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Laguerre RSI

Ehlers DSP oscillator rsi ehlers dsp laguerre momentum

RSI calculated over Laguerre-warped time for faster response.

Visual Example

Laguerre RSI — 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 Laguerre RSI indicator is a technical analysis tool that rsi calculated over laguerre-warped time for faster response.

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 faster lower-lag alternative to traditional RSI. Laguerre smoothing produces fewer whipsaws while remaining responsive to genuine momentum shifts.

Ehlers constructs the Laguerre RSI in Cybernetic Analysis by computing RSI on the four outputs of a Laguerre filter bank. The result has RSI-like scaling (0 to 1) but dramatically less lag and smoother behaviour than conventional RSI.

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/laguerre_rsi.rs):

[ L_0 = (1 - \gamma) \cdot Close + \gamma \cdot L_{0,t-1} ] [ L_1 = -\gamma L_0 + L_{0,t-1} + \gamma L_{1,t-1} ] [ L_2 = -\gamma L_1 + L_{1,t-1} + \gamma L_{2,t-1} ] [ L_3 = -\gamma L_2 + L_{2,t-1} + \gamma L_{3,t-1} ] [ CU = \sum \max(L_{i} - L_{i+1}, 0) ] [ CD = \sum \max(L_{i+1} - L_{i}, 0) ] [ RSI = \frac{CU}{CU + CD} ]

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

Parameters

Parameter Default Description
gamma 0.5 Smoothing factor (0.0 to 1.0)

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::LAGUERRE_RSI;
use quantwave_core::traits::Next;

let mut ind = LAGUERRE_RSI::new(0.5);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import LAGUERRE_RSI

ind = LAGUERRE_RSI(0.5)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_laguerre_rsi(series: pl.Series) -> pl.Series:
    ind = qw.LAGUERRE_RSI(0.5)
    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_laguerre_rsi, return_dtype=pl.Float64).alias("laguerre_rsi")
    )
    .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/TimeWarp.pdf

Implementation: quantwave-core/src/indicators/laguerre_rsi.rs (LAGUERRE_RSI / LAGUERRE_RSI_METADATA). Parity: quantwave-core/tests/gold_standard/laguerre_rsi.json

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