ChoppinessIndex

Choppiness Index — is the market trending or just chopping sideways?

Quick reference

Field	Value
Family	Trend & Directional
Input type	Candle (uses high, low, close)
Output type	f64
Output range	[0, 100] (typical)
Default parameters	period = 14 (Python)
Warmup period	period
Interpretation	High = choppy/ranging, low = trending; 61.8 / 38.2 thresholds.

Formula

CI = 100 · log10( Σ(TR, n) / (highest_high(n) − lowest_low(n)) ) / log10(n)

The ratio compares the distance price actually travelled (the summed true range) with the net ground it covered (the high-low span of the window). A clean trend travels almost exactly its span, so the ratio is near 1 and CI near 0; a choppy market criss-crosses far more than its span, so the ratio is large and CI climbs toward 100. The conventional reading is CI > 61.8 ranging, CI < 38.2 trending.

Parameters

period — the lookback window. Must be at least 2 (the log10(period) denominator is zero for period == 1). The Python binding defaults it to 14.

Inputs / Outputs

From crates/wickra-core/src/indicators/choppiness_index.rs:

use wickra::{Indicator, ChoppinessIndex, Candle};
// ChoppinessIndex: Input = Candle, Output = f64
const _: fn(&mut ChoppinessIndex, Candle) -> Option<f64> = <ChoppinessIndex as Indicator>::update;

ChoppinessIndex is a candle-input indicator that reads high, low and close (the close drives the true range across bar gaps). Python's streaming update accepts a 6-tuple or a dict; the batch helper takes high, low, close numpy arrays. Node and WASM expose update(high, low, close) and the matching batch.

Warmup

ChoppinessIndex::new(14).warmup_period() == 14. The first value lands once the window holds a full period bars.

Edge cases

• Flat window. A window with high == low everywhere has a zero span; CI is defined as 100 (maximal choppiness).
• Steady trend. A one-directional march reads well below 50.
• period < 2. Rejected at construction.
• Reset. ci.reset() clears the true-range and high/low windows.

Examples

Rust

use wickra::{BatchExt, Candle, Indicator, ChoppinessIndex};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut ci = ChoppinessIndex::new(2)?;
    // Two H=11 L=9 C=10 bars: ΣTR = 4, span = 2 -> CI = 100·log10(2)/log10(2).
    let out = ci.batch(&[
        Candle::new(10.0, 11.0, 9.0, 10.0, 1.0, 0)?,
        Candle::new(10.0, 11.0, 9.0, 10.0, 1.0, 1)?,
    ]);
    println!("{:?}", out);
    Ok(())
}

Output:

[None, Some(100.0)]

Python

import numpy as np
import wickra as ta

ci = ta.ChoppinessIndex(2)
high = np.array([11.0, 11.0])
low = np.array([9.0, 9.0])
close = np.array([10.0, 10.0])
print(ci.batch(high, low, close))

Output:

[ nan 100.]

Node

const ta = require('wickra');
const ci = new ta.ChoppinessIndex(2);
console.log(ci.batch([11, 11], [9, 9], [10, 10]));

Output:

[ NaN, 100 ]

Interpretation

The Choppiness Index is not directional — it does not say which way price is going, only whether it is going anywhere. Use it as a regime filter: above 61.8 favour mean-reversion / range tactics; below 38.2 favour trend-following. It pairs naturally with a directional indicator that picks the side once a trend is confirmed.

Common pitfalls

• Expecting a direction. It has none — combine it with a trend indicator.
• Tiny periods. period = 2 is allowed but noisy; 14 is conventional.

References

E. W. Dreiss' Choppiness Index; the summed-true-range formulation here is the standard one.

See also

• Indicator-VerticalHorizontalFilter — the same trending-vs-ranging question on an inverted scale.
• Indicators-Overview — the full taxonomy.