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Rate of Change (ROC)

Classic momentum classic oscillator

A momentum-based technical indicator that measures the percentage change in price between the current price and the price n periods ago.

Visual Example

Rate of Change (ROC) — 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

A momentum-based technical indicator that measures the percentage change in price between the current price and the price n periods ago.

Use to measure the speed at which price is changing. It is often used to identify overbought/oversold conditions and trend reversals.

Native Rust implementation with gold-standard or TA-Lib parity tests where applicable.

The Rate of Change (ROC) indicator is a pure momentum oscillator that measures the percentage change in price from one period to the next. It is highly effective at identifying the velocity of a move and anticipating when that velocity is slowing down. — StockCharts ChartSchool

Typical applications:

  • Fade extremes in ranges; trade with trend on recoveries from oversold/overbought
  • Use divergences as early warning — confirm with structure or volume
  • Parameter default 10 — shorten for sensitivity, lengthen for stability
  • Drop into build_feature_matrix() for ML research

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/momentum.rs):

\[ ROC = \frac{Price_t - Price_{t-n}}{Price_{t-n}} \times 100 \]

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

Parameters

Parameter Default Description
timeperiod 10 Lookback period

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::ROC;
use quantwave_core::traits::Next;

let mut ind = ROC::new(10);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import ROC

ind = ROC(10)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave  # registers pl.col().ta

df = (
    pl.read_csv('ohlcv.csv')
    .lazy()
    .with_columns(
        pl.col("close").ta.roc(10).alias("rate_of_change_roc")
    )
    .collect()
)

All surfaces are bit-identical via the single Next<T> implementation and proptests.

Edge Cases & Limitations

  • Warm-up: first 10 bars may return NaN or partial state per implementation.
  • Parameter sensitivity: smaller periods increase noise; larger periods increase lag.
  • Sudden gaps or bad ticks can distort rolling windows — consider pre-filtering.
  • Single-series indicators ignore volume unless otherwise documented.
  • Validated via proptests against gold-standard vectors where available.
  • No look-ahead bias; streaming and Polars batch paths are bit-identical.

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.investopedia.com/terms/r/rateofchange.asp

Implementation: quantwave-core/src/indicators/momentum.rs (ROC / ROC_METADATA). Parity: quantwave-core/tests/gold_standard/roc.json

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