Using optimize.portfolio

Foger,

See the vignette for custom moment and objective functions:

https://cran.r-project.org/web/packages/PortfolioAnalytics/vignettes/custom_moments_objectives.pdf


You can simply pass mu and sigma, but you probably want to do a little bit
more work to make it more extensible.

Hopefully this is enough of a pointer. If not, I can work on an example
based on your example later.

Regards,

Brian

--

Brian G. Peterson
ph: +1.773.459.4973
im: bgpbraverock

On Fri, 2020-06-05 at 14:16 -0400, Roger Bos 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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