def handle_minute_close(self, dt):
todays_date = dt.replace(hour=0, minute=0, second=0, microsecond=0)
minute_returns = self.minute_performance.returns
self.minute_performance.rollover()
algo_minute_returns = pd.Series({dt: minute_returns})
bench_minute_returns = pd.Series({dt: self.all_benchmark_returns[dt]})
# the intraday risk is calculated on top of minute performance
# returns for the bench and the algo
self.intraday_risk_metrics.update(dt, algo_minute_returns,
bench_minute_returns)
bench_since_open = \
self.intraday_risk_metrics.benchmark_period_returns[-1]
benchmark_returns = pd.Series({todays_date: bench_since_open})
# if we've reached market close, check on dividends
if dt == self.market_close:
for perf_period in self.perf_periods:
perf_period.update_dividends(todays_date)
algorithm_returns = pd.Series(
{todays_date: self.todays_performance.returns})
self.cumulative_risk_metrics.update(todays_date, algorithm_returns,
benchmark_returns)
# if this is the close, save the returns objects for cumulative
# risk calculations
if dt == self.market_close:
todays_return_obj = zp.DailyReturn(todays_date,
self.todays_performance.returns)
self.returns.append(todays_return_obj)
def handle_market_close(self):
# add the return results from today to the list of DailyReturn objects.
todays_date = self.market_close.replace(hour=0,
minute=0,
second=0,
microsecond=0)
self.cumulative_performance.update_dividends(todays_date)
self.todays_performance.update_dividends(todays_date)
todays_return_obj = zp.DailyReturn(todays_date,
self.todays_performance.returns)
self.returns.append(todays_return_obj)
#update risk metrics for cumulative performance
self.cumulative_risk_metrics.update(
todays_return_obj.date, todays_return_obj.returns,
self.all_benchmark_returns[todays_return_obj.date])
# increment the day counter before we move markers forward.
self.day_count += 1.0
# Take a snapshot of our current peformance to return to the
# browser.
daily_update = self.to_dict()
# On the last day of the test, don't create tomorrow's performance
# period. We may not be able to find the next trading day if we're
# at the end of our historical data
if self.market_close >= self.last_close:
return daily_update
#move the market day markers forward
self.market_open, self.market_close = \
trading.environment.next_open_and_close(self.market_open)
# Roll over positions to current day.
self.todays_performance.rollover()
self.todays_performance.period_open = self.market_open
self.todays_performance.period_close = self.market_close
# The dividend calculation for the daily needs to be made
# after the rollover. midnight_between is the last midnight
# hour between the close of markets and the next open. To
# make sure midnight_between matches identically with
# dividend data dates, it is in UTC.
midnight_between = self.market_open.replace(hour=0,
minute=0,
second=0,
microsecond=0)
self.cumulative_performance.update_dividends(midnight_between)
self.todays_performance.update_dividends(midnight_between)
return daily_update