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This study calculates and displays the Inertia 2 study for the Price Data.

Let \(C\) be a random variable denoting the Close Price, and let the Standard Deviation Length, Relative Volatility Index Length, and Linear Regression Length Inputs be denoted as \(n_\sigma\), \(n_{RVIX}\), and \(n_{LR}\), respectively. We denote the Relative Volatility Index Up and Relative Volatility Index Down at Index \(t\) as \(RVIX^{(U)}_t(n_\sigma)\) and \(RVIX^{(D)}_t(n_\sigma)\), respectively, and we compute them in terms of a Standard Deviation for \(t \geq \max\{n_\sigma, n_{RVIX}, n_{LR}\}\) as follows.

Next we compute the Smoothed Relative Volatility Index Up and Smoothed Relative Volatility Index Down. The values of these at Index \(t\) are denoted as \(\overline{RVIX}^{(U)}_t(n_\sigma,n_{RVIX})\) and \(\overline{RVIX}^{(D)}_t(n_\sigma,n_{RVIX})\), respectively. These both have the value \(0\) for \(t < \max\{n_\sigma, n_{RVIX}, n_{LR}\}\). We compute them for \(t \geq \max\{n_\sigma, n_{RVIX}, n_{LR}\}\) as follows.

We denote the Relative Volatility Index at Index \(t\) as \(RVIX_t(n_\sigma,n_{RVIX})\), and we compute it for \(t \geq \max\{n_\sigma, n_{RVIX}, n_{LR}\}\) as follows.

Finally, we denote Inertia 2 at Index \(t\) as \(Inertia^{(2)}_t(n_\sigma,n_{RVIX},n_{LR})\). It is a Moving Linear Regression of the Relative Volatility Index, and we compute it for \(t \geq \max\{n_\sigma,n_{RVIX},n_{LR}\}\) as follows.