def stock_zh_kcb_spot() -> pd.DataFrame:
"""
从新浪财经-A股获取所有A股的实时行情数据, 大量抓取容易封IP
http://vip.stock.finance.sina.com.cn/mkt/#qbgg_hk
:return: pandas.DataFrame
"""
big_df = pd.DataFrame()
page_count = get_zh_kcb_page_count()
zh_sina_stock_payload_copy = zh_sina_kcb_stock_payload.copy()
for page in tqdm(range(1, page_count + 1)):
zh_sina_stock_payload_copy.update({"page": page})
res = requests.get(zh_sina_kcb_stock_url, params=zh_sina_kcb_stock_payload)
data_json = demjson.decode(res.text)
big_df = big_df.append(pd.DataFrame(data_json), ignore_index=True)
return big_df
def stock_zh_kcb_spot():
"""
从新浪财经-A股获取所有A股的实时行情数据, 大量抓取容易封IP
http://vip.stock.finance.sina.com.cn/mkt/#qbgg_hk
:return: pandas.DataFrame
symbol code name trade pricechange changepercent buy \
0 sh688001 688001 华兴源创 36.020 -1.550 -4.126 36.020
1 sh688002 688002 睿创微纳 34.560 -0.590 -1.679 34.550
2 sh688003 688003 天准科技 26.800 -0.400 -1.471 26.740
3 sh688005 688005 容百科技 30.930 0.000 0.000 0.000
4 sh688006 688006 杭可科技 39.400 -1.610 -3.926 39.400
5 sh688007 688007 光峰科技 27.780 -0.400 -1.419 27.780
6 sh688008 688008 澜起科技 61.130 -1.030 -1.657 61.120
7 sh688009 688009 中国通号 7.550 -0.040 -0.527 7.540
8 sh688010 688010 福光股份 45.120 0.260 0.580 45.120
9 sh688011 688011 新光光电 40.620 -0.550 -1.336 40.620
10 sh688012 688012 中微公司 66.760 -2.710 -3.901 66.760
11 sh688015 688015 交控科技 34.540 -0.120 -0.346 34.540
12 sh688016 688016 心脉医疗 138.800 2.520 1.849 138.580
13 sh688018 688018 乐鑫科技 140.160 -0.840 -0.596 140.160
14 sh688019 688019 安集科技 113.800 -1.560 -1.352 113.800
15 sh688020 688020 方邦股份 78.650 0.250 0.319 78.640
16 sh688021 688021 N奥福 33.210 7.040 26.901 33.200
17 sh688022 688022 瀚川智能 41.040 -0.710 -1.701 41.040
18 sh688023 688023 安恒信息 88.510 9.450 11.953 88.510
19 sh688025 688025 杰普特 45.140 -1.230 -2.653 45.010
20 sh688028 688028 沃尔德 60.010 -0.100 -0.166 60.010
21 sh688029 688029 南微医学 180.120 2.820 1.591 180.120
22 sh688030 688030 山石网科 40.500 0.610 1.529 40.500
23 sh688033 688033 天宜上佳 28.880 -0.130 -0.448 28.810
24 sh688036 688036 传音控股 40.910 -0.550 -1.327 40.910
25 sh688058 688058 宝兰德 87.700 -2.300 -2.556 87.670
26 sh688066 688066 航天宏图 37.920 0.020 0.053 37.920
27 sh688068 688068 热景生物 47.060 -0.940 -1.958 47.050
28 sh688088 688088 虹软科技 42.170 -0.150 -0.354 42.170
29 sh688098 688098 申联生物 17.590 -1.010 -5.430 17.580
30 sh688099 688099 晶晨股份 53.800 0.060 0.112 53.800
31 sh688108 688108 赛诺医疗 17.410 -1.080 -5.841 17.410
32 sh688116 688116 天奈科技 28.320 -0.210 -0.736 28.310
33 sh688122 688122 西部超导 30.910 0.400 1.311 30.900
34 sh688128 688128 中国电研 21.930 -4.990 -18.536 21.920
35 sh688139 688139 海尔生物 26.670 -0.930 -3.370 26.660
36 sh688168 688168 安博通 89.690 0.490 0.549 89.560
37 sh688188 688188 柏楚电子 132.020 2.250 1.734 132.020
38 sh688199 688199 久日新材 66.350 -4.610 -6.497 66.350
39 sh688202 688202 美迪西 66.380 -4.620 -6.507 66.250
