Changing tracks, I want to now look at portfolio optimization. Although this is very different from developing trading strategies, it is useful to know how to construct minimum-variance portfolios and the like, if only for curiosity’s sake. Also, just a -I hope unnecessary- note, portfolio optimization and parameter optimization (which I covered in the last post) are two completely different things.

Minimum-variance portfolio optimization has a lot of problems associated with it, but it makes for a good starting point as it is the most commonly discussed optimization technique in classroom-finance. One of my biggest issues is with the measurement of risk via volatility. Security out-performance contributes as much to volatility -hence risk- as security under-performance, which ideally shouldn’t be the case.

First, install the package tseries:
install.packages(‘tseries’)

The function of interest is portfolio.optim(). I decided to write my own function to enter in a vector of tickers, start and end dates for the dataset, min and max weight constraints and short-selling constraints. This function first processes the data and then passes it to portfolio.optim to determine the minimum variance portfolio for a given level of return. It then cycles through increasingly higher returns to check how high the Sharpe ratio can go.

Here is the code with comments:

minVarPortfolio= function(tickers,start='2000-01-01',end=Sys.Date(),
riskfree=0,short=TRUE,lowestWeight=-1,highestWeight=1){

# Load up the package
require(tseries)

#Initialize all the variables we will be using. returnMatrix is 
#initailized as a vector,with length equal to one of the input 
#ticker vectors (dependent on the start and end dates).
#Sharpe is set to 0. The weights vector is set equal in 
#length to the number of tickers. The portfolio is set to 
#NULL. A 'constraint' variable is created to pass on the 
#short parameter to the portfolio.optim function. And vectors 
#are created with the low and high weight restrictions, which
#are then passed to the portfolio.optim function as well. ##

returnMatrix=vector(length=length(getSymbols(tickers[1],
auto.assign=FALSE,from=start,to=end)))
sharpe=0
weights=vector(,length(tickers))
port=NULL
constraint=short
lowVec=rep(lowestWeight,length(tickers))
hiVec=rep(highestWeight,length(tickers))

#This is a for-loop which cycles through the tickers, calculates 
#their return, and stores the returns in a matrix, adding 
#the return vector for each ticker to the matrix

   for(i in 1:length(tickers)){
	temp=getSymbols(tickers[i],auto.assign=FALSE,from=start,to=end)
	if(i==1){
	returnMatrix=diff(log(Ad(temp)))
	}
	else{
	returnMatrix=cbind(returnMatrix,diff(log(Ad(temp))))
	}
    }

    returnMatrix[is.na(returnMatrix)]=0
    it

#This for-loop cycles through returns to test the portfolio.optim function 
#for the highest Sharpe ratio.
    for(j in 1:100){

#Stores the log of the return in retcalc
	retcalc=log((1+j/100))
	retcalc=retcalc/252
	print(paste("Ret Calc:",retcalc))

#Tries to see if the specified return from retcalc can result 
#in an efficient portfolio
	try(port<-portfolio.optim(returnMatrix,pm=retcalc,shorts=constraint,
reslow=lowVec,reshigh=hiVec,riskfree=riskfree),silent=T)

#If the portfolio exists, it is compared against previous portfolios 
#for different returns using the #Sharpe ratio. If it has the highest 
#Sharpe ratio, it is stored and the old one is discarded.
        if(!is.null(port)){
        print('Not Null')
        sd=port$ps
        tSharpe=((retcalc-riskfree)/sd)
        print(paste("Sharpe",tSharpe))

        if(tSharpe>sharpe){
	    sharpe=tSharpe
	    weights=port$pw
	}}

  }
    print(paste('Sharpe:', sharpe))
    print(rbind(tickers,weights))
    return(returnMatrix)

}

This code works fine except for when the restrictions are too strict, the portfolio.optim function can’t find a minimum variance portfolio. This happens if the optimum portfolio has negative returns, which my code doesn’t test for. For this reason, I wanted to try out other ways of finding the highest Sharpe portfolio. There are numerous tutorials out there on how to do this. Some of them are:

1-http://blog.streeteye.com/blog/2012/01/portfolio-optimization-and-efficient-frontiers-in-r/
2-http://quantivity.wordpress.com/2011/04/17/minimum-variance-portfolios/
3-http://www.rinfinance.com/RinFinance2009/presentations/yollin_slides.pdf
4-http://systematicinvestor.wordpress.com/2013/03/22/maximum-sharpe-portfolio/
5-http://alphaism.wordpress.com/2012/05/04/finding-efficient-frontier-and-optimal-portfolio-in-r/

After I run my function, with the following tickers and constraints:

matrix=minVarPortfolio(c(‘NVDA’, ‘YHOO’, ‘GOOG’, ‘CAT’, ‘BNS’, ‘POT’, ‘STO’, ‘MBT’ ,’SNE’),lowestWeight=0,highestWeight=0.2,start=’2000-01-01′, end=’2013-06-01′)

This is the output I get:

[1] “Sharpe: 0.177751547083007″

tickers                ”NVDA”                                   “YHOO”                        ”GOOG”
weights “-1.58276161084957e-19″      ”2.02785605793095e-17″           “0.2″
tickers                 “CAT”                                       “BNS”                           “POT”
weights “0.104269676769825″                           “0.2″                             “0.2″

tickers                 “STO”                                       “MBT”
weights “0.189985091184918″             “0.105745232045257″

tickers                 “SNE”
weights “-2.85654465380669e-17″

The ‘e-XX’ weights basically indicate a weighting of zero on that particular security (NVDA, YHOO and SNE above). In the next post I will look at how all this can be done using a package called ‘fPortfolio’. Happy trading!