# $$_ Lines starting with # $$_* autogenerated by jup_mini. Do not modify these
# $$_code
# $$_ %%checkall
import numpy as np
import pandas as pd
from collections.abc import Iterable
import datetime
import os
import inspect
import calendar as cal
import dateutil.relativedelta as rd
from typing import Tuple, Union, MutableMapping
from types import FrameType
DateTimeType = Union[pd.Timestamp, str, np.datetime64, datetime.datetime, datetime.date]
def _as_np_date(val: DateTimeType) -> Union[np.datetime64, np.ndarray, None]:
'''
Convert a pandas timestamp, string, np.datetime64('M8[ns]'), datetime, date to a numpy datetime64 [D] and remove time info.
Returns None if the value cannot be converted
>>> _as_np_date(pd.Timestamp('2016-05-01 3:55:00'))
numpy.datetime64('2016-05-01')
>>> _as_np_date('2016-05-01')
numpy.datetime64('2016-05-01')
>>> x = pd.DataFrame({'x' : [np.datetime64('2015-01-01 05:00:00'), np.datetime64('2015-02-01 06:00:00')]})
>>> _as_np_date(x.x)
array(['2015-01-01', '2015-02-01'], dtype='datetime64[D]')
>>> _as_np_date(pd.Series([np.datetime64('2015-01-01 05:00:00'), np.datetime64('2015-02-01 06:00:00')]))
array(['2015-01-01', '2015-02-01'], dtype='datetime64[D]')
>>> x = pd.DataFrame({'x' : [1, 2]}, index = [np.datetime64('2015-01-01 05:00:00'), np.datetime64('2015-02-01 06:00:00')])
>>> _as_np_date(x.index)
array(['2015-01-01', '2015-02-01'], dtype='datetime64[D]')
'''
if isinstance(val, np.datetime64):
return val.astype('M8[D]')
if isinstance(val, str) or isinstance(val, datetime.date) or isinstance(val, datetime.datetime):
np_date = np.datetime64(val).astype('M8[D]') # type: ignore
if isinstance(np_date.astype(datetime.datetime), int): # User can pass in a string like 20180101 which gets parsed as a year
raise Exception(f'invalid date: {val}')
return np_date
if isinstance(val, pd.Timestamp):
return val.to_datetime64().astype('M8[D]')
if isinstance(val, pd.Series) or isinstance(val, pd.DatetimeIndex):
return val.values.astype('M8[D]')
if isinstance(val, np.ndarray) and np.issubdtype(val.dtype, np.datetime64):
return val.astype('M8[D]')
return None
def _normalize_datetime(val: DateTimeType) -> Tuple[np.datetime64, np.timedelta64]:
'''
Break up a datetime into numpy date and numpy timedelta.
Args:
val: The datetime to normalize. Can be an array or a single datetime as a string, pandas timestamp, numpy datetime
or python date or datetime
>>> print(_normalize_datetime(pd.Timestamp('2016-05-01 3:55:00')))
(numpy.datetime64('2016-05-01'), numpy.timedelta64(14100000000000,'ns'))
>>> print(_normalize_datetime('2016-05-01'))
(numpy.datetime64('2016-05-01'), numpy.timedelta64(0,'D'))
>>> x = pd.DataFrame({'x' : [np.datetime64('2015-01-01 05:00:00'), np.datetime64('2015-02-01 06:00:00')]})
>>> print(_normalize_datetime(x.x))
(array(['2015-01-01', '2015-02-01'], dtype='datetime64[D]'), array([18000000000000, 21600000000000], dtype='timedelta64[ns]'))
>>> x = pd.DataFrame({'x' : [1, 2]}, index = [np.datetime64('2015-01-01 05:00:00'), np.datetime64('2015-02-01 06:00:00')])
>>> print(_normalize_datetime(x.index))
(array(['2015-01-01', '2015-02-01'], dtype='datetime64[D]'), array([18000000000000, 21600000000000], dtype='timedelta64[ns]'))
'''
if isinstance(val, pd.Timestamp):
dtime = val.to_datetime64()
elif isinstance(val, pd.Series) or isinstance(val, pd.DatetimeIndex):
dtime = val.values
elif isinstance(val, np.ndarray) and np.issubdtype(val.dtype, np.datetime64):
dtime = val
else:
assert isinstance(val, datetime.datetime) or isinstance(val, datetime.date) or isinstance(val, str) or isinstance(val, np.datetime64)
dtime = np.datetime64(val) # type: ignore
date = dtime.astype('M8[D]')
time_delta = dtime - date
return date, time_delta
def _normalize(start: DateTimeType,
end: DateTimeType,
include_first: bool,
include_last: bool) -> Union[Tuple[np.datetime64, np.datetime64], Tuple[np.ndarray, np.ndarray]]:
'''
Given a start and end date, return a new start and end date, taking into account include_first and include_last flags
Args:
start: start date
end: end_date
include_first: whether to increment start date by 1
include_last: whether to increment end date by 1
Return:
new start and end dates
>>> x = np.arange(np.datetime64('2017-01-01'), np.datetime64('2017-01-10'))
>>> y = x.astype('M8[ns]')
>>> xcp = x.copy()
>>> ycp = y.copy()
>>> _normalize(x, y, False, False) # doctest:+ELLIPSIS +NORMALIZE_WHITESPACE
(array(...
