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GriffithsPredictor

Ehlers DSP prediction cycle ehlers dsp

Adaptive LMS linear predictive filter for signal forecasting.

Visual Example

GriffithsPredictor — annotated preview mapping to core implementation

Synthetic ideal per library logic. Generated 2026-07-01 IST via docs/generate_all_previews.py (reproducible; maps to core Next<T> implementation).

Description

Adaptive LMS linear predictive filter for signal forecasting.

Use for short-horizon price prediction by projecting the dominant market cycle one or two bars forward. Works best in oscillating markets; disable in strong trends.

Part of QuantWave's Ehlers digital signal processing suite. Designed for low-lag cycle and trend work — pair with Roofing Filter or SuperSmoother on noisy inputs.

The Griffiths Predictor uses autoregressive coefficients from the Griffiths cycle measurement to extrapolate the current dominant cycle one bar ahead. By fitting an AR model to cycle-filtered price, it generates a one-step-ahead forecast useful for anticipatory entries at predicted cycle turns.

Typical applications:

  • Use for cycle timing in mean-reverting regimes
  • Gate with Hurst exponent or ADX before taking cycle signals
  • Allow 18+ bars warm-up for filter state to stabilise
  • Chain with Roofing Filter when input is noisy

QuantWave implements this via the universal Next<T> trait — bit-identical across Rust streaming, Python streaming, and Polars .ta() batch plugins.

Formula / Specification

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

[ \mu = 1/L ] [ \bar{x} = \sum_{i=1}^L xx_{L-i} \cdot coef_i ] [ coef_i = coef_i + \mu(xx_L - \bar{x})xx_{L-i} ]

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

Parameters

Parameter Default Description
lower_bound 18 Lower frequency bound (SS length)
upper_bound 40 Upper frequency bound (HP length)
length 18 LMS filter length
bars_fwd 2 Number of bars to predict forward

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::GRIFFITHS_PREDICTOR;
use quantwave_core::traits::Next;

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

Streaming (Python)

from quantwave import GRIFFITHS_PREDICTOR

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

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_griffithspredictor(series: pl.Series) -> pl.Series:
    ind = qw.GRIFFITHS_PREDICTOR(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_griffithspredictor, return_dtype=pl.Float64).alias("griffithspredictor")
    )
    .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_predictor.rs (GRIFFITHS_PREDICTOR / GRIFFITHS_PREDICTOR_METADATA). Parity: quantwave-core/tests/gold_standard/griffiths_predictor.json

Provenance: Standards bulk upgrade 2026-07-01 IST — see docs/DOCUMENTATION_STANDARDS.md.