[R-SIG-Finance] Using optimize.portfolio

[Enrico Schumann]

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","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))
>

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


-- 
Enrico Schumann
Lucerne, Switzerland
http://enricoschumann.net