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

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#' title: "Maximizing 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

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#' portfolio optimization problem to maximize Sharpe Ratio.

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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="StdDev")

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init.portf

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#' Maximizing Sharpe Ratio can be formulated as a quadratic programming 

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

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#' was specified as an objective, the quadratic programming problem uses the 

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#' variance-covariance matrix in the objective function.

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

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#' optimize_method="ROI" is to maximize quadratic utility. If we want to maximize

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#' Sharpe Ratio, we need to pass in maxSR=TRUE to optimize.portfolio.

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

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                                   optimize_method="ROI", 

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                                   maxSR=TRUE, trace=TRUE)

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maxSR.lo.ROI

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#' Although the maximum Sharpe 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 return).

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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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maxSR.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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maxSR.lo.RP

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chart.RiskReward(maxSR.lo.RP, risk.col="StdDev", return.col="mean")

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# Use DEoptim to run the optimization.

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maxSR.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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maxSR.lo.DE

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chart.RiskReward(maxSR.lo.DE, risk.col="StdDev", return.col="mean")

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