Multi asset portfolio optimization R - r

Question

I am new to Portfolio Optimization in R. When I add more than 25 assets (each asset has about 25 observations) to my portfolio, optimize.portfolio does not find any solutions. When I run this program with 25 assets or less, it works properly and plots the efficient frontier. Any help on this is much appreciated.
library(data.table)
library(readxl)
library(PerformanceAnalytics)
library(PortfolioAnalytics)
library(ROI)
library(foreach)
library(DEoptim)
library(iterators)
library(fGarch)
library(Rglpk)
library(quadprog)
library(ROI.plugin.glpk)
library(ROI.plugin.quadprog)
library(ROI.plugin.symphony)
library(pso)
library(GenSA)
library(corpcor)
library(testthat)
library(nloptr)
library(MASS)
library(robustbase)
library(ggplot2)
setwd("~/R")
#BRING IN DATA
returns.data <- read_excel("portfolio_sample_4asset.xlsx", sheet = "portfolio")
returns.data <- data.frame(returns.data)
row.names(returns.data) <- (returns.data$year)
returns.data$year <- NULL
meanReturns <- colMeans(returns.data)
#GENERATE COVARIANCE AND CORRELATION TABLES
cov.pop <- function(x,y=NULL) {
cov(x,y)*(NROW(x)-1)/NROW(x)
}
covMat <- cov.pop(returns.data)
corMat <- cor(returns.data)
#SPECIFY PORTFOLIO OBJECT
port <- portfolio.spec(assets = colnames(returns.data))
#CONSTRAINTS
port <- add.constraint(port,type="weight_sum",min=0.99, max=1.01)
#rportfolios <- random_portfolios(port, permutations = 500, rp_method = "sample", eliminate = TRUE)
#OPTIMIZATION SETUP
minreturnLimit <- min(colMeans(returns.data))
maxreturnLimit <- max(colMeans(returns.data))
minret <- minreturnLimit
maxret <- maxreturnLimit
vec <- seq(minret, maxret, length.out = 100)
eff.frontier <- data.frame(Risk = rep(NA, length(vec)), Return = rep(NA, length(vec)))
frontier.weights <- mat.or.vec(nr = length(vec), nc = ncol(returns.data))
colnames(frontier.weights) <- colnames(returns.data)
#GENERATE EFFICIENT FRONTIER
#In add.constraint...the type is return, as in, it is targeting a specific return specified by vec
#Subsequently, it looks for the portfolio that minimizes StdDev for that return constraint...this is the objective
for(i in 1:length(vec)){
eff.port <- add.constraint(port, type = "return", name = "mean", return_target = vec[i])
eff.port <- add.objective(eff.port, type = "risk", name = "var")
# eff.port <- add.objective(eff.port, type = "weight_concentration", name = "HHI",
# conc_aversion = 0.001)
eff.port <- optimize.portfolio(returns.data, eff.port, optimize_method = "ROI")
eff.frontier$Risk[i] <- sqrt(t(eff.port$weights) %*% covMat %*% eff.port$weights)
eff.frontier$Return[i] <- eff.port$weights %*% meanReturns
eff.frontier$Sharperatio[i] <- eff.port$Return[i] / eff.port$Risk[i]
frontier.weights[i,] = eff.port$weights
print(paste(round(i/length(vec) * 100, 0), "% done..."))
}
#PLOT EFFICIENT FRONTIER
ggplot(eff.frontier, aes(x=eff.frontier$Risk, y=eff.frontier$Return)) + geom_point(shape = 18, color = "limegreen", size = 2) + ggtitle("Portfolio Optimization") + labs(x="Risk",y="Return")
eff.frontier$Sharpe <- eff.frontier$Return / eff.frontier$Risk

Answer 1

I figured out why my optimization wasn't working - my dataset contained a rank-deficient matrix which could not be solved (more columns than rows).