fix(qav3): revert candle pattern usage to label reversal pivots

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 ce4c0fc7a972daed17d6a443fe7c31ceb8f5ec90..d2b91de8fcedabeb3b30deab8279adbd4e8c4cc5 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 = "3.7.27"
+    version = "3.7.28"
 
     @cached_property
     def _optuna_config(self) -> dict:
@@ -840,12 +840,6 @@ def hp_objective(
     return error
 
 
-class TrendDirection(IntEnum):
-    NEUTRAL = 0
-    UP = 1
-    DOWN = -1
-
-
 def find_fractals(df: pd.DataFrame, fractal_period: int) -> tuple[list[int], list[int]]:
     if len(df) < 2 * fractal_period + 1:
         return [], []
@@ -876,26 +870,22 @@ def find_fractals(df: pd.DataFrame, fractal_period: int) -> tuple[list[int], lis
     )
 
 
+class TrendDirection(IntEnum):
+    NEUTRAL = 0
+    UP = 1
+    DOWN = -1
+
+
 def zigzag(
     df: pd.DataFrame,
     natr_period: int = 14,
     natr_ratio: float = 1.0,
-    fractal_period: int = 2,
     confirmation_window: int = 2,
     depth: int = 12,
 ) -> tuple[list[int], list[float], list[int]]:
-    if (
-        df.empty
-        or len(df) < max(natr_period, 2 * fractal_period + 1) + confirmation_window
-    ):
+    if df.empty or len(df) < natr_period + confirmation_window:
         return [], [], []
 
-    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 = (
         (ta.NATR(df, timeperiod=natr_period) * natr_ratio).fillna(method="bfill").values
@@ -930,7 +920,7 @@ def zigzag(
             return all(c > lows[pos] for c in next_closes)
         return False
 
-    start_pos = fractal_period
+    start_pos = 0
     initial_high_pos = start_pos
     initial_low_pos = start_pos
     initial_high = highs[initial_high_pos]
@@ -943,18 +933,14 @@ def zigzag(
 
         initial_move_from_high = (initial_high - lows[i]) / initial_high
         initial_move_from_low = (highs[i] - initial_low) / initial_low
-        if (
-            initial_move_from_high >= thresholds[i]
-            and is_fractal_high[initial_high_pos]
-            and is_reversal_confirmed(initial_high_pos, TrendDirection.DOWN)
+        if initial_move_from_high >= thresholds[i] and is_reversal_confirmed(
+            initial_high_pos, TrendDirection.DOWN
         ):
             add_pivot(initial_high_pos, initial_high, TrendDirection.UP)
             state = TrendDirection.DOWN
             break
-        elif (
-            initial_move_from_low >= thresholds[i]
-            and is_fractal_low[initial_low_pos]
-            and is_reversal_confirmed(initial_low_pos, TrendDirection.UP)
+        elif initial_move_from_low >= thresholds[i] and is_reversal_confirmed(
+            initial_low_pos, TrendDirection.UP
         ):
             add_pivot(initial_low_pos, initial_low, TrendDirection.DOWN)
             state = TrendDirection.UP
@@ -972,7 +958,6 @@ def zigzag(
             elif (
                 (last_pivot_val - current_low) / last_pivot_val >= thresholds[i]
                 and (i - last_pivot_pos) >= depth
-                and is_fractal_low[i]
                 and is_reversal_confirmed(i, TrendDirection.DOWN)
             ):
                 add_pivot(i, current_low, TrendDirection.DOWN)
@@ -983,7 +968,6 @@ def zigzag(
             elif (
                 (current_high - last_pivot_val) / last_pivot_val >= thresholds[i]
                 and (i - last_pivot_pos) >= depth
-                and is_fractal_high[i]
                 and is_reversal_confirmed(i, TrendDirection.UP)
             ):
                 add_pivot(i, current_high, TrendDirection.UP)

diff --git a/quickadapter/user_data/strategies/QuickAdapterV3.py b/quickadapter/user_data/strategies/QuickAdapterV3.py
index 9d830a66093c26211d3f88669c16defd8e8b8bfd..d2e8f8850418287455ac4748498d6a06eb328b43 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 "3.3.21"
+        return "3.3.22"
 
