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# Working with real data

It is useful to know that portfolio optimization is totally integrated in various R packages that we will discuss later. However, it's better to walk before we run; so let's start with a simple self-made `R` function that we would also itemize line by line as follows:

```minvariance <- function(assets, mu = 0.005) {
return  <- log(tail(assets, -1) / head(assets, -1))
Q       <- rbind(cov(return), rep(1, ncol(assets)),
colMeans(return))
Q       <- cbind(Q, rbind(t(tail(Q, 2)), matrix(0, 2, 2)))
b       <- c(rep(0, ncol(assets)), 1, mu)
solve(Q, b)
}```

This is a direct implementation of the algorithm that we discussed in the Theorem (Lagrange) section.

For demonstration purposes, we have fetched some IT stock prices from a Quandl superset (http://www.quandl.com/USER_1KR/1KT ...

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