PortfolioAnalytics question re: showing results

 
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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