[R-SIG-Finance] PortfolioAnalytics question re: showing results

[Ross Bennett]

Hi Matt,

You are very close in your script. Note that create.EfficientFrontier with
type="mean-StdDev" is a special case for an efficient frontier that can be
formulated and solved with a QP solver.

Also note that your second call to add.objective should add to the SD.portf
portfolio and not init.portf
# Add measure 2, annualized standard deviation
# note that you want to add this to the SD.portf portfolio, not init.portf
SD.portf <- add.objective(portfolio=SD.portf,
                            type="risk", # the kind of objective this is
                            name="pasd1", # to minimize from the sample
                            enabled=TRUE, # enable or disable the objective
                            multiplier=0 # calculate it but don't use it in
the objective
)

I recommend actually running an optimization using random portfolios so you
get the entire feasible space given the constraints and objectives in your
portfolio.

rp <- random_portfolios(SD.portf, 5000)
# make sure to run with trace=TRUE for the extract stats output
opt <- optimize.portfolio(R, SD.portf, optimize_method="random", trace=TRUE)
chart.RiskReward(opt, risk.col="pasd1.pasd1", return.col="pamean1.pamean1")

# use the output of extractStats to find portfolio with max return at a
given
# risk level and portfolio with min risk at a given return level
ex <- extractStats(opt)
head(ex)

# This should get you started
# order by max pamean1
head(ex[order(ex[,"pamean1.pamean1"], decreasing=TRUE),])

# order by min pasd1
head(ex[order(ex[,"pasd1.pasd1"], decreasing=FALSE),])

Hope this helps, let me know if you need any other pointers.

Regards,
Ross

On Fri, Mar 18, 2016 at 8:13 AM, <matt at considine.net> wrote:

> Hi Brian,
>
> Thanks for the offer of some code.  I had wanted to try to figure this out
> for myself, but I'm not making the headway I thought.  IF you have some
> code or a worked example you can send, I'd be appreciative.
>
> That said here is what I am working with.   Perhaps someone can suggest
> what I am doing wrong?
>
> Goal : generate/plot an efficient frontier (with annualized axes) using
> PortfolioAnalytics, using monthly return data.  (Ideally, I'd also want to
> isolate the tangency/max Sharpe portfolio, a portfolio with max return at a
> specific risk level and a portfolio with a min risk at a specific return.
> But I'll deal with that later.)
>
> Code : I tried to use code from some of the presentations, demos (DEoptim
> and random portfolios, specifically) and vignettes.  Also, I'm using the
> latest version of the code from R-forge.
>
> #-----------------------------
> library(PortfolioAnalytics)
>
> # Define pamean function
> pamean1 <- function(R, weights, n=60, geometric=FALSE){
>   as.vector(sum(Return.annualized(last(R,n), geometric=geometric)*weights))
> }
>
> # Define pasd function
> pasd1 <- function(R, weights=NULL){
>   as.numeric(StdDev(R=R, weights=weights)*sqrt(12)) # hardcoded for
> monthly data
> }
>
> data(edhec)
>
> # Use the first 4 columns in edhec for a returns object
> R <- edhec[, 1:4]
> colnames(R) <- c("CA", "CTAG", "DS", "EM")
> head(R, 5)
>
> # Get a character vector of the fund names
> funds <- colnames(R)
>
> # Construct initial portfolio with basic constraints.
> init.portf <- portfolio.spec(assets=funds)
> init.portf <- add.constraint(portfolio=init.portf, type="full_investment")
> init.portf <- add.constraint(portfolio=init.portf, type="box", min=0.0,
> max=1.0)
>
> # Portfolio with standard deviation as an objective
> #SD.portf <- add.objective(portfolio=init.portf, type="risk",
> name="pasd1") #pasd1 doesn't work?
> #SD.portf <- add.objective(portfolio=SD.portf, type="return", name="mean")
> #pamean1 doesn't work?
>
> #Ok, let's try this :
> #Add measure 1, annualized return
> SD.portf <- add.objective(portfolio=init.portf,
>                             type="return", # the kind of objective this is
>                             name="pamean1", # name of the function
>                             enabled=TRUE, # enable or disable the objective
>                             multiplier=0 # calculate it but don't use it
> in the objective
> )
>
> # Add measure 2, annualized standard deviation
> SD.portf <- add.objective(portfolio=init.portf,
>                             type="risk", # the kind of objective this is
>                             name="pasd1", # to minimize from the sample
>                             enabled=TRUE, # enable or disable the objective
>                             multiplier=0 # calculate it but don't use it
> in the objective
> )
>
> #Create efficient frontier
> init.portf.ef <- create.EfficientFrontier(R=R, portfolio=SD.portf,
> type="mean-StdDev")
>
> #This chart never seems to show annualized axes
> chart.EfficientFrontier(init.portf.ef, match.col="StdDev")
>
> sd.moments <- set.portfolio.moments(R, SD.portf)
> names(sd.moments) #returning NULL with pasd1/pamean1
> print(sd.moments) #returning NULL with pasd1/pamean1
>
> #Just a reality check to see what the axes ranges should roughly look like
> ra <- Return.annualized(R[, , drop = FALSE], scale = 12, geometric = FALSE)
> sda <- StdDev.annualized(R[, , drop = FALSE], scale = 12)
> sra <- SharpeRatio.annualized(R[, , drop = FALSE], scale = 12, Rf = 0.00,
> geometric = FALSE)
>
> pamean1(R)
> ra
>
> pasd1(R)
> sda
> #----------------------------------
> Regards,
> Matt
>
>
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