RocketRSI
Highly responsive RSI variant using SuperSmoother and Fisher Transform.
Visual Example

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 RocketRSI indicator is a technical analysis tool that highly responsive rsi variant using supersmoother and fisher transform.
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 rapid cycle identification and reversal detection. The Fisher Transform converts the RSI distribution into a Gaussian-like distribution with sharp peaks at reversals.
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.
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/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)
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. |
Related Indicators & See Also
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 2026-06-25 IST — see docs/DOCUMENTATION_STANDARDS.md.