perf(qav3): fine tune prediction thresholds and stoploss distance

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

diff --git a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
index 2bdf26c18496afbc6dbdd5c910823bca0ef9c0d0..c584bfb4b7f80951965695bb6d4d725f0f806ac7 100644 (file)
--- a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
+++ b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
@@ -44,7 +44,7 @@ class QuickAdapterRegressorV3(BaseRegressionModel):
     https://github.com/sponsors/robcaulk
     """
 
-    version = "3.7.10"
+    version = "3.7.11"
 
     @cached_property
     def _optuna_config(self) -> dict:
@@ -372,7 +372,7 @@ class QuickAdapterRegressorV3(BaseRegressionModel):
 
     def min_max_pred(self, pred_df: pd.DataFrame) -> tuple[float, float]:
         temperature = float(
-            self.freqai_info.get("prediction_thresholds_temperature", 150.0)
+            self.freqai_info.get("prediction_thresholds_temperature", 125.0)
         )
         min_pred = smoothed_min(
             pred_df[EXTREMA_COLUMN],

diff --git a/quickadapter/user_data/strategies/QuickAdapterV3.py b/quickadapter/user_data/strategies/QuickAdapterV3.py
index 350327fc7b1c9655e3cde348ed371be025dee42e..b19bbce0264643319785d604438064e9301e5f55 100644 (file)
--- a/quickadapter/user_data/strategies/QuickAdapterV3.py
+++ b/quickadapter/user_data/strategies/QuickAdapterV3.py
@@ -58,7 +58,7 @@ class QuickAdapterV3(IStrategy):
     INTERFACE_VERSION = 3
 
     def version(self) -> str:
-        return "3.3.7"
+        return "3.3.8"
 
     timeframe = "5m"
 
@@ -377,7 +377,7 @@ class QuickAdapterV3(IStrategy):
         return self.get_label_natr_ratio(pair) * 0.025
 
     def get_trailing_stoploss_natr_ratio(self, pair: str) -> float:
-        return self.get_label_natr_ratio(pair) * 0.5
+        return self.get_label_natr_ratio(pair) * 0.75
 
     def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs):
         pair = str(metadata.get("pair"))