Update! I’ve extended and polished the library and published it as pyyahoofinance at github. Feel free to critizise my coding style and laugh at me, and of course to use it and/or contribute.
I’m working on a project where I need to analyze the ups and downs of different stock values along the history and find correlations between the contraction and expansion of the market, the evolution of the indexes and simulate certain trading strategies to become rich and famous. ;)
Yahoo! Finance lets you download historical financial data in CSV, but only provides data for one stock at a time. Clicking, saving, cutting and pasting is always tedious, error-prone and a waste of time in general, so I’ve taken advantage of my good friend Python to write a script to plunder (yeah, I’m a pirate, you know ;) Yahoo!’s Standard & Poor’s 500 index in a couple of minutes and save it in a space-separated-values file named “results.txt”.
You can then generate beautiful charts like this one:
Code follows:
#!/usr/bin/python
import urllib2
CLOSE_COLUMN = 4 # the index of the column containing the close value
TICKER_COLUMN = 0 # the index of the column containing the ticker name
# get the Standard & Poor stock tickers
tickers = []
for n in range(0, 500, 50):
url = urllib2.urlopen("http://download.finance.yahoo.com/d/quotes.csv?s=@%5EGSPC&f=sl1d1t1c1ohgv&e=.csv&h=PAGE".replace('PAGE', str(n)))
data = url.read()
stocks = data.split('\r\n')
for stock in stocks:
try:
ticker = stock.split(',')[TICKER_COLUMN]
ticker = ticker.replace('"', '') # remove surrounding quotes
if ticker: # not empty ticker
tickers.append(ticker)
except IndexError: # empty row
pass
global_closes = {}
for ticker in tickers:
print "getting data from ticker: %s" % ticker
url = urllib2.urlopen("http://ichart.finance.yahoo.com/table.csv?s=%s&a=00&b=1&c=2000&d=00&e=1&f=2009&g=m&ignore=.csv" % ticker)
history = url.read()
measures = history.split('\n')
measures = measures[1:-1] # the last row is empty and the first
# one contains the labels
closes = [measure.split(',')[CLOSE_COLUMN] for measure in measures]
global_closes[ticker] = closes
columns = [[ticker] + global_closes[ticker] for ticker in global_closes.keys()]
rows = zip(*columns)
out = open('results.txt', 'w')
for row in rows:
out.write(' '.join(row))
out.write('\n')
out.close()
Recent Comments