Skip to content

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

Description

The Chande Momentum Oscillator (CMO) indicator is a technical analysis tool that an advanced momentum oscillator developed by tushar chande that measures the difference between up and down days.

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

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

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/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

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