Comments (3)
yutiansut
commented on June 6, 2024
1
import requests
data_=[]
for item in requests.get('http://d.10jqka.com.cn/v2/line/hs_600010/01/2016.js ').text.split('\"')[3].split(';'):
data_.append(item.split(','))
data_df= pd.DataFrame(data_,index=list(np.asarray(data_).T[0]),columns=['date','open','high','low','close','volume','amount','factor'])from quantaxis.
yutiansut
commented on June 6, 2024
import requests
requests.get('http://d.10jqka.com.cn/v2/line/hs_600010/01/2016.js ').text.split('\"')[3].split(';')['20160104,2.59,2.59,2.32,2.33,69437299,242332010.00,0.618',
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'20161107,2.01,2.06,2.00,2.05,143604690,407763910.00,1.277',
'20161108,2.04,2.05,2.02,2.04,84196726,239327460.00,0.749',
'20161109,2.03,2.04,2.00,2.02,94759388,267876050.00,0.843',
'20161110,2.04,2.06,2.04,2.04,134513920,385101030.00,1.196',
'20161111,2.04,2.12,2.04,2.11,352646660,1029576700.00,3.136',
'20161114,2.09,2.11,2.07,2.11,213135690,625032990.00,1.896',
'20161115,2.10,2.14,2.08,2.09,148732680,437437890.00,1.323',
'20161116,2.07,2.16,2.06,2.14,406777190,1209374580.00,3.618',
'20161117,2.13,2.13,2.11,2.12,161270820,477916840.00,1.434',
'20161118,2.11,2.14,2.10,2.11,108666447,322200120.00,0.966',
'20161121,2.11,2.14,2.10,2.11,114557751,339795860.00,1.019',
'20161122,2.11,2.15,2.11,2.14,167943830,502591470.00,1.494',
'20161123,2.14,2.16,2.11,2.12,101154491,301503020.00,0.900',
'20161124,2.11,2.14,2.11,2.12,91680268,272503710.00,0.815',
'20161125,2.14,2.14,2.09,2.14,172758960,512616630.00,1.536',
'20161128,2.14,2.16,2.12,2.14,149050060,446304570.00,1.326',
'20161129,2.14,2.19,2.13,2.16,285636000,869041780.00,2.540',
'20161130,2.15,2.16,2.11,2.13,118658206,354947140.00,1.055',
'20161201,2.13,2.19,2.12,2.19,187748290,569961340.00,1.670',
'20161202,2.19,2.21,2.16,2.16,152353480,464076130.00,1.355',
'20161205,2.13,2.16,2.11,2.11,117903892,351497450.00,1.049',
'20161206,2.12,2.13,2.10,2.11,58917294,174115600.00,0.524',
'20161207,2.11,2.16,2.11,2.16,156476360,469002600.00,1.392',
'20161208,2.16,2.16,2.11,2.11,117791919,351791910.00,1.048',
'20161209,2.11,2.15,2.11,2.12,115871897,343863510.00,1.031',
'20161212,2.14,2.16,2.05,2.06,206053250,609641440.00,1.833',
'20161213,2.05,2.08,2.04,2.07,83568115,241203240.00,0.743',
'20161214,2.07,2.07,2.01,2.03,101446171,290766760.00,0.902',
'20161215,2.01,2.04,2.01,2.04,55941526,158843460.00,0.498',
'20161216,2.03,2.04,2.01,2.04,53356473,151462500.00,0.475',
'20161219,2.04,2.05,2.02,2.02,55753284,158820550.00,0.496',
'20161220,2.03,2.03,2.00,2.01,60642763,170695500.00,0.539',
'20161221,2.01,2.03,2.01,2.03,70177026,198392800.00,0.624',
'20161222,2.02,2.03,2.01,2.01,52148443,146992360.00,0.464',
'20161223,2.01,2.01,1.99,2.01,51380195,144042030.00,0.457',
'20161226,2.00,2.01,1.98,2.01,54378359,151634810.00,0.484',
'20161227,2.00,2.01,1.99,2.00,29679659,83301045.00,0.264',
'20161228,2.01,2.01,1.99,1.99,31189281,87149698.00,0.277',
'20161229,1.99,2.01,1.98,1.99,31461786,87826414.00,0.280',
'20161230,1.99,2.00,1.98,1.99,36529644,101796632.00,0.325']from quantaxis.
