def test_quantile_analysis_simple(self):
f_df = pd.DataFrame(self.x)
calculated = quantile_analysis(f_df,
self.x_w,
self.r,
n_bins=self.n_bins,
pre_process=[],
post_process=[])
er = self.x_w @ self.x.T
expected = er_quantile_analysis(er, self.n_bins, self.r)
np.testing.assert_array_almost_equal(calculated, expected)
def test_quantile_analysis_with_factor_processing(self):
f_df = pd.DataFrame(self.x)
calculated = quantile_analysis(f_df,
self.x_w,
self.r,
n_bins=self.n_bins,
risk_exp=self.risk_exp,
pre_process=[winsorize_normal, standardize],
post_process=[standardize])
er = self.x_w @ factor_processing(self.x,
[winsorize_normal, standardize],
self.risk_exp,
[standardize]).T
expected = er_quantile_analysis(er, self.n_bins, self.r)
np.testing.assert_array_almost_equal(calculated, expected)
def test_quantile_analysis_with_benchmark(self):
f_df = pd.DataFrame(self.x)
calculated = quantile_analysis(f_df,
self.x_w,
self.r,
n_bins=self.n_bins,
do_neutralize=True,
benchmark=self.b_w,
risk_exp=self.risk_exp,
pre_process=[winsorize_normal, standardize],
post_process=[standardize])
er = self.x_w @ factor_processing(self.x,
[winsorize_normal, standardize],
self.risk_exp,
[standardize]).T
raw_er = er_quantile_analysis(er, self.n_bins, self.r)
expected = raw_er * self.b_w.sum() - np.dot(self.b_w, self.r)
np.testing.assert_array_almost_equal(calculated, expected)