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

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.