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.
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"""
- version = "3.11.7"
+ version = "3.11.8"
_TEST_SIZE: Final[float] = 0.1
df,
natr_period=label_period_candles,
natr_multiplier=label_natr_multiplier,
- normalize=False,
)
median_amplitude = np.nanmedian(np.asarray(pivots_amplitudes, dtype=float))
_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)
df: pd.DataFrame,
natr_period: int = 14,
natr_multiplier: float = 9.0,
- normalize: bool = True,
+ normalize: bool = False,
) -> tuple[
list[int],
list[float],
repositories / freqai-strategies.git / commitdiff
