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braverock
GitHub Repository: braverock/portfolioanalytics
 Path: blob/master/demo/demo_max_STARR.R
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#' ---

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#' title: "Maximizing Modified Sharpe Ratio Demo"

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#' author: Ross Bennett

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#' date: "7/17/2014"

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#' ---

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#' This script demonstrates how to solve a constrained portfolio optimization 

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#' problem to maximize modified Sharpe Ratio using ES as the risk measure.

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#' Load the package and data

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library(PortfolioAnalytics)

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data(edhec)

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R <- edhec[, 1:8]

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funds <- colnames(R)

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#' Construct initial portfolio with basic constraints.

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init.portf <- portfolio.spec(assets=funds)

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init.portf <- add.constraint(portfolio=init.portf, type="full_investment")

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init.portf <- add.constraint(portfolio=init.portf, type="long_only")

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init.portf <- add.objective(portfolio=init.portf, type="return", name="mean")

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init.portf <- add.objective(portfolio=init.portf, type="risk", name="ES",

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                            arguments=list(p=0.925))

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init.portf

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#' Maximizing STARR Ratio can be formulated as a linear programming 

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#' problem and solved very quickly using optimize_method="ROI". 

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#' The default action if "mean" and "ES" are specified as objectives with

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#' optimize_method="ROI" is to maximize STARR. If we want to use

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#' both mean and ES in the objective function, but only minimize ES, we need to 

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#' pass in maxSTARR=FALSE to optimize.portfolio.

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maxSTARR.lo.ROI <- optimize.portfolio(R=R, portfolio=init.portf, 

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                                      optimize_method="ROI",

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                                      trace=TRUE)

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maxSTARR.lo.ROI

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#' Although the maximum STARR Ratio objective can be solved quickly and accurately

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#' with optimize_method="ROI", it is also possible to solve this optimization

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#' problem using other solvers such as random portfolios or DEoptim. These

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#' solvers have the added flexibility of using different methods to calculate

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#' the Sharpe Ratio (e.g. we could specify annualized measures of risk and 

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#' return or use modified, guassian, or historical ES).

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#' For random portfolios and DEoptim, the leverage constraints should be 

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#' relaxed slightly.

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init.portf$constraints[[1]]$min_sum=0.99

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init.portf$constraints[[1]]$max_sum=1.01

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# Use random portfolios to run the optimization.

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maxSTARR.lo.RP <- optimize.portfolio(R=R, portfolio=init.portf, 

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                                  optimize_method="random",

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                                  search_size=2000,

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                                  trace=TRUE)

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maxSTARR.lo.RP

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chart.RiskReward(maxSTARR.lo.RP, risk.col="ES", return.col="mean")

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# Use DEoptim to run the optimization.

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maxSTARR.lo.DE <- optimize.portfolio(R=R, portfolio=init.portf, 

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                                     optimize_method="DEoptim",

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                                     search_size=2000,

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                                     trace=TRUE)

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maxSTARR.lo.DE

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chart.RiskReward(maxSTARR.lo.DE, risk.col="ES", return.col="mean",

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                 xlim=c(0.01, 0.08), ylim=c(0.004,0.008))

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