CoCalc -- testing_ROI

GitHub Repository: braverock/portfolioanalytics
Path: blob/master/sandbox/testing_ROI_Martin.R
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# Testing for replicating professor Martin's examples
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# The numbered examples corresopond to 1. theory review weights constrained mvo v5.pdf
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# data = crsp.short.Rdata
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# returns = midcap.ts[, 1:10]
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rm(list=ls())
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# Load packages
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library(PortfolioAnalytics)
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library(ROI)
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library(ROI.plugin.glpk)
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library(ROI.plugin.quadprog)
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# Use edhec data set from PerformanceAnalytics for reproducing if user does not
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# have the crsp.short.Rdata data
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# data(edhec)
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# returns <- edhec[, 1:10]
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# Use crsp.short.Rdata from Prof Martin
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# data file should be in working directory or specify path
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# Can we include this as a data set in the PortfolioAnalytics package?
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load("/Users/rossbennett/Desktop/Testing/crsp.short.Rdata")
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returns <- midcap.ts[, 1:10]
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funds <- colnames(returns)
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##### Example 1.1: Global Minimum Variance (GMV) Portfolio #####
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# GMV portfolio using new interface
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pspec <- portfolio.spec(assets=funds)
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pspec <- add.constraint(portfolio=pspec, type="full_investment", enabled=TRUE)
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pspec <- add.constraint(portfolio=pspec, type="box", min=-Inf, max=Inf, enabled=TRUE)
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pspec <- add.objective(portfolio=pspec, type="risk", name="var", enabled=TRUE)
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opt <- optimize.portfolio(R=returns, portfolio=pspec, optimize_method="ROI")
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# Optimal weights
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round(opt$weights, 3)
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# Portfolio standard deviation
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sqrt(opt$out)
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##### Example 1.2: Long Only GMV Portfolio #####
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# GMV long only portfolio using new interface
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pspec <- portfolio.spec(assets=funds)
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pspec <- add.constraint(portfolio=pspec, type="full_investment", enabled=TRUE)
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pspec <- add.constraint(portfolio=pspec, type="box", min=0, max=1, enabled=TRUE)
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pspec <- add.objective(portfolio=pspec, type="risk", name="var", enabled=TRUE)
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opt <- optimize.portfolio(R=returns, portfolio=pspec, optimize_method="ROI")
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# Optimal weights
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round(opt$weights, 3)
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# Portfolio standard deviation
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sqrt(opt$out)
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##### Example 1.3: GMV Box Constraints #####
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# GMV box constraints portfolio using new interface
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pspec <- portfolio.spec(assets=funds)
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pspec <- add.constraint(portfolio=pspec, type="full_investment", enabled=TRUE)
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pspec <- add.constraint(portfolio=pspec, type="box", min=0.03, max=0.25, enabled=TRUE)
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pspec <- add.objective(portfolio=pspec, type="risk", name="var", enabled=TRUE)
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opt <- optimize.portfolio(R=returns, portfolio=pspec, optimize_method="ROI")
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# Optimal weights
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round(opt$weights, 3)
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# Portfolio standard deviation
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sqrt(opt$out)
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##### Example 1.4: GMV long only with Group Constraints #####
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# Combine returns from different market cap groups
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returns.cap <- cbind(microcap.ts[, 1:2],
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                     smallcap.ts[, 1:2],
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                     midcap.ts[, 1:2],
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                     largecap.ts[, 1:2])
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funds.cap <- colnames(returns.cap)
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# GMV group constraints portfolio using new interface
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pspec <- portfolio.spec(assets=funds.cap)
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pspec <- add.constraint(portfolio=pspec, type="full_investment", enabled=TRUE)
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pspec <- add.constraint(portfolio=pspec, type="box", min=0, max=1, enabled=TRUE)
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pspec <- add.constraint(portfolio=pspec, type="group", enabled=TRUE,
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                        groups=c(2, 2, 2, 2),
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                        group_min=c(0.1, 0.15, 0, 0), 
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                        group_max=c(0.25, .35, 0.35, 0.45),
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                        group_labels=c("MICRO", "SMALL", "MID", "LARGE"))
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pspec <- add.objective(portfolio=pspec, type="risk", name="var", enabled=TRUE)
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opt <- optimize.portfolio(R=returns.cap, portfolio=pspec, optimize_method="ROI")
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# Optimal weights
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round(opt$weights, 3)
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# Get the group weights
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# This is something I will work to include in the summary.optimize.portfolio.ROI
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groups <- pspec$constraints[[3]]$groups
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group_labels <- pspec$constraints[[3]]$group_labels
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n.groups <- length(groups)
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group_weights <- rep(0, n.groups)
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k <- 1
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l <- 0
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for(i in 1:n.groups){
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  j <- groups[i]
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  group_weights[i] <- sum(opt$weights[k:(l+j)])
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  k <- k + j
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  l <- k - 1
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}
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names(group_weights) <- group_labels
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group_weights
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# Portfolio standard deviation
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sqrt(opt$out)
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# In the previous examples, we were solving global minimum variance with optmize_method="ROI". 
