Multi asset portfolio optimization 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

我弄清楚了为什么我的优化无法正常工作-我的数据集包含无法解决的秩不足矩阵（列多于行）。