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HannFilter

Ehlers DSP filter ehlers dsp windowing spectral

Hann windowed lowpass FIR filter.

Visual Example

HannFilter — annotated preview mapping to core implementation

Synthetic ideal per library logic. Generated 2026-06-25 IST via docs/generate_all_previews.py (reproducible; maps to core Next<T> implementation).

Description

The HannFilter indicator is a technical analysis tool that hann windowed lowpass fir filter.

This indicator is primarily used for identifying key market conditions. It provides a robust signal that can be easily integrated into both simple strategies and more complex machine learning feature pipelines. Compared to its alternatives, it offers a distinct balance of responsiveness and stability.

Traders often combine this with other metrics to confirm signals and avoid false positives during sideways market regimes. It remains a standard tool for systematic trading models.

Use as a windowing function before FFT-based dominant cycle measurement to achieve clean spectral separation between market cycles.

The Hann window provides a smooth bell-shaped taper achieving -31.5 dB first sidelobe suppression. Ehlers uses it in Cycle Analytics for Traders as the preferred DFT window because it offers the best trade-off between frequency resolution and leakage rejection.

QuantWave implements this indicator via the universal Next<T> trait, guaranteeing bit-identical results between Rust streaming, Python streaming, and Polars batch (.ta() / map_batches) surfaces.

Formula / Specification

Implementation (quantwave-core/src/indicators/hann.rs):

[ H(n) = 1 - \cos\left(\frac{2\pi n}{L+1}\right) ] [ Filt = \frac{\sum_{n=1}^L H(n) \cdot Price_{t-n+1}}{\sum H(n)} ]

Gold-standard parity vectors: quantwave-core/tests/gold_standard/hann_filter.json.

Parameters

Parameter Default Description
length 20 Filter length

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::HANN_FILTER;
use quantwave_core::traits::Next;

let mut ind = HANN_FILTER::new(20);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import HANN_FILTER

ind = HANN_FILTER(20)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_hannfilter(series: pl.Series) -> pl.Series:
    ind = qw.HANN_FILTER(20)
    return pl.Series([ind.next(float(v)) for v in series.to_list()])

df = (
    pl.read_csv('ohlcv.csv')
    .lazy()
    .with_columns(
        pl.col("close").map_batches(apply_hannfilter, return_dtype=pl.Float64).alias("hannfilter")
    )
    .collect()
)

All surfaces are bit-identical via the single Next<T> implementation and proptests.

Edge Cases & Limitations

  • Recursive DSP filters require a warm-up period; first N bars may be unstable or raw-pass-through.
  • Designed for cyclic/mean-reverting regimes; trending markets can produce lag or drift.
  • Parameter period (or equivalent) controls cutoff — too small adds noise, too large adds lag.
  • Prefer chaining with other Ehlers tools (Roofing Filter, SuperSmoother) on noisy inputs.
  • Validated via proptests against gold-standard vectors where available.
  • No look-ahead bias; suitable for live streaming and batch feature pipelines.

Boundary Behavior

Condition Behavior
Warm-up Leading bars return NaN until warmup_bars is satisfied.
period > len When period exceeds series length, output is all NaN.
NaN inputs NaN in input propagates to output (NaN out).
Invalid params Non-positive period or missing required params raise ValueError.
Empty data Empty input returns an empty result series.

Sources & References

Primary Source: https://github.com/lavs9/quantwave/blob/main/references/Ehlers%20Papers/JustIgnoreThem.pdf

Implementation: quantwave-core/src/indicators/hann.rs (HANN_FILTER / HANN_FILTER_METADATA). Parity: quantwave-core/tests/gold_standard/hann_filter.json

Provenance: Standards bulk upgrade 2026-06-25 IST — see docs/DOCUMENTATION_STANDARDS.md.