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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