Time for action – charting stock price distributions

Let's chart the stock price distribution of quotes from Yahoo Finance.

  1. Download the data going back one year:
    today = date.today()
start = (today.year - 1, today.month, today.day)

quotes = quotes_historical_yahoo(symbol, start, today)
  2. The quotes data in the previous step is stored in a Python list. Convert this to a NumPy array and extract the close prices:
    quotes = np.array(quotes)
close = quotes.T[4]
  3. Draw the histogram with a reasonable number of bars:
    plt.hist(close, np.sqrt(len(close)))
plt.show()

    The histogram for DISH appears as follows:

    Time for action – charting stock price distributions

What just happened?

We charted the stock price distribution of DISH as a histogram (see stockhistogram.py):

from matplotlib.finance import quotes_historical_yahoo
import sys
from datetime import date
import matplotlib.pyplot as plt
import numpy as np

today = date.today()
start = (today.year - 1, today.month, today.day)

symbol = 'DISH'

if len(sys.argv) == 2:
   symbol = sys.argv[1]

quotes = quotes_historical_yahoo(symbol, start, today)
quotes = np.array(quotes)
close = quotes.T[4]

plt.hist(close, np.sqrt(len(close)))
plt.show()

Have a go hero – drawing a bell curve

Overlay a bell curve (related to the Gaussian or normal distribution) using the average price and standard deviation. This is, of course, only an exercise.

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