def test_cla_custom_bounds():
bounds = [(0.01, 0.13), (0.02, 0.11)] * 10
cla = CLA(*setup_cla(data_only=True), weight_bounds=bounds)
df = get_data()
cla.cov_matrix = risk_models.exp_cov(df).values
w = cla.min_volatility()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
assert (0.01 <= cla.weights[::2]).all() and (cla.weights[::2] <=
0.13).all()
assert (0.02 <= cla.weights[1::2]).all() and (cla.weights[1::2] <=
0.11).all()
# Test polymorphism of the weight_bounds param.
bounds2 = ([bounds[0][0], bounds[1][0]] * 10,
[bounds[0][1], bounds[1][1]] * 10)
cla2 = CLA(*setup_cla(data_only=True), weight_bounds=bounds2)
cla2.cov_matrix = risk_models.exp_cov(df).values
w2 = cla2.min_volatility()
assert dict(w2) == dict(w)
def test_cla_min_volatility_exp_cov_short():
cla = CLA(*setup_cla(data_only=True), weight_bounds=(-1, 1))
df = get_data()
cla.cov_matrix = risk_models.exp_cov(df).values
w = cla.min_volatility()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
np.testing.assert_allclose(
cla.portfolio_performance(),
(0.23215576461823062, 0.1325959061825329, 1.6000174569958052),
)
def test_cla_min_volatility_exp_cov_short():
cla = CLA(*setup_cla(data_only=True), weight_bounds=(-1, 1))
df = get_data()
cla.cov_matrix = risk_models.exp_cov(df).values
w = cla.min_volatility()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
np.testing.assert_allclose(
cla.portfolio_performance(),
(0.2634735528776959, 0.13259590618253303, 1.8362071642131053),
)
def test_cla_custom_bounds():
bounds = [(0.01, 0.13), (0.02, 0.11)] * 10
cla = CLA(*setup_cla(data_only=True), weight_bounds=bounds)
df = get_data()
cla.cov_matrix = risk_models.exp_cov(df).values
w = cla.min_volatility()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
assert (0.01 <= cla.weights[::2]).all() and (cla.weights[::2] <=
0.13).all()
assert (0.02 <= cla.weights[1::2]).all() and (cla.weights[1::2] <=
0.11).all()
e_return = {}
e_cov = {}
for x in epochs.keys():
sub_price = assets.loc[epochs[x]['start']:epochs[x]['end']]
e_return[x] = mean_historical_return(assets, frequency=252)
e_cov[x] = CovarianceShrinkage(sub_price).ledoit_wolf()
# Display the efficient covariance matrices for all epochs
print("Efficient Covariance Matrices\n", e_cov)
# Initialize the Crtical Line Algorithm object
efficient_portfolio_during = CLA(e_return["during"], e_cov["during"])
# Find the minimum volatility portfolio weights and display them
print(efficient_portfolio_during.min_volatility())
# Compute the efficient frontier
(ret, vol, weights) = efficient_portfolio_during.efficient_frontier()
# Add the frontier to the plot showing the 'before' and 'after' frontiers
plt.scatter(vol, ret, s=4, c='g', marker='.', label='During')
plt.legend()
plt.show()
# plotting using PyPortfolioOpt
pplot.plot_covariance(cs, plot_correlation=False, show_tickers=True)
pplot.plot_efficient_frontier(efficient_portfolio_during,
points=100,
show_assets=True)
pplot.plot_weights(cw)
