Download stocks data from google finance

Question

I'm trying to download data from Google Finance from a list of stocks symbols inside a .csv file.

This is the class that I'm trying to adapt from this site:

import urllib,time,datetime
import csv

class Quote(object):

  DATE_FMT = '%Y-%m-%d'
  TIME_FMT = '%H:%M:%S'

  def __init__(self):
    self.symbol = ''
    self.date,self.time,self.open_,self.high,self.low,self.close,self.volume = ([] for _ in range(7))

  def append(self,dt,open_,high,low,close,volume):
    self.date.append(dt.date())
    self.time.append(dt.time())
    self.open_.append(float(open_))
    self.high.append(float(high))
    self.low.append(float(low))
    self.close.append(float(close))
    self.volume.append(int(volume))


  def append_csv(self, filename):
    with open(filename, 'a') as f:
      f.write(self.to_csv())

  def __repr__(self):
    return self.to_csv()

  def get_symbols(self, filename):
    for line in open(filename,'r'):
      if line != 'codigo':
        print line
        q = GoogleQuote(line,'2014-01-01','2014-06-20')
        q.append_csv('data.csv')


class GoogleQuote(Quote):
  ''' Daily quotes from Google. Date format='yyyy-mm-dd' '''
  def __init__(self,symbol,start_date,end_date=datetime.date.today().isoformat()):
    super(GoogleQuote,self).__init__()
    self.symbol = symbol.upper()
    start = datetime.date(int(start_date[0:4]),int(start_date[5:7]),int(start_date[8:10]))
    end = datetime.date(int(end_date[0:4]),int(end_date[5:7]),int(end_date[8:10]))
    url_string = "http://www.google.com/finance/historical?q={0}".format(self.symbol)
    url_string += "&startdate={0}&enddate={1}&output=csv".format(
                      start.strftime('%b %d, %Y'),end.strftime('%b %d, %Y'))
    csv = urllib.urlopen(url_string).readlines()
    csv.reverse()
for bar in xrange(0,len(csv)-1):
  try: 
    #ds,open_,high,low,close,volume = csv[bar].rstrip().split(',')
    #open_,high,low,close = [float(x) for x in [open_,high,low,close]]
    #dt = datetime.datetime.strptime(ds,'%d-%b-%y')
    #self.append(dt,open_,high,low,close,volume)
    data = csv[bar].rstrip().split(',')
    dt = datetime.datetime.strftime(data[0],'%d-%b-%y')
    close = data[4]
    self.append(dt,close)
  except:
    print "error " + str(len(csv)-1)
    print "error " + csv[bar]


if __name__ == '__main__':
  q = Quote()                                       # create a generic quote object
  q.get_symbols('list.csv')

But, for some quotes, the code doesn't return all data (e.g. BIOM3), some fields return as '-'. How can I handle the split in these cases? For last, at some point of the script, it stops of download the data because the script stops, it doesn't return any message. How can I handle this problem?

Answer 1

It should work, but notice that the ticker should be: BVMF:ABRE11

In [250]:

import pandas.io.data as web
import datetime
start = datetime.datetime(2010, 1, 1)
end = datetime.datetime(2013, 1, 27)
df=web.DataReader("BVMF:ABRE11", 'google', start, end)
print df.head(10)
             Open   High    Low  Close   Volume
?Date                                          
2011-07-26  19.79  19.79  18.30  18.50  1843700
2011-07-27  18.45  18.60  17.65  17.89  1475100
2011-07-28  18.00  18.50  18.00  18.30   441700
2011-07-29  18.30  18.84  18.20  18.70   392800
2011-08-01  18.29  19.50  18.29  18.86   217800
2011-08-02  18.86  18.86  18.60  18.80   154600
2011-08-03  18.90  18.90  18.00  18.00   168700
2011-08-04  17.50  17.85  16.50  16.90   238700
2011-08-05  17.00  17.00  15.63  16.00   253000
2011-08-08  15.50  15.96  14.35  14.50   224300

[10 rows x 5 columns]

In [251]:

df=web.DataReader("BVMF:BIOM3", 'google', start, end)
print df.head(10)
            Open  High   Low  Close  Volume
?Date                                      
2010-01-04  2.90  2.90  2.90   2.90       0
2010-01-05  3.00  3.00  3.00   3.00       0
2010-01-06  3.01  3.01  3.01   3.01       0
2010-01-07  3.01  3.09  3.01   3.09    2000
2010-01-08  3.01  3.01  3.01   3.01       0
2010-01-11  3.00  3.00  3.00   3.00       0
2010-01-12  3.00  3.00  3.00   3.00       0
2010-01-13  3.00  3.10  3.00   3.00    7000
2010-01-14  3.00  3.00  3.00   3.00       0
2010-01-15  3.00  3.00  3.00   3.00    1000

[10 rows x 5 columns]