In [1]:
#!/Tsan/bin/python
# -*- coding: utf-8 -*-
In [279]:
# Libraries to use
from __future__ import division
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime
import statsmodels
import cvxopt as cv
from cvxopt import solvers
In [2]:
# Import My own library for factor testing
from SingleFactorTest import factorFilterFunctions as ff
from BarraModel import barraRiskModel as brm
#from config import *
In [4]:
%matplotlib inline
In [5]:
# basic stocl pool
ZZ500Weight = 'LZ_GPA_INDEX_CSI500WEIGHT.csv'
HS300Weight = 'LZ_GPA_INDEX_HS300WEIGHT.csv'
In [6]:
filenamePrice ='LZ_GPA_DERI_AdjustedPriceForward_20.csv'
In [7]:
# Factors
# Value factor
filenamePE='LZ_GPA_VAL_PE.csv' # 市盈率
filenamePB='LZ_GPA_VAL_PB.csv' # 市净率
filenamePS = 'LZ_GPA_VAL_PS.csv' # 市销率
filenamePCF = 'LZ_GPA_VAL_PC.csv' # 市现率
# Growth factor
filenameYOYGR = 'LZ_GPA_FIN_IND_QFA_YOYGR.csv' # 单季度.营业总收入同比增长率(%)
filenameYOYNETPROFIT = 'LZ_GPA_FIN_IND_QFA_YOYNETPROFIT.csv' # 单季度.归属母公司股东的净利润同比增长率(%)
filenameYOYOCF = 'LZ_GPA_FIN_IND_YOYOCF.csv' # 同比增长率-经营活动产生的现金流量净额(%)
filenameYOYROE = 'LZ_GPA_FIN_IND_YOYROE.csv' # 同比增长率-净资产收益率(摊薄)(%)
filenameYOYBPS = 'LZ_GPA_FIN_IND_YOYBPS.csv' # 相对年初增长率-每股净资产(%)
# Financial factor
filenameCAPITALIZEDTODA = 'LZ_GPA_FIN_IND_CAPITALIZEDTODA.csv' # 资本支出/折旧和摊销
filenameCASHRATIO = 'LZ_GPA_FIN_IND_CASHRATIO.csv' # 保守速动比率
filenameCASHTOLIQDEBT = 'LZ_GPA_FIN_IND_CASHTOLIQDEBT.csv' # 货币资金／流动负债
filenameOCFTODEBT = 'LZ_GPA_FIN_IND_OCFTODEBT.csv' # 经营活动产生的现金流量净额/负债合计
filenamePROFITTOOP = 'LZ_GPA_FIN_IND_PROFITTOOP.csv' # 利润总额／营业收入
# Momentum factor
filenameTRUEMOM = 'LZ_GPA_USR_MOM.csv' # 真动量
filenameMOM1M = 'LZ_GPA_DERI_Momentum_1M.csv' # 一月反转
filenameMOM3M = 'LZ_GPA_TURNOVER_TurnoverAvg_3M.csv' # 三月反转
filenamePVO = 'LZ_GPA_DERI_PVO.csv' # 一年偏度
filenameRV1Y = 'LZ_GPA_DERI_RealizedVolatility_1Y.csv' # 一年收益波动
filenameABNORMALVOLUME = 'LZ_GPA_DERI_NormalizedAbormalVolume.csv' # 归一化异常交易量
filenameSKEW = 'LZ_GPA_DERI_TSKEW.csv'# 偏度
# Volatility factor
# Liquidity factor
filenameTURNOVER1M = 'LZ_GPA_TURNOVER_TurnoverAvg_1M.csv' # 一月换手率均值
filenameAMOUNTAVG1M = 'LZ_GPA_DERI_AmountAvg_1M.csv' # 日均成交量
filenameILLIQ = 'LZ_GPA_DERI_ILLIQ.csv' # 非流动性因子
filenameVOLUME = 'LZ_GPA_QUOTE_TVOLUME.csv' # 成交量
In [8]:
filenameFCAP = 'LZ_GPA_VAL_A_FCAP.csv'
filenameBENCH = 'LZ_GPA_INDXQUOTE_CLOSE.csv'
filenamePriceOrg = 'LZ_GPA_QUOTE_TCLOSE.csv'
In [10]:
# some useful parameters
startTime = datetime.strptime('20120101', '%Y%m%d')
endTime = datetime.strptime('20161231', '%Y%m%d')
facNum = 5 # factor Num
path = ff.data_path # path
timeStampNum = 2500 # Number of time period
thresholdNum = 0.05 # thresholdNum to filter stocks by Nan's amount
HS300Index ='000300.SH' # HS300 index code
ZZ500Index = '000905.SH' # ZZ500 index code
In [11]:
orgPrice = pd.read_csv(path+filenamePriceOrg,infer_datetime_format=True,parse_dates=[0],index_col=0)
orgPrice.shape
Out[11]:
(2989, 3294)
In [12]:
stockPool = pd.read_csv(path+ZZ500Weight,infer_datetime_format=True,parse_dates=[0],index_col=0).loc[startTime:endTime]
#stockPool = stockPool.iloc[-1].dropna(how='any').index.tolist() # get today's ZX500 stock pool
stockPool1 = pd.read_csv(path+HS300Weight,infer_datetime_format=True,parse_dates=[0],index_col=0).loc[startTime:endTime]
In [13]:
stockPool = stockPool.dropna(how = 'all',axis =1).columns.tolist() # stock pool
stockPool1 = stockPool1.dropna(how = 'all',axis =1).columns.tolist()
In [14]:
stockPool = list((set(stockPool1)|set(stockPool)))
In [15]:
len(stockPool)
Out[15]:
1186
In [16]:
class dataCleaning():
def __init__(self,data):
self.__data = data
#@property
#def _constructor(self):
# return dataCleaning
#@property
#def _constructor_sliced(self):
# return SubclassedSeries
def narrowData(self):
return self.__data[stockPool].loc[startTime:endTime]
def Normalize(self,narrowedData):
dataWinsorized = narrowedData.copy()
dataWinsorizedTrans = dataWinsorized.T
MAD = 1.483*np.abs(dataWinsorizedTrans-dataWinsorizedTrans.median(skipna=True))
return ((dataWinsorizedTrans - dataWinsorizedTrans.mean(axis=0, skipna=True))/dataWinsorizedTrans.std(axis=0, skipna=True)).T
In [17]:
PriceDF = pd.read_csv(path+filenamePrice,infer_datetime_format=True,parse_dates=[0],index_col=0)[stockPool].loc[startTime:endTime]
In [18]:
LFCAPData = np.log10(pd.read_csv(path+filenameFCAP,infer_datetime_format=True,parse_dates=[0],index_col=0))
PBData = pd.read_csv(path+filenamePB,infer_datetime_format=True,parse_dates=[0],index_col=0)
YOYBPSData = pd.read_csv(path+filenameYOYBPS,infer_datetime_format=True,parse_dates=[0],index_col=0)
AMOUNTAVG1MData = pd.read_csv(path+filenameAMOUNTAVG1M,infer_datetime_format=True,parse_dates=[0],index_col=0)
TURNOVER1MData = pd.read_csv(path+filenameTURNOVER1M,infer_datetime_format=True,parse_dates=[0],index_col=0)
PROFITOOPData = pd.read_csv(path+filenamePROFITTOOP,infer_datetime_format=True,parse_dates=[0],index_col=0)
benchmarkData = pd.read_csv(path+filenameBENCH,infer_datetime_format=True,parse_dates=[0],index_col=0)[ZZ500Index].loc[startTime:endTime]
In [19]:
#isinstance(dataCleaning(PBData).narrowData(),dataCleaning)
In [20]:
PBData = dataCleaning(PBData).Normalize(dataCleaning(PBData).narrowData())
LFCAPData = dataCleaning(LFCAPData ).Normalize(dataCleaning(LFCAPData ).narrowData())
YOYBPSData = dataCleaning(YOYBPSData).Normalize(dataCleaning(YOYBPSData).narrowData())
AMOUNTAVG1MData = dataCleaning(AMOUNTAVG1MData).Normalize(dataCleaning(AMOUNTAVG1MData).narrowData())
TURNOVER1MData = dataCleaning(TURNOVER1MData).Normalize(dataCleaning(TURNOVER1MData).narrowData())
PROFITOOPData = dataCleaning(PROFITOOPData).Normalize(dataCleaning(PROFITOOPData).narrowData())
In [21]:
print PBData.shape[1], LFCAPData.shape[1], YOYBPSData.shape[1], AMOUNTAVG1MData.shape[1], TURNOVER1MData.shape[1], PROFITOOPData.shape[1]
1186 1186 1186 1186 1186 1186
In [22]:
#PROFITOOPData
In [23]:
# filter and fill data
def filterAndfill(data,threshold):
df = data.loc[:,data.isnull().sum() < data.shape[1] * threshold]
return df.fillna(method = 'ffill').fillna(method = 'bfill')
In [24]:
PBData = PBData .loc[:, PBData .isnull().sum() < (len(PBData) * thresholdNum)]
LFCAPData = LFCAPData .loc[:, LFCAPData .isnull().sum() < (len(LFCAPData) * thresholdNum)]
YOYBPSData = YOYBPSData .loc[:, YOYBPSData .isnull().sum() < (len(YOYBPSData) * thresholdNum)]
AMOUNTAVG1MData = AMOUNTAVG1MData .loc[:, AMOUNTAVG1MData .isnull().sum() < (len(AMOUNTAVG1MData) * thresholdNum)]
TURNOVER1MData = TURNOVER1MData .loc[:, TURNOVER1MData .isnull().sum() < (len(TURNOVER1MData) * thresholdNum)]
PROFITOOPData = PROFITOOPData .loc[:, PROFITOOPData .isnull().sum() < (len(PROFITOOPData ) * thresholdNum)]
PriceDF = PriceDF.loc[:, (PriceDF.isnull().sum() < (len(PriceDF) * thresholdNum))]
In [25]:
TURNOVER1MData
Out[25]:
002344.SZ
000793.SZ
600059.SH
000713.SZ
600360.SH
000528.SZ
000063.SZ
601101.SH
000685.SZ
000636.SZ
...
000002.SZ
600122.SH
600469.SH
600282.SH
600739.SH
600406.SH
600866.SH
600121.SH
601908.SH
002195.SZ
LZ_GPA_TURNOVER_TurnoverAvg_1M-f
2012-01-04
-0.176351
-0.241920
0.050054
0.869193
-0.366411
-0.093829
-0.393634
0.019835
0.223272
-0.362456
...
-0.364535
-0.065026
-0.095687
-0.491847
-0.103122
-0.417364
-0.226168
-0.572395
1.834449
0.389762
2012-01-05
-0.148203
-0.249227
0.053181
0.922777
-0.369314
-0.073094
-0.399965
0.024752
0.273700
-0.367420
...
-0.363392
-0.064554
-0.104441
-0.497757
-0.094877
-0.417484
-0.227977
-0.583313
1.908621
0.397108
2012-01-06
-0.150965
-0.253463
0.056583
0.966051
-0.375157
-0.076563
-0.406325
0.021471
0.292534
-0.373791
...
-0.362520
-0.064029
-0.096429
-0.502588
-0.090162
-0.411667
-0.235200
-0.589101
1.945360
0.382742
2012-01-09
-0.147897
-0.271938
0.053587
0.999738
-0.383023
-0.083020
-0.407480
0.022990
0.325065
-0.376640
...
-0.365216
-0.071729
-0.099137
-0.512626
-0.072496
-0.411301
-0.236356
-0.601228
1.995226
0.356548
2012-01-10
-0.152183
-0.279319
0.037945
0.942761
-0.370101
-0.076421
-0.406706
0.089766
0.378797
-0.370965
...
-0.351053
-0.088790
-0.062426
-0.505284
-0.053147
-0.402723
-0.247932
-0.591522
1.897973
0.282578
2012-01-11
-0.168841
-0.322439
0.010055
1.036801
-0.383707
-0.020533
-0.428879
0.217896
0.474164
-0.387665
...
-0.358099
-0.089980
-0.025234
-0.534745
-0.022924
-0.417929
-0.241646
-0.626192
1.758969
0.237720
2012-01-12
-0.162372
-0.322084
-0.033919
0.959788
-0.360970
-0.013654
-0.410265
0.260215
0.470223
-0.370419
...
-0.349857
-0.099265
-0.037710
-0.505792
-0.028520
-0.400478
-0.223688
-0.592075
1.550123
0.312365
2012-01-13
-0.183801
-0.346556
-0.062592
0.961231
-0.368488
-0.000548
-0.428325
0.294343
0.510059
-0.369837
...
-0.366494
-0.093252
-0.034006
-0.528796
-0.036445
-0.414237
-0.225953
-0.616371
1.684493
0.424654
2012-01-16
-0.212042
-0.376568
-0.082489
0.984949
-0.380708
0.016878
-0.437954
0.333461
0.590162
-0.374992
...
-0.378573
-0.094583
-0.031932
-0.545493
-0.042599
-0.425377
-0.232737
-0.573792
1.777184
0.468624
2012-01-17
-0.219125
-0.394498
-0.088873
0.917117
-0.390129
0.038771
-0.444035
0.372458
0.623077
-0.379241
...
-0.384374
-0.104654
-0.017012
-0.558088
-0.051787
-0.410341
-0.239160
-0.503438
1.811306
0.500328
2012-01-18
-0.245147
-0.411192
-0.110104
0.871144
-0.401055
0.040412
-0.454860
0.417836
0.627728
-0.393604
...
-0.384941
-0.125616
-0.004364
-0.567185
-0.067020
-0.397362
-0.257404
-0.407806
1.813784
0.518071
2012-01-19
-0.274954
-0.430320
-0.125285
0.869315
-0.417791
0.056413
-0.466226
0.482096
0.633287
-0.397418
...
-0.403628
-0.163700
0.023308
-0.582603
-0.060625
-0.397930
-0.267881
-0.354266
1.813680
0.546191
2012-01-20
-0.287666
-0.438861
-0.137945
0.812346
-0.432491
0.078376
-0.432853
0.510944
0.615313
-0.399626
...
-0.395634
-0.170500
0.030190
-0.579570
-0.045881
-0.408720
-0.267592
-0.308893
1.656007
0.544229
2012-01-30
-0.306125
-0.457798
-0.134974
0.857664
-0.444669
0.127795
-0.387081
0.562299
0.648002
-0.409160
...
-0.403123
-0.201241
0.031396
-0.595858
-0.024422
-0.417538
-0.275965
-0.282890
1.709626
0.586615
2012-01-31
-0.299987
-0.440980
-0.137565
0.789206
-0.421361
0.128308
-0.355007
0.526171
0.583492
-0.385952
...
-0.364868
-0.200108
0.008899
-0.561914
-0.024017
-0.406620
-0.267634
-0.252964
1.554799
0.534224
2012-02-01
-0.301852
-0.468548
-0.145177
0.814509
-0.432690
0.146472
-0.353794
0.559221
0.595519
-0.391784
...
-0.370900
-0.211667
0.009198
-0.575289
-0.031840
-0.412883
-0.273020
-0.226196
1.633841
0.554533
2012-02-02
-0.326846
-0.482990
-0.152960
0.815525
-0.435350
0.136561
-0.353160
0.592577
0.587722
-0.400793
...
-0.377825
-0.217205
0.016963
-0.589346
-0.025147
-0.422286
-0.279675
-0.207650
1.669252
0.579474
2012-02-03
-0.336597
-0.485897
-0.160608
0.764376
-0.436665
0.147052
-0.352183
0.647395
0.576620
-0.409051
...
-0.383313
-0.223898
0.014160
-0.600853
-0.018022
-0.420203
-0.281479
-0.181641
1.687573
0.578504
2012-02-06
-0.350271
-0.499601
-0.173187
0.698719
-0.400753
0.147716
-0.352297
0.658856
0.543902
-0.407624
...
-0.387502
-0.220205
0.011300
-0.610527
-0.027678
-0.421104
-0.246912
-0.146946
1.670068
0.581189
2012-02-07
-0.334052
-0.514300
-0.186763
0.685674
-0.391928
0.142268
-0.345534
0.710393
0.511572
-0.412133
...
-0.397465
-0.231599
0.015186
-0.621467
-0.034022
-0.421312
-0.237994
-0.121478
1.831008
0.588126
2012-02-08
-0.336347
-0.521263
-0.198244
0.656236
-0.387846
0.138273
-0.349784
0.724136
0.484572
-0.417289
...
-0.393781
-0.229638
0.014006
-0.628526
-0.022207
-0.430481
-0.235447
-0.091689
1.872689
0.601832
2012-02-09
-0.356701
-0.528646
-0.197149
0.620384
-0.386122
0.165222
-0.357099
0.792308
0.448954
-0.427058
...
-0.393200
-0.239033
0.049933
-0.635407
-0.040774
-0.440361
-0.231101
-0.018088
1.964097
0.603965
2012-02-10
-0.377991
-0.539581
-0.196030
0.584913
-0.385109
0.148882
-0.360883
0.842562
0.410270
-0.430896
...
-0.392298
-0.241976
0.049180
-0.649815
-0.062422
-0.457095
-0.218549
0.019884
2.018375
0.614870
2012-02-13
-0.397476
-0.557135
-0.213564
0.582310
-0.392934
0.164331
-0.369005
0.876639
0.400119
-0.432317
...
-0.375599
-0.243280
0.054585
-0.653671
-0.073503
-0.480147
-0.221327
0.035366
2.008112
0.616491
2012-02-14
-0.415274
-0.568373
-0.211204
0.562843
-0.350739
0.164588
-0.381545
0.911076
0.372311
-0.414295
...
-0.382098
-0.242748
0.055005
-0.661910
-0.098881
-0.495954
-0.226104
0.044354
2.035851
0.652825
2012-02-15
-0.426435
-0.579982
-0.223918
0.570665
-0.342374
0.145326
-0.383730
0.868075
0.295250
-0.399825
...
-0.388246
-0.254687
0.031297
-0.670751
-0.139479
-0.509338
-0.231509
0.062828
2.064115
0.696201
2012-02-16
-0.425443
-0.576007
-0.236791
0.498661
-0.315325
0.099245
-0.357816
0.790114
0.242605
-0.377186
...
-0.402805
-0.253713
0.017101
-0.672348
-0.134634
-0.514295
-0.231553
0.102238
2.069179
0.724048
2012-02-17
-0.433813
-0.572508
-0.232145
0.490395
-0.300185
0.068313
-0.337932
0.740062
0.191899
-0.369159
...
-0.411948
-0.224619
0.021272
-0.683131
-0.137650
-0.521680
-0.237403
0.128969
2.208334
0.584542
2012-02-20
-0.432552
-0.567778
-0.238432
0.496084
-0.286920
0.044491
-0.333549
0.713215
0.176222
-0.377078
...
-0.416579
-0.238442
0.012377
-0.684973
-0.122271
-0.526266
-0.241663
0.144508
2.202496
0.476408
2012-02-21
-0.435873
-0.574868
-0.241613
0.477905
-0.284171
0.029205
-0.332529
0.687086
0.122953
-0.379727
...
-0.415824
-0.246459
0.008495
-0.693053
-0.123263
-0.523904
-0.254238
0.097749
2.118811
0.442097
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
2016-11-21
0.339211
0.327051
-0.478526
0.216833
1.009662
0.122831
-0.258610
-1.011798
-0.480532
0.350209
...
-0.356853
-0.405601
-0.242672
-0.445705
-0.029647
-0.505557
0.021167
0.309672
-0.592936
0.870772
2016-11-22
0.348309
0.337006
-0.477614
0.228182
0.989635
0.098060
-0.245707
-1.012429
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0.357895
...
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-0.409292
-0.238683
-0.423313
-0.006477
-0.503478
0.007337
0.293464
-0.521057
0.929354
2016-11-23
0.348850
0.335159
-0.476456
0.229714
0.995550
0.067371
-0.285965
-1.016022
-0.426617
0.357303
...
-0.344394
-0.414025
-0.245770
-0.372995
0.104248
-0.502326
0.001805
0.247501
-0.434856
1.004881
2016-11-24
0.367093
0.337730
-0.474700
0.245077
0.992066
0.078542
-0.303518
-1.019543
-0.393657
0.312578
...
-0.348902
-0.258886
-0.257183
-0.318815
0.135082
-0.506026
-0.011809
0.170837
-0.408522
1.039779
2016-11-25
0.378846
0.332780
-0.473555
0.230566
1.020618
0.060976
-0.297504
-1.023122
-0.236251
0.312612
...
-0.357601
-0.224203
-0.257299
-0.262019
0.237898
-0.490263
-0.049177
0.165737
-0.414943
1.181434
2016-11-28
0.381775
0.296359
-0.479544
0.223207
1.046428
0.067307
-0.274996
-1.033295
-0.174653
0.303401
...
-0.361522
-0.273774
-0.267017
-0.227405
0.281875
-0.495471
-0.037072
0.131145
-0.416549
1.228019
2016-11-29
0.382884
0.303165
-0.475943
0.192036
0.966235
0.065597
-0.296858
-1.043822
-0.111336
0.283537
...
-0.352027
-0.312521
-0.286869
-0.205921
0.312418
-0.486903
-0.044388
0.117805
-0.418700
1.167163
2016-11-30
0.374142
0.230828
-0.467690
0.178063
0.939709
0.064111
-0.287178
-1.057634
-0.083427
0.245842
...
-0.360965
-0.346877
-0.273983
-0.155535
0.320914
-0.556055
-0.078779
0.098878
-0.431606
1.119928
2016-12-01
0.391853
0.188441
-0.453535
0.164641
0.923601
0.087166
-0.281280
-1.064245
-0.062633
0.236595
...
-0.372864
-0.375743
-0.283123
-0.123600
0.348119
-0.577364
-0.067803
0.092138
-0.442613
1.168412
2016-12-02
0.403345
0.141081
-0.440295
0.153185
0.905264
0.087818
-0.227830
-1.070498
-0.044099
0.216368
...
-0.376245
-0.404441
-0.296049
-0.092577
0.365942
-0.582938
-0.094018
0.079462
-0.439269
1.090950
2016-12-05
0.396403
0.007547
-0.451989
0.129430
0.902360
0.085735
-0.211575
-1.076894
-0.040607
0.212192
...
-0.377224
-0.428853
-0.272605
-0.060784
0.356046
-0.583934
-0.087519
0.065595
-0.443127
1.067728
2016-12-06
0.435023
-0.059724
-0.452034
0.112123
0.906201
0.101734
-0.183822
-1.088633
-0.007200
0.213928
...
-0.374652
-0.455101
-0.251837
-0.041369
0.428467
-0.580779
-0.094789
0.045681
-0.443831
1.076773
2016-12-07
0.440893
-0.066906
-0.447040
0.073963
0.929112
0.105765
-0.170952
-1.090142
0.015985
0.240810
...
-0.370060
-0.479381
-0.222863
-0.045225
0.456582
-0.549729
-0.113448
-0.013276
-0.439214
1.112534
2016-12-08
0.378036
-0.061253
-0.446720
0.030825
0.644921
0.106202
-0.238813
-1.091342
0.029054
0.231864
...
-0.357204
-0.502794
-0.200598
-0.015788
0.480091
-0.529847
-0.112802
-0.041478
-0.435094
1.120293
2016-12-09
0.169782
-0.043610
-0.431119
0.030145
0.465799
0.133322
-0.272516
-1.088982
0.042460
0.202115
...
-0.462313
-0.337505
-0.235240
0.009180
0.496282
-0.515746
-0.126452
-0.061747
-0.425869
1.145664
2016-12-12
0.081109
-0.033808
-0.432806
0.008084
0.294452
0.094596
-0.269675
-1.090375
0.051776
0.165231
...
-0.518058
-0.270382
-0.298083
-0.003551
0.542411
-0.495399
-0.174348
-0.136509
-0.414601
1.180259
2016-12-13
0.056150
-0.042762
-0.416011
0.008414
0.274752
0.012084
-0.273143
-1.108492
0.062561
0.160186
...
-0.526692
-0.160998
-0.293781
-0.002460
0.612004
-0.476690
-0.190242
-0.201323
-0.408319
1.245668
2016-12-14
-0.006133
-0.028240
-0.406396
0.078970
0.244948
-0.118562
-0.275224
-1.042991
0.073449
0.092316
...
-0.526085
-0.057184
-0.288198
0.000890
0.645049
-0.452468
-0.202820
-0.272731
-0.448315
1.250050
2016-12-15
-0.042187
-0.029670
-0.415814
0.128432
0.222281
-0.183330
-0.280087
-0.929227
0.085458
0.063167
...
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-0.005828
-0.207580
-0.014168
0.663679
-0.379908
-0.189359
-0.304291
-0.457505
1.183270
2016-12-16
-0.065592
-0.045096
-0.405525
0.141736
0.237604
-0.198915
-0.271643
-0.867567
0.126693
0.053820
...
-0.567067
0.043004
-0.154633
-0.014044
0.669048
-0.328086
-0.180149
-0.312134
-0.474979
0.937109
2016-12-19
-0.083675
-0.008960
-0.384299
0.248362
0.203078
-0.235518
-0.258607
-0.810034
0.143088
-0.014755
...
-0.569690
0.076896
-0.109907
-0.028524
0.672902
-0.320174
-0.162324
-0.328041
-0.472701
0.879488
2016-12-20
-0.153944
0.035289
-0.383900
0.362226
0.161799
-0.261483
-0.253889
-0.777796
0.152505
-0.100052
...
-0.593818
0.131212
-0.038110
-0.005918
0.673076
-0.294732
-0.154440
-0.326380
-0.478959
0.797175
2016-12-21
-0.196152
0.044170
-0.387187
0.398765
0.174860
-0.292603
-0.264438
-0.740030
0.162656
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...
-0.612233
0.227336
0.005686
-0.017992
0.638360
-0.267010
-0.128415
-0.361149
-0.564060
0.717394
2016-12-22
-0.212938
0.054204
-0.378527
0.476347
0.151573
-0.298470
-0.250567
-0.697605
0.069651
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...
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0.280681
0.109095
-0.069863
0.472788
-0.236246
-0.116745
-0.400989
-0.658733
0.622029
2016-12-23
-0.232255
0.091244
-0.375569
0.488009
0.150086
-0.315375
-0.243510
-0.655893
0.035301
-0.238426
...
-0.586612
0.138315
0.206033
-0.049636
0.419994
-0.161437
-0.120293
-0.430476
-0.682183
0.585456
2016-12-26
-0.234240
0.175453
-0.380516
0.503552
0.111011
-0.308345
-0.255892
-0.620602
-0.142386
-0.267007
...
-0.570472
0.117711
0.255970
-0.053093
0.212314
-0.121033
-0.111490
-0.474126
-0.693503
0.423348
2016-12-27
-0.233237
0.207867
-0.362428
0.483924
0.058840
-0.319714
-0.278398
-0.580884
-0.208771
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...
-0.559975
0.186083
0.302554
-0.046797
0.118723
-0.070017
-0.129127
-0.470867
-0.696086
0.386464
2016-12-28
-0.225549
0.176722
-0.328304
0.492511
0.032220
-0.322516
-0.268330
-0.544023
-0.283736
-0.166854
...
-0.554182
0.249068
0.356346
-0.030349
0.041503
-0.012552
-0.106335
-0.470765
-0.691265
0.358643
2016-12-29
-0.209622
0.190835
-0.335515
0.515818
0.017362
-0.331125
-0.277758
-0.496093
-0.295346
-0.130025
...
-0.543827
0.316363
0.445902
-0.068804
0.009896
0.020193
-0.063188
-0.471845
-0.686699
0.360484
2016-12-30
-0.224775
0.206770
-0.329140
0.519387
0.031108
-0.357354
-0.273219
-0.455438
-0.315036
-0.137832
...
-0.558144
0.398626
0.510387
-0.006404
0.006963
0.033327
-0.059319
-0.469659
-0.671464
0.261797
1214 rows × 1086 columns
In [26]:
print PBData.shape[1], LFCAPData.shape[1], YOYBPSData.shape[1] ,AMOUNTAVG1MData.shape[1], TURNOVER1MData.shape[1], PROFITOOPData.shape[1]
1078 1093 1140 1086 1086 1143
In [27]:
#len(list((DFcolumns(PBData)) & (DFcolumns(YOYBPSData))))
In [28]:
# get intersection
def getInterStk(dfList):
'''
To get the columns intersections of several dataframe.
output: List, the intersection of the columns of dataframes.
input:
dfList: List, which contains dataframe u want to get the intersection, len(dfList) should be more than 1.
'''
columnsList = map(lambda x: set(x.columns.tolist()), dfList)
stkList = reduce(lambda x,y: x & y, columnsList)
return stkList
In [29]:
SA = getInterStk([PBData,YOYBPSData,AMOUNTAVG1MData,TURNOVER1MData,PROFITOOPData,PriceDF])
In [30]:
len(SA)
Out[30]:
1071
In [31]:
def DFcolumns(df):
return set(df.columns.tolist())
In [32]:
##
stkPool = list((DFcolumns(PBData)) & (DFcolumns(YOYBPSData)) & (DFcolumns(AMOUNTAVG1MData)) & (DFcolumns(TURNOVER1MData)) & (DFcolumns(PROFITOOPData))\
& (DFcolumns(PriceDF)))
len(stkPool)
Out[32]:
1071
In [33]:
#
def fillData(df,stkPool):
return df[stkPool].fillna(method='ffill').fillna(method='bfill')
In [34]:
PBData = fillData(PBData,stkPool)
LFCAPData = fillData(LFCAPData,stkPool)
YOYBPSData = fillData(YOYBPSData,stkPool)
AMOUNTAVG1MData = fillData(AMOUNTAVG1MData,stkPool)
TURNOVER1MData = fillData(TURNOVER1MData,stkPool)
PROFITOOPData = fillData(PROFITOOPData,stkPool)
PriceDF = fillData(PriceDF,stkPool)
In [35]:
# independentfactor should be a list contains of dataframe
def orthoFactor(factordf, independentfactor,WLS =False, weightdf = None):
'''
Muti variable regression for return.
returndf and dataframes in factorDict should have same index and same columns
output: Dataframe, the orthogonalized result of factordf
input:
factordf: Dataframe, factor to be orthogonalized
independentfactor: List, the values are the factor dataframe as independence in regression(all \
with same columns and index)
'''
emptydf = pd.DataFrame(index = factordf.index, columns = factordf.columns, data= None, dtype =float)
dfNum = len(independentfactor)
if dfNum == 0:
print 'Input is an empty list!'
raise ValueError
for date in factordf.index:
factordfSlice = factordf.loc[date]
mapfunction = map(lambda x: x.loc[date], independentfactor)
if dfNum > 1:
totaldf = pd.concat(mapfunction, axis=1)
else:
totaldf = independentfactor[0].loc[date]
if WLS:
w = weightdf.loc[date]
result = sm.WLS(factordfSlice.T,totaldf,weights = 1/w ).fit()
else:
result = sm.OLS(factordfSlice.T,totaldf ).fit()
emptydf .loc[date] = result.resid
return emptydf
In [36]:
LFCAPData.shape
Out[36]:
(1214, 1071)
In [37]:
YOYBPSData
Out[37]:
600507.SH
002344.SZ
000793.SZ
300039.SZ
600059.SH
600501.SH
000713.SZ
002233.SZ
600360.SH
600565.SH
...
600150.SH
600469.SH
600282.SH
600739.SH
600406.SH
600687.SH
600866.SH
000860.SZ
601908.SH
002195.SZ
LZ_GPA_FIN_IND_YOYBPS-d
2012-01-04
0.240171
-0.992352
-0.041829
-0.724594
-0.052970
-0.092335
-0.141171
-0.859772
-0.085465
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...
-0.757088
0.006821
-0.172923
0.298903
-1.132173
0.492759
0.009977
-0.040529
5.103138
-0.619124
2012-01-05
0.239668
-0.992292
-0.042203
-0.724656
-0.053339
-0.092687
-0.141500
-0.859774
-0.085819
-0.141871
...
-0.757137
0.006424
-0.173237
0.298374
-1.132050
0.492141
0.009579
-0.040904
5.100416
-0.619235
2012-01-06
0.239805
-0.993245
-0.042316
-0.725372
-0.053462
-0.092844
-0.141700
-0.860609
-0.085970
-0.142071
...
-0.757881
0.006355
-0.173465
0.298562
-1.133126
0.492501
0.009513
-0.041015
5.104851
-0.619858
2012-01-09
0.240006
-0.993583
-0.042238
-0.725593
-0.053389
-0.092788
-0.141665
-0.860889
-0.085912
-0.142037
...
-0.758116
0.006454
-0.173445
0.298789
-1.133525
0.492813
0.009613
-0.040937
5.107181
-0.620033
2012-01-10
0.240006
-0.993583
-0.042238
-0.725593
-0.053389
-0.092788
-0.141665
-0.860889
-0.085912
-0.142037
...
-0.758116
0.006454
-0.173445
0.298789
-1.133525
0.492813
0.009613
-0.040937
5.107181
-0.620033
2012-01-11
0.239869
-0.994227
-0.042491
-0.726127
-0.053647
-0.093062
-0.141960
-0.861479
-0.086183
-0.142331
...
-0.758664
0.006220
-0.173752
0.298676
-1.134227
0.492779
0.009381
-0.041190
5.109043
-0.620524
2012-01-12
0.239869
-0.994227
-0.042491
-0.726127
-0.053647
-0.093062
-0.141960
-0.861479
-0.086183
-0.142331
...
-0.758664
0.006220
-0.173752
0.298676
-1.134227
0.492779
0.009381
-0.041190
5.109043
-0.620524
2012-01-13
0.239869
-0.994227
-0.042491
-0.726127
-0.053647
-0.093062
-0.141960
-0.861479
-0.086183
-0.142331
...
-0.758664
0.006220
-0.173752
0.298676
-1.134227
0.492779
0.009381
-0.041190
5.109043
-0.620524
2012-01-16
0.239794
-0.994252
-0.042555
-0.726163
-0.053709
-0.093123
-0.142019
-0.861509
-0.086245
-0.142391
...