>>> (x == xcp).all()
True
>>> (y == ycp).all()
True
'''
s = _as_np_date(start)
e = _as_np_date(end)
assert s is not None and e is not None
if not include_first:
s += np.timedelta64(1, 'D')
if include_last:
e += np.timedelta64(1, 'D')
return s, e # type: ignore
[docs]def read_holidays(calendar_name: str, dirname: str = None) -> np.ndarray:
'''
Reads a csv with a holidays column containing holidays (not including weekends)
'''
curr_frame: FrameType = inspect.currentframe() # type: ignore # wants Optional[FrameType]
if dirname is None: dirname = os.path.dirname(os.path.abspath(inspect.getfile(curr_frame)))
if '/ipykernel_' in dirname: dirname = os.getcwd() # sometimes we get jupyter cache dir
if not os.path.isdir(dirname + '/refdata'):
if os.path.isdir(dirname + '/../refdata'):
dirname = dirname + '/../'
else:
raise Exception(f'path {dirname}/refdata and {dirname}/../refdata do not exist')
df = pd.read_csv(f'{dirname}/refdata/holiday_calendars/{calendar_name}.csv')
holidays = pd.to_datetime(df.holidays, format='%Y-%m-%d').values.astype('M8[D]')
return holidays
[docs]class Calendar:
NYSE = "nyse"
EUREX = "eurex"
_calendars: MutableMapping[str, 'Calendar'] = {}
[docs] def __init__(self, holidays: np.ndarray) -> None:
'''
Do not use this function directly. Use Calendar.get_calendar instead
Args:
holidays (np.array of datetime64[D]): holidays for this calendar, excluding weekends
'''
self.bus_day_cal = np.busdaycalendar(holidays=holidays)
[docs] def is_trading_day(self, dates: Union[np.datetime64, pd.Series, np.ndarray, str, datetime.datetime, datetime.date]) -> Union[bool, np.ndarray]:
'''
Returns whether the date is not a holiday or a weekend
Args:
dates: date times to check
Return:
Whether this date is a trading day
>>> import datetime
>>> eurex = Calendar.get_calendar(Calendar.EUREX)
>>> eurex.is_trading_day('2016-12-25')
False
>>> eurex.is_trading_day(datetime.date(2016, 12, 22))
True
>>> nyse = Calendar.get_calendar(Calendar.NYSE)
>>> nyse.is_trading_day('2017-04-01') # Weekend
False
>>> nyse.is_trading_day(np.arange('2017-04-01', '2017-04-09', dtype = np.datetime64)) # doctest:+ELLIPSIS
array([False, False, True, True, True, True, True, False]...)
