第十一课 经济,金融数据应用

  • Python当中的matplotlib module有一个finance module能够获取各公司的股票历史数据并绘图。

from pylab import figure, show  
from import quotes_historical_yahoo  
from matplotlib.dates import YearLocator, MonthLocator, DateFormatter  
import datetime  
date1 = 2013, 1, 1 )  
date2 = 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  
# format the coords message box  
def price(x): return '$%1.2f'%x  
ax.fmt_xdata = DateFormatter('%Y-%m-%d')  
ax.fmt_ydata = price  
  • quotes_historical_yahoo是一个获取yahoo历史数据的函数,需要输入公司的Ticker Symbol和查询起止日期,输出为一缓冲文件,具体代码如下:
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. 
    sp = f.quotes_historical_yahoo('^GSPC', d1, d2, 
                                asobject=True, adjusted=True) 
    returns = ([1:] -[:-1])/[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)  
        ret = parse_yahoo_historical(fh, asobject=asobject,  
        if len(ret) == 0:  
            return None  
    except IOError as exc:  
        warnings.warn('fh failure\n%s'%(exc.strerror[1]))  
        return None  
    return ret  
  • parse_yahoo_historical函数可对历史数据进行解析,读取文件,对文件部分内容进行操作,代码如下:
def parse_yahoo_historical(fh, adjusted=True, asobject=False):  
    Parse the historical data in file handle fh from yahoo finance. 
      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. 
      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 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 =*time.strptime(datestr, datefmt)[:3])  
        # Using strptime doubles the runtime. With the present  
        # format, we don't need it.  
        dt =*[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,,  
                        dnum, open, close, high, low, volume, aclose))  
    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  
  • 另外,如果无需操作历史数据,只需下载存储到本地文件可参考下面代码:
#this example can download the data in 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])  
urlFmt = ''  
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  
  • course/python/lesson11.txt
  • 最后更改: 2014/05/11 20:47
  • (外部编辑)