-0.758698
0.006155
-0.173810
0.298599
-1.134246
0.492694
0.009316
-0.041253
5.108771
-0.620563
2012-01-17
0.239569
-0.994165
-0.042708
-0.726144
-0.053860
-0.093264
-0.142148
-0.861456
-0.086387
-0.142519
...
-0.758671
0.005989
-0.173931
0.298359
-1.134123
0.492405
0.009149
-0.041407
5.107314
-0.620571
2012-01-18
0.239569
-0.994165
-0.042708
-0.726144
-0.053860
-0.093264
-0.142148
-0.861456
-0.086387
-0.142519
...
-0.758671
0.005989
-0.173931
0.298359
-1.134123
0.492405
0.009149
-0.041407
5.107314
-0.620571
2012-01-19
0.240100
-0.994156
-0.042297
-0.726021
-0.053454
-0.092874
-0.141778
-0.861390
-0.085994
-0.142150
...
-0.758562
0.006421
-0.173575
0.298914
-1.134173
0.493042
0.009582
-0.040995
5.109901
-0.620404
2012-01-20
0.238777
-0.993341
-0.043131
-0.725671
-0.054268
-0.093621
-0.142440
-0.860805
-0.086753
-0.142811
...
-0.758155
0.005503
-0.174182
0.297489
-1.133116
0.616949
0.008658
-0.041831
5.100143
-0.620236
2012-01-30
0.238896
-0.993764
-0.043135
-0.725976
-0.054278
-0.093647
-0.142488
-0.861170
-0.086776
-0.142859
...
-0.758475
0.005520
-0.174244
0.297635
-1.133601
0.617235
0.008677
-0.041835
5.102407
-0.620495
2012-01-31
0.238502
-0.994094
-0.043515
-0.726320
-0.054657
-0.094025
-0.142863
-0.861507
-0.087154
-0.143234
...
-0.758817
0.005137
-0.174617
0.297237
-1.133923
0.616820
0.008294
-0.042215
5.101755
-0.620845
2012-02-01
0.238410
-0.994348
-0.043644
-0.726539
-0.054787
-0.094160
-0.143005
-0.861743
-0.087288
-0.143376
...
-0.759040
0.005015
-0.174763
0.297153
-1.134195
0.616778
0.008172
-0.042344
5.102302
-0.621049
2012-02-02
0.238306
-0.994633
-0.043790
-0.726784
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...
-0.759290
0.004876
-0.174927
0.297058
-0.976615
0.616730
0.008034
-0.042489
5.102912
-0.621280
2012-02-03
0.238288
-0.994688
-0.043816
-0.726831
-0.054961
-0.094341
-0.143194
-0.862060
-0.087468
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...
-0.759338
0.004852
-0.174958
0.297042
-0.976670
0.616724
0.008009
-0.042516
5.103041
-0.621323
2012-02-06
0.238109
-0.994863
-0.043994
-0.727007
-0.055139
-0.094519
-0.143372
-0.862235
-0.087646
-0.143743
...
-0.759514
0.004673
-0.175135
0.296863
-0.976845
0.616544
0.007831
-0.042694
5.102849
-0.621500
2012-02-07
0.237622
-0.994911
-0.044380
-0.727150
-0.055522
-0.094887
-0.143723
-0.862330
-0.088017
-0.144094
...
-0.759646
0.004270
-0.175475
0.296355
-0.976899
0.615922
0.007426
-0.043081
5.100628
-0.621680
2012-02-08
0.237194
-0.995264
-0.044792
-0.727520
-0.055932
-0.095295
-0.144128
-0.862692
-0.088425
-0.144499
...
-0.760013
0.003855
-0.175878
0.295923
-0.977253
0.615470
0.007012
-0.043492
5.099903
-0.622056
2012-02-09
0.237133
-0.995315
-0.044850
-0.727573
-0.055991
-0.095353
-0.144186
-0.862744
-0.088484
-0.144557
...
-0.760066
0.003796
-0.175936
0.295861
-0.977304
0.615406
0.006953
-0.043550
5.099801
-0.622110
2012-02-10
0.237011
-0.995410
-0.044966
-0.727674
-0.056106
-0.095468
-0.144299
-0.862842
-0.088598
-0.144670
...
-0.760166
0.003680
-0.176049
0.295739
-0.977400
0.615277
0.006836
-0.043666
5.099577
-0.622213
2012-02-13
0.237808
-0.991026
-0.043348
-0.724069
-0.054456
-0.093703
-0.142392
-0.858843
-0.086853
-0.142762
...
-0.756467
0.005156
-0.174049
0.296365
-0.973068
0.614972
0.008303
-0.042052
5.086220
-0.618916
2012-02-14
0.236598
-1.006774
-0.047885
-0.736659
-0.059124
-0.098835
-0.148101
-0.873027
-0.091905
-0.148475
...
-0.769440
0.001193
-0.180132
0.295847
-0.988604
0.618224
0.004378
-0.046573
5.142367
-0.630261
2012-02-15
0.237170
-1.006570
-0.047396
-0.736375
-0.058639
-0.098362
-0.147642
-0.872784
-0.091430
-0.148017
...
-0.769166
0.001696
-0.179683
0.296437
-0.988395
0.618910
0.004881
-0.046085
5.144396
-0.629946
2012-02-16
0.237138
-1.006607
-0.047430
-0.736411
-0.058672
-0.098396
-0.147676
-0.872820
-0.091463
-0.148051
...
-0.769202
0.001663
-0.179717
0.296405
-0.988431
0.618879
0.004848
-0.046118
5.144381
-0.629981
2012-02-17
0.237054
-1.006724
-0.047521
-0.736521
-0.058764
-0.098489
-0.147770
-0.872934
-0.091556
-0.148145
...
-0.769312
0.001573
-0.179812
0.296323
-0.988548
0.618805
0.004758
-0.046210
5.144427
-0.630088
2012-02-20
0.497486
-1.007413
-0.048541
-0.737303
-0.059780
-0.099491
-0.148755
-0.873669
-0.092560
-0.149130
...
-0.770083
0.000536
-0.180786
0.295185
-0.989243
0.617556
0.003721
-0.047229
5.141618
-0.630907
2012-02-21
0.492604
-1.003322
-0.050167
-0.734823
-0.061339
-0.100814
-0.149784
-0.870376
-0.093924
-0.150157
...
-0.767408
-0.001383
-0.181624
0.291508
-0.985261
0.611958
0.001782
-0.048864
5.109045
-0.629061
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
2016-11-21
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-11-22
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-11-23
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-11-24
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-11-25
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-11-28
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-11-29
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-11-30
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-12-01
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-12-02
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-12-05
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-12-06
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-12-07
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-12-08
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-12-09
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-12-12
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-12-13
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-12-14
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-12-15
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-12-16
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-12-19
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-12-20
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-12-21
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-12-22
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-12-23
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-12-26
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-12-27
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-12-28
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-12-29
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
2016-12-30
0.340788
0.146407
0.103897
0.148159
0.038965
-0.011702
-0.082949
0.025408
0.016802
-0.004202
...
-0.041196
-0.607518
0.061022
0.070768
0.020463
0.087341
0.021476
0.351489
0.053659
-0.558160
1214 rows × 1071 columns
In [43]:
orthorizedDF = orthoFactor(YOYBPSData,[PROFITOOPData],True, weight)
In [44]:
orthorizedDF.head()
Out[44]:
600507.SH
002344.SZ
000793.SZ
300039.SZ
600059.SH
600501.SH
000713.SZ
002233.SZ
600360.SH
600565.SH
...
600150.SH
600469.SH
600282.SH
600739.SH
600406.SH
600687.SH
600866.SH
000860.SZ
601908.SH
002195.SZ
LZ_GPA_FIN_IND_YOYBPS-d
2012-01-04
0.151757
-1.078286
-0.129363
-0.811546
-0.140540
-0.181008
-0.229801
-0.947120
-0.173590
-0.230349
...
-0.845153
-0.081833
-0.261602
0.213348
-1.219860
0.404714
-0.077751
-0.128960
5.016732
-0.706591
2012-01-05
0.149857
-1.079584
-0.131120
-0.812983
-0.142293
-0.182761
-0.231530
-0.948502
-0.175337
-0.232081
...
-0.846592
-0.083630
-0.263317
0.211466
-1.221122
0.402704
-0.079536
-0.130732
5.012645
-0.708084
2012-01-06
0.149248
-1.081257
-0.131969
-0.814429
-0.143152
-0.183666
-0.232478
-0.950072
-0.176231
-0.233031
...
-0.848079
-0.084448
-0.264294
0.210939
-1.222937
0.402323
-0.080341
-0.131589
5.016355
-0.709442
2012-01-09
0.148981
-1.082048
-0.132354
-0.815109
-0.143542
-0.184080
-0.232913
-0.950813
-0.176638
-0.233468
...
-0.848780
-0.084818
-0.264743
0.210715
-1.223800
0.402169
-0.080704
-0.131979
5.018230
-0.710079
2012-01-10
0.148740
-1.082282
-0.132592
-0.815346
-0.143780
-0.184321
-0.233154
-0.951051
-0.176878
-0.233709
...
-0.849020
-0.085060
-0.264984
0.210482
-1.224038
0.401929
-0.080943
-0.132219
5.017994
-0.710317
5 rows × 1071 columns
In [45]:
correlationDF1 = ff.showCorrelation(orthorizedDF ,PROFITOOPData, orthorizedDF.index, filterdic = None).astype(float)
correlationDF1.describe()
Out[45]:
Pearson
Spearman
count
1214.000000
1214.000000
mean
0.003473
0.221383
std
0.080167
0.229138
min
-0.198467
-0.388118
25%
-0.007971
0.178505
50%
0.005107
0.223134
75%
0.022895
0.327686
max
0.135320
0.587530
In [46]:
correlationDF = ff.showCorrelation(YOYBPSData ,PROFITOOPData, orthorizedDF.index, filterdic = None).astype(float)
correlationDF.describe()
Out[46]:
Pearson
Spearman
count
1214.000000
1214.000000
mean
0.095275
0.363892
std
0.061983
0.155586
min
0.008090
0.145976
25%
0.051428
0.261719
50%
0.084243
0.284862
75%
0.128098
0.560356
max
0.284805
0.634266
In [47]:
orthoFactor(PBData,[LFCAPData,YOYBPSData]).head()
Out[47]:
600507.SH
002344.SZ
000793.SZ
300039.SZ
600059.SH
600501.SH
000713.SZ
002233.SZ
600360.SH
600565.SH
...
600150.SH
600469.SH
600282.SH
600739.SH
600406.SH
600687.SH
600866.SH
000860.SZ
601908.SH
002195.SZ
LZ_GPA_VAL_PB-d
2012-01-04
-0.071250
0.053166
-0.035383
0.000753
-0.037675
-0.052847
-0.014652
-0.054814
-0.088625
-0.075696
...
-0.076637
-0.096018
-0.103514
-0.073245
0.360620
0.075080
-0.083320
-0.066678
-0.066159
0.039261
2012-01-05
-0.377263
0.817910
-0.019514
0.336389
-0.093000
-0.182212
0.080297
-0.260826
-0.537475
-0.373930
...
-0.525790
-0.599379
-0.700208
-0.452856
3.866487
1.164736
-0.499238
-0.372012
-0.310752
0.661021
2012-01-06
-0.370344
0.807951
-0.033858
0.331630
-0.083475
-0.174873
-0.008016
-0.224641
-0.529716
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...
-0.519258
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-0.695577
-0.428696
3.870769
1.150617
-0.514991
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-0.306836
0.694119
2012-01-09
-0.071479
0.059052
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0.007236
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-0.032796
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...
-0.081841
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-0.073139
0.398672
0.086776
-0.089531
-0.069367
-0.069264
0.036300
2012-01-10
-0.384463
0.819567
-0.052424
0.333428
-0.084771
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0.052174
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...
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5 rows × 1071 columns
In [48]:
# calc active return
returnDF = PriceDF.pct_change()[1:]
returnOfBench = benchmarkData.pct_change()[1:]
activeReturnDF = returnDF .apply(lambda x: x - returnOfBench )
activeReturnDF.head().iloc[:,2].values
Out[48]:
array([ 0.04409056, -0.01318288, -0.01008094, -0.01510446, -0.00549635])
In [49]:
factorReturn = pd.DataFrame(index = returnDF.index, columns = ['LFCA', 'PB', 'YOYBPS', 'AMOUNTAVG1MData', 'TURNOVER1M' ,\
'PROFITOOP'],data= None ,dtype =float)
factorPvalue = pd.DataFrame(index = returnDF.index, columns = ['LFCA', 'PB', 'YOYBPS', 'AMOUNTAVG1MData', 'TURNOVER1M' ,\
'PROFITOOP'],data= None ,dtype =float)
specificReturn = pd.DataFrame(index = returnDF.index, columns = LFCAPData.columns ,dtype =float )
In [50]:
# construct the multiple factor structural risk model
def multiFactorReg(returndf,factorDict,WLS =False, weightdf = None):
'''
Multi variable regression for return.
returndf and dataframes in factorDict should have same index and same columns.
output: 4 Dataframe, respectively idiosyncratic return for each stock, factor Return, factor P-value and
R-Square of the linear regression model.
input:
returndf: Dataframe, can either be return or acticve return.
factorDict: Dictionary, the keys are the names of factors and the values are the corresponding factor dataframe(all\
with same columns and index).
WLS: True to use WLS , False to use OLS. If True, then weightdf should not be none.
weightdf: Dataframe , which has no nan and the shape is same as dataframes in factorDict.
'''
specificReturn = pd.DataFrame(index = returndf.index, columns =returndf.columns , data= None, dtype =float)
factorReturn = pd.DataFrame(index = returndf.index, columns =factorDict.keys(), data= None, dtype =float )
factorPvalue = pd.DataFrame(index = returndf.index, columns =factorDict.keys(), data= None, dtype =float )
RSquare = pd.DataFrame(index = returndf.index, columns =['R-Square'], data= None, dtype =float )
dfNum = len(factorDict.keys())
if dfNum == 0:
print 'Input is an empty list!'
raise ValueError
for date in returndf.index:
returndfSlice = returndf.loc[date]
mapfunction = map(lambda x: x.loc[date], factorDict.values())
if dfNum > 1:
totaldf = pd.concat(mapfunction, axis=1)
else:
totaldf = factorDict.values()[0].loc[date]
if WLS:
w = weightdf.loc[date]
result = sm.WLS(returndfSlice.T,totaldf,weights = 1/w ).fit()
else:
result = sm.OLS(returndfSlice.T,totaldf ).fit()
specificReturn .loc[date] = result.resid
factorReturn .loc[date] = result.params.values
factorPvalue . loc[date] = result.pvalues.values
RSquare .loc[date] = result.rsquared
return specificReturn, factorReturn, factorPvalue, RSquare
In [51]:
factordic = {'LFCA':LFCAPData, 'PB':PBData, 'YOYBPS':YOYBPSData, 'AMOUNTAVG1MData':AMOUNTAVG1MData, 'TURNOVER1M' :TURNOVER1MData,\
'PROFITOOP':PROFITOOPData}
In [ ]:
In [52]:
help(multiFactorReg)
Help on function multiFactorReg in module __main__:
multiFactorReg(returndf, factorDict, WLS=False, weightdf=None)
Multi variable regression for return.
returndf and dataframes in factorDict should have same index and same columns.
output: 4 Dataframe, respectively idiosyncratic return for each stock, factor Return, factor P-value and
R-Square of the linear regression model.
input:
returndf: Dataframe, can either be return or acticve return.
factorDict: Dictionary, the keys are the names of factors and the values are the corresponding factor dataframe(all with same columns and index).
WLS: True to use WLS , False to use OLS. If True, then weightdf should not be none.
weightdf: Dataframe , which has no nan and the shape is same as dataframes in factorDict.
In [53]:
# set weight for WLS
weightdf = pd.read_csv(path+filenameFCAP,infer_datetime_format=True,parse_dates=[0],index_col=0).loc[startTime:endTime]
weightdf = fillData(weightdf,stkPool)
# the square root is the weight
weight = np.sqrt(weightdf)
In [54]:
weightdf[stkPool].isnull().sum().sum()
Out[54]:
0
In [55]:
weightdf
Out[55]:
600507.SH
002344.SZ
000793.SZ
300039.SZ
600059.SH
600501.SH
000713.SZ
002233.SZ
600360.SH
600565.SH
...
600150.SH
600469.SH
600282.SH
600739.SH
600406.SH
600687.SH
600866.SH
000860.SZ
601908.SH
002195.SZ
LZ_GPA_VAL_A_FCAP-d
2012-01-04
5.059064e+05
3.296545e+05
7.585292e+05
275650.5960
608818.6904
159668.3269
403180.4931
408744.0281
265129.28
2.203200e+05
...
2.717012e+06
281206.6110
4.582656e+05
1.662217e+06
3.366400e+06
1.795473e+05
296507.3390
5.959299e+05
1.255200e+05
1.430554e+05
2012-01-05
5.007042e+05
3.149170e+05
7.639667e+05
266325.1032
578619.3506
150797.8643
362832.5565
379710.0461
254958.08
2.203200e+05
...
2.647046e+06
277082.2474
4.498416e+05
1.652664e+06
3.340141e+06
1.795473e+05
281395.3657
5.656730e+05
1.152600e+05
1.320444e+05
2012-01-06
5.072069e+05
3.149170e+05
7.571698e+05
266736.5220
586471.1789
152654.4727
337727.1737
396948.9729
259026.56
2.304000e+05
...
2.676728e+06
285330.9746
4.548960e+05
1.718170e+06
3.363249e+06
1.795473e+05
275142.1353
5.674270e+05
1.170000e+05
1.352156e+05
2012-01-09
5.267148e+05
3.254660e+05
7.802791e+05
279627.6444
608818.6904
159874.6167
348486.6234
422807.3631
270553.92
2.347200e+05
...
2.785918e+06
293579.7019
4.683744e+05
1.794593e+06
3.392659e+06
1.795473e+05
287648.5960
6.016305e+05
1.231800e+05
1.352156e+05
2012-01-10
5.449223e+05
3.369457e+05
8.006697e+05
286210.3452
631770.1886
166682.1810
383454.8352
450026.7212
282081.28
2.397600e+05
...
2.894047e+06
307452.5614
4.885920e+05
1.828711e+06
3.465134e+06
1.795473e+05
308492.6972
6.261869e+05
1.300800e+05
1.437601e+05
2012-01-11
5.696324e+05
3.425305e+05
7.965916e+05
281136.1800
624522.3471
167301.0505
390627.8017
440499.9459
280725.12
2.368800e+05
...
2.860124e+06
309327.2721
4.902768e+05
1.809605e+06
3.496645e+06
1.795473e+05
304844.9795
6.209248e+05
1.288800e+05
1.581185e+05
2012-01-12
5.631297e+05
3.395830e+05
7.925135e+05
276336.2940
622106.3999
168332.4996
383155.9616
443221.8817
280725.12
2.361600e+05
...
2.862244e+06
304078.0820
4.869072e+05
1.801417e+06
3.446227e+06
1.795473e+05
303802.7744
6.165397e+05
1.321200e+05
1.552997e+05
2012-01-13
5.384196e+05
3.347739e+05
7.666854e+05
254393.9580
602174.8356
161112.3557
376879.6159
424168.3310
267841.60
2.282400e+05
...
2.788038e+06
289830.2804
4.734288e+05
1.761840e+06
3.334889e+06
1.795473e+05
287127.4935
5.867213e+05
1.314000e+05
1.447291e+05
2012-01-16
5.332175e+05
3.164684e+05
7.503730e+05
245617.0236
578015.3638
154511.0812
371499.8910
410558.6519
261738.88
2.217600e+05
...
2.736093e+06
283081.3217
4.683744e+05
1.755017e+06
3.004026e+06
1.795473e+05
274621.0328
5.757587e+05
1.245600e+05
1.389153e+05
2012-01-17
5.332175e+05
3.327572e+05
7.829979e+05
254393.9580
611838.6243
162143.8048
392122.1698
445036.5056
276656.64
2.325600e+05
...
2.896167e+06
300703.6027
4.953312e+05
1.865558e+06
3.069149e+06
1.795473e+05
290775.2112
6.055771e+05
1.319400e+05
1.497502e+05
2012-01-18
5.332175e+05
3.291891e+05
7.748417e+05
231080.2260
594323.0073
164825.5726
379868.3520
448665.7533
272588.16
2.304000e+05
...
2.866485e+06
299953.7184
4.852224e+05
1.823252e+06
2.852775e+06
1.795473e+05
282958.6732
5.915449e+05
1.267800e+05
1.436721e+05
2012-01-19
5.722334e+05
3.294994e+05
7.626073e+05
235605.8328
602778.8224
165857.0217
378672.8575
451387.6892
277334.72
2.332800e+05
...
2.908888e+06
305952.7928
4.919616e+05
1.869652e+06
3.036587e+06
1.795473e+05
284521.9808
5.989995e+05
1.312200e+05
1.442887e+05
2012-01-20
5.696324e+05
3.316713e+05
7.734823e+05
242051.3940
620898.4263
167507.3403
394214.2850
462729.0884
281403.20
2.361600e+05
...
3.018078e+06
308577.3878
4.970160e+05
1.907864e+06
3.089105e+06
1.795473e+05
291817.4162
6.117162e+05
1.319400e+05
1.444649e+05
2012-01-30
5.709329e+05
3.254660e+05
7.734823e+05
239720.0208
623314.3735
167094.7607
390328.9281
445943.8175
280725.12
2.332800e+05
...
2.936451e+06
301078.5448
4.919616e+05
1.890123e+06
3.071249e+06
1.795473e+05
288690.8011
6.213633e+05
1.308000e+05
1.439363e+05
2012-01-31
5.683318e+05
3.201915e+05
7.897947e+05
232588.7616
621502.4131
169363.9488
389432.3073
445943.8175
281403.20
2.361600e+05
...
2.991575e+06
305202.9085
4.835376e+05
1.887394e+06
3.044990e+06
1.795473e+05
286606.3910
6.275024e+05
1.251000e+05
1.436721e+05
2012-02-01
5.579276e+05
3.201915e+05
7.816385e+05
233548.7388
610026.6640
166063.3115
390030.0545
430519.5146
282759.36
2.376000e+05
...
2.918429e+06
306327.7349
4.801680e+05
1.857370e+06
3.030285e+06
1.795473e+05
286606.3910
6.226788e+05
1.227600e+05
1.426150e+05
2012-02-02
5.644302e+05
3.243801e+05
7.952322e+05
246714.1404
615462.5451
168951.3691
394214.2850
441407.2578
288862.08
2.419200e+05
...
2.960833e+06
311576.9250
4.869072e+05
1.881935e+06
3.050242e+06
1.795473e+05
292338.5188
6.345185e+05
1.253400e+05
1.462266e+05
2012-02-03
5.696324e+05
3.318264e+05
7.938729e+05
248908.3740
622106.3999
171220.5572
393915.4114
441407.2578
300389.44
2.433600e+05
...
3.011717e+06
315326.3465
4.902768e+05
1.880570e+06
3.203595e+06
1.795473e+05
310056.0048
6.314489e+05
1.287600e+05
1.495740e+05
2012-02-06
5.709329e+05
3.366355e+05
7.897947e+05
258508.1460
624522.3471
171014.2674
399295.1363
451387.6892
298355.20
2.419200e+05
...
3.000056e+06
314576.4622
4.885920e+05
1.895582e+06
3.307580e+06
1.795473e+05
309013.7997
6.336415e+05
1.341000e+05
1.500144e+05
2012-02-07
5.657308e+05
3.304302e+05
7.843573e+05
252336.8640
608214.7036
167713.6302
392421.0434
437324.3541
289540.16
2.397600e+05
...
2.925850e+06
308202.4457
4.835376e+05
1.815064e+06
3.271868e+06
1.795473e+05
299112.8516
6.187323e+05
1.314600e+05
1.461385e+05
2012-02-08
5.735339e+05
3.355495e+05
7.938729e+05
257685.3084
626334.3075
172458.2962
397203.0210
454109.6250
299711.36
2.448000e+05
...
2.990515e+06
320575.5365
4.936464e+05
1.877841e+06
3.356947e+06
1.795473e+05
309534.9022
6.318874e+05
1.364400e+05
1.478382e+05
2012-02-09
5.865392e+05
3.366355e+05
7.965916e+05
256313.9124
637206.0698
172045.7166
396904.1474
457285.2168
303101.76
2.484000e+05
...
3.001116e+06
318325.8837
4.953312e+05
1.865558e+06
3.383206e+06
1.795473e+05
318914.7477
6.362725e+05
1.357800e+05
1.504394e+05
2012-02-10
5.904408e+05
3.392727e+05
7.979510e+05
253708.2600
634790.1226
173077.1657
403180.4931
460914.4645
303101.76
2.498400e+05
...
2.991575e+06
319075.7679
5.003856e+05
1.881935e+06
3.368501e+06
1.795473e+05
316830.3376
6.503047e+05
1.375800e+05
1.503527e+05
2012-02-13
5.930419e+05
3.395830e+05
8.020291e+05
267010.8012
647473.8453
174933.7741
399295.1363
455470.5929
315307.20
2.498400e+05
...
2.989455e+06
322075.3051
4.987008e+05
1.886029e+06
3.374803e+06
1.795473e+05
322562.4655
6.533743e+05
1.372800e+05
1.521736e+05
2012-02-14
5.904408e+05
3.420651e+05
8.020291e+05
275787.7356
645661.8849
176584.0928
396306.4002
461821.7764
311238.72
2.534400e+05
...
3.014897e+06
327699.4374
4.936464e+05
1.872382e+06
3.348544e+06
1.795473e+05
320478.0553
6.529357e+05
1.354800e+05
1.515666e+05
2012-02-15
5.982440e+05
3.459434e+05
8.197009e+05
279627.6444
646265.8717
180503.5995
397800.7683
466811.9921
322766.08
2.548800e+05
...
3.074262e+06
329949.0902
5.054400e+05
1.940617e+06
3.329638e+06
1.795473e+05
335068.9261
6.581978e+05
1.386600e+05
1.538211e+05
2012-02-16
6.099488e+05
3.473395e+05
8.237790e+05
279079.0860
646265.8717
179678.4402
402881.6195
462729.0884
320053.76
2.592000e+05
...
3.038219e+06
333698.5117
5.020704e+05
1.918782e+06
3.304429e+06
1.795473e+05
331942.3110
6.498662e+05
1.422000e+05
1.533875e+05
2012-02-17
6.047467e+05
3.459434e+05
8.156228e+05
274279.2000
640226.0038
182360.2080
396007.5266
456831.5608
314629.12
2.599200e+05
...
3.052000e+06
331823.8010
5.003856e+05
1.948806e+06
3.313882e+06
1.795473e+05
326210.1832
6.437271e+05
1.437000e+05
1.499192e+05
2012-02-20
6.047467e+05
3.428407e+05
8.101853e+05
263993.7300
645661.8849
181328.7588
395110.9058
458646.1847
312594.88
2.556000e+05
...
3.053061e+06
328824.2638
4.987008e+05
1.921511e+06
3.419968e+06
1.795473e+05
325689.0806
6.467967e+05
1.418400e+05
1.501793e+05
2012-02-21
6.008451e+05
3.437715e+05
8.224197e+05
266873.6616
674049.2643
182153.9181
396306.4002
462275.4324
316663.36
2.599200e+05
...
3.060481e+06
329574.1481
5.020704e+05
1.950170e+06
3.460932e+06
1.795473e+05
328815.6958
6.647754e+05
1.429200e+05
1.543413e+05
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
2016-11-21
8.566561e+05
1.414394e+06
2.090657e+06
694497.9562
827120.2208
746224.5224
362826.4865
789212.0183
755793.92
1.054097e+06
...
3.253736e+06
606281.4533
1.050329e+06
2.665278e+06
3.504942e+06
1.770962e+06
395732.9013
1.165971e+06
1.334363e+06
1.052462e+06
2016-11-22
8.712431e+05
1.418818e+06
2.081520e+06
702509.0240
828737.2691
751514.0092
367010.6469
777840.0872
752841.60
1.058514e+06
...
3.312995e+06
612467.9988
1.104589e+06
2.824949e+06
3.485091e+06
1.770962e+06
398484.8687
1.172164e+06
1.415182e+06
1.034429e+06
2016-11-23
8.712431e+05
1.372371e+06
2.057762e+06
693881.7202
827928.7450
761686.0992
361929.8807
771016.9286
752103.52
1.048208e+06
...
3.288189e+06
605156.6269
1.123968e+06
2.792196e+06
3.476268e+06
1.770962e+06
392430.5405
1.178920e+06
1.380791e+06
1.025412e+06
2016-11-24
8.738953e+05
1.384536e+06
2.054107e+06
687719.3604
844907.7524
761686.0992
357147.9830
771016.9286
766127.04
1.045264e+06
...
3.228930e+06
596158.0153
1.147223e+06
2.984620e+06
3.394655e+06
1.770962e+06
384725.0320
1.190743e+06
1.339522e+06
9.827893e+05
2016-11-25
9.096998e+05
1.369054e+06
2.063245e+06
688951.8323
836822.5108
758024.1468
354159.2969
763435.6412
751365.44
1.037903e+06
...
3.263382e+06
602906.9740
1.154974e+06
2.953232e+06
3.394655e+06
1.770962e+06
380872.2778
1.183424e+06
1.330924e+06
1.065481e+06
2016-11-28
9.030693e+05
1.348043e+06
2.092485e+06
695114.1922
838439.5591
748258.9404
355354.7713
771775.0573
738080.00
1.076180e+06
...
3.290945e+06
600094.9079
1.162726e+06
2.940950e+06
3.436564e+06
1.770962e+06
377569.9170
1.182861e+06
1.554554e+06
1.054052e+06
2016-11-29
9.256129e+05
1.352466e+06
2.035832e+06
688951.8323
842482.1799
777961.4432
349676.2679
747514.9376
718889.92
1.061458e+06
...
3.376388e+06
579848.0319
1.127844e+06
2.900008e+06
3.423330e+06
1.770962e+06
372616.3758
1.192432e+06
1.516686e+06
1.039987e+06
2016-11-30
9.110259e+05
1.306020e+06
2.048625e+06
710520.0919
838439.5591
772265.0728
349676.2679
734626.7491
723318.40
1.043792e+06
...
3.285432e+06
580972.8583
1.112341e+06
2.921844e+06
3.410095e+06
1.770962e+06
371515.5889
1.167660e+06
1.486791e+06
1.069876e+06
2016-12-01
9.295912e+05
1.315973e+06
2.034005e+06
708671.3839
836822.5108
773078.8400
352067.2167
743724.2939
727008.80
1.039375e+06
...
3.388791e+06
596720.4285
1.131720e+06
2.920479e+06
3.414507e+06
1.770962e+06
375368.3431
1.164845e+06
1.498749e+06
1.068997e+06
2016-12-02
8.858301e+05
1.289432e+06
2.035832e+06
687719.3604
814183.8342
748665.8240
345790.9760
731594.2341
710032.96
1.037903e+06
...
3.497662e+06
602906.9740
1.108465e+06
2.924573e+06
3.440976e+06
1.770962e+06
366562.0477
1.144014e+06
1.462875e+06
1.047019e+06
2016-12-05
8.845040e+05
1.246304e+06
2.019385e+06
672929.6967
817417.9308
748258.9404
345193.2388
728561.7191
704866.40
1.034958e+06
...
3.763639e+06
606281.4533
1.096838e+06
2.632525e+06
3.491708e+06
1.770962e+06
367112.4412
1.146829e+06
1.454903e+06
1.057569e+06
2016-12-06
8.791996e+05
1.246304e+06
2.002937e+06
670464.7528
810949.7375
734018.0144
347883.0562
740691.7790
719628.00
1.027597e+06
...
3.703002e+06
611343.1723
1.100714e+06
2.605231e+06
3.546852e+06
1.770962e+06
372065.9823
1.146266e+06
1.458889e+06
1.070755e+06
2016-12-07
9.295912e+05
1.287220e+06
1.991972e+06
679708.2926
816609.4067
736459.3160
352366.0853
764193.7699
719628.00
1.030542e+06
...
3.806361e+06
612467.9988
1.135595e+06
2.617513e+06
3.562292e+06
1.770962e+06
378120.3105
1.155837e+06
1.472840e+06
1.080426e+06
2016-12-08
9.309173e+05
1.276162e+06
2.043142e+06
670464.7528
818226.4550
720997.7392
350572.8737
749789.3239
718151.84
1.052625e+06
...
3.744346e+06
622591.4368
1.120092e+06
2.591584e+06
3.502737e+06
1.770962e+06
373717.1627
1.164845e+06
1.456896e+06
1.073393e+06
2016-12-09
9.136781e+05
1.277268e+06
2.035832e+06
666767.3369
819034.9791
721811.5064
349377.3992
734626.7491
711509.12
1.054097e+06
...
3.850461e+06
624841.0896
1.131720e+06
2.603866e+06
3.489502e+06
1.770962e+06
374267.5562
1.170475e+06
1.432980e+06
1.073393e+06
2016-12-12
8.884823e+05
1.210916e+06
1.966387e+06
626095.7618
776991.7226
696584.7232
328456.5969
686864.6384
660581.60
1.027597e+06
...
3.726430e+06
595033.1889
1.092962e+06
2.431913e+06
3.476268e+06
1.770962e+06
355554.1784
1.141762e+06
1.363224e+06
1.000427e+06
2016-12-13
9.401999e+05
1.216445e+06
1.966387e+06
630409.4137
790736.6334
712046.3000
334732.8376
712641.0156
656153.12
1.027597e+06
...
4.002054e+06
594470.7757
1.108465e+06
2.457842e+06
3.385832e+06
1.770962e+06
357755.7523
1.176668e+06
1.351266e+06
1.006580e+06
2016-12-14
9.150042e+05
1.206493e+06
1.948112e+06
627328.2337
787502.5367
731576.7128
325169.0422
700510.9558
653200.80
1.006987e+06
...
