refactor(quickadapter): harmonize eps values across the codebase

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 4017c4232111a5281ea8bdb60b067b53aa424efe..238ee542202b8f99b82c71e06f7e882d8ebf6d6f 100644 (file)
--- a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
+++ b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
@@ -96,7 +96,7 @@ class QuickAdapterRegressorV3(BaseRegressionModel):
     https://github.com/sponsors/robcaulk
     """
 
-    version = "3.11.4"
+    version = "3.11.5"
 
     _TEST_SIZE: Final[float] = 0.1
 
@@ -2029,7 +2029,7 @@ class QuickAdapterRegressorV3(BaseRegressionModel):
             )
 
         elif selection_method == EXTREMA_SELECTION_METHODS[2]:  # "partition"
-            eps = np.finfo(float).eps
+            eps = 10 * np.finfo(float).eps
 
             pred_label_maxima = pred_label[pred_label > eps]
             pred_label_minima = pred_label[pred_label < -eps]

diff --git a/quickadapter/user_data/strategies/QuickAdapterV3.py b/quickadapter/user_data/strategies/QuickAdapterV3.py
index d00ae210551aa65c7fb79a8684bcc292c568ad3a..c0105302faee8d8587ccfce936875e54c8d24507 100644 (file)
--- a/quickadapter/user_data/strategies/QuickAdapterV3.py
+++ b/quickadapter/user_data/strategies/QuickAdapterV3.py
@@ -109,7 +109,7 @@ class QuickAdapterV3(IStrategy):
     _PLOT_EXTREMA_MIN_EPS: Final[float] = 0.01
 
     def version(self) -> str:
-        return "3.11.4"
+        return "3.11.5"
 
     timeframe = "5m"
     timeframe_minutes = timeframe_to_minutes(timeframe)

diff --git a/quickadapter/user_data/strategies/Utils.py b/quickadapter/user_data/strategies/Utils.py
index f333adca95eb494dfa8fa016f2c498c197a73c69..49e706dffc2cecbd7ded9d0226db4bc350ab927f 100644 (file)
--- a/quickadapter/user_data/strategies/Utils.py
+++ b/quickadapter/user_data/strategies/Utils.py
@@ -1739,8 +1739,7 @@ def zigzag(
 
         arr = np.asarray(values, dtype=float)
         valid_mask = np.isfinite(arr)
-        n_valid = valid_mask.sum()
-        if n_valid < 2:
+        if not valid_mask.any():
             return values
 
         min_val = np.nanmin(arr)