[R-SIG-Finance] Using optimize.portfolio

[alexios galanos]

You might (or not) find parma useful (for mainly convex problems):

library(parma)

# with covariance matrix
parmspec <- parmaspec(S = sigma, risk = "EV", riskType = "minrisk", LB = 
reslow, UB = reshigh)
parm <- parmasolve(parmspec, solver = "QP")
round(weights(parm),3)
sqrt(parmarisk(parm))

# with scenario matrix
parmspec <- parmaspec(scenario = returns, risk = "EV", riskType = 
"minrisk", LB = reslow, UB = reshigh)
parm <- parmasolve(parmspec, solver = "NLP")
round(weights(parm),3)
sqrt(parmarisk(parm))

# optimal reward to risk (scenario matrix)
parmspec <- parmaspec(scenario = returns, risk = "EV", forecast = 
colMeans(returns), riskType = "optimal", LB = reslow, UB = reshigh)
parm <- parmasolve(parmspec)
round(weights(parm),3)

# optimal reward to risk (covariance matrix)
parmspec <- parmaspec(S = sigma, risk = "EV", forecast = 
colMeans(returns), riskType = "optimal", LB = reslow, UB = reshigh)
parm <- parmasolve(parmspec)
round(weights(parm),3)

Alexios



On 6/5/20 1:31 PM, Roger Bos wrote:
> Thanks Brian.  That should get me started.  Appreciate the help.
> 
> On Fri, Jun 5, 2020, 4:05 PM Brian G. Peterson <brian using braverock.com> wrote:
> 
>> 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))
>>
>>
>> 	[[alternative HTML version deleted]]
>>
>>
>> _______________________________________________
>>
>> R-SIG-Finance using r-project.org
>>
>>   mailing list
>>
>> https://stat.ethz.ch/mailman/listinfo/r-sig-finance
>>
>>
>> -- Subscriber-posting only. If you want to post, subscribe first.
>>
>> -- Also note that this is not the r-help list where general R questions should go.
>>
>>
> 
> 	[[alternative HTML version deleted]]
> 
> _______________________________________________
> R-SIG-Finance using r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-finance
> -- Subscriber-posting only. If you want to post, subscribe first.
> -- Also note that this is not the r-help list where general R questions should go.
>