3.902829e+06
630465.2219
1.085211e+06
2.422360e+06
3.608613e+06
1.770962e+06
361058.1130
1.176105e+06
1.351266e+06
9.916355e+05
2016-12-15
1.006505e+06
1.251833e+06
1.973697e+06
631025.6497
789119.5850
740528.1520
329353.2027
702027.2132
670176.64
1.057041e+06
...
3.875267e+06
636651.7673
1.104589e+06
2.420995e+06
3.635082e+06
1.770962e+06
368763.6216
1.186802e+06
1.359238e+06
1.005701e+06
2016-12-16
1.063527e+06
1.263997e+06
2.081520e+06
637804.2455
801247.4475
749479.5912
346687.5818
711124.7581
671652.80
1.087958e+06
...
3.696111e+06
630465.2219
1.100714e+06
2.479678e+06
3.588761e+06
1.770962e+06
374817.9497
1.181735e+06
1.375182e+06
1.031195e+06
2016-12-19
1.029048e+06
1.244092e+06
2.134517e+06
630409.4137
799630.3992
752734.6600
351170.6109
712641.0156
659843.52
1.087958e+06
...
3.753992e+06
640026.2466
1.123968e+06
2.464666e+06
3.604201e+06
1.770962e+06
375918.7366
1.197499e+06
1.375182e+06
1.018009e+06
2016-12-20
9.799827e+05
1.254045e+06
2.130862e+06
632874.3576
791545.1575
743783.2208
342802.2899
698994.6983
674605.12
1.074708e+06
...
3.722296e+06
623716.2632
1.120092e+06
2.445560e+06
3.509354e+06
1.770962e+06
379221.0974
1.190743e+06
1.365217e+06
1.024163e+06
2016-12-21
9.786566e+05
1.260680e+06
2.123552e+06
637188.0095
798013.3509
760058.5648
347584.1876
700510.9558
677557.44
1.074708e+06
...
3.989650e+06
668709.3210
1.139471e+06
2.471489e+06
3.555675e+06
1.770962e+06
379771.4909
1.195247e+06
1.363224e+06
1.028558e+06
2016-12-22
9.760044e+05
1.256256e+06
2.105277e+06
634106.8296
800438.9234
745003.8716
343698.8958
739933.6502
666486.24
1.098263e+06
...
4.087497e+06
674895.8664
1.193732e+06
2.468760e+06
3.676991e+06
1.770962e+06
379221.0974
1.181735e+06
1.361231e+06
1.008339e+06
2016-12-23
9.348956e+05
1.221975e+06
2.158275e+06
626711.9978
810141.2133
740121.2684
334433.9690
719464.1743
665010.08
1.101208e+06
...
4.104034e+06
658585.8830
1.193732e+06
2.437372e+06
3.716695e+06
1.770962e+06
373717.1627
1.162030e+06
1.349273e+06
9.969102e+05
2016-12-26
9.163303e+05
1.238563e+06
2.143655e+06
632874.3576
822269.0758
746631.4060
338319.2609
727803.5904
664272.00
1.099735e+06
...
4.130218e+06
661960.3623
1.166601e+06
2.440101e+06
3.690226e+06
1.770962e+06
378670.7039
1.158652e+06
1.363224e+06
1.018888e+06
2016-12-27
9.136781e+05
1.238563e+06
2.149137e+06
632874.3576
828737.2691
748665.8240
342503.4213
730836.1054
669438.56
1.129179e+06
...
4.051666e+06
661960.3623
1.158850e+06
2.429183e+06
3.701255e+06
1.770962e+06
384174.6386
1.169349e+06
1.353259e+06
1.011855e+06
2016-12-28
9.004171e+05
1.238563e+06
2.097967e+06
627328.2337
823077.6000
752734.6600
342802.2899
720980.4317
671652.80
1.114457e+06
...
4.044775e+06
683332.0647
1.154974e+06
2.405983e+06
3.668168e+06
1.770962e+06
390228.9667
1.176105e+06
1.355252e+06
1.008339e+06
2016-12-29
9.070476e+05
1.269527e+06
2.107105e+06
634723.0656
827120.2208
754362.1944
345193.2388
731594.2341
661319.68
1.089430e+06
...
3.948307e+06
673208.6267
1.201483e+06
2.467395e+06
3.668168e+06
1.770962e+06
385275.4255
1.175542e+06
1.383154e+06
1.010976e+06
2016-12-30
8.977650e+05
1.251833e+06
2.063245e+06
630409.4137
827928.7450
742155.6864
348181.9248
708850.3719
653938.88
1.092374e+06
...
3.804983e+06
669271.7342
1.201483e+06
2.451019e+06
3.668168e+06
1.770962e+06
389678.5732
1.238598e+06
1.419029e+06
9.951520e+05
1214 rows × 1071 columns
In [56]:
weight
Out[56]:
600507.SH
002344.SZ
000793.SZ
300039.SZ
600059.SH
600501.SH
000713.SZ
002233.SZ
600360.SH
600565.SH
...
600150.SH
600469.SH
600282.SH
600739.SH
600406.SH
600687.SH
600866.SH
000860.SZ
601908.SH
002195.SZ
LZ_GPA_VAL_A_FCAP-d
2012-01-04
711.270946
574.155505
870.935818
525.024377
780.268345
399.585194
634.964954
639.330922
514.907060
469.382573
...
1648.336085
530.289177
676.953174
1289.269776
1834.775229
423.730240
544.524875
771.964985
354.288018
378.226706
2012-01-05
707.604577
561.174702
874.051878
516.066956
760.670330
388.327007
602.355839
616.206172
504.933738
469.382573
...
1626.974455
526.386025
670.702318
1285.559640
1827.605310
423.730240
530.467120
752.112365
339.499632
363.379142
2012-01-06
712.184589
561.174702
870.155060
516.465412
765.814063
390.710216
581.142989
630.038866
508.946520
480.000000
...
1636.071029
534.163809
674.459784
1310.789708
1833.916319
423.730240
524.539927
753.277528
342.052628
367.716712
2012-01-09
725.751229
570.496268
883.334105
528.798302
780.268345
399.843240
590.327556
650.236390
520.147979
484.479102
...
1669.106866
541.829957
684.378842
1339.624353
1841.917238
423.730240
536.328813
775.648450
350.970084
367.716712
2012-01-10
738.188508
580.470263
894.801493
534.986304
794.839725
408.267291
619.237301
670.840310
531.113246
489.652938
...
1701.189865
554.484050
698.993562
1352.298471
1861.487006
423.730240
555.421189
791.319699
360.666051
379.157155
2012-01-11
754.739924
585.261021
892.519802
530.222765
790.267263
409.024511
625.002241
663.701699
529.834993
486.703195
...
1691.190132
556.171981
700.197686
1345.215676
1869.931720
423.730240
552.127684
787.987812
358.998607
397.641227
2012-01-12
750.419683
582.737473
890.232264
525.676986
788.737219
410.283438
618.995930
665.749113
529.834993
485.962962
...
1691.816846
551.432754
697.787360
1342.168751
1856.401738
423.730240
551.183068
785.200439
363.483150
394.080853
2012-01-13
733.770823
578.596478
875.605749
504.374819
775.999250
401.388036
613.905217
651.282067
517.534153
477.744702
...
1669.741869
538.358877
688.061625
1327.343351
1826.167948
423.730240
535.842788
765.977354
362.491379
380.432806
2012-01-16
730.217433
562.555208
866.240713
495.597643
760.273217
393.078976
609.507909
640.748509
511.604222
470.914005
...
1654.114106
532.053871
684.378842
1324.770480
1733.212720
423.730240
524.042968
758.787620
352.930588
372.713416
2012-01-17
730.217433
576.851085
884.871676
504.374819
782.201141
402.670839
626.196590
667.110565
525.981597
482.244751
...
1701.812897
548.364480
703.797698
1365.854428
1751.898565
423.730240
539.235766
778.188328
363.235461
386.975651
2012-01-18
730.217433
573.750075
880.250905
480.708047
770.923477
405.987158
616.334610
669.825166
522.099761
480.000000
...
1693.069580
547.680307
696.579069
1350.278606
1689.015885
423.730240
531.938599
769.119546
356.061792
379.040974
2012-01-19
756.461112
574.020394
873.273905
485.392452
776.388319
407.255475
615.364004
671.853920
526.625787
482.990683
...
1705.546312
553.129996
701.399743
1367.352350
1742.580689
423.730240
533.406019
773.950568
362.243012
379.853495
2012-01-20
754.739924
575.909067
879.478416
491.987189
787.971082
409.276606
627.864862
680.241934
530.474505
485.962962
...
1737.261506
555.497424
704.993617
1381.254619
1757.585105
423.730240
540.201274
782.122856
363.235461
380.085325
2012-01-30
755.601008
570.496268
879.478416
489.612113
789.502611
408.772260
624.763098
667.790250
529.834993
482.990683
...
1713.607474
548.706246
701.399743
1374.817477
1752.498017
423.730240
537.299545
788.266007
361.662826
379.389410
2012-01-31
753.877856
565.854680
888.703967
482.274571
788.354243
411.538514
624.045116
667.790250
530.474505
485.962962
...
1729.617090
552.451725
695.368679
1373.824470
1744.990066
423.730240
535.356322
792.150481
353.694784
379.040974
2012-02-01
746.945499
565.854680
884.103225
483.268806
781.042037
407.508664
624.523862
656.139859
531.751220
487.442304
...
1708.341019
553.468820
692.941556
1362.853646
1740.771468
423.730240
535.356322
789.100004
350.371232
377.644012
2012-02-02
751.285718
569.543739
891.757941
496.703272
784.514210
411.036944
627.864862
664.384872
537.458910
491.853637
...
1720.707022
558.190760
697.787360
1371.836301
1746.494238
423.730240
540.683381
796.566685
354.033897
382.395896
2012-02-03
754.739924
576.043736
890.995429
498.907180
788.737219
413.788058
627.626809
664.384872
548.077951
493.315315
...
1735.429917
561.539265
700.197686
1371.338808
1789.858798
423.730240
556.826728
794.637616
358.831437
386.747951
2012-02-06
755.601008
580.202950
888.703967
508.436964
790.267263
413.538713
631.898043
671.853920
546.219004
491.853637
...
1732.066974
560.871164
698.993562
1376.801342
1818.675354
423.730240
555.890097
796.016000
366.196668
387.316949
2012-02-07
752.150757
574.830585
885.639461
502.331428
779.881211
409.528546
626.435187
661.305039
538.089361
489.652938
...
1710.511523
555.159838
695.368679
1347.243131
1808.830518
423.730240
546.912106
786.595360
362.574130
382.280699
2012-02-08
757.320239
579.266383
890.995429
507.627135
791.412855
415.280985
630.240447
673.876565
547.459003
494.772675
...
1729.310611
566.193904
702.599744
1370.343281
1832.197290
423.730240
556.358609
794.913484
369.377855
384.497282
2012-02-09
765.858504
580.202950
892.519802
506.274543
798.251884
414.783940
630.003292
676.228672
550.546783
498.397432
...
1732.372966
564.203761
703.797698
1365.854428
1839.349309
423.730240
564.725374
797.666916
368.483378
387.865205
2012-02-10
768.401484
582.471201
893.281013
503.694610
796.737173
416.025439
634.964954
678.906816
550.546783
499.839974
...
1729.617090
564.867921
707.379389
1371.836301
1835.347612
423.730240
562.876841
806.414725
370.917781
387.753412
2012-02-13
770.092138
582.737473
895.560764
516.730879
804.657595
418.250851
631.898043
674.885615
561.522217
499.839974
...
1729.004077
567.516788
706.187511
1373.327698
1837.063693
423.730240
567.945830
808.315688
370.513158
390.094334
2012-02-14
768.401484
584.863290
895.560764
525.154963
803.530886
420.219101
629.528713
679.574703
557.887731
503.428247
...
1736.345953
572.450380
702.599744
1368.350053
1829.902740
423.730240
566.107812
808.044396
368.076079
389.315591
2012-02-15
773.462360
588.169496
905.373354
528.798302
803.906631
424.857152
630.714490
683.236410
568.125057
504.856415
...
1753.357446
574.411952
710.943036
1393.060429
1824.729454
423.730240
578.851385
811.293917
372.370783
392.200271
2012-02-16
780.992188
589.355190
907.622732
528.279364
803.906631
423.884937
634.729564
680.241934
565.732941
509.116882
...
1743.048840
577.666436
708.569263
1385.201069
1817.808837
423.730240
576.144349
806.142792
377.094153
391.647175
2012-02-17
777.654599
588.169496
903.118373
523.716717
800.141240
427.036542
629.291289
675.893158
560.918105
509.823499
...
1746.997548
576.041492
707.379389
1395.996281
1820.407151
423.730240
571.148127
802.326065
379.077828
387.193964
2012-02-20
777.654599
585.526025
900.102944
513.803202
803.530886
425.827147
628.578480
677.234217
559.101851
505.568986
...
1747.300926
573.432005
706.187511
1386.185926
1849.315668
423.730240
570.691756
804.236693
376.616516
387.529730
2012-02-21
775.141977
586.320318
906.873559
516.598163
821.005033
426.794937
629.528713
679.908400
562.728496
509.823499
...
1749.423093
574.085488
708.569263
1396.484990
1860.358148
423.730240
573.424534
815.337607
378.047616
392.862959
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
2016-11-21
925.557166
1189.283040
1445.910513
833.365440
909.461500
863.842881
602.350800
888.376057
869.364089
1026.692281
...
1803.811423
778.640773
1024.855559
1632.567999
1872.149159
1330.774865
629.073049
1079.801530
1155.146511
1025.895512
2016-11-22
933.404034
1191.141296
1442.747283
838.158114
910.350081
866.899077
605.814037
881.952429
867.664451
1028.840925
...
1820.163373
782.603347
1050.994505
1680.758555
1866.839760
1330.774865
631.256579
1082.665384
1189.614168
1017.068714
2016-11-23
933.404034
1171.482605
1434.490242
832.995630
909.905899
872.746297
601.606084
878.075696
867.239021
1023.820409
...
1813.336314
777.918136
1060.173671
1670.986620
1864.475175
1330.774865
626.442767
1085.780975
1175.070660
1012.626463
2016-11-24
934.823661
1176.663056
1433.215706
829.288466
919.188638
872.746297
597.618593
878.075696
875.286833
1022.381448
...
1796.922239
772.112696
1071.084837
1727.605385
1842.458899
1330.774865
620.262067
1091.211853
1157.377244
991.357301
2016-11-25
953.781834
1170.065774
1436.399926
830.031224
914.780034
870.645822
595.112844
873.748042
866.813382
1018.775153
...
1806.483455
776.470846
1074.697275
1718.497030
1842.458899
1330.774865
617.148505
1087.853078
1153.656960
1032.221270
2016-11-28
950.299596
1161.052362
1446.542329
833.735085
915.663453
865.019619
596.116408
878.507289
859.115825
1037.390996
...
1814.096145
774.657930
1078.297611
1714.919722
1853.797214
1330.774865
614.467181
1087.594281
1246.817471
1026.670505
2016-11-29
962.087784
1162.955728
1426.825947
830.031224
917.868280
882.021226
591.334311
864.589462
847.873764
1030.270864
...
1837.495065
761.477532
1061.999984
1702.941091
1850.224200
1330.774865
610.423112
1091.985488
1231.538254
1019.797320
2016-11-30
954.476758
1142.812290
1431.301774
842.923539
915.663453
878.786136
591.334311
857.103698
850.481276
1021.661207
...
1812.576165
762.215756
1054.675758
1709.340141
1846.644272
1330.774865
609.520786
1080.583334
1219.340479
1034.348248
2016-12-01
964.153091
1147.158518
1426.185397
841.826220
914.780034
879.249020
593.352523
862.394512
852.648110
1019.497433
...
1840.866962
772.476814
1063.823161
1708.940902
1847.838352
1330.774865
612.673113
1079.280012
1224.234173
1033.923202
2016-12-02
941.185483
1135.531611
1426.825947
829.288466
902.321359
865.254774
588.039944
855.332821
842.634535
1018.775153
...
1870.203856
776.470846
1052.836741
1710.138338
1854.986690
1330.774865
605.443678
1069.586076
1209.493693
1023.239684
2016-12-05
940.480739
1116.379650
1421.050599
820.322922
904.111680
865.019619
587.531479
853.558269
839.563220
1017.329056
...
1940.010093
778.640773
1047.300313
1622.505851
1868.611236
1330.774865
605.898045
1070.901195
1206.193567
1028.381624
2016-12-06
937.656466
1116.379650
1415.251684
818.819121
900.527477
856.748513
589.816121
860.634521
848.308906
1013.704787
...
1924.318577
781.884373
1049.149035
1614.072811
1883.308735
1330.774865
609.972116
1070.638301
1207.844757
1034.773119
2016-12-07
964.153091
1134.557324
1411.372503
824.444233
903.664433
858.172078
593.604317
874.181772
848.308906
1015.156047
...
1950.989699
782.603347
1065.643219
1617.873118
1887.403525
1330.774865
614.914881
1075.098766
1213.606230
1039.435237
2016-12-08
964.840544
1129.673283
1429.385278
818.819121
904.558707
849.115857
592.091947
865.903761
847.438399
1025.975067
...
1935.031140
789.044635
1058.344207
1609.839725
1871.559970
1330.774865
611.324106
1079.280012
1207.019444
1036.046686
2016-12-09
955.865088
1130.162637
1426.825947
816.558226
905.005513
849.594907
591.081550
857.103698
843.510000
1026.692281
...
1962.259047
790.468905
1063.823161
1613.650002
1868.020931
1330.774865
611.774106
1081.885085
1197.071312
1036.046686
2016-12-12
942.593391
1100.416295
1402.279350
791.262132
881.471340
834.616513
573.111330
828.772972
812.761712
1013.704787
...
1930.396328
771.383944
1045.448321
1559.459167
1864.475175
1330.774865
596.283639
1068.532815
1167.571885
1000.213300
2016-12-13
969.639070
1102.925800
1402.279350
793.983258
889.233734
843.828359
578.561006
844.180677
810.032789
1013.704787
...
2000.513310
771.019310
1052.836741
1567.750739
1840.062982
1330.774865
598.126870
1084.743439
1162.439676
1003.284811
2016-12-14
956.558498
1098.404564
1395.747972
792.040551
887.413397
855.322578
570.235953
836.965325
808.208389
1003.487199
...
1975.557905
794.018401
1041.734461
1556.393242
1899.634937
1330.774865
600.881114
1084.483900
1162.439676
995.808993
2016-12-15
1003.247016
1118.853351
1404.883400
794.371229
888.324032
860.539454
573.893024
837.870642
818.643170
1028.125209
...
1968.569706
797.904610
1050.994505
1555.954759
1906.589084
1330.774865
607.259106
1089.404568
1165.863659
1002.846600
2016-12-16
1031.274248
1124.276333
1442.747283
798.626474
895.124264
865.724893
588.801819
843.282134
819.544264
1043.052108
...
1922.527346
794.018401
1049.149035
1574.699252
1894.402574
1330.774865
612.223774
1087.076502
1172.681634
1015.477956
2016-12-19
1014.420108
1115.388634
1460.998683
793.983258
894.220554
867.602824
592.596499
844.180677
812.307528
1043.052108
...
1937.522216
800.016404
1060.173671
1569.925453
1898.473436
1330.774865
613.122122
1094.303111
1172.681634
1008.964239
2016-12-20
989.940764
1119.841301
1459.747292
795.534008
889.688236
862.428676
585.493202
836.059028
821.343485
1036.681184
...
1929.325175
789.757091
1058.344207
1563.828632
1873.326982
1330.774865
615.809303
1091.211853
1168.425062
1012.009191
2016-12-21
989.270753
1122.799936
1457.241286
798.240571
893.315930
871.813377
589.562709
836.965325
823.138773
1036.681184
...
1997.410936
817.746489
1067.460174
1572.097159
1885.649704
1330.774865
616.256027
1093.273663
1167.571885
1014.178559
2016-12-22
987.929368
1120.828380
1450.957336
796.308250
894.672523
863.136068
586.258387
860.193961
816.386085
1047.980495
...
2021.755869
821.520460
1092.580321
1571.228837
1917.548240
1330.774865
615.809303
1087.076502
1166.718085
1004.160660
2016-12-23
966.899971
1105.429607
1469.106742
791.651437
900.078448
860.303010
578.302662
848.212340
815.481502
1049.384354
...
2025.841605
811.533045
1092.580321
1561.208420
1927.873134
1330.774865
611.324106
1077.975115
1161.582104
998.453908
2016-12-26
957.251405
1112.907232
1464.122481
795.534008
906.790536
864.078356
581.652182
853.114055
815.028834
1048.682660
...
2032.293887
813.609465
1080.093278
1562.082312
1920.996038
1330.774865
615.362254
1076.407150
1167.571885
1009.399795
2016-12-27
955.865088
1112.907232
1465.993565
795.534008
910.350081
865.254774
585.237919
854.889528
818.192251
1062.628520
...
2012.874993
813.609465
1076.498948
1558.583803
1923.864482
1330.774865
619.818230
1081.364572
1163.296617
1005.910070
2016-12-28
948.903123
1112.907232
1448.436123
792.040551
907.236243
867.602824
585.493202
849.105666
819.544264
1055.678619
...
2011.162636
826.639017
1074.697275
1551.123272
1915.246261
1330.774865
624.683093
1084.483900
1164.152927
1004.160660
2016-12-29
952.390467
1126.732697
1451.586955
796.695089
909.461500
868.540266
587.531479
855.332821
813.215642
1043.757588
...
1987.034705
820.492917
1096.121919
1570.794496
1915.246261
1330.774865
620.705587
1084.224299
1176.075799
1005.473003
2016-12-30
947.504591
1118.853351
1436.399926
793.983258
909.905899
861.484583
590.069424
841.932522
808.664875
1045.167120
...
1950.636483
818.090297
1096.121919
1565.573004
1915.246261
1330.774865
624.242399
1112.923286
1191.229870
997.573049
1214 rows × 1071 columns
In [57]:
specDF = multiFactorReg(activeReturnDF,factordic,WLS=True,weightdf = weight)[0]
specDF.index.name = 'Specifc_Return_for_ZX500'
specDF = specDF.rolling(min_periods=20,window=60,center=False).std()
specDF
Out[57]:
600507.SH
002344.SZ
000793.SZ
300039.SZ
600059.SH
600501.SH
000713.SZ
002233.SZ
600360.SH
600565.SH
...
600150.SH
600469.SH
600282.SH
600739.SH
600406.SH
600687.SH
600866.SH
000860.SZ
601908.SH
002195.SZ
Specifc_Return_for_ZX500
2012-01-05
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
2012-01-06
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
2012-01-09
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
2012-01-10
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
2012-01-11
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
2012-01-12
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
2012-01-13
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
2012-01-16
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
2012-01-17
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
2012-01-18
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
2012-01-19
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
2012-01-20
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
2012-01-30
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
2012-01-31
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
2012-02-01
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
2012-02-02
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
2012-02-03
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
2012-02-06
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
2012-02-07
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
2012-02-08
0.024681
0.010271
0.015044
0.023673
0.008274
0.012507
0.029383
0.019861
0.008383
0.018102
...
0.008983
0.013302
0.009655
0.013662
0.030834
0.026065
0.015539
0.011284
0.021302
0.027655
2012-02-09
0.024230
0.010023
0.014664
0.023144
0.008584
0.012537
0.028653
0.019363
0.008171
0.017659
...
0.008757
0.013599
0.009456
0.013550
0.030172
0.025437
0.015928
0.011001
0.020986
0.027008
2012-02-10
0.023650
0.009824
0.014313
0.022690
0.008516
0.012239
0.028129
0.018897
0.008216
0.017246
...
0.008640
0.013334
0.009286
0.013251
0.029454
0.024838
0.015789
0.011379
0.020515
0.026413
2012-02-13
0.023168
0.009683
0.013984
0.024383
0.008793
0.011970
0.027700
0.019013
0.009803
0.017082
...
0.008473
0.013053
0.009441
0.012962
0.028794
0.024352
0.015505
0.011153
0.020833
0.025808
2012-02-14
0.022721
0.009650
0.013680
0.024985
0.008606
0.011816
0.027100
0.018812
0.010242
0.016873
...
0.008498
0.013117
0.009481
0.012768
0.028168
0.023821
0.015239
0.010909
0.020601
0.025260
2012-02-15
0.022258
0.009464
0.013527
0.024459
0.008798
0.011596
0.026581
0.018486
0.010598
0.016733
...
0.008444
0.013081
0.009453
0.013262
0.027693
0.023448
0.016020
0.010772
0.020275
0.024736
2012-02-16
0.022065
0.009337
0.013339
0.023965
0.008642
0.011440
0.026299
0.018168
0.010670
0.016627
...
0.008498
0.012943
0.009351
0.013097
0.027135
0.022981
0.015831
0.010793
0.020609
0.024244
2012-02-17
0.021694
0.009159
0.013122
0.023584
0.008517
0.011743
0.025870
0.017940
0.010940
0.016329
...
0.008439
0.012726
0.009172
0.013254
0.026660
0.022589
0.015765
0.010642
0.020299
0.024070
2012-02-20
0.021297
0.009155
0.012914
0.024164
0.008560
0.011699
0.025387
0.017606
0.010967
0.016542
...
0.008305
0.012764
0.009030
0.013295
0.027210
0.022167
0.015489
0.010485
0.020217
0.023623
2012-02-21
0.021225
0.009057
0.012732
0.023746
0.010557
0.011576
0.024948
0.017318
0.010769
0.016269
...
0.008319
0.012705
0.008882
0.013062
0.026728
0.021798
0.015213
0.010779
0.019924
0.023392
2012-02-22
0.021023
0.008922
0.012528
0.023351
0.010620
0.011801
0.024514
0.017036
0.010835
0.016124
...
0.008227
0.012552
0.008882
0.012837
0.026272
0.021598
0.014948
0.010684
0.019821
0.023048
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
2016-11-21
0.015091
0.017818
0.020984
0.010135
0.007493
0.009722
0.009287
0.015742
0.018431
0.013953
...
0.012222
0.011742
0.007463
0.012499
0.016296
0.006733
0.013836
0.009382
0.011381
0.009434
2016-11-22
0.015130
0.017784
0.020957
0.010131
0.007492
0.009687
0.008445
0.015927
0.018421
0.013908
...
0.012303
0.011742
0.009045
0.014103
0.016385
0.006764
0.013825
0.009320
0.013064
0.010094
2016-11-23
0.015126
0.018146
0.020943
0.010166
0.007295
0.009928
0.008389
0.015922
0.018467
0.013572
...
0.012277
0.011723
0.009451
0.014159
0.016069
0.006687
0.013873
0.008772
0.013287
0.010093
2016-11-24
0.015051
0.018168
0.020818
0.009450
0.007840
0.009927
0.008416
0.015922
0.017048
0.012325
...
0.012506
0.011744
0.009753
0.016350
0.016392
0.006684
0.014046
0.008900
0.013808
0.011086
2016-11-25
0.015560
0.018247
0.020398
0.009446
0.007796
0.009970
0.008178
0.016004
0.017166
0.012265
...
0.012516
0.011824
0.009706
0.016488
0.016399
0.006728
0.014121
0.008956
0.013873
0.011129
2016-11-28
0.015531
0.018441
0.014274
0.009494
0.007798
0.009746
0.008180
0.016009
0.017154
0.012946
...
0.012503
0.011764
0.009399
0.016536
0.016346
0.006683
0.014190
0.008942
0.013842
0.011266
2016-11-29
0.016011
0.018477
0.014449
0.008280
0.007778
0.011732
0.008061
0.016234
0.017177
0.012568
...
0.012930
0.011912
0.009796
0.016629
0.016343
0.006655
0.012990
0.009155
0.013960
0.011264
2016-11-30
0.016068
0.018794
0.014604
0.009618
0.007601
0.011700
0.008096
0.016301
0.017211
0.012423
...
0.013208
0.011885
0.009724
0.016704
0.016332
0.006678
0.012941
0.009284
0.014063
0.012237
2016-12-01
0.016150
0.018766
0.014537
0.009582
0.007546
0.011584
0.007936
0.016310
0.017210
0.012442
...
0.013590
0.012181
0.009730
0.016745
0.016349
0.006719
0.012944
0.009253
0.014063
0.012268
2016-12-02
0.016631
0.018654
0.014630
0.009655
0.007160
0.011704
0.007922
0.016297
0.017149
0.012318
...
0.013971
0.012603
0.009566
0.016777
0.016510
0.006879
0.012960
0.009253
0.014121
0.012170
2016-12-05
0.016543
0.018814
0.014598
0.009515
0.007005
0.011661
0.007901
0.016170
0.017000
0.012351
...
0.017550
0.012685
0.009515
0.020645
0.016460
0.006862
0.012658
0.009111
0.013987
0.012290
2016-12-06
0.016568
0.018815
0.014473
0.009524
0.007008
0.011982
0.007910
0.016178
0.017147
0.012402
...
0.017678
0.012696
0.009522
0.020680
0.015872
0.006835
0.012713
0.009045
0.013971
0.012337
2016-12-07
0.017179
0.018821
0.013697
0.009524
0.006993
0.012024
0.007618
0.016318
0.017216
0.012413
...
0.017800
0.012751
0.009754
0.020693
0.015770
0.006882
0.012686
0.009049
0.013971
0.012333
2016-12-08
0.017066
0.018805
0.014050
0.009588
0.007036
0.012202
0.007611
0.016451
0.017179
0.012785
...
0.017891
0.013017
0.009838
0.020601
0.015840
0.006647
0.012632
0.009189
0.013993
0.012338
2016-12-09
0.016510
0.018675
0.013823
0.009497
0.006818
0.012197
0.007599
0.016673
0.017191
0.012093
...
0.018135
0.012999
0.009847
0.020532
0.015844
0.006651
0.012625
0.009221
0.014136
0.012331
2016-12-12
0.016614
0.018716
0.013432
0.008921
0.006889
0.012283
0.007651
0.016003
0.017405
0.012188
...
0.018129
0.012987
0.009848
0.020808
0.016137
0.007737
0.012599
0.009437
0.014197
0.012516
2016-12-13
0.017696
0.018634
0.013428
0.008689
0.007127
0.012448
0.007819
0.015957
0.017450
0.012207
...
0.019850
0.013007
0.009883
0.020810
0.016460
0.007691
0.012591
0.009957
0.014290
0.012498
2016-12-14
0.017696
0.018556
0.013354
0.008637
0.006994
0.013057
0.008567
0.016071
0.017217
0.012066
...
0.019974
0.015269
0.010090
0.020780
0.018720
0.007119
0.012164
0.009952
0.014298
0.012590
2016-12-15
0.021338
0.018951
0.013302
0.008600
0.007013
0.013064
0.008418
0.014865
0.017366
0.013130
...
0.020082
0.015190
0.010054
0.020743
0.018727
0.007119
0.012143
0.009940
0.014271
0.012582
2016-12-16
0.021683
0.018929
0.014713
0.008533
0.007010
0.013063
0.009930
0.014520
0.017341
0.013383
...
0.021107
0.015427
0.010150
0.020848
0.018846
0.007114
0.012156
0.009983
0.014235
0.012789
2016-12-19
0.022125
0.019104
0.014994
0.008652
0.007008
0.012984
0.009964
0.014514
0.017478
0.013382
...
0.021140
0.015459
0.010392
0.020874
0.018823
0.007026
0.012069
0.010079
0.014221
0.012963
2016-12-20
0.022915
0.019149
0.014934
0.008705
0.007059
0.013021
0.010380
0.014530
0.017799
0.013351
...
0.021065
0.015691
0.010392
0.020854
0.019010
0.007040
0.010119
0.010053
0.014217
0.013023
2016-12-21
0.022921
0.019158
0.015028
0.008703
0.007057
0.013140
0.010401
0.014536
0.017801
0.013191
...
0.022525
0.017515
0.010406
0.020824
0.018693
0.007122
0.010167
0.010062
0.014268
0.013030
2016-12-22
0.022927
0.019117
0.015066
0.008478
0.006981
0.013391
0.010491
0.016197
0.017904
0.013482
...
0.022659
0.017531
0.011958
0.020819
0.019221
0.007086
0.010159
0.010163
0.014256
0.013249
2016-12-23
0.023276
0.019184
0.015716
0.008454
0.007677
0.013287
0.010538
0.016344
0.017898
0.013461
...
0.022681
0.017600
0.011995
0.020739
0.019381
0.007179
0.010122
0.010105
0.014250
0.013251
2016-12-26
0.023457
0.019165
0.015226
0.008491
0.007805
0.013256
0.010283
0.016313
0.017799
0.013475
...
0.022653
0.017598
0.012533
0.020715
0.019424
0.006771
0.009983
0.010088
0.014294
0.013398
2016-12-27
0.022837
0.019168
0.015226
0.008454
0.007852
0.013252
0.010317
0.016279
0.017753
0.013812
...
0.022858
0.017591
0.012593
0.020341
0.019427
0.006713
0.010060
0.010125
0.014318
0.013408
2016-12-28
0.022897
0.019166
0.015460
0.008467
0.007868
0.013256
0.010302
0.016172
0.017703
0.013906
...
0.022845
0.018035
0.012599
0.020351
0.019407
0.006727
0.010221
0.010165
0.014331
0.013358
2016-12-29
0.022880
0.019226
0.015481
0.008547
0.007908
0.013211
0.010285
0.016149
0.017719
0.014199
...
0.023107
0.018135
0.013591
0.020601
0.019405
0.006726
0.010214
0.010192
0.014635
0.013376
2016-12-30
0.022904
0.019233
0.015446
0.008495
0.007912
0.013320
0.010356
0.016215
0.017770
0.014154
...
