def plotpe(ax):
global profit, hs300
daterange = pd.date_range('01/01/2012', end='03/03/2017', freq='B')
#daterange = pd.date_range('03/01/2014', end = '05/20/2014', freq='B')
convertrange = daterange.to_pydatetime()
sz50code, namedf, price, volume = getdata.GetPriceVolume()
epsall = getdata.GetProfit('eps')
peseries = Series()
validpe = Series()
for date in convertrange:
if date not in price.index:
continue
combinepe = 0
for code in hs300.index:
weight = hs300['weight'][code]
eps = epsall.ix[code]
quarter = date.month / 4 + 1
if date.year == 2012:
earning = eps[20124]
elif date.year > 2016:
earning = eps[20163] + eps[20154] - eps[20153]
else:
if quarter == 1:
earning = eps[(date.year - 1) * 10 + 4]
else:
earning = eps[date.year * 10 + quarter - 1] + eps[
(date.year - 1) * 10 + 4] - eps[(date.year - 1) * 10 +
quarter - 1]
temppe = float(price[code][date]) / earning
if not isnan(temppe):
validpe[code] = temppe
if isnan(temppe) and code in validpe.index:
temppe = validpe[code]
if (not isnan(temppe)) and temppe > 0:
combinepe = combinepe + weight * temppe
#print combinepe
#if date.year == 2014 and date.month==4 and date.day == 3 and temppe < 0:
# print date, code, temppe, earning
peseries[date] = combinepe
print date, combinepe
#normalizedpe = (peseries - peseries.min()) / (peseries.max() - peseries.min())
ax.set_title("hs300PE")
peseries.plot(ax=ax)
volume1 = volume1.apply(lambda x: (x - np.mean(x)) /
(np.max(x) - np.min(x)))
volume_ma = pd.rolling_mean(volume1, window=30)
#price_ma = pd.rolling_mean(price1, window = 30)
#ss = getdata.GetWindowCorr(price_ma[code1], volume_ma[code1], 90)
#price_ma[code1].plot(color='r')
price1.plot(label="PE")
#volume_ma[code1].plot(color='y')
#ss.plot(color='b')
plt.show()
if __name__ == "__main__":
global profit, hs300, roe, growth_mbrg, growth_nprg, growth_nav
profit = getdata.GetProfit('net_profits')
basic = getdata.GetBasic()['totals']
roe = getdata.GetProfit('roe')
growth_mbrg = getdata.GetGrowth('mbrg')
growth_nprg = getdata.GetGrowth('nprg')
#growth_nav = getdata.GetGrowth('nav')
hs300 = getdata.GetHs300()
zz500 = getdata.GetZz500()
#retpearray = Series()
#retpbarray = Series()
#plotcorrelation()
#plot use subplot
ratio.plot(ax=ax)
plt.show()
def calcdivratio(code):
global divi, hs300, eps
zzidx = [2012, 2013, 2014, 2015, 2016]
codelist = [
'600015', '601166', '000001', '600000', '600036', '600016', '601818',
'601998'
]
ret = DataFrame(index=codelist, columns=zzidx)
#print divi.ix[code]
#print eps.ix[code]
for code in ret.index:
for year in zzidx:
ret[year][code] = divi[year][code] / eps[year * 10 + 3][code] * 10
print hs300['name'][code].decode('UTF-8'), ret.ix[code]
if __name__ == "__main__":
global divi, hs300, eps
divi = getdata.GetDiv('divi')
eps = getdata.GetProfit('eps')
#print divi
hs300 = getdata.GetHs300()
#plotdivratio('601166')
calcdivratio('601166')