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 In [260]: from pandas.tseries.holiday import ( .....: Holiday , .....: USMemorialDay , .....: AbstractHolidayCalendar , .....: nearest_workday , .....: MO , .....: ) .....: In [261]: class ExampleCalendar ( AbstractHolidayCalendar ): .....: rules = [ .....: USMemorialDay , .....: Holiday ( "July 4th" , month = 7 , day = 4 , observance = nearest_workday ), .....: Holiday ( .....: "Columbus Day" , .....: month = 10 , .....: day = 1 , .....: offset = pd . DateOffset ( weekday = MO ( 2 )), .....: ), .....: ] .....: In [262]: cal = ExampleCalendar () In [263]: cal . holidays ( datetime . datetime ( 2012 , 1 , 1 ), datetime . datetime ( 2012 , 12 , 31 )) Out[263]: DatetimeIndex(['2012-05-28', '2012-07-04', '2012-10-08'], dtype='datetime64[ns]', freq=None) hint : Date times: A specific date and time with timezone support. Similar to datetime.datetime from the standard library. By having trial and error, I could only find the correct output by combining the method as below: from datetime import datetime, timedelta

command refers to 20 days INCLUDING weekends, but I want it to refer to 20 WEEKDAYS; e.g. something like this: df["window"].loc[beg: beg + pd.to_timedelta(20, "Weekdays_only")] = 2 from secondly to every 250 milliseconds In [306]: ts [: 2 ] . resample ( "250L" ) . asfreq () Out[306]: 2012-01-01 00:00:00.000 308.0 2012-01-01 00:00:00.250 NaN 2012-01-01 00:00:00.500 NaN 2012-01-01 00:00:00.750 NaN 2012-01-01 00:00:01.000 204.0 Freq: 250L, dtype: float64 In [307]: ts [: 2 ] . resample ( "250L" ) . ffill () Out[307]: 2012-01-01 00:00:00.000 308 2012-01-01 00:00:00.250 308 2012-01-01 00:00:00.500 308 2012-01-01 00:00:00.750 308 2012-01-01 00:00:01.000 204 Freq: 250L, dtype: int64 In [308]: ts [: 2 ] . resample ( "250L" ) . ffill ( limit = 2 ) Out[308]: 2012-01-01 00:00:00.000 308.0 2012-01-01 00:00:00.250 308.0 2012-01-01 00:00:00.500 308.0 2012-01-01 00:00:00.750 NaN 2012-01-01 00:00:01.000 204.0 Freq: 250L, dtype: float64 Sparse resampling # Holiday: Dr. Martin Luther King Jr. (month=1, day=1, offset=), BUSINESS_DATE += "', Next (" + DAY_NAME[TOMORROW.weekday()] + "):'" + TOMORROW.strftime('%y%m%d') + "']" Holiday: New Years Day (month=1, day=1, observance=),In [13]: friday = pd . Timestamp ( "2018-01-05" ) In [14]: friday . day_name () Out[14]: 'Friday' # Add 1 day In [15]: saturday = friday + pd . Timedelta ( "1 day" ) In [16]: saturday . day_name () Out[16]: 'Saturday' # Add 1 business day (Friday --> Monday) In [17]: monday = friday + pd . offsets . BDay () In [18]: monday . day_name () Out[18]: 'Monday' Cell In [ 489 ], line 1 ----> 1 dti . tz_localize ( 'Europe/Warsaw' ) File ~/work/pandas/pandas/pandas/core/indexes/datetimes.py:291, in DatetimeIndex.tz_localize (self, tz, ambiguous, nonexistent) 284 @doc ( DatetimeArray . tz_localize ) 285 def tz_localize ( 286 self , ( ... ) 289 nonexistent : TimeNonexistent = "raise" , 290 ) -> Self : --> 291 arr = self . _data . tz_localize ( tz , ambiguous , nonexistent ) 292 return type ( self ) . _simple_new ( arr , name = self . name ) File ~/work/pandas/pandas/pandas/core/arrays/_mixins.py:80, in ravel_compat..method (self, *args, **kwargs) 77 @wraps ( meth ) 78 def method ( self , * args , ** kwargs ): 79 if self . ndim == 1 : ---> 80 return meth ( self , * args , ** kwargs ) 82 flags = self . _ndarray . flags 83 flat = self . ravel ( "K" ) File ~/work/pandas/pandas/pandas/core/arrays/datetimes.py:1066, in DatetimeArray.tz_localize (self, tz, ambiguous, nonexistent) 1063 tz = timezones . maybe_get_tz ( tz ) 1064 # Convert to UTC -> 1066 new_dates = tzconversion . tz_localize_to_utc ( 1067 self . asi8 , 1068 tz , 1069 ambiguous = ambiguous , 1070 nonexistent = nonexistent , 1071 creso = self . _creso , 1072 ) 1073 new_dates_dt64 = new_dates . view ( f "M8[ { self . unit } ]" ) 1074 dtype = tz_to_dtype ( tz , unit = self . unit ) File tzconversion.pyx:426, in pandas._libs.tslibs.tzconversion.tz_localize_to_utc () NonExistentTimeError: 2015-03-29 02:30:00 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') Is there an easy fix so that I can replace the "D" with something else or do I have to rewrite everything? 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]')

