From: Jérôme Benoit <jerome.benoit@piment-noir.org>
Date: Sat, 23 May 2026 22:23:35 +0000 (+0200)
Subject: fix(zigzag): default normalize to False to prevent label magnitude leak (#71)
X-Git-Url: https://git.piment-noir.org/?a=commitdiff_plain;h=900a646d1220189e1df8ebec31a07a0c65e792e3;p=freqai-strategies.git

fix(zigzag): default normalize to False to prevent label magnitude leak (#71)

When set_freqai_targets is invoked by FreqAI's backtesting loop, the dataframe
passed to _generate_extrema_label spans the full historical window
(right-truncated to the current train-window stop), not just train_period_days.
With normalize=True, zigzag applies a global minmax scaling across all detected
pivots in that wider window to amplitudes, amplitude_threshold_ratios,
volume_rates and speeds. The resulting label magnitudes therefore depend on the
global pivot distribution, including pivots outside the current training slice
— a magnitude leak from out-of-train data into training labels.

Switching the zigzag default to normalize=False emits raw log-amplitude values
(|log(P2/P1)|) and defers any scaling to LabelTransformer, which is fitted
strictly on the train slice and is therefore leak-free. The two existing call
sites — _generate_extrema_label (label generation) and label_objective (Optuna
hyperopt) — both want the unnormalized output, so the redundant
normalize=False kwargs are dropped at the call sites in favor of the default.

Strategy and regressor patch versions are bumped to 3.11.8.

Caveat: with apply_label_weighting strategy="combined", the metrics now sit on
heterogeneous scales (raw log-amplitudes ~[0.005, 0.5] mix with bounded ratios
in [0, 1] like efficiency_ratio). Users relying on "combined" aggregation
(power means, weighted_median, softmax) may need to introduce metric-specific
rescaling on the train slice before aggregation. Direction-only (strategy=
"none") and single-metric strategies (e.g. strategy="amplitude") are
unaffected.
---

diff --git a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
index 7f7b19b..a9aebce 100644
--- a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
+++ b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
@@ -98,7 +98,7 @@ class QuickAdapterRegressorV3(BaseRegressionModel):
     https://github.com/sponsors/robcaulk
     """
 
-    version = "3.11.7"
+    version = "3.11.8"
 
     _TEST_SIZE: Final[float] = 0.1
 
@@ -3592,7 +3592,6 @@ def label_objective(
         df,
         natr_period=label_period_candles,
         natr_multiplier=label_natr_multiplier,
-        normalize=False,
     )
 
     median_amplitude = np.nanmedian(np.asarray(pivots_amplitudes, dtype=float))
diff --git a/quickadapter/user_data/strategies/QuickAdapterV3.py b/quickadapter/user_data/strategies/QuickAdapterV3.py
index d7a2358..f02eba2 100644
--- 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 "3.11.7"
+        return "3.11.8"
 
     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 c5f8e74..a1def00 100644
--- a/quickadapter/user_data/strategies/Utils.py
+++ b/quickadapter/user_data/strategies/Utils.py
@@ -1669,7 +1669,7 @@ def zigzag(
     df: pd.DataFrame,
     natr_period: int = 14,
     natr_multiplier: float = 9.0,
-    normalize: bool = True,
+    normalize: bool = False,
 ) -> tuple[
     list[int],
     list[float],