Time for action – smoothing stock prices with the Blackman window

Let's smooth the close prices from the small AAPL stock prices data file:

  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 = 5
window = np.blackman(N)
smoothed = np.convolve(window/window.sum(),
  closes, mode='same')
  2. Plot the smoothed prices with matplotlib. In this example, we will omit the first five data points and the last five data points. The reason for this is that there is a strong boundary effect:
    plt.plot(smoothed[N:-N], lw=2, label="smoothed")
plt.plot(closes[N:-N], label="closes")
plt.legend(loc='best')
plt.show()

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

    Time for action – smoothing stock prices with the Blackman window

What just happened?

We plotted the closing price of AAPL from our sample data file that was smoothed using the Blackman window with the NumPy blackman() function (see plot_blackman.py):

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.dates import datestr2num

closes=np.loadtxt('AAPL.csv', delimiter=',', usecols=(6,), converters={1:datestr2num}, unpack=True)
N = 5
window = np.blackman(N)
smoothed = np.convolve(window/window.sum(), closes, mode='same')
plt.plot(smoothed[N:-N], lw=2, label="smoothed")
plt.plot(closes[N:-N], '--', label="closes")
plt.title('Blackman window')
plt.xlabel('Days')
plt.ylabel('Price ($)')
plt.grid()
plt.legend(loc='best')
plt.show()
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