def test_get_distance_to_equal_risk_contrib(self):
factors_covariance = self.factors_df.cov()
weights = pd.Series([0.5, 0.5], index=self.factors_df.columns)
actual_result = RiskContributionAnalysis.get_distance_to_equal_risk_contrib(factors_covariance, weights)
expected_result = 0.342309791791
self.assertAlmostEqual(actual_result, expected_result, places=10)
weights = pd.Series([0.25, 0.75], index=self.factors_df.columns)
actual_result = RiskContributionAnalysis.get_distance_to_equal_risk_contrib(factors_covariance, weights)
expected_result = 0.72012126510882146
self.assertAlmostEqual(actual_result, expected_result, places=10)
def test_get_risk_contribution_optimised(self):
weights = pd.Series([0.5, 0.5], index=self.factors_df.columns)
actual_result = RiskContributionAnalysis.get_risk_contribution_optimised(assets_rets=self.factors_df,
weights_of_assets=weights)
expected_result = pd.Series([0.328845104104390, 0.671154895895610], index=self.factors_df.columns)
assert_series_equal(expected_result, actual_result)
weights = pd.Series([0.25, 0.75], index=self.factors_df.columns)
actual_result = RiskContributionAnalysis.get_risk_contribution_optimised(assets_rets=self.factors_df,
weights_of_assets=weights)
expected_result = pd.Series([0.139939367445589, 0.860060632554411], index=self.factors_df.columns)
assert_series_equal(expected_result, actual_result)
def test_is_equal_risk_contribution(self):
asset_a_tms = self.factors_df.loc[:, 'a']
factors_df = pd.concat((asset_a_tms, asset_a_tms), axis=1)
factors_df = cast_dataframe(factors_df, SimpleReturnsDataFrame)
factors_df.columns = ['a', 'b']
factors_covariance = factors_df.cov()
weights = pd.Series([0.25, 0.75], index=self.factors_df.columns)
actual_result = RiskContributionAnalysis.is_equal_risk_contribution(factors_covariance, weights)
self.assertFalse(actual_result)
weights = pd.Series([0.5, 0.5], index=self.factors_df.columns)
actual_result = RiskContributionAnalysis.is_equal_risk_contribution(factors_covariance, weights)
self.assertTrue(actual_result)
def test_get_risk_contribution(self):
weights = pd.Series([0.5, 0.5], index=self.factors_df.columns)
actual_result = RiskContributionAnalysis.get_risk_contribution(factors_rets=self.factors_df,
weigths_of_assets=weights,
portfolio_rets=self.portfolio_tms)
expected_result = pd.Series([0.32739478440485410, 0.672605215595146], index=self.factors_df.columns)
assert_series_equal(expected_result, actual_result)
weights = pd.Series([0.25, 0.75], index=self.factors_df.columns)
actual_result = RiskContributionAnalysis.get_risk_contribution(factors_rets=self.factors_df,
weigths_of_assets=weights,
portfolio_rets=self.portfolio_tms)
expected_result = pd.Series([0.139601453264340, 0.860398546735660], index=self.factors_df.columns)
assert_series_equal(expected_result, actual_result)
def setup(self):
self._calc_coefficients()
self._setup_return_analysis_of_fund_and_fit(self.fitted_tms)
self._setup_out_of_sample_fit()
residuals = self.fit_model.resid
self.durbin_watson_test = durbin_watson(residuals)
regressors_df = self.input_data.regressors_df
analysed_tms = self.input_data.analysed_tms
self.risk_contribution = RiskContributionAnalysis.get_risk_contribution(
regressors_df, self.coefficients, analysed_tms)
factors_perf_attrib, unexplained_perf_attrib = ReturnAttributionAnalysis.get_factor_return_attribution(
analysed_tms, self.fitted_tms, regressors_df, self.coefficients,
self.intercept)
self.factors_performance_attribution_ret = factors_perf_attrib
self.unexplained_performance_attribution_ret = unexplained_perf_attrib
self._setup_correlations(self.fitted_tms)
self.condition_number = cond(regressors_df.values)
self._setup_r_square_of_each_predictor()
self._setup_autocorrelation(residuals)
_, _, _, self.heteroskedasticity = het_breuschpagan(
residuals, self.fit_model.model.exog)
self._setup_cooks_distance(self.fit_model)
def get_weights(self) -> QFSeries:
def minimised_func(weights_values: Sequence[float]):
weights_series = QFSeries(data=weights_values,
index=self.cov_matrix.columns)
return RiskContributionAnalysis.get_distance_to_equal_risk_contrib(
self.cov_matrix, weights_series)
weights = NonlinearFunctionOptimizer.get_weights(
minimised_func,
max_iter=self.max_iter,
upper_constraints=self.upper_constraint,
num_of_assets=self.cov_matrix.shape[1])
weights = QFSeries(data=weights, index=self.cov_matrix.columns.copy())
if not RiskContributionAnalysis.is_equal_risk_contribution(
self.cov_matrix, weights):
self.logger.warning(
"EqualRiskContributionPortfolio: calculated weights do not create an ERC Portfolio."
)
return weights
def minimised_func(weights_values: Sequence[float]):
weights_series = QFSeries(data=weights_values,
index=self.cov_matrix.columns)
return RiskContributionAnalysis.get_distance_to_equal_risk_contrib(
self.cov_matrix, weights_series)