def test_simples_settle(self): calc_ret = simple_settle(self.weights, self.ret_series) ret_series = self.ret_series.reshape((-1, 1)) expected_ret = (self.weights * ret_series).sum(axis=0) np.testing.assert_array_almost_equal(calc_ret, expected_ret) ret_series = np.random.randn(self.n_samples, 1) calc_ret = simple_settle(self.weights, ret_series) expected_ret = (self.weights * ret_series).sum(axis=0) np.testing.assert_array_almost_equal(calc_ret, expected_ret)
def benchmark_simple_settle_with_group(n_samples: int, n_portfolios: int, n_loops: int, n_groups: int) -> None: print("-" * 60) print("Starting simple settle with group-by values benchmarking") print( "Parameters(n_samples: {0}, n_portfolios: {1}, n_loops: {2}, n_groups: {3})" .format(n_samples, n_portfolios, n_loops, n_groups)) weights = np.random.randn(n_samples, n_portfolios) ret_series = np.random.randn(n_samples) groups = np.random.randint(n_groups, size=n_samples) start = dt.datetime.now() for _ in range(n_loops): calc_ret = simple_settle(weights, ret_series, groups=groups) impl_model_time = dt.datetime.now() - start print('{0:20s}: {1}'.format('Implemented model', impl_model_time)) start = dt.datetime.now() ret_series.shape = -1, 1 for _ in range(n_loops): ret_mat = weights * ret_series exp_ret = pd.DataFrame(ret_mat).groupby(groups).sum().values benchmark_model_time = dt.datetime.now() - start np.testing.assert_array_almost_equal(calc_ret, exp_ret) print('{0:20s}: {1}'.format('Benchmark model', benchmark_model_time))
def benchmark_simple_settle(n_samples: int, n_portfolios: int, n_loops: int) -> None: print("-" * 60) print("Starting simple settle benchmarking") print("Parameters(n_samples: {0}, n_portfolios: {1}, n_loops: {2})".format( n_samples, n_portfolios, n_loops)) weights = np.random.randn(n_samples, n_portfolios) ret_series = np.random.randn(n_samples) start = dt.datetime.now() for _ in range(n_loops): calc_ret = simple_settle(weights, ret_series) impl_model_time = dt.datetime.now() - start print('{0:20s}: {1}'.format('Implemented model', impl_model_time)) start = dt.datetime.now() ret_series.shape = -1, 1 for _ in range(n_loops): exp_ret = (weights * ret_series).sum(axis=0) benchmark_model_time = dt.datetime.now() - start np.testing.assert_array_almost_equal(calc_ret, exp_ret) print('{0:20s}: {1}'.format('Benchmark model', benchmark_model_time))
def test_simples_settle(self): calc_ret = simple_settle(self.weights, self.ret_series) ret_series = self.ret_series.reshape((-1, 1)) expected_ret = self.weights @ ret_series self.assertAlmostEqual(calc_ret['er'][0], expected_ret[0])
def test_simple_settle_with_group(self): calc_ret = simple_settle(self.weights, self.ret_series, self.groups) ret_series = self.ret_series.reshape((-1, 1)) ret_mat = self.weights * ret_series expected_ret = pd.DataFrame(ret_mat).groupby(self.groups).sum().values np.testing.assert_array_almost_equal(calc_ret, expected_ret) ret_series = np.random.randn(self.n_samples, 1) calc_ret = simple_settle(self.weights, ret_series, self.groups) ret_mat = self.weights * ret_series expected_ret = pd.DataFrame(ret_mat).groupby(self.groups).sum().values np.testing.assert_array_almost_equal(calc_ret, expected_ret)
def test_simple_settle_with_group(self): calc_ret = simple_settle(self.weights, self.ret_series, self.groups) ret_series = self.weights * self.ret_series expected_ret = pd.Series(ret_series).groupby(self.groups).sum().values np.testing.assert_array_almost_equal(calc_ret['er'].values[:-1], expected_ret) self.assertAlmostEqual(calc_ret['er'].values[-1], expected_ret.sum())
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