Chande Momentum Oscillator (CMO)

Classic momentum oscillator classic overbought oversold

An advanced momentum oscillator developed by Tushar Chande that measures the difference between up and down days.

Visual Example

Chande Momentum Oscillator (CMO) — 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

An advanced momentum oscillator developed by Tushar Chande that measures the difference between up and down days.

Use to identify extreme overbought and oversold conditions. CMO is more sensitive to price action than RSI as it uses unsmoothed data in its internal calculations.

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

Developed by Tushar Chande in 1994, the CMO is similar to the RSI but uses the net sum of up and down moves in both the numerator and denominator. This makes it more sensitive to price movements and useful for identifying short-term overextensions in the market. — The New Technical Trader

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 14 — 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):

\[ CMO = 100 \times \frac{S_u - S_d}{S_u + S_d} \]

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

Parameters

Parameter	Default	Description
`timeperiod`	14	Lookback period

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::CMO;
use quantwave_core::traits::Next;

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

Streaming (Python)

from quantwave import CMO

ind = CMO(14)
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.cmo(14).alias("chande_momentum_oscillator_cmo")
    )
    .collect()
)

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

Edge Cases & Limitations

Warm-up: first 14 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/c/chandemomentumoscillator.asp

Implementation: quantwave-core/src/indicators/momentum.rs (CMO / CMO_METADATA). Parity: quantwave-core/tests/gold_standard/cmo.json

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