pandas-datareader和yahoo返回的数据不在指定的不同股票行情自动收录器的开始和结束日期之内

如何解决pandas-datareader和yahoo返回的数据不在指定的不同股票行情自动收录器的开始和结束日期之内

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？