PortfolioOptimization#
- class PortfolioOptimization(expected_returns, covariances, risk_factor, budget, bounds=None)[source]#
Bases:
OptimizationApplicationOptimization 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:
- 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:
- 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:
- 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:
- to_quadratic_program()[source]#
Convert a portfolio optimization problem instance into a
QuadraticProgram.- Returns:
The
QuadraticProgramcreated from the portfolio optimization problem instance.- Return type: