第十一课 经济，金融数据应用

利用matplotlib抓取yahoo finance里的历史数据并绘图

from pylab import figure, show  
from matplotlib.finance import quotes_historical_yahoo  
from matplotlib.dates import YearLocator, MonthLocator, DateFormatter  
import datetime  
date1 = datetime.date( 2013, 1, 1 )  
date2 = datetime.date( 2013, 11, 11 )  
  
daysFmt  = DateFormatter('%m-%d-%Y')  
  
quotes = quotes_historical_yahoo('MSFT', date1, date2)              # 获取在date1和date2期间的微软股票
if len(quotes) == 0:  
    raise SystemExit  
  
dates = [q[0] for q in quotes]  
opens = [q[1] for q in quotes]  
  
fig = figure()  
ax = fig.add_subplot(111)  
ax.plot_date(dates, opens, '-')  
  
# format the ticks  
ax.xaxis.set_major_formatter(daysFmt)  
ax.autoscale_view()  
  
# format the coords message box  
def price(x): return '$%1.2f'%x  
ax.fmt_xdata = DateFormatter('%Y-%m-%d')  
ax.fmt_ydata = price  
ax.grid(True)  
  
fig.autofmt_xdate()  
show()
def quotes_historical_yahoo(ticker, date1, date2, asobject=False,  
                                        adjusted=True, cachename=None):  
    """ 
    Get historical data for ticker between date1 and date2.  date1 and 
    date2 are datetime instances or (year, month, day) sequences. 
 
    See :func:`parse_yahoo_historical` for explanation of output formats 
    and the *asobject* and *adjusted* kwargs. 
 
    Ex: 
    sp = f.quotes_historical_yahoo('^GSPC', d1, d2, 
                                asobject=True, adjusted=True) 
    returns = (sp.open[1:] - sp.open[:-1])/sp.open[1:] 
    [n,bins,patches] = hist(returns, 100) 
    mu = mean(returns) 
    sigma = std(returns) 
    x = normpdf(bins, mu, sigma) 
    plot(bins, x, color='red', lw=2) 
 
    cachename is the name of the local file cache.  If None, will 
    default to the md5 hash or the url (which incorporates the ticker 
    and date range) 
    """  
    # Maybe enable a warning later as part of a slow transition  
    # to using None instead of False.  
    #if asobject is False:  
    #    warnings.warn("Recommend changing to asobject=None")  
  
    fh = fetch_historical_yahoo(ticker, date1, date2, cachename)  
  
    try:  
        ret = parse_yahoo_historical(fh, asobject=asobject,  
                                            adjusted=adjusted)  
        if len(ret) == 0:  
            return None  
    except IOError as exc:  
        warnings.warn('fh failure\n%s'%(exc.strerror[1]))  
        return None  
  
    return ret  
def parse_yahoo_historical(fh, adjusted=True, asobject=False):  
    """ 
    Parse the historical data in file handle fh from yahoo finance. 
 
    *adjusted* 
      If True (default) replace open, close, high, and low prices with 
      their adjusted values. The adjustment is by a scale factor, S = 
      adjusted_close/close. Adjusted prices are actual prices 
      multiplied by S. 
 
      Volume is not adjusted as it is already backward split adjusted 
      by Yahoo. If you want to compute dollars traded, multiply volume 
      by the adjusted close, regardless of whether you choose adjusted 
      = True|False. 
 
 
    *asobject* 
      If False (default for compatibility with earlier versions) 
      return a list of tuples containing 
 
        d, open, close, high, low, volume 
 
      If None (preferred alternative to False), return 
      a 2-D ndarray corresponding to the list of tuples. 
 
      Otherwise return a numpy recarray with 
 
        date, year, month, day, d, open, close, high, low, 
        volume, adjusted_close 
 
      where d is a floating poing representation of date, 
      as returned by date2num, and date is a python standard 
      library datetime.date instance. 
 
      The name of this kwarg is a historical artifact.  Formerly, 
      True returned a cbook Bunch 
      holding 1-D ndarrays.  The behavior of a numpy recarray is 
      very similar to the Bunch. 
 
    """  
  
    lines = fh.readlines()  
  
    results = []  
  
    datefmt = '%Y-%m-%d'  
  
    for line in lines[1:]:  
  
        vals = line.split(',')  
        if len(vals)!=7:  
            continue      # add warning?  
        datestr = vals[0]  
        #dt = datetime.date(*time.strptime(datestr, datefmt)[:3])  
        # Using strptime doubles the runtime. With the present  
        # format, we don't need it.  
        dt = datetime.date(*[int(val) for val in datestr.split('-')])  
        dnum = date2num(dt)  
        open, high, low, close =  [float(val) for val in vals[1:5]]  
        volume = float(vals[5])  
        aclose = float(vals[6])  
  
        results.append((dt, dt.year, dt.month, dt.day,  
                        dnum, open, close, high, low, volume, aclose))  
    results.reverse()  
    d = np.array(results, dtype=stock_dt)  
    if adjusted:  
        scale = d['aclose'] / d['close']  
        scale[np.isinf(scale)] = np.nan  
        d['open'] *= scale  
        d['close'] *= scale  
        d['high'] *= scale  
        d['low'] *= scale  
  
    if not asobject:  
        # 2-D sequence; formerly list of tuples, now ndarray  
        ret = np.zeros((len(d), 6), dtype=np.float)  
        ret[:,0] = d['d']  
        ret[:,1] = d['open']  
        ret[:,2] = d['close']  
        ret[:,3] = d['high']  
        ret[:,4] = d['low']  
        ret[:,5] = d['volume']  
        if asobject is None:  
            return ret  
        return [tuple(row) for row in ret]  
  
    return d.view(np.recarray)  # Close enough to former Bunch return  
#this example can download the data in finance.yahoo and put in our computers  
  
import os,urllib2,urllib  
  
ticker = 'MSFT'           #the Ticker Symbol  
date1 = ( 2012, 1, 1 )    #begining time  
date2 = ( 2012, 11, 11 )  #ending time  
  
  
d1 = (date1[1]-1, date1[2], date1[0])  
      
d2 = (date2[1]-1, date2[2], date2[0])  
  
g='d'  
  
urlFmt = 'http://table.finance.yahoo.com/table.csv?a=%d&b=%d&c=%d&d=%d&e=%d&f=%d&s=%s&y=0&g=%s&ignore=.csv'  
url =  urlFmt % (d1[0], d1[1], d1[2],  
                     d2[0], d2[1], d2[2], ticker, g)  #the url of historical data  
print url  
  
path = r'C:\Users\yinyao\Desktop\Python code'  #Saving path  
file_name = r'\ticker.csv'                #file name  
dest_dir = os.path.join(path,file_name)   #located file  
urllib.urlretrieve(url,dest_dir)        #download the data and put in located file