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Technical Studies Reference


Adaptive RSI Moving Average with Smoothing

This study calculates a type of adaptive moving average of the data specified by the Input Data. Unlike the Adaptive Moving Average, this study incorporates the Relative Strength Index (RSI) of the Input Data. The study allows for optional smoothing of both the Input Data and the RSI.

Smoothed vs Unsmoothed Input Data

Let \(X\) be a random variable denoting the Input Data, and let \(X_t\) be the value of the Input Data at chart bar \(t\). If the value of the Set Price Smoothing? Input is No, then the unsmoothed Input Data \(X\) is used in the subsequent calculations. If the value is Yes (default value), then the smoothed Input Data is used instead. The smoothing is done with an Exponential Moving Average whose Length is given by the Price Smoothing Period Input, denoted as \(n_{PS}\).

We define a new random variable \(P\) denoting the Price (smoothed or unsmoothed). We denote the value of \(P\) at chart bar \(t\) as \(P_t\), and we compute it as follows.

\(P_t =\left\{ \begin{matrix} X_t & Price \space Smoothing = No \\ EMA_t\left(X,n_{PS}\right) & Price \space Smoothing = Yes \end{matrix}\right .\)

Note: Depending on the setting of the Input Price Smoothing Average Type, the Exponential Moving Average in the calculation of the smoothed Input Data could be replaced with a Linear Regression Moving Average, a Simple Moving Average, a Weighted Moving Average, a Wilders Moving Average, a Simple Moving Average - Skip Zeros, or a Smoothed Moving Average.

Smoothed vs Unsmoothed RSI

The RSI is calculated for \(P\) with Length given by the ARSI Period, denoted as \(n_{ARSI}\). The type of Moving Average used in the calculation of the RSI is determined by the ARSI Moving Average Type Input. If the value of the Set RSI Smoothing? Input is No, then the RSI at chart bar \(t\) is given by \(RSI_t\left(P,n_{ARSI}\right)\). If the value is Yes (default value), then the smoothed RSI is used instead. The smoothing is done with an Exponential Moving Average whose Length is given by the RSI Smoothing Period Input, denoted as \(n_{RSIS}\).

We define a new random variable \(R\) denoting the RSI (smoothed or unsmoothed). We denote the value of \(R\) at chart bar \(t\) as \(R_t\), and we compute it as follows.

\(R_t =\left\{ \begin{matrix} RSI_t\left(P,n_{ARSI}\right) & RSI \space Smoothing = No \\ EMA_t\left(RSI\left(P,n_{ARSI}\right)\right) & RSI \space Smoothing = Yes \end{matrix}\right .\)

Note: Depending on the setting of the Input RSI Smoothing Average Type, the Exponential Moving Average in the calculation of the smoothed Input Data could be replaced with a Linear Regression Moving Average, a Simple Moving Average, a Weighted Moving Average, a Wilders Moving Average, a Simple Moving Average - Skip Zeros, or a Smoothed Moving Average.

Scaling Factor

Let \(SF\) be a random variable denoting the Scaling Factor, and let \(s\) denote the ARSI Scale Factor Input. We denote the value of \(SF\) at chart bar \(t\) as \(SF_t\), and we compute it as follows.

\(\displaystyle{SF_t = 2s\left|\frac{R_t}{100} - 0.5\right|}\)

Adaptive RSI Moving Average

The Adaptive RSI Moving Average at chart bar \(t\) for Price \(P\) and Scaling Factor \(SF\) is denoted as \(ARSIMA_t(P,SF)\), and we compute it with the following recursion relation.

\(ARSIMA_0(P,SF) = 0\)

\(ARSIMA_t(P,SF) = \left(P_t - ARSIMA_{t - 1}(P,SF)\right)SF_t + ARSIMA_{t - 1}(P,SF)\)

Inputs


*Last modified Friday, 09th June, 2017.