def init_instance_fixtures(self):
super(TestRisk, self).init_instance_fixtures()
self.start_session = pd.Timestamp("2006-01-01", tz='UTC')
self.end_session = self.trading_calendar.minute_to_session_label(
pd.Timestamp("2006-12-31", tz='UTC'),
direction="previous"
)
self.sim_params = SimulationParameters(
start_session=self.start_session,
end_session=self.end_session,
trading_calendar=self.trading_calendar,
)
self.algo_returns = factory.create_returns_from_list(
RETURNS,
self.sim_params
)
self.benchmark_returns = factory.create_returns_from_list(
BENCHMARK,
self.sim_params
)
self.metrics = risk.RiskReport(
self.algo_returns,
self.sim_params,
benchmark_returns=self.benchmark_returns,
trading_calendar=self.trading_calendar,
treasury_curves=self.env.treasury_curves,
)
def _test_treasury_returns(self):
returns = factory.create_returns_from_range(self.sim_params)
metrics = risk.RiskReport(returns,
self.sim_params,
trading_calendar=self.trading_calendar,
treasury_curves=self.env.treasury_curves,
benchmark_returns=self.env.benchmark_returns)
self.assertEqual([
round(x.treasury_period_return, 4) for x in metrics.month_periods
], [
0.0037, 0.0034, 0.0039, 0.0038, 0.0040, 0.0037, 0.0043, 0.0043,
0.0038, 0.0044, 0.0043, 0.004
])
self.assertEqual([
round(x.treasury_period_return, 4)
for x in metrics.three_month_periods
], [
0.0114, 0.0116, 0.0122, 0.0125, 0.0129, 0.0127, 0.0123, 0.0128,
0.0125, 0.0127
])
self.assertEqual([
round(x.treasury_period_return, 4)
for x in metrics.six_month_periods
], [0.0260, 0.0257, 0.0258, 0.0252, 0.0259, 0.0256, 0.0257])
self.assertEqual(
[round(x.treasury_period_return, 4) for x in metrics.year_periods],
[0.0500])
def handle_simulation_end(self):
"""
When the simulation is complete, run the full period risk report
and send it out on the results socket.
"""
log_msg = "Simulated {n} trading days out of {m}."
log.info(log_msg.format(n=int(self.session_count),
m=self.total_session_count))
log.info("first open: {d}".format(
d=self.sim_params.first_open))
log.info("last close: {d}".format(
d=self.sim_params.last_close))
bms = pd.Series(
index=self.cumulative_risk_metrics.cont_index,
data=self.cumulative_risk_metrics.benchmark_returns_cont)
ars = pd.Series(
index=self.cumulative_risk_metrics.cont_index,
data=self.cumulative_risk_metrics.algorithm_returns_cont)
acl = self.cumulative_risk_metrics.algorithm_cumulative_leverages
risk_report = risk.RiskReport(
ars,
self.sim_params,
benchmark_returns=bms,
algorithm_leverages=acl,
trading_calendar=self.trading_calendar,
treasury_curves=self.treasury_curves,
)
return risk_report.to_dict()
def _test_benchmarkrange(self):
start_session = self.trading_calendar.minute_to_session_label(
pd.Timestamp("2008-01-01", tz='UTC'))
end_session = self.trading_calendar.minute_to_session_label(
pd.Timestamp("2010-01-01", tz='UTC'), direction="previous")
sim_params = SimulationParameters(
start_session=start_session,
end_session=end_session,
trading_calendar=self.trading_calendar,
)
returns = factory.create_returns_from_range(sim_params)
metrics = risk.RiskReport(returns,
self.sim_params,
trading_calendar=self.trading_calendar,
treasury_curves=self.env.treasury_curves,
benchmark_returns=self.env.benchmark_returns)
self.check_metrics(metrics, 24, start_session)
def _test_algorithm_sortino(self):
# The sortino ratio is calculated by a empyrical function so testing
# of period sortino ratios will be limited to determine if the value is
# numerical. This tests for its existence and format.
# This test needs a different result set that, with some
# negative results, otherwise fails in a legitimate way.
RETURNS = (np.random.rand(251) * 0.1) - 0.05
self.algo_returns = factory.create_returns_from_list(
RETURNS, self.sim_params)
self.metrics = risk.RiskReport(
self.algo_returns,
self.sim_params,
benchmark_returns=self.benchmark_returns,
trading_calendar=self.trading_calendar,
treasury_curves=self.env.treasury_curves,
)
for x in self.metrics.month_periods:
print(type(x.sortino))
np.testing.assert_equal(
all(
isinstance(x.sortino, float)
for x in self.metrics.month_periods), True)
np.testing.assert_equal(
all(
isinstance(x.sortino, float)
for x in self.metrics.three_month_periods), True)
np.testing.assert_equal(
all(
isinstance(x.sortino, float)
for x in self.metrics.six_month_periods), True)
np.testing.assert_equal(
all(
isinstance(x.sortino, float)
for x in self.metrics.year_periods), True)
def test_partial_month(self):
start_session = self.trading_calendar.minute_to_session_label(
pd.Timestamp("1993-02-01", tz='UTC')
)
# 1992 and 1996 were leap years
total_days = 365 * 5 + 2
end_session = start_session + datetime.timedelta(days=total_days)
sim_params90s = SimulationParameters(
start_session=start_session,
end_session=end_session,
trading_calendar=self.trading_calendar,
)
returns = factory.create_returns_from_range(sim_params90s)
returns = returns[:-10] # truncate the returns series to end mid-month
metrics = risk.RiskReport(returns, sim_params90s,
trading_calendar=self.trading_calendar,
treasury_curves=self.env.treasury_curves,
benchmark_returns=self.env.benchmark_returns)
total_months = 60
self.check_metrics(metrics, total_months, start_session)
