When I query data for an American company (e.g. Google; `GOOG.O`) and a British (e.g. RaspberryPi; `RPI.L`) the data frame returned is a full merge of American and British trading days resulting in NAs in the respective time series if e.g. American markets have a non trading day as of `2024-06-19` (see example below). Is that default behavior adjustable to returning a list with a time series per ticker in order to avoid having NAs in the time series when pulling a mixed/international universe?
>>> res=rd.get_history(
... ['GOOG.O', 'RPI.L'],
... start = '2023-01-01',
... end = datetime.today().strftime('%Y-%m-%d'),
... # end = pd.to_datetime('today').strftime('%Y-%m-%d'),
... interval='daily',
... # fields = ['CLOSE', 'OPEN', 'HIGH', 'LOW']
... fields = [
... 'TR.OPENPRICE',
... 'TR.HIGHPRICE',
... 'TR.LOWPRICE' ,
... 'TR.CLOSEPRICE'
... ]
... )
>>> res
GOOG.O ... RPI.L
Open Price High Price Low Price ... High Price Low Price Close Price
Date ...
2023-01-03 89.83 91.55 89.02 ... <NA> <NA> <NA>
2023-01-04 91.01 91.24 87.8 ... <NA> <NA> <NA>
2023-01-05 88.07 88.21 86.56 ... <NA> <NA> <NA>
2023-01-06 87.36 88.47 85.57 ... <NA> <NA> <NA>
2023-01-09 89.195 90.83 88.58 ... <NA> <NA> <NA>
... ... ... ... ... ... ... ...
2024-06-18 178.79 178.91 175.62 ... 465.0 422.05 425.0
2024-06-19 <NA> <NA> <NA> ... 435.0 400.0 415.0
2024-06-20 176.71 178.74 176.46 ... 415.0 370.0 372.0
2024-06-21 178.49 182.5117 178.06 ... 389.5 365.0 373.5
2024-06-24 181.28 182.08 180.23 ... 399.0 362.0 390.0
[379 rows x 8 columns]
