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Technical Studies Reference

Moving Average - Adaptive

This study calculates and displays an Adaptive Moving Average of the data specified by the Input Data Input. This moving average was developed by Perry Kaufman. Reference: Stocks & Commodities V13:6: (267): Sidebar: Adaptive Moving Average.

Let \(X\) be a random variable denoting the Input Data, and let \(X_t\) be the value of the Input Data at Index \(t\). Let the Inputs Fast Smoothing Constant and Slow Smoothing Constant be denoted as \(c_F\) and \(c_S\), respectively, and let the Input Length be denoted as \(n\). We denote the values of the Direction, Volatility, and Smoothing Constant for the given Inputs at Index \(t\) as \(Dir_t(X,n)\), \(Vol_t(X,n)\), and \(c_t(X,n)\), respectively. We compute these for \(t \geq n\) as follows.

\(Dir_t(X,n) = X_t - X_{t-n}\)

\(\displaystyle{Vol_t(X,n) = \left\{ \begin{matrix} 0.000001 & \sum_{i=t-n+1}^t\left|X_i - X_{i-1}\right| = 0 \\ \sum_{i=t-n+1}^t\left|X_i - X_{i-1}\right| & \sum_{i=t-n+1}^t\left|X_i - X_{i-1}\right| \neq 0 \end{matrix}\right .}\)

\(\displaystyle{c_t(X,n) = \left[\left|\frac{Dir_t(X,n)}{Vol_t(X,n)}\right|\left(\frac{2}{c_F + 1} - \frac{2}{c_S + 1}\right) + \frac{2}{c_S + 1}\right]^2}\)

We denote the Moving Average - Adaptive at Index \(t\) for the given Inputs as \(AMA_t\left(X,n,c_F,c_S\right)\), and we compute it with the following recursion relation for \(t \geq n\).

\(\displaystyle{AMA_t\left(X,n,c_F,c_S\right) = \left\{ \begin{matrix} X_{t-1} + c_t(X,n)\cdot\left(X_t - X_{t - 1}\right) & AMA_{t-1}\left(X,n,c_F,c_S\right) = 0 \\ AMA_{t-1}\left(X,n,c_F,c_S\right) + c_t(X,n)\cdot(X_t - AMA_{t-1}\left(X,n,c_F,c_S\right)) & AMA_{t-1}\left(X,n,c_F,c_S\right) \neq 0 \end{matrix}\right.}\)

For an explanation of the Sigma (\(\Sigma\)) notation for summation, refer to our description here.



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*Last modified Monday, 26th September, 2022.