# Technical Studies Reference

- Technical Studies Reference
- Common Study Inputs (Opens a new page)
- Using Studies (Opens a new page)

### Stochastic RSI

This staudy calculates and displays the Stochastic RSI for Close (Last) Price data. The RSI is used in the calculation.

Let \(C\) be a random variable denoting the Close Price, and let \(C_t\) be the value of the Close Price at Index \(t\). Then the RSI of \(C\) with **RSI Length** \(n_{RSI}\) at Index \(t\) is denoted as \(RSI_t(C,n_{RSI})\), and it is calculated in terms of a Simple Moving Average for \(t \geq n_{RSI} - 1\).

**Note**: Depending on the setting of the Input **Average Type**, the Simple Moving Averages in the calculations of \(RS_t\left(X,n_{RSI}\right)\) and \(\overline{RSI}_t(RSI(X,n_{RSI}),n)\) could be replaced with Exponential Moving Averages, Linear Regression Moving Averages, Weighted Moving Averages, Wilders Moving Averages, Simple Moving Averages - Skip Zeros, or Smoothed Moving Averages. The types of all three Moving Averages in the calculation are determined by this one Input.

Let the **RSI HighestLowest Length** Input be denoted as \(n_{HL}\). We denote the maximum and minimum values of \(RSI_t(C,n)\) over a sliding window of Length \(n_{HL}\) at Index \(t\) as \(MaxRSI_t\left(C,n,n_{HL}\right)\) and \(MinRSI_t\left(C,n,n_{HL}\right)\), respectively. We compute them for \(t \geq n_{RSI} + n_{HL} - 1\) as follows.

\(MinRSI_t\left(C,n,n_{HL}\right) = \min\{RSI_{t - n_{HL} + 1}(C,n), RSI_{t - n_{HL} + 2}(C,n), ... , RSI_t(C,n)\}\)

We denote the **Stochastic RSI** for the given Inputs at Index \(t\) as \(StochRSI_t\left(C,n,n_{HL}\right)\), and we compute it for \(t \geq n_{RSI} + n_{HL} - 1\) with the following recursion relation.

#### Inputs

#### 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 Wednesday, 03rd January, 2018.