yutiansut
commented on June 6, 2024
data_=[]
for item in data:
data_.append(item.split(',')
pd.DataFrame(data_,index=list(np.asarray(data_).T[0]),columns=['date','open','high','low','close','volume','amount','factor'])In [54]: pd.DataFrame(data_,index=list(np.asarray(data_).T[0]),columns=['date','open','high','low','close','volume','amount','factor'])
Out[54]:
date open high low close volume amount factor
20160104 20160104 2.59 2.59 2.32 2.33 69437299 242332010.00 0.618
20160105 20160105 2.28 2.44 2.23 2.39 114924349 378947390.00 1.022
20160106 20160106 2.41 2.61 2.37 2.59 183924160 648683940.00 1.636
20160107 20160107 2.51 2.54 2.33 2.34 51144318 173416790.00 0.455
20160108 20160108 2.41 2.57 2.34 2.54 269896420 947258110.00 2.400
20160111 20160111 2.49 2.49 2.34 2.36 196359560 661641860.00 1.746
20160112 20160112 2.36 2.39 2.24 2.31 98270625 317175040.00 0.874
20160113 20160113 2.34 2.46 2.29 2.31 125690384 417415510.00 1.118
20160114 20160114 2.23 2.39 2.21 2.35 98322435 316861960.00 0.874
20160115 20160115 2.32 2.33 2.23 2.24 93528953 297474680.00 0.832
20160118 20160118 2.23 2.27 2.20 2.24 64590691 202761380.00 0.574
20160119 20160119 2.26 2.31 2.24 2.29 96680129 307322480.00 0.860
20160120 20160120 2.26 2.34 2.24 2.26 101959509 324735300.00 0.907
20160121 20160121 2.22 2.27 2.18 2.19 87420957 272307120.00 0.778
20160122 20160122 2.22 2.26 2.16 2.24 80863272 251230110.00 0.719
20160125 20160125 2.26 2.28 2.23 2.24 63713723 201200220.00 0.567
20160126 20160126 2.22 2.24 2.02 2.09 113888133 344049080.00 1.013
20160127 20160127 2.11 2.13 2.03 2.11 103701991 302859880.00 0.922
20160128 20160128 2.08 2.09 1.90 1.92 97593135 272647830.00 0.868
20160129 20160129 1.94 2.11 1.91 2.11 197683570 572679180.00 1.758
20160201 20160201 2.09 2.10 1.96 2.01 131984720 375889720.00 1.174
20160202 20160202 1.99 2.17 1.99 2.06 108175246 315234680.00 0.962
20160203 20160203 2.04 2.06 1.99 2.02 91365909 258485030.00 0.813
20160204 20160204 2.04 2.09 2.02 2.06 83281507 240504290.00 0.741
20160205 20160205 2.08 2.09 2.02 2.03 52837897 151741470.00 0.470
20160215 20160215 1.96 2.06 1.94 2.04 58809771 164621270.00 0.523
20160216 20160216 2.03 2.10 2.01 2.09 93010401 271062480.00 0.827
20160217 20160217 2.09 2.16 2.07 2.11 115949211 342859230.00 1.031
20160218 20160218 2.13 2.15 2.09 2.11 99191116 294640880.00 0.882
20160219 20160219 2.09 2.12 2.08 2.10 58237732 170913540.00 0.518
... ... ... ... ... ... ... ... ...
20161121 20161121 2.11 2.14 2.10 2.11 114557751 339795860.00 1.019
20161122 20161122 2.11 2.15 2.11 2.14 167943830 502591470.00 1.494
20161123 20161123 2.14 2.16 2.11 2.12 101154491 301503020.00 0.900
20161124 20161124 2.11 2.14 2.11 2.12 91680268 272503710.00 0.815
20161125 20161125 2.14 2.14 2.09 2.14 172758960 512616630.00 1.536
20161128 20161128 2.14 2.16 2.12 2.14 149050060 446304570.00 1.326
20161129 20161129 2.14 2.19 2.13 2.16 285636000 869041780.00 2.540
20161130 20161130 2.15 2.16 2.11 2.13 118658206 354947140.00 1.055
20161201 20161201 2.13 2.19 2.12 2.19 187748290 569961340.00 1.670
20161202 20161202 2.19 2.21 2.16 2.16 152353480 464076130.00 1.355
20161205 20161205 2.13 2.16 2.11 2.11 117903892 351497450.00 1.049
20161206 20161206 2.12 2.13 2.10 2.11 58917294 174115600.00 0.524
20161207 20161207 2.11 2.16 2.11 2.16 156476360 469002600.00 1.392
20161208 20161208 2.16 2.16 2.11 2.11 117791919 351791910.00 1.048
20161209 20161209 2.11 2.15 2.11 2.12 115871897 343863510.00 1.031
20161212 20161212 2.14 2.16 2.05 2.06 206053250 609641440.00 1.833
20161213 20161213 2.05 2.08 2.04 2.07 83568115 241203240.00 0.743
20161214 20161214 2.07 2.07 2.01 2.03 101446171 290766760.00 0.902
20161215 20161215 2.01 2.04 2.01 2.04 55941526 158843460.00 0.498
20161216 20161216 2.03 2.04 2.01 2.04 53356473 151462500.00 0.475
20161219 20161219 2.04 2.05 2.02 2.02 55753284 158820550.00 0.496
20161220 20161220 2.03 2.03 2.00 2.01 60642763 170695500.00 0.539
20161221 20161221 2.01 2.03 2.01 2.03 70177026 198392800.00 0.624
20161222 20161222 2.02 2.03 2.01 2.01 52148443 146992360.00 0.464
20161223 20161223 2.01 2.01 1.99 2.01 51380195 144042030.00 0.457
20161226 20161226 2.00 2.01 1.98 2.01 54378359 151634810.00 0.484
20161227 20161227 2.00 2.01 1.99 2.00 29679659 83301045.00 0.264
20161228 20161228 2.01 2.01 1.99 1.99 31189281 87149698.00 0.277
20161229 20161229 1.99 2.01 1.98 1.99 31461786 87826414.00 0.280
20161230 20161230 1.99 2.00 1.98 1.99 36529644 101796632.00 0.325
[244 rows x 8 columns]from quantaxis.
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from quantaxis.