40 sh688288 688288 N鸿泉 31.260 6.270 25.090 31.260
41 sh688299 688299 N长阳 17.860 4.150 30.270 17.860
42 sh688321 688321 微芯生物 54.110 2.270 4.379 54.110
43 sh688333 688333 铂力特 55.090 -1.770 -3.113 55.090
44 sh688363 688363 N华熙 85.100 37.310 78.071 85.100
45 sh688366 688366 昊海生科 89.850 -0.010 -0.011 89.830
46 sh688368 688368 晶丰明源 64.300 -0.980 -1.501 64.210
47 sh688369 688369 致远互联 64.000 -0.610 -0.944 64.000
48 sh688388 688388 嘉元科技 44.740 1.040 2.380 44.710
49 sh688389 688389 普门科技 18.100 -1.530 -7.794 18.090
sell settlement open high low volume amount \
0 36.030 37.570 37.850 37.920 35.810 2380984 87476401
1 34.560 35.150 35.170 35.690 34.560 2103123 73990175
2 26.800 27.200 27.200 27.360 26.600 2057578 55542380
3 0.000 30.930 0.000 0.000 0.000 0 0
4 39.410 41.010 41.160 42.000 38.810 4930690 198288639
5 27.790 28.180 28.180 28.480 27.540 1713538 48026144
6 61.130 62.160 62.320 62.620 60.900 2331354 143703073
7 7.550 7.590 7.570 7.660 7.530 20843563 157970856
8 45.140 44.860 44.710 46.200 44.550 2088806 94878090
9 40.630 41.170 41.030 41.730 40.300 924537 37802171
10 66.770 69.470 69.050 70.700 66.360 3166810 217462568
11 34.650 34.660 34.980 35.530 34.410 1690038 59026743
12 138.800 136.280 136.030 141.680 135.010 890307 123543447
13 140.190 141.000 140.000 145.000 138.300 949958 134262776
14 113.810 115.360 114.000 116.850 113.520 543447 62396461
15 78.650 78.400 77.540 80.270 77.530 880072 69705273
16 33.210 26.170 30.000 34.900 30.000 13693192 444011825
17 41.060 41.750 41.750 42.480 40.900 1143725 47732979
18 88.530 79.060 77.400 91.900 77.400 7154366 605779685
19 45.140 46.370 45.790 46.850 44.950 2533248 115958362
20 60.050 60.110 60.400 61.360 59.200 1243836 74998220
21 180.150 177.300 178.100 181.010 175.720 1649325 294724332
22 40.510 39.890 40.000 41.960 39.700 5295281 215776050
23 28.880 29.010 29.210 29.440 28.580 1118531 32345154
24 40.920 41.460 41.100 41.990 40.690 4809262 198377404
25 87.700 90.000 88.150 89.800 86.550 1759660 154998681
26 37.930 37.900 37.910 38.830 37.850 1361196 52129856
27 47.060 48.000 47.960 48.330 46.500 1113358 52776362
28 42.180 42.320 42.420 42.690 41.900 1706984 72205682
29 17.590 18.600 18.040 18.590 17.460 8656313 155253889
30 53.820 53.740 53.610 55.430 53.550 1811039 98609660
31 17.420 18.490 17.500 18.450 17.220 9959654 175741647
32 28.320 28.530 28.600 29.250 28.200 4119339 118200733
33 30.910 30.510 30.580 31.550 30.580 1790005 55652309
34 21.930 26.920 23.800 24.200 21.880 21824232 505020080
35 26.670 27.600 27.150 27.990 26.660 9026072 245111328
36 89.690 89.200 89.190 92.730 88.880 790196 71559830
37 132.040 129.770 129.490 135.050 128.600 1745272 232065547
38 66.360 70.960 66.950 68.230 66.010 8803968 588840957
39 66.380 71.000 68.200 71.000 64.220 5959905 404831463
40 31.270 24.990 35.000 35.000 30.500 15451343 500316962
