[R-SIG-Finance] Using optimize.portfolio

[Brian G. Peterson]

On Sat, 2020-06-06 at 14:33 +0200, Enrico Schumann wrote:
> On Fri, 05 Jun 2020, Roger Bos writes:
> 
> > 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","G
> > ILD")
> > 
> > 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))
> > 
> 
> If all else fails, and supposing that 'PortfolioAnalytics' per
> default computes means and covariances in the standard way, you could
> create input data (time series) that have exactly the desired
> covariances and means:
> 
>   
> https://stackoverflow.com/questions/58293991/how-to-use-fportfolio-package-in-r-for-non-time-series-input/58302451#58302451
> 

per default, PortfolioAnalytics uses sample moments as most users would
expect.  

As I already told the OP, the user may pass mu and sigma and m3 and m4
directly, or may construct custom moment functions to compute the
moments using any method they choose.

This is outlined in section 2 of the vignette:

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


and, of course, in the manual.