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# The solve.QP plugin is selected automatically by optimize.portfolio when "var" is the objective
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##### Example 1.6: Maximize mean-return with box constraints #####
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# Maximize mean return with box constraints portfolio using new interface
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pspec <- portfolio.spec(assets=funds)
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pspec <- add.constraint(portfolio=pspec, type="full_investment", enabled=TRUE)
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pspec <- add.constraint(portfolio=pspec, type="box", min=0.03, max=0.25, enabled=TRUE)
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pspec <- add.objective(portfolio=pspec, type="return", name="mean", enabled=TRUE)
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opt <- optimize.portfolio(R=returns, portfolio=pspec, optimize_method="ROI")
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# Optimal weights
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round(opt$weights, 3)
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# Portfolio standard deviation
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sqrt(opt$out)
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##### Example 1.7 Maximize mean-return Long Only with Group Constraints #####
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# GMV group constraints portfolio using new interface
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pspec <- portfolio.spec(assets=funds.cap)
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pspec <- add.constraint(portfolio=pspec, type="full_investment", enabled=TRUE)
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pspec <- add.constraint(portfolio=pspec, type="box", min=0, max=1, enabled=TRUE)
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pspec <- add.constraint(portfolio=pspec, type="group", enabled=TRUE,
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                        groups=c(2, 2, 2, 2),
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                        group_min=c(0.1, 0.15, 0, 0), 
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                        group_max=c(0.25, .35, 0.35, 0.45),
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                        group_labels=c("MICRO", "SMALL", "MID", "LARGE"))
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pspec <- add.objective(portfolio=pspec, type="return", name="mean", enabled=TRUE)
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opt <- optimize.portfolio(R=returns.cap, portfolio=pspec, optimize_method="ROI")
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# Optimal weights
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round(opt$weights, 3)
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# Get the group weights
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# This is something I will work to include in the summary.optimize.portfolio.ROI
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groups <- pspec$constraints[[3]]$groups
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group_labels <- pspec$constraints[[3]]$group_labels
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group_weights <- rep(0, n.groups)
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n.groups <- length(groups)
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k <- 1
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l <- 0
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for(i in 1:n.groups){
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  j <- groups[i]
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  group_weights[i] <- sum(opt$weights[k:(l+j)])
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  k <- k + j
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  l <- k - 1
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}
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names(group_weights) <- group_labels
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group_weights
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# Portfolio standard deviation
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sqrt(opt$out)
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# Check results for quadratic utility with manual code
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p <- ncol(returns)
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V <- var(returns)
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mu <- colMeans(returns)
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lambda <- 20
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min_wt <- 0
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max_wt <- 1
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# parameters for solve.QP
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A <- cbind(rep(1, p), diag(p), -diag(p))
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b <- c(1, rep(min_wt, p), rep(-max_wt, p))
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d <- mu
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res <- quadprog:::solve.QP(Dmat=2*lambda*V, dvec=d, Amat=A, bvec=b, meq=1)
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wts2 <- round(res$solution, 4)
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names(wts2) <- colnames(returns)
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wts2
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# Note that target mean return CANNOT be specified as a constraint currently
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# It is specified as a target in the return objective
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# Can do quadratic utility optimization with target return
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##### Example X: Mean Variance Optimization (MVO) with target mean return constraint #####
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# MVO with target mean return
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pspec <- portfolio.spec(assets=funds)
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pspec <- add.constraint(portfolio=pspec, type="full_investment", enabled=TRUE)
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pspec <- add.constraint(portfolio=pspec, type="box", min=-Inf, max=Inf, enabled=TRUE)
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pspec <- add.objective(portfolio=pspec, type="risk", name="var", risk_aversion=1e6, enabled=TRUE)
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pspec <- add.objective(portfolio=pspec, type="return", name="mean", target=0.014, enabled=TRUE)
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opt <- optimize.portfolio(R=returns, portfolio=pspec, optimize_method="ROI")
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# Optimal weights
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round(opt$weights, 3)
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# Portfolio return
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t(opt$weights) %*% colMeans(returns)
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##### Example X: Mean Variance Optimization (MVO) with target mean return and long only constraints #####
