Using optimize.portfolio

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