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 N length window (in this example, N is 8):

   N = int(sys.argv[1])
   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=',', usecols=(6,), unpack=True)
   vale_returns = np.diff(vale) / vale[ : -1]
   smooth_vale = np.convolve(weights/weights.sum(), vale_returns)[N-1:-N+1]

3. Plotting with Matplotlib:

   t = np.arange(N - 1, len(bhp_returns))
   plot(t, bhp_returns...