def test_risk_analysis(self):
n_samples = 36000
n_dates = 20
n_risk_factors = 35
dates = np.sort(np.random.randint(n_dates, size=n_samples))
weights_series = pd.Series(data=np.random.randn(n_samples),
index=dates)
bm_series = pd.Series(data=np.random.randn(n_samples), index=dates)
next_bar_return_series = pd.Series(data=np.random.randn(n_samples),
index=dates)
risk_table = pd.DataFrame(data=np.random.randn(n_samples,
n_risk_factors),
columns=list(range(n_risk_factors)),
index=dates)
explained_table, _ = risk_analysis(weights_series - bm_series,
next_bar_return_series, risk_table)
to_explain = (weights_series - bm_series).multiply(
next_bar_return_series, axis=0)
aggregated = explained_table.sum(axis=1)
np.testing.assert_array_almost_equal(to_explain.values,
aggregated.values)
def perf_attribution_by_pos(net_weight_series: pd.Series,
next_bar_return_series: pd.Series,
benchmark_table: pd.DataFrame) -> pd.DataFrame:
explained_table, _ = risk_analysis(net_weight_series,
next_bar_return_series, benchmark_table)
return explained_table.groupby(level=0).sum()