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 2026-06-25 IST via docs/generate_all_previews.py (reproducible; maps to core Next<T> implementation).

Description

The Sine Wave indicator is a technical analysis tool that plots a sine wave and a lead-sine wave based on the cyclic phase of price movement.

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 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.

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.

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/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 2026-06-25 IST — see docs/DOCUMENTATION_STANDARDS.md.