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In [234]: bhour_mon = pd . offsets . CustomBusinessHour ( start = "10:00" , weekmask = "Tue Wed Thu Fri" ) # Monday is skipped because it's a holiday, business hour starts from 10:00 In [235]: dt + bhour_mon * 2 Out[235]: Timestamp('2014-01-21 10:00:00') Offset aliases # If it helps, I had a similar need for exchange trading calendars. There was some excellent code buried in the Zipline project by Quantopian. I extracted out the relevant part and created a new project for creating market exchange trading calendars in pandas. The links are here, with some of the functionality described below. In certain countries, such as the United States, there are laws ( Uniform Monday Holiday Act of 1968), whose rules are included in Pandas, print(USFederalHolidayCalendar.rules) as an example for developing other calendars. However, if we want more accuracy, we must consider bank holidays (for example, if we calculate costs that depend on the exact days between coupon and coupon, 1 day off in 20 is a 5% error). Knowing the holidays within a period is especially useful when estimating human habits and behaviors (medical care, travels, etc.). In the insurance sector, these patterns can directly affect accounting reserves; for example, when calculating costs incurred but not reported (IBNR). In fact, in some insurance companies it’s common to slightly increase the claims ratio in leap years due to having one calendar day more than the rest.

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 There are several time/date properties that one can access from Timestamp or a collection of timestamps like a DatetimeIndex.In [218]: bh = pd . offsets . BusinessHour ( start = "17:00" , end = "09:00" ) In [219]: bh Out[219]: In [220]: pd . Timestamp ( "2014-08-01 17:00" ) + bh Out[220]: Timestamp('2014-08-01 18:00:00') In [221]: pd . Timestamp ( "2014-08-01 23:00" ) + bh Out[221]: Timestamp('2014-08-02 00:00:00') # Although 2014-08-02 is Saturday, # it is valid because it starts from 08-01 (Friday). In [222]: pd . Timestamp ( "2014-08-02 04:00" ) + bh Out[222]: Timestamp('2014-08-02 05:00:00') # Although 2014-08-04 is Monday, # it is out of business hours because it starts from 08-03 (Sunday). In [223]: pd . Timestamp ( "2014-08-04 04:00" ) + bh Out[223]: Timestamp('2014-08-04 18:00:00') The tradingCal instance seems to work in that I'm able to view the Holiday rules. In[10]: tradingCal.rules The CustomBusinessDay class has now been merged into the upcoming 0.12 release of Pandas where you will be able to do something like the following: >>> from pandas.tseries.offsets import CustomBusinessDay In [264]: pd . date_range ( .....: start = "7/1/2012" , end = "7/10/2012" , freq = pd . offsets . CDay ( calendar = cal ) .....: ) . to_pydatetime () .....: Out[264]: array([datetime.datetime(2012, 7, 2, 0, 0), datetime.datetime(2012, 7, 3, 0, 0), datetime.datetime(2012, 7, 5, 0, 0), datetime.datetime(2012, 7, 6, 0, 0), datetime.datetime(2012, 7, 9, 0, 0), datetime.datetime(2012, 7, 10, 0, 0)], dtype=object) In [265]: offset = pd . offsets . CustomBusinessDay ( calendar = cal ) In [266]: datetime . datetime ( 2012 , 5 , 25 ) + offset Out[266]: Timestamp('2012-05-29 00:00:00') In [267]: datetime . datetime ( 2012 , 7 , 3 ) + offset Out[267]: Timestamp('2012-07-05 00:00:00') In [268]: datetime . datetime ( 2012 , 7 , 3 ) + 2 * offset Out[268]: Timestamp('2012-07-06 00:00:00') In [269]: datetime . datetime ( 2012 , 7 , 6 ) + offset Out[269]: Timestamp('2012-07-09 00:00:00')

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 # By having trial and error, I could only find the correct output by combining the method as below: from datetime import datetime, timedelta In [379]: idx = pd . period_range ( "2014-07-01 09:00" , periods = 5 , freq = "H" ) In [380]: idx Out[380]: PeriodIndex(['2014-07-01 09:00', '2014-07-01 10:00', '2014-07-01 11:00', '2014-07-01 12:00', '2014-07-01 13:00'], dtype='period[H]') In [381]: idx + pd . offsets . Hour ( 2 ) Out[381]: PeriodIndex(['2014-07-01 11:00', '2014-07-01 12:00', '2014-07-01 13:00', '2014-07-01 14:00', '2014-07-01 15:00'], dtype='period[H]') In [382]: idx = pd . period_range ( "2014-07" , periods = 5 , freq = "M" ) In [383]: idx Out[383]: PeriodIndex(['2014-07', '2014-08', '2014-09', '2014-10', '2014-11'], dtype='period[M]') In [384]: idx + pd . offsets . MonthEnd ( 3 ) Out[384]: PeriodIndex(['2014-10', '2014-11', '2014-12', '2015-01', '2015-02'], dtype='period[M]') into freq keyword arguments. The available date offsets and associated frequency strings can be found below:Holiday: Dr. Martin Luther King Jr. (month=1, day=1, offset=),