Create trading holiday calendar with Pandas

Perhaps it is more straightforward to create the trade calendar from scratch, like so:

import datetime as dt

from pandas.tseries.holiday import AbstractHolidayCalendar, Holiday, nearest_workday, \
    USMartinLutherKingJr, USPresidentsDay, GoodFriday, USMemorialDay, \
    USLaborDay, USThanksgivingDay


class USTradingCalendar(AbstractHolidayCalendar):
    rules = [
        Holiday('NewYearsDay', month=1, day=1, observance=nearest_workday),
        USMartinLutherKingJr,
        USPresidentsDay,
        GoodFriday,
        USMemorialDay,
        Holiday('USIndependenceDay', month=7, day=4, observance=nearest_workday),
        USLaborDay,
        USThanksgivingDay,
        Holiday('Christmas', month=12, day=25, observance=nearest_workday)
    ]


def get_trading_close_holidays(year):
    inst = USTradingCalendar()

    return inst.holidays(dt.datetime(year-1, 12, 31), dt.datetime(year, 12, 31))


if __name__ == '__main__':
    print(get_trading_close_holidays(2016))
    #    DatetimeIndex(['2016-01-01', '2016-01-18', '2016-02-15', '2016-03-25',
    #                   '2016-05-30', '2016-07-04', '2016-09-05', '2016-11-24',
    #                   '2016-12-26'],
    #                  dtype='datetime64[ns]', freq=None)

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.

https://github.com/rsheftel/pandas_market_calendars

https://pypi.python.org/pypi/pandas-market-calendars

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
nyse = mcal.get_calendar('NYSE')

early = nyse.schedule(start_date='2012-07-01', end_date='2012-07-10')
early

                  market_open             market_close
=========== ========================= =========================
2012-07-02 2012-07-02 13:30:00+00:00 2012-07-02 20:00:00+00:00
2012-07-03 2012-07-03 13:30:00+00:00 2012-07-03 17:00:00+00:00
2012-07-05 2012-07-05 13:30:00+00:00 2012-07-05 20:00:00+00:00
2012-07-06 2012-07-06 13:30:00+00:00 2012-07-06 20:00:00+00:00
2012-07-09 2012-07-09 13:30:00+00:00 2012-07-09 20:00:00+00:00
2012-07-10 2012-07-10 13:30:00+00:00 2012-07-10 20:00:00+00:00

mcal.date_range(early, frequency='1D')

DatetimeIndex(['2012-07-02 20:00:00+00:00', '2012-07-03 17:00:00+00:00',
               '2012-07-05 20:00:00+00:00', '2012-07-06 20:00:00+00:00',
               '2012-07-09 20:00:00+00:00', '2012-07-10 20:00:00+00:00'],
               dtype='datetime64[ns, UTC]', freq=None)

mcal.date_range(early, frequency='1H')

DatetimeIndex(['2012-07-02 14:30:00+00:00', '2012-07-02 15:30:00+00:00',
               '2012-07-02 16:30:00+00:00', '2012-07-02 17:30:00+00:00',
               '2012-07-02 18:30:00+00:00', '2012-07-02 19:30:00+00:00',
               '2012-07-02 20:00:00+00:00', '2012-07-03 14:30:00+00:00',
               '2012-07-03 15:30:00+00:00', '2012-07-03 16:30:00+00:00',
               '2012-07-03 17:00:00+00:00', '2012-07-05 14:30:00+00:00',
               '2012-07-05 15:30:00+00:00', '2012-07-05 16:30:00+00:00',
               '2012-07-05 17:30:00+00:00', '2012-07-05 18:30:00+00:00',
               '2012-07-05 19:30:00+00:00', '2012-07-05 20:00:00+00:00',
               '2012-07-06 14:30:00+00:00', '2012-07-06 15:30:00+00:00',
               '2012-07-06 16:30:00+00:00', '2012-07-06 17:30:00+00:00',
               '2012-07-06 18:30:00+00:00', '2012-07-06 19:30:00+00:00',
               '2012-07-06 20:00:00+00:00', '2012-07-09 14:30:00+00:00',
               '2012-07-09 15:30:00+00:00', '2012-07-09 16:30:00+00:00',
               '2012-07-09 17:30:00+00:00', '2012-07-09 18:30:00+00:00',
               '2012-07-09 19:30:00+00:00', '2012-07-09 20:00:00+00:00',
               '2012-07-10 14:30:00+00:00', '2012-07-10 15:30:00+00:00',
               '2012-07-10 16:30:00+00:00', '2012-07-10 17:30:00+00:00',
               '2012-07-10 18:30:00+00:00', '2012-07-10 19:30:00+00:00',
               '2012-07-10 20:00:00+00:00'],
              dtype='datetime64[ns, UTC]', freq=None)

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()

holidays.holidays[-5:]
(numpy.datetime64('2030-05-27'),
 numpy.datetime64('2030-07-04'),
 numpy.datetime64('2030-09-02'),
 numpy.datetime64('2030-11-28'),
 numpy.datetime64('2030-12-25'))

You have to create new instance of class: cal1 = tradingCal(). This works for me.

from pandas.tseries.holiday import get_calendar, HolidayCalendarFactory, GoodFriday
from datetime import datetime

cal = get_calendar('USFederalHolidayCalendar')  # Create calendar instance
cal.rules.pop(7)                                # Remove Veteran's Day rule
cal.rules.pop(6)                                # Remove Columbus Day rule
tradingCal = HolidayCalendarFactory('TradingCalendar', cal, GoodFriday)
print tradingCal.rules

#new instance of class
cal1 = tradingCal()

print cal1.holidays(datetime(2014, 12, 31), datetime(2016, 12, 31))

#DatetimeIndex(['2015-01-01', '2015-01-19', '2015-02-16', '2015-04-03',
#               '2015-05-25', '2015-07-03', '2015-09-07', '2015-11-26',
#               '2015-12-25', '2016-01-01', '2016-01-18', '2016-02-15',
#              '2016-03-25', '2016-05-30', '2016-07-04', '2016-09-05',
#               '2016-11-24', '2016-12-26'],
#              dtype='datetime64[ns]', freq=None, tz=None)

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