41 17.870 13.710 18.450 19.680 17.220 47843863 862422893
42 54.120 51.840 51.900 55.740 51.900 4021213 217774905
43 55.100 56.860 56.100 57.800 55.070 1591126 89698024
44 85.120 47.790 78.000 92.200 78.000 33823982 2807767312
45 89.850 89.860 88.530 92.740 88.530 2802240 255113439
46 64.300 65.280 65.000 65.860 64.130 1157674 75158797
47 64.010 64.610 64.100 66.330 63.500 2663678 172052487
48 44.740 43.700 43.360 45.960 42.910 4445248 199015034
49 18.100 19.630 18.500 19.090 17.980 15342408 282404361
ticktime per pb mktcap nmc turnoverratio
0 15:29:59 53.761 7.594 1.444402e+06 130570.987160 6.56831
1 15:29:59 92.779 6.862 1.537920e+06 178220.760192 4.07831
2 15:29:59 40.606 3.309 5.188480e+05 118398.334440 4.65742
3 15:29:59 55.232 3.128 1.371083e+06 125999.748231 0.00000
4 15:29:59 49.560 7.124 1.579940e+06 144936.729680 13.40372
5 15:29:59 38.055 6.575 1.254418e+06 158881.912314 2.99607
6 15:29:59 70.264 9.642 6.906552e+06 454714.502468 3.13418
7 15:29:59 19.868 2.101 7.995313e+06 894199.803000 1.75989
8 15:29:59 56.669 3.976 6.929617e+05 159931.061568 5.89297
9 15:29:59 41.876 3.449 4.062000e+05 92511.810342 4.05945
10 15:29:59 333.800 9.672 3.570740e+06 323295.801348 6.53941
11 15:29:59 62.800 5.500 5.526400e+05 113077.684082 5.16228
12 15:29:59 82.619 9.637 9.990567e+05 194054.225760 6.36805
13 15:29:59 89.576 7.245 1.121280e+06 244398.589824 5.44791
14 15:29:59 100.708 6.963 6.043734e+05 131722.999280 4.69502
15 15:29:59 40.333 4.197 6.292000e+05 143561.048345 4.82148
16 15:29:59 40.654 5.263 2.566588e+05 60443.262720 75.23600
17 15:29:59 47.172 5.285 4.432320e+05 100239.149376 4.68265
18 15:29:59 58.230 10.575 6.556296e+05 137047.184608 46.20547
19 15:29:59 32.014 4.833 4.169518e+05 95248.487576 12.00553
20 15:29:59 54.555 5.651 4.800800e+05 108933.026479 6.85216
21 15:29:59 93.472 10.013 2.401720e+06 551024.743092 5.39134
22 15:29:59 75.828 5.588 7.299050e+05 148434.820650 14.44802
23 15:29:59 43.758 5.675 1.295953e+06 125443.124680 2.57513
24 15:29:59 44.956 4.227 3.272800e+06 292763.619350 6.72033
25 15:29:59 51.287 18.541 3.508000e+05 79778.059000 19.34394
26 15:29:59 75.840 5.607 6.294088e+05 142758.064848 3.61567
27 15:29:59 45.689 4.686 2.926960e+05 66542.905884 7.87381
28 15:29:59 86.061 7.041 1.712102e+06 160272.166485 4.49133
29 15:29:59 67.654 7.180 7.206623e+05 79421.824469 19.17163
30 15:29:59 70.789 7.979 2.211826e+06 200811.028600 4.85202
31 15:29:59 69.640 8.174 7.138100e+05 78640.992633 22.04926
32 15:29:59 70.800 4.215 6.566222e+05 149461.023120 7.80536
33 15:29:59 90.939 5.405 1.363972e+06 122719.781481 4.50857
34 15:29:59 37.810 6.709 8.870685e+05 99732.922857 47.98871
35 15:29:59 47.625 5.090 8.456304e+05 194102.345094 12.40198
36 15:29:59 56.056 4.843 4.590334e+05 104090.913408 6.80873
37 15:29:59 70.978 6.308 1.320200e+06 302449.489538 7.61816
