问题描述
我尝试获取一些热门股票行情自动收录器的历史数据，并根据行情自动收录器在一天前开始和/或结束返回的数据框。
这是使用S＆P500（'^GSPC'）和Vanguard的澳大利亚ETF（'VDHG.AX'）的最低示例
import pandas as pd
from pandas_datareader import data as pdr
start_date = '2020-4-15'
end_date = '2020-10-15'
sp500 = pdr.DataReader('^GSPC',data_source='yahoo',start=start_date,end=end_date)
VangETF = pdr.DataReader('VDHG.AX',end=end_date)
产生用于S＆P500的数据框：
Open High Low Close Adj Close Volume
Date
2020-04-14 2805.100098 2851.850098 2805.100098 2846.060059 2846.060059 5567400000
2020-04-15 2795.639893 2801.879883 2761.540039 2783.360107 2783.360107 5203390000
2020-04-16 2799.340088 2806.510010 2764.320068 2799.550049 2799.550049 5179990000
2020-04-17 2842.429932 2879.219971 2830.879883 2874.560059 2874.560059 5792140000
2020-04-20 2845.620117 2868.979980 2820.429932 2823.159912 2823.159912 5220160000
... ... ... ... ... ...
2020-10-08 3434.280029 3447.280029 3428.149902 3446.830078 3446.830078 3856190000
2020-10-09 3459.669922 3482.340088 3458.070068 3477.139893 3477.139893 3939060000
2020-10-12 3500.020020 3549.850098 3499.610107 3534.219971 3534.219971 3428970000
2020-10-13 3534.010010 3534.010010 3500.860107 3511.929932 3511.929932 3605150000
2020-10-14 3515.469971 3527.939941 3480.550049 3488.669922 3488.669922 3840630000
[129 rows x 6 columns]
和ETF的数据框：
Open High Low Close Adj Close Volume
Date
2020-04-15 50.990002 51.000000 49.700001 49.880001 49.880001 42774
2020-04-16 49.849998 49.880001 49.150002 49.490002 49.490002 39799
2020-04-17 49.950001 50.950001 49.950001 50.389999 50.389999 26522
2020-04-20 50.980000 50.990002 49.889999 49.889999 49.889999 38152
2020-04-21 49.650002 49.700001 48.950001 49.049999 49.049999 28250
... ... ... ... ... ...
2020-10-08 53.369999 53.889999 53.369999 53.630001 53.630001 24254
2020-10-09 54.000000 54.099998 53.900002 54.000000 54.000000 15536
2020-10-12 53.990002 54.150002 53.910000 54.150002 54.150002 13745
2020-10-13 54.360001 54.740002 54.360001 54.599998 54.599998 17989
2020-10-14 54.610001 54.610001 54.500000 54.590000 54.590000 11177
[126 rows x 6 columns]
这可能是由于股票所在的时区引起的吗？
解决方法
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