CoCalc -- demo_efficient

GitHub Repository: braverock/portfolioanalytics
Path: blob/master/demo/demo_efficient_frontier.R
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#' ---
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#' title: "Efficient Frontier Demo"
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#' date: "7/17/2014"
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#' ---
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#' This script demonstrates how to compute and plot the efficient frontier
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#' given different constraints and objectives.
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#' Efficient frontiers can be plotted two ways
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#' 1. Run optimize.portfolio with trace=TRUE and then chart that object.
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#' 2. create an efficient frontier and then chart that object.
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#' Load required packages
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library(PortfolioAnalytics)
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library(DEoptim)
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library(ROI)
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require(ROI.plugin.quadprog)
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require(ROI.plugin.glpk)
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#' Load the data and change the column names for better legends in plotting.
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data(edhec)
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R <- edhec[, 1:5]
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colnames(R) <- c("CA", "CTAG", "DS", "EM", "EQM")
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funds <- colnames(R)
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#' Set up the initial portfolio object with some basic constraints.
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init <- portfolio.spec(assets=funds)
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init <- add.constraint(portfolio=init, type="full_investment")
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init <- add.constraint(portfolio=init, type="box", min=0.15, max=0.45)
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init <- add.constraint(portfolio=init, type="group",
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                       groups=list(c(1, 3),
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                                   c(2, 4, 5)),
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                       group_min=0.05,
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                       group_max=0.7)
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#' Add objectives for mean-ES (Expected Shortfall) portfolio.
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meanetl.portf <- add.objective(portfolio=init, type="risk", name="ES")
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meanetl.portf <- add.objective(portfolio=meanetl.portf, type="return", name="mean")
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#' Add objectives for mean-variance portfolio.
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meanvar.portf <- add.objective(portfolio=init, type="risk", name="var", risk_aversion=10)
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meanvar.portf <- add.objective(portfolio=meanvar.portf, type="return", name="mean")
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#' Compute the mean-variance efficient frontier.
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meanvar.ef <- create.EfficientFrontier(R=R, portfolio=init, type="mean-StdDev")
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meanvar.ef
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summary(meanvar.ef, digits=2)
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meanvar.ef$frontier
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#' The RAR.text argument can be used for the risk-adjusted-return name on the 
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#' legend, by default it is 'Modified Sharpe Ratio'.
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chart.EfficientFrontier(meanvar.ef, match.col="StdDev", type="l", 
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                        RAR.text="Sharpe Ratio", pch=4)
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#' The tangency portfolio and line are plotted by default, these can be 
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#' ommitted by setting rf=NULL.
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chart.EfficientFrontier(meanvar.ef, match.col="StdDev", type="b", rf=NULL)
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#' The tangency line can be omitted with tangent.line=FALSE. The tangent 
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#' portfolio, risk-free rate and Sharpe Ratio are still included in the plot.
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chart.EfficientFrontier(meanvar.ef, match.col="StdDev", type="l", tangent.line=FALSE)
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#' The assets can be omitted with chart.assets=FALSE.
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chart.EfficientFrontier(meanvar.ef, match.col="StdDev", type="l", 
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                        tangent.line=FALSE, chart.assets=FALSE)
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#' Just the names of the assets can be omitted with labels.assets=FALSE and the 
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#' plotting character can be changed with pch.assets.
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chart.EfficientFrontier(meanvar.ef, match.col="StdDev", type="l", 
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                        tangent.line=FALSE, labels.assets=FALSE, pch.assets=1)
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#' Chart the asset weights along the efficient frontier.
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chart.EF.Weights(meanvar.ef, colorset=bluemono, match.col="StdDev")
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#' Chart the group weights along the efficient frontier.
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chart.EF.Weights(meanvar.ef, colorset=bluemono, by.groups=TRUE, match.col="StdDev")
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#' The labels for Mean, Weight, and StdDev can be increased or decreased with
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#' the cex.lab argument. The default is cex.lab=0.8.
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chart.EF.Weights(meanvar.ef, colorset=bluemono, match.col="StdDev", main="", cex.lab=1)
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#' If you have a lot of assets and they don't fit with the default legend, you
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#' can set legend.loc=NULL and customize the plot.
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par(mar=c(8, 4, 4, 2)+0.1, xpd=TRUE)
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chart.EF.Weights(meanvar.ef, colorset=bluemono, match.col="StdDev", legend.loc=NULL)
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legend("bottom", legend=colnames(R), inset=-1, fill=bluemono, bty="n", ncol=3, cex=0.8)
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par(mar=c(5, 4, 4, 2)+0.1, xpd=FALSE)
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#' Run optimize.portfolio and chart the efficient frontier of the optimal
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#' portfolio object.
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opt_meanvar <- optimize.portfolio(R=R, portfolio=meanvar.portf, 
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                                  optimize_method="ROI", trace=TRUE)
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#' The efficient frontier is created from the 'opt_meanvar' object by getting.
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#' The portfolio and returns objects and then passing those to create.EfficientFrontier.
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chart.EfficientFrontier(opt_meanvar, match.col="StdDev", n.portfolios=25, type="l")
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#' Rerun the optimization with a new risk aversion parameter to change where 
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#' the portfolio is along the efficient frontier. The 'optimal' portfolio 
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#' plotted on the efficient frontier is the optimal portfolio returned by 
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#' optimize.portfolio.
