e2ecdcfe33
Former-commit-id:a02aeb236c[formerly9f19e3f712] [formerlya02aeb236c[formerly9f19e3f712] [formerly06a8b51d6d[formerly 64fa9254b946eae7e61bbc3f513b7c3696c4f54f]]] Former-commit-id:06a8b51d6dFormer-commit-id:8e80217e59[formerly3360eb6c5f] Former-commit-id:377dcd10b9
47 lines
1.6 KiB
Python
Executable file
47 lines
1.6 KiB
Python
Executable file
"""
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Illustrate the rec array utility funcitons by loading prices from a
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csv file, computing the daily returns, appending the results to the
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record arrays, joining on date
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"""
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import urllib
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.mlab as mlab
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# grab the price data off yahoo
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u1 = urllib.urlretrieve('http://ichart.finance.yahoo.com/table.csv?s=AAPL&d=9&e=14&f=2008&g=d&a=8&b=7&c=1984&ignore=.csv')
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u2 = urllib.urlretrieve('http://ichart.finance.yahoo.com/table.csv?s=GOOG&d=9&e=14&f=2008&g=d&a=8&b=7&c=1984&ignore=.csv')
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# load the CSV files into record arrays
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r1 = mlab.csv2rec(file(u1[0]))
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r2 = mlab.csv2rec(file(u2[0]))
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# compute the daily returns and add these columns to the arrays
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gains1 = np.zeros_like(r1.adj_close)
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gains2 = np.zeros_like(r2.adj_close)
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gains1[1:] = np.diff(r1.adj_close)/r1.adj_close[:-1]
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gains2[1:] = np.diff(r2.adj_close)/r2.adj_close[:-1]
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r1 = mlab.rec_append_fields(r1, 'gains', gains1)
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r2 = mlab.rec_append_fields(r2, 'gains', gains2)
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# now join them by date; the default postfixes are 1 and 2. The
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# default jointype is inner so it will do an intersection of dates and
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# drop the dates in AAPL which occurred before GOOG started trading in
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# 2004. r1 and r2 are reverse ordered by date since Yahoo returns
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# most recent first in the CSV files, but rec_join will sort by key so
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# r below will be properly sorted
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r = mlab.rec_join('date', r1, r2)
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# long appl, short goog
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g = r.gains1-r.gains2
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tr = (1+g).cumprod() # the total return
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# plot the return
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fig = plt.figure()
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ax = fig.add_subplot(111)
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ax.plot(r.date, tr)
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ax.set_title('total return: long APPL, short GOOG')
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ax.grid()
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fig.autofmt_xdate()
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plt.show()
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