def get_economic_event_ret_over_custom_event_day(self, data_frame_in, event_dates, name, event, start, end, lagged = False,
NYC_cutoff = 10):
time_series_filter = TimeSeriesFilter()
event_dates = time_series_filter.filter_time_series_by_date(start, end, event_dates)
data_frame = data_frame_in.copy(deep=True) # because we change the dates!
time_series_tz = TimeSeriesTimezone()
calendar = Calendar()
bday = CustomBusinessDay(weekmask='Mon Tue Wed Thu Fri')
event_dates_nyc = time_series_tz.convert_index_from_UTC_to_new_york_time(event_dates)
average_hour_nyc = numpy.average(event_dates_nyc.index.hour)
event_dates = calendar.floor_date(event_dates)
# realised is traditionally on later day eg. 3rd Jan realised ON is 2nd-3rd Jan realised
# so if Fed meeting is on 2nd Jan later, then we need realised labelled on 3rd (so minus a day)
# implied expires on next day eg. 3rd Jan implied ON is 3rd-4th Jan implied
# TODO smarter way of adjusting dates, as sometimes events can be before/after 10am NY cut
if (lagged and average_hour_nyc >= NYC_cutoff):
data_frame.index = data_frame.index - bday
elif (not lagged and average_hour_nyc < NYC_cutoff): # ie. implied
data_frame.index = data_frame.index + bday
# set as New York time and select only those ON vols at the 10am NY cut just before the event
data_frame_events = data_frame.ix[event_dates.index]
data_frame_events.columns = data_frame.columns.values + '-' + name + ' ' + event
return data_frame_events
def average_by_month_day_hour_min_by_bus_day(self, data_frame, cal = "FX"):
date_index = data_frame.index
return data_frame.\
groupby([date_index.month,
Calendar().get_bus_day_of_month(date_index, cal),
date_index.hour, date_index.minute]).mean()
def get_economic_event_ret_over_custom_event_day(self,
data_frame_in,
event_dates,
name,
event,
start,
end,
lagged=False,
NYC_cutoff=10):
time_series_filter = TimeSeriesFilter()
event_dates = time_series_filter.filter_time_series_by_date(
start, end, event_dates)
data_frame = data_frame_in.copy(
deep=True) # because we change the dates!
time_series_tz = TimeSeriesTimezone()
calendar = Calendar()
bday = CustomBusinessDay(weekmask='Mon Tue Wed Thu Fri')
event_dates_nyc = time_series_tz.convert_index_from_UTC_to_new_york_time(
event_dates)
average_hour_nyc = numpy.average(event_dates_nyc.index.hour)
event_dates = calendar.floor_date(event_dates)
# realised is traditionally on later day eg. 3rd Jan realised ON is 2nd-3rd Jan realised
# so if Fed meeting is on 2nd Jan later, then we need realised labelled on 3rd (so minus a day)
# implied expires on next day eg. 3rd Jan implied ON is 3rd-4th Jan implied
# TODO smarter way of adjusting dates, as sometimes events can be before/after 10am NY cut
if (lagged and average_hour_nyc >= NYC_cutoff):
data_frame.index = data_frame.index - bday
elif (not lagged and average_hour_nyc < NYC_cutoff): # ie. implied
data_frame.index = data_frame.index + bday
# set as New York time and select only those ON vols at the 10am NY cut just before the event
data_frame_events = data_frame.ix[event_dates.index]
data_frame_events.columns = data_frame.columns.values + '-' + name + ' ' + event
return data_frame_events
def average_by_day_hour_min_by_bus_day(self, data_frame):
date_index = data_frame.index
return data_frame.\
groupby([Calendar().get_bus_day_of_month(date_index),
date_index.hour, date_index.minute]).mean()
def average_by_bus_day(self, data_frame, cal = "FX"):
date_index = data_frame.index
return data_frame.\
groupby([Calendar().get_bus_day_of_month(date_index, cal)]).mean()
