数据采集20200628(指数估值数据组)

#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

3 comments

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