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 BUSINESS_DATE = "[Previous (" + DAY_NAME[YESTERDAY.weekday()] + "):'" + YESTERDAY.strftime('%y%m%d') under the default business hours (9:00 - 17:00), there is no gap (0 minutes) between 2014-08-01 17:00 and is similar to a Timedelta that represents a duration of time but follows specific calendar duration rules.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 # In [76]: start = datetime . datetime ( 2011 , 1 , 1 ) In [77]: end = datetime . datetime ( 2012 , 1 , 1 ) In [78]: index = pd . date_range ( start , end ) In [79]: index Out[79]: DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03', '2011-01-04', '2011-01-05', '2011-01-06', '2011-01-07', '2011-01-08', '2011-01-09', '2011-01-10', ... '2011-12-23', '2011-12-24', '2011-12-25', '2011-12-26', '2011-12-27', '2011-12-28', '2011-12-29', '2011-12-30', '2011-12-31', '2012-01-01'], dtype='datetime64[ns]', length=366, freq='D') In [80]: index = pd . bdate_range ( start , end ) In [81]: index Out[81]: DatetimeIndex(['2011-01-03', '2011-01-04', '2011-01-05', '2011-01-06', '2011-01-07', '2011-01-10', '2011-01-11', '2011-01-12', '2011-01-13', '2011-01-14', ... '2011-12-19', '2011-12-20', '2011-12-21', '2011-12-22', '2011-12-23', '2011-12-26', '2011-12-27', '2011-12-28', '2011-12-29', '2011-12-30'], dtype='datetime64[ns]', length=260, freq='B') TypeError: unbound method holidays() must be called with TradingCalendar instance as first argument (got datetime instance instead) Holiday: Christmas (month=12, day=25, observance=),

In [256]: pd . Timestamp ( "2014-01-02" ) + pd . offsets . MonthBegin ( n = 0 ) Out[256]: Timestamp('2014-02-01 00:00:00') In [257]: pd . Timestamp ( "2014-01-02" ) + pd . offsets . MonthEnd ( n = 0 ) Out[257]: Timestamp('2014-01-31 00:00:00') In [258]: pd . Timestamp ( "2014-01-01" ) + pd . offsets . MonthBegin ( n = 0 ) Out[258]: Timestamp('2014-01-01 00:00:00') In [259]: pd . Timestamp ( "2014-01-31" ) + pd . offsets . MonthEnd ( n = 0 ) Out[259]: Timestamp('2014-01-31 00:00:00') Holidays / holiday calendars #

Perhaps it is more straightforward to create the trade calendar from scratch, like so: import datetime as dt ts = pd . Timestamp ( 2022 , 12 , 9 , 15 ) >>> ts + pd . offsets . BusinessDay ( normalize = True ) Timestamp('2022-12-12 00:00:00') For time series data, it’s conventional to represent the time component in the index of a Series or DataFrame whenever the dob is greater than now. You may want to subtract a few years to now in the condition df['dob'] < now since it may be slightly more likely to have a 101 year old worker than a 1 year old worker... This is because one day’s business hour end is equal to next day’s business hour start. For example,