maximum=0.2 ) @@ -206,7 +206,7 @@ class QuickAdapterV3(IStrategy): dataframe["%-vwap_width"] = ( (dataframe["vwap_upperband"] - dataframe["vwap_lowerband"]) / dataframe["vwap_middleband"] - ) * 100 + ) dataframe["%-dist_to_vwap_upperband"] = get_distance( dataframe["close"], dataframe["vwap_upperband"] ) -- 2.43.0