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

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#' title: "ROI Solvers 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 with different solvers available through the ROI 

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#' (R Optimization Interface) package. See 

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#' http://cran.r-project.org/web/packages/ROI/index.html for more details.

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#' Load the packages

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library(PortfolioAnalytics)

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library(ROI)

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library(ROI.plugin.quadprog)

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library(ROI.plugin.glpk)

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library(ROI.plugin.symphony)

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#' Load data

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data(edhec)

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R <- edhec[, 1:4]

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funds <- colnames(R)

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#' Set up 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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#' Add objectives.

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

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

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minsd.portf <- add.objective(portfolio=init.portf, type="risk", name="StdDev")

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

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                          risk_aversion=0.25)

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

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#' Solve optimization problem to maximize return. Note that 

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#' optimize_method="ROI" defaults to using the glpk solver for linear

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#' problems.

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opt.maxret.roi <- optimize.portfolio(R, maxret.portf, optimize_method="ROI")

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opt.maxret.glpk <- optimize.portfolio(R, maxret.portf, optimize_method="glpk")

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opt.maxret.symphony <- optimize.portfolio(R, maxret.portf, optimize_method="symphony")

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all.equal(extractStats(opt.maxret.roi), extractStats(opt.maxret.glpk))

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all.equal(extractStats(opt.maxret.roi), extractStats(opt.maxret.symphony))

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#' This fails because an optimization problem with a linear objective cannot

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#' be solved with a quadratic programming solver.

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# opt.maxret.qp <- optimize.portfolio(R, maxret.portf, optimize_method="quadprog")

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#' Solve optimization problem to minimize ES. Note that 

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#' optimize_method="ROI" defaults to using the glpk solver for linear 

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#' problems.

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opt.mines.roi <- optimize.portfolio(R, mines.portf, optimize_method="ROI")

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opt.mines.glpk <- optimize.portfolio(R, mines.portf, optimize_method="glpk")

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opt.mines.symphony <- optimize.portfolio(R, mines.portf, optimize_method="symphony")

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all.equal(extractStats(opt.mines.roi), extractStats(opt.mines.glpk))

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all.equal(extractStats(opt.mines.roi), extractStats(opt.mines.symphony))

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#' Solve optimization problem to minimize standard deviation. Note that 

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#' optimize_method="ROI" defaults to using the quadprog solver for quadratic 

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#' problems.

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opt.minsd.roi <- optimize.portfolio(R, minsd.portf, optimize_method="ROI")

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opt.minsd.qp <- optimize.portfolio(R, minsd.portf, optimize_method="quadprog")

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all.equal(extractStats(opt.minsd.roi), extractStats(opt.minsd.qp))

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#' This fails because an optimization problem with a quadratic objective cannot

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#' be solved with a linear programming solver.

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# opt.minsd.glpk <- optimize.portfolio(R, minsd.portf, optimize_method="glpk")

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#' Solve optimization problem to maximize quadratic utility. Note that 

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#' optimize_method="ROI" defaults to using the quadprog solver for quadratic 

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#' problems.

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opt.qu.roi <- optimize.portfolio(R, qu.portf, optimize_method="ROI")

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opt.qu.qp <- optimize.portfolio(R, qu.portf, optimize_method="quadprog")

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all.equal(extractStats(opt.qu.roi), extractStats(opt.qu.qp))

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