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 align_to_NY_cut_in_UTC(self, date_time):
tstz = TimeSeriesTimezone()
date_time = tstz.localise_index_as_new_york_time(date_time)
date_time.index = date_time.index + timedelta(hours=10)
return tstz.convert_index_aware_to_UTC_time(date_time)
def run_strategy_returns_stats(self, strategy):
"""
run_strategy_returns_stats - Plots useful statistics for the trading strategy (using PyFolio)
Parameters
----------
strategy : StrategyTemplate
defining trading strategy
"""
pnl = strategy.get_strategy_pnl()
tz = TimeSeriesTimezone()
tsc = TimeSeriesCalcs()
# PyFolio assumes UTC time based DataFrames (so force this localisation)
try:
pnl = tz.localise_index_as_UTC(pnl)
except: pass
# TODO for intraday strategy make daily
# convert DataFrame (assumed to have only one column) to Series
pnl = tsc.calculate_returns(pnl)
pnl = pnl[pnl.columns[0]]
fig = pf.create_returns_tear_sheet(pnl, return_fig=True)
try:
plt.savefig (strategy.DUMP_PATH + "stats.png")
except: pass
plt.show()
def run_strategy_returns_stats(self, strategy):
"""
run_strategy_returns_stats - Plots useful statistics for the trading strategy (using PyFolio)
Parameters
----------
strategy : StrategyTemplate
defining trading strategy
"""
pnl = strategy.get_strategy_pnl()
tz = TimeSeriesTimezone()
tsc = TimeSeriesCalcs()
# PyFolio assumes UTC time based DataFrames (so force this localisation)
try:
pnl = tz.localise_index_as_UTC(pnl)
except:
pass
# TODO for intraday strategy make daily
# convert DataFrame (assumed to have only one column) to Series
pnl = tsc.calculate_returns(pnl)
pnl = pnl[pnl.columns[0]]
fig = pf.create_returns_tear_sheet(pnl, return_fig=True)
try:
plt.savefig(strategy.DUMP_PATH + "stats.png")
except:
pass
plt.show()
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 run_strategy_returns_stats(self, strategy):
"""
run_strategy_returns_stats - Plots useful statistics for the trading strategy (using PyFolio)
Parameters
----------
strategy : StrategyTemplate
defining trading strategy
"""
pnl = strategy.get_strategy_pnl()
tz = TimeSeriesTimezone()
tsc = TimeSeriesCalcs()
# PyFolio assumes UTC time based DataFrames (so force this localisation)
try:
pnl = tz.localise_index_as_UTC(pnl)
except: pass
# set the matplotlib style sheet & defaults
# at present this only works in Matplotlib engine
try:
matplotlib.rcdefaults()
plt.style.use(GraphicsConstants().plotfactory_pythalesians_style_sheet['pythalesians-pyfolio'])
except: pass
# TODO for intraday strategies, make daily
# convert DataFrame (assumed to have only one column) to Series
pnl = tsc.calculate_returns(pnl)
pnl = pnl.dropna()
pnl = pnl[pnl.columns[0]]
fig = pf.create_returns_tear_sheet(pnl, return_fig=True)
try:
plt.savefig (strategy.DUMP_PATH + "stats.png")
except: pass
plt.show()
