Generate random portfolios using the 'sample', 'simplex', or 'grid' method. See details.
random_portfolios(
portfolio,
permutations = 100,
rp_method = "sample",
eliminate = TRUE,
...
)matrix of random portfolio weights
an object of class 'portfolio' specifying the constraints for the optimization, see portfolio.spec
integer: number of unique constrained random portfolios to generate
method to generate random portfolios. Currently "sample", "simplex", or "grid". See Details.
TRUE/FALSE, eliminate portfolios that do not satisfy constraints
any other passthru parameters
Peter Carl, Brian G. Peterson, Ross Bennett
Random portfolios can be generate using one of three methods.
The 'sample' method to generate random portfolios is based on an idea pioneerd by Pat Burns. This is the most flexible method, but also the slowest, and can generate portfolios to satisfy leverage, box, group, position limit, and leverage exposure constraints.
The 'simplex' method to generate random portfolios is
based on a paper by W. T. Shaw. The simplex method is useful to generate
random portfolios with the full investment constraint, where the sum of the
weights is equal to 1, and min box constraints. Values for min_sum
and max_sum of the leverage constraint will be ignored, the sum of
weights will equal 1. All other constraints such as group and position
limit constraints will be handled by elimination. If the constraints are
very restrictive, this may result in very few feasible portfolios remaining.
The 'grid' method to generate random portfolios is based on
the gridSearch function in package 'NMOF'. The grid search method
only satisfies the min and max box constraints. The
min_sum and max_sum leverage constraints will likely be
violated and the weights in the random portfolios should be normalized.
Normalization may cause the box constraints to be violated and will be
penalized in constrained_objective.
The constraint types checked are leverage, box, group, position limit, and
leverage exposure. Any
portfolio that does not satisfy all these constraints will be eliminated. This
function is particularly sensitive to min_sum and max_sum
leverage constraints. For the sample method, there should be some
"wiggle room" between min_sum and max_sum in order to generate
a sufficient number of feasible portfolios. For example, min_sum=0.99
and max_sum=1.01 is recommended instead of min_sum=1
and max_sum=1. If min_sum=1 and max_sum=1, the number of
feasible portfolios may be 1/3 or less depending on the other constraints.
portfolio.spec,
objective,
rp_sample,
rp_simplex,
rp_grid