0.023505
0.018137
0.013592
0.020590
0.019133
0.006735
0.010186
0.011793
0.014912
0.013397
1213 rows × 1071 columns
In [58]:
#specDF.to_csv(path+'Own_Factor_Specific_Volatility.csv',na_rep='NaN',date_format='%Y%m%d')
In [59]:
multiFactorReg(activeReturnDF,factordic)[3].describe()
Out[59]:
R-Square
count
1213.000000
mean
0.058747
std
0.055401
min
0.000807
25%
0.022558
50%
0.040173
75%
0.076018
max
0.383183
In [60]:
multiFactorReg(activeReturnDF,factordic,True,weight)[3].describe()
Out[60]:
R-Square
count
1213.000000
mean
0.052555
std
0.048237
min
0.001143
25%
0.020351
50%
0.037204
75%
0.067413
max
0.313538
In [61]:
for date in returnDF.index:
activeReturnSlice = activeReturnDF .loc[date]
LFCAPDataSlice = LFCAPData.loc[date]
PBDataSlice = PBData.loc[date]
YOYBPSDataSlice = YOYBPSData.loc[date]
AMOUNTAVG1MDataSlice = AMOUNTAVG1MData .loc[date]
TURNOVER1MDataSlice = TURNOVER1MData.loc[date]
PROFITOOPDataSlice = PROFITOOPData.loc[date]
totalDF = pd.concat([LFCAPDataSlice,PBDataSlice,YOYBPSDataSlice,AMOUNTAVG1MDataSlice,TURNOVER1MDataSlice,PROFITOOPDataSlice],axis=1)
#print totalDF,PBDataSlice
result = sm.OLS(activeReturnSlice.T,totalDF ).fit()
factorReturn . loc[date] = result.params.values
factorPvalue . loc[date] = result.pvalues.values
specificReturn .loc[date] = result.resid
result.summary()
Out[61]:
OLS Regression Results
Dep. Variable: 2016-12-30 00:00:00 R-squared: 0.091
Model: OLS Adj. R-squared: 0.085
Method: Least Squares F-statistic: 17.68
Date: Wed, 26 Apr 2017 Prob (F-statistic): 1.33e-19
Time: 09:40:34 Log-Likelihood: 3088.4
No. Observations: 1071 AIC: -6165.
Df Residuals: 1065 BIC: -6135.
Df Model: 6
Covariance Type: nonrobust
coef std err t P>|t| [0.025 0.975]
2016-12-30 00:00:00 0.0008 0.001 1.482 0.139 -0.000 0.002
2016-12-30 00:00:00 0.0018 0.014 0.133 0.894 -0.025 0.028
2016-12-30 00:00:00 -0.0008 0.001 -0.868 0.386 -0.003 0.001
2016-12-30 00:00:00 -0.0016 0.000 -3.344 0.001 -0.003 -0.001
2016-12-30 00:00:00 -0.0038 0.001 -7.448 0.000 -0.005 -0.003
2016-12-30 00:00:00 4.907e-05 0.000 0.113 0.910 -0.001 0.001
Omnibus: 236.185 Durbin-Watson: 1.991
Prob(Omnibus): 0.000 Jarque-Bera (JB): 2483.395
Skew: 0.693 Prob(JB): 0.00
Kurtosis: 10.330 Cond. No. 39.8
In [62]:
factorReturn.head()
Out[62]:
LFCA
PB
YOYBPS
AMOUNTAVG1MData
TURNOVER1M
PROFITOOP
Specifc_Return_for_ZX500
2012-01-05
0.010763
-0.001083
-0.000036
0.001894
0.001312
-0.039442
2012-01-06
0.000101
-0.000361
0.000318
0.002294
0.001647
-0.022881
2012-01-09
-0.002122
-0.032656
0.000115
0.000528
0.000192
0.050028
2012-01-10
-0.001900
-0.001688
-0.001064
0.000217
-0.001661
0.029090
2012-01-11
-0.003104
0.002084
-0.000630
0.000545
-0.000638
-0.013783
In [63]:
def calEWMcovariance(facRetdf,decay=0.94):
'''
To calculate EWM covariance matrix of given facRetdf
output: Dataframe, the ewm cov-matrix of the factors
input:
facRetdf: Dataframe, factor return dataframe
decay: float, decay-factors
Decay factors were set at:
− 0.94 (1-day) from 112 days of data;
− 0.97 (1-month) from 227 days of data.
'''
m,n = facRetdf.shape
facDF = facRetdf - facRetdf.mean()
for i in xrange(m):
facDF.iloc[i] = np.sqrt(decay**(m-1-i)) * facDF.iloc[i]
ewmCovMatrix = facDF.T.dot(facDF) * (1-decay)/(1-decay**m)
return ewmCovMatrix
In [64]:
compara = 1/0.94 - 1
ewmMatrix = factorReturn.ewm(ignore_na=True, min_periods=0, com=compara).cov(pairwise = True)[-200:].iloc[-1]
In [65]:
calEWMcovariance(factorReturn.iloc[-200:], decay=0.94)
Out[65]:
LFCA
PB
YOYBPS
AMOUNTAVG1MData
TURNOVER1M
PROFITOOP
LFCA
4.650162e-06
-0.000001
2.518765e-07
-4.502851e-07
-3.139638e-06
-2.471350e-07
PB
-1.216889e-06
0.001375
-6.708782e-06
-2.063559e-05
-1.669693e-05
6.297053e-06
YOYBPS
2.518765e-07
-0.000007
1.296555e-06
2.905198e-07
2.570304e-07
-1.963979e-08
AMOUNTAVG1MData
-4.502851e-07
-0.000021
2.905198e-07
2.454811e-06
-2.380790e-08
-2.879563e-08
TURNOVER1M
-3.139638e-06
-0.000017
2.570304e-07
-2.380790e-08
4.739274e-06
5.153030e-07
PROFITOOP
-2.471350e-07
0.000006
-1.963979e-08
-2.879563e-08
5.153030e-07
5.995070e-07
In [66]:
# calculate monthly scaled variance forecast for the market index by DEWIV
alphaS = 21*returnOfBench.ewm(ignore_na=True, min_periods=0, com=compara).cov(pairwise = True)[-200:].iloc[-1]
In [67]:
alphaS
Out[67]:
0.0002192128126956748
In [68]:
benchmarkWeight = pd.read_csv(path+ZZ500Weight,infer_datetime_format=True,parse_dates=[0],index_col=0)[stkPool].loc[startTime:endTime].fillna(0)
In [69]:
benchmarkWeight
Out[69]:
600507.SH
002344.SZ
000793.SZ
300039.SZ
600059.SH
600501.SH
000713.SZ
002233.SZ
600360.SH
600565.SH
...
600150.SH
600469.SH
600282.SH
600739.SH
600406.SH
600687.SH
600866.SH
000860.SZ
601908.SH
002195.SZ
LZ_GPA_INDEX_CSI500WEIGHT-d
2012-01-04
0.1384
0.0000
0.3659
0.0000
0.2749
0.1272
0.1678
0.2312
0.1507
0.0000
...
0.0
0.1177
0.0000
0.0
0.0
0.0000
0.1689
0.2531
0.0000
0.0
2012-01-05
0.1424
0.0000
0.3737
0.0000
0.2701
0.1272
0.1702
0.2268
0.1492
0.0000
...
0.0
0.1187
0.0000
0.0
0.0
0.0000
0.1763
0.2516
0.0000
0.0
2012-01-06
0.1463
0.0000
0.3908
0.0000
0.2666
0.1247
0.1590
0.2188
0.1490
0.0000
...
0.0
0.1214
0.0000
0.0
0.0
0.0000
0.1737
0.2479
0.0000
0.0
2012-01-09
0.1476
0.0000
0.3857
0.0000
0.2690
0.1257
0.1474
0.2278
0.1507
0.0000
...
0.0
0.1245
0.0000
0.0
0.0
0.0000
0.1691
0.2476
0.0000
0.0
2012-01-10
0.1473
0.0000
0.3820
0.0000
0.2684
0.1265
0.1461
0.2331
0.1513
0.0000
...
0.0
0.1231
0.0000
0.0
0.0
0.0000
0.1699
0.2523
0.0000
0.0
2012-01-11
0.1463
0.0000
0.3764
0.0000
0.2675
0.1267
0.1544
0.2383
0.1515
0.0000
...
0.0
0.1238
0.0000
0.0
0.0
0.0000
0.1750
0.2522
0.0000
0.0
2012-01-12
0.1529
0.0000
0.3744
0.0000
0.2643
0.1271
0.1573
0.2332
0.1507
0.0000
...
0.0
0.1245
0.0000
0.0
0.0
0.0000
0.1728
0.2500
0.0000
0.0
2012-01-13
0.1512
0.0000
0.3726
0.0000
0.2633
0.1279
0.1543
0.2347
0.1507
0.0000
...
0.0
0.1225
0.0000
0.0
0.0
0.0000
0.1723
0.2483
0.0000
0.0
2012-01-16
0.1496
0.0000
0.3729
0.0000
0.2637
0.1267
0.1570
0.2324
0.1488
0.0000
...
0.0
0.1208
0.0000
0.0
0.0
0.0000
0.1685
0.2445
0.0000
0.0
2012-01-17
0.1530
0.0000
0.3770
0.0000
0.2615
0.1255
0.1599
0.2323
0.1502
0.0000
...
0.0
0.1218
0.0000
0.0
0.0
0.0000
0.1665
0.2478
0.0000
0.0
2012-01-18
0.1449
0.0000
0.3726
0.0000
0.2622
0.1247
0.1599
0.2385
0.1504
0.0000
...
0.0
0.1226
0.0000
0.0
0.0
0.0000
0.1670
0.2469
0.0000
0.0
2012-01-19
0.1479
0.0000
0.3764
0.0000
0.2600
0.1294
0.1581
0.2455
0.1512
0.0000
...
0.0
0.1248
0.0000
0.0
0.0
0.0000
0.1658
0.2462
0.0000
0.0
2012-01-20
0.1572
0.0000
0.3667
0.0000
0.2610
0.1289
0.1560
0.2445
0.1523
0.0000
...
0.0
0.1260
0.0000
0.0
0.0
0.0000
0.1651
0.2468
0.0000
0.0
2012-01-30
0.1543
0.0000
0.3669
0.0000
0.2652
0.1285
0.1602
0.2472
0.1525
0.0000
...
0.0
0.1254
0.0000
0.0
0.0
0.0000
0.1670
0.2486
0.0000
0.0
2012-01-31
0.1558
0.0000
0.3695
0.0000
0.2681
0.1290
0.1597
0.2399
0.1532
0.0000
...
0.0
0.1232
0.0000
0.0
0.0
0.0000
0.1664
0.2543
0.0000
0.0
2012-02-01
0.1550
0.0000
0.3771
0.0000
0.2672
0.1307
0.1593
0.2398
0.1535
0.0000
...
0.0
0.1248
0.0000
0.0
0.0
0.0000
0.1651
0.2567
0.0000
0.0
2012-02-02
0.1534
0.0000
0.3764
0.0000
0.2645
0.1293
0.1609
0.2335
0.1555
0.0000
...
0.0
0.1264
0.0000
0.0
0.0
0.0000
0.1665
0.2569
0.0000
0.0
2012-02-03
0.1521
0.0000
0.3752
0.0000
0.2615
0.1289
0.1593
0.2346
0.1557
0.0000
...
0.0
0.1259
0.0000
0.0
0.0
0.0000
0.1664
0.2565
0.0000
0.0
2012-02-06
0.1510
0.0000
0.3685
0.0000
0.2600
0.1285
0.1566
0.2308
0.1593
0.0000
...
0.0
0.1254
0.0000
0.0
0.0
0.0000
0.1736
0.2511
0.0000
0.0
2012-02-07
0.1501
0.0000
0.3635
0.0000
0.2588
0.1272
0.1574
0.2340
0.1568
0.0000
...
0.0
0.1240
0.0000
0.0
0.0
0.0000
0.1716
0.2498
0.0000
0.0
2012-02-08
0.1511
0.0000
0.3669
0.0000
0.2562
0.1268
0.1573
0.2304
0.1547
0.0000
...
0.0
0.1235
0.0000
0.0
0.0
0.0000
0.1688
0.2480
0.0000
0.0
2012-02-09
0.1493
0.0000
0.3618
0.0000
0.2570
0.1271
0.1551
0.2331
0.1560
0.0000
...
0.0
0.1252
0.0000
0.0
0.0
0.0000
0.1702
0.2467
0.0000
0.0
2012-02-10
0.1516
0.0000
0.3605
0.0000
0.2597
0.1259
0.1539
0.2331
0.1567
0.0000
...
0.0
0.1234
0.0000
0.0
0.0
0.0000
0.1741
0.2467
0.0000
0.0
2012-02-13
0.1518
0.0000
0.3592
0.0000
0.2573
0.1260
0.1555
0.2337
0.1559
0.0000
...
0.0
0.1231
0.0000
0.0
0.0
0.0000
0.1721
0.2508
0.0000
0.0
2012-02-14
0.1511
0.0000
0.3578
0.0000
0.2601
0.1262
0.1526
0.2289
0.1607
0.0000
...
0.0
0.1231
0.0000
0.0
0.0
0.0000
0.1736
0.2497
0.0000
0.0
2012-02-15
0.1502
0.0000
0.3573
0.0000
0.2590
0.1272
0.1513
0.2317
0.1584
0.0000
...
0.0
0.1251
0.0000
0.0
0.0
0.0000
0.1723
0.2492
0.0000
0.0
2012-02-16
0.1499
0.0000
0.3597
0.0000
0.2554
0.1281
0.1496
0.2307
0.1618
0.0000
...
0.0
0.1241
0.0000
0.0
0.0
0.0000
0.1774
0.2475
0.0000
0.0
2012-02-17
0.1529
0.0000
0.3616
0.0000
0.2555
0.1275
0.1515
0.2288
0.1605
0.0000
...
0.0
0.1255
0.0000
0.0
0.0
0.0000
0.1758
0.2444
0.0000
0.0
2012-02-20
0.1521
0.0000
0.3592
0.0000
0.2539
0.1298
0.1494
0.2266
0.1583
0.0000
...
0.0
0.1252
0.0000
0.0
0.0
0.0000
0.1733
0.2429
0.0000
0.0
2012-02-21
0.1518
0.0000
0.3560
0.0000
0.2555
0.1288
0.1487
0.2270
0.1569
0.0000
...
0.0
0.1238
0.0000
0.0
0.0
0.0000
0.1727
0.2435
0.0000
0.0
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
2016-11-21
0.0846
0.1798
0.0000
0.1389
0.1233
0.0000
0.0000
0.0934
0.0000
0.2512
...
0.0
0.0000
0.1317
0.0
0.0
0.2434
0.0000
0.2052
0.1120
0.0
2016-11-22
0.0852
0.1780
0.0000
0.1394
0.1234
0.0000
0.0000
0.0926
0.0000
0.2505
...
0.0
0.0000
0.1334
0.0
0.0
0.2430
0.0000
0.2056
0.1156
0.0
2016-11-23
0.0860
0.1773
0.0000
0.1400
0.1227
0.0000
0.0000
0.0906
0.0000
0.2497
...
0.0
0.0000
0.1393
0.0
0.0
0.2412
0.0000
0.2052
0.1217
0.0
2016-11-24
0.0864
0.1722
0.0000
0.1389
0.1231
0.0000
0.0000
0.0902
0.0000
0.2484
...
0.0
0.0000
0.1424
0.0
0.0
0.2423
0.0000
0.2073
0.1192
0.0
2016-11-25
0.0867
0.1740
0.0000
0.1378
0.1258
0.0000
0.0000
0.0903
0.0000
0.2480
...
0.0
0.0000
0.1455
0.0
0.0
0.2426
0.0000
0.2096
0.1158
0.0
2016-11-28
0.0899
0.1713
0.0000
0.1375
0.1240
0.0000
0.0000
0.0890
0.0000
0.2451
...
0.0
0.0000
0.1458
0.0
0.0
0.2415
0.0000
0.2074
0.1146
0.0
2016-11-29
0.0890
0.1682
0.0000
0.1383
0.1239
0.0000
0.0000
0.0897
0.0000
0.2535
...
0.0
0.0000
0.1464
0.0
0.0
0.2409
0.0000
0.2067
0.1151
0.0
2016-11-30
0.0919
0.1700
0.0000
0.1381
0.1255
0.0000
0.0000
0.0876
0.0000
0.2519
...
0.0
0.0000
0.1431
0.0
0.0
0.2427
0.0000
0.2100
0.1132
0.0
2016-12-01
0.0909
0.1650
0.0000
0.1432
0.1255
0.0000
0.0000
0.0865
0.0000
0.2490
...
0.0
0.0000
0.1419
0.0
0.0
0.2440
0.0000
0.2067
0.1115
0.0
2016-12-02
0.0922
0.1653
0.0000
0.1420
0.1245
0.0000
0.0000
0.0871
0.0000
0.2465
...
0.0
0.0000
0.1435
0.0
0.0
0.2425
0.0000
0.2050
0.1118
0.0
2016-12-05
0.0892
0.1644
0.0000
0.1399
0.1230
0.0000
0.0000
0.0870
0.0000
0.2499
...
0.0
0.0000
0.1427
0.0
0.0
0.2462
0.0000
0.2044
0.1108
0.0
2016-12-06
0.0896
0.1598
0.0000
0.1376
0.1242
0.0000
0.0000
0.0871
0.0000
0.2505
...
0.0
0.0000
0.1420
0.0
0.0
0.2476
0.0000
0.2060
0.1108
0.0
2016-12-07
0.0890
0.1597
0.0000
0.1370
0.1231
0.0000
0.0000
0.0885
0.0000
0.2486
...
0.0
0.0000
0.1423
0.0
0.0
0.2474
0.0000
0.2057
0.1110
0.0
2016-12-08
0.0931
0.1632
0.0000
0.1375
0.1227
0.0000
0.0000
0.0903
0.0000
0.2467
...
0.0
0.0000
0.1454
0.0
0.0
0.2449
0.0000
0.2053
0.1109
0.0
2016-12-09
0.0936
0.1625
0.0000
0.1362
0.1235
0.0000
0.0000
0.0890
0.0000
0.2531
...
0.0
0.0000
0.1440
0.0
0.0
0.2459
0.0000
0.2078
0.1101
0.0
2016-12-12
0.0000
0.1578
0.0000
0.1314
0.1199
0.0000
0.0000
0.0846
0.0000
0.2535
...
0.0
0.0000
0.1411
0.0
0.0
0.2385
0.0000
0.2026
0.1051
0.0
2016-12-13
0.0000
0.1559
0.0000
0.1286
0.1186
0.0000
0.0000
0.0824
0.0000
0.2576
...
0.0
0.0000
0.1421
0.0
0.0
0.2486
0.0000
0.2060
0.1042
0.0
2016-12-14
0.0000
0.1559
0.0000
0.1289
0.1201
0.0000
0.0000
0.0851
0.0000
0.2564
...
0.0
0.0000
0.1434
0.0
0.0
0.2475
0.0000
0.2113
0.1028
0.0
2016-12-15
0.0000
0.1556
0.0000
0.1290
0.1203
0.0000
0.0000
0.0842
0.0000
0.2528
...
0.0
0.0000
0.1413
0.0
0.0
0.2490
0.0000
0.2125
0.1035
0.0
2016-12-16
0.0000
0.1604
0.0000
0.1289
0.1198
0.0000
0.0000
0.0838
0.0000
0.2637
...
0.0
0.0000
0.1429
0.0
0.0
0.2474
0.0000
0.2130
0.1034
0.0
2016-12-19
0.0000
0.1610
0.0000
0.1295
0.1209
0.0000
0.0000
0.0844
0.0000
0.2697
...
0.0
0.0000
0.1415
0.0
0.0
0.2459
0.0000
0.2109
0.1040
0.0
2016-12-20
0.0000
0.1584
0.0000
0.1280
0.1206
0.0000
0.0000
0.0846
0.0000
0.2696
...
0.0
0.0000
0.1444
0.0
0.0
0.2458
0.0000
0.2136
0.1039
0.0
2016-12-21
0.0000
0.1601
0.0000
0.1289
0.1198
0.0000
0.0000
0.0832
0.0000
0.2671
...
0.0
0.0000
0.1444
0.0
0.0
0.2465
0.0000
0.2130
0.1035
0.0
2016-12-22
0.0000
0.1598
0.0000
0.1288
0.1199
0.0000
0.0000
0.0828
0.0000
0.2653
...
0.0
0.0000
0.1458
0.0
0.0
0.2448
0.0000
0.2123
0.1026
0.0
2016-12-23
0.0000
0.1594
0.0000
0.1283
0.1203
0.0000
0.0000
0.0875
0.0000
0.2713
...
0.0
0.0000
0.1529
0.0
0.0
0.2450
0.0000
0.2101
0.1025
0.0
2016-12-26
0.0000
0.1434
0.0000
0.1280
0.1230
0.0000
0.0000
0.0859
0.0000
0.2746
...
0.0
0.0000
0.1544
0.0
0.0
0.2474
0.0000
0.2086
0.1026
0.0
2016-12-27
0.0000
0.1451
0.0000
0.1291
0.1246
0.0000
0.0000
0.0868
0.0000
0.2738
...
0.0
0.0000
0.1506
0.0
0.0
0.2470
0.0000
0.2076
0.1035
0.0
2016-12-28
0.0000
0.1453
0.0000
0.1292
0.1257
0.0000
0.0000
0.0872
0.0000
0.2814
...
0.0
0.0000
0.1497
0.0
0.0
0.2472
0.0000
0.2097
0.1029
0.0
2016-12-29
0.0000
0.1457
0.0000
0.1285
0.1252
0.0000
0.0000
0.0863
0.0000
0.2786
...
0.0
0.0000
0.1497
0.0
0.0
0.2479
0.0000
0.2116
0.1033
0.0
2016-12-30
0.0000
0.1479
0.0000
0.1297
0.1262
0.0000
0.0000
0.0867
0.0000
0.2731
...
0.0
0.0000
0.1561
0.0
0.0
0.2486
0.0000
0.2133
0.1057
0.0
1214 rows × 1071 columns
In [70]:
riskExposureDF = pd.concat(map(lambda x: x.iloc[-1], factordic .values()), axis=1)
In [71]:
factordic .values()[3].iloc[-1].head()
Out[71]:
600507.SH 0.116149
002344.SZ -0.233700
000793.SZ 0.247426
300039.SZ -0.350758
600059.SH -0.355085
Name: 2016-12-30 00:00:00, dtype: float64
In [72]:
# note that dictionary is not used in the given order, so make sure keys pair the values!
factordic.keys()
Out[72]:
['YOYBPS', 'LFCA', 'PB', 'AMOUNTAVG1MData', 'PROFITOOP', 'TURNOVER1M']
In [73]:
riskExposureDF.columns= factordic.keys()
In [74]:
riskExposureDF.head()
Out[74]:
YOYBPS
LFCA
PB
AMOUNTAVG1MData
PROFITOOP
TURNOVER1M
600507.SH
0.340788
-0.469033
-0.029513
0.116149
0.022185
0.396599
002344.SZ
0.146407
-0.094015
-0.033699
-0.233700
0.475590
-0.224775
000793.SZ
0.103897
0.469627
-0.032676
0.247426
0.328214
0.206770
300039.SZ
0.148159
-0.867833
-0.028938
-0.350758
0.231069
-0.123416
600059.SH
0.038965
-0.560382
-0.033503
-0.355085
0.037209
-0.329140
In [75]:
## Only conserve the diagonal element
spEwmMatrix = calEWMcovariance(specificReturn[-200:],decay=0.94)
tempo = spEwmMatrix .copy()
digVal = np.diag(spEwmMatrix)
a = np.fill_diagonal(tempo.values ,0)
finalSpMat = spEwmMatrix -tempo
In [76]:
finalSpMat.head()
Out[76]:
600507.SH
002344.SZ
000793.SZ
300039.SZ
600059.SH
600501.SH
000713.SZ
002233.SZ
600360.SH
600565.SH
...
600150.SH
600469.SH
600282.SH
600739.SH
600406.SH
600687.SH
600866.SH
000860.SZ
601908.SH
002195.SZ
600507.SH
0.000776
0.000000
0.000000
0.000000
0.000000
0.0
0.0
0.0
0.0
0.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
002344.SZ
0.000000
0.000232
0.000000
0.000000
0.000000
0.0
0.0
0.0
0.0
0.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
000793.SZ
0.000000
0.000000
0.000292
0.000000
0.000000
0.0
0.0
0.0
0.0
0.0
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In [77]:
# calculate monthly specific risk of the market index
alphaSP = reduce(lambda x,y: x.dot(y),[benchmarkWeight.iloc[-1], finalSpMat, benchmarkWeight.iloc[-1].T ])
In [78]:
alphaSP
Out[78]:
0.0056262027636875909
In [79]:
# calculate adjusted factor covriance matrix
def calFinlCov(ewmMatrix, benchMarkWeight, benchMarkRet, riskExposureDF, finalSpMat, compara=1/0.94 - 1):
'''
To calculate final to optimize covriance, with factor covriance matrix adjusted by Barra method,
P32 ,chapter-2,Barra Risk Model Handbook.
Output: Dataframe, namely adjusted factor covriance matrix(k*k, k is the factor amount).
Inpput:
ewmMatrix: Dataframe, the factor covriance matrix calculated directell by the outcome of multiFactorReg.
benchMarkWeight: Dataframe or Series, benchMarkWeight of the stocks on a given date!.
benchMarkRet: Dataframe or Series, notice the index !
riskExposureDF: Dataframe , riskExposureDF of stocks on factors in specific date.
finalSpMat: Dataframe, the digonal covriance matrix of specific returns.
'''
# calculate monthly scaled variance forecast for the market index by DEWIV
alphaS = 21*benchMarkRet.ewm(ignore_na=True, min_periods=0, com=compara).cov(pairwise = True)[-200:].iloc[-1]
# calculate monthly specific risk of the market index
alphaSP = reduce(lambda x,y: x.dot(y),[benchMarkWeight, finalSpMat, benchMarkWeight.T ])
# calculate total variance of the market index
alphaM = (reduce(lambda x,y: x.dot(y),[benchMarkWeight, riskExposureDF, ewmMatrix, riskExposureDF.T, benchMarkWeight.T ])\
+alphaSP)
# can not use np.dot on two series to construct a matrix
benchMarkWeightDF = pd.DataFrame(benchMarkWeight)
lastPart = reduce(lambda x,y: x.dot(y),[ewmMatrix, riskExposureDF.T, benchMarkWeightDF, \
benchMarkWeightDF.T, riskExposureDF,ewmMatrix ])
finalCovMatrix = ewmMatrix + ((alphaS - alphaM)/(alphaS - alphaSP)) * lastPart
return finalCovMatrix
In [80]:
calFinlCov(ewmMatrix, benchmarkWeight.iloc[-1], returnOfBench, riskExposureDF, finalSpMat, compara=1/0.94 - 1)
Out[80]:
LFCA
PB
YOYBPS
AMOUNTAVG1MData
TURNOVER1M
PROFITOOP
LFCA
1.467257e-07
0.000007
-6.467844e-07
-3.561044e-07
-9.388647e-07
1.981803e-08
PB
7.089570e-06
0.000491
-4.578150e-05
-2.320465e-05
-5.793758e-05
1.374818e-06
YOYBPS
-6.467844e-07
-0.000046
4.285751e-06
2.166568e-06
5.390764e-06
-1.285029e-07
AMOUNTAVG1MData
-3.561044e-07
-0.000023
2.166568e-06
1.114499e-06
2.803689e-06
-6.511143e-08
TURNOVER1M
-9.388647e-07
-0.000058
5.390764e-06
2.803689e-06
7.110081e-06
-1.626258e-07
PROFITOOP
1.981803e-08
0.000001
-1.285029e-07
-6.511143e-08
-1.626258e-07
3.887640e-09
In [81]:
# calculate total variance of the market index
alphaM = reduce(lambda x,y: x.dot(y),[benchmarkWeight.iloc[-1], riskExposureDF, ewmMatrix, riskExposureDF.T, benchmarkWeight.iloc[-1].T ])+alphaSP
In [82]:
alphaM
Out[82]:
0.0090002235911027344
In [83]:
ewmMatrix
Out[83]:
LFCA
PB
YOYBPS
AMOUNTAVG1MData
TURNOVER1M
PROFITOOP
LFCA
1.461664e-07
0.000007
-6.431792e-07
-3.542772e-07
-9.343010e-07
1.970972e-08
PB
7.050887e-06
0.000488
-4.553217e-05
-2.307828e-05
-5.762197e-05
1.367327e-06
YOYBPS
-6.431792e-07
-0.000046
4.262513e-06
2.154790e-06
5.361348e-06
-1.278048e-07
AMOUNTAVG1MData
-3.542772e-07
-0.000023
2.154790e-06
1.108529e-06
2.788780e-06
-6.475759e-08
TURNOVER1M
-9.343010e-07
-0.000058
5.361348e-06
2.788780e-06
7.072846e-06
-1.617420e-07
PROFITOOP
1.970972e-08
0.000001
-1.278048e-07
-6.475759e-08
-1.617420e-07
3.866666e-09
In [84]:
# can not use np.dot on two series to construct a matrix
benchmarkWeightDF = pd.DataFrame(benchmarkWeight.iloc[-1])
In [85]:
lastPart = reduce(lambda x,y: x.dot(y),[ewmMatrix, riskExposureDF.T, benchmarkWeightDF, \
benchmarkWeightDF.T, riskExposureDF,ewmMatrix ])
lastPart
Out[85]:
LFCA
PB
YOYBPS
AMOUNTAVG1MData
TURNOVER1M
PROFITOOP
LFCA
3.444220e-10
2.381932e-08
-2.219976e-09
-1.125144e-09
-2.810122e-09
6.669474e-11
PB
2.381932e-08
1.647282e-06
-1.535277e-07
-7.781202e-08
-1.943407e-07
4.612433e-09
YOYBPS
-2.219976e-09
-1.535277e-07
1.430889e-08
7.252131e-09
1.811268e-08
-4.298818e-10
AMOUNTAVG1MData
-1.125144e-09
-7.781202e-08
7.252131e-09
3.675576e-09
9.179995e-09
-2.178757e-10
TURNOVER1M
-2.810122e-09
-1.943407e-07
1.811268e-08
9.179995e-09
2.292765e-08
-5.441591e-10
PROFITOOP
6.669474e-11
4.612433e-09
-4.298818e-10
-2.178757e-10
-5.441591e-10
1.291494e-11
In [86]:
finalCovMatrix = ewmMatrix + ((alphaS - alphaM)/(alphaS - alphaSP)) * lastPart
finalCovMatrix
Out[86]:
LFCA
PB
YOYBPS
AMOUNTAVG1MData
TURNOVER1M
PROFITOOP
LFCA
1.467257e-07
0.000007
-6.467844e-07
-3.561044e-07
-9.388647e-07
1.981803e-08
PB
7.089570e-06
0.000491
-4.578150e-05
-2.320465e-05
-5.793758e-05
1.374818e-06
YOYBPS
-6.467844e-07
-0.000046
4.285751e-06
2.166568e-06
5.390764e-06
-1.285029e-07
AMOUNTAVG1MData
-3.561044e-07
-0.000023
2.166568e-06
1.114499e-06
2.803689e-06
-6.511143e-08
TURNOVER1M
-9.388647e-07
-0.000058
5.390764e-06
2.803689e-06
7.110081e-06
-1.626258e-07
PROFITOOP
1.981803e-08
0.000001
-1.285029e-07
-6.511143e-08
-1.626258e-07
3.887640e-09
In [87]:
stocks = pd.read_csv(path+ZZ500Weight,infer_datetime_format=True,parse_dates=[0],index_col=0)[stkPool].loc[startTime:endTime].iloc[-1].dropna()
optStkPool = stocks.index.tolist()
riskExposureDFSlice = riskExposureDF.loc[optStkPool]
In [88]:
# final covriance to optimize
toOptCov = reduce(lambda x,y: x.dot(y),[riskExposureDFSlice,finalCovMatrix,riskExposureDFSlice.T])
In [89]:
toOptCov
Out[89]:
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In [90]:
# optimize
factorNum = riskExposureDFSlice.shape[1]
stkNum = toOptCov.shape[1]
P = cv.matrix(toOptCov.values)
q = cv.matrix(0.0, (stkNum, 1))
G = cv.matrix(np.concatenate((riskExposureDFSlice.T.values, np.diag(np.ones(stkNum)), - np.diag(np.ones(stkNum)))))
h = cv.matrix(reduce(lambda x,y : np.append(x,y),[0.01 * np.ones(factorNum),0.005 * np.ones(stkNum), np.zeros(stkNum)]))
A = cv.matrix(np.ones(stkNum)).T
b = cv.matrix(1.0).T
sol = solvers.qp(P, q, G, h, A, b)
pcost dcost gap pres dres
0: 2.5034e-07 -2.4052e+00 1e+03 3e+01 4e+01
1: 9.1761e-06 -2.5895e+00 4e+01 1e+00 2e+00
2: 4.6722e-08 -2.2694e+00 3e+00 4e-02 5e-02
3: 7.3297e-08 -2.7486e-01 3e-01 2e-04 3e-04
4: 7.3431e-08 -2.7620e-03 3e-03 2e-06 3e-06
5: 7.2726e-08 -3.9199e-05 4e-05 3e-08 4e-08
6: 4.3407e-08 -4.8314e-06 5e-06 4e-09 5e-09
7: 9.9927e-09 -9.2092e-07 9e-07 6e-10 8e-10
8: 3.6460e-09 -2.3577e-07 2e-07 3e-16 3e-21
9: 3.0658e-09 -4.2605e-08 5e-08 9e-16 3e-21
Optimal solution found.
