simple moving average 1.png

Theory

Moving averages are used as a basis for many technical indicators. A moving average is a sort of local average around a given point. In signal processing terms the moving average acts as a lowpass filter, removing high frequency "noise" from the signal.

Implementation

The signal parameter is a one dimensional array. It makes sense to use the typical price as this input.

import numpy as np

def simple_moving_average(signal, points):
    """
    Calculate the N-point simple moving average of a signal


    Inputs:
        signal: numpy array -   A sequence of price points in time
        points: int         -   The size of the moving average

    Outputs:
        moving_average:     numpy array -   The moving average at each point in the signal
    """
    moving_average = np.zeros(len(signal))

    for i in range(points):
        moving_average[i] = np.sum(signal[0:i + 1])/(i+1)

    for i in range(points, len(signal)):
        moving_average[i] = np.sum(signal[i + 1 - points:i + 1])/(points)

    return moving_average

Results

Comparing the simple moving average to the typical price reveals the behaviour of the SMA: it is smoother than the typical price signal and it lags behind the typical price signal.

sma vs typical price.png

It is also worth comparing moving averages of different periods:

sma 10 vs 20.png

Note that the 20 point moving average is smoother than the 10 point moving average and lags even further behind the typical price.

Posted Using LeoFinance

Calculating the Simple Moving Average in Python

Theory

Implementation

Results