def test_drawdown(self):
returns = factory.create_returns_from_list(
[1.0, -0.5, 0.8, .17, 1.0, -0.1, -0.45], self.sim_params)
#200, 100, 180, 210.6, 421.2, 379.8, 208.494
metrics = risk.RiskMetricsBatch(returns[0].date, returns[-1].date,
returns)
self.assertEqual(metrics.max_drawdown, 0.505)
def test_risk_metrics_returns(self):
trading.environment = trading.TradingEnvironment()
# Advance start date to first date in the trading calendar
if trading.environment.is_trading_day(self.start_date):
start_date = self.start_date
else:
start_date = trading.environment.next_trading_day(self.start_date)
self.all_benchmark_returns = pd.Series({
x.date: x.returns
for x in trading.environment.benchmark_returns
if x.date >= self.start_date
})
start_index = trading.environment.trading_days.searchsorted(start_date)
end_date = trading.environment.trading_days[start_index + len(RETURNS)]
sim_params = SimulationParameters(start_date, end_date)
risk_metrics_refactor = risk.RiskMetricsIterative(sim_params)
todays_date = start_date
cur_returns = []
for i, ret in enumerate(RETURNS):
todays_return_obj = DailyReturn(todays_date, ret)
cur_returns.append(todays_return_obj)
try:
risk_metrics_original = risk.RiskMetricsBatch(
start_date=start_date,
end_date=todays_date,
returns=cur_returns)
except Exception as e:
#assert that when original raises exception, same
#exception is raised by risk_metrics_refactor
np.testing.assert_raises(
type(e), risk_metrics_refactor.update, todays_date,
self.all_benchmark_returns[todays_return_obj.date])
continue
risk_metrics_refactor.update(
todays_date, ret,
self.all_benchmark_returns[todays_return_obj.date])
# Move forward day counter to next trading day
todays_date = trading.environment.next_trading_day(todays_date)
self.assertEqual(risk_metrics_original.start_date,
risk_metrics_refactor.start_date)
self.assertEqual(risk_metrics_original.end_date,
risk_metrics_refactor.algorithm_returns.index[-1])
self.assertEqual(risk_metrics_original.treasury_period_return,
risk_metrics_refactor.treasury_period_return)
np.testing.assert_allclose(risk_metrics_original.benchmark_returns,
risk_metrics_refactor.benchmark_returns,
rtol=0.001)
np.testing.assert_allclose(risk_metrics_original.algorithm_returns,
risk_metrics_refactor.algorithm_returns,
rtol=0.001)
risk_original_dict = risk_metrics_original.to_dict()
risk_refactor_dict = risk_metrics_refactor.to_dict()
self.assertEqual(set(risk_original_dict.keys()),
set(risk_refactor_dict.keys()))
err_msg_format = """\
"In update step {iter}: {measure} should be {truth} but is {returned}!"""
for measure in risk_original_dict.iterkeys():
if measure == 'max_drawdown':
np.testing.assert_almost_equal(
risk_refactor_dict[measure],
risk_original_dict[measure],
err_msg=err_msg_format.format(
iter=i,
measure=measure,
truth=risk_original_dict[measure],
returned=risk_refactor_dict[measure]))
else:
if isinstance(risk_original_dict[measure], numbers.Real):
np.testing.assert_allclose(
risk_original_dict[measure],
risk_refactor_dict[measure],
rtol=0.001,
err_msg=err_msg_format.format(
iter=i,
measure=measure,
truth=risk_original_dict[measure],
returned=risk_refactor_dict[measure]))
else:
np.testing.assert_equal(
risk_original_dict[measure],
risk_refactor_dict[measure],
err_msg=err_msg_format.format(
iter=i,
measure=measure,
truth=risk_original_dict[measure],
returned=risk_refactor_dict[measure]))
def test_risk_metrics_returns(self):
risk_metrics_refactor = risk.RiskMetricsIterative(
self.start_date, self.trading_env)
todays_date = self.start_date
cur_returns = []
for i, ret in enumerate(RETURNS):
todays_return_obj = risk.DailyReturn(
todays_date,
ret
)
cur_returns.append(todays_return_obj)
# Move forward day counter to next trading day
todays_date += self.oneday
while not self.trading_env.is_trading_day(todays_date):
todays_date += self.oneday
try:
risk_metrics_original = risk.RiskMetricsBatch(
start_date=self.start_date,
end_date=todays_date,
returns=cur_returns,
trading_environment=self.trading_env
)
except Exception as e:
#assert that when original raises exception, same
#exception is raised by risk_metrics_refactor
np.testing.assert_raises(
type(e), risk_metrics_refactor.update, ret)
continue
risk_metrics_refactor.update(ret)
self.assertEqual(
risk_metrics_original.start_date,
risk_metrics_refactor.start_date)
self.assertEqual(
risk_metrics_original.end_date,
risk_metrics_refactor.end_date)
self.assertEqual(
risk_metrics_original.treasury_duration,
risk_metrics_refactor.treasury_duration)
self.assertEqual(
risk_metrics_original.treasury_curve,
risk_metrics_refactor.treasury_curve)
self.assertEqual(
risk_metrics_original.treasury_period_return,
risk_metrics_refactor.treasury_period_return)
self.assertEqual(
risk_metrics_original.benchmark_returns,
risk_metrics_refactor.benchmark_returns)
self.assertEqual(
risk_metrics_original.algorithm_returns,
risk_metrics_refactor.algorithm_returns)
risk_original_dict = risk_metrics_original.to_dict()
risk_refactor_dict = risk_metrics_refactor.to_dict()
self.assertEqual(set(risk_original_dict.keys()),
set(risk_refactor_dict.keys()))
err_msg_format = """\
"In update step {iter}: {measure} should be {truth} but is {returned}!"""
for measure in risk_original_dict.iterkeys():
if measure == 'max_drawdown':
np.testing.assert_almost_equal(
risk_refactor_dict[measure],
risk_original_dict[measure],
err_msg=err_msg_format.format(
iter=i,
measure=measure,
truth=risk_original_dict[measure],
returned=risk_refactor_dict[measure]))
else:
np.testing.assert_equal(
risk_original_dict[measure],
risk_refactor_dict[measure],
err_msg_format.format(
iter=i,
measure=measure,
truth=risk_original_dict[measure],
returned=risk_refactor_dict[measure])
)