[R-SIG-Finance] Using optimize.portfolio

[Ilya Kipnis]

Roger, I believe what you'd need is some custom risk/return functions. I
don't remember the exact syntax off the top of my head, but I do remember
that Ross Bennett covers this in his course on Datacamp. I highly recommend
it if you're going to be using PortfA.

On Fri, Jun 5, 2020 at 2:16 PM Roger Bos <roger.bos using gmail.com> wrote:

> All,
>
> I am comparing optimize.portfolio from the PortfolioAnalytics package to
> portfolio.optim from the tseries package.  portfolio.optim seems a bit
> easier to use, but I like the set up of optimize.portfolio.  I have created
> a minimal reprex below that compares the output of both in case that helps
> answer my questions.
> Here are my two primary questions:
>
> 1) What if I wanted to pass a custom covariance matrix to
> optimize.portfolio, like from a risk model.  Is that possible?  I can pass
> it to portfolio.optim because covmat is one of the parameters.
> 2) What if I wanted to pass forecasted returns to optimize.portfolio?  How
> would that be done.
>
> If there is anything that can be improved in this example, that would be
> helpful as well.  Thank you in advance for any assistance, Roger.
>
> ###
>
> library(PortfolioAnalytics)
> library(tidyquant)
>
> symbols <-
> c("MSFT","AAPL","AMZN","NVDA","CSCO","ADBE","AMGN","ORCL","QCOM","GILD")
>
> getYahooReturns <- function(symbols, return_column = "Ad") {
>   returns <- list()
>   for (symbol in symbols) {
>     getSymbols(symbol, from = '2000-01-01', adjustOHLC = TRUE, env =
> .GlobalEnv, auto.assign = TRUE)
>     return <- Return.calculate(Ad(get(symbol)))
>     colnames(return) <- gsub("\\.Adjusted", "", colnames(return))
>     returns[[symbol]] <- return
>   }
>   returns <- do.call(cbind, returns)
>   return(returns)
> }
>
> returns <- getYahooReturns(symbols)
> returns <- returns[-1, ]
> returns[is.na(returns)] <- 0
>
> # portfolio.optim from tseries package
> library(tseries)
> sigma <- cov(returns)
> reslow <- rep(0, ncol(returns))
> reshigh <- rep(1, ncol(returns))
> popt <- portfolio.optim(x = returns, covmat = sigma, reslow = reslow,
> reshigh = reshigh)
> popt$pw
>
> pspec <- portfolio.spec(assets = symbols)
> pspec <- add.constraint(portfolio=pspec, type="box",
>                     min = 0, max = 1, min_sum = 0.99, max_sum = 1.01)
> pspec <- add.objective(portfolio=pspec,
>                        type="return",
>                        name="mean")
> pspec <- add.objective(portfolio=pspec,
>                        type="risk",
>                        name="var")
>
> opt <- optimize.portfolio(R = returns,
>                    portfolio = pspec,
>                    optimize_method = "DEoptim", )
> data.frame(optimize.portfolio = opt$weights, portfolio.optim =
> round(popt$pw, 3))
>
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>
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