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

[matt at considine.net]

Hi Ross, 

Thanks - that helps alot. It looks like part of what was tripping me up
is that the guts of the create/chart EfficientFrontier functions expect
hard-coded column headings. I.e. that those routines as written aren't
flexible enough to deal with the custom optimizing functions. Is that
correct or is there a more flexible set of those that I have overlooked?
The presentation graphs the create are quite clean. 

In any case, thank you for the example and feedback. 

Matt 

On 2016-03-18 10:54, Ross Bennett wrote: 

> 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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