def test_q_anl_impl(self):
n_bins = 5
x = self.x[:, 0]
q_groups = quantile(x, n_bins)
s = pd.Series(self.r, index=q_groups)
expected_res = s.groupby(level=0).mean()
calculated_res = er_quantile_analysis(x, n_bins, self.r)
np.testing.assert_array_almost_equal(expected_res.values, calculated_res)
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 calc_quantile(self, factors, bins = 5, de_trend = True):
n_bins = 5
quantile_dict = {}
for column in self._columns:
df = pd.DataFrame(columns=['q' + str(i) for i in range(1, n_bins+1)])
grouped = factors.groupby('trade_date')
for k, g in grouped:
er = g[column].fillna(0).values
dx_return = g['chgPct'].values
res = er_quantile_analysis(er, n_bins=5, dx_return=dx_return, de_trend=de_trend)
df.loc[k, :] = res
quantile_dict[column] = df.reset_index().rename(columns={'index':'trade_date'}).set_index('trade_date')
return quantile_dict
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)
def test_q_anl_impl(self):
n_bins = 5
x = self.x[:, 0]
q_groups = quantile(x, n_bins)
s = pd.Series(self.r, index=q_groups)
grouped_return = s.groupby(level=0).mean().values.flatten()
expected_res = grouped_return.copy()
res = n_bins - 1
res_weight = 1. / res
for i, value in enumerate(expected_res):
expected_res[i] = (1. + res_weight) * value - res_weight * grouped_return.sum()
calculated_res = er_quantile_analysis(x, n_bins, self.r, de_trend=True)
np.testing.assert_array_almost_equal(expected_res, calculated_res)