     timeframe = "5m"
 

diff --git a/quickadapter/user_data/strategies/Utils.py b/quickadapter/user_data/strategies/Utils.py
index 91798973daa596f92edc79acd2a5c42aca132abc..615c21c923c9fda7b8967ae405ba657f5cef310e 100644 (file)
--- a/quickadapter/user_data/strategies/Utils.py
+++ b/quickadapter/user_data/strategies/Utils.py
@@ -299,12 +299,6 @@ def alligator(
     return jaw, teeth, lips
 
 
-class TrendDirection(IntEnum):
-    NEUTRAL = 0
-    UP = 1
-    DOWN = -1
-
-
 def find_fractals(df: pd.DataFrame, fractal_period: int) -> tuple[list[int], list[int]]:
     if len(df) < 2 * fractal_period + 1:
         return [], []
@@ -335,26 +329,22 @@ def find_fractals(df: pd.DataFrame, fractal_period: int) -> tuple[list[int], lis
     )
 
 
+class TrendDirection(IntEnum):
+    NEUTRAL = 0
+    UP = 1
+    DOWN = -1
+
+
 def zigzag(
     df: pd.DataFrame,
     natr_period: int = 14,
     natr_ratio: float = 1.0,
-    fractal_period: int = 2,
     confirmation_window: int = 2,
     depth: int = 12,
 ) -> tuple[list[int], list[float], list[int]]:
-    if (
-        df.empty
-        or len(df) < max(natr_period, 2 * fractal_period + 1) + confirmation_window
-    ):
+    if df.empty or len(df) < natr_period + confirmation_window:
         return [], [], []
 
-    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 = (
         (ta.NATR(df, timeperiod=natr_period) * natr_ratio).fillna(method="bfill").values
@@ -389,7 +379,7 @@ def zigzag(
             return all(c > lows[pos] for c in next_closes)
         return False
 
-    start_pos = fractal_period
+    start_pos = 0
     initial_high_pos = start_pos
     initial_low_pos = start_pos
     initial_high = highs[initial_high_pos]
@@ -402,18 +392,14 @@ def zigzag(
 
         initial_move_from_high = (initial_high - lows[i]) / initial_high
         initial_move_from_low = (highs[i] - initial_low) / initial_low
-        if (
-            initial_move_from_high >= thresholds[i]
-            and is_fractal_high[initial_high_pos]
-            and is_reversal_confirmed(initial_high_pos, TrendDirection.DOWN)
+        if initial_move_from_high >= thresholds[i] and is_reversal_confirmed(
+            initial_high_pos, TrendDirection.DOWN
         ):
             add_pivot(initial_high_pos, initial_high, TrendDirection.UP)
             state = TrendDirection.DOWN
             break
-        elif (
-            initial_move_from_low >= thresholds[i]
-            and is_fractal_low[initial_low_pos]
-            and is_reversal_confirmed(initial_low_pos, TrendDirection.UP)
+        elif initial_move_from_low >= thresholds[i] and is_reversal_confirmed(
+            initial_low_pos, TrendDirection.UP
         ):
             add_pivot(initial_low_pos, initial_low, TrendDirection.DOWN)
             state = TrendDirection.UP
@@ -431,7 +417,6 @@ def zigzag(
             elif (
                 (last_pivot_val - current_low) / last_pivot_val >= thresholds[i]
                 and (i - last_pivot_pos) >= depth
-                and is_fractal_low[i]
                 and is_reversal_confirmed(i, TrendDirection.DOWN)
             ):
                 add_pivot(i, current_low, TrendDirection.DOWN)
@@ -442,7 +427,6 @@ def zigzag(
             elif (
                 (current_high - last_pivot_val) / last_pivot_val >= thresholds[i]
                 and (i - last_pivot_pos) >= depth
-                and is_fractal_high[i]
                 and is_reversal_confirmed(i, TrendDirection.UP)
             ):
                 add_pivot(i, current_high, TrendDirection.UP)