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Sine Wave

Rocket Science cycle oscillator ehlers dsp phase

Plots a sine wave and a lead-sine wave based on the cyclic phase of price movement.

Visual Example

Sine Wave — 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

Plots a sine wave and a lead-sine wave based on the cyclic phase of price movement.

Use to confirm whether the market is in cycle or trend mode. When price follows the sine wave trade cycle reversals; when it diverges switch to trend-following.

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.

Introduced in Rocket Science for Traders, the Sine Wave Indicator plots the sine and cosine of measured instantaneous phase. In cycling markets price tracks the sine wave; in trending markets price breaks through the lead line signaling a mode change.

Typical applications:

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

[ \text{Sine} = \sin(\text{Phase}) ] [ \text{LeadSine} = \sin(\text{Phase} + 45^\circ) ]

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

Parameters

Parameter Default Description
(none) — No tunable parameters for this detector.

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::SINE_WAVE;
use quantwave_core::traits::Next;

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

Streaming (Python)

from quantwave import SINE_WAVE

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

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_sine_wave(series: pl.Series) -> pl.Series:
    ind = qw.SINE_WAVE(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_sine_wave, return_dtype=pl.Float64).alias("sine_wave")
    )
    .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/ROCKET%20SCIENCE%20FOR%20TRADER.pdf

Implementation: quantwave-core/src/indicators/sine_wave.rs (SINE_WAVE / SINE_WAVE_METADATA). Parity: quantwave-core/tests/gold_standard/sine_wave.json

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