PairsRotation
Relative rotation of two securities using normalized roofing filters.
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 PairsRotation indicator is a technical analysis tool that relative rotation of two securities using normalized roofing filters.
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 to detect and trade rotation between two correlated assets. When one asset leads and the other lags, the indicator signals a rotation trade opportunity.
Pairs Rotation analysis measures the relative cycle phase between two correlated assets. When one asset is at a cycle peak while its correlated partner is at a trough, a statistical rotation trade can be placed — long the laggard, short the leader — anticipating mean reversion of the spread.
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/pairs_rotation.rs):
[ Filt = SuperSmoother(HighPass(Price, HPLen), LPLen) ] [ MS = 0.0242 \cdot Filt^2 + 0.9758 \cdot MS_{t-1} ] [ Normalized = \frac{Filt}{\sqrt{MS}} ]
Gold-standard parity vectors: quantwave-core/tests/gold_standard/pairs_rotation.json.
Parameters
| Parameter | Default | Description |
|---|---|---|
hp_len |
125 | HighPass filter length |
lp_len |
20 | LowPass (SuperSmoother) length |
Usage Examples
Streaming (Rust)
use quantwave_core::indicators::PAIRS_ROTATION;
use quantwave_core::traits::Next;
let mut ind = PAIRS_ROTATION::new(125);
for price in &prices {
let value = ind.next(price);
}
Streaming (Python)
from quantwave import PAIRS_ROTATION
ind = PAIRS_ROTATION(125)
for price in prices:
value = ind.next(price)
Polars Batch (Python)
import polars as pl
import quantwave as qw
def apply_pairsrotation(series: pl.Series) -> pl.Series:
ind = qw.PAIRS_ROTATION(125)
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_pairsrotation, return_dtype=pl.Float64).alias("pairsrotation")
)
.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://github.com/lavs9/quantwave/blob/main/references/Ehlers%20Papers/PAIRS%20ROTATION.pdf
Implementation: quantwave-core/src/indicators/pairs_rotation.rs (PAIRS_ROTATION / PAIRS_ROTATION_METADATA).
Parity: quantwave-core/tests/gold_standard/pairs_rotation.json
Provenance: Standards bulk upgrade 2026-06-25 IST — see docs/DOCUMENTATION_STANDARDS.md.