In [91]:
print sol['x']
[ 2.01e-03]
[ 2.25e-03]
[ 2.39e-03]
[ 2.30e-03]
[ 1.47e-03]
[ 2.35e-03]
[ 2.41e-03]
[ 2.29e-03]
[ 2.25e-03]
[ 3.02e-03]
[ 2.35e-03]
[ 2.42e-03]
[ 1.83e-03]
[ 1.75e-03]
[ 2.61e-03]
[ 2.61e-03]
[ 1.85e-03]
[ 2.92e-03]
[ 3.14e-03]
[ 1.81e-03]
[ 3.06e-03]
[ 2.00e-03]
[ 2.43e-03]
[ 2.21e-03]
[ 3.36e-03]
[ 2.68e-03]
[ 1.61e-03]
[ 2.89e-03]
[ 3.05e-03]
[ 1.72e-03]
[ 1.92e-03]
[ 2.40e-03]
[ 1.42e-03]
[ 2.01e-03]
[ 2.11e-03]
[ 2.72e-03]
[ 2.30e-03]
[ 2.15e-03]
[ 2.24e-03]
[ 2.47e-03]
[ 1.09e-03]
[ 1.84e-03]
[ 2.23e-03]
[ 2.90e-03]
[ 2.16e-03]
[ 1.33e-03]
[ 2.09e-03]
[ 2.52e-03]
[ 1.44e-03]
[ 2.39e-03]
[ 1.97e-03]
[ 2.15e-03]
[ 2.52e-03]
[ 2.10e-03]
[ 3.03e-03]
[ 3.00e-03]
[ 1.33e-03]
[ 2.62e-03]
[ 2.28e-03]
[ 1.28e-03]
[ 2.76e-03]
[ 2.35e-03]
[ 1.93e-03]
[ 2.38e-03]
[ 2.83e-03]
[ 2.21e-03]
[ 2.58e-03]
[ 1.94e-03]
[ 1.89e-03]
[ 1.63e-03]
[ 2.68e-03]
[ 1.44e-03]
[ 2.07e-03]
[ 2.03e-03]
[ 1.62e-03]
[ 8.58e-04]
[ 2.22e-03]
[ 2.40e-03]
[ 1.49e-03]
[ 2.17e-03]
[ 2.10e-03]
[ 8.01e-04]
[ 1.07e-03]
[ 2.47e-03]
[ 1.88e-03]
[ 2.48e-03]
[ 2.68e-03]
[ 2.06e-03]
[ 2.42e-03]
[ 2.41e-03]
[ 2.33e-03]
[ 2.81e-03]
[ 3.43e-03]
[ 1.97e-03]
[ 2.86e-03]
[ 1.63e-03]
[ 2.16e-03]
[ 2.28e-03]
[ 1.97e-03]
[ 2.52e-03]
[ 2.39e-03]
[ 3.33e-03]
[ 1.12e-03]
[ 1.05e-03]
[ 2.70e-03]
[ 2.18e-03]
[ 2.54e-03]
[ 2.18e-03]
[ 2.20e-03]
[ 1.79e-03]
[ 1.61e-03]
[ 2.65e-03]
[ 2.27e-03]
[ 2.38e-03]
[ 2.81e-03]
[ 2.27e-03]
[ 2.68e-03]
[ 1.33e-03]
[ 2.54e-03]
[ 1.82e-03]
[ 2.80e-03]
[ 2.15e-03]
[ 3.33e-03]
[ 2.53e-03]
[ 2.74e-03]
[ 2.14e-03]
[ 2.17e-03]
[ 1.57e-03]
[ 1.42e-03]
[ 2.42e-03]
[ 2.05e-03]
[ 2.40e-03]
[ 2.25e-03]
[ 2.83e-03]
[ 2.44e-03]
[ 2.03e-03]
[ 2.19e-03]
[ 2.43e-03]
[ 1.20e-03]
[ 1.78e-03]
[ 1.74e-03]
[ 2.20e-03]
[ 2.88e-03]
[ 3.18e-03]
[ 2.45e-03]
[ 1.73e-03]
[ 1.84e-03]
[ 9.27e-04]
[ 1.84e-03]
[ 3.12e-03]
[ 2.75e-03]
[ 2.09e-03]
[ 2.39e-03]
[ 1.76e-03]
[ 2.53e-03]
[ 2.79e-03]
[ 1.82e-03]
[ 2.90e-03]
[ 1.72e-03]
[ 2.19e-03]
[ 2.23e-03]
[ 2.33e-03]
[ 3.19e-03]
[ 1.94e-03]
[ 2.25e-03]
[ 2.28e-03]
[ 2.25e-03]
[ 3.12e-03]
[ 1.29e-03]
[ 3.38e-03]
[ 2.79e-03]
[ 3.30e-03]
[ 2.82e-03]
[ 1.29e-03]
[ 1.68e-03]
[ 3.42e-03]
[ 2.49e-03]
[ 1.75e-03]
[ 1.90e-03]
[ 2.21e-03]
[ 2.38e-03]
[ 1.95e-03]
[ 1.07e-03]
[ 2.06e-03]
[ 3.24e-03]
[ 2.69e-03]
[ 2.55e-03]
[ 2.19e-03]
[ 2.05e-03]
[ 2.41e-03]
[ 2.19e-03]
[ 2.00e-03]
[ 2.53e-03]
[ 1.98e-03]
[ 1.47e-03]
[ 2.54e-03]
[ 2.18e-03]
[ 2.71e-03]
[ 1.81e-03]
[ 2.55e-03]
[ 2.36e-03]
[ 2.28e-03]
[ 2.25e-03]
[ 2.07e-03]
[ 2.51e-03]
[ 2.40e-03]
[ 2.17e-03]
[ 2.62e-03]
[ 1.77e-03]
[ 2.42e-03]
[ 1.92e-03]
[ 2.61e-03]
[ 2.59e-03]
[ 2.15e-03]
[ 2.71e-03]
[ 2.36e-03]
[ 2.27e-03]
[ 2.52e-03]
[ 2.92e-03]
[ 2.09e-03]
[ 1.98e-03]
[ 2.15e-03]
[ 2.01e-03]
[ 2.08e-03]
[ 3.02e-03]
[ 2.38e-03]
[ 2.76e-03]
[ 1.68e-03]
[ 2.21e-03]
[ 1.41e-03]
[ 2.56e-03]
[ 2.00e-03]
[ 2.37e-03]
[ 2.39e-03]
[ 2.13e-03]
[ 1.33e-03]
[ 7.64e-04]
[ 2.24e-03]
[ 1.97e-03]
[ 2.68e-03]
[ 9.39e-04]
[ 2.42e-03]
[ 2.77e-03]
[ 1.07e-03]
[ 3.40e-03]
[ 2.72e-03]
[ 1.97e-03]
[ 1.72e-03]
[ 2.64e-03]
[ 2.47e-03]
[ 9.82e-04]
[ 2.40e-03]
[ 2.28e-03]
[ 2.78e-03]
[ 2.65e-03]
[ 1.92e-03]
[ 2.02e-03]
[ 2.30e-03]
[ 2.03e-03]
[ 2.06e-03]
[ 2.56e-03]
[ 2.72e-03]
[ 1.88e-03]
[ 2.50e-03]
[ 1.35e-03]
[ 1.61e-03]
[ 2.43e-03]
[ 1.29e-03]
[ 1.58e-03]
[ 1.59e-03]
[ 9.84e-04]
[ 2.27e-03]
[ 2.10e-03]
[ 2.62e-03]
[ 2.43e-03]
[ 2.49e-03]
[ 2.26e-03]
[ 2.25e-03]
[ 2.49e-03]
[ 2.15e-03]
[ 1.51e-03]
[ 1.75e-03]
[ 2.72e-03]
[ 1.34e-03]
[ 1.37e-03]
[ 2.71e-03]
[ 2.52e-03]
[ 2.84e-03]
[ 1.57e-03]
[ 2.12e-03]
[ 3.06e-03]
[ 2.32e-03]
[ 2.28e-03]
[ 1.62e-03]
[ 2.28e-03]
[ 2.16e-03]
[ 1.49e-03]
[ 2.32e-03]
[ 2.41e-03]
[ 2.37e-03]
[ 1.53e-03]
[ 2.56e-03]
[ 3.11e-03]
[ 2.32e-03]
[ 1.53e-03]
[ 2.14e-03]
[ 2.33e-03]
[ 1.73e-03]
[ 2.05e-03]
[ 2.75e-03]
[ 2.22e-03]
[ 2.52e-03]
[ 1.88e-03]
[ 2.61e-03]
[ 7.67e-04]
[ 1.84e-03]
[ 1.70e-03]
[ 2.39e-03]
[ 2.40e-03]
[ 2.41e-03]
[ 2.24e-03]
[ 2.91e-03]
[ 1.76e-03]
[ 3.22e-03]
[ 2.43e-03]
[ 1.38e-03]
[ 2.16e-03]
[ 2.69e-03]
[ 1.89e-03]
[ 3.61e-03]
[ 2.42e-03]
[ 2.61e-03]
[ 2.38e-03]
[ 2.64e-03]
[ 1.70e-03]
[ 2.19e-03]
[ 2.27e-03]
[ 1.50e-03]
[ 1.96e-03]
[ 2.52e-03]
[ 3.01e-03]
[ 2.57e-03]
[ 2.51e-03]
[ 2.16e-03]
[ 1.94e-03]
[ 1.86e-03]
[ 1.90e-03]
[ 1.51e-03]
[ 2.49e-03]
[ 2.16e-03]
[ 2.23e-03]
[ 2.11e-03]
[ 2.52e-03]
[ 1.18e-03]
[ 1.42e-03]
[ 1.11e-03]
[ 2.43e-03]
[ 1.74e-03]
[ 1.36e-03]
[ 2.78e-03]
[ 7.76e-04]
[ 1.52e-03]
[ 1.68e-03]
[ 1.84e-03]
[ 3.23e-03]
[ 1.44e-03]
[ 1.28e-03]
[ 2.42e-03]
[ 2.62e-03]
[ 1.67e-03]
[ 3.21e-03]
[ 2.17e-03]
[ 2.24e-03]
[ 2.30e-03]
[ 2.22e-03]
[ 1.93e-03]
[ 2.46e-03]
[ 1.99e-03]
[ 2.53e-03]
[ 2.26e-03]
[ 2.87e-03]
[ 1.86e-03]
[ 2.15e-03]
[ 2.98e-03]
[ 2.38e-03]
[ 2.42e-03]
[ 2.47e-03]
[ 1.76e-03]
[ 1.89e-03]
[ 2.16e-03]
[ 2.07e-03]
[ 2.19e-03]
[ 2.92e-03]
[ 1.58e-03]
[ 3.59e-03]
[ 2.57e-03]
[ 1.43e-03]
[ 1.86e-03]
[ 2.31e-03]
[ 1.53e-03]
[ 2.47e-03]
[ 2.13e-03]
[ 2.22e-03]
[ 2.34e-03]
[ 1.78e-03]
[ 2.13e-03]
[ 2.11e-03]
[ 2.14e-03]
[ 2.04e-03]
[ 2.09e-03]
[ 1.10e-03]
[ 3.21e-03]
[ 1.19e-03]
[ 2.50e-03]
[ 2.78e-03]
[ 2.39e-03]
[ 2.31e-03]
[ 2.51e-03]
[ 2.10e-03]
[ 1.61e-03]
[ 3.49e-03]
[ 2.44e-03]
[ 2.02e-03]
[ 9.73e-04]
[ 2.61e-03]
[ 2.98e-03]
[ 3.41e-03]
[ 3.29e-03]
[ 1.65e-03]
[ 3.10e-03]
[ 2.35e-03]
[ 1.54e-03]
[ 2.79e-03]
[ 1.56e-03]
[ 1.08e-03]
[ 1.83e-03]
[ 1.83e-03]
[ 2.37e-03]
[ 1.78e-03]
[ 2.89e-03]
[ 1.24e-03]
[ 2.04e-03]
[ 2.37e-03]
[ 9.83e-04]
[ 2.26e-03]
[ 1.27e-03]
[ 1.73e-03]
[ 3.54e-03]
[ 2.05e-03]
[ 2.52e-03]
[ 1.70e-03]
[ 1.69e-03]
[ 2.66e-03]
[ 1.62e-03]
[ 2.40e-03]
In [92]:
len(sol['x'])
Out[92]:
455
In [93]:
####
dates = [pd.Timestamp('2012-05-01'), pd.Timestamp('2012-05-02'), pd.Timestamp('2012-05-03')]
s = pd.DataFrame(index = dates ,data =[[5,6,7],[8,6,0],[1,2,3]],columns=[1,2,3] )
d = pd.DataFrame(index = dates,data =[[1,2,3,1],[2,3,1,2],[3,1,2,3]],columns=['hs','hc','hv','hg'] )
In [94]:
dd =d.copy()
for i in dates:
sSlice = s.loc[i]
dSlice = d.loc[i]
dd.loc[i] = sSlice.loc[dSlice].values
print dd
hs hc hv hg
2012-05-01 5 6 7 5
2012-05-02 6 0 8 6
2012-05-03 3 1 2 3
In [ ]:
In [95]:
#d.apply(lambda x: s[x.values] ,axis=0)
In [96]:
color_set=sns.light_palette((210, 90, 60), input="husl")
(factorPvalue[factorPvalue < 0.05].count()/len(factorPvalue)).plot(figsize=(18,12),kind='bar',color=sns.color_palette(color_set,10))
Out[96]:
<matplotlib.axes._subplots.AxesSubplot at 0x3b98ab70>
In [97]:
factorReturn.cumsum().plot(figsize=(20,14))
Out[97]:
<matplotlib.axes._subplots.AxesSubplot at 0x3921a128>
In [98]:
factorReturn.describe()
Out[98]:
LFCA
PB
YOYBPS
AMOUNTAVG1MData
TURNOVER1M
PROFITOOP
count
1213.000000
1213.000000
1213.000000
1213.000000
1213.000000
1213.000000
mean
0.000138
0.001844
-0.000530
-0.000184
-0.000306
-0.002801
std
0.003104
0.018440
0.023450
0.001884
0.004886
0.026907
min
-0.018767
-0.125413
-0.164897
-0.008586
-0.025073
-0.214041
25%
-0.001447
-0.001921
-0.001335
-0.001211
-0.002268
-0.001897
50%
-0.000213
0.000765
-0.000054
-0.000234
-0.000122
-0.000158
75%
0.001137
0.003673
0.001293
0.000902
0.001962
0.000922
max
0.023948
0.167759
0.166723
0.007942
0.022233
0.134320
In [99]:
result2 = sm.OLS(factorReturn['PB'],factorReturn['LFCA']).fit()
In [100]:
result2.summary()
Out[100]:
OLS Regression Results
Dep. Variable: PB R-squared: 0.001
Model: OLS Adj. R-squared: -0.000
Method: Least Squares F-statistic: 0.6427
Date: Wed, 26 Apr 2017 Prob (F-statistic): 0.423
Time: 09:40:43 Log-Likelihood: 3117.4
No. Observations: 1213 AIC: -6233.
Df Residuals: 1212 BIC: -6228.
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [0.025 0.975]
LFCA -0.1373 0.171 -0.802 0.423 -0.473 0.199
Omnibus: 527.561 Durbin-Watson: 1.762
Prob(Omnibus): 0.000 Jarque-Bera (JB): 18261.231
Skew: 1.346 Prob(JB): 0.00
Kurtosis: 21.817 Cond. No. 1.00
In [101]:
specificReturn
Out[101]:
600507.SH
002344.SZ
000793.SZ
300039.SZ
600059.SH
600501.SH
000713.SZ
002233.SZ
600360.SH
600565.SH
...
600150.SH
600469.SH
600282.SH
600739.SH
600406.SH
600687.SH
600866.SH
000860.SZ
601908.SH
002195.SZ
Specifc_Return_for_ZX500
2012-01-05
0.025697
-0.003473
0.037547
0.008942
-0.016753
-0.008696
-0.063853
-0.037908
0.003391
0.043417
...
-0.006358
0.025833
0.017932
0.015733
0.014046
0.047862
-0.010460
-0.016782
-0.034919
-0.028392
2012-01-06
0.009798
-0.002888
-0.014237
-0.001026
0.008056
0.008518
-0.076295
0.035727
0.012290
0.042268
...
0.007569
0.025506
0.007685
0.032604
0.004787
-0.002140
-0.026344
-0.000980
0.006110
0.020083
2012-01-09
-0.002461
-0.003984
-0.008911
0.008975
-0.001940
0.004855
-0.009167
0.023284
0.001697
-0.023664
...
0.002377
-0.014051
-0.012759
0.004765
-0.013659
-0.037539
0.002730
0.019514
0.008412
-0.039856
2012-01-10
-0.006802
-0.005576
-0.014091
-0.018444
-0.002437
-0.000084
0.061007
0.024528
-0.000246
-0.021472
...
-0.000985
0.005040
0.000618
-0.019920
-0.011203
-0.040650
0.030107
-0.000083
0.021606
0.021835
2012-01-11
0.045727
0.012534
-0.004737
-0.021131
-0.011484
0.000324
0.017528
-0.022103
-0.006171
-0.014216
...
-0.007070
0.005367
0.003865
-0.006986
0.004961
-0.005496
-0.012906
-0.007782
-0.008659
0.094105
2012-01-12
-0.009627
-0.009251
-0.003970
-0.016977
-0.002378
0.007949
-0.016876
0.007746
0.001761
-0.001463
...
0.000756
-0.014743
-0.005372
-0.002584
-0.018490
0.001313
-0.001444
-0.005423
0.036531
-0.017246
2012-01-13
-0.010242
0.020970
-0.002322
-0.040364
-0.000066
0.000146
0.017963
-0.008312
-0.005817
0.007578
...
-0.004806
-0.007751
0.008295
0.002108
-0.022852
0.039016
-0.015669
-0.015496
0.032646
-0.026736
2012-01-16
0.020199
-0.021679
0.007341
-0.001592
-0.010316
-0.006683
0.018280
-0.001341
0.008870
0.003953
...
0.005323
0.008468
0.018731
0.021012
-0.070429
0.035611
-0.011880
0.010993
-0.011707
-0.003663
2012-01-17
-0.054877
-0.000032
-0.011086
-0.017507
0.003734
-0.005409
0.000674
0.026957
0.001241
-0.006605
...
0.004167
0.006175
0.001760
0.006243
-0.022848
-0.051361
0.002953
-0.003269
0.002274
0.025790
2012-01-18
0.022522
0.011270
0.009700
-0.068346
-0.007530
0.042251
-0.008548
0.026919
0.008811
0.014793
...
0.004430
0.021011
0.000996
-0.006043
-0.054979
0.027621
-0.003274
-0.001775
0.001269
-0.014766
2012-01-19
0.060079
-0.006803
-0.028688
0.011397
0.002379
-0.002305
-0.012110
-0.001179
0.006911
0.002572
...
-0.000286
0.009633
0.001779
0.009646
0.052840
-0.009797
-0.005038
0.000501
0.018976
-0.001058
2012-01-20
-0.019647
-0.008150
-0.000180
0.012686
0.015826
-0.004234
0.027664
0.013963
0.000335
-0.001984
...
0.021521
-0.005428
-0.004488
0.007368
0.001899
-0.014612
0.011480
0.006558
-0.008054
-0.012639
2012-01-30
0.011053
-0.013475
0.009726
-0.004199
0.012627
0.001401
-0.003782
-0.028172
0.004519
-0.006218
...
-0.010550
-0.017300
-0.000519
0.004420
0.002340
0.000927
-0.003856
0.025063
-0.012913
-0.002350
2012-01-31
-0.003756
-0.017357
0.020712
-0.029218
-0.003476
0.014373
-0.004233
-0.003062
0.003812
0.013702
...
0.018583
0.014493
-0.015569
-0.002926
-0.016167
-0.000671
-0.006074
0.009975
-0.044988
-0.003558
2012-02-01
-0.010210
0.005883
-0.001170
0.009586
-0.009513
-0.014536
0.010392
-0.023494
0.011447
0.011954
...
-0.011139
0.011274
0.001201
-0.002798
0.005141
0.003310
0.006928
0.001441
-0.012411
-0.002608
2012-02-02
-0.006556
-0.005135
-0.001330
0.037808
-0.010369
-0.001339
-0.009855
0.001722
0.002519
-0.000582
...
-0.005014
-0.002695
-0.005149
-0.007672
-0.010057
-0.016110
0.000715
-0.000305
0.004030
0.006156
2012-02-03
-0.007349
0.003108
-0.016817
-0.011857
-0.004797
-0.007903
-0.016840
-0.015055
0.020752
-0.014320
...
0.006986
-0.006016
-0.010460
-0.010506
0.039603
-0.021608
0.041952
-0.020348
0.012629
0.001543
2012-02-06
-0.005528
0.005469
-0.011822
0.029207
-0.003050
-0.011378
0.006300
0.017259
-0.015340
-0.015194
...
-0.007103
-0.010491
-0.010896
0.004785
0.027431
-0.011732
-0.011777
-0.003412
0.028627
-0.007435
2012-02-07
0.008390
-0.003556
0.010067
-0.007387
-0.009078
-0.002071
-0.000861
-0.013572
-0.011307
0.008977
...
-0.006898
-0.001997
0.008342
-0.024295
-0.001304
0.014379
-0.013875
-0.005976
-0.004512
-0.011058
2012-02-08
-0.012152
-0.010044
-0.014624
-0.003835
0.002906
0.003406
-0.015299
0.009846
0.009580
-0.003992
...
-0.006144
0.013945
-0.004910
0.005237
-0.002903
-0.024295
0.009027
-0.005572
0.010185
0.001977
2012-02-09
0.016116
-0.005459
-0.002377
-0.014785
0.011079
-0.012502
-0.007977
-0.000302
0.002661
0.005397
...
0.001038
-0.015223
-0.004035
-0.009586
0.006196
-0.009539
0.021959
0.000662
-0.010483
0.007370
2012-02-10
0.001162
0.001826
-0.003241
-0.016826
-0.008935
-0.001425
0.010385
0.002829
-0.006626
-0.001218
...
-0.005290
-0.003878
0.004429
0.005094
-0.006322
-0.007867
-0.012987
0.017081
0.006077
-0.007489
2012-02-13
-0.004348
-0.009434
-0.002377
0.041533
0.011631
-0.002109
-0.020070
-0.020366
0.029998
-0.011101
...
-0.002728
-0.001064
-0.011708
-0.001984
-0.002504
-0.014350
0.007706
-0.003517
-0.023715
-0.001887
2012-02-14
-0.005020
0.005827
-0.000060
0.031157
-0.002835
0.007338
-0.007546
0.014853
-0.014081
0.012637
...
0.010812
0.016797
-0.010790
-0.005326
-0.006415
-0.003178
-0.007490
-0.000516
-0.014311
-0.005706
2012-02-15
-0.001737
-0.005584
0.007412
-0.003420
-0.014295
0.004322
-0.012637
-0.006615
0.020089
-0.011643
...
0.007085
-0.009986
0.007822
0.023290
-0.016891
-0.016486
0.028790
-0.007172
0.010785
-0.003552
2012-02-16
0.019497
0.002845
0.005799
-0.003650
0.000985
-0.006408
0.014222
-0.006275
-0.009168
0.015122
...
-0.008492
0.011382
-0.007275
-0.007312
-0.003675
-0.002756
-0.009829
-0.011589
0.029805
-0.003292
2012-02-17
-0.004990
-0.000247
-0.006921
-0.012979
-0.006523
0.019146
-0.014689
-0.010776
-0.013079
0.007198
...
0.006536
-0.002213
0.000637
0.017015
0.003368
0.004459
-0.013537
-0.006585
0.010342
-0.019308
2012-02-20
-0.000762
-0.011651
-0.007823
-0.040286
0.006905
-0.008681
-0.004858
0.000353
-0.009046
-0.019164
...
0.000677
-0.011483
-0.005031
-0.014838
0.032045
-0.002110
-0.003969
0.003308
-0.013040
-0.002108
2012-02-21
-0.016275
-0.008949
0.004663
-0.000365
0.032768
-0.006971
-0.009862
-0.005791
0.002142
0.006485
...
-0.008162
-0.009223
-0.003224
0.004059
-0.000326
-0.010086
-0.001350
0.016590
-0.006795
0.014290
2012-02-22
-0.011953
-0.000159
-0.005044
-0.000245
-0.013014
0.017540
-0.004819
-0.004893
-0.010375
-0.009904
...
-0.004839
-0.005477
-0.009693
0.003160
-0.000570
-0.019620
-0.000302
-0.007680
0.020105
0.007744
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
2016-11-21
0.004116
-0.012315
-0.000965
0.002540
-0.001069
0.015198
-0.003783
-0.009837
0.002850
-0.005221
...
0.006744
0.012791
0.010766
0.007151
-0.021069
-0.003086
-0.000848
-0.000633
0.029898
0.018065
2016-11-22
0.008588
-0.006524
-0.014546
0.002471
-0.006451
-0.001184
0.001560
-0.024366
-0.014749
-0.006824
...
0.011174
0.001036
0.041534
0.051665
-0.012534
-0.006076
-0.002769
-0.002800
0.052490
-0.027845
2016-11-23
0.003757
-0.026598
-0.005973
-0.006287
0.002678
0.017555
-0.006563
-0.002171
0.007746
-0.002714
...
-0.007023
-0.007030
0.021765
-0.008500
-0.002021
-0.000128
-0.008328
0.009204
-0.021191
-0.000150
2016-11-24
0.002655
0.010088
-0.001484
-0.007074
0.021118
0.001684
-0.008638
0.002739
0.023288
-0.000461
...
-0.019155
-0.011349
0.020292
0.068609
-0.025388
-0.001966
-0.016330
0.010067
-0.030220
-0.035994
2016-11-25
0.035908
-0.014605
0.000058
-0.001145
-0.013527
-0.007813
-0.008431
-0.011698
-0.019321
-0.009498
...
0.004688
0.010013
0.002062
-0.015354
-0.006859
-0.006832
-0.011420
-0.010807
-0.011395
0.011546
2016-11-28
-0.009264
-0.018041
0.009752
0.007162
-0.000162
-0.014589
0.001061
0.008392
-0.020504
0.032465
...
0.006745
-0.008057
-0.000886
-0.006892
0.010231
-0.000727
-0.011330
-0.001330
0.005123
-0.014616
2016-11-29
0.032540
0.012524
-0.021000
0.002891
0.013319
0.049836
0.000907
-0.018818
-0.010295
-0.003335
...
0.026431
-0.021797
-0.023068
-0.009793
-0.004073
0.001587
0.002279
0.015202
-0.019326
0.001329
2016-11-30
-0.010226
-0.026371
0.014858
0.037224
0.000731
-0.001730
0.005836
-0.010111
0.014362
-0.008539
...
-0.019896
0.007475
-0.005221
0.016167
0.002437
0.004305
0.002454
-0.014744
-0.013338
0.037680
2016-12-01
0.013128
0.000494
-0.014953
-0.009253
-0.009240
-0.005666
0.000146
0.005404
-0.001360
-0.011849
...
0.024119
0.019769
0.007970
-0.007834
-0.006012
-0.007108
0.003505
-0.009391
0.000424
-0.007404
2016-12-02
-0.031747
-0.003301
0.015371
-0.011835
-0.012148
-0.015657
0.001290
0.001381
-0.003682
0.015541
...
0.042484
0.025365
-0.004533
0.014798
0.017513
0.010304
-0.004684
-0.003220
-0.010651
-0.002368
2016-12-05
0.005434
-0.023616
0.001484
-0.014046
0.010283
0.005787
0.005068
0.004562
0.002820
0.007331
...
0.084771
0.010870
0.001622
-0.089297
0.021249
0.003972
0.007877
0.009958
0.001822
0.019856
2016-12-06
-0.006845
-0.000693
-0.007595
-0.005681
-0.009284
-0.020962
0.004289
0.014763
0.018684
-0.008087
...
-0.014768
0.005650
0.003751
-0.009610
0.017321
0.000288
0.010379
-0.001049
0.002666
0.010346
2016-12-07
0.045892
0.020282
-0.015689
0.000494
-0.004245
-0.008839
-0.001693
0.018272
-0.015131
-0.010187
...
0.018825
-0.009593
0.018710
-0.006145
-0.002597
-0.007274
0.002232
-0.002943
-0.000263
-0.004393
2016-12-08
0.005831
-0.004326
0.029605
-0.008784
0.006654
-0.016217
0.000350
-0.014138
0.002712
0.025956
...
-0.012993
0.021909
-0.008978
-0.006309
-0.013237
0.003997
-0.006220
0.011827
-0.006600
-0.001618
2016-12-09
-0.018018
0.000732
-0.004448
-0.004510
0.001487
0.002110
-0.002175
-0.020003
-0.008717
0.000906
...
0.026872
0.004223
0.007711
0.003453
-0.004103
0.000866
0.002818
0.005212
-0.016663
-0.000147
2016-12-12
0.013583
-0.009366
0.003169
-0.012749
-0.009388
0.010733
-0.007133
-0.016055
-0.022612
0.019399
...
0.003842
-0.001923
0.008784
-0.026577
0.024233
0.029109
0.000933
0.015489
-0.011285
-0.014813
2016-12-13
0.052545
-0.001282
-0.005146
0.000161
0.012430
0.015913
0.012024
0.030629
-0.013771
-0.006450
...
0.067248
-0.005695
0.008372
0.003918
-0.029522
-0.002800
-0.000237
0.024233
-0.013403
-0.001608
2016-12-14
-0.021991
-0.003708
-0.004227
-0.001113
-0.000090
0.030691
-0.026090
-0.013705
-0.001227
-0.015985
...
-0.021127
0.063488
-0.014712
-0.010451
0.071338
0.005249
0.012816
0.003353
0.004883
-0.012642
2016-12-15
0.093530
0.031947
0.010375
-0.004773
-0.005439
0.002772
-0.002247
-0.007773
0.015203
0.041862
...
-0.009560
-0.000193
0.011235
-0.004244
0.009400
-0.000135
0.007963
0.003303
0.002096
0.002556
2016-12-16
0.049880
0.003535
0.049998
0.002677
0.007463
0.004271
0.042843
0.005243
-0.005540
0.023208
...
-0.047765
-0.018137
-0.009253
0.020822
-0.016290
-0.006583
0.006597
-0.010722
0.005390
0.019541
2016-12-19
-0.034419
-0.017775
0.024785
-0.015057
-0.005119
0.001086
0.007792
-0.001322
-0.021083
-0.002237
...
0.015954
0.011016
0.019557
-0.006675
0.004989
-0.000995
-0.001658
0.011185
-0.001744
-0.015854
2016-12-20
-0.044345
0.011837
0.003587
0.006492
-0.007609
-0.008795
-0.021780
-0.016357
0.025412
-0.007512
...
0.001017
-0.022421
-0.000326
-0.000752
-0.020449
0.002981
0.009770
-0.001188
-0.003776
0.011593
2016-12-21
-0.008973
-0.003105
-0.011989
-0.000767
-0.000366
0.014113
0.006825
-0.006050
-0.003281
-0.007760
...
0.064926
0.063497
0.007640
0.002840
0.004354
-0.009241
-0.006415
-0.003403
-0.010551
-0.003575
2016-12-22
-0.002591
-0.003378
-0.008123
-0.004816
0.002814
-0.019702
-0.011148
0.056149
-0.016243
0.022487
...
0.026384
0.009699
0.047396
-0.000297
0.034828
-0.000194
-0.001773
-0.011049
-0.001616
-0.018613
2016-12-23
-0.029777
-0.015928
0.036201
0.000889
0.023664
0.005538
-0.012470
-0.015434
0.010483
0.015981
...
0.017602
-0.011120
0.011213
-0.001032
0.020064
0.008623
-0.001696
-0.004138
0.001410
0.001846
2016-12-26
-0.023122
0.010769
-0.009817
0.006947
0.012481
0.006357
0.008649
0.008887
-0.003876
-0.004927
...
0.002493
0.002654
-0.026140
-0.001668
-0.009431
-0.001582
0.010529
-0.005836
0.008052
0.019346
2016-12-27
-0.004435
-0.000807
0.002591
-0.001878
0.006556
0.001184
0.009099
0.002489
0.005987
0.025400
...
-0.019135
-0.001682
-0.007102
-0.004429
0.004407
0.000556
0.012094
0.007637
-0.007560
-0.007801
2016-12-28
-0.012017
0.002621
-0.020772
-0.006607
-0.004481
0.007649
0.002448
-0.011259
0.005490
-0.010482
...
0.001505
0.034261
-0.000343
-0.006536
-0.005476
0.002957
0.017636
0.008229
0.004232
-0.001179
2016-12-29
0.008727
0.013987
-0.004611
0.008750
0.006758
0.004271
0.006866
0.004027
-0.013295
-0.020404
...
-0.022136
-0.011416
0.042035
0.026898
0.001633
0.001583
-0.010350
0.006314
0.022208
-0.005476
2016-12-30
-0.009606
-0.004526
-0.011325
-0.003919
-0.002088
-0.018438
0.012067
-0.020959
-0.012541
0.006351
...
-0.028871
-0.005890
-0.000246
-0.008862
-0.002014
-0.006388
0.010013
0.047173
0.021147
-0.005549
1213 rows × 1071 columns
In [102]:
result1 = sm.OLS(returnOfBench, factorReturn).fit()
In [103]:
result1 .summary()
Out[103]:
OLS Regression Results
Dep. Variable: 000905.SH R-squared: 0.626
Model: OLS Adj. R-squared: 0.624
Method: Least Squares F-statistic: 336.2
Date: Wed, 26 Apr 2017 Prob (F-statistic): 2.15e-253
Time: 09:40:44 Log-Likelihood: 3696.9
No. Observations: 1213 AIC: -7382.
Df Residuals: 1207 BIC: -7351.
