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MyRSI

Ehlers DSP oscillator rsi ehlers momentum smoothing

Ehlers' version of RSI that swings between -1 and +1.

Visual Example

MyRSI — 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

Ehlers' version of RSI that swings between -1 and +1.

Use as Ehlers smoothed RSI variant that applies cycle-aware filtering to reduce whipsaws while maintaining RSI-style overbought/oversold interpretation.

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.

Ehlers presents a smoothed RSI formulation that applies a Laguerre or SuperSmoother filter to the up/down ratio before computing the RSI index. This reduces the noise and oscillation of standard RSI without significantly increasing lag, producing more reliable overbought and oversold readings.

Typical applications:

  • Use for cycle timing in mean-reverting regimes
  • Gate with Hurst exponent or ADX before taking cycle signals
  • Allow 14+ 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/my_rsi.rs):

[ CU = \sum_{i=0}^{length-1} \max(0, Price_i - Price_{i+1}) ] [ CD = \sum_{i=0}^{length-1} \max(0, Price_{i+1} - Price_i) ] [ MyRSI = \frac{CU - CD}{CU + CD} ]

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

Parameters

Parameter Default Description
length 14 Smoothing length

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::MY_RSI;
use quantwave_core::traits::Next;

let mut ind = MY_RSI::new(14);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import MY_RSI

ind = MY_RSI(14)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_myrsi(series: pl.Series) -> pl.Series:
    ind = qw.MY_RSI(14)
    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_myrsi, return_dtype=pl.Float64).alias("myrsi")
    )
    .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/Noise%20Elimination%20Technology.pdf

Implementation: quantwave-core/src/indicators/my_rsi.rs (MY_RSI / MY_RSI_METADATA). Parity: quantwave-core/tests/gold_standard/my_rsi.json

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