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

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#' title: "ROI Demo"

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#' date: "7/17/2014"

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#' ---

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#' This script demonstrates running optimizations using ROI as the 

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#' optimization backend. Note that this script uses the v1 specification

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#' previous to version 0.8.3.

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#' Load packages

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library(PortfolioAnalytics)

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#' General Parameters for sample code

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data(edhec)

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funds <- names(edhec)

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mu.port <- mean(colMeans(edhec))

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N <- length(funds)

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#' Define problem with constraints and objectives

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gen.constr <- constraint(assets = colnames(edhec), min=-Inf, max =Inf, min_sum=1, max_sum=1, risk_aversion=1)

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gen.constr <- add.objective(constraints=gen.constr, type="return", name="mean", enabled=FALSE, multiplier=0, target=mu.port)

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gen.constr <- add.objective(constraints=gen.constr, type="risk", name="var", enabled=FALSE, multiplier=0, risk_aversion=10)

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gen.constr <- add.objective(constraints=gen.constr, type="risk", name="CVaR", enabled=FALSE, multiplier=0)

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#' Max return under box constraints, fully invested

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max.port <- gen.constr

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max.port$min <- rep(0.01,N)

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max.port$max <- rep(0.30,N)

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max.port$objectives[[1]]$enabled <- TRUE

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max.port$objectives[[1]]$target <- NA

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max.solution <- optimize.portfolio(R=edhec, constraints=max.port, optimize_method="ROI")

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#' Mean-variance:  Fully invested, Global Minimum Variance Portfolio

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gmv.port <- gen.constr

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gmv.port$objectives[[2]]$enabled <- TRUE

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gmv.port$objectives[[2]]$risk_aversion <- 1

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gmv.solution <- optimize.portfolio(R=edhec, constraints=gmv.port, optimize_method="ROI")

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#' Mean-variance:  Maximize quadratic utility, fully invested, target portfolio return

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target.port <- gen.constr

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target.port$objectives[[1]]$enabled <- TRUE

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target.port$objectives[[2]]$enabled <- TRUE

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target.solution <- optimize.portfolio(R=edhec, constraints=target.port, optimize_method="ROI")

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#' Mean-variance:  Maximize quadratic utility, dollar-neutral, target portfolio return

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dollar.neu.port <- gen.constr

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dollar.neu.port$min_sum <- 0

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dollar.neu.port$max_sum <- 0

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dollar.neu.port$objectives[[1]]$enabled <- TRUE

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dollar.neu.port$objectives[[2]]$enabled <- TRUE

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dollar.neu.solution <- optimize.portfolio(R=edhec, constraints=dollar.neu.port, optimize_method="ROI")

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#' Minimize CVaR with target return

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cvar.port <- gen.constr

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cvar.port$objectives[[1]]$enabled <- TRUE

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cvar.port$objectives[[3]]$enabled <- TRUE

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cvar.solution <- optimize.portfolio(R=edhec, constraints=cvar.port, optimize_method="ROI")

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#' Mean-variance:  Fully invested, Global Minimum Variance Portfolio, Groups Constraints

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groups.port <- gen.constr

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groups <- list(1:3, 4:6, 7:9, 10:13)

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groups.port$groups <- groups 

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groups.port$cLO <- rep(0.15,length(groups))

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groups.port$cUP <- rep(0.30,length(groups)) 

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groups.port$objectives[[2]]$enabled <- TRUE

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groups.port$objectives[[2]]$risk_aversion <- 1

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groups.solution <- optimize.portfolio(R=edhec, constraints=groups.port, optimize_method="ROI")

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#' Minimize CVaR with target return and group constraints

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group.cvar.port <- gen.constr

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groups <- list(1:3, 4:6, 7:9, 10:13)

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group.cvar.port$groups <- groups

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group.cvar.port$cLO <- rep(0.15,length(groups))

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group.cvar.port$cUP <- rep(0.30,length(groups))

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group.cvar.port$objectives[[1]]$enabled <- TRUE

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group.cvar.port$objectives[[3]]$enabled <- TRUE

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group.cvar.solution <- optimize.portfolio(R=edhec, constraints=group.cvar.port, optimize_method="ROI", maxSTARR=FALSE)

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