#指数估值数据组
EDate=”20200624″
code=[“000001″,”000016″,”000300″,”000905″,”000922″,”399001″,”399005″,”399006″,”000807″,”000986″,”000987″,”000988”,
“000989”,”000990″,”000991″,”000992″,”000993″]
factor_list=[]
for tickers in code:
zhishu=DataAPI.MktIdxdEvalGet(secID=u””,ticker=tickers,beginDate=u”20000101″,endDate=EDate,
field=u”ticker,secShortName,tradeDate,PEType,PEValue,PB”,pandas=”1″)
zhishu=zhishu[zhishu.PEType==1]
d={‘0maxPE’:([np.max(zhishu.PEValue)]),’1minPE’:([np.min(zhishu.PEValue)]),’2APE’:([np.mean(zhishu.PEValue)]),
‘3MPE’:([np.median(zhishu.PEValue)]),’4SPE’:([np.std(zhishu.PEValue)]),’5maxPB’:([np.max(zhishu.PB)]),
‘6minPB’:([np.min(zhishu.PB)]),’7APB’:([np.mean(zhishu.PB)]),’8MPB’:([np.median(zhishu.PB)]),’9SPB’:([np.std(zhishu.PB)])}
jg=pd.DataFrame(d)
factor_list.append(jg)
data=pd.concat(factor_list)
data=np.transpose(data)
data
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0maxPE 28.530000 27.030000 27.970000 67.720000 34.530000 49.820000 60.980000 131.930000 39.630000 44.270000 62.890000 54.230000 42.060000 42.640000 70.750000 11.890000 120.140000
1minPE 8.350000 6.950000 7.880000 14.280000 6.010000 10.850000 14.350000 26.070000 14.750000 10.180000 11.210000 13.900000 13.690000 17.660000 22.390000 5.650000 24.600000
2APE 13.855501 11.450284 13.234334 30.043417 10.628552 21.905971 29.959868 49.225840 25.342818 18.353514 27.631036 22.100402 20.886112 27.294508 35.978442 8.244105 46.069334
3MPE 13.045000 10.185000 12.265000 27.770000 9.070000 21.195000 29.550000 46.090000 26.040000 14.160000 24.610000 18.350000 19.470000 27.660000 35.940000 8.310000 44.280000
4SPE 3.970077 3.745802 3.934397 9.102845 5.139973 6.842980 6.552809 15.760306 5.997218 8.629391 11.385788 7.126046 4.507191 5.189311 7.172878 1.254244 14.428814
5maxPB 4.340000 4.370000 4.490000 5.920000 3.360000 5.810000 7.670000 14.890000 8.340000 2.210000 4.360000 5.160000 5.500000 6.690000 8.430000 1.870000 11.210000
6minPB 1.150000 0.960000 1.170000 1.440000 0.780000 1.770000 2.380000 2.520000 3.010000 0.740000 1.480000 1.420000 1.650000 2.770000 2.750000 0.830000 2.340000
7APB 1.861323 1.645359 1.849838 2.764215 1.475573 2.916881 4.240074 5.302333 5.040064 1.333324 2.142006 2.135192 2.602376 4.282829 4.353037 1.220393 4.321313
8MPB 1.620000 1.340000 1.580000 2.640000 1.250000 2.860000 4.030000 4.840000 4.690000 1.250000 2.050000 1.930000 2.500000 4.120000 4.280000 1.160000 4.130000
9SPB 0.620727 0.650223 0.631637 0.785571 0.554429 0.715275 0.972717 1.895860 1.289442 0.306038 0.468804 0.623761 0.629510 0.821584 0.805830 0.220965 1.345774
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