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

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#' title: "Leverage Exposure Constraint 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 portfolio optimization problem with

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#' leverage exposure constraints.

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#' 

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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[, 1:10]

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funds <- colnames(R)

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#' Set up an initial portfolio object with basic constraints.

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

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#' Add an objective to maximize mean return per unit expected shortfall.

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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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#' The leverage_exposure constraint type is supported for random, DEoptim, pso,

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#' and GenSA solvers. The following examples use DEoptim for solving the

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#' optimization problem.

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#' Dollar neutral portfolio with max 2:1 leverage constraint.

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dollar.neutral.portf <- init.portf

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dollar.neutral.portf <- add.constraint(portfolio=dollar.neutral.portf, 

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                                       type="weight_sum", 

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                                       min_sum=-0.01, max_sum=0.01)

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dollar.neutral.portf <- add.constraint(portfolio=dollar.neutral.portf, 

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                                       type="box", min=-0.5, max=0.5)

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dollar.neutral.portf <- add.constraint(portfolio=dollar.neutral.portf, 

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                                       type="leverage_exposure", leverage=2)

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#' Run optimization using DEoptim as optimization backend.

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dollar.neutral.opt <- optimize.portfolio(R=R, portfolio=dollar.neutral.portf, 

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                                         optimize_method="DEoptim",

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                                         search_size=2000)

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dollar.neutral.opt

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#' Set up leveraged portfolio with max 1.6:1 leverage exposure constraint.

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leveraged.portf <- init.portf

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leveraged.portf <- add.constraint(portfolio=leveraged.portf, 

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                                       type="weight_sum", 

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                                       min_sum=0.99, max_sum=1.01)

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leveraged.portf <- add.constraint(portfolio=leveraged.portf, 

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                                       type="box", min=-0.3, max=0.8)

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leveraged.portf <- add.constraint(portfolio=leveraged.portf, 

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                                       type="leverage_exposure", leverage=1.6)

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#' Run optimization using DEoptim as optimization backend.

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leveraged.opt <- optimize.portfolio(R=R, portfolio=leveraged.portf, 

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                                         optimize_method="DEoptim",

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                                         search_size=2000)

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leveraged.opt

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