def load_yahoo_data(ticker, date1=datetime.date(2004, 1, 1), date2=datetime.date.today()):
data = getYahooData(ticker, date1, date2)
data = np.sort(data, 0, order=['Date'])
data = data[np.where(data['Date'] > datetime.date(2004, 1, 1))]
iso_week, new_data = rearrange(data)
return ticker, iso_week, new_data
def get_return(dat):
dates = dat['Date'][1:]
ret = np.diff(dat['Close']) / dat['Close'][:-1]
new_dat = np.array(zip(dates, ret), dtype = [('Date', datetime.date),
('Return', float)])
return new_dat
stockList = ['FDX', 'UPS', 'OHI', 'HCN', 'XOM', 'CVX']
date1 = datetime.date(1990, 1, 1)
date2 = datetime.date(2012, 10, 10)
dataList = [getYahooData(ticker, date1, date2) for ticker in stockList]
cdataList = [get_return(dat) for dat in dataList]
average_return = mean_estimation([dat['Return'] for dat in cdataList], use_stein=False)
covariance = cov_estimation(cdataList, 'Date', pair_wise=True)
average_return_mod = average_return
#average_return_mod[0] *= 1.3
#average_return_mod[1] *= 1.3
#average_return_mod[2] *= 1.0
mv = MeanVariance()
mv.nameList = stockList
mv.meanList = average_return_mod
mv.covMat = covariance
mv.fixedPosition = {0: 0.378, 1:0.622}
