s1 <- read.csv("http://ichart.finance.yahoo.com/table.csv?s=IBM&a=00&b=31&c=2005&d=02&e=31&f=2012&g=m&ignore=.csv", sep=",", header=TRUE)

s2 <- read.csv("http://ichart.finance.yahoo.com/table.csv?s=XOM&a=00&b=31&c=2005&d=02&e=31&f=2012&g=m&ignore=.csv", sep=",", header=TRUE)

s3 <- read.csv("http://ichart.finance.yahoo.com/table.csv?s=AAPL&a=00&b=31&c=2005&d=02&e=31&f=2012&g=m&ignore=.csv", sep=",", header=TRUE)

head(s1)

head(s2)

head(s3)

#Select only the adjusted close prices from each file:
prices <- as.data.frame(cbind(s1$Adj.Close, s2$Adj.Close, s3$Adj.Close))

#You can rename the adjusted close prices as follows:
names(prices) <-c("p1", "p2", "p3")

#Convert adjusted close prices into returns:
ribm <- (prices$p1[-length(prices$p1)]-prices$p1[-1])/prices$p1[-1]
rxom <- (prices$p2[-length(prices$p2)]-prices$p2[-1])/prices$p2[-1]
raapl <- (prices$p3[-length(prices$p3)]-prices$p3[-1])/prices$p3[-1]

#Place the returns together in a data frame:
returns <- as.data.frame(cbind(ribm,rxom,raapl))

mean(returns)

#Or use
sapply(returns, mean)

#Or
colMeans(returns)

cov(returns)

a <- seq(0, 1, 0.01)
b <- 1-a

#Construct portfolio possibilities curve using IBM and AAPL:
rp_bar <- a*mean(ribm) + b*mean(raapl)
var_p <- a^2*var(ribm) + b^2*var(raapl) + 2*a*b*cov(ribm,raapl)
sd_p <- sqrt(var_p)

plot(sd_p, rp_bar)