perf(qav3): recalibrate pivot labeling momentum interval under low volatility

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 90045ad5e0376f639dfe9c6fecbfd5f9599d4bad..37ad09115dd340e3be5e7dcc82e6835c7a8c0aa7 100644 (file)
--- a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
+++ b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
@@ -45,7 +45,7 @@ class QuickAdapterRegressorV3(BaseRegressionModel):
     https://github.com/sponsors/robcaulk
     """
 
-    version = "3.7.63"
+    version = "3.7.64"
 
     @cached_property
     def _optuna_config(self) -> dict:
@@ -963,8 +963,8 @@ def zigzag(
 
     def calculate_min_slope_strength(
         pos: int,
-        min_strength: float = 0.8,
-        max_strength: float = 1.6,
+        min_strength: float = 0.5,
+        max_strength: float = 1.5,
     ) -> float:
         volatility_quantile = calculate_volatility_quantile(pos)
         if np.isnan(volatility_quantile):

diff --git a/quickadapter/user_data/strategies/QuickAdapterV3.py b/quickadapter/user_data/strategies/QuickAdapterV3.py
index 3d1bfaf83409b718e6a997eed4f67dedf2c060e0..ab310451adaf020c17b530eae82f60d9c07afc78 100644 (file)
--- a/quickadapter/user_data/strategies/QuickAdapterV3.py
+++ b/quickadapter/user_data/strategies/QuickAdapterV3.py
@@ -60,7 +60,7 @@ class QuickAdapterV3(IStrategy):
     INTERFACE_VERSION = 3
 
     def version(self) -> str:
-        return "3.3.65"
+        return "3.3.66"
 
     timeframe = "5m"
 

diff --git a/quickadapter/user_data/strategies/Utils.py b/quickadapter/user_data/strategies/Utils.py
index a4a1a8ebf89d2c9fa4ea8efcd8207e1c99bc658a..b7a6f7f29a46f26e6da3721b440af3adba3d5e78 100644 (file)
--- a/quickadapter/user_data/strategies/Utils.py
+++ b/quickadapter/user_data/strategies/Utils.py
@@ -441,8 +441,8 @@ def zigzag(
 
     def calculate_min_slope_strength(
         pos: int,
-        min_strength: float = 0.8,
-        max_strength: float = 1.6,
+        min_strength: float = 0.5,
+        max_strength: float = 1.5,
     ) -> float:
         volatility_quantile = calculate_volatility_quantile(pos)
         if np.isnan(volatility_quantile):