'''
if isinstance(dates, str) or isinstance(dates, datetime.date):
dates = np.datetime64(dates, 'D') # type: ignore
if isinstance(dates.astype(datetime.datetime), int): # user can pass in a string like 20180101 which gets parsed as a date
raise Exception(f'invalid date: {dates}')
if isinstance(dates, pd.Series): dates = dates.values
return np.is_busday(dates.astype('M8[D]'), busdaycal=self.bus_day_cal)
[docs] def num_trading_days(self,
start: Union[np.datetime64, pd.Series, np.ndarray, str, datetime.datetime, datetime.date],
end: Union[np.datetime64, pd.Series, np.ndarray, str, datetime.datetime, datetime.date],
include_first: bool = False,
include_last: bool = True) -> Union[float, np.ndarray]:
'''
Count the number of trading days between two date series including those two dates
>>> eurex = Calendar.get_calendar(Calendar.EUREX)
>>> eurex.num_trading_days('2009-01-01', '2011-12-31')
772.0
>>> dates = pd.date_range('20130101',periods=8)
>>> increments = np.array([5, 0, 3, 9, 4, 10, 15, 29])
>>> import warnings
>>> import pandas as pd
>>> warnings.filterwarnings(action = 'ignore', category = pd.errors.PerformanceWarning)
>>> dates2 = dates + increments * dates.freq
>>> df = pd.DataFrame({'x': dates, 'y' : dates2})
>>> df.iloc[4]['x'] = np.nan
>>> df.iloc[6]['y'] = np.nan
>>> nyse = Calendar.get_calendar(Calendar.NYSE)
>>> np.set_printoptions(formatter = {'float' : lambda x : f'{x:.1f}'}) # After numpy 1.13 positive floats don't have a leading space for sign
>>> print(nyse.num_trading_days(df.x, df.y))
[3.0 0.0 1.0 5.0 nan 8.0 nan 20.0]
'''
iterable = isinstance(start, Iterable) and not isinstance(start, str)
s_tmp, e_tmp = _normalize(start, end, include_first, include_last)
# np.busday_count does not like nat dates
if iterable:
assert isinstance(s_tmp, Iterable)
ret = np.full(len(s_tmp), np.nan)
mask = ~(np.isnat(s_tmp) | np.isnat(e_tmp))
count = np.busday_count(s_tmp[mask], e_tmp[mask], busdaycal=self.bus_day_cal) # type: ignore
ret[mask] = count
return ret
else:
if np.isnat(s_tmp) or np.isnat(s_tmp): return np.nan
count = np.busday_count(s_tmp, e_tmp, busdaycal=self.bus_day_cal) # type: ignore
return count.astype(float)
[docs] def get_trading_days(self,
start: Union[np.datetime64, pd.Series, np.ndarray, str, datetime.datetime, datetime.date],
end: Union[np.datetime64, pd.Series, np.ndarray, str, datetime.datetime, datetime.date],
include_first: bool = False,
include_last: bool = True) -> Union[int, np.ndarray]:
'''
Get back a list of numpy dates that are trading days between the start and end
>>> nyse = Calendar.get_calendar(Calendar.NYSE)
>>> nyse.get_trading_days('2005-01-01', '2005-01-08')
array(['2005-01-03', '2005-01-04', '2005-01-05', '2005-01-06', '2005-01-07'], dtype='datetime64[D]')
>>> nyse.get_trading_days(datetime.date(2005, 1, 1), datetime.date(2005, 2, 1))
array(['2005-01-03', '2005-01-04', '2005-01-05', '2005-01-06',
'2005-01-07', '2005-01-10', '2005-01-11', '2005-01-12',
'2005-01-13', '2005-01-14', '2005-01-18', '2005-01-19',
'2005-01-20', '2005-01-21', '2005-01-24', '2005-01-25',
'2005-01-26', '2005-01-27', '2005-01-28', '2005-01-31', '2005-02-01'], dtype='datetime64[D]')
>>> nyse.get_trading_days(datetime.date(2016, 1, 5), datetime.date(2016, 1, 29), include_last = False)
array(['2016-01-06', '2016-01-07', '2016-01-08', '2016-01-11',
'2016-01-12', '2016-01-13', '2016-01-14', '2016-01-15',
'2016-01-19', '2016-01-20', '2016-01-21', '2016-01-22',
'2016-01-25', '2016-01-26', '2016-01-27', '2016-01-28'], dtype='datetime64[D]')
>>> nyse.get_trading_days('2017-07-04', '2017-07-08', include_first = False)
array(['2017-07-05', '2017-07-06', '2017-07-07'], dtype='datetime64[D]')
>>> nyse.get_trading_days(np.datetime64('2017-07-04'), np.datetime64('2017-07-08'), include_first = False)
array(['2017-07-05', '2017-07-06', '2017-07-07'], dtype='datetime64[D]')
'''
s, e = _normalize(start, end, include_first, include_last)
dates = np.arange(s, e, dtype='datetime64[D]')
dates = dates[np.is_busday(dates, busdaycal=self.bus_day_cal)]
return dates
[docs] def third_friday_of_month(self, month: int, year: int, roll: str = 'backward') -> np.datetime64:
'''
>>> nyse = Calendar.get_calendar(Calendar.NYSE)
>>> nyse.third_friday_of_month(3, 2017)
numpy.datetime64('2017-03-17')
'''
# From https://stackoverflow.com/questions/18424467/python-third-friday-of-a-month
FRIDAY = 4
first_day_of_month = datetime.datetime(year, month, 1)
first_friday = first_day_of_month + datetime.timedelta(days=((FRIDAY - cal.monthrange(year, month)[0]) + 7) % 7)
# 4 is friday of week
third_friday_date = first_friday + datetime.timedelta(days=14)
third_friday_dt = third_friday_date.date()
third_friday = self.add_trading_days(third_friday_dt, 0, roll)
assert isinstance(third_friday, np.datetime64)
return third_friday
[docs] def add_trading_days(self,
start: Union[np.datetime64, pd.Series, np.ndarray, str, datetime.datetime, datetime.date],
num_days: Union[int, np.ndarray],
roll: str = 'raise') -> Union[np.datetime64, np.ndarray]:
'''
Adds trading days to a start date
Args:
start: start datetimes(s)
num_days: number of trading days to add
roll: one of 'raise', 'nat', 'forward', 'following', 'backward', 'preceding', 'modifiedfollowing', 'modifiedpreceding' or 'allow'}
'allow' is a special case in which case, adding 1 day to a holiday will act as if it was not a holiday, and give you the next business day'
The rest of the values are the same as in the numpy busday_offset function
From numpy documentation:
How to treat dates that do not fall on a valid day. The default is ‘raise’.
