方式
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
数据读取方式
import pandas as pd
df = pd.read_csv("./price/000001.XSHE.csv")
print(df)
下面是我在学习量化交易过程中自行保存的4000多只量化数据接口,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。
文件片
方式
文章为作者独立观点，不代表观点
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