refactor(qav3): differentiate candles step for training and labeling

Signed-off-by: Jérôme Benoit <jerome.benoit@piment-noir.org>

diff --git a/quickadapter/user_data/config-template.json b/quickadapter/user_data/config-template.json
index 66dec2b818bb804e8f39cd268d3bd739714b5242..7067924c955009e5f7fa872e4ff3d3bca769243a 100644 (file)
--- a/quickadapter/user_data/config-template.json
+++ b/quickadapter/user_data/config-template.json
@@ -126,7 +126,8 @@
       "n_jobs": 6,
       "n_trials": 36,
       "timeout": 7200,
-      "candles_step": 4,
+      "label_candles_step": 4,
+      "train_candles_step": 10,
       "storage": "file"
     },
     "extra_returns_per_train": {

diff --git a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
index d3f719a9dd59d826ddf54136a5cc2bd9e7266736..c48c47201ca224929f926d9a7aa5ea76b71f059b 100644 (file)
--- a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
+++ b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
@@ -66,7 +66,8 @@ class QuickAdapterRegressorV3(BaseRegressionModel):
             "n_startup_trials": 15,
             "n_trials": 36,
             "timeout": 7200,
-            "candles_step": 4,
+            "label_candles_step": 4,
+            "train_candles_step": 10,
             "expansion_factor": 0.4,
             "seed": 1,
         }
@@ -321,7 +322,7 @@ class QuickAdapterRegressorV3(BaseRegressionModel):
                     test_weights,
                     self.data_split_parameters.get("test_size", TEST_SIZE),
                     self.freqai_info.get("fit_live_predictions_candles", 100),
-                    self._optuna_config.get("candles_step"),
+                    self._optuna_config.get("train_candles_step"),
                     model_training_parameters,
                 ),
                 direction=optuna.study.StudyDirection.MINIMIZE,
@@ -412,7 +413,7 @@ class QuickAdapterRegressorV3(BaseRegressionModel):
                             pair=pair, timeframe=self.config.get("timeframe")
                         ),
                         fit_live_predictions_candles,
-                        self._optuna_config.get("candles_step"),
+                        self._optuna_config.get("label_candles_step"),
                     ),
                     directions=[
                         optuna.study.StudyDirection.MAXIMIZE,