Commodity Channel Index (CCI)

Classic momentum oscillator classic mean-reversion

A versatile indicator that can be used to identify a new trend or warn of extreme conditions.

Visual Example

Commodity Channel Index (CCI) — 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 Commodity Channel Index (CCI) indicator is a technical analysis tool that a versatile indicator that can be used to identify a new trend or warn of extreme conditions.

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 cyclical turns in commodities or stocks. Readings above +100 imply a strong uptrend, while readings below -100 imply a strong downtrend.

Developed by Donald Lambert in 1980, the CCI measures the current price level relative to an average price level over a given period. CCI is relatively high when prices are far above their average and relatively low when prices are far below their average. — StockCharts ChartSchool

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):

\[ CCI = \frac{Price - SMA}{0.015 \times \text{Mean Deviation}} \]

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

Parameters

Parameter	Default	Description
`timeperiod`	14	Lookback period

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::CCI;
use quantwave_core::traits::Next;

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

Streaming (Python)

from quantwave import CCI

ind = CCI(14)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_commodity_channel_index_cci(series: pl.Series) -> pl.Series:
    ind = qw.CCI(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_commodity_channel_index_cci, return_dtype=pl.Float64).alias("commodity_channel_index_cci")
    )
    .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/commoditychannelindex.asp

Implementation: quantwave-core/src/indicators/momentum.rs (CCI / CCI_METADATA). Parity: quantwave-core/tests/gold_standard/cci.json

Provenance: Standards bulk upgrade 2026-06-25 IST — see docs/DOCUMENTATION_STANDARDS.md.