38 15:29:59 30.023 5.872 7.379898e+05 170954.178755 34.16958
39 15:29:59 52.268 7.828 4.115560e+05 83779.339736 47.22149
40 15:29:59 40.597 8.518 3.126000e+05 63633.081072 75.90533
41 15:29:59 43.561 5.363 5.046675e+05 115465.435800 74.00409
42 15:29:59 622.670 15.324 2.218510e+06 219569.316575 9.90976
43 15:29:59 57.989 4.316 4.407200e+05 98938.543942 8.85955
44 15:29:59 0.000 17.123 4.084800e+06 388614.570870 74.06878
45 15:29:59 34.691 3.790 1.597940e+06 131740.765500 19.11187
46 15:29:59 36.534 9.239 3.960880e+05 90504.179000 8.22486
47 15:29:59 50.794 10.579 4.927333e+05 113015.558400 15.08424
48 15:29:59 43.863 4.195 1.032939e+06 236401.028322 8.41284
49 15:29:59 86.190 10.169 7.641820e+05 62900.767050 44.14852
"""
big_df = pd.DataFrame()
page_count = get_zh_kcb_page_count()
zh_sina_stock_payload_copy = zh_sina_kcb_stock_payload.copy()
for page in range(1, page_count + 1):
print(page)
zh_sina_stock_payload_copy.update({"page": page})
res = requests.get(zh_sina_kcb_stock_url,
params=zh_sina_kcb_stock_payload)
data_json = demjson.decode(res.text)
big_df = big_df.append(pd.DataFrame(data_json), ignore_index=True)
return big_df
def stock_zh_kcb_spot() -> pd.DataFrame:
"""
新浪财经-科创板实时行情数据, 大量抓取容易封IP
https://vip.stock.finance.sina.com.cn/mkt/#kcb
:return: 科创板实时行情数据
:rtype: pandas.DataFrame
"""
big_df = pd.DataFrame()
page_count = get_zh_kcb_page_count()
zh_sina_stock_payload_copy = zh_sina_kcb_stock_payload.copy()
for page in tqdm(range(1, page_count + 1), leave=False):
zh_sina_stock_payload_copy.update({"page": page})
zh_sina_stock_payload_copy.update({"_s_r_a": "page"})
res = requests.get(zh_sina_kcb_stock_url,
params=zh_sina_stock_payload_copy)
data_json = demjson.decode(res.text)
big_df = pd.concat([big_df, pd.DataFrame(data_json)],
ignore_index=True)
big_df.columns = [
"代码",
"-",
"名称",
"最新价",
"涨跌额",
"涨跌幅",
'买入',
'卖出',
'昨收',
'今开',
'最高',
'最低',
'成交量',
'成交额',
'时点',
'市盈率',
'市净率',
'流通市值',
'总市值',
'换手率',
]
big_df = big_df[[
"代码",
"名称",
"最新价",
"涨跌额",
"涨跌幅",
'买入',
'卖出',
'昨收',
'今开',
'最高',
'最低',
'成交量',
'成交额',
'时点',
'市盈率',
'市净率',
'流通市值',
'总市值',
'换手率',
]]
big_df['最新价'] = pd.to_numeric(big_df['最新价'])
big_df['涨跌额'] = pd.to_numeric(big_df['涨跌额'])
big_df['涨跌幅'] = pd.to_numeric(big_df['涨跌幅'])
big_df['买入'] = pd.to_numeric(big_df['买入'])
big_df['卖出'] = pd.to_numeric(big_df['卖出'])
big_df['昨收'] = pd.to_numeric(big_df['昨收'])
big_df['今开'] = pd.to_numeric(big_df['今开'])
big_df['最高'] = pd.to_numeric(big_df['最高'])
big_df['最低'] = pd.to_numeric(big_df['最低'])
big_df['成交量'] = pd.to_numeric(big_df['成交量'])
big_df['成交额'] = pd.to_numeric(big_df['成交额'])
big_df['市盈率'] = pd.to_numeric(big_df['市盈率'])
big_df['市净率'] = pd.to_numeric(big_df['市净率'])
big_df['流通市值'] = pd.to_numeric(big_df['流通市值'])
big_df['总市值'] = pd.to_numeric(big_df['总市值'])
big_df['换手率'] = pd.to_numeric(big_df['换手率'])
return big_df