def test_long_short_build(self): x = self.x[:, 0].flatten() calc_weights = long_short_builder(x).flatten() expected_weights = x / np.abs(x).sum() np.testing.assert_array_almost_equal(calc_weights, expected_weights) calc_weights = long_short_builder(self.x, leverage=2) expected_weights = self.x / np.abs(self.x).sum(axis=0) * 2 np.testing.assert_array_almost_equal(calc_weights, expected_weights)
def test_long_short_build_with_group(self): x = self.x[:, 0].flatten() calc_weights = long_short_builder(x, groups=self.groups).flatten() expected_weights = pd.Series(x).groupby(self.groups).apply(lambda s: s / np.abs(s).sum()) np.testing.assert_array_almost_equal(calc_weights, expected_weights) calc_weights = long_short_builder(self.x, groups=self.groups) expected_weights = pd.DataFrame(self.x).groupby(self.groups).apply( lambda s: s / np.abs(s).sum(axis=0)) np.testing.assert_array_almost_equal(calc_weights, expected_weights)
def test_long_short_build_with_masks(self): x = self.x[:, 0].flatten() calc_weights = long_short_builder(x, masks=self.masks, leverage=1.).flatten() self.assertAlmostEqual(calc_weights.sum(), 0.) masked_x = x.copy() masked_x[self.masks] = 0. masked_x[~self.masks] = masked_x[~self.masks] - masked_x[~self.masks].mean() expected_weights = masked_x / np.abs(masked_x).sum() np.testing.assert_array_almost_equal(calc_weights, expected_weights)
def er_portfolio_analysis( er: np.ndarray, industry: np.ndarray, dx_return: np.ndarray, constraints: Optional[Union[LinearConstraints, Constraints]] = None, detail_analysis=True, benchmark: Optional[np.ndarray] = None, is_tradable: Optional[np.ndarray] = None, method='risk_neutral', **kwargs) -> Tuple[pd.DataFrame, Optional[pd.DataFrame]]: er = er.flatten() def create_constraints(benchmark, **kwargs): if 'lbound' in kwargs: lbound = kwargs['lbound'].copy() del kwargs['lbound'] else: lbound = np.maximum(0., benchmark - 0.01) if 'ubound' in kwargs: ubound = kwargs['ubound'].copy() del kwargs['ubound'] else: ubound = 0.01 + benchmark if is_tradable is not None: ubound[~is_tradable] = np.minimum(lbound, ubound)[~is_tradable] risk_lbound, risk_ubound = constraints.risk_targets() cons_exp = constraints.risk_exp return lbound, ubound, cons_exp, risk_lbound, risk_ubound if method == 'risk_neutral': lbound, ubound, cons_exp, risk_lbound, risk_ubound = create_constraints( benchmark, **kwargs) turn_over_target = kwargs.get('turn_over_target') current_position = kwargs.get('current_position') status, _, weights = linear_builder(er, risk_constraints=cons_exp, lbound=lbound, ubound=ubound, risk_target=(risk_lbound, risk_ubound), turn_over_target=turn_over_target, current_position=current_position) if status != 'optimal': raise ValueError( 'linear programming optimizer in status: {0}'.format(status)) elif method == 'rank': weights = rank_build( er, use_rank=kwargs['use_rank'], masks=is_tradable).flatten() * benchmark.sum() / kwargs['use_rank'] elif method == 'ls' or method == 'long_short': weights = long_short_builder(er).flatten() elif method == 'mv' or method == 'mean_variance': lbound, ubound, cons_exp, risk_lbound, risk_ubound = create_constraints( benchmark, **kwargs) cov = kwargs['cov'] if 'lam' in kwargs: lam = kwargs['lam'] else: lam = 1. status, _, weights = mean_variance_builder(er, cov=cov, bm=benchmark, lbound=lbound, ubound=ubound, risk_exposure=cons_exp, risk_target=(risk_lbound, risk_ubound), lam=lam) if status != 'optimal': raise ValueError( 'mean variance optimizer in status: {0}'.format(status)) elif method == 'tv' or method == 'target_vol': lbound, ubound, cons_exp, risk_lbound, risk_ubound = create_constraints( benchmark, **kwargs) cov = kwargs['cov'] if 'target_vol' in kwargs: target_vol = kwargs['target_vol'] else: target_vol = 1. status, _, weights = target_vol_builder(er, cov=cov, bm=benchmark, lbound=lbound, ubound=ubound, risk_exposure=cons_exp, risk_target=(risk_lbound, risk_ubound), vol_low=0, vol_high=target_vol) else: raise ValueError("Unknown building type ({0})".format(method)) if detail_analysis: analysis = simple_settle(weights, dx_return, industry, benchmark) else: analysis = None return pd.DataFrame({'weight': weights, 'industry': industry, 'er': er}), \ analysis