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braverock
GitHub Repository: braverock/portfolioanalytics
 Path: blob/master/sandbox/leverage_example.R
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# Examples demonstrating the difference of specifying a leverage constraint 

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# using max_sum compared to leverage_exposure.

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library(PortfolioAnalytics)

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data(edhec)

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R <- edhec

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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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# dollar neutral portfolio

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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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# Here is a dollar neutral portfolio with no constraint on leverage

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opt1 <- optimize.portfolio(R=R, portfolio=dollar.neutral.portf, 

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                           optimize_method="DEoptim", search_size=2000, 

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                           trace=TRUE)

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sum(opt1$weights)

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# Total portfolio leverage is actually greater than 4

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sum(abs(opt1$weights))

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# now add the leverage exposure constraint for 2:1 leverage

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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

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opt2 <- 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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sum(opt2$weights)

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sum(abs(opt2$weights))

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# Leveraged portfolio

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leveraged.portf <- init.portf

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# Add a "leverage" constraint using max_sum

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

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                                  type="leverage", 

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                                  min_sum=1.29, max_sum=1.31)

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

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                                  type="box", min=-0.3, max=0.6)

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opt3 <- 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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sum(opt3$weights)

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# total portfolio leverage is approximately 3.9

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sum(abs(opt3$weights))

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# add a leverage exposure constraint

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

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                                  type="leverage_exposure", leverage=1.5)

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# change min_sum and max_sum such that the weights sum to 1

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leveraged.portf$constraints[[1]]$min_sum <- 0.99

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leveraged.portf$constraints[[1]]$max_sum <- 1.01

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# Run optimization

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opt4 <- 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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sum(opt4$weights)

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# total portfolio leverage is less than 1.5

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sum(abs(opt4$weights))

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