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# MVO with long only and target mean return
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pspec <- portfolio.spec(assets=funds)
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pspec <- add.constraint(portfolio=pspec, type="full_investment", enabled=TRUE)
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pspec <- add.constraint(portfolio=pspec, type="box", min=0, max=1, enabled=TRUE)
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pspec <- add.objective(portfolio=pspec, type="risk", name="var", risk_aversion=1e6, enabled=TRUE)
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pspec <- add.objective(portfolio=pspec, type="return", name="mean", target=0.014, enabled=TRUE)
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opt <- optimize.portfolio(R=returns, portfolio=pspec, optimize_method="ROI")
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# Optimal weights
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round(opt$weights, 3)
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# Portfolio return
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t(opt$weights) %*% colMeans(returns)
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##### Example X: Mean Variance Optimization (MVO) with target mean return and box constraints #####
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# MVO with box constraints and target mean return
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pspec <- portfolio.spec(assets=funds)
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pspec <- add.constraint(portfolio=pspec, type="full_investment", enabled=TRUE)
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pspec <- add.constraint(portfolio=pspec, type="box", min=0.03, max=0.25, enabled=TRUE)
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pspec <- add.objective(portfolio=pspec, type="risk", name="var", risk_aversion=1e6, enabled=TRUE)
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pspec <- add.objective(portfolio=pspec, type="return", name="mean", target=0.014, enabled=TRUE)
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opt <- optimize.portfolio(R=returns, portfolio=pspec, optimize_method="ROI")
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# Optimal weights
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round(opt$weights, 3)
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# Portfolio return
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t(opt$weights) %*% colMeans(returns)
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##### Example X: ETL Long Only #####
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pspec <- portfolio.spec(assets=funds)
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pspec <- add.constraint(portfolio=pspec, type="full_investment", enabled=TRUE)
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pspec <- add.constraint(portfolio=pspec, type="box", min=0, max=1, enabled=TRUE)
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# This can be specified with ETL, ES, or CVaR for name
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pspec <- add.objective(portfolio=pspec, type="risk", name="ETL", alpha=0.05, enabled=TRUE)
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opt <- optimize.portfolio(R=returns, portfolio=pspec, optimize_method="ROI")
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# Optimal weights
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round(opt$weights, 3)
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##### Example X: ETL with box constraints #####
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pspec <- portfolio.spec(assets=funds)
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pspec <- add.constraint(portfolio=pspec, type="full_investment", enabled=TRUE)
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pspec <- add.constraint(portfolio=pspec, type="box", min=0.03, max=0.25, enabled=TRUE)
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# This can be specified with ETL, ES, or CVaR for name
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pspec <- add.objective(portfolio=pspec, type="risk", name="ETL", alpha=0.05, enabled=TRUE)
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opt <- optimize.portfolio(R=returns, portfolio=pspec, optimize_method="ROI")
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# Optimal weights
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round(opt$weights, 3)
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##### Example X: ETL long only with group constraints #####
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# GMV group constraints portfolio using new interface
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pspec <- portfolio.spec(assets=funds.cap)
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pspec <- add.constraint(portfolio=pspec, type="full_investment", enabled=TRUE)
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pspec <- add.constraint(portfolio=pspec, type="box", min=0, max=1, enabled=TRUE)
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pspec <- add.constraint(portfolio=pspec, type="group", enabled=TRUE,
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                        groups=c(2, 2, 2, 2),
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                        group_min=c(0.1, 0.15, 0, 0), 
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                        group_max=c(0.25, .35, 0.35, 0.45),
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                        group_labels=c("MICRO", "SMALL", "MID", "LARGE"))
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pspec <- add.objective(portfolio=pspec, type="risk", name="ETL", alpha=0.05, enabled=TRUE)
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opt <- optimize.portfolio(R=returns.cap, portfolio=pspec, optimize_method="ROI")
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# Optimal weights
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round(opt$weights, 3)
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# Get the group weights
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# This is something I will work to include in the summary.optimize.portfolio.ROI
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groups <- pspec$constraints[[3]]$groups
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group_labels <- pspec$constraints[[3]]$group_labels
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group_weights <- rep(0, n.groups)
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n.groups <- length(groups)
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k <- 1
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l <- 0
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for(i in 1:n.groups){
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  j <- groups[i]
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  group_weights[i] <- sum(opt$weights[k:(l+j)])
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  k <- k + j
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  l <- k - 1
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}
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names(group_weights) <- group_labels
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group_weights
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