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AutoTune Filter

Ehlers DSP adaptive filter cycle ehlers dsp autotune

An adaptive BandPass filter that dynamically tunes itself to the market's dominant cycle.

Visual Example

AutoTune Filter — 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 AutoTune Filter indicator is a technical analysis tool that an adaptive bandpass filter that dynamically tunes itself to the market's dominant cycle.

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 to isolate the cyclical component of price while automatically adapting to changes in cycle length. Zero crossings of the output or its rate of change can be used as trading signals.

The AutoTune filter provides a bridge between the time domain and frequency domain by using a rolling autocorrelation function to measure the Dominant Cycle in real time. By dynamically tuning a Bandpass filter to twice the lag at which autocorrelation is minimized, it maintains consistent performance and avoids the destructive phase shifts typical of fixed-tuned filters.

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/autotune.rs):

[ R(lag) = \frac{n \sum X_i Y_i - \sum X_i \sum Y_i}{\sqrt{(n \sum X_i^2 - (\sum X_i)^2)(n \sum Y_i^2 - (\sum Y_i)^2)}} ] [ DC = 2 \times \text{argmin}_{lag} R(lag) ] [ BP = \text{BandPass}(Price, DC, BW) ]

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

Parameters

Parameter Default Description
window 20 Window length for autocorrelation and HighPass filter
bandwidth 0.25 Bandwidth of the tuned BandPass filter

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::AUTOTUNE_FILTER;
use quantwave_core::traits::Next;

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

Streaming (Python)

from quantwave import AUTOTUNE_FILTER

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

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_autotune_filter(series: pl.Series) -> pl.Series:
    ind = qw.AUTOTUNE_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_autotune_filter, return_dtype=pl.Float64).alias("autotune_filter")
    )
    .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: references/Ehlers Papers/The AutoTune Filter.pdf

Implementation: quantwave-core/src/indicators/autotune.rs (AUTOTUNE_FILTER / AUTOTUNE_FILTER_METADATA). Parity: quantwave-core/tests/gold_standard/autotune_filter.json

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