I'm trying to create a Trading calendar using Pandas. I'm able to create a cal instance based on the USFederalHolidayCalendar. The USFederalHolidayCalendar is not consistent with the Trading calendar in that the Trading calendar doesn't include Columbus Day and Veteran's Day. However, the Trading calendar includes Good Friday (not included in the USFederalHolidayCalendar). Everything except for the last line in following code works: from pandas.tseries.holiday import get_calendar, HolidayCalendarFactory, GoodFriday

In [242]: pd . date_range ( start , periods = 10 , freq = "2h20min" ) Out[242]: DatetimeIndex(['2011-01-01 00:00:00', '2011-01-01 02:20:00', '2011-01-01 04:40:00', '2011-01-01 07:00:00', '2011-01-01 09:20:00', '2011-01-01 11:40:00', '2011-01-01 14:00:00', '2011-01-01 16:20:00', '2011-01-01 18:40:00', '2011-01-01 21:00:00'], dtype='datetime64[ns]', freq='140T') In [243]: pd . date_range ( start , periods = 10 , freq = "1D10U" ) Out[243]: DatetimeIndex([ '2011-01-01 00:00:00', '2011-01-02 00:00:00.000010', '2011-01-03 00:00:00.000020', '2011-01-04 00:00:00.000030', '2011-01-05 00:00:00.000040', '2011-01-06 00:00:00.000050', '2011-01-07 00:00:00.000060', '2011-01-08 00:00:00.000070', '2011-01-09 00:00:00.000080', '2011-01-10 00:00:00.000090'], dtype='datetime64[ns]', freq='86400000010U') Anchored offsets # In [476]: didx = pd . date_range ( start = "2014-08-01 09:00" , freq = "H" , periods = 3 , tz = "US/Eastern" ) In [477]: didx Out[477]: DatetimeIndex(['2014-08-01 09:00:00-04:00', '2014-08-01 10:00:00-04:00', '2014-08-01 11:00:00-04:00'], dtype='datetime64[ns, US/Eastern]', freq='H') In [478]: didx . tz_localize ( None ) Out[478]: DatetimeIndex(['2014-08-01 09:00:00', '2014-08-01 10:00:00', '2014-08-01 11:00:00'], dtype='datetime64[ns]', freq=None) In [479]: didx . tz_convert ( None ) Out[479]: DatetimeIndex(['2014-08-01 13:00:00', '2014-08-01 14:00:00', '2014-08-01 15:00:00'], dtype='datetime64[ns]', freq='H') # tz_convert(None) is identical to tz_convert('UTC').tz_localize(None) In [480]: didx . tz_convert ( "UTC" ) . tz_localize ( None ) Out[480]: DatetimeIndex(['2014-08-01 13:00:00', '2014-08-01 14:00:00', '2014-08-01 15:00:00'], dtype='datetime64[ns]', freq=None) Fold # In [143]: idx = pd . date_range ( start = "2019-12-29" , freq = "D" , periods = 4 ) In [144]: idx . isocalendar () Out[144]: year week day 2019-12-29 2019 52 7 2019-12-30 2020 1 1 2019-12-31 2020 1 2 2020-01-01 2020 1 3 In [145]: idx . to_series () . dt . isocalendar () Out[145]: year week day 2019-12-29 2019 52 7 2019-12-30 2020 1 1 2019-12-31 2020 1 2 2020-01-01 2020 1 3 DateOffset objects #In [82]: pd . date_range ( start , periods = 1000 , freq = "M" ) Out[82]: DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31', '2011-04-30', '2011-05-31', '2011-06-30', '2011-07-31', '2011-08-31', '2011-09-30', '2011-10-31', ... '2093-07-31', '2093-08-31', '2093-09-30', '2093-10-31', '2093-11-30', '2093-12-31', '2094-01-31', '2094-02-28', '2094-03-31', '2094-04-30'], dtype='datetime64[ns]', length=1000, freq='M') In [83]: pd . bdate_range ( start , periods = 250 , freq = "BQS" ) Out[83]: