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Technical Studies Reference

Choppiness Index

This study calculates and displays a Choppiness Index of the price data.

Let \(H\) and \(L\) be random variables denoting the High Price and Low Price, respectively, and let \(H_t\) and \(L_t\) be their respective values at Index \(t\). Let the Inputs Summation Period and ATR Period be denoted as \(n_S\) and \(n_{ATR}\), respectively. Let \(ATR(n_{ATR})\) denote the Average True Range with Length \(n_{ATR}\). Then we denote the Choppiness Index at Index \(t\) for the given Inputs as \(CI_t(n_S,n_{ATR})\), and we compute it as follows.

\(\displaystyle{CI_t(n_S,n_{ATR}) = \frac{\left. 100\log\left(\sum_{i = t - n_S + 1}^t ATR_i(n_{ATR}) \middle/ (\max_t(H,n_S) - \min_t(L,n_S)\right) \right.}{\log(n_S)}}\)

This Subgraph is displayed for \(t > \max(n_S, n_{ATR}) - 1\).

The above formula is used as long as \(\max_t(H,n_S) - \min_t(L,n_S) \neq 0\). Otherwise, \(CI_t(n_S,n_{ATR}) = 0\).

For an explanation of the Sigma (\(\Sigma\)) notation for summation, refer to our description here: Summation.

For an explanation of the functions \(\max_t()\) and \(\min_t()\), refer to our descriptions here: Moving Maximum and Moving Minimum.



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*Last modified Monday, 26th September, 2022.