My aim is to get historical stock datas of different stocks from europe and us. With the function:
JavaScript
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3
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import pandas_datareader as web2
print(web.DataReader('ALB', 'yahoo', '01.01.2020', '01.01.2021'))3
Output:
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14
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High Low Volume Adj Close2
Date 3
2020-01-02 73.879997 72.190002 1620838.0 70.9426194
2020-01-03 72.389999 71.220001 1337602.0 69.9264985
2020-01-06 72.169998 70.188698 1330518.0 69.8483286
2020-01-07 73.489998 71.130997 1497506.0 70.7667477
2020-01-08 71.500000 69.610001 2029494.0 69.5454568
9
2021-12-06 252.690002 239.110001 1035976.0 248.53999310
2021-12-07 263.190887 253.259995 888158.0 259.52999911
2021-12-08 265.859985 258.690002 832756.0 264.64001512
2021-12-09 266.489990 257.299988 535738.0 257.47000113
2021-12-10 263.559998 257.220001 598893.0 262.86999514
returns the historical stock data in USD.
Otherwise
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print(web.DataReader('BMW.DE', 'yahoo', '01.01.2020', '01.01.2021'))2
Output:
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14
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High Low Open Close Volume Adj Close2
Date 3
2020-01-02 74.629997 73.059998 73.139999 74.220001 1232319.0 67.1035314
2020-01-03 73.830002 72.330002 73.730003 73.320000 1153245.0 66.2898185
2020-01-06 73.050003 72.040001 72.830002 73.050003 1039192.0 66.0457156
2020-01-07 74.320000 73.339996 73.599998 74.220001 1302124.0 67.1035317
2020-01-08 74.500000 73.750000 73.940002 74.410004 941807.0 67.2753078
9
2021-12-06 88.800003 86.820000 88.029999 88.419998 1100120.0 88.41999810
2021-12-07 88.419998 88.419998 88.419998 88.419998 0.0 88.41999811
2021-12-08 91.209999 89.129997 90.849998 91.129997 1338471.0 91.12999712
2021-12-09 91.870003 90.000000 91.489998 90.220001 1050166.0 90.22000113
2021-12-10 90.190002 88.949997 89.529999 89.660004 1415043.0 89.66000414
return the historical stock data in EUR.
Is there a possibility to unify the currency in both cases?
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Answer
Download EURUSD%3DX to convert your data from EUR to USD. The important part is to reindex correctly your dataframes to align dates between them.
JavaScript
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alb = web.DataReader('ALB', 'yahoo', '01.01.2020', '01.01.2021')2
bmw = web.DataReader('BMW.DE', 'yahoo', '01.01.2020', '01.01.2021')3
eurusd = web.data.DataReader('EURUSD%3DX', 'yahoo', '01.01.2020', '01.01.2021')4
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38
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# Euro2
>>> bmw[['Open', 'High', 'Low', 'Close', 'Adj Close']] 3
* eurusd[['Adj Close']].reindex(bmw.index).values4
5
Open High Low Close Adj Close6
Date 7
2020-01-02 82.069120 83.741021 81.979351 83.280971 75.2957048
2020-01-03 82.367012 82.478725 80.803009 81.908979 74.0552569
2020-01-06 81.292559 81.538124 80.410763 81.538124 73.71996610
2020-01-07 82.417241 83.223498 82.126091 83.111520 75.14250011
2020-01-08 82.478140 83.102803 82.266198 83.002414 75.04384612
13
2020-12-22 88.717571 89.219288 88.032307 88.228092 84.24179914
2020-12-23 88.365411 89.766877 87.756078 89.425643 85.38524515
2020-12-28 91.599244 92.111856 89.524372 89.695243 85.64266716
2020-12-29 90.624622 90.820201 88.840000 89.426729 85.38628417
2020-12-30 89.446540 89.728362 88.503067 88.503067 84.50435818
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[254 rows x 5 columns]20
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# Dollar22
>>> bmw[['Open', 'High', 'Low', 'Close', 'Adj Close']]23
Open High Low Close Adj Close24
Date 25
2020-01-02 73.139999 74.629997 73.059998 74.220001 67.10353126
2020-01-03 73.730003 73.830002 72.330002 73.320000 66.28981827
2020-01-06 72.830002 73.050003 72.040001 73.050003 66.04571528
2020-01-07 73.599998 74.320000 73.339996 74.220001 67.10353129
2020-01-08 73.940002 74.500000 73.750000 74.410004 67.27530730
31
2020-12-22 72.500000 72.910004 71.940002 72.099998 68.84240032
2020-12-23 72.510002 73.660004 72.010002 73.379997 70.06456833
2020-12-28 75.050003 75.470001 73.349998 73.489998 70.16960134
2020-12-29 74.139999 74.300003 72.680000 73.160004 69.85451535
2020-12-30 73.000000 73.230003 72.230003 72.230003 68.96653736
37
[254 rows x 5 columns]38