def test_es_return_sample(): df = get_data() mu = expected_returns.mean_historical_return(df) S = risk_models.sample_cov(df) # Generate a 1y sample of daily data np.random.seed(0) mu_daily = (1 + mu)**(1 / 252) - 1 S_daily = S / 252 sample_rets = pd.DataFrame(np.random.multivariate_normal( mu_daily, S_daily, 300), columns=mu.index) es = EfficientSemivariance(mu, sample_rets) w = es.efficient_return(0.2) assert isinstance(w, dict) assert set(w.keys()) == set(es.tickers) np.testing.assert_almost_equal(es.weights.sum(), 1) assert all([i >= -1e-5 for i in w.values()]) np.testing.assert_allclose( es.portfolio_performance(), (0.20, 0.10050247458629837, 1.7910016727029479), rtol=1e-4, atol=1e-4, ) # Cover verbose param case np.testing.assert_equal(es.portfolio_performance(verbose=True), es.portfolio_performance())
def test_efficient_semivariance_vs_heuristic_weekly(): benchmark = 0 _, historic_returns = setup_efficient_semivariance(data_only=True) weekly_returns = historic_returns.resample("W").sum() mean_weekly_returns = weekly_returns.mean(axis=0) es = EfficientSemivariance(mean_weekly_returns, weekly_returns, frequency=52) es.efficient_return(0.20 / 52) mu_es, semi_deviation, _ = es.portfolio_performance() pairwise_semivariance = risk_models.semicovariance(weekly_returns, returns_data=True, benchmark=0, frequency=1) ef = EfficientFrontier(mean_weekly_returns, pairwise_semivariance) ef.efficient_return(0.20 / 52) mu_ef, _, _ = ef.portfolio_performance() portfolio_returns = historic_returns @ ef.weights drops = np.fmin(portfolio_returns - benchmark, 0) T = weekly_returns.shape[0] semivariance = np.sum(np.square(drops)) / T * 52 semi_deviation_ef = np.sqrt(semivariance) assert semi_deviation < semi_deviation_ef assert mu_es / semi_deviation > mu_ef / semi_deviation_ef
def test_efficient_return_short(): es = EfficientSemivariance(*setup_efficient_semivariance(data_only=True), weight_bounds=(None, None)) w = es.efficient_return(0.25) assert isinstance(w, dict) assert set(w.keys()) == set(es.tickers) np.testing.assert_almost_equal(es.weights.sum(), 1) np.testing.assert_allclose( es.portfolio_performance(), (0.25, 0.09073654273906914, 2.534811096726317), rtol=1e-4, atol=1e-4, ) sortino = es.portfolio_performance()[2] ef_long_only = setup_efficient_semivariance() ef_long_only.efficient_return(0.25) long_only_sortino = ef_long_only.portfolio_performance()[2] assert sortino > long_only_sortino
def test_max_quadratic_utility_market_neutral(): es = EfficientSemivariance(*setup_efficient_semivariance(data_only=True), weight_bounds=(-1, 1), solver="ECOS") es.max_quadratic_utility(market_neutral=True) np.testing.assert_almost_equal(es.weights.sum(), 0) np.testing.assert_allclose( es.portfolio_performance(), (3.106826930927578, 0.9789014670659041, 3.153358161960706), rtol=1e-4, atol=1e-4, )
def test_max_quadratic_utility_with_shorts(): es = EfficientSemivariance(*setup_efficient_semivariance(data_only=True), weight_bounds=(-1, 1)) es.max_quadratic_utility() np.testing.assert_almost_equal(es.weights.sum(), 1) np.testing.assert_allclose( es.portfolio_performance(), (3.2806086360944846, 1.0219729896939227, 3.190503730505662), rtol=1e-4, atol=1e-4, )
def test_es_example_weekly(): df = get_data() df = df.resample("W").first() mu = expected_returns.mean_historical_return(df, frequency=52) historical_rets = expected_returns.returns_from_prices(df).dropna() es = EfficientSemivariance(mu, historical_rets, frequency=52) es.efficient_return(0.2) np.testing.assert_allclose( es.portfolio_performance(), (0.2000000562544616, 0.07667633475531543, 2.3475307841574087), rtol=1e-4, atol=1e-4, )
def test_es_example(): df = get_data() mu = expected_returns.mean_historical_return(df) historical_rets = expected_returns.returns_from_prices(df).dropna() es = EfficientSemivariance(mu, historical_rets) w = es.efficient_return(0.2) assert isinstance(w, dict) assert set(w.keys()) == set(es.tickers) np.testing.assert_almost_equal(es.weights.sum(), 1) assert all([i >= -1e-5 for i in w.values()]) np.testing.assert_allclose( es.portfolio_performance(), (0.20, 0.08558991313395496, 2.1030523036993265), rtol=1e-4, atol=1e-4, ) # Cover verbose param case np.testing.assert_equal(es.portfolio_performance(verbose=True), es.portfolio_performance())
def test_es_example_monthly(): df = get_data() df = df.resample("M").first() mu = expected_returns.mean_historical_return(df, frequency=12) historical_rets = expected_returns.returns_from_prices(df).dropna() es = EfficientSemivariance(mu, historical_rets, frequency=12) es.efficient_return(0.3) np.testing.assert_allclose( es.portfolio_performance(), (0.3, 0.04746519522734184, 5.899059271933824), rtol=1e-4, atol=1e-4, )
def test_efficient_risk_market_neutral(): es = EfficientSemivariance(*setup_efficient_semivariance(data_only=True), weight_bounds=(-1, 1)) w = es.efficient_risk(0.21, market_neutral=True) assert isinstance(w, dict) assert set(w.keys()) == set(es.tickers) np.testing.assert_almost_equal(es.weights.sum(), 0) assert (es.weights < 1).all() and (es.weights > -1).all() np.testing.assert_allclose( es.portfolio_performance(), (0.9257112257221027, 0.21, 4.312873624163129), rtol=1e-4, atol=1e-4, )