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GriffithsSpectrum

Ehlers DSP spectrum cycle ehlers dsp periodogram

Normalized power spectrum estimation using Griffiths adaptive filters.

Visual Example

GriffithsSpectrum — 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 GriffithsSpectrum indicator is a technical analysis tool that normalized power spectrum estimation using griffiths adaptive filters.

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 generate a high-resolution periodogram for cycle analysis. Best visualized as a heatmap to identify and track multiple market cycles simultaneously.

The Griffiths Spectrum is an adaptive spectral estimation method that provides higher resolution than a standard DFT for short data segments. It fits an all-pole model to the signal using an LMS algorithm, allowing for instantaneous frequency measurement without the windowing artifacts of FFT-based methods.

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/griffiths_spectrum.rs):

[ Pwr(P) = \frac{0.1}{(1 - \sum coef_i \cos(2\pi i/P))^2 + (\sum coef_i \sin(2\pi i/P))^2} ] [ Pwr_{norm}(P) = \frac{Pwr(P)}{\max(Pwr)} ]

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

Parameters

Parameter Default Description
lower_bound 18 Lower period bound
upper_bound 40 Upper period bound
length 40 LMS filter length

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::GRIFFITHS_SPECTRUM;
use quantwave_core::traits::Next;

let mut ind = GRIFFITHS_SPECTRUM::new(18);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import GRIFFITHS_SPECTRUM

ind = GRIFFITHS_SPECTRUM(18)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_griffithsspectrum(series: pl.Series) -> pl.Series:
    ind = qw.GRIFFITHS_SPECTRUM(18)
    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_griffithsspectrum, return_dtype=pl.Float64).alias("griffithsspectrum")
    )
    .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/traderstipsreference/TRADERS’%20TIPS%20-%20JANUARY%202025.html

Implementation: quantwave-core/src/indicators/griffiths_spectrum.rs (GRIFFITHS_SPECTRUM / GRIFFITHS_SPECTRUM_METADATA). Parity: quantwave-core/tests/gold_standard/griffiths_spectrum.json

Provenance: Standards bulk upgrade 2026-06-25 IST (DOCUMENTATION_STANDARDS.md).