Portfolio optimization.

R Example 8.1:  Mean-variance portfolio, Efficient Frontier, minimum-variance portfolio, maximum return, and a  graphical description of all portfolios in the efficient frontier, for five small-cap US equities monthly recorded return history   between 1997 and 2001.  The main package to use is fPortfolio from the suite of methods RMetrics. The equities are BKE, FCEL, GG, OII and SEB, contained in the dataset  SMALLCAP.RET, which comes with the fPortfolio package.
 

library(fPortfolio)
library(lpSolve)

names(SMALLCAP)  #to see the contents of the data set
Data = SMALLCAP.RET #to get returns 

##Extract a small sample for playing
Data = Data[, c("BKE","FCEL","GG","OII","SEB")]

##Compute the sample mean of the returns of each stock 
## and the sample covariance matrix.
covData <- covEstimator(Data) ; covData

## Compute the unrestricted (long-short) Mean-Variance (MV) portfolio
shortSpec <- portfolioSpec()
setSolver(shortSpec) <- "solveRshortExact"
shortFrontier <- portfolioFrontier(Data,spec=shortSpec,
    constraints="Short")
print(shortFrontier) #report results for portfolio:1,13,25,37,50

##Plot the Efficient Frontier
Frontier <- shortFrontier  
frontierPlot(Frontier,frontier="both",risk="Sigma",type="l")

## Plot some portfolios 
minvariancePoints(Frontier,pch=19,col="red") #the MVP point
##Position of each asset in the sigma-mu plane
singleAssetPoints(Frontier,risk="Sigma",pch=19,cex=1.5,
      col=topo.colors(6))

## To compute the minimum variance portfolio (MVP), 
## and a particular efficient portfolio for a given target return
##MVP: the minimum Variance. 
minvariancePortfolio(Data)
##EP(mu): an efficient portfolio for given target return
mu = 0.05; Spec = portfolioSpec()
setSolver(Spec) = "solveRshortExact" 
setTargetReturn(Spec) = mu 
efficientPortfolio(Data, Spec) 

##To compute the global maximum return portfolio we use the 
##linear programming solver lp to resolve the optimization
## problem for a vector  of 5 unknown weights.
##MaxR: Global maximum return portfolio 
## maximize: (w1,w2,w3,w4,w5)*covData$mu
## subject to: w1+w2+w3+w4+w5 = 1
##Use the linear programming solver lp from lpSolve:
f.obj <- covData$mu
f.con <- matrix(c(1,1,1,1,1), nrow=1, byrow=TRUE)
f.dir <- "="
f.rhs <- 1
lp ("max", f.obj, f.con, f.dir, f.rhs)
lp ("max", f.obj, f.con, f.dir, f.rhs)$solution

##Result: a portfolio containing only FCEL

## A plot of all 50 portfolios along the Efficient Frontier for 
## the given assets. The bold vertical line marks the MVP
weightsPlot(Frontier)

#####END