Df Model: 6
Covariance Type: nonrobust
coef std err t P>|t| [0.025 0.975]
LFCA -0.8877 0.110 -8.092 0.000 -1.103 -0.672
PB -0.0405 0.022 -1.871 0.062 -0.083 0.002
YOYBPS -0.0398 0.015 -2.708 0.007 -0.069 -0.011
AMOUNTAVG1MData 0.9825 0.191 5.140 0.000 0.607 1.357
TURNOVER1M 3.0035 0.074 40.779 0.000 2.859 3.148
PROFITOOP 0.0772 0.015 5.298 0.000 0.049 0.106
Omnibus: 77.445 Durbin-Watson: 1.694
Prob(Omnibus): 0.000 Jarque-Bera (JB): 306.322
Skew: -0.130 Prob(JB): 3.04e-67
Kurtosis: 5.448 Cond. No. 17.4
In [104]:
result1.resid.head(10)
Out[104]:
Specifc_Return_for_ZX500
2012-01-05 -0.030171
2012-01-06 -0.001059
2012-01-09 0.032445
2012-01-10 0.041967
2012-01-11 0.000153
2012-01-12 -0.000846
2012-01-13 -0.028417
2012-01-16 -0.024800
2012-01-17 0.050525
2012-01-18 -0.021567
dtype: float64
In [105]:
1.247**5
Out[105]:
3.0153120786310086
In [106]:
print PBData.shape,YOYBPSData.shape,AMOUNTAVG1MData .shape, TURNOVER1MData.shape,PROFITOOPData.shape
(1214, 1071) (1214, 1071) (1214, 1071) (1214, 1071) (1214, 1071)
In [107]:
PriceDF
Out[107]:
600507.SH
002344.SZ
000793.SZ
300039.SZ
600059.SH
600501.SH
000713.SZ
002233.SZ
600360.SH
600565.SH
...
600150.SH
600469.SH
600282.SH
600739.SH
600406.SH
600687.SH
600866.SH
000860.SZ
601908.SH
002195.SZ
LZ_GPA_DERI_AdjustedPriceForward_20-d
2012-01-04
2.95251
10.25091
5.49539
5.93078
9.33043
7.62428
13.27401
8.09095
3.81523
2.97514
...
19.48821
4.54020
2.65200
12.10778
14.57299
4.87935
5.69
13.19030
5.08136
3.17793
2012-01-05
2.92215
9.79264
5.53478
5.73013
8.86761
7.20070
11.94563
7.51623
3.66887
2.97514
...
18.98636
4.47361
2.60325
12.03819
14.45932
4.87935
5.40
12.52060
4.66601
2.93332
2012-01-06
2.96010
9.79264
5.48554
5.73899
8.98794
7.28936
11.11907
7.85747
3.72742
3.11126
...
19.19927
4.60679
2.63250
12.51535
14.55935
4.87935
5.28
12.55942
4.73645
3.00377
2012-01-09
3.07395
10.12067
5.65296
6.01635
9.33043
7.63413
11.47331
8.36933
3.89329
3.16959
...
19.98244
4.73997
2.71050
13.07203
14.68666
4.87935
5.52
13.31648
4.98663
3.00377
2012-01-10
3.18021
10.47764
5.80068
6.15798
9.68217
7.95919
12.62458
8.90813
4.05917
3.23765
...
20.75802
4.96395
2.82750
13.32055
15.00040
4.87935
5.92
13.86001
5.26596
3.19358
2012-01-11
3.32442
10.65130
5.77114
6.04880
9.57110
7.98874
12.86074
8.71955
4.03966
3.19876
...
20.51470
4.99422
2.83725
13.18138
15.13681
4.87935
5.85
13.74354
5.21738
3.51255
2012-01-12
3.28647
10.55965
5.74159
5.94553
9.53407
8.03800
12.61474
8.77343
4.03966
3.18904
...
20.52991
4.90947
2.81775
13.12173
14.91856
4.87935
5.83
13.64648
5.34854
3.44993
2012-01-13
3.14226
10.41010
5.55448
5.47343
9.22861
7.69323
12.40810
8.39627
3.85426
3.08209
...
19.99765
4.67944
2.73975
12.83345
14.43658
4.87935
5.51
12.98648
5.31940
3.21511
2012-01-16
3.11190
9.84088
5.43630
5.28459
8.85836
7.37801
12.23098
8.12687
3.76645
2.99458
...
19.62507
4.57047
2.71050
12.78375
13.00429
4.87935
5.27
12.74383
5.04250
3.08596
2012-01-17
3.11190
10.34739
5.67266
5.47343
9.37671
7.74248
12.90993
8.80935
3.98111
3.14042
...
20.77323
4.85499
2.86650
13.58894
13.28620
4.87935
5.58
13.40383
5.34126
3.32665
2012-01-18
3.11190
10.23644
5.61357
4.97182
9.10828
7.87054
12.50650
8.88119
3.92257
3.11126
...
20.56032
4.84288
2.80800
13.28078
12.34953
4.87935
5.43
13.09324
5.13237
3.19163
2012-01-19
3.33960
10.24609
5.52493
5.06919
9.23787
7.91979
12.46714
8.93507
3.99087
3.15015
...
20.86447
4.93974
2.84700
13.61877
13.14525
4.87935
5.46
13.25824
5.31211
3.20532
2012-01-20
3.32442
10.31363
5.60372
5.20787
9.51556
7.99859
12.97881
9.15957
4.04942
3.18904
...
21.64765
4.98212
2.87625
13.89711
13.37259
4.87935
5.60
13.53971
5.34126
3.20924
2012-01-30
3.33201
10.12067
5.60372
5.15771
9.55258
7.97889
12.85090
8.82731
4.03966
3.15015
...
21.06217
4.86104
2.84700
13.76788
13.29529
4.87935
5.54
13.75324
5.29511
3.19750
2012-01-31
3.31683
9.95665
5.72190
5.00428
9.52481
8.08725
12.82138
8.82731
4.04942
3.18904
...
21.45756
4.92763
2.79825
13.74800
13.18162
4.87935
5.50
13.88913
5.06436
3.19163
2012-02-01
3.25611
9.95665
5.66281
5.02493
9.34894
7.92964
12.84106
8.52199
4.06893
3.20848
...
20.93290
4.94579
2.77875
13.52930
13.11796
4.87935
5.50
13.78236
4.96963
3.16814
2012-02-02
3.29406
10.08690
5.76129
5.30819
9.43225
8.06755
12.97881
8.73751
4.15675
3.26682
...
21.23705
5.03054
2.81775
13.70823
13.20436
4.87935
5.61
14.04442
5.07407
3.24838
2012-02-03
3.32442
10.31845
5.75144
5.35540
9.53407
8.17590
12.96897
8.73751
4.32263
3.28626
...
21.60203
5.09108
2.83725
13.69829
13.86821
4.87935
5.95
13.97648
5.21252
3.32274
2012-02-06
3.33201
10.46799
5.72190
5.56195
9.57110
8.16605
13.14609
8.93507
4.29336
3.26682
...
21.51839
5.07897
2.82750
13.80764
14.31836
4.87935
5.93
14.02501
5.42870
3.33252
2012-02-07
3.30165
10.27503
5.68250
5.42917
9.32117
8.00844
12.91977
8.65669
4.16651
3.23765
...
20.98613
4.97606
2.79825
13.22114
14.16376
4.87935
5.74
13.69501
5.32183
3.24642
2012-02-08
3.34719
10.43422
5.75144
5.54424
9.59887
8.23501
13.07721
8.98895
4.31287
3.30571
...
21.44995
5.17583
2.85675
13.67841
14.53207
4.87935
5.94
13.98618
5.52343
3.33643
2012-02-09
3.42309
10.46799
5.77114
5.51474
9.76548
8.21530
13.06737
9.05181
4.36166
3.35432
...
21.52599
5.13951
2.86650
13.58894
14.64574
4.87935
6.12
14.08324
5.49671
3.39514
2012-02-10
3.44586
10.55000
5.78099
5.45868
9.72846
8.26456
13.27401
9.12365
4.36166
3.37377
...
21.45756
5.15162
2.89575
13.70823
14.58208
4.87935
6.08
14.39383
5.56958
3.39318
2012-02-13
3.46104
10.55965
5.81053
5.74489
9.92284
8.35321
13.14609
9.01589
4.53730
3.37377
...
21.44235
5.20005
2.88600
13.73805
14.60936
4.87935
6.19
14.46177
5.55743
3.43428
2012-02-14
3.44586
10.63683
5.81053
5.93373
9.89507
8.43202
13.04769
9.14161
4.47875
3.42238
...
21.62484
5.29085
2.85675
13.63865
14.49569
4.87935
6.15
14.45207
5.48457
3.42058
2012-02-15
3.49140
10.75743
5.93856
6.01635
9.90433
8.61917
13.09689
9.24039
4.64463
3.44183
...
22.05064
5.32717
2.92500
14.13568
14.41385
4.87935
6.43
14.56854
5.61330
3.47146
2012-02-16
3.55971
10.80085
5.96811
6.00454
9.90433
8.57977
13.26417
9.15957
4.60560
3.50016
...
21.79212
5.38771
2.90550
13.97663
14.30472
4.87935
6.37
14.38413
5.75661
3.46167
2012-02-17
3.52935
10.75743
5.90902
5.90127
9.81176
8.70783
13.03785
9.04283
4.52754
3.50989
...
21.89097
5.35744
2.89575
14.19533
14.34564
4.87935
6.26
14.24824
5.81733
3.38340
2012-02-20
3.52935
10.66095
5.86962
5.67997
9.89507
8.65858
13.00833
9.07875
4.49827
3.45155
...
21.89857
5.30901
2.88600
13.99651
14.80488
4.87935
6.25
14.31618
5.74203
3.38927
2012-02-21
3.50658
10.68989
5.95826
5.74194
10.33012
8.69798
13.04769
9.15059
4.55681
3.50989
...
21.95179
5.32112
2.90550
14.20527
14.98222
4.87935
6.31
14.71413
5.78576
3.48320
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
2016-11-21
6.46000
12.79000
11.44000
11.27000
10.23000
18.34000
12.14000
10.41000
10.24000
7.16000
...
23.61000
10.78000
2.71000
19.53000
15.89000
16.42000
7.19
20.71000
7.76000
12.84000
2016-11-22
6.57000
12.83000
11.39000
11.40000
10.25000
18.47000
12.28000
10.26000
10.20000
7.19000
...
24.04000
10.89000
2.85000
20.70000
15.80000
16.42000
7.24
20.82000
8.23000
12.62000
2016-11-23
6.57000
12.41000
11.26000
11.26000
10.24000
18.72000
12.11000
10.17000
10.19000
7.12000
...
23.86000
10.76000
2.90000
20.46000
15.76000
16.42000
7.13
20.94000
8.03000
12.51000
2016-11-24
6.59000
12.52000
11.24000
11.16000
10.45000
18.72000
11.95000
10.17000
10.38000
7.10000
...
23.43000
10.60000
2.96000
21.87000
15.39000
16.42000
6.99
21.15000
7.79000
11.99000
2016-11-25
6.86000
12.38000
11.29000
11.18000
10.35000
18.63000
11.85000
10.07000
10.18000
7.05000
...
23.68000
10.72000
2.98000
21.64000
15.39000
16.42000
6.92
21.02000
7.74000
12.12000
2016-11-28
6.81000
12.19000
11.45000
11.28000
10.37000
18.39000
11.89000
10.18000
10.00000
7.31000
...
23.88000
10.67000
3.00000
21.55000
15.58000
16.42000
6.86
21.01000
7.80000
11.99000
2016-11-29
6.98000
12.23000
11.14000
11.18000
10.42000
19.12000
11.70000
9.86000
9.74000
7.21000
...
24.50000
10.31000
2.91000
21.25000
15.52000
16.42000
6.77
21.18000
7.61000
11.83000
2016-11-30
6.87000
11.81000
11.21000
11.53000
10.37000
18.98000
11.70000
9.69000
9.80000
7.09000
...
23.84000
10.33000
2.87000
21.41000
15.46000
16.42000
6.75
20.74000
7.46000
12.17000
2016-12-01
7.01000
11.90000
11.13000
11.50000
10.35000
19.00000
11.78000
9.81000
9.85000
7.06000
...
24.59000
10.61000
2.92000
21.40000
15.48000
16.42000
6.82
20.69000
7.52000
12.16000
2016-12-02
6.68000
11.66000
11.14000
11.16000
10.07000
18.40000
11.57000
9.65000
9.62000
7.05000
...
25.38000
10.72000
2.86000
21.43000
15.60000
16.42000
6.66
20.32000
7.34000
11.91000
2016-12-05
6.67000
11.27000
11.05000
10.92000
10.11000
18.39000
11.55000
9.61000
9.55000
7.03000
...
27.31000
10.78000
2.83000
19.29000
15.83000
16.42000
6.67
20.37000
7.30000
12.03000
2016-12-06
6.63000
11.27000
10.96000
10.88000
10.03000
18.04000
11.64000
9.77000
9.75000
6.98000
...
26.87000
10.87000
2.84000
19.09000
16.08000
16.42000
6.76
20.36000
7.32000
12.18000
2016-12-07
7.01000
11.64000
10.90000
11.03000
10.10000
18.10000
11.79000
10.08000
9.75000
7.00000
...
27.62000
10.89000
2.93000
19.18000
16.15000
16.42000
6.87
20.53000
7.39000
12.29000
2016-12-08
7.02000
11.54000
11.18000
10.88000
10.12000
17.72000
11.73000
9.89000
9.73000
7.15000
...
27.17000
11.07000
2.89000
18.99000
15.88000
16.42000
6.79
20.69000
7.31000
12.21000
2016-12-09
6.89000
11.55000
11.14000
10.82000
10.13000
17.74000
11.69000
9.69000
9.64000
7.16000
...
27.94000
11.11000
2.92000
19.08000
15.82000
16.42000
6.80
20.79000
7.19000
12.21000
2016-12-12
6.70000
10.95000
10.76000
10.16000
9.61000
17.12000
10.99000
9.06000
8.95000
6.98000
...
27.04000
10.58000
2.82000
17.82000
15.76000
16.42000
6.46
20.28000
6.84000
11.38000
2016-12-13
7.09000
11.00000
10.76000
10.23000
9.78000
17.50000
11.20000
9.40000
8.89000
6.98000
...
29.04000
10.57000
2.86000
18.01000
15.35000
16.42000
6.50
20.90000
6.78000
11.45000
2016-12-14
6.90000
10.91000
10.66000
10.18000
9.74000
17.98000
10.88000
9.24000
8.85000
6.84000
...
28.32000
11.21000
2.80000
17.75000
16.36000
16.42000
6.56
20.89000
6.78000
11.28000
2016-12-15
7.59000
11.32000
10.80000
10.24000
9.76000
18.20000
11.02000
9.26000
9.08000
7.18000
...
28.12000
11.32000
2.85000
17.74000
16.48000
16.42000
6.70
21.08000
6.82000
11.44000
2016-12-16
8.02000
11.43000
11.39000
10.35000
9.91000
18.42000
11.60000
9.38000
9.10000
7.39000
...
26.82000
11.21000
2.84000
18.17000
16.27000
16.42000
6.81
20.99000
6.90000
11.73000
2016-12-19
7.76000
11.25000
11.68000
10.23000
9.89000
18.50000
11.75000
9.40000
8.94000
7.39000
...
27.24000
11.38000
2.90000
18.06000
16.34000
16.42000
6.83
21.27000
6.90000
11.58000
2016-12-20
7.39000
11.34000
11.66000
10.27000
9.79000
18.28000
11.47000
9.22000
9.14000
7.30000
...
27.01000
11.09000
2.89000
17.92000
15.91000
16.42000
6.89
21.15000
6.85000
11.65000
2016-12-21
7.38000
11.40000
11.62000
10.34000
9.87000
18.68000
11.63000
9.24000
9.18000
7.30000
...
28.95000
11.89000
2.94000
18.11000
16.12000
16.42000
6.90
21.23000
6.84000
11.70000
2016-12-22
7.36000
11.36000
11.52000
10.29000
9.90000
18.31000
11.50000
9.76000
9.03000
7.46000
...
29.66000
12.00000
3.08000
18.09000
16.67000
16.42000
6.89
20.99000
6.83000
11.47000
2016-12-23
7.05000
11.05000
11.81000
10.17000
10.02000
18.19000
11.19000
9.49000
9.01000
7.48000
...
29.78000
11.71000
3.08000
17.86000
16.85000
16.42000
6.79
20.64000
6.77000
11.34000
2016-12-26
6.91000
11.20000
11.73000
10.27000
10.17000
18.35000
11.32000
9.60000
9.00000
7.47000
...
29.97000
11.77000
3.01000
17.88000
16.73000
16.42000
6.88
20.58000
6.84000
11.59000
2016-12-27
6.89000
11.20000
11.76000
10.27000
10.25000
18.40000
11.46000
9.64000
9.07000
7.67000
...
29.40000
11.77000
2.99000
17.80000
16.78000
16.42000
6.98
20.77000
6.79000
11.51000
2016-12-28
6.79000
11.20000
11.48000
10.18000
10.18000
18.50000
11.47000
9.51000
9.10000
7.57000
...
29.35000
12.15000
2.98000
17.63000
16.63000
16.42000
7.09
20.89000
6.80000
11.47000
2016-12-29
6.84000
11.34000
11.41000
10.25000
10.23000
18.54000
11.52000
9.53000
8.96000
7.40000
...
28.65000
11.97000
3.10000
18.08000
16.63000
16.42000
7.00
21.00000
6.94000
11.37000
2016-12-30
6.77000
11.32000
11.29000
10.23000
10.24000
18.24000
11.65000
9.35000
8.86000
7.42000
...
27.61000
11.90000
3.10000
17.96000
16.63000
16.42000
7.08
22.00000
7.12000
11.32000
1214 rows × 1071 columns
In [108]:
def f(series, i, l):
s = rc.iloc[(i*l):((i+1)*l)]
try:
return s.loc[[s.idxmax()]]
except:
return None
In [109]:
len(PriceDF.iloc[:,0])
Out[109]:
1214
In [110]:
%timeit a = pd.concat([f(PriceDF.iloc[:,0], i, 20) for i in range(len(PriceDF.iloc[:,0]))])
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-110-d690aed19933> in <module>()
----> 1 get_ipython().magic(u'timeit a = pd.concat([f(PriceDF.iloc[:,0], i, 20) for i in range(len(PriceDF.iloc[:,0]))])')
c:\python27\lib\site-packages\IPython\core\interactiveshell.pyc in magic(self, arg_s)
2156 magic_name, _, magic_arg_s = arg_s.partition(' ')
2157 magic_name = magic_name.lstrip(prefilter.ESC_MAGIC)
-> 2158 return self.run_line_magic(magic_name, magic_arg_s)
2159
2160 #-------------------------------------------------------------------------
c:\python27\lib\site-packages\IPython\core\interactiveshell.pyc in run_line_magic(self, magic_name, line)
2077 kwargs['local_ns'] = sys._getframe(stack_depth).f_locals
2078 with self.builtin_trap:
-> 2079 result = fn(*args,**kwargs)
2080 return result
2081
<decorator-gen-59> in timeit(self, line, cell)
c:\python27\lib\site-packages\IPython\core\magic.pyc in <lambda>(f, *a, **k)
186 # but it's overkill for just that one bit of state.
187 def magic_deco(arg):
--> 188 call = lambda f, *a, **k: f(*a, **k)
189
190 if callable(arg):
c:\python27\lib\site-packages\IPython\core\magics\execution.pyc in timeit(self, line, cell)
1047 number = 1
1048 for _ in range(1, 10):
-> 1049 time_number = timer.timeit(number)
1050 worst_tuning = max(worst_tuning, time_number / number)
1051 if time_number >= 0.2:
c:\python27\lib\site-packages\IPython\core\magics\execution.pyc in timeit(self, number)
137 gc.disable()
138 try:
--> 139 timing = self.inner(it, self.timer)
140 finally:
141 if gcold:
<magic-timeit> in inner(_it, _timer)
<ipython-input-108-6d1e4e9c5e78> in f(series, i, l)
1 def f(series, i, l):
----> 2 s = rc.iloc[(i*l):((i+1)*l)]
3 try:
4 return s.loc[[s.idxmax()]]
5 except:
NameError: global name 'rc' is not defined
In [1]:
from __future__ import print_function
import sys
print(sys.argv, len(sys.argv))
['c:\\python27\\lib\\site-packages\\ipykernel\\__main__.py', '-f', 'C:\\Users\\LZJF_02\\AppData\\Roaming\\jupyter\\runtime\\kernel-edbf3667-ffdf-4939-b13f-716e7e28e5bb.json'] 3
In [3]:
import requests
from bs4 import BeautifulSoup
In [69]:
url = 'http://gs.amac.org.cn/amac-infodisc/res/pof/manager/managerList.html'
r = requests.get(url)
r.headers['content-type']
Out[69]:
'text/html'
In [81]:
url = 'http://gs.amac.org.cn/amac-infodisc/res/pof/manager/managerList.html'
r = requests.get(url)
r.encoding = 'utf-8'
html_doc = BeautifulSoup(r.text,"html")
In [82]:
html_doc
Out[82]:
<!DOCTYPE html>\n<html><head lang="en">\n <meta charset="unicode-escape"/>\n <title>\u79c1\u52df\u57fa\u91d1\u7ba1\u7406\u4eba\u5206\u7c7b\u516c\u793a - \u4e2d\u56fd\u57fa\u91d1\u4e1a\u534f\u4f1a</title>\n <link href="../../common/css/reset.css" rel="stylesheet" type="text/css"/>\n <link href="../../common/css/unit.css" rel="stylesheet" type="text/css"/>\n <link href="../../common/css/grid.css" rel="stylesheet" type="text/css"/>\n <link href="../../common/css/global.css" rel="stylesheet" type="text/css"/>\n <link href="../../common/css/module.css" rel="stylesheet" type="text/css"/>\n <link href="../../lib/dataTable/css/jquery.dataTables.css" rel="stylesheet" type="text/css"/>\n <style type="text/css">\n .w100 {\n width: 100px;\n }\n\n .controls ul li {\n cursor: pointer;\n line-height: 180%;\n }\n\n .controls ul li:hover {\n background: #eee;\n }\n\n #controls div {\n display: inline-block;\n }\n\n #controls div {\n *display: inline;\n }\n\n .a1 {\n color: #173bf4;\n font-size: 1.1em;\n }\n\n #aa table tr td {\n width: 30%;\n }\n\n .ui-dialog .ui-dialog-buttonpane {\n height: 40px;\n }\n\n .controls a:hover {\n text-decoration: none !important;\n background-color: #dbdbdb;\n }\n\n .active:hover {\n background-color: #c3d7f3 !important;\n }\n\n #aa a {\n color: blue;\n text-decoration: none !important;\n }\n\n #managerList_length {\n margin-left: 10px;\n margin-top: 6px;\n }\n\n #managerList_length select {\n width: 70px;\n }\n </style>\n <script language="javascript" src="../../lib/dataTable/js/jquery.js" type="text/javascript"></script>\n <script language="javascript" src="../../lib/jquery/plugins/jquery-ui-1.11.4/external/jquery/jquery.js" type="text/javascript"></script>\n <script language="javascript" src="../../lib/dataTable/js/jquery.dataTables.js" type="text/javascript"></script>\n <script language="javascript" src="../../lib/dataTable/js/ellipses.js" type="text/javascript"></script>\n <!--\n <script type="text/javascript" language="javascript" src="../../../lib/dataTable/js/shCore.js"></script>\n -->\n <link href="../../lib/jquery/plugins/artDialog4/skins/blue.css" rel="stylesheet" type="text/css"/>\n <script language="javascript" src="../../lib/jquery/plugins/artDialog4/artDialog.js" type="text/javascript"></script>\n <script language="javascript" src="../../lib/jquery/plugins/artDialog4/jquery.artDialog.js" type="text/javascript"></script>\n <script language="javascript" src="../../lib/jquery/plugins/artDialog4/plugins/iframeTools.js" type="text/javascript"></script>\n <script language="javascript" src="../../lib/jquery/plugins/jquery-ui-1.11.4/jquery-ui.js" type="text/javascript"></script>\n <link href="../../lib/jquery/plugins/jquery-ui-1.11.4/jquery-ui.css" rel="stylesheet" type="text/css"/>\n <script language="javascript" src="../../lib/datepicker/WdatePicker.js" type="text/javascript"></script>\n <script language="javascript" src="../../common/js/xdate.js" type="text/javascript"></script>\n <!--[if lt IE 8]>\n <script type="text/javascript" language="javascript" src="../../common/js/json2.js"></script>\n <![endif]-->\n\n <script language="javascript" src="js/manager_list.js" type="text/javascript"></script>\n</head>\n<body class="g-container">\n<div>\n <!-- header start -->\n <div class="g-header clearfix">\n <a class="logo pull-left" href="/"><img alt="\u4e2d\u56fd\u8bc1\u5238\u6295\u8d44\u57fa\u91d1\u4e1a\u534f\u4f1a" src="../../common/images/logo.png"/></a>\n <div class="info pull-right">\n <span id="IE67" style="display:none;"><span class="fontSpace">\u7ec4\u7ec7\u673a\u6784\u4ee3\u7801</span>\uff1a71783348<br/></span>\n <span id="otherBrowser"><span class="fontSpace">\u7ec4\u7ec7\u673a\u6784\u4ee3</span>\u7801\uff1a71783348<br/></span>\n \u793e\u4f1a\u56e2\u4f53\u6cd5\u4eba\u767b\u8bb0\u8bc1\u4e66\uff1a\u793e\u8bc1\u5b57\u7b2c4951\u53f7\n </div>\n </div>\n <!-- header end -->\n\n <!-- body start -->\n <div class="g-body">\n <!-- \u79c1\u52df\u57fa\u91d1\u7ba1\u7406\u4eba\u516c\u793a start -->\n <div class="m-manager-list">\n <h3 class="text-center font-xbig text-bold mb30">\u79c1\u52df\u57fa\u91d1\u7ba1\u7406\u4eba\u5206\u7c7b\u516c\u793a</h3>\n <p class="mb15"><a class="text-danger ml15" href="#" onclick="openAMACInfo();"><\n <\u4e2d\u56fd\u8bc1\u5238\u6295\u8d44\u57fa\u91d1\u4e1a\u534f\u4f1a\u63d0\u793a>>\n </a></p>\n <div id="tooggleAMACInfo" style="display:none;">\n\n </div>\n\n <!-- \u67e5\u8be2\u6761\u4ef6\u533a\u57df start -->\n <div class="m-search-bar clearfix mb15">\n <!-- <a href="javascript:openMap();" class="btn-map">\u6309\u5730\u56fe\u67e5\u8be2</a> -->\n <form action="" class="form-horizontal" name="manager-list-form">\n <input id="fundType" type="hidden"/>\n <input id="remindItem" type="hidden"/>\n <input id="faithInfo" type="hidden"/>\n <input id="fundScaleFrom" type="hidden"/>\n <input id="fundScaleTo" type="hidden"/>\n <div class="control-group">\n <div class="control-label w100"><img onmouseout="hide(this,'fundTypeHint')" onmouseover="javascript:show(this,'fundTypeHint');" src="../../common/images/icon_tips.jpg" style="vertical-align:middle;"/>\u57fa\u91d1\u89c4\u6a21\uff1a\n </div>\n <div class="m-modal" id="fundTypeHint" style="display:none;position:absolute;width:650px;">\n <div class="modal-title">\u57fa\u91d1\u89c4\u6a21</div>\n <div class="modal-content">\n <p>1\u3001 \u5404\u533a\u95f4\u5185\u79c1\u52df\u57fa\u91d1\u7ba1\u7406\u4eba\u4ee5\u5728\u4e2d\u56fd\u8bc1\u5238\u6295\u8d44\u57fa\u91d1\u4e1a\u534f\u4f1a\u7684\u767b\u8bb0\u65f6\u95f4\u987a\u5e8f\u8fdb\u884c\u6392\u5217\uff1b</p>\n <p>2\u3001 \u57fa\u91d1\u89c4\u6a21\u4ee5\u7ba1\u7406\u4eba\u5728\u767b\u8bb0\u5907\u6848\u7cfb\u7edf\u4e2d\u586b\u62a5\u7684\u8ba4\u7f34\u89c4\u6a21\u4e3a\u51c6\uff1b</p>\n <p>3\u3001 \u57fa\u91d1\u6307\u6b63\u5728\u8fd0\u4f5c\u7684\u57fa\u91d1\uff0c\u4e0d\u5305\u62ec\u5df2\u7ecf\u6e05\u76d8\u7684\u57fa\u91d1\uff1b</p>\n <p>4\u3001 \u6839\u636e\u79c1\u52df\u57fa\u91d1\u7ba1\u7406\u4eba\u6240\u7ba1\u7406\u57fa\u91d1\u7684\u4e0d\u540c\u7c7b\u578b\uff0c\u89c4\u6a21\u7c7b\u516c\u793a\u9009\u53d6\u4e0d\u540c\u7684\u5212\u5206\u6807\u51c6\uff0c\u7ba1\u7406\u89c4\u6a21\u5728\u533a\u95f4\u4e0b\u9650\u4ee5\u4e0b\u7684\u7ba1\u7406\u4eba\u4e0d\u5217\u5165\u5206\u7c7b\u516c\u793a\u8303\u56f4\uff1b</p>\n <p>5\u3001 \u7ba1\u7406\u4eba\u7ba1\u7406\u4e0d\u540c\u7c7b\u578b\u79c1\u52df\u57fa\u91d1\u7684\uff0c\u6839\u636e\u6240\u7ba1\u7406\u7684\u4e0d\u540c\u7c7b\u578b\u57fa\u91d1\u89c4\u6a21\u5206\u522b\u8fdb\u884c\u7edf\u8ba1\uff1b</p>\n <p>6\u3001 \u5176\u4ed6\u79c1\u52df\u57fa\u91d1\u4e3b\u8981\u662f\u6307\u4ece\u4e8b\u827a\u672f\u54c1\u3001\u5546\u54c1\u7b49\u6295\u8d44\u7684\u79c1\u52df\u57fa\u91d1\uff1b </p>\n <p>7\u3001 \u533a\u95f4\u5212\u5206\u6807\u51c6\u5c06\u6839\u636e\u884c\u4e1a\u53d1\u5c55\u60c5\u51b5\u8fdb\u884c\u52a8\u6001\u8c03\u6574 \u3002</p>\n </div>\n </div>\n <div class="controls" id="controls">\n <div onmouseout="hide(this,'showFundScale1')" onmouseover="javascript:chooseFundType(this,'showFundScale1');" value="\u79c1\u52df\u8bc1\u5238\u81ea\u4e3b\u53d1\u884c">\n <a class="btn-link mr10" href="javascript:void(0);" onclick="removeFundType(this);" value="\u79c1\u52df\u8bc1\u5238\u57fa\u91d1(\u81ea\u4e3b\u53d1\u884c)">\u79c1\u52df\u8bc1\u5238\u57fa\u91d1(\u81ea\u4e3b\u53d1\u884c)</a>\n <ul id="showFundScale1" style="display:none;background-color:rgb(222,230,248);position:absolute;">\n <li onclick="javascript:chooseFundScale(this,'','');">\u5168\u90e8</li>\n <li onclick="javascript:chooseFundScale(this,'500000','');">50 \u4ebf\u4ee5\u4e0a</li>\n <li onclick="javascript:chooseFundScale(this,'200000','500000');">20-50 \u4ebf</li>\n <li onclick="javascript:chooseFundScale(this,'100000','200000');">10-20 \u4ebf</li>\n <li onclick="javascript:chooseFundScale(this,'10000','100000');">1-10 \u4ebf</li>\n <li onclick="javascript:chooseFundScale(this,'0','10000');">0-1 \u4ebf</li>\n </ul>\n </div>\n\n <div onmouseout="hide(this,'showFundScale2')" onmouseover="javascript:chooseFundType(this,'showFundScale2');" value="\u79c1\u52df\u8bc1\u5238\u987e\u95ee\u7ba1\u7406">\n <a class="btn-link mr10" href="javascript:void(0);" onclick="removeFundType(this);" value="\u79c1\u52df\u8bc1\u5238\u57fa\u91d1(\u987e\u95ee\u7ba1\u7406)">\u79c1\u52df\u8bc1\u5238\u57fa\u91d1(\u987e\u95ee\u7ba1\u7406)</a>\n <ul id="showFundScale2" style="display:none;background-color:rgb(222,230,248);position:absolute;">\n <li onclick="javascript:chooseFundScale(this,'','');">\u5168\u90e8</li>\n <li onclick="javascript:chooseFundScale(this,'500000','');">50 \u4ebf\u4ee5\u4e0a</li>\n <li onclick="javascript:chooseFundScale(this,'200000','500000');">20-50 \u4ebf</li>\n <li onclick="javascript:chooseFundScale(this,'100000','200000');">10-20 \u4ebf</li>\n <li onclick="javascript:chooseFundScale(this,'10000','100000');">1-10 \u4ebf</li>\n <li onclick="javascript:chooseFundScale(this,'0','10000');">0-1 \u4ebf</li>\n </ul>\n\n </div>\n\n <div onmouseout="hide(this,'showFundScale3')" onmouseover="javascript:chooseFundType(this,'showFundScale3');" value="\u79c1\u52df\u80a1\u6743">\n <a class="btn-link mr10" href="javascript:void(0);" onclick="removeFundType(this);" value="\u79c1\u52df\u80a1\u6743\u57fa\u91d1">\u79c1\u52df\u80a1\u6743\u57fa\u91d1</a>\n <ul id="showFundScale3" style="display:none;background-color:rgb(222,230,248);position:absolute;">\n <li onclick="javascript:chooseFundScale(this,'','');">\u5168\u90e8</li>\n <li onclick="javascript:chooseFundScale(this,'1000000','');">100 \u4ebf\u4ee5\u4e0a</li>\n <li onclick="javascript:chooseFundScale(this,'500000','1000000');">50-100 \u4ebf</li>\n <li onclick="javascript:chooseFundScale(this,'200000','500000');">20-50 \u4ebf</li>\n <li onclick="javascript:chooseFundScale(this,'0','200000');">0-20 \u4ebf</li>\n </ul>\n </div>\n\n <div