使用Python获取金融数据

俗话说，“巧媳妇难为无米之炊”。做金融数据研究，首先要有可用的数据源。
Python下，pandas_datareader模块可以用于获取研究数据。例子如下：

>>> from pandas_datareader.data import DataReader
>>>
>>> datas = DataReader(name='AAPL', data_source='yahoo', start='2018-01-01')
>>>
>>> type(datas)

>>> datas
                  Open        High         Low       Close   Adj Close  \
Date
2018-01-02  170.160004  172.300003  169.259995  172.259995  172.259995
2018-01-03  172.529999  174.550003  171.960007  172.229996  172.229996
2018-01-04  172.539993  173.470001  172.080002  173.029999  173.029999
2018-01-05  173.440002  175.369995  173.050003  175.000000  175.000000
2018-01-08  174.350006  175.610001  173.929993  174.350006  174.350006
2018-01-09  174.550003  175.059998  173.410004  174.330002  174.330002
2018-01-10  173.160004  174.300003  173.000000  174.289993  174.289993
2018-01-11  174.589996  175.490005  174.490005  175.279999  175.279999
2018-01-12  176.179993  177.360001  175.649994  177.089996  177.089996

              Volume
Date
2018-01-02  25555900
2018-01-03  29517900
2018-01-04  22434600
2018-01-05  23660000
2018-01-08  20567800
2018-01-09  21584000
2018-01-10  23959900
2018-01-11  18667700
2018-01-12  25226000
>>>
>>> print(datas.to_csv())
Date,Open,High,Low,Close,Adj Close,Volume
2018-01-02,170.160004,172.300003,169.259995,172.259995,172.259995,25555900
2018-01-03,172.529999,174.550003,171.960007,172.229996,172.229996,29517900
2018-01-04,172.539993,173.470001,172.080002,173.029999,173.029999,22434600
2018-01-05,173.440002,175.369995,173.050003,175.0,175.0,23660000
2018-01-08,174.350006,175.610001,173.929993,174.350006,174.350006,20567800
2018-01-09,174.550003,175.059998,173.410004,174.330002,174.330002,21584000
2018-01-10,173.160004,174.300003,173.0,174.289993,174.289993,23959900
2018-01-11,174.589996,175.490005,174.490005,175.279999,175.279999,18667700
2018-01-12,176.179993,177.360001,175.649994,177.089996,177.089996,25226000

>>>

首先，引入DataReader方法；
然后，以股票代码(name)、数据源(data_source)以及开始日期(start)为参数，调用DataReader获取数据；
获取到的数据(datas)是一个DataFrame，以日期(Date)为索引，包含Open、High、Low、Close等多列指标。
DataFrame数据也可以非常方便地转换成csv格式并输出。

注意到，pandas_datareader还可以使用其他数据源，比如Google Finance。
更多关于pandas_datareader使用方式，请参考：pandas-datareader文档。