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