Python：pandas_datareader 以欧元导入历史股票数据答案

【问题标题】：Python: pandas_datareader import historical stock data in euroPython：pandas_datareader 以欧元导入历史股票数据
【发布时间】：2022-01-16 11:03:36
【问题描述】：

我的目标是获取欧洲和美国不同股票的历史股票数据。配合功能：

import pandas_datareader as web
print(web.DataReader('ALB', 'yahoo', '01.01.2020', '01.01.2021'))

输出：

                  High         Low  ...     Volume   Adj Close
Date                                ...                       
2020-01-02   73.879997   72.190002  ...  1620838.0   70.942619
2020-01-03   72.389999   71.220001  ...  1337602.0   69.926498
2020-01-06   72.169998   70.188698  ...  1330518.0   69.848328
2020-01-07   73.489998   71.130997  ...  1497506.0   70.766747
2020-01-08   71.500000   69.610001  ...  2029494.0   69.545456
...                ...         ...  ...        ...         ...
2021-12-06  252.690002  239.110001  ...  1035976.0  248.539993
2021-12-07  263.190887  253.259995  ...   888158.0  259.529999
2021-12-08  265.859985  258.690002  ...   832756.0  264.640015
2021-12-09  266.489990  257.299988  ...   535738.0  257.470001
2021-12-10  263.559998  257.220001  ...   598893.0  262.869995

以美元返回历史股票数据。

否则

print(web.DataReader('BMW.DE', 'yahoo', '01.01.2020', '01.01.2021'))

输出：

                 High        Low       Open      Close     Volume  Adj Close
Date                                                                        
2020-01-02  74.629997  73.059998  73.139999  74.220001  1232319.0  67.103531
2020-01-03  73.830002  72.330002  73.730003  73.320000  1153245.0  66.289818
2020-01-06  73.050003  72.040001  72.830002  73.050003  1039192.0  66.045715
2020-01-07  74.320000  73.339996  73.599998  74.220001  1302124.0  67.103531
2020-01-08  74.500000  73.750000  73.940002  74.410004   941807.0  67.275307
...               ...        ...        ...        ...        ...        ...
2021-12-06  88.800003  86.820000  88.029999  88.419998  1100120.0  88.419998
2021-12-07  88.419998  88.419998  88.419998  88.419998        0.0  88.419998
2021-12-08  91.209999  89.129997  90.849998  91.129997  1338471.0  91.129997
2021-12-09  91.870003  90.000000  91.489998  90.220001  1050166.0  90.220001
2021-12-10  90.190002  88.949997  89.529999  89.660004  1415043.0  89.660004

以欧元返回历史股票数据。

有没有可能在这两种情况下统一货币？

【问题讨论】：

您能否将这两个函数调用的输出示例添加到问题中？
您正在从不同的交易所/来源检索数据。为了统一它们，除以汇率。
谢谢，有没有一种简单的方法可以得到我检查的当前股票的货币？我的问题是，我有一份包含各种货币的股票清单。

标签： python pandas datareader pandas-datareader

【解决方案1】：

下载EURUSD%3DX 将您的数据从EUR 转换为USD。重要的部分是正确地重新索引您的数据框以对齐它们之间的日期。

alb = web.DataReader('ALB', 'yahoo', '01.01.2020', '01.01.2021')
bmw = web.DataReader('BMW.DE', 'yahoo', '01.01.2020', '01.01.2021')
eurusd = web.data.DataReader('EURUSD%3DX', 'yahoo', '01.01.2020', '01.01.2021')

# Euro
>>> bmw[['Open', 'High', 'Low', 'Close', 'Adj Close']] \
        * eurusd[['Adj Close']].reindex(bmw.index).values

                 Open       High        Low      Close  Adj Close
Date                                                             
2020-01-02  82.069120  83.741021  81.979351  83.280971  75.295704
2020-01-03  82.367012  82.478725  80.803009  81.908979  74.055256
2020-01-06  81.292559  81.538124  80.410763  81.538124  73.719966
2020-01-07  82.417241  83.223498  82.126091  83.111520  75.142500
2020-01-08  82.478140  83.102803  82.266198  83.002414  75.043846
...               ...        ...        ...        ...        ...
2020-12-22  88.717571  89.219288  88.032307  88.228092  84.241799
2020-12-23  88.365411  89.766877  87.756078  89.425643  85.385245
2020-12-28  91.599244  92.111856  89.524372  89.695243  85.642667
2020-12-29  90.624622  90.820201  88.840000  89.426729  85.386284
2020-12-30  89.446540  89.728362  88.503067  88.503067  84.504358

[254 rows x 5 columns]

# Dollar
>>> bmw[['Open', 'High', 'Low', 'Close', 'Adj Close']]
                 Open       High        Low      Close  Adj Close
Date                                                             
2020-01-02  73.139999  74.629997  73.059998  74.220001  67.103531
2020-01-03  73.730003  73.830002  72.330002  73.320000  66.289818
2020-01-06  72.830002  73.050003  72.040001  73.050003  66.045715
2020-01-07  73.599998  74.320000  73.339996  74.220001  67.103531
2020-01-08  73.940002  74.500000  73.750000  74.410004  67.275307
...               ...        ...        ...        ...        ...
2020-12-22  72.500000  72.910004  71.940002  72.099998  68.842400
2020-12-23  72.510002  73.660004  72.010002  73.379997  70.064568
2020-12-28  75.050003  75.470001  73.349998  73.489998  70.169601
2020-12-29  74.139999  74.300003  72.680000  73.160004  69.854515
2020-12-30  73.000000  73.230003  72.230003  72.230003  68.966537

[254 rows x 5 columns]

【讨论】：

@MartinKunze。是你要找的吗？
为什么是eurusd[['Adj Close']]？你不觉得它涉及更多一点吗？
真的，我不知道 :) 我让 OP 决定。您会选择关闭列吗？
不。您需要 OHLC 的时间，检索这些时间的汇率，并相应地划分它们。或者，更好的是，将原始数据转换为感兴趣的货币，然后再进行汇总。