def test_representation(self):
test_period = ClassicRiskMetrics.risk_metric_period(
start_session=self.start_session,
end_session=self.end_session,
algorithm_returns=self.algo_returns,
benchmark_returns=self.benchmark_returns,
algorithm_leverages=pd.Series(0.0, index=self.algo_returns.index))
metrics = {
"algorithm_period_return",
"benchmark_period_return",
"treasury_period_return",
"period_label",
"excess_return",
"trading_days",
"benchmark_volatility",
"algo_volatility",
"sharpe",
"sortino",
"beta",
"alpha",
"max_drawdown",
"max_leverage",
}
self.assertEqual(set(test_period), metrics)
def test_representation(self):
test_period = ClassicRiskMetrics.risk_metric_period(
start_session=self.start_session,
end_session=self.end_session,
algorithm_returns=self.algo_returns,
benchmark_returns=self.benchmark_returns,
algorithm_leverages=pd.Series(
0.0,
index=self.algo_returns.index
)
)
metrics = {
"algorithm_period_return",
"benchmark_period_return",
"treasury_period_return",
"period_label",
"excess_return",
"trading_days",
"benchmark_volatility",
"algo_volatility",
"sharpe",
"sortino",
"beta",
"alpha",
"max_drawdown",
"max_leverage",
}
self.assertEqual(set(test_period), metrics)
def test_sharpe_value_when_benchmark_null(self):
# Sharpe is displayed as '0.0' instead of np.nan
null_returns = factory.create_returns_from_list([0.0] * 251,
self.sim_params)
test_period = ClassicRiskMetrics.risk_metric_period(
start_session=self.start_session,
end_session=self.end_session,
algorithm_returns=null_returns,
benchmark_returns=null_returns,
algorithm_leverages=pd.Series(0.0, index=self.algo_returns.index))
self.assertEqual(test_period['sharpe'], 0.0)
def test_sharpe_value_when_null(self):
# Sharpe is displayed as '0.0' instead of np.nan
null_returns = factory.create_returns_from_list(
[0.0]*251,
self.sim_params
)
test_period = ClassicRiskMetrics.risk_metric_period(
start_session=self.start_session,
end_session=self.end_session,
algorithm_returns=null_returns,
benchmark_returns=self.benchmark_returns,
algorithm_leverages=pd.Series(
0.0,
index=self.algo_returns.index
)
)
self.assertEqual(test_period['sharpe'], 0.0)
def test_algorithm_leverages(self):
# Max leverage for an algorithm with 'None' as leverage is 0.
for period, expected_len in zip(PERIODS, [12, 10, 7, 1]):
self.assertEqual(
[x['max_leverage'] for x in self.metrics[period]],
[0.0] * expected_len,
)
test_period = ClassicRiskMetrics.risk_metric_period(
start_session=self.start_session,
end_session=self.end_session,
algorithm_returns=self.algo_returns,
benchmark_returns=self.benchmark_returns,
algorithm_leverages=pd.Series([.01, .02, .03]))
# This return period has a list instead of None for algorithm_leverages
# Confirm that max_leverage is set to the max of those values
self.assertEqual(test_period['max_leverage'], .03)
def test_algorithm_leverages(self):
# Max leverage for an algorithm with 'None' as leverage is 0.
for period, expected_len in zip(PERIODS, [12, 10, 7, 1]):
self.assertEqual(
[x['max_leverage'] for x in self.metrics[period]],
[0.0] * expected_len,
)
test_period = ClassicRiskMetrics.risk_metric_period(
start_session=self.start_session,
end_session=self.end_session,
algorithm_returns=self.algo_returns,
benchmark_returns=self.benchmark_returns,
algorithm_leverages=pd.Series([.01, .02, .03])
)
# This return period has a list instead of None for algorithm_leverages
# Confirm that max_leverage is set to the max of those values
self.assertEqual(test_period['max_leverage'], .03)
