量化交易分析：4000多只股票2015-2022年历史数据分享，供小伙伴们学习交流

量化交易学习数据

下面是我在学习量化交易过程中自行保存的4000多只股票的基本数据，数据时间是从2015年1月–2022年9月，每只股票数据为一个.csv文件，包括每天开盘价、收盘价、最高价、最低价、成交量、成交额等数据，提供给需要的小伙伴分析使用，需要的可以自行下载。

文件图片

数据读取方式

import pandas as pd
df = pd.read_csv('./price/000001.XSHE.csv')
print(df)

读取结果如下：

 date   open  close      ...         low       volume         money
0     2015-01-05   9.98  10.00      ...        9.74  458099037.0  4.565388e+09
1     2015-01-06   9.90   9.85      ...        9.71  346952496.0  3.453446e+09
2     2015-01-07   9.72   9.67      ...        9.55  272274401.0  2.634796e+09
3     2015-01-08   9.68   9.34      ...        9.30  225445502.0  2.128003e+09
4     2015-01-09   9.30   9.42      ...        9.19  401736419.0  3.835378e+09
5     2015-01-12   9.29   9.22      ...        9.05  248759608.0  2.293105e+09
6     2015-01-13   9.15   9.17      ...        9.12  130822538.0  1.204987e+09
7     2015-01-14   9.23   9.25      ...        9.18  202274250.0  1.889297e+09
8     2015-01-15   9.27   9.58      ...        9.19  198933635.0  1.868796e+09
9     2015-01-16   9.62   9.60      ...        9.48  249168874.0  2.403346e+09
10    2015-01-19   8.75   8.64      ...        8.64  342260458.0  3.016203e+09
11    2015-01-20   8.64   8.64      ...        8.47  238786616.0  2.064281e+09
12    2015-01-21   8.67   9.00      ...        8.59  310776035.0  2.758193e+09
13    2015-01-22   8.95   8.93      ...        8.84  200990884.0  1.801436e+09
14    2015-01-23   8.97   8.99      ...        8.93  233688053.0  2.108747e+09
15    2015-01-26   8.97   8.95      ...        8.84  169375612.0  1.508447e+09
16    2015-01-27   8.96   8.74      ...        8.64  214520130.0  1.881059e+09
17    2015-01-28   8.66   8.78      ...        8.62  198726593.0  1.742176e+09
18    2015-01-29   8.63   8.68      ...        8.59  162833086.0  1.408825e+09
19    2015-01-30   8.70   8.70      ...        8.59  148958237.0  1.298736e+09
20    2015-02-02   8.49   8.51      ...        8.46  137878326.0  1.176950e+09
21    2015-02-03   8.60   8.71      ...        8.50  141468403.0  1.217877e+09
22    2015-02-04   8.74   8.56      ...        8.55  129340881.0  1.122667e+09
23    2015-02-05   8.93   8.61      ...        8.59  306483809.0  2.710524e+09
24    2015-02-06   8.55   8.44      ...        8.37  165019979.0  1.411299e+09
25    2015-02-09   8.43   8.44      ...        8.25  151595831.0  1.273141e+09
26    2015-02-10   8.42   8.60      ...        8.38  116088823.0  9.918237e+08
27    2015-02-11   8.60   8.57      ...        8.54   88779102.0  7.634146e+08
28    2015-02-12   8.59   8.65      ...        8.50   97485845.0  8.386113e+08
29    2015-02-13   8.70   8.71      ...        8.64  142172103.0  1.244515e+09
...          ...    ...    ...      ...         ...          ...           ...
1866  2022-09-01  12.65  12.61      ...       12.58   86198195.0  1.092666e+09
1867  2022-09-02  12.62  12.51      ...       12.43   78636281.0  9.834339e+08
1868  2022-09-05  12.46  12.57      ...       12.37   63203998.0  7.884511e+08
1869  2022-09-06  12.58  12.50      ...       12.43   73129499.0  9.146922e+08
1870  2022-09-07  12.42  12.33      ...       12.28   97981281.0  1.208330e+09
1871  2022-09-08  12.32  12.36      ...       12.30   62111692.0  7.689135e+08
1872  2022-09-09  12.40  12.72      ...       12.36  195129731.0  2.469131e+09
1873  2022-09-13  12.88  12.95      ...       12.66  172268989.0  2.223707e+09
1874  2022-09-14  12.75  12.73      ...       12.70   85803584.0  1.095055e+09
1875  2022-09-15  12.80  13.00      ...       12.77  184101788.0  2.393239e+09
1876  2022-09-16  12.92  12.56      ...       12.56  135744781.0  1.719253e+09
1877  2022-09-19  12.54  12.57      ...       12.48   63212104.0  7.934913e+08
1878  2022-09-20  12.61  12.34      ...       12.32   88998853.0  1.102212e+09
1879  2022-09-21  12.31  12.43      ...       12.20   68419739.0  8.454772e+08
1880  2022-09-22  12.33  12.29      ...       12.25   58613338.0  7.200582e+08
1881  2022-09-23  12.24  12.29      ...       12.23   58644106.0  7.227031e+08
1882  2022-09-26  12.16  12.00      ...       11.99   90372904.0  1.094061e+09
1883  2022-09-27  12.00  12.15      ...       11.81   78962229.0  9.467330e+08
1884  2022-09-28  12.09  12.11      ...       11.92   78092558.0  9.462118e+08
1885  2022-09-29  12.25  11.86      ...       11.82   91771804.0  1.100405e+09
1886  2022-09-30  11.87  11.84      ...       11.83   53723019.0  6.389069e+08
1887  2022-10-10  11.70  11.47      ...       11.46   96608018.0  1.119090e+09
1888  2022-10-11  11.54  11.48      ...       11.41   41525337.0  4.767492e+08
1889  2022-10-12  11.45  11.60      ...       11.35   55957243.0  6.417765e+08
1890  2022-10-13  11.51  11.34      ...       11.31   85261597.0  9.700217e+08
1891  2022-10-14  11.45  11.53      ...       11.40  109606158.0  1.265487e+09
1892  2022-10-17  11.42  11.48      ...       11.34  102482271.0  1.171524e+09
1893  2022-10-18  11.55  11.48      ...       11.46   94683709.0  1.091647e+09
1894  2022-10-19  11.42  11.29      ...       11.28   92398968.0  1.049701e+09
1895  2022-10-20  11.23  11.20      ...       11.16   78192323.0  8.769672e+08

完整数据

方式一：

文件已上传至我的csdn资源文件中,资源名：‘4000多只股票基本历史数据’。

方式二：

关注下面公众号：‘阿旭算法与机器学习’。然后输入：股票数据，即可获取。

如果内容对你有帮助，记得点赞、关注哦！也欢迎小伙伴们和我共同学习交流。