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

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

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.