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Momentum (MOM)

Classic momentum classic trend

A simple indicator that measures the amount that a security's price has changed over a given span of time.

Visual Example

Momentum (MOM) — 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 simple indicator that measures the amount that a security's price has changed over a given span of time.

Use to measure the velocity of price changes. Positive values indicate an uptrend, while negative values indicate a downtrend.

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

Momentum is one of the most basic and powerful concepts in technical analysis. It measures the rate of change of an asset's price, providing a clear indication of trend strength and potential exhaustion before the actual price reversal occurs. — 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):

\[ MOM = Price_t - Price_{t-n} \]

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

Parameters

Parameter Default Description
timeperiod 10 Lookback period

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::MOM;
use quantwave_core::traits::Next;

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

Streaming (Python)

from quantwave import MOM

ind = MOM(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.mom(10).alias("momentum_mom")
    )
    .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/m/momentum.asp

Implementation: quantwave-core/src/indicators/momentum.rs (MOM / MOM_METADATA). Parity: quantwave-core/tests/gold_standard/mom.json

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