[R-SIG-Finance] PortfolioAnalytics Group Constraint
Ross Bennett
rossbennett34 at gmail.com
Sat Apr 18 03:01:27 CEST 2015
Hi Peter,
This can certainly be done with PortfolioAnalytics. One of the key aspects
of the package is the modular design that allows you to define your own
objective function. Here are two ways to accomplish this:
1. custom objective function - define a custom objective function to
penalize portfolio weights that violate benchmark weights
2. loop over the returns and benchmark weights, modifying the constraints
each iteration
Here is an example with box constraints. I know your question asks for
group constraints, but the same concept applies and doing this first with
box constraints should be easier to understand.
###########
library(PortfolioAnalytics)
data(edhec)
data(weights)
# 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)
# benchmark weights
bweights <- weights[,1:4]
# normalize to sum to 1
bweights <- xts(t(apply(bweights, 1, function(x) x / sum(x))),
index(weights))
colnames(bweights) <- c("CA", "CTAG", "DS", "EM")
head(bweights)
# subset the returns object to begin after bweights
R <- R[paste(first(index(bweights)),"/")]
# 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="weight_sum",
min_sum=0.99, max_sum=1.01)
init.portf <- add.constraint(portfolio=init.portf, type="long_only")
# objective to minimize portfolio standard deviation
init.portf <- add.objective(portfolio=init.portf, type="risk",
name="StdDev")
##### Solution 1: custom objective function #####
# objective function to penalize portfolio weights that violate benchmark
# weights by +/- 2%.
foo <- function(R, weights, benchmark_weights){
idx <- index(last(R))
# this will use the last observation before idx of weights in the
benchmark
# if the index of the returns does not match the index of
benchmark_weights
# e.g. annual benchmark weights and quarterly rebalancing period
# should do some other checks in here to make sure you get valid weights
bw <- as.numeric(last(benchmark_weights[paste("/",idx,sep="")]))
# penalize weights that are outside of the benchmark weights +/- 2%
out <- 0
penalty <- 1e4
N <- length(bw)
bmax <- bw + 0.02
# Only go to penalty term if any of the weights violate max
if(any(weights > bmax)){
out <- out + sum(weights[which(weights > bmax[1:N])] -
bmax[which(weights > bmax[1:N])]) * penalty
}
bmin <- bw - 0.02
# Only go to penalty term if any of the weights violate min
if(any(weights < bmin)){
out <- out + sum(bmin[which(weights < bmin[1:N])] -
weights[which(weights < bmin[1:N])]) * penalty
}
# objective function must return a single value to minimize
return(out)
}
# use type = "risk" because we want to minimize the penalty term for
# portfolio weights that are outside of the bounds of the benchmark weights
portf <- add.objective(portfolio=init.portf, type="risk", name="foo",
arguments=list(benchmark_weights=bweights))
rp <- random_portfolios(init.portf, 1000)
rebal <- "years"
train <- 36
opt <- optimize.portfolio.rebalancing(R, portf,
training_period = train,
rebalance_on = rebal,
optimize_method="random", rp=rp,
trace=TRUE)
opt
extractWeights(opt)
chart.Weights(