perf(qav3): optimize reversal pivots labeling

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 8254abdb1c35405ac15eb829093f462f90ca467c..16625d1e0549125eed8ebeecf849c1dc772133c9 100644 (file)
--- a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
+++ b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
@@ -847,25 +847,33 @@ class TrendDirection(IntEnum):
 
 
 def find_fractals(df: pd.DataFrame, fractal_period: int) -> tuple[list[int], list[int]]:
+    if len(df) < 2 * fractal_period + 1:
+        return [], []
+
     highs = df["high"].values
     lows = df["low"].values
-    fractal_highs = []
-    fractal_lows = []
-    for i in range(fractal_period, len(df) - fractal_period):
-        valid_high = True
-        valid_low = True
-        for j in range(1, fractal_period + 1):
-            if highs[i] <= highs[i - j] or highs[i] <= highs[i + j]:
-                valid_high = False
-            if lows[i] >= lows[i - j] or lows[i] >= lows[i + j]:
-                valid_low = False
-            if not valid_high and not valid_low:
-                break
-        if valid_high:
-            fractal_highs.append(i)
-        if valid_low:
-            fractal_lows.append(i)
-    return fractal_highs, fractal_lows
+
+    fractal_candidate_indices = np.arange(fractal_period, len(df) - fractal_period)
+
+    is_fractal_high = np.ones(len(fractal_candidate_indices), dtype=bool)
+    is_fractal_low = np.ones(len(fractal_candidate_indices), dtype=bool)
+
+    for i in range(1, fractal_period + 1):
+        is_fractal_high &= (
+            highs[fractal_candidate_indices] > highs[fractal_candidate_indices - i]
+        ) & (highs[fractal_candidate_indices] > highs[fractal_candidate_indices + i])
+
+        is_fractal_low &= (
+            lows[fractal_candidate_indices] < lows[fractal_candidate_indices - i]
+        ) & (lows[fractal_candidate_indices] < lows[fractal_candidate_indices + i])
+
+        if not np.any(is_fractal_high) and not np.any(is_fractal_low):
+            break
+
+    return (
+        fractal_candidate_indices[is_fractal_high].tolist(),
+        fractal_candidate_indices[is_fractal_low].tolist(),
+    )
 
 
 def zigzag(
@@ -878,11 +886,11 @@ def zigzag(
     if df.empty or len(df) < 2:
         return [], [], []
 
-    fractal_highs, fractal_lows = find_fractals(df, fractal_period)
-    fractal_highs_set = set(fractal_highs)
-    fractal_lows_set = set(fractal_lows)
-    is_fractal_high = [i in fractal_highs_set for i in range(len(df))]
-    is_fractal_low = [i in fractal_lows_set for i in range(len(df))]
+    fractal_high_indices, fractal_low_indices = find_fractals(df, fractal_period)
+    is_fractal_high = np.zeros(len(df), dtype=bool)
+    is_fractal_high[fractal_high_indices] = True
+    is_fractal_low = np.zeros(len(df), dtype=bool)
+    is_fractal_low[fractal_low_indices] = True
 
     indices = df.index.tolist()
     thresholds = (
@@ -935,7 +943,7 @@ def zigzag(
     else:
         return [], [], []
 
-    for i in range(i + 1, len(df)):
+    for i in range(last_pivot_pos + 1, len(df)):
         last_pivot_val = pivots_values[-1]
         if state == TrendDirection.UP:
             if highs[i] > last_pivot_val and is_fractal_high[i]:

diff --git a/quickadapter/user_data/strategies/Utils.py b/quickadapter/user_data/strategies/Utils.py
index 7dee6692dfb78594a94dc7f80f62b88358e87067..1a9089a0be978a0c56583240ad7c26ce37bd181d 100644 (file)
--- a/quickadapter/user_data/strategies/Utils.py
+++ b/quickadapter/user_data/strategies/Utils.py
@@ -306,25 +306,33 @@ class TrendDirection(IntEnum):
 
 
 def find_fractals(df: pd.DataFrame, fractal_period: int) -> tuple[list[int], list[int]]:
+    if len(df) < 2 * fractal_period + 1:
+        return [], []
+
     highs = df["high"].values
     lows = df["low"].values
-    fractal_highs = []
-    fractal_lows = []
-    for i in range(fractal_period, len(df) - fractal_period):
-        valid_high = True
-        valid_low = True
-        for j in range(1, fractal_period + 1):
-            if highs[i] <= highs[i - j] or highs[i] <= highs[i + j]:
-                valid_high = False
-            if lows[i] >= lows[i - j] or lows[i] >= lows[i + j]:
-                valid_low = False
-            if not valid_high and not valid_low:
-                break
-        if valid_high:
-            fractal_highs.append(i)
-        if valid_low:
-            fractal_lows.append(i)
-    return fractal_highs, fractal_lows
+
+    fractal_candidate_indices = np.arange(fractal_period, len(df) - fractal_period)
+
+    is_fractal_high = np.ones(len(fractal_candidate_indices), dtype=bool)
+    is_fractal_low = np.ones(len(fractal_candidate_indices), dtype=bool)
+
+    for i in range(1, fractal_period + 1):
+        is_fractal_high &= (
+            highs[fractal_candidate_indices] > highs[fractal_candidate_indices - i]
+        ) & (highs[fractal_candidate_indices] > highs[fractal_candidate_indices + i])
+
+        is_fractal_low &= (
+            lows[fractal_candidate_indices] < lows[fractal_candidate_indices - i]
+        ) & (lows[fractal_candidate_indices] < lows[fractal_candidate_indices + i])
+
+        if not np.any(is_fractal_high) and not np.any(is_fractal_low):
+            break
+
+    return (
+        fractal_candidate_indices[is_fractal_high].tolist(),
+        fractal_candidate_indices[is_fractal_low].tolist(),
+    )
 
 
 def zigzag(
@@ -337,11 +345,11 @@ def zigzag(
     if df.empty or len(df) < 2:
         return [], [], []
 
-    fractal_highs, fractal_lows = find_fractals(df, fractal_period)
-    fractal_highs_set = set(fractal_highs)
-    fractal_lows_set = set(fractal_lows)
-    is_fractal_high = [i in fractal_highs_set for i in range(len(df))]
-    is_fractal_low = [i in fractal_lows_set for i in range(len(df))]
+    fractal_high_indices, fractal_low_indices = find_fractals(df, fractal_period)
+    is_fractal_high = np.zeros(len(df), dtype=bool)
+    is_fractal_high[fractal_high_indices] = True
+    is_fractal_low = np.zeros(len(df), dtype=bool)
+    is_fractal_low[fractal_low_indices] = True
 
     indices = df.index.tolist()
     thresholds = (
@@ -394,7 +402,7 @@ def zigzag(
     else:
         return [], [], []
 
-    for i in range(i + 1, len(df)):
+    for i in range(last_pivot_pos + 1, len(df)):
         last_pivot_val = pivots_values[-1]
         if state == TrendDirection.UP:
             if highs[i] > last_pivot_val and is_fractal_high[i]: