import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import bisect#指定日期的指数PE（等权重）
def get_index_pe_date(index_code,date):stocks = get_index_stocks(index_code, date)q = query(valuation).filter(valuation.code.in_(stocks))df = get_fundamentals(q, date)if len(df)>0:pe = len(df)/sum([1/p if p>0 else 0 for p in df.pe_ratio])return peelse:return float('NaN')#指定日期的指数PB（等权重）
def get_index_pb_date(index_code,date):stocks = get_index_stocks(index_code, date)q = query(valuation).filter(valuation.code.in_(stocks))df = get_fundamentals(q, date)if len(df)>0:pb = len(df)/sum([1/p if p>0 else 0 for p in df.pb_ratio])return pbelse:return float('NaN')#指数历史PEPB
def get_index_pe_pb(index_code):start='2005-1-1'#start='2015-1-1'end = pd.datetime.today();dates=[]pes=[]pbs=[]for d in pd.date_range(start,end,freq='M'): #频率为月dates.append(d)pes.append(get_index_pe_date(index_code,d))pbs.append(get_index_pb_date(index_code,d))d = {'PE' : pd.Series(pes, index=dates),'PB' : pd.Series(pbs, index=dates)}PB_PE = pd.DataFrame(d)return PB_PEall_index = get_all_securities(['index'])
index_choose =['000016.XSHG',                        '000300.XSHG','000902.XSHG','000905.XSHG','399106.XSHE',               '399316.XSHE','000036.XSHG','000037.XSHG','000038.XSHG','000039.XSHG','000158.XSHG']
df_pe_pb = pd.DataFrame()
frames=pd.DataFrame()
today= pd.datetime.today()
for code in index_choose:index_name = all_index.ix[code].display_name  print u'正在处理: ',index_name   df_pe_pb=get_index_pe_pb(code)    results=[]pe = get_index_pe_date(code,today)q_pes = [df_pe_pb['PE'].quantile(i/10.0)  for i in range(11)]    idx = bisect.bisect(q_pes,pe)quantile = idx-(q_pes[idx]-pe)/(q_pes[idx]-q_pes[idx-1])   #index_name = all_index.ix[code].display_nameresults.append([index_name,'%.2f'% pe,'%.2f'% (quantile*10)]+['%.2f'%q  for q in q_pes]+[df_pe_pb['PE'].count()])pb = get_index_pb_date(code,today)q_pbs = [df_pe_pb['PB'].quantile(i/10.0)  for i in range(11)] idx = bisect.bisect(q_pbs,pb)quantile = idx-(q_pbs[idx]-pb)/(q_pbs[idx]-q_pbs[idx-1])   #index_name = all_index.ix[code].display_nameresults.append([index_name,'%.2f'% pb,'%.2f'% (quantile*10)]+['%.2f'%q  for q in q_pbs]+[df_pe_pb['PB'].count()])df_pe_pb['10% PE']=q_pes[1]df_pe_pb['50% PE']=q_pes[5]df_pe_pb['90% PE']=q_pes[9]df_pe_pb['10% PB']=q_pbs[1]df_pe_pb['50% PB']=q_pbs[5]df_pe_pb['90% PB']=q_pbs[9]df_pe_pb.plot(secondary_y=['PB','10% PB','50% PB','90% PB'],figsize=(14,8),title=index_name,style=['k-.', 'k', 'g', 'y', 'r', 'g-.', 'y-.', 'r-.']) columns=[u'名称',u'当前估值',u'分位点%',u'最小估值']+['%d%%'% (i*10) for i in range(1,10)]+[u'最大估值' , u"数据个数"]df= pd.DataFrame(data=results,index=['PE','PB'],columns=columns)frames = pd.concat([frames, df])
frames