onmouseout="hide(this,'showFundScale4')" onmouseover="javascript:chooseFundType(this,'showFundScale4');" value="\u521b\u4e1a\u6295\u8d44">\n <a class="btn-link mr10" href="javascript:void(0);" onclick="removeFundType(this);" value="\u521b\u4e1a\u6295\u8d44\u57fa\u91d1">\u521b\u4e1a\u6295\u8d44\u57fa\u91d1</a>\n <ul id="showFundScale4" style="display:none;background-color:rgb(222,230,248);position:absolute;">\n <li onclick="javascript:chooseFundScale(this,'','');">\u5168\u90e8</li>\n <li onclick="javascript:chooseFundScale(this,'100000','');">10 \u4ebf\u4ee5\u4e0a</li>\n <li onclick="javascript:chooseFundScale(this,'50000','100000');">5-10 \u4ebf</li>\n <li onclick="javascript:chooseFundScale(this,'20000','50000');">2-5 \u4ebf</li>\n <li onclick="javascript:chooseFundScale(this,'0','20000');">0-2 \u4ebf</li>\n </ul>\n </div>\n\n <div onmouseout="hide(this,'showFundScale5')" onmouseover="javascript:chooseFundType(this,'showFundScale5');" value="\u5176\u5b83\u79c1\u52df">\n <a class="btn-link mr10" href="javascript:void(0);" onclick="removeFundType(this);" value="\u5176\u4ed6\u79c1\u52df\u57fa\u91d1">\u5176\u4ed6\u79c1\u52df\u57fa\u91d1</a>\n <ul id="showFundScale5" style="display:none;background-color:rgb(222,230,248);position:absolute;">\n <li onclick="javascript:chooseFundScale(this,'','');">\u5168\u90e8</li>\n <li onclick="javascript:chooseFundScale(this,'100000','');">10 \u4ebf\u4ee5\u4e0a</li>\n <li onclick="javascript:chooseFundScale(this,'50000','100000');">5-10 \u4ebf</li>\n <li onclick="javascript:chooseFundScale(this,'20000','50000');">2-5 \u4ebf</li>\n <li onclick="javascript:chooseFundScale(this,'0','20000');">0-2 \u4ebf</li>\n </ul>\n </div>\n </div>\n </div>\n\n <div class="control-group">\n <div class="control-label w100">\u63d0\u793a\u4e8b\u9879\uff1a</div>\n <div id="itemHint" style="display:none;position:absolute;solid:silver;background-color:#CCCCCC">\n \u8bf4\u660e\uff1a\u6b64\u7c7b\u516c\u793a\u4ee5\u79c1\u52df\u57fa\u91d1\u7ba1\u7406\u4eba\u6210\u7acb\u65f6\u95f4\u5148\u540e\u987a\u5e8f\u8fdb\u884c\u6392\u5217\n </div>\n\n <div class="controls">\n <a class="btn-link mr10" href="javascript:void(0);" onclick="javascript:chooseRemindItem(this);" value="manageScalaZero">\u7ba1\u7406\u89c4\u6a21\u4e3a\u96f6</a>\n <a class="btn-link mr10" href="javascript:void(0);" onclick="javascript:chooseRemindItem(this);" value="oneYearManageScalaZero">\u767b\u8bb0\u4e00\u5e74\u4ee5\u4e0a\u7ba1\u7406\u89c4\u6a21\u4e3a\u96f6</a>\n <a class="btn-link mr10" href="javascript:void(0);" onclick="javascript:chooseRemindItem(this);" value="handInUnderRegister25P">\u7ba1\u7406\u4eba\u5b9e\u7f34\u8d44\u672c\u4f4e\u4e8e\u6ce8\u518c\u8d44\u672c25%</a>\n <a class="btn-link mr10" href="javascript:void(0);" onclick="javascript:chooseRemindItem(this);" value="handInLe100W">\u7ba1\u7406\u4eba\u5b9e\u7f34\u8d44\u672c\u4f4e\u4e8e100\u4e07</a>\n <a class="btn-link mr10" href="javascript:void(0);" onclick="javascript:chooseRemindItem(this);" value="notNormalLiquidation">\u975e\u6b63\u5e38\u6e05\u7b97</a>\n </div>\n </div>\n\n <div class="control-group">\n <div class="control-label w100"><img onmouseout="hide(this,'faithInfoHint')" onmouseover="javascript:show(this,'faithInfoHint');" src="../../common/images/icon_tips.jpg" style="vertical-align:middle;"/>\u8bda\u4fe1\u4fe1\u606f\uff1a\n </div>\n <div class="m-modal" id="faithInfoHint" style="display:none;position:absolute;width:700px;z-index:10000;">\n <div class="modal-title">\u8bda\u4fe1\u4fe1\u606f</div>\n <div class="modal-content">\n <p>1\u3001\u6b64\u7c7b\u516c\u793a\u4ee5\u79c1\u52df\u57fa\u91d1\u7ba1\u7406\u4eba\u5728\u4e2d\u56fd\u8bc1\u5238\u6295\u8d44\u57fa\u91d1\u4e1a\u534f\u4f1a\u7684\u767b\u8bb0\u65f6\u95f4\u987a\u5e8f\u8fdb\u884c\u6392\u5217\uff1b</p>\n <p>\n 2\u3001\u4e0d\u826f\u8bda\u4fe1\u8bb0\u5f55\u4e3b\u8981\u5305\u62ec\u79c1\u52df\u57fa\u91d1\u7ba1\u7406\u4eba\u3001\u5b9e\u9645\u63a7\u5236\u4eba\u3001\u76f8\u5173\u9ad8\u7ba1\u4eba\u5458\u53d7\u5230\u4e2d\u56fd\u8bc1\u76d1\u4f1a\u884c\u653f\u5904\u7f5a\u6216\u8005\u4e2d\u56fd\u8bc1\u5238\u6295\u8d44\u57fa\u91d1\u4e1a\u534f\u4f1a\u7eaa\u5f8b\u5904\u5206\uff0c\u672c\u516c\u793a\u63d0\u4f9b\u8bc1\u76d1\u4f1a\u4fe1\u606f\u516c\u5f00\u548c\u4e2d\u56fd\u8bc1\u5238\u6295\u8d44\u57fa\u91d1\u4e1a\u534f\u4f1a\u7eaa\u5f8b\u5904\u5206\u7684\u67e5\u8be2\u94fe\u63a5\uff1b</p>\n <p>3\u3001\u5217\u5165\u865a\u5047\u586b\u62a5\u3001\u91cd\u5927\u9057\u6f0f\u3001\u8fdd\u53cd\u4e09\u6761\u5e95\u7ebf\u7684\u79c1\u52df\u57fa\u91d1\u7ba1\u7406\u4eba\uff0c\u5982\u5df2\u7ecf\u5b8c\u6210\u6574\u6539\uff0c\u5219\u4e0d\u518d\u5217\u5165\u6b64\u7c7b\u516c\u793a\u3002</p>\n </div>\n </div>\n <div class="controls">\n <a class="btn-link mr10" href="javascript:void(0);" onclick="javascript:chooseFaithInfo(this);" value="isLostContactMechanism">\u5931\u8054\u673a\u6784</a>\n <a class="btn-link mr10" href="javascript:void(0);" onclick="javascript:chooseFaithInfo(this);" value="identification">\u5f02\u5e38\u673a\u6784</a>\n <a class="btn-link mr10" href="javascript:void(0);" onclick="javascript:chooseFaithInfo(this);" value="shamStatement">\u865a\u5047\u586b\u62a5</a>\n <a class="btn-link mr10" href="javascript:void(0);" onclick="javascript:chooseFaithInfo(this);" value="materRialOmission">\u91cd\u5927\u9057\u6f0f</a>\n <a class="btn-link mr10" href="javascript:void(0);" onclick="javascript:chooseFaithInfo(this);" value="eightLine">\u8fdd\u53cd\u516b\u6761\u5e95\u7ebf</a>\n <a class="btn-link mr10" href="javascript:void(0);" onclick="javascript:chooseFaithInfo(this);" value="badCredit">\u76f8\u5173\u4e3b\u4f53\u5b58\u5728\u4e0d\u826f\u8bda\u4fe1\u8bb0\u5f55</a>\n </div>\n </div>\n\n <div id="foldArea" style="display:none;">\n <div class="control-group">\n <div class="control-label w100">\u6210\u7acb\u65f6\u95f4\uff1a</div>\n <div class="controls">\n <a class="btn-link mr10" href="javascript:void(0);" onclick="javascript:choosePeriods(this);" value="1">\u6700\u8fd1\u4e00\u5b63\u5ea6</a>\n <a class="btn-link mr10" href="javascript:void(0);" onclick="javascript:choosePeriods(this);" value="2">\u6700\u8fd1\u4e00\u5e74</a>\n <input class="datepicker" id="foundBeginDate" onclick="WdatePicker({dateFmt:'yyyy-MM-dd'})" type="text" value=""/>\xa0-\xa0\n <input class="datepicker" id="foundEndDate" onclick="WdatePicker({dateFmt:'yyyy-MM-dd'})" type="text" value=""/>\n </div>\n </div>\n <div class="control-group">\n <div class="control-label w100">\u767b\u8bb0\u65f6\u95f4\uff1a</div>\n <div class="controls">\n <a class="btn-link mr10" href="javascript:void(0);" onclick="javascript:chooseCheckTime(this);" value="1">\u6700\u8fd1\u4e00\u6708</a>\n <a class="btn-link mr10" href="javascript:void(0);" onclick="javascript:chooseCheckTime(this);" value="2">\u6700\u8fd1\u4e00\u5b63\u5ea6</a>\n <input class="datepicker" id="checkBeginDate" onclick="WdatePicker({dateFmt:'yyyy-MM-dd'})" type="text" value=""/>\xa0-\xa0\n <input class="datepicker" id="checkEndDate" onclick="WdatePicker({dateFmt:'yyyy-MM-dd'})" type="text" value=""/>\n </div>\n </div>\n </div>\n <!-- <div style="text-align:center;top:-1px;border-top: 1px solid #ddd;padding-top:0px;">\n <span onclick="queryCollapsed(this);" style="cursor:pointer;width:100px;background:#F6F6F6;display:inline-block;margin-top:-1px;line-height:28px; text-align:center;border: 1px solid #ddd;padding:0px 10px 4px 10px;border-top:0px;">\u66f4\u591a\u9009\u9879\u2193</span>\n </div> -->\n\n <div class="control-group text-right">\n <input class="btn btn-primary" onclick="gotoManagerList();" style="padding:0px;" type="button" value="\u7efc\u5408\u67e5\u8be2"/>\n <input class="btn btn-primary" onclick="reload();" style="display:none;" type="button" value="\u67e5\u8be2"/>\n <input class="btn btn-clear ml10 mr40" id="reset" style="display:none;" type="button" value="\u6e05\u7a7a\u6761\u4ef6"/>\n </div>\n </form>\n </div>\n <!-- \u67e5\u8be2\u6761\u4ef6\u533a\u57df end -->\n\n <!-- \u8bf4\u660e -->\n <div class="m-pager">\n <span class="label-tips text-mult pull-left text-danger">\u6ce8\uff1a\u5e26\xa0<span class="text-danger">*</span>\xa0\u53f7\u7684\u673a\u6784\u76ee\u524d\u6ca1\u6709\u6b63\u5728\u7ba1\u7406\u7684\u79c1\u52df\u57fa\u91d1</span>\n <br/>\n </div>\n <!-- \u6570\u636e\u8868\u683c start -->\n <table class="table table-center" id="managerList">\n <colgroup>\n <col width="7%"/>\n <col width="27%"/>\n <col width="18%"/>\n <col width="14%"/>\n <col width="12%"/>\n <col width="12%"/>\n <col width="12%"/>\n </colgroup>\n <thead>\n <tr>\n <th>\u7f16\u53f7</th>\n <th>\u79c1\u52df\u57fa\u91d1\u7ba1\u7406\u4eba\u540d\u79f0</th>\n <th>\u6cd5\u5b9a\u4ee3\u8868\u4eba/\u6267\u884c\u4e8b\u52a1\u5408\u4f19\u4eba(\u59d4\u6d3e\u4ee3\u8868)\u59d3\u540d</th>\n <th>\u6ce8\u518c\u5730</th>\n <th>\u767b\u8bb0\u7f16\u53f7</th>\n <th>\u6210\u7acb\u65f6\u95f4</th>\n <th>\u767b\u8bb0\u65f6\u95f4</th>\n </tr>\n </thead>\n </table>\n </div>\n <!-- \u6570\u636e\u8868\u683c end -->\n </div>\n <!-- \u79c1\u52df\u57fa\u91d1\u7ba1\u7406\u4eba\u516c\u793a end -->\n\n</div>\n<!-- body end -->\n<div align="center" class="g-body" id="dialog-title" style="display:none" title="\u4e2d\u56fd\u8bc1\u5238\u6295\u8d44\u57fa\u91d1\u4e1a\u534f\u4f1a\u63d0\u793a">\n <!-- \u79c1\u52df\u57fa\u91d1\u7ba1\u7406\u4eba\u516c\u793a start -->\n <div class="m-manager-list">\n <br/>\n <div id="noticeInfo">\n <p class="mb15" style="text-align: left;">\xa0\xa0\xa0\xa0\xa0\xa0\u79c1\u52df\u57fa\u91d1\u7ba1\u7406\u4eba\u89c4\u6a21\u7c7b\u3001\u63d0\u793a\u7c7b\u516c\u793a\u4fe1\u606f\u5747\u6765\u6e90\u4e8e\u4e2d\u56fd\u8bc1\u5238\u6295\u8d44\u57fa\u91d1\u4e1a\u534f\u4f1a\u5df2\u767b\u8bb0\u79c1\u52df\u57fa\u91d1\u7ba1\u7406\u4eba\u63d0\u4f9b\u7684\u4fe1\u606f\uff0c\u57fa\u91d1\u7ba1\u7406\u4eba\u5df2\u4e66\u9762\u627f\u8bfa\u6240\u6709\u586b\u62a5\u4fe1\u606f\u771f\u5b9e\u3001\u51c6\u786e\u3001\u5b8c\u6574\uff0c\u5e76\u627f\u8bfa\u627f\u62c5\u6240\u6709\u76f8\u5173\u6cd5\u5f8b\u8d23\u4efb\u3002</p>\n <p class="mb15" style="text-align: left;">\xa0\xa0\xa0\xa0\xa0\xa0\u4e2d\u56fd\u8bc1\u5238\u6295\u8d44\u57fa\u91d1\u4e1a\u534f\u4f1a\u5bf9\u79c1\u52df\u57fa\u91d1\u767b\u8bb0\u5907\u6848\u4fe1\u606f\u4e0d\u505a\u5b9e\u8d28\u6027\u4e8b\u524d\u5ba1\u67e5\u3002\u516c\u793a\u4fe1\u606f\u4e0d\u6784\u6210\u5bf9\u79c1\u52df\u57fa\u91d1\u7ba1\u7406\u4eba\u6295\u8d44\u7ba1\u7406\u80fd\u529b\u3001\u6301\u7eed\u5408\u89c4\u60c5\u51b5\u7684\u8ba4\u53ef\uff0c\u4e0d\u4f5c\u4e3a\u57fa\u91d1\u8d44\u4ea7\u5b89\u5168\u7684\u4fdd\u8bc1\u3002</p>\n <p class="mb15" style="text-align: left;">\xa0\xa0\xa0\xa0\xa0\xa0\u6295\u8d44\u8005\u8fdb\u884c\u79c1\u52df\u57fa\u91d1\u6295\u8d44\u65f6\u987b\u8c28\u614e\u5224\u65ad\u548c\u8bc6\u522b\u98ce\u9669\u3002</p>\n <p class="mb15" style="text-align: left;">\xa0\xa0\xa0\xa0\xa0\xa0\u793e\u4f1a\u516c\u4f17\u82e5\u53d1\u73b0\u79c1\u52df\u57fa\u91d1\u7ba1\u7406\u4eba\u5206\u7c7b\u516c\u793a\u4fe1\u606f\u4e2d\u5b58\u5728\u4efb\u4f55\u865a\u5047\u6216\u4e0d\u5b9e\u5185\u5bb9\uff0c\u8bf7\u5c06\u76f8\u5173\u5b9e\u540d\u4e3e\u62a5\u6216\u6295\u8bc9\u60c5\u51b5\u53d1\u9001\u81f3\u90ae\u7bb1\uff1a<a class="a1" href="mailto:tousu@amac.org.cn" style="color: #173bf4;">tousu@amac.org.cn</a>\uff0c\u6216\u90ae\u5bc4\u81f3\u4e2d\u56fd\u8bc1\u5238\u6295\u8d44\u57fa\u91d1\u4e1a\u534f\u4f1a\uff08\u5730\u5740\uff1a<font class="a1">\u5317\u4eac\u5e02\u897f\u57ce\u533a\u91d1\u878d\u5927\u885720\u53f7\u4ea4\u901a\u94f6\u884c\u5927\u53a6B\u5ea79\u5c42\uff0c\u90ae\u7f16100033\uff0c\u8bf7\u6807\u660e\u201c\u6cd5\u5f8b\u90e8\u6536\u201d</font>\uff09\u3002\u8be6\u89c1\u534f\u4f1a\u5b98\u7f51\u201c\u6295\u8d44\u8005\u6559\u80b2\u201d\u680f\u76ee\u4e2d\u201c\u6295\u8bc9\u987b\u77e5\u201d\u3002</p>\n <p class="mb15" style="text-align: left;">\xa0\xa0\xa0\xa0\xa0\xa0\u5217\u5165\u5206\u7c7b\u516c\u793a\u7684\u79c1\u52df\u57fa\u91d1\u7ba1\u7406\u4eba\u5982\u5bf9\u5206\u7c7b\u516c\u793a\u6709\u5f02\u8bae\uff0c\u8bf7\u53d1\u51fd\u81f3\u90ae\u7bb1\uff1a<a class="a1" href="mailto:pf@amac.org.cn" style="color: #173bf4;">pf@amac.org.cn</a>\u3002</p>\n </div>\n <div id="timeShow" style="font-size:20px;font-weight:800;color:#FF9900">5 S</div>\n </div>\n</div>\n\n<!-- footer start -->\n<div class="g-footer" style="position:relative;">\u7248\u6743\u6240\u6709\uff1a\u4e2d\u56fd\u8bc1\u5238\u6295\u8d44\u57fa\u91d1\u4e1a\u534f\u4f1a \u4eacICP\u590716045718\u53f7\xa0\xa0\xa0\xa0\n \u6d4f\u89c8\u91cf\uff1a<span id="clickCount"></span>\n</div>\n<!-- footer end -->\n\n</body></html>
In [26]:
# coding=utf-8
import urllib
import urllib2
import json
#url地址
url='https://www.baidu.com/s'
#参数
values={
'ie':'UTF-8',
'wd':'test'
}
#进行参数封装
data=urllib.urlencode(values)
#组装完整url
req=urllib2.Request(url,data)
#访问完整url
response = urllib2.urlopen(req)
html = response.read()
print (html)
<!DOCTYPE html>
<!--STATUS OK-->
<html>
<head>
<meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1">
<meta http-equiv="content-type" content="text/html;charset=utf-8">
<meta content="always" name="referrer">
<title>页面不存在_百度搜索</title>
<style data-for="result">
body {color: #333; background: #fff; padding: 0; margin: 0; position: relative; min-width: 700px; font-family: arial; font-size: 12px }
p, form, ol, ul, li, dl, dt, dd, h3 {margin: 0; padding: 0; list-style: none }
input {padding-top: 0; padding-bottom: 0; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; box-sizing: border-box } img {border: none; }
.logo {width: 117px; height: 38px; cursor: pointer }
#wrapper {_zoom: 1 }
#head {padding-left: 35px; margin-bottom: 20px; width: 900px }
.fm {clear: both; position: relative; z-index: 297 }
.btn, #more {font-size: 14px }
.s_btn {width: 95px; height: 32px; padding-top: 2px\9; font-size: 14px; padding: 0; background-color: #ddd; background-position: 0 -48px; border: 0; cursor: pointer }
.s_btn_h {background-position: -240px -48px }
.s_btn_wr {width: 97px; height: 34px; display: inline-block; background-position: -120px -48px; *position: relative; z-index: 0; vertical-align: top }
#foot {}
#foot span {color: #666 }
.s_ipt_wr {height: 32px }
.s_form:after, .s_tab:after {content: "."; display: block; height: 0; clear: both; visibility: hidden }
.s_form {zoom: 1; height: 55px; padding: 0 0 0 10px }
#result_logo {float: left; margin: 7px 0 0 }
#result_logo img {width: 101px }
#head {padding: 0; margin: 0; width: 100%; position: absolute; z-index: 301; min-width: 1000px; background: #fff; border-bottom: 1px solid #ebebeb; position: fixed; _position: absolute; -webkit-transform: translateZ(0) }
#head .head_wrapper {_width: 1000px }
#head.s_down {box-shadow: 0 0 5px #888 }
.fm {clear: none; float: left; margin: 11px 0 0 10px }
#s_tab {background: #f8f8f8; line-height: 36px; height: 38px; padding: 55px 0 0 121px; float: none; zoom: 1 }
#s_tab a, #s_tab b {width: 54px; display: inline-block; text-decoration: none; text-align: center; color: #666; font-size: 14px }
#s_tab b {border-bottom: 2px solid #38f; font-weight: bold; color: #323232 }
#s_tab a:hover {color: #323232 }
#content_left {width: 540px; padding-left: 121px; padding-top: 5px }
.to_tieba, .to_zhidao_bottom {margin: 10px 0 0 121px }
#help {background: #f5f6f5; zoom: 1; padding: 0 0 0 50px; float: right }
#help a {color: #777; padding: 0 15px; text-decoration: none }
#help a:hover {color: #333 }
#foot {position: fixed; bottom:0; width: 100%; background: #f5f6f5; border-top: 1px solid #ebebeb; text-align: left; height: 42px; line-height: 42px; margin-top: 40px; *margin-top: 0; _position:absolute; _bottom:auto; _top:expression(eval(document.documentElement.scrollTop+document.documentElement.clientHeight-this.offsetHeight-(parseInt(this.currentStyle.marginTop,10)||0)-(parseInt(this.currentStyle.marginBottom,10)||0))); }
.content_none {padding: 45px 0 25px 121px } .s_ipt_wr.bg,
.s_btn_wr.bg, #su.bg {background-image: none }
.s_ipt_wr.bg {background: 0 }
.s_btn_wr {width: auto; height: auto; border-bottom: 1px solid transparent; *border-bottom: 0 }
.s_btn {width: 100px; height: 34px; color: white; letter-spacing: 1px; background: #3385ff; border-bottom: 1px solid #2d78f4; outline: medium; *border-bottom: 0; -webkit-appearance: none; -webkit-border-radius: 0 }
.s_btn:hover {background: #317ef3; border-bottom: 1px solid #2868c8; *border-bottom: 0; box-shadow: 1px 1px 1px #ccc }
.s_btn:active {background: #3075dc; box-shadow: inset 1px 1px 3px #2964bb; -webkit-box-shadow: inset 1px 1px 3px #2964bb; -moz-box-shadow: inset 1px 1px 3px #2964bb; -o-box-shadow: inset 1px 1px 3px #2964bb }
#lg {display: none }
#head .headBlock {margin: -5px 0 6px 121px }
#content_left .leftBlock {margin-bottom: 14px; padding-bottom: 5px; border-bottom: 1px solid #f3f3f3 }
.s_ipt_wr {border: 1px solid #b6b6b6; border-color: #7b7b7b #b6b6b6 #b6b6b6 #7b7b7b; background: #fff; display: inline-block; vertical-align: top; width: 539px; margin-right: 0; border-right-width: 0; border-color: #b8b8b8 transparent #ccc #b8b8b8; overflow: hidden }
.s_ipt_wr.ip_short {width: 439px; }
.s_ipt_wr:hover, .s_ipt_wr.ipthover {border-color: #999 transparent #b3b3b3 #999 }
.s_ipt_wr.iptfocus {border-color: #4791ff transparent #4791ff #4791ff }
.s_ipt_tip {color: #aaa; position: absolute; z-index: -10; font: 16px/22px arial; height: 32px; line-height: 32px; padding-left: 7px; overflow: hidden; width: 526px }
.s_ipt {width: 526px; height: 22px; font: 16px/18px arial; line-height: 22px\9; margin: 6px 0 0 7px; padding: 0; background: transparent; border: 0; outline: 0; -webkit-appearance: none }
#kw {position: relative;display: inline-block;}
input::-ms-clear {display: none }
/*Error page css*/
.norsSuggest {display: inline-block; color: #333; font-family: arial; font-size: 13px; position: relative; }
.norsTitle {font-size: 22px; font-family: Microsoft Yahei; font-weight: normal; color: #333; margin: 35px 0 25px 0; }
.norsTitle2 {font-family: arial; font-size: 13px; color: #666; }
.norsSuggest ol {margin-left: 47px; }
.norsSuggest li {margin: 13px 0; }
</style>
</head>
<body link="#0000cc">
<div id="wrapper" class="wrapper_l">
<div id="head">
<div class="head_wrapper">
<div class="s_form">
<div class="s_form_wrapper">
<a href="/" id="result_logo"><img src="//www.baidu.com/img/baidu_jgylogo3.gif" alt="到百度首页" title="到百度首页"></a>
<form id="form" name="f" action="/s" class="fm">
<input type="hidden" name="ie" value="utf-8">
<input type="hidden" name="f" value="8">
<input type="hidden" name="rsv_bp" value="1">
<input type="hidden" name="ch" value="">
<input type="hidden" name="tn" value="baiduerr">
<input type="hidden" name="bar" value="">
<span class="bg s_ipt_wr iptfocus">
<input id="kw" name="wd" class="s_ipt" value="" maxlength="255" autocomplete="off" autofocus>
</span><span class="bg s_btn_wr">
<input type="submit" id="su" value="百度一下" class="bg s_btn">
</span>
</form>
</div>
</div>
</div>
</div>
<div class="s_tab" id="s_tab"><b>网页</b><a href="http://news.baidu.com/ns?cl=2&rn=20&tn=news&word=" wdfield="word">新闻</a><a href="http://tieba.baidu.com/f?kw=&fr=wwwt" wdfield="kw">贴吧</a><a href="http://zhidao.baidu.com/q?ct=17&pn=0&tn=ikaslist&rn=10&word=&fr=wwwt" wdfield="word">知道</a><a href="http://music.baidu.com/search?fr=ps&ie=utf-8&key=" wdfield="key">音乐</a><a href="http://image.baidu.com/i?tn=baiduimage&ps=1&ct=201326592&lm=-1&cl=2&nc=1&ie=utf-8&word=" wdfield="word">图片</a><a href="http://v.baidu.com/v?ct=301989888&rn=20&pn=0&db=0&s=25&ie=utf-8&word=" wdfield="word">视频</a><a href="http://map.baidu.com/m?word=&fr=ps01000" wdfield="word">地图</a><a href="http://wenku.baidu.com/search?word=&lm=0&od=0&ie=utf-8" wdfield="word">文库</a><a href="//www.baidu.com/more/">更多»</a></div>
<div id="wrapper_wrapper">
<div id="content_left">
<div class="nors">
<div class="norsSuggest">
<h3 class="norsTitle">很抱歉，您要访问的页面不存在！</h3>
<p class="norsTitle2">温馨提示：</p>
<ol>
<li>请检查您访问的网址是否正确</li>
<li>如果您不能确认访问的网址，请浏览<a href="//www.baidu.com/more/index.html">百度更多</a>页面查看更多网址。</li>
<li>回到顶部重新发起搜索</li>
<li>如有任何意见或建议，请及时<a href="http://qingting.baidu.com/index">反馈给我们</a>。</li>
</ol>
</div>
</div>
</div>
</div>
<div id="foot">
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In [31]:
json.loads(response.read())
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-31-90e1349c89f5> in <module>()
----> 1 json.loads(response.read())
c:\python27\lib\json\__init__.pyc in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
337 parse_int is None and parse_float is None and
338 parse_constant is None and object_pairs_hook is None and not kw):
--> 339 return _default_decoder.decode(s)
340 if cls is None:
341 cls = JSONDecoder
c:\python27\lib\json\decoder.pyc in decode(self, s, _w)
362
363 """
--> 364 obj, end = self.raw_decode(s, idx=_w(s, 0).end())
365 end = _w(s, end).end()
366 if end != len(s):
c:\python27\lib\json\decoder.pyc in raw_decode(self, s, idx)
380 obj, end = self.scan_once(s, idx)
381 except StopIteration:
--> 382 raise ValueError("No JSON object could be decoded")
383 return obj, end
ValueError: No JSON object could be decoded
In [127]:
import requests
from bs4 import BeautifulSoup
import json
import re
# 确定测试爬虫页面
#page_index = input('Pls input the page_index you want to try:')
surl = 'http://gs.amac.org.cn/amac-infodisc/res/pof/manager/101000000138.html'
# 使用requests获取网页
r = requests.get( surl)
#content = re.sub(r'(?m)^\<!--.*\n?', '', r.content)
#content = re.sub(r'(?m)^\-->.*\n?', '', content)
soup = BeautifulSoup(r.content, 'html.parser')
#print len(soup.find_all('table'))
for table in soup.find_all('table'):
if table.findParent("table") is None:
print "\n\n", str(table)
# 使用BS提取内容
#soup = BeautifulSoup(req.content,'lxml')
# 使用json将其转换为dict
#res_json = json.loads(res)
#json.dumps(res_json, indent = 4)
<table class="table table-center table-info">
<colgroup>
<col width="25%"/>
<col width="25%"/>
<col width="25%"/>
<col width="25%"/>
</colgroup>
<tbody>
<tr>
<td class="td-title">机构诚信信息:</td>
<td class="td-content" colspan="3">
<table class="table table-center table-info">
</table>
</td>
</tr>
<tr>
<td colspan="4" style="padding: 0px 0px;text-align: left;">
<div style="background-color: #DFDFDF;padding: 8px 10px;font-weight:bold;">机构信息</div>
</td>
</tr>
<tr>
<td class="td-title">基金管理人全称(中文):</td>
<td class="td-content" colspan="2">
<div id="complaint1" style="display: none;">平安道远投资管理（上海）有限公司 </div>
<div id="complaint2">平安道远投资管理（上海）有限公司 <input id="complaint" onclick="complain();" style="border:none;background-color:#b64545;color:#FFF;width:50px;border-radius:4px;" type="button" value="投诉"/>
</div>
</td>
<td class="td-code" rowspan="3">
<div id="qrcode"></div>
</td>
</tr>
<tr>
<td class="td-title">基金管理人全称(英文):</td>
<td class="td-content" colspan="2">Ping An DaoYuan Investment Management (Shanghai) Co., Ltd.</td>
</tr>
<tr>
<td class="td-title">登记编号:</td>
<td class="td-content" colspan="2">P1000182</td>
</tr>
<tr>
<td class="td-title">组织机构代码:</td>
<td class="td-content" colspan="3">57075680-3</td>
</tr>
<tr>
<td class="td-title">登记时间:</td>
<td class="td-content">
2014-03-17
</td>
<td class="td-title">成立时间:</td>
<td class="td-content">
2011-03-15
</td>
</tr>
<tr>
<td class="td-title">注册地址:</td>
<td class="td-content" colspan="3">上海市浦东新区浦东新区张杨路707号生命人寿大厦39楼03室</td>
</tr>
<tr>
<td class="td-title">办公地址:</td>
<td class="td-content" colspan="3">上海市浦东新区浦东新区陆家嘴环路1333号平安金融大厦11楼</td>
</tr>
<tr>
<td class="td-title">注册资本(万元)(人民币):</td>
<td class="td-content">20,000</td>
<td class="td-title">实缴资本(万元)(人民币):</td>
<td class="td-content">20,000</td>
</tr>
<tr>
<td class="td-title">企业性质:</td>
<td class="td-content">内资企业</td>
<td class="td-title">注册资本实缴比例:</td>
<td class="td-content">
100%
</td>
</tr>
<tr>
<td class="td-title">管理基金主要类别:</td>
<td class="td-content">证券投资基金</td>
<td class="td-title">申请的其他业务类型:</td>
<td class="td-content">
</td>
</tr>
<tr>
<td class="td-title">员工人数:</td>
<td class="td-content">34</td>
<td class="td-title">机构网址:</td>
<td class="td-content">
<a href="javascript:void(0);" onclick="javascript:gotoWebsite('http://russell.pingan.com/index.shtml');">http://russell.pingan.com/index.shtml</a>
</td>
</tr>
<!-- 会员开始 -->
<tr>
<td colspan="4" style="padding: 0px 0px;text-align: left;">
<div style="background-color: #DFDFDF;padding: 8px 10px;font-weight:bold;">会员信息</div>
</td>
</tr>
<tr>
<td class="td-title">是否为会员:</td>
<td class="td-content" colspan="3">是</td>
</tr>
<tr>
<td class="td-title">当前会员类型:</td>
<td class="td-content">观察会员</td>
<td class="td-title">入会时间:</td>
<td class="td-content">2013-05-08</td>
</tr>
<!-- 会员结束 -->
<!-- 法律意见书信息开始 -->
<tr>
<td colspan="4" style="padding: 0px 0px;text-align: left;">
<div style="background-color: #DFDFDF;padding: 8px 10px;font-weight:bold;">法律意见书信息</div>
</td>
</tr>
<tr>
<td class="td-title">法律意见书状态:</td>
<td class="td-content" colspan="3">
</td>
</tr>
<!-- 法律意见书信息结束 -->
<tr>
<td colspan="4" style="padding: 0px 0px;text-align: left;">
<div style="background-color: #DFDFDF;padding: 8px 10px;font-weight:bold;">高管信息</div>
</td>
</tr>
<tr>
