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#' ---
#' title: "Maximizing Modified Sharpe Ratio Demo"
#' author: Ross Bennett
#' date: "7/17/2014"
#' ---
#' This script demonstrates how to solve a constrained portfolio optimization
#' problem to maximize modified Sharpe Ratio using ES as the risk measure.
#' Load the package and data
library(PortfolioAnalytics)
data(edhec)
R <- edhec[, 1:8]
funds <- colnames(R)
#' Construct initial portfolio with basic constraints.
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 to run the optimization.
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 to run the optimization.
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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