DatetimeIndex(['2011-01-03', '2011-04-01', '2011-07-01', '2011-10-03', '2012-01-02', '2012-04-02', '2012-07-02', '2012-10-01', '2013-01-01', '2013-04-01', ... '2071-01-01', '2071-04-01', '2071-07-01', '2071-10-01', '2072-01-01', '2072-04-01', '2072-07-01', '2072-10-03', '2073-01-02', '2073-04-03'], dtype='datetime64[ns]', length=250, freq='BQS-JAN') If you just want to get the pandas Holiday Calendar that can be used in other pandas functions that take that as an argument: holidays = nyse.holidays() In [106]: dft = pd . DataFrame ( .....: np . random . randn ( 100000 , 1 ), .....: columns = [ "A" ], .....: index = pd . date_range ( "20130101" , periods = 100000 , freq = "T" ), .....: ) .....: In [107]: dft Out[107]: A 2013-01-01 00:00:00 0.276232 2013-01-01 00:01:00 -1.087401 2013-01-01 00:02:00 -0.673690 2013-01-01 00:03:00 0.113648 2013-01-01 00:04:00 -1.478427 ... ... 2013-03-11 10:35:00 -0.747967 2013-03-11 10:36:00 -0.034523 2013-03-11 10:37:00 -0.201754 2013-03-11 10:38:00 -1.509067 2013-03-11 10:39:00 -1.693043 [100000 rows x 1 columns] In [108]: dft . loc [ "2013" ] Out[108]: A 2013-01-01 00:00:00 0.276232 2013-01-01 00:01:00 -1.087401 2013-01-01 00:02:00 -0.673690 2013-01-01 00:03:00 0.113648 2013-01-01 00:04:00 -1.478427 ... ... 2013-03-11 10:35:00 -0.747967 2013-03-11 10:36:00 -0.034523 2013-03-11 10:37:00 -0.201754 2013-03-11 10:38:00 -1.509067 2013-03-11 10:39:00 -1.693043 [100000 rows x 1 columns]

There are several time/date properties that one can access from Timestamp or a collection of timestamps like a DatetimeIndex. Here is what it can do by creating a pandas DatetimeIndex of all of the valid open hours for the NYSE: import pandas_market_calendars as mcal BUSINESS_DATE += "', Next (" + DAY_NAME[TOMORROW.weekday()] + "):'" + TOMORROW.strftime('%y%m%d') + "']" In [481]: pd . Timestamp ( .....: datetime . datetime ( 2019 , 10 , 27 , 1 , 30 , 0 , 0 ), .....: tz = "dateutil/Europe/London" , .....: fold = 0 , .....: ) .....: Out[481]: Timestamp('2019-10-27 01:30:00+0100', tz='dateutil//usr/share/zoneinfo/Europe/London') In [482]: pd . Timestamp ( .....: year = 2019 , .....: month = 10 , .....: day = 27 , .....: hour = 1 , .....: minute = 30 , .....: tz = "dateutil/Europe/London" , .....: fold = 1 , .....: ) .....: Out[482]: Timestamp('2019-10-27 01:30:00+0000', tz='dateutil//usr/share/zoneinfo/Europe/London') Ambiguous times when localizing #In [275]: from pandas.tseries.holiday import get_calendar , HolidayCalendarFactory , USLaborDay In [276]: cal = get_calendar ( "ExampleCalendar" ) In [277]: cal . rules Out[277]: [Holiday: Memorial Day (month=5, day=31, offset=), Holiday: July 4th (month=7, day=4, observance=), Holiday: Columbus Day (month=10, day=1, offset=)] In [278]: new_cal = HolidayCalendarFactory ( "NewExampleCalendar" , cal , USLaborDay ) In [279]: new_cal . rules Out[279]: [Holiday: Labor Day (month=9, day=1, offset=), Holiday: Memorial Day (month=5, day=31, offset=), Holiday: July 4th (month=7, day=4, observance=), Holiday: Columbus Day (month=10, day=1, offset=)] Time Series-related instance methods # Shifting / lagging #