Numpy Beginner's Guide (Update)

Time for action – smoothing with the hanning() function

We will use the hanning() function to smooth arrays of stock returns, as shown in the following steps:

1. Call the hanning() function to compute weights for a certain length window (in this example 8) as follows:

   N = 8
   weights = np.hanning(N)
   print("Weights", weights)

   The weights are as follows:

   Weights [ 0.          0.1882551   0.61126047  0.95048443  0.95048443  0.61126047
     0.1882551   0.        ]
   

2. Calculate the stock returns for the BHP and VALE quotes using convolve() with normalized weights:

   bhp = np.loadtxt('BHP.csv', delimiter=',', usecols=(6,), unpack=True) bhp_returns = np.diff(bhp) / bhp[ : -1] smooth_bhp = np.convolve(weights/weights.sum(), bhp_returns)[N-1:-N+1] vale = np.loadtxt('VALE.csv', delimiter=',', ...