[R-SIG-Finance] PortfolioAnalytics with turnover constraint

[Bos, Roger]

Dear All,

I am trying to use the PortfolioAnalytics package to run an optimization with a turnover constraint.  I didn't see any examples in the demos.  The example code below runs in I set the portfolio up using equal weights:

init.portf <- portfolio.spec(assets=funds)

But if I instead try to use a named vector of initial weights, as described in the docs, as such:

init.portf <- portfolio.spec(assets=wgts) # Does not work with this line added

I get an error when I try to run the optimization:

11:57:01  > minStdDev.DE <- optimize.portfolio(R=R, portfolio=init.portf, optimize_method="DEoptim", search_size = 2000)
 Show Traceback

 Rerun with Debug
 Error in result[2, ] <- rep(1/length(seed), length(seed)) :
  incorrect number of subscripts on matrix

The portfolio.spec function did take in the initial weights properly, as shown using the print function:

11:57:01  > print.default(init.portf)
$assets
   CA CTAG  DS  EM EQM
1 0.1  0.3 0.4 0.1 0.1

Can anyone provide me with a working example using both initial weights and a turnover constraint?  I don't care it if uses DEoptim or some other optimizer, I just want to get started with something.

Thanks,

Roger


library(PortfolioAnalytics)

data(edhec)
R <- edhec[, 1:5]
colnames(R) <- c("CA", "CTAG", "DS", "EM", "EQM")
funds <- colnames(R)
wgts <- data.frame("CA" = .1, "CTAG" = .3, "DS" = .4, "EM" = .1, "EQM" = .1)
wgts

# Set up portfolio with objectives and constraints
init.portf <- portfolio.spec(assets=funds)
init.portf <- portfolio.spec(assets=wgts) # Does not work with this line added
init.portf <- add.constraint(portfolio = init.portf, type="weight_sum", min_sum = .99, max_sum = 1.01)
init.portf <- add.constraint(portfolio = init.portf, type="long_only")
init.portf <- add.constraint(portfolio = init.portf, type="turnover", turnover_target = .2)

# Add an objective to minimize portfolio standard deviation
init.portf <- add.objective(portfolio=init.portf, type="risk", name="StdDev")
print.default(init.portf)

# Solve with DEoptim
minStdDev.DE <- optimize.portfolio(R=R, portfolio=init.portf, optimize_method="DEoptim", search_size = 2000)
minStdDev.DE





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