refactor(qav3)!: refine features list

Signed-off-by: Jérôme Benoit <jerome.benoit@sap.com>

diff --git a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
index 3107ae717eb8a1a6d97c56ef508a02df7063cafd..561f619802a64e6019c4a6738bdffb60d85f9464 100644 (file)
--- 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 4e8d66b75c1d28be901e88ebcdf02e0c9bc630b9..7c8693d0d1bda019f8767c2835c0289359f47900 100644 (file)
--- 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"]
         )