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meanvar.portf$objectives[[2]]$risk_aversion=0.25
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opt_meanvar <- optimize.portfolio(R=R, portfolio=meanvar.portf, optimize_method="ROI", trace=TRUE)
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chart.EfficientFrontier(opt_meanvar, match.col="StdDev", n.portfolios=25, type="l")
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#' The weights along the efficient frontier can be plotted by passing in the
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#' optimize.portfolio output object.
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chart.EF.Weights(opt_meanvar, match.col="StdDev")
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chart.EF.Weights(opt_meanvar, match.col="StdDev", by.groups=TRUE)
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#' Extract the efficient frontier and then plot it.
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#' Note that if you want to do multiple charts of the efficient frontier from
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#' the optimize.portfolio object, it is best to extractEfficientFrontier as 
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#' shown below.
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ef <- extractEfficientFrontier(object=opt_meanvar, match.col="StdDev", n.portfolios=15)
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ef
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summary(ef, digits=5)
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chart.EF.Weights(ef, match.col="StdDev", colorset=bluemono)
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chart.EF.Weights(ef, match.col="StdDev", colorset=bluemono, by.groups=TRUE)
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#' Compute the mean-ES efficient frontier.
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meanetl.ef <- create.EfficientFrontier(R=R, portfolio=init, type="mean-ES")
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meanetl.ef
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summary(meanetl.ef)
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meanetl.ef$frontier
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#' Chart the mean-ES efficient frontier.
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chart.EfficientFrontier(meanetl.ef, match.col="ES", main="mean-ETL Efficient Frontier", type="l", col="blue", RAR.text="STARR")
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chart.EF.Weights(meanetl.ef, colorset=bluemono, match.col="ES")
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chart.EF.Weights(meanetl.ef, by.groups=TRUE, colorset=bluemono, match.col="ES")
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#' Compute the mean-ES efficient frontier using random portfolios to solve
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#' the optimization problem.
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meanetl.rp.ef <- create.EfficientFrontier(R=R, portfolio=meanetl.portf, type="random", match.col="ES")
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chart.EfficientFrontier(meanetl.rp.ef, match.col="ES", main="mean-ETL RP Efficient Frontier", type="l", col="blue", rf=0)
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chart.EF.Weights(meanetl.rp.ef, colorset=bluemono, match.col="ES")
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# mean-etl efficient frontier with optimize.portfolio output
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opt_meanetl <- optimize.portfolio(R=R, portfolio=meanetl.portf, optimize_method="random", search_size=2000, trace=TRUE)
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chart.EfficientFrontier(meanetl.rp.ef, match.col="ES", main="mean-ETL RP Efficient Frontier", type="l", col="blue", rf=0, RAR.text="STARR")
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#' Create a mean-var efficient frontier for multiple portfolios and overlay 
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#' the efficient frontier lines. Set up an initial portfolio with the full 
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#' investment constraint and mean and var objectives.
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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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#' Portfolio with long only constraints.
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lo.portf <- add.constraint(portfolio=init.portf, type="long_only")
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#' Portfolio with box constraints.
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box.portf <- add.constraint(portfolio=init.portf, type="box", min=0.05, max=0.65)
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#' Portfolio with group constraints (also add long only constraints to the 
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#' group portfolio).
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group.portf <- add.constraint(portfolio=init.portf, type="group", 
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                              groups=list(groupA=c(1, 3),
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                                          groupB=c(2, 4, 5)),
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                              group_min=c(0.25, 0.15), 
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                              group_max=c(0.75, 0.55))
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group.portf <- add.constraint(portfolio=group.portf, type="long_only")
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#' Combine the portfolios into a list.
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portf.list <- combine.portfolios(list(lo.portf, box.portf, group.portf))
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#' Plot the efficient frontier overlay of the portfolios with varying constraints.
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legend.labels <- c("Long Only", "Box", "Group + Long Only")
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chart.EfficientFrontierOverlay(R=R, portfolio_list=portf.list, type="mean-StdDev", 
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                               match.col="StdDev", legend.loc="topleft", 
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                               legend.labels=legend.labels, cex.legend=0.6,
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                               labels.assets=FALSE, pch.assets=18)
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#' Efficient frontier in mean-ES space with varying confidence leves for
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#' ES calculation.
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ES90 <- add.objective(portfolio=lo.portf, type="risk", name="ES", 
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                          arguments=list(p=0.9))
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ES92 <- add.objective(portfolio=lo.portf, type="risk", name="ES", 
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                          arguments=list(p=0.92))
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ES95 <- add.objective(portfolio=lo.portf, type="risk", name="ES", 
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                      arguments=list(p=0.95))
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#' Combine the portfolios into a list.
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portf.list <- combine.portfolios(list(ES.90=ES90, ES.92=ES92, ES.95=ES95))
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#' Plot the efficient frontier overlay of the portfolios with varying 
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#' confidence levels fot he ES calculation.
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legend.labels <- c("ES (p=0.9)", "ES (p=0.92)", "ES (p=0.95)")
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chart.EfficientFrontierOverlay(R=R, portfolio_list=portf.list, type="mean-ES", 
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                               match.col="ES", legend.loc="topleft", 
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                               legend.labels=legend.labels, cex.legend=0.6,
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                               labels.assets=FALSE, pch.assets=18)
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