def test_budget(self):
"""test budget"""
portfolio_optimization = PortfolioOptimization(
self.expected_returns, self.covariances, self.risk_factor, self.budget
)
portfolio_optimization.budget = 3
self.assertEqual(portfolio_optimization.budget, 3)
def test_bounds(self):
"""test bounds"""
portfolio_optimization = PortfolioOptimization(
self.expected_returns, self.covariances, self.risk_factor, self.budget, self.bounds
)
portfolio_optimization.bounds = [(0, 4), (0, 4), (0, 4), (1, 4)]
self.assertEqual(portfolio_optimization.bounds, [(0, 4), (0, 4), (0, 4), (1, 4)])
def test_risk_factor(self):
"""test risk factor"""
portfolio_optimization = PortfolioOptimization(
self.expected_returns, self.covariances, self.risk_factor, self.budget
)
portfolio_optimization.risk_factor = 0.898989
self.assertEqual(portfolio_optimization.risk_factor, 0.898989)
def test_interpret(self):
"""test interpret"""
portfolio_optimization = PortfolioOptimization(
self.expected_returns, self.covariances, self.risk_factor, self.budget
)
result_x = np.array([0, 1, 0, 1])
self.assertEqual(portfolio_optimization.interpret(result_x), [1, 3])
def test_portfolio_variance(self):
"""test portfolio_variance"""
portfolio_optimization = PortfolioOptimization(
self.expected_returns, self.covariances, self.risk_factor, self.budget
)
result_x = np.array([0, 1, 0, 1])
variance = np.dot(result_x, np.dot(self.covariances, result_x))
self.assertEqual(portfolio_optimization.portfolio_variance(result_x), variance)
def test_to_quadratic_program(self):
"""Test to_quadratic_program"""
portfolio_optimization = PortfolioOptimization(
self.expected_returns, self.covariances, self.risk_factor, self.budget
)
actual_op = portfolio_optimization.to_quadratic_program()
expected_op = QuadraticProgram(name="Portfolio optimization")
for i in range(self.num_assets):
expected_op.binary_var(name="x_{0}".format(i))
quadratic = {
(i, j): self.risk_factor * self.covariances[i][j]
for i in range(self.num_assets)
for j in range(self.num_assets)
}
linear = {i: -self.expected_returns[i] for i in range(self.num_assets)}
expected_op.minimize(quadratic=quadratic, linear=linear)
linear = {i: 1 for i in range(self.num_assets)}
expected_op.linear_constraint(linear=linear, sense="==", rhs=self.budget)
self.assertEqualQuadraticProgram(actual_op, expected_op)
def test_to_quadratic_program(self):
"""Test to_quadratic_program"""
# When bounds is None
portfolio_optimization = PortfolioOptimization(
self.expected_returns, self.covariances, self.risk_factor, self.budget
)
actual_op = portfolio_optimization.to_quadratic_program()
expected_op = QuadraticProgram(name="Portfolio optimization")
for i in range(self.num_assets):
expected_op.binary_var(name=f"x_{i}")
quadratic = {
(i, j): self.risk_factor * self.covariances[i][j]
for i in range(self.num_assets)
for j in range(self.num_assets)
}
linear = {i: -self.expected_returns[i] for i in range(self.num_assets)}
expected_op.minimize(quadratic=quadratic, linear=linear)
linear = {i: 1 for i in range(self.num_assets)}
expected_op.linear_constraint(linear=linear, sense="==", rhs=self.budget)
self.assertEqualQuadraticProgram(actual_op, expected_op)
# When bounds is provided
portfolio_optimization = PortfolioOptimization(
self.expected_returns, self.covariances, self.risk_factor, self.budget, self.bounds
)
actual_op = portfolio_optimization.to_quadratic_program()
expected_op = QuadraticProgram(name="Portfolio optimization")
for i in range(self.num_assets):
expected_op.integer_var(
lowerbound=self.bounds[i][0], upperbound=self.bounds[i][1], name=f"x_{i}"
)
quadratic = {
(i, j): self.risk_factor * self.covariances[i][j]
for i in range(self.num_assets)
for j in range(self.num_assets)
}
linear = {i: -self.expected_returns[i] for i in range(self.num_assets)}
expected_op.minimize(quadratic=quadratic, linear=linear)
linear = {i: 1 for i in range(self.num_assets)}
expected_op.linear_constraint(linear=linear, sense="==", rhs=self.budget)
self.assertEqualQuadraticProgram(actual_op, expected_op)
def test_is_compatibility(self, expected_returns, covariances, risk_factor, budget, bounds):
"""Test error cases in _is_compatibility"""
with self.assertRaises(QiskitFinanceError):
_ = PortfolioOptimization(expected_returns, covariances, risk_factor, budget, bounds)
