PortfolioOptimization#

class PortfolioOptimization(expected_returns, covariances, risk_factor, budget, bounds=None)[source]#

Bases: OptimizationApplication

Optimization application for the “portfolio optimization” [1] problem.

References

[1]: “Portfolio optimization”, https://en.wikipedia.org/wiki/Portfolio_optimization

Parameters:

  • expected_returns (ndarray) – The expected returns for the assets.

  • covariances (ndarray) – The covariances between the assets.

  • risk_factor (float) – The risk appetite of the decision maker.

  • budget (int) – The budget, i.e. the number of assets to be selected.

  • bounds (List[Tuple[int, int]] | None) – The list of tuples for the lower bounds and the upper bounds of each variable. e.g. [(lower bound1, upper bound1), (lower bound2, upper bound2), …]. Default is None which means all the variables are binary variables.

Attributes

bounds#

Getter of the lower bounds and upper bounds of each selectable assets.

Returns:

The lower bounds and upper bounds of each assets selectable

budget#

Getter of budget

Returns:

The budget, i.e. the number of assets to be selected.

covariances#

Getter of covariances

Returns:

The covariances between the assets.

expected_returns#

Getter of expected_returns

Returns:

The expected returns for the assets.

risk_factor#

Getter of risk_factor

Returns:

The risk appetite of the decision maker.

Methods

interpret(result)[source]#

Interpret a result as a list of asset indices

Parameters:

result (OptimizationResult | ndarray) – The calculated result of the problem

Returns:

The list of asset indices whose corresponding variable is 1

Return type:

List[int]

portfolio_expected_value(result)[source]#

Returns the portfolio expected value based on the result.

Parameters:

result (OptimizationResult | ndarray) – The calculated result of the problem

Returns:

The portfolio expected value

Return type:

float

portfolio_variance(result)[source]#

Returns the portfolio variance based on the result

Parameters:

result (OptimizationResult | ndarray) – The calculated result of the problem

Returns:

The portfolio variance

Return type:

float

static sample_most_likely(state_vector)#

Compute the most likely binary string from state vector.

Parameters:

state_vector (QuasiDistribution | Statevector | ndarray | Dict) – state vector or counts or quasi-probabilities.

Returns:

binary string as numpy.ndarray of ints.

Raises:

ValueError – if state_vector is not QuasiDistribution, Statevector, np.ndarray, or dict.

Return type:

ndarray

to_quadratic_program()[source]#

Convert a portfolio optimization problem instance into a QuadraticProgram.

Returns:

The QuadraticProgram created from the portfolio optimization problem instance.

Return type:

QuadraticProgram