The McGinley Moving Average

Coding the McGinley Dynamic in Python

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Moving averages come in all shapes and forms and each type has its advantages and limitations. In this article, we will discuss a less common type called the McGinley dynamic, an unusual form of smoothing.

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Contrarian Trading Strategies in Python

Moving Averages

Moving averages come in all shapes and types. The most basic type is the simple moving average which is simply the sum divided by the quantity. The next mathematical representation shows how to calculate a simple mean given a dataset:

Therefore, the simple moving average is the sum of the values divided by their number. In technical analysis, you generally use moving averages to understand the underlying trend and to find trading signals. Check the next Figure which shows a 60-period simple moving average applied on hourly values of Ethereum versus USD.

ETHUSD with its 60-period simple moving average.

Assume you have an OHLC data array imported in Python (which I have shown how to do many times in previous articles). Write the below primal functions that allow you to better manipulate data:

def add_column(data, times):
    
    for i in range(1, times + 1):
    
        new = np.zeros((len(data), 1), dtype = float)
        
        data = np.append(data, new, axis = 1)
    return data
def delete_column(data, index, times):
    
    for i in range(1, times + 1):
    
        data =…