by Guangming Lang
2 min read


  • r

In a previous post, I showed how to calculate the efficient portfolio given a target return using R and vanguard funds in my retirement account. All assets I used previously are risky assets. However, we can also hold risk free assets in our portfolio, for example, 1 year t-bills. After having included risk free assets, we can define something called the Sharpe Slope as the difference between our portfolio return and the risk free rate divided by the volatility of our portfolio. The bigger the Sharpe Slope the better. So our objective is to find the portfolio with the biggest Sharpe Slope, and this is called tangency portfolio.

Step 0. Load libraries and define helper functions.

## Error in library(tseries): there is no package called 'tseries'
download_data = function(symb, begin, end) {
        get.hist.quote(instrument=symb, start=begin, end=end, 
                       origin="1970-01-01", quote="AdjClose", 
                       provider="yahoo", compression="m", 

to_yearmon = function(data) {
        index(data) = as.yearmon(index(data))

Step 1. I choose assets from three broad classes: stocks, bonds, and commodities. For stocks, I choose funds that cover total US market, total international markets, and real estate. For bonds, I choose funds that invest in the total US bond market and inflation protected securities. For commodities, I choose funds that invest in gold and other precious metals and their mining companies and oil & gas and energy companies. First, I download the monthly adjusted closing price data of these funds between June 2000 and Oct 2014 from Yahoo.

# initialize the fund symbols 
stocks = c("VTSMX", "VGTSX", "VGSIX")
bonds = c("VIPSX", "VBMFX")
commodities = c("VGPMX", "VGENX")
symbols = c(stocks, bonds, commodities)

# download adj. price data
port = list()
for (symbol in symbols)
        port[[symbol]] = download_data(symbol, "2000-06-29", "2014-10-31")
## Error in get.hist.quote(instrument = symb, start = begin, end = end, origin = "1970-01-01", : could not find function "get.hist.quote"
# change the class of the time index to yearmon
lapply(port, to_yearmon)

# put both all price data in one data frame
prices ="cbind", port)
colnames(prices) = symbols
## Error in `colnames<-`(`*tmp*`, value = c("VTSMX", "VGTSX", "VGSIX", "VIPSX", : attempt to set 'colnames' on an object with less than two dimensions

Step 2. Calculate monthly continuously compounded returns as difference in log prices. = diff(log(prices))
## Error in log(prices): non-numeric argument to mathematical function
# inspect the return data
cat("Start: ", as.character(start(, "  End: ", as.character(end(
## Error in start( object '' not found
head(, 3)
## Error in head(, 3): object '' not found

Step 3. Calculate annualized sample average returns of the underlying assets and the sample covariance matrix of the returns.

mu.hat.annual = apply(, 2, mean) * 12   
## Error in apply(, 2, mean): object '' not found
cov.mat.annual = cov( * 12 
## Error in object '' not found

Step 4. Set the risk free rate as 0.12% and calculate the tangency portfolio using a helper function written by Eric Zivot and Hezky Varon from U of Washington.

t.bill.rate.1yr = 0.12/100
helper = "~/Coding/R-related/R/portfolio-optim/portfolio_noshorts.r"
## Warning in file(filename, "r", encoding = encoding): cannot open file '/
## Users/gmlang/Coding/R-related/R/portfolio-optim/portfolio_noshorts.r': No
## such file or directory
## Error in file(filename, "r", encoding = encoding): cannot open the connection
tan.port = tangency.portfolio(mu.hat.annual, cov.mat.annual, 
                    , shorts=T)
## Error in tangency.portfolio(mu.hat.annual, cov.mat.annual, = t.bill.rate.1yr, : could not find function "tangency.portfolio"
## Error in summary(tan.port): object 'tan.port' not found
# port weights
plot(tan.port, col="blue", lwd=2)
## Error in plot(tan.port, col = "blue", lwd = 2): object 'tan.port' not found