'raise' means to raise an exception for an invalid day.
'nat' means to return a NaT (not-a-time) for an invalid day.
'forward' and 'following’ mean to take the first valid day later in time.
'backward' and 'preceding' mean to take the first valid day earlier in time.
'modifiedfollowing' means to take the first valid day later in time unless it is across a Month boundary,
in which case to take the first valid day earlier in time.
'modifiedpreceding' means to take the first valid day earlier in time unless it is across a Month boundary,
in which case to take the first valid day later in time.
Return:
The datetime num_days trading days after start
>>> calendar = Calendar.get_calendar(Calendar.NYSE)
>>> calendar.add_trading_days(datetime.date(2015, 12, 24), 1)
numpy.datetime64('2015-12-28')
>>> calendar.add_trading_days(np.datetime64('2017-04-15'), 0, roll = 'preceding') # 4/14/2017 is a Friday and a holiday
numpy.datetime64('2017-04-13')
>>> calendar.add_trading_days(np.datetime64('2017-04-08'), 0, roll = 'preceding') # 4/7/2017 is a Friday and not a holiday
numpy.datetime64('2017-04-07')
>>> calendar.add_trading_days(np.datetime64('2019-02-17 15:25'), 1, roll = 'allow')
numpy.datetime64('2019-02-19T15:25')
>>> calendar.add_trading_days(np.datetime64('2019-02-17 15:25'), -1, roll = 'allow')
numpy.datetime64('2019-02-15T15:25')
'''
start_date, time_delta = _normalize_datetime(start)
if roll == 'allow':
# If today is a holiday, roll forward but subtract 1 day so
num_days = np.where(self.is_trading_day(start) | (num_days < 1), num_days, num_days - 1) # type: ignore
roll = 'forward'
out = np.busday_offset(start_date, num_days, roll=roll, busdaycal=self.bus_day_cal) # type: ignore
out = out + time_delta # for some reason += does not work correctly here.
return out
[docs] @staticmethod
def add_calendar(exchange_name: str, holidays: np.ndarray) -> None:
'''
Add a trading calendar to the class level calendars dict
Args:
exchange_name: Name of the exchange.
holidays: holidays for this exchange, excluding weekends
'''
Calendar._calendars[exchange_name] = Calendar(holidays)
[docs] @staticmethod
def get_calendar(exchange_name: str) -> 'Calendar':
'''
Get a calendar object for the given exchange:
Args:
exchange_name: The exchange for which you want a calendar. Calendar.NYSE, Calendar.EUREX are predefined.
If you want to add a new calendar, use the add_calendar class level function
Return:
The calendar object
'''
if exchange_name not in Calendar._calendars:
if exchange_name not in [Calendar.NYSE, Calendar.EUREX]:
raise Exception(f'calendar not found: {exchange_name}')
holidays = read_holidays(exchange_name)
Calendar.add_calendar(exchange_name, holidays)
return Calendar._calendars[exchange_name]
[docs]def get_date_from_weekday(weekday: int, year: int, month: int, week: int) -> np.datetime64:
'''
Return the date that falls on a given weekday (Monday = 0) on a week, year and month
>>> get_date_from_weekday(1, 2019, 10, 4)
numpy.datetime64('2019-10-22')
'''
if week == -1: # Last day of month
_, last_day = cal.monthrange(year, month)
return np.datetime64(datetime.datetime(year, month, last_day)).astype('M8[D]')
first_day_of_month = datetime.datetime(year, month, 1)
date = first_day_of_month + rd.relativedelta(weeks=week - 1, weekday=weekday)
return np.datetime64(date).astype('M8[D]')
if __name__ == "__main__":
import doctest
doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)
# $$_end_code