<td class="td-title">法定代表人/执行事务合伙人(委派代表)姓名:</td>
<td class="td-content" colspan="3">杨晓华</td>
</tr>
<tr>
<td class="td-title">是否有从业资格:</td>
<td class="td-content">
是
</td>
<td class="td-title">资格取得方式:</td>
<td class="td-content">资格认定</td>
</tr>
<tr>
<td class="td-title">法定代表人/执行事务合伙人(委派代表)工作履历:</td>
<td class="td-content" colspan="3">
<table class="table table-center table-noborder" style="width:80%;">
<colgroup>
<col width="30%"/>
<col width="40%"/>
<col width="30%"/>
</colgroup>
<thead>
<tr>
<th>时间</th>
<th>任职单位</th>
<th>职务</th>
</tr>
</thead>
<tbody>
<tr>
<td>2015.09 -
2017.04
</td>
<td>平安道远投资管理（上海）有限公司</td>
<td>董事长兼总经理</td>
</tr>
<tr>
<td>2010.10 -
2015.09
</td>
<td>平安罗素</td>
<td>董事长兼总经理</td>
</tr>
<tr>
<td>2009.11 -
2010.08
</td>
<td>平安香港投资管理公司</td>
<td>董事常务副总</td>
</tr>
<tr>
<td>1999.10 -
2009.10
</td>
<td>平安香港保险</td>
<td>董事总经理</td>
</tr>
<tr>
<td>1996.11 -
1999.10
</td>
<td>平安香港海外</td>
<td>负责人</td>
</tr>
<tr>
<td>1996.02 -
1996.11
</td>
<td>平安总部核赔部</td>
<td>总经理</td>
</tr>
<tr>
<td>1995.03 -
1996.02
</td>
<td>平安总部核保部</td>
<td>总经理</td>
</tr>
<tr>
<td>1992.04 -
1995.03
</td>
<td>平安总部国际部</td>
<td>总经理</td>
</tr>
<tr>
<td>1989.09 -
1992.04
</td>
<td>深圳平安公司对外业务部</td>
<td>副经理</td>
</tr>
<tr>
<td>1987.09 -
1989.09
</td>
<td>大连海运学院</td>
<td>教师</td>
</tr>
<tr>
<td>1983.09 -
1985.09
</td>
<td>大连海运学院</td>
<td>教师</td>
</tr>
</tbody>
</table>
<table class="table table-noborder table-responsive">
<tbody>
<tr>
<td>时间：2015.09 -
2017.04
</td>
</tr>
<tr>
<td>任职单位：平安道远投资管理（上海）有限公司</td>
</tr>
<tr>
<td>职务：董事长兼总经理</td>
</tr>
<tr>
<td>时间：2010.10 -
2015.09
</td>
</tr>
<tr>
<td>任职单位：平安罗素</td>
</tr>
<tr>
<td>职务：董事长兼总经理</td>
</tr>
<tr>
<td>时间：2009.11 -
2010.08
</td>
</tr>
<tr>
<td>任职单位：平安香港投资管理公司</td>
</tr>
<tr>
<td>职务：董事常务副总</td>
</tr>
<tr>
<td>时间：1999.10 -
2009.10
</td>
</tr>
<tr>
<td>任职单位：平安香港保险</td>
</tr>
<tr>
<td>职务：董事总经理</td>
</tr>
<tr>
<td>时间：1996.11 -
1999.10
</td>
</tr>
<tr>
<td>任职单位：平安香港海外</td>
</tr>
<tr>
<td>职务：负责人</td>
</tr>
<tr>
<td>时间：1996.02 -
1996.11
</td>
</tr>
<tr>
<td>任职单位：平安总部核赔部</td>
</tr>
<tr>
<td>职务：总经理</td>
</tr>
<tr>
<td>时间：1995.03 -
1996.02
</td>
</tr>
<tr>
<td>任职单位：平安总部核保部</td>
</tr>
<tr>
<td>职务：总经理</td>
</tr>
<tr>
<td>时间：1992.04 -
1995.03
</td>
</tr>
<tr>
<td>任职单位：平安总部国际部</td>
</tr>
<tr>
<td>职务：总经理</td>
</tr>
<tr>
<td>时间：1989.09 -
1992.04
</td>
</tr>
<tr>
<td>任职单位：深圳平安公司对外业务部</td>
</tr>
<tr>
<td>职务：副经理</td>
</tr>
<tr>
<td>时间：1987.09 -
1989.09
</td>
</tr>
<tr>
<td>任职单位：大连海运学院</td>
</tr>
<tr>
<td>职务：教师</td>
</tr>
<tr>
<td>时间：1983.09 -
1985.09
</td>
</tr>
<tr>
<td>任职单位：大连海运学院</td>
</tr>
<tr>
<td>职务：教师</td>
</tr>
</tbody>
</table>
</td>
</tr>
<tr>
<td class="td-title">高管情况:</td>
<td class="td-content" colspan="3">
<table class="table table-center table-noborder" style="width:80%;">
<colgroup>
<col width="30%"/>
<col width="40%"/>
<col width="30%"/>
</colgroup>
<thead>
<tr>
<th>高管姓名</th>
<th>职务</th>
<th>是否具有基金从业资格</th>
</tr>
</thead>
<tbody>
<tr>
<td>杨晓华</td>
<td>法定代表人 董事长 总经理 其他</td>
<td>
是(资格认定)
</td>
</tr>
<tr>
<td>周娟</td>
<td>合规风控 信息填报负责人 其他</td>
<td>
是(通过考试)
</td>
</tr>
<tr>
<td>张存相</td>
<td>其他</td>
<td>
是(通过考试)
</td>
</tr>
<tr>
<td>瞿立杰</td>
<td>其他</td>
<td>
是(通过考试)
</td>
</tr>
</tbody>
</table>
<table class="table table-noborder table-responsive">
<tbody>
<tr>
<td>高管姓名：杨晓华</td>
</tr>
<tr>
<td>职务：法定代表人 董事长 总经理 其他</td>
</tr>
<tr>
<td>
是否具有基金从业资格：
是(资格认定)
</td>
</tr>
<tr>
<td>高管姓名：周娟</td>
</tr>
<tr>
<td>职务：合规风控 信息填报负责人 其他</td>
</tr>
<tr>
<td>
是否具有基金从业资格：
是(通过考试)
</td>
</tr>
<tr>
<td>高管姓名：张存相</td>
</tr>
<tr>
<td>职务：其他</td>
</tr>
<tr>
<td>
是否具有基金从业资格：
是(通过考试)
</td>
</tr>
<tr>
<td>高管姓名：瞿立杰</td>
</tr>
<tr>
<td>职务：其他</td>
</tr>
<tr>
<td>
是否具有基金从业资格：
是(通过考试)
</td>
</tr>
</tbody>
</table>
</td>
</tr>
<tr>
<td colspan="4" style="padding: 0px 0px;text-align: left;">
<div style="background-color: #DFDFDF;padding: 8px 10px;font-weight:bold;">产品信息</div>
</td>
</tr>
<tr>
<td class="td-title">暂行办法实施前成立的基金:</td>
<td class="td-content" colspan="3">
<p><a href="../fund/351000131340.html">平安资产MOM1号资产管理产品</a></p>
<p><span class="fontweightB">月报：</span>应披露0条；<span class="fontweightB">季报：</span>应披露0条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露0条；</p>
<p><a href="../fund/351000128351.html">罗素投资合格投资者中国基金</a></p>
<p><span class="fontweightB">月报：</span>应披露0条；<span class="fontweightB">季报：</span>应披露0条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露0条；</p>
<p><a href="../fund/351000128367.html">平安人寿绝对收益账户</a></p>
<p><span class="fontweightB">月报：</span>应披露0条；<span class="fontweightB">季报：</span>应披露0条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露0条；</p>
<p><a href="../fund/351000128350.html">交银国信 平安罗素MOM一期多元投资集合资金信托计划</a></p>
<p><span class="fontweightB">月报：</span>应披露0条；<span class="fontweightB">季报：</span>应披露0条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露0条；</p>
<p><a href="../fund/351000144727.html">平安资产MOM2号保险资产管理产品</a></p>
<p><span class="fontweightB">月报：</span>应披露0条；<span class="fontweightB">季报：</span>应披露0条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露0条；</p>
<p><a href="../fund/351000144730.html">平安资产MOM3号保险资产管理产品</a></p>
<p><span class="fontweightB">月报：</span>应披露0条；<span class="fontweightB">季报：</span>应披露0条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露0条；</p>
</td>
</tr>
<tr>
<td class="td-title">暂行办法实施后成立的基金:</td>
<td class="td-content" colspan="3">
<p><a href="../fund/1705030955106523.html">平安道远STAR21号私募基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露0条；<span class="fontweightB">季报：</span>应披露0条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露0条；</p>
<p><a href="../fund/351000411156.html">平安道远STAR27号私募基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露7条；<span class="fontweightB">季报：</span>应披露3条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露1条；</p>
<p><a href="../fund/351000398101.html">平安道远STAR19号私募基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露7条；<span class="fontweightB">季报：</span>应披露3条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露1条；</p>
<p><a href="../fund/351000337686.html">平安道远STAR2号私募基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露6条；<span class="fontweightB">季报：</span>应披露3条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露1条；</p>
<p><a href="../fund/351000411159.html">平安道远STAR28号私募基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露8条；<span class="fontweightB">季报：</span>应披露3条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露1条；</p>
<p><a href="../fund/351000337690.html">平安道远STAR3号私募基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露8条；<span class="fontweightB">季报：</span>应披露3条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露1条；</p>
<p><a href="../fund/351000398107.html">平安道远STAR20号私募基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露7条；<span class="fontweightB">季报：</span>应披露3条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露1条；</p>
<p><a href="../fund/351000337693.html">平安道远STAR4号私募基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露8条；<span class="fontweightB">季报：</span>应披露3条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露1条；</p>
<p><a href="../fund/351000398044.html">平安道远STAR10号私募基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露8条；<span class="fontweightB">季报：</span>应披露3条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露1条；</p>
<p><a href="../fund/351000411165.html">平安道远STAR29号私募基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露0条；<span class="fontweightB">季报：</span>应披露0条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露0条；</p>
<p><a href="../fund/351000747226.html">平安道远投资管理（上海）有限公司稳盈1号私募投资基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露0条；<span class="fontweightB">季报：</span>应披露1条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露0条；</p>
<p><a href="../fund/351000221216.html">平安养老-平安罗素道远1号资产管理产品 </a></p>
<p><span class="fontweightB">月报：</span>应披露0条；<span class="fontweightB">季报：</span>应披露0条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露0条；</p>
<p><a href="../fund/351000398083.html">平安道远STAR17号私募基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露8条；<span class="fontweightB">季报：</span>应披露3条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露1条；</p>
<p><a href="../fund/351000398027.html">平安道远STAR8号私募基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露8条；<span class="fontweightB">季报：</span>应披露3条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露1条；</p>
<p><a href="../fund/351000411149.html">平安道远STAR26号私募基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露7条；<span class="fontweightB">季报：</span>应披露3条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露1条；</p>
<p><a href="../fund/351000398095.html">平安道远STAR18号私募基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露2条；<span class="fontweightB">季报：</span>应披露3条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露1条；</p>
<p><a href="../fund/351000337328.html">平安道远STAR1号私募基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露8条；<span class="fontweightB">季报：</span>应披露3条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露1条；</p>
<p><a href="../fund/351000398070.html">平安道远STAR15号私募基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露8条；<span class="fontweightB">季报：</span>应披露3条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露1条；</p>
<p><a href="../fund/351000264901.html">平安道远金锁2号基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露0条；<span class="fontweightB">季报：</span>应披露0条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露0条；</p>
<p><a href="../fund/351000398079.html">平安道远STAR16号私募基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露8条；<span class="fontweightB">季报：</span>应披露3条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露1条；</p>
<p><a href="../fund/351000398143.html">平安道远STAR25号私募基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露0条；<span class="fontweightB">季报：</span>应披露3条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露1条；</p>
<p><a href="../fund/351000337697.html">平安道远STAR5号私募基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露8条；<span class="fontweightB">季报：</span>应披露3条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露1条；</p>
<p><a href="../fund/351000144734.html">民生通惠优选1号保险资产管理产品 </a></p>
<p><span class="fontweightB">月报：</span>应披露0条；<span class="fontweightB">季报：</span>应披露0条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露0条；</p>
<p><a href="../fund/351000411170.html">平安道远STAR30号私募基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露0条；<span class="fontweightB">季报：</span>应披露0条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露0条；</p>
<p><a href="../fund/1705111733104153.html">平安道远投资管理（上海）有限公司安享1号私募投资基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露0条；<span class="fontweightB">季报：</span>应披露0条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露0条；</p>
<p><a href="../fund/351000337698.html">平安道远STAR6号私募基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露8条；<span class="fontweightB">季报：</span>应披露3条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露1条；</p>
<p><a href="../fund/351000337700.html">平安道远STAR7号私募基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露8条；<span class="fontweightB">季报：</span>应披露3条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露1条；</p>
<p><a href="../fund/351000767714.html">平安道远投资管理（上海）有限公司稳盈2号私募投资基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露0条；<span class="fontweightB">季报：</span>应披露0条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露0条；</p>
<p><a href="../fund/351000398054.html">平安道远STAR11号私募基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露8条；<span class="fontweightB">季报：</span>应披露3条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露1条；</p>
<p><a href="../fund/351000227027.html">平安道远金锁1号资产管理计划 </a></p>
<p><span class="fontweightB">月报：</span>应披露0条；<span class="fontweightB">季报：</span>应披露0条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露0条；</p>
<p><a href="../fund/351000398063.html">平安道远STAR12号私募基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露0条；<span class="fontweightB">季报：</span>应披露3条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露1条；</p>
<p><a href="../fund/351000398127.html">平安道远STAR22号私募基金 </a></p>
<p><span class="fontweightB">月报：</span>应披露0条；<span class="fontweightB">季报：</span>应披露3条；<span class="fontweightB">半年报：</span>应披露0条；<span class="fontweightB">年报：</span>应披露1条；</p>
</td>
</tr>
<tr>
<td colspan="4" style="padding: 0px 0px;text-align: left;">
<div style="background-color: #DFDFDF;padding: 8px 10px;font-weight:bold;">诚信信息</div>
</td>
</tr>
<tr>
<td class="td-title">机构信息最后更新时间:</td>
<td class="td-content" colspan="3">2017-04-28</td>
</tr>
<tr>
<td class="td-title">特别提示信息:</td>
<td class="td-content" colspan="3" id="specialInfos">
<!-- 法律意见书 -->
<!-- 法律意见书 -->
</td>
</tr>
</tbody>
</table>
In [167]:
soup.find_all('a')
Out[167]:
[<a class="logo pull-left" href="/">\n<img alt="\u4e2d\u56fd\u57fa\u91d1\u4e1a\u534f\u4f1a\u4fe1\u606f\u516c\u793a" class="img-desktop" src="../../common/images/logo.png"/>\n<img alt="\u4e2d\u56fd\u57fa\u91d1\u4e1a\u534f\u4f1a\u4fe1\u606f\u516c\u793a" class="img-responsive" src="../../common/images/logo_mobile.png"/>\n</a>,
<a class="text-danger ml15" href="#" id="comfirmMsg" onclick="openAMACInfo();"><\r\n <\u4e2d\u56fd\u8bc1\u5238\u6295\u8d44\u57fa\u91d1\u4e1a\u534f\u4f1a\u63d0\u793a>>\r\n </a>,
<a class="a1" href="mailto:pf@amac.org.cn" style="color: #173bf4;">pf@amac.org.cn</a>,
<a class="a1" href="mailto:pf@amac.org.cn" style="color: #173bf4;">pf@amac.org.cn</a>,
<a href="javascript:void(0);" onclick="javascript:gotoWebsite('http://russell.pingan.com/index.shtml');">http://russell.pingan.com/index.shtml</a>,
<a href="../fund/351000131340.html">\u5e73\u5b89\u8d44\u4ea7MOM1\u53f7\u8d44\u4ea7\u7ba1\u7406\u4ea7\u54c1</a>,
<a href="../fund/351000128351.html">\u7f57\u7d20\u6295\u8d44\u5408\u683c\u6295\u8d44\u8005\u4e2d\u56fd\u57fa\u91d1</a>,
<a href="../fund/351000128367.html">\u5e73\u5b89\u4eba\u5bff\u7edd\u5bf9\u6536\u76ca\u8d26\u6237</a>,
<a href="../fund/351000128350.html">\u4ea4\u94f6\u56fd\u4fe1 \u5e73\u5b89\u7f57\u7d20MOM\u4e00\u671f\u591a\u5143\u6295\u8d44\u96c6\u5408\u8d44\u91d1\u4fe1\u6258\u8ba1\u5212</a>,
<a href="../fund/351000144727.html">\u5e73\u5b89\u8d44\u4ea7MOM2\u53f7\u4fdd\u9669\u8d44\u4ea7\u7ba1\u7406\u4ea7\u54c1</a>,
<a href="../fund/351000144730.html">\u5e73\u5b89\u8d44\u4ea7MOM3\u53f7\u4fdd\u9669\u8d44\u4ea7\u7ba1\u7406\u4ea7\u54c1</a>,
<a href="../fund/1705030955106523.html">\u5e73\u5b89\u9053\u8fdcSTAR21\u53f7\u79c1\u52df\u57fa\u91d1 </a>,
<a href="../fund/351000411156.html">\u5e73\u5b89\u9053\u8fdcSTAR27\u53f7\u79c1\u52df\u57fa\u91d1 </a>,
<a href="../fund/351000398101.html">\u5e73\u5b89\u9053\u8fdcSTAR19\u53f7\u79c1\u52df\u57fa\u91d1 </a>,
<a href="../fund/351000337686.html">\u5e73\u5b89\u9053\u8fdcSTAR2\u53f7\u79c1\u52df\u57fa\u91d1 </a>,
<a href="../fund/351000411159.html">\u5e73\u5b89\u9053\u8fdcSTAR28\u53f7\u79c1\u52df\u57fa\u91d1 </a>,
<a href="../fund/351000337690.html">\u5e73\u5b89\u9053\u8fdcSTAR3\u53f7\u79c1\u52df\u57fa\u91d1 </a>,
<a href="../fund/351000398107.html">\u5e73\u5b89\u9053\u8fdcSTAR20\u53f7\u79c1\u52df\u57fa\u91d1 </a>,
<a href="../fund/351000337693.html">\u5e73\u5b89\u9053\u8fdcSTAR4\u53f7\u79c1\u52df\u57fa\u91d1 </a>,
<a href="../fund/351000398044.html">\u5e73\u5b89\u9053\u8fdcSTAR10\u53f7\u79c1\u52df\u57fa\u91d1 </a>,
<a href="../fund/351000411165.html">\u5e73\u5b89\u9053\u8fdcSTAR29\u53f7\u79c1\u52df\u57fa\u91d1 </a>,
<a href="../fund/351000747226.html">\u5e73\u5b89\u9053\u8fdc\u6295\u8d44\u7ba1\u7406\uff08\u4e0a\u6d77\uff09\u6709\u9650\u516c\u53f8\u7a33\u76c81\u53f7\u79c1\u52df\u6295\u8d44\u57fa\u91d1 </a>,
<a href="../fund/351000221216.html">\u5e73\u5b89\u517b\u8001-\u5e73\u5b89\u7f57\u7d20\u9053\u8fdc1\u53f7\u8d44\u4ea7\u7ba1\u7406\u4ea7\u54c1 </a>,
<a href="../fund/351000398083.html">\u5e73\u5b89\u9053\u8fdcSTAR17\u53f7\u79c1\u52df\u57fa\u91d1 </a>,
<a href="../fund/351000398027.html">\u5e73\u5b89\u9053\u8fdcSTAR8\u53f7\u79c1\u52df\u57fa\u91d1 </a>,
<a href="../fund/351000411149.html">\u5e73\u5b89\u9053\u8fdcSTAR26\u53f7\u79c1\u52df\u57fa\u91d1 </a>,
<a href="../fund/351000398095.html">\u5e73\u5b89\u9053\u8fdcSTAR18\u53f7\u79c1\u52df\u57fa\u91d1 </a>,
<a href="../fund/351000337328.html">\u5e73\u5b89\u9053\u8fdcSTAR1\u53f7\u79c1\u52df\u57fa\u91d1 </a>,
<a href="../fund/351000398070.html">\u5e73\u5b89\u9053\u8fdcSTAR15\u53f7\u79c1\u52df\u57fa\u91d1 </a>,
<a href="../fund/351000264901.html">\u5e73\u5b89\u9053\u8fdc\u91d1\u95012\u53f7\u57fa\u91d1 </a>,
<a href="../fund/351000398079.html">\u5e73\u5b89\u9053\u8fdcSTAR16\u53f7\u79c1\u52df\u57fa\u91d1 </a>,
<a href="../fund/351000398143.html">\u5e73\u5b89\u9053\u8fdcSTAR25\u53f7\u79c1\u52df\u57fa\u91d1 </a>,
<a href="../fund/351000337697.html">\u5e73\u5b89\u9053\u8fdcSTAR5\u53f7\u79c1\u52df\u57fa\u91d1 </a>,
<a href="../fund/351000144734.html">\u6c11\u751f\u901a\u60e0\u4f18\u90091\u53f7\u4fdd\u9669\u8d44\u4ea7\u7ba1\u7406\u4ea7\u54c1 </a>,
<a href="../fund/351000411170.html">\u5e73\u5b89\u9053\u8fdcSTAR30\u53f7\u79c1\u52df\u57fa\u91d1 </a>,
<a href="../fund/1705111733104153.html">\u5e73\u5b89\u9053\u8fdc\u6295\u8d44\u7ba1\u7406\uff08\u4e0a\u6d77\uff09\u6709\u9650\u516c\u53f8\u5b89\u4eab1\u53f7\u79c1\u52df\u6295\u8d44\u57fa\u91d1 </a>,
<a href="../fund/351000337698.html">\u5e73\u5b89\u9053\u8fdcSTAR6\u53f7\u79c1\u52df\u57fa\u91d1 </a>,
<a href="../fund/351000337700.html">\u5e73\u5b89\u9053\u8fdcSTAR7\u53f7\u79c1\u52df\u57fa\u91d1 </a>,
<a href="../fund/351000767714.html">\u5e73\u5b89\u9053\u8fdc\u6295\u8d44\u7ba1\u7406\uff08\u4e0a\u6d77\uff09\u6709\u9650\u516c\u53f8\u7a33\u76c82\u53f7\u79c1\u52df\u6295\u8d44\u57fa\u91d1 </a>,
<a href="../fund/351000398054.html">\u5e73\u5b89\u9053\u8fdcSTAR11\u53f7\u79c1\u52df\u57fa\u91d1 </a>,
<a href="../fund/351000227027.html">\u5e73\u5b89\u9053\u8fdc\u91d1\u95011\u53f7\u8d44\u4ea7\u7ba1\u7406\u8ba1\u5212 </a>,
<a href="../fund/351000398063.html">\u5e73\u5b89\u9053\u8fdcSTAR12\u53f7\u79c1\u52df\u57fa\u91d1 </a>,
<a href="../fund/351000398127.html">\u5e73\u5b89\u9053\u8fdcSTAR22\u53f7\u79c1\u52df\u57fa\u91d1 </a>]
In [174]:
soup.find_all('a', class_=re.compile(ur'bgwhite\st-[\u4e00-\u9fff]+'))
Out[174]:
[]
In [228]:
u'量化'
Out[228]:
u'\u91cf\u5316'
In [193]:
u'对冲'.decode('gb2312')
---------------------------------------------------------------------------
UnicodeEncodeError Traceback (most recent call last)
<ipython-input-193-85351923c68c> in <module>()
----> 1 u'对冲'.decode('gb2312')
UnicodeEncodeError: 'ascii' codec can't encode characters in position 0-1: ordinal not in range(128)
In [277]:
import requests
from bs4 import BeautifulSoup
suffix = '101000000138.html'
target = [u'量化',u'对冲']
def is_true_quant(suffix):
Flag = False
surl = 'http://gs.amac.org.cn/amac-infodisc/res/pof/manager/' + suffix
# 使用requests获取网页
r = requests.get( surl)
soup = BeautifulSoup(r.content, 'html.parser')
for item in soup.find_all('a', href=True):
if item['href'].endswith('html'):
base = soup('a',{'href':item['href']})[0].contents[0] # 返回 href后面对应的文字
print base
if target[0] in base or target[1] in base:
Flag = True
break
return Flag
In [278]:
is_true_quant(suffix)
平安资产MOM1号资产管理产品
罗素投资合格投资者中国基金
平安人寿绝对收益账户
交银国信 平安罗素MOM一期多元投资集合资金信托计划
平安资产MOM2号保险资产管理产品
平安资产MOM3号保险资产管理产品
平安道远STAR21号私募基金
平安道远STAR27号私募基金
平安道远STAR19号私募基金
平安道远STAR2号私募基金
平安道远STAR28号私募基金
平安道远STAR3号私募基金
平安道远STAR20号私募基金
平安道远STAR4号私募基金
平安道远STAR10号私募基金
平安道远STAR29号私募基金
平安道远投资管理（上海）有限公司稳盈1号私募投资基金
平安养老-平安罗素道远1号资产管理产品
平安道远STAR17号私募基金
平安道远STAR8号私募基金
平安道远STAR26号私募基金
平安道远STAR18号私募基金
平安道远STAR1号私募基金
平安道远STAR15号私募基金
平安道远金锁2号基金
平安道远STAR16号私募基金
平安道远STAR25号私募基金
平安道远STAR5号私募基金
民生通惠优选1号保险资产管理产品
平安道远STAR30号私募基金
平安道远投资管理（上海）有限公司安享1号私募投资基金
平安道远STAR6号私募基金
平安道远STAR7号私募基金
平安道远投资管理（上海）有限公司稳盈2号私募投资基金
平安道远STAR11号私募基金
平安道远金锁1号资产管理计划
平安道远STAR12号私募基金
平安道远STAR22号私募基金
Out[278]:
False
In [281]:
#soup('a',{'href':})[0].contents[0]
In [ ]:
In [282]:
fund_path = 'C:/Users/LZJF_02/Desktop/myjob/web_crawling/fundlist'
filename0 = 'zizhu1-10.csv'
filename1 = 'guwen1-10.csv'
In [337]:
data1 = pd.read_csv(fund_path+'/'+filename0,encoding ='gbk')
In [341]:
data1['type'] = u'自主'
data1['size']
In [342]:
data1.fundScale
Out[342]:
0 9.309265e+05
1 3.418153e+05
2 7.501422e+04
3 6.705248e+05
4 9.839621e+05
5 5.485847e+05
6 8.902925e+04
7 1.089406e+05
8 3.135787e+05
9 2.021778e+04
10 1.600695e+05
11 1.244000e+04
12 4.315295e+04
13 7.704000e+05
14 9.407056e+04
15 1.247753e+06
16 9.224500e+04
17 1.619245e+05
18 9.789640e+04
19 1.650072e+04
20 3.279377e+05
21 1.111600e+05
22 3.677000e+04
23 9.428028e+04
24 8.609573e+04
25 5.747272e+05
26 3.516764e+04
27 4.683099e+04
28 1.246248e+05
29 1.156022e+05
...
1190 1.996811e+04
1191 5.888500e+04
1192 1.471550e+04
1193 2.870986e+05
1194 1.325998e+04
1195 2.740000e+04
1196 3.820738e+04
1197 3.401300e+04
1198 3.020000e+04
1199 2.000050e+04
1200 1.719913e+04
1201 1.100000e+04
1202 7.480869e+04
1203 1.836200e+04
1204 1.200027e+04
1205 1.153000e+04
1206 1.905600e+04
1207 1.740746e+04
1208 1.465000e+04
1209 1.261800e+04
1210 1.912123e+04
1211 1.192374e+04
1212 1.033000e+04
1213 1.178000e+04
1214 1.042800e+04
1215 1.357800e+04
1216 1.500000e+04
1217 1.510000e+04
1218 1.484900e+04
1219 2.000000e+04
Name: fundScale, dtype: float64
In [344]:
Out[344]:
930926.5
In [331]:
city = u'上海'
In [332]:
data1['city'] = data1['managerName'].apply(lambda x : 1 if city in x else 0 )
In [333]:
data1[(data1['city'] ==1) & (data1[u'含量化或对冲'] ==True )]
Out[333]:
id
managerName
fundScale
含量化或对冲
city
type
0
101000000172
富舜资产管理（上海）有限公司
9.309265e+05
True
1
自主
2
101000000578
上海尚雅投资管理有限公司
7.501422e+04
True
1
自主
5
101000000196
上海国富投资管理有限公司
5.485847e+05
True
1
自主
6
101000000458
上海万丰友方投资管理有限公司
8.902925e+04
True
1
自主
14
101000000374
上海涌峰投资管理有限公司
9.407056e+04
True
1
自主
31
101000001316
浙商控股集团上海资产管理有限公司
2.754900e+04
True
1
自主
34
101000000694
上海游马地投资中心（有限合伙）
4.192697e+04
True
1
自主
39
101000001661
上海鼎锋资产管理有限公司
1.778855e+05
True
1
自主
44
101000000491
上海理石投资管理有限公司
2.301286e+05
True
1
自主
45
101000001221
上海恒如投资管理有限公司
6.176445e+04
True
1
自主
47
101000002228
上海仁和智本资产管理有限公司
8.046300e+04
True
1
自主
49
101000000534
上海睿信投资管理有限公司
1.142423e+05
True
1
自主
54
101000000934
上海弈泰资产管理有限公司
1.474782e+04
True
1
自主
71
101000000229
上海通晟资产管理有限公司
8.136947e+04
True
1
自主
72
101000000500
上海无花果资产管理中心（普通合伙）
1.669681e+04
True
1
自主
81
101000002351
上海玖歌投资管理有限公司
3.814694e+05
True
1
自主
83
101000002708
上海毅扬投资管理有限公司
8.954819e+04
True
1
自主
84
101000003353
上海天迪资产管理有限公司
8.088482e+04
True
1
自主
89
101000001632
上海元葵资产管理中心（有限合伙）
5.547037e+04
True
1
自主
105
101000001084
上海明湾资产管理有限公司
2.461179e+04
True
1
自主
106
101000001726
上海珺容投资管理有限公司
9.309078e+04
True
1
自主
109
101000001974
德骏达隆（上海）资产管理有限公司
1.383652e+04
True
1
自主
115
101000000375
上海银叶投资有限公司
1.038920e+06
True
1
自主
117
101000002967
上海喜世润投资管理有限公司
4.107349e+04
True
1
自主
128
101000002204
上海知几投资发展中心（有限合伙）
1.931802e+04
True
1
自主
161
101000001702
上海远策投资管理中心（有限合伙）
6.716260e+05
True
1
自主
162
101000002244
上海河东资产管理咨询有限公司
3.060343e+04
True
1
自主
179
101000001003
上海国时资产管理有限公司
2.704305e+04
True
1
自主
182
101000003885
上海天倚道投资管理有限公司
2.873217e+04
True
1
自主
187
101000004064
上海和君股权投资管理合伙企业（有限合伙）
5.502301e+04
True
1
自主
...
...
...
...
...
...
...
801
101000023324
上海量金资产管理有限公司
7.369595e+04
True
1
自主
805
101000021686
上海璞琢资产管理有限公司
1.341200e+04
True
1
自主
832
101000023458
上海砥俊资产管理中心（有限合伙）
2.422734e+04
True
1
自主
838
101000019197
上海洛书投资管理有限公司
3.950401e+04
True
1
自主
856
101000025638
上海珺容资产管理有限公司
1.287097e+05
True
1
自主
857
101000019574
上海融启投资管理有限公司
4.746424e+04
True
1
自主
858
101000023239
上海汇贝投资管理有限公司
1.262950e+04
True
1
自主
871
101000025565
上海永柏联投投资管理有限公司
8.764566e+04
True
1
自主
887
101000017063
上海道亨资产管理有限公司
3.451567e+04
True
1
自主
905
101000027980
上海小鳄资产管理有限公司
3.308000e+04
True
1
自主
949
101000030443
上海千波资产管理有限公司
2.074652e+04
True
1
自主
970
101000030365
上海邦孚资产管理有限公司
2.280638e+04
True
1
自主
974
101000032177
上海盈衡资产管理有限公司
1.882030e+04
True
1
自主
983
101000033374
和瑾（上海）资产管理有限公司
9.219357e+04
True
1
自主
988
101000030655
上海招焱投资管理有限公司
3.744824e+04
True
1
自主
991
101000032835
上海迎水投资管理有限公司
1.306746e+04
True
1
自主
1059
101000037020
上海钜澎资产管理有限公司
3.840371e+05
True
1
自主
1061
101000038007
上海步耘投资管理有限公司
3.250000e+04
True
1
自主
1099
101000023875
上海涌乐股权投资基金管理有限公司
1.114872e+04
True
1
自主
1103
101000039741
贝莱德海外投资基金管理（上海）有限公司
7.717569e+04
True
1
自主
1119
101000030090
上海稳博投资管理有限公司
1.197616e+04
True
1
自主
1132
101000044810
石担（上海）资产管理有限公司
1.077336e+04
True
1
自主
1135
101000044746
锐汇（上海）资产管理有限公司
1.552799e+04
True
1
自主
1149
101000043338
上海联创永泉资产管理有限公司
3.423920e+04
True
1
自主
1155
101000045294
上海弘润资产管理有限责任公司
9.536936e+04
True
1
自主
1162
101000043772
上海久期量和投资有限公司
5.856086e+04
True
1
自主
1168
101000051261
上海岳海资产管理有限公司
1.099564e+04
True
1
自主
1187
101000063062
上海橘颂资产管理有限公司
1.378692e+04
True
1
自主
1196
101000050644
上海思勰投资管理有限公司
3.820738e+04
True
1
自主
1217
1612231709102433
上海崇正投资管理合伙企业（有限合伙）
1.510000e+04
True
1
自主
119 rows × 6 columns
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data
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In [19]:
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Content source: kamiseko/factor-test
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