使用Tushare获取多只股票数据
tushare ID：393042
Tushare 简介
Tushare 是一个免费、开源的 python 财经数据接口包。Tushare 返回的绝大部分的数据格式都是pandas DataFrame 类型，非常便于用 pandas / NumPy / Matplotlib 进行数据分析和可视化。
Tushare 包的安装
在 Anaconda prompt 中通过：
pip install tushare命令安装，python的大多数第三包都可以通过这种方式安装。安装完成后，可以通过 conda list 检查是否按照成功。此外，也可以在 Jupyter Notebook 中通过 import 导入检查。需要更新时，可以在 Anaconda prompt 中通过以下命令完成：
pip install tushare --upgradeTushare Pro
一般的 Tushare 抓取的数据有一定时限，要想抓取长时间的数据，需要注册 Tushare 会员，获取积分，如何获取Tushare积分。
第一次使用 ts.pro_api 需要设置TOKEN凭证， 如何获取TOKEN凭证 。
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import tushare as ts
token='********************************************************'
ts.set_token(token)
pro=ts.pro_api()
SSEC_pro = pro.index_daily(ts_code='000001.SH', start_date='20180101', end_date='20210915')
SSEC_pro.tail()
ts_code trade_date close open high low pre_close change pct_chg vol amount
898 000001.SH 20180108 3409.4795 3391.5528 3412.7307 3384.5591 3391.7501 17.7294 0.5227 236165106.0 286213219.1
899 000001.SH 20180105 3391.7501 3386.4640 3402.0694 3380.2450 3385.7102 6.0399 0.1784 213060681.0 248187840.5
900 000001.SH 20180104 3385.7102 3371.0000 3392.8264 3365.2954 3369.1084 16.6018 0.4928 206955288.0 243090768.7
901 000001.SH 20180103 3369.1084 3347.7428 3379.9152 3345.2887 3348.3259 20.7825 0.6207 213836149.0 258366523.2
902 000001.SH 20180102 3348.3259 3314.0307 3349.0530 3314.0307 3307.1721 41.1538 1.2444 202278860.0 227788461.1
SSEC_pro.describe()
close open high low pre_close change pct_chg vol amount
count 903.000000 903.000000 903.000000 903.000000 903.000000 903.000000 903.000000 9.030000e+02 9.030000e+02
mean 3097.503049 3094.822599 3115.893445 3074.233051 3097.116514 0.387218 0.018157 2.426987e+08 2.855748e+08
std 309.423173 309.152421 309.868581 307.585213 308.942065 36.162055 1.183221 1.074083e+08 1.442081e+08
min 2464.362800 2446.019300 2488.479000 2440.906600 2464.362800 -229.922500 -7.724500 8.823889e+07 8.540412e+07
25% 2874.505700 2871.951900 2888.813600 2851.706500 2874.505700 -17.994000 -0.580400 1.545743e+08 1.684175e+08
50% 3050.124000 3041.922000 3074.257100 3029.463200 3050.124000 0.974800 0.031300 2.185987e+08 2.517639e+08
75% 3379.144300 3380.980800 3407.084750 3358.585500 3378.381450 20.332900 0.632550 3.093207e+08 3.728400e+08
max 3715.372300 3721.086500 3731.687000 3692.815700 3715.372300 180.068100 5.711300 6.672763e+08 8.425290e+08
获取多只股票数据
利用 for 循环从股票代码里抓取多只股票的收盘价：
S50 = pro.index_weight(index_code='000016.SH', start_date='20210730')
S50
index_code con_code trade_date weight
0 000016.SH 603986.SH 20210730 1.675
1 000016.SH 603501.SH 20210730 2.156
2 000016.SH 603288.SH 20210730 1.985
3 000016.SH 603259.SH 20210730 3.685
4 000016.SH 601995.SH 20210730 0.186
5 000016.SH 601899.SH 20210730 1.940
6 000016.SH 601888.SH 20210730 3.223
7 000016.SH 601857.SH 20210730 0.623
8 000016.SH 601818.SH 20210730 0.748
9 000016.SH 601688.SH 20210730 1.019
10 000016.SH 601668.SH 20210730 1.282
11 000016.SH 601628.SH 20210730 0.639
12 000016.SH 601601.SH 20210730 1.220
13 000016.SH 601398.SH 20210730 2.204
14 000016.SH 601336.SH 20210730 0.463
15 000016.SH 601318.SH 20210730 7.965
