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# Rapid Adaptive Variance Indicator

This study calculates and displays a Rapid Adaptive Variance Indicator (RAVI) of the data specified by the **Input Data** Input. This study was developed by Tushar S. Chande, Ph.D., in his article *Adapting Moving Averages to Market Volatility*, which was published in Stocks & Commodities V. 10:3 (pp 108-114). The closely related studies Variable Index Dynamic Moving Average (VIDYA) and Turbo MACD were presented in the same article. Refer to the documentation on the VIDYA study for an explanation of its notation.

Let \(X\) be a random variable denoting the **Input Data**. Let the Inputs **Long VIDYA Length**, **Long Std Dev Length**, **Long Reference Standard Deviation**, **Short VIDYA Length**, **Short Std Dev Length**, and **Short Reference Standard Deviation** be denoted as \(n_V^{(L)}\), \(n_{\sigma}^{(L)}\), \(\sigma_{ref}^{(L)}\), \(n_V^{(S)}\), \(n_{\sigma}^{(S)}\), and \(\sigma_{ref}^{(S)}\), respectively. Then we denote the **Rapid Adaptive Variance Indicator** at Index \(t\) for the given Inputs as \(RAVI_t\left(X, n_V^{(L)}, n_{\sigma}^{(L)}, \sigma_{ref}^{(L)}, n_V^{(S)}, n_{\sigma}^{(S)}, \sigma_{ref}^{(S)}\right)\), and we compute it in terms of the VIDYA as follows.

For \(0 \leq t < \max\left\{n_{\sigma}^{(L)}, n_{\sigma}^{(S)}\right\} - 2\):

\(\displaystyle{RAVI_t\left(X, n_V^{(L)}, n_{\sigma}^{(L)}, \sigma_{ref}^{(L)}, n_V^{(S)}, n_{\sigma}^{(S)}, \sigma_{ref}^{(S)}\right) = 0}\)For \(t > \max\left\{n_{\sigma}^{(L)}, n_{\sigma}^{(S)}\right\} - 2\):

\(\displaystyle{RAVI_t\left(X, n_V^{(L)}, n_{\sigma}^{(L)}, \sigma_{ref}^{(L)}, n_V^{(S)}, n_{\sigma}^{(S)}, \sigma_{ref}^{(S)}\right) = VIDYA_t\left(X, n_V^{(S)}, n_{\sigma}^{(S)}, \sigma_{ref}^{(S)}\right) - VIDYA_t\left(X,n_V^{(L)}, n_{\sigma}^{(L)}, \sigma_{ref}^{(L)}\right)}\)The first term on the right hand side is called the Short VIDYA, and the second term is called the Long VIDYA.

RAVI can be thought of as a sort of MACD, with the VIDYA playing the role of the EMA.

#### Inputs

- Input Data
- Long VIDYA Length
- Long Std Dev Length
**Long Reference Standard Deviation**: A scaling divisor. The volatility of the**Input Data**in the Long VIDYA is measured as a number of Long Reference Standard Deviations.- Short VIDYA Length
- Short Std Dev Length
**Short Reference Standard Deviation**: A scaling divisor. The volatility of the**Input Data**in the Short VIDYA is measured as a number of Reference Standard Deviations.

#### Spreadsheet

The spreadsheet below contains the formulas for this study in Spreadsheet format. Save this Spreadsheet to the Data Files Folder.

Open it through **File >> Open Spreadsheet**.

*Last modified Monday, 03rd October, 2022.