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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 2026-06-25 IST via docs/generate_all_previews.py (reproducible; maps to core Next<T> implementation).

Description

The GriffithsPredictor indicator is a technical analysis tool that adaptive lms linear predictive filter for signal forecasting.

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 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.

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.

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_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 2026-06-25 IST — see docs/DOCUMENTATION_STANDARDS.md.