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RocketRSI

Ehlers DSP oscillator rsi ehlers dsp fisher momentum

Highly responsive RSI variant using SuperSmoother and Fisher Transform.

Visual Example

RocketRSI — 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

Highly responsive RSI variant using SuperSmoother and Fisher Transform.

Use for rapid cycle identification and reversal detection. The Fisher Transform converts the RSI distribution into a Gaussian-like distribution with sharp peaks at reversals.

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.

RocketRSI improves upon standard RSI by first smoothing the momentum with a SuperSmoother filter to eliminate high-frequency noise. The resulting RSI is then passed through a Fisher Transform to create clear, actionable signals at cyclical turning points.

Typical applications:

  • Use for cycle timing in mean-reverting regimes
  • Gate with Hurst exponent or ADX before taking cycle signals
  • Allow 8+ 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/rocket_rsi.rs):

[ Mom = Price - Price_{t-(L-1)} ] [ Filt = \text{SuperSmoother}(Mom, SL) ] [ MyRSI = \frac{\sum \max(0, \Delta Filt) - \sum \max(0, -\Delta Filt)}{\sum |\Delta Filt|} ] [ RocketRSI = 0.5 \cdot \ln\left(\frac{1 + MyRSI}{1 - MyRSI}\right) ]

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

Parameters

Parameter Default Description
rsi_length 8 RSI calculation period
smooth_length 10 SuperSmoother filter period

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::ROCKET_RSI;
use quantwave_core::traits::Next;

let mut ind = ROCKET_RSI::new(8);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import ROCKET_RSI

ind = ROCKET_RSI(8)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_rocketrsi(series: pl.Series) -> pl.Series:
    ind = qw.ROCKET_RSI(8)
    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_rocketrsi, return_dtype=pl.Float64).alias("rocketrsi")
    )
    .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://www.traders.com/Documentation/FEEDbk_docs/2018/05/TradersTips.html

Implementation: quantwave-core/src/indicators/rocket_rsi.rs (ROCKET_RSI / ROCKET_RSI_METADATA). Parity: quantwave-core/tests/gold_standard/rocket_rsi.json

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