The following ProBuilder code snippet demonstrates how to implement a triple smoothed moving average, which is a technique used to reduce the noise from price data in financial time series. This method involves smoothing a moving average multiple times to provide a clearer trend indication.

//Multiple smoothing moving average
// MA type (0=SMA,1=EMA,2=WMA,3=Wilder,4=Triangular,5=End point,6=Time series,7 = Hull (PRT v11 only),8 = ZeroLag (PRT v11 only))
period1=14
type1 = 0
period2=10
type2 = 0
period3=9
type3 = 0
src = customclose
avg = average[period1,type1](average[period2,type2](average[period3,type3](src)))
return avg

This code snippet creates a triple smoothed moving average based on user-defined parameters. Here’s a breakdown of how it works:

• Definition of Parameters: The variables period1, period2, and period3 define the periods of the moving averages, and type1, type2, and type3 specify the types of moving averages. The types can range from simple moving average (SMA) to more complex types like Hull and ZeroLag moving averages.
• Source Data: The variable src is set to customclose, which typically represents the closing prices of a financial instrument, although it can be customized to other price types or data points.
• Calculating Nested Averages: The function average is used three times, each nested within the other. This nesting results in the source data being smoothed multiple times. The innermost average function calculates the first moving average based on period3 and type3. The result is then used as the input for the second average function, and so on, until the outermost average function computes the final smoothed moving average using period1 and type1.
• Output: The final result, stored in the variable avg, is returned. This value represents the triple smoothed moving average of the input data, providing a smoother and potentially more reliable indicator than a single moving average.

This method is particularly useful in technical analysis for identifying longer-term trends and reducing the impact of short-term fluctuations.