# demo/max_STARR.R
library(PortfolioAnalytics)
# Examples of solving optimization problems to maximize mean return per unit ES
data(edhec)
R <- edhec[, 1:8]
funds <- colnames(R)
# Construct initial portfolio
init.portf <- portfolio.spec(assets=funds)
init.portf <- add.constraint(portfolio=init.portf, type="full_investment")
init.portf <- add.constraint(portfolio=init.portf, type="long_only")
init.portf <- add.objective(portfolio=init.portf, type="return", name="mean")
init.portf <- add.objective(portfolio=init.portf, type="risk", name="ES",
arguments=list(p=0.925))
init.portf
# Maximizing STARR Ratio can be formulated as a linear programming
# problem and solved very quickly using optimize_method="ROI".
# The default action if "mean" and "ES" are specified as objectives with
# optimize_method="ROI" is to maximize STARR. If we want to use
# both mean and ES in the objective function, but only minimize ES, we need to
# pass in maxSTARR=FALSE to optimize.portfolio.
maxSTARR.lo.ROI <- optimize.portfolio(R=R, portfolio=init.portf,
optimize_method="ROI",
trace=TRUE)
maxSTARR.lo.ROI
# Although the maximum STARR Ratio objective can be solved quickly and accurately
# with optimize_method="ROI", it is also possible to solve this optimization
# problem using other solvers such as random portfolios or DEoptim. These
# solvers have the added flexibility of using different methods to calculate
# the Sharpe Ratio (e.g. we could specify annualized measures of risk and
# return or use modified, guassian, or historical ES).
# For random portfolios and DEoptim, the leverage constraints should be
# relaxed slightly.
init.portf$constraints[[1]]$min_sum=0.99
init.portf$constraints[[1]]$max_sum=1.01
# Use random portfolios
maxSTARR.lo.RP <- optimize.portfolio(R=R, portfolio=init.portf,
optimize_method="random",
search_size=2000,
trace=TRUE)
maxSTARR.lo.RP
chart.RiskReward(maxSTARR.lo.RP, risk.col="ES", return.col="mean")
# Use DEoptim
maxSTARR.lo.DE <- optimize.portfolio(R=R, portfolio=init.portf,
optimize_method="DEoptim",
search_size=2000,
trace=TRUE)
maxSTARR.lo.DE
chart.RiskReward(maxSTARR.lo.DE, risk.col="ES", return.col="mean",
xlim=c(0.01, 0.08), ylim=c(0.004,0.008))
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