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 = ClassicRiskMetrics.risk_report(
algorithm_returns=self.algo_returns,
benchmark_returns=self.benchmark_returns,
algorithm_leverages=pd.Series(0.0, index=self.algo_returns.index)
)
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 = ClassicRiskMetrics.risk_report(
algorithm_returns=returns,
# use returns from the fixture to ensure that we have enough data.
benchmark_returns=self.BENCHMARK_RETURNS,
algorithm_leverages=pd.Series(0.0, index=returns.index),
)
total_months = 60
self.check_metrics(metrics, total_months, start_session)
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 = ClassicRiskMetrics.risk_report(
algorithm_returns=returns,
# use returns from the fixture to ensure that we have enough data.
benchmark_returns=self.BENCHMARK_RETURNS,
algorithm_leverages=pd.Series(0.0, index=returns.index)
)
total_months = 60
self.check_metrics(metrics, total_months, start_session)
def test_treasury_returns(self):
returns = factory.create_returns_from_range(self.sim_params)
metrics = ClassicRiskMetrics.risk_report(
algorithm_returns=returns,
benchmark_returns=self.benchmark_returns,
algorithm_leverages=pd.Series(0.0, index=returns.index))
# These values are all expected to be zero because we explicity zero
# out the treasury period returns as they are no longer actually used.
for period in PERIODS:
self.assertEqual(
[x['treasury_period_return'] for x in metrics[period]],
[0.0] * len(metrics[period]),
)
def test_treasury_returns(self):
returns = factory.create_returns_from_range(self.sim_params)
metrics = ClassicRiskMetrics.risk_report(
algorithm_returns=returns,
benchmark_returns=self.env.benchmark_returns,
algorithm_leverages=pd.Series(0.0, index=returns.index)
)
# These values are all expected to be zero because we explicity zero
# out the treasury period returns as they are no longer actually used.
for period in PERIODS:
self.assertEqual(
[x['treasury_period_return'] for x in metrics[period]],
[0.0] * len(metrics[period]),
)
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 = ClassicRiskMetrics.risk_report(
algorithm_returns=returns,
# use returns from the fixture to ensure that we have enough data.
benchmark_returns=self.BENCHMARK_RETURNS,
algorithm_leverages=pd.Series(0.0, index=returns.index))
self.check_metrics(metrics, 24, start_session)
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 = ClassicRiskMetrics.risk_report(
algorithm_returns=returns,
benchmark_returns=self.env.benchmark_returns,
algorithm_leverages=pd.Series(0.0, index=returns.index)
)
self.check_metrics(metrics, 24, start_session)