16 000016.SH 601288.SH 20210730 1.153
17 000016.SH 601211.SH 20210730 1.003
18 000016.SH 601166.SH 20210730 3.520
19 000016.SH 601138.SH 20210730 0.458
20 000016.SH 601088.SH 20210730 0.768
21 000016.SH 601066.SH 20210730 0.352
22 000016.SH 601012.SH 20210730 5.097
23 000016.SH 600918.SH 20210730 0.096
24 000016.SH 600893.SH 20210730 0.801
25 000016.SH 600887.SH 20210730 2.783
26 000016.SH 600837.SH 20210730 1.460
27 000016.SH 600809.SH 20210730 1.912
28 000016.SH 600745.SH 20210730 0.932
29 000016.SH 600703.SH 20210730 1.568
30 000016.SH 600690.SH 20210730 1.295
31 000016.SH 600588.SH 20210730 0.802
32 000016.SH 600585.SH 20210730 1.164
33 000016.SH 600570.SH 20210730 0.892
34 000016.SH 600547.SH 20210730 0.435
35 000016.SH 600519.SH 20210730 14.448
36 000016.SH 600438.SH 20210730 1.599
37 000016.SH 600309.SH 20210730 2.446
38 000016.SH 600276.SH 20210730 3.336
39 000016.SH 600196.SH 20210730 1.354
40 000016.SH 600104.SH 20210730 0.881
41 000016.SH 600050.SH 20210730 1.050
42 000016.SH 600048.SH 20210730 0.985
43 000016.SH 600036.SH 20210730 7.885
44 000016.SH 600031.SH 20210730 2.020
45 000016.SH 600030.SH 20210730 2.636
46 000016.SH 600028.SH 20210730 0.731
47 000016.SH 600016.SH 20210730 1.160
48 000016.SH 600009.SH 20210730 0.509
49 000016.SH 600000.SH 20210730 1.453
S50_stock_list = S50.con_code
S50_stock_list.values
array(['603986.SH', '603501.SH', '603288.SH', '603259.SH', '601995.SH',
'601899.SH', '601888.SH', '601857.SH', '601818.SH', '601688.SH',
'601668.SH', '601628.SH', '601601.SH', '601398.SH', '601336.SH',
'601318.SH', '601288.SH', '601211.SH', '601166.SH', '601138.SH',
'601088.SH', '601066.SH', '601012.SH', '600918.SH', '600893.SH',
'600887.SH', '600837.SH', '600809.SH', '600745.SH', '600703.SH',
'600690.SH', '600588.SH', '600585.SH', '600570.SH', '600547.SH',
'600519.SH', '600438.SH', '600309.SH', '600276.SH', '600196.SH',
'600104.SH', '600050.SH', '600048.SH', '600036.SH', '600031.SH',
'600030.SH', '600028.SH', '600016.SH', '600009.SH', '600000.SH'],
dtype=object)
S50_data = pd.DataFrame()
for stock in S50_stock_list:
data = pro.daily(ts_code=stock, start_date='20190101', end_date='20210915').set_index(['trade_date'])
S50_data[stock] = data['close']
S50_data.head()
603986.SH 603501.SH 603288.SH 603259.SH 601995.SH 601899.SH 601888.SH 601857.SH 601818.SH 601688.SH ... 600104.SH 600050.SH 600048.SH 600036.SH 600031.SH 600030.SH 600028.SH 600016.SH 600009.SH 600000.SH
trade_date
20210915 151.55 231.99 86.32 141.73 56.76 11.52 234.97 5.99 3.45 18.09 ... 20.21 4.22 12.62 51.40 26.58 26.24 4.73 4.02 42.85 9.19
20210914 154.64 234.90 88.18 141.96 57.71 11.72 246.85 5.71 3.48 18.56 ... 20.43 4.23 12.99 52.10 27.01 26.48 4.65 4.03 43.32 9.21
20210913 155.30 237.35 88.96 136.32 60.05 12.39 251.25 5.50 3.56 19.15 ... 20.67 4.31 13.15 53.92 27.52 27.19 4.70 4.12 44.45 9.39
20210910 159.90 243.13 90.77 140.80 58.01 11.97 255.00 5.19 3.56 18.98 ... 21.00 4.32 12.46 54.12 28.31 27.27 4.55 4.16 45.78 9.41
20210909 154.25 245.81 90.00 140.78 57.30 11.43 245.07 5.27 3.54 18.34 ... 20.96 4.39 12.08 52.10 28.29 26.73 4.65 4.15 45.09 9.30
5 rows × 50 columns