Time for action – smoothing stock prices with the Blackman window

Let's smooth the close prices from the small AAPL stock prices data file. Perform the following steps to do so:

1. Load the data into a NumPy array. Call the NumPy blackman function to form a window and then use this window to smooth the price signal.

   closes=np.loadtxt('AAPL.csv', delimiter=',', usecols=(6,), converters={1:datestr2num}, unpack=True)
   N = int(sys.argv[1])
   window = np.blackman(N)
   smoothed = np.convolve(window/window.sum(),
     closes, mode='same')

2. Plot the smoothed prices with Matplotlib. We will omit the first five and the last five data points in this example. The reason for this is that there is a strong boundary effect.

   plot(smoothed[N:-N], lw=2, label="smoothed")
   plot(closes[N:-N], label="closes")
   legend(loc='best')
   show()

   The closing prices of AAPL smoothed with the Blackman window should appear, as follows:

   

What just happened?

We plotted the closing price of AAPL from our sample data file that was smoothed using...