refactor(qav3): use sensible defaults

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

diff --git a/ReforceXY/user_data/config-template.json b/ReforceXY/user_data/config-template.json
index 1de66d2df962cba48ad67dee49e04a7dc87220b8..a4b97a69594ad087fef41b900437ebc500318d52 100644 (file)
--- a/ReforceXY/user_data/config-template.json
+++ b/ReforceXY/user_data/config-template.json
@@ -114,7 +114,7 @@
     "backtest_period_days": 2,
     "write_metrics_to_disk": false,
     "identifier": "ReforceXY-PPO",
-    "fit_live_predictions_candles": 300,
+    "fit_live_predictions_candles": 600,
     "data_kitchen_thread_count": 6, // set to number of CPU threads / 4
     "track_performance": false,
     "feature_parameters": {

diff --git a/quickadapter/user_data/config-template.json b/quickadapter/user_data/config-template.json
index 5c77e1fdfb8280b49850d76db403d175d6e6e0ba..e9a59311b1f6b9ec4d8a9aeb1f6fd7a4e79f4e5b 100644 (file)
--- a/quickadapter/user_data/config-template.json
+++ b/quickadapter/user_data/config-template.json
@@ -114,7 +114,7 @@
     "fit_live_predictions_candles": 300,
     "data_kitchen_thread_count": 6, // set to number of CPU threads / 4
     "track_performance": false,
-    "predictions_smoothing": "log-sum-exp",
+    "predictions_smoothing": "mean",
     "outlier_threshold": 0.999,
     "optuna_hyperopt": {
       "enabled": true,

diff --git a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
index 7da35646fbcececef275a9d993b9cc5fdaa66899..1760a7ba648bd7953328629c5d8342828ab90449 100644 (file)
--- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
@@ -271,9 +271,7 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel):
         fit_live_predictions_candles: int,
         label_period_candles: int,
     ) -> tuple[float, float]:
-        predictions_smoothing = self.freqai_info.get(
-            "predictions_smoothing", "log-sum-exp"
-        )
+        predictions_smoothing = self.freqai_info.get("predictions_smoothing", "mean")
         if predictions_smoothing == "log-sum-exp":
             return log_sum_exp_min_max_pred(
                 pred_df, fit_live_predictions_candles, label_period_candles

diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
index b2a9f7f18609301fd0fcab22b62cdb577c749b54..2a5bf96cc5165c6ad8e9cd4aed3931efa1628652 100644 (file)
--- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
@@ -272,9 +272,7 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel):
         fit_live_predictions_candles: int,
         label_period_candles: int,
     ) -> tuple[float, float]:
-        predictions_smoothing = self.freqai_info.get(
-            "predictions_smoothing", "log-sum-exp"
-        )
+        predictions_smoothing = self.freqai_info.get("predictions_smoothing", "mean")
         if predictions_smoothing == "log-sum-exp":
             return log_sum_exp_min_max_pred(
                 pred_df, fit_live_predictions_candles, label_period_candles

diff --git a/quickadapter/user_data/strategies/QuickAdapterV3.py b/quickadapter/user_data/strategies/QuickAdapterV3.py
index a857e6cc22e6e3f8579d617739a8b361d5747edd..bbf89ecded7ba05f50118dd16cb9ad7d101219ed 100644 (file)
--- a/quickadapter/user_data/strategies/QuickAdapterV3.py
+++ b/quickadapter/user_data/strategies/QuickAdapterV3.py
@@ -38,8 +38,6 @@ class QuickAdapterV3(IStrategy):
     https://github.com/sponsors/robcaulk
     """
 
-    position_adjustment_enable = False
-
     stoploss = -0.02
     # Trailing stop:
     trailing_stop = True
@@ -58,9 +56,8 @@ class QuickAdapterV3(IStrategy):
         "stoploss_on_exchange_interval": 120,
     }
 
-    # Example specific variables
+    position_adjustment_enable = False
     max_entry_position_adjustment = 1
-    # This number is explained a bit further down
     max_dca_multiplier = 2
 
     minimal_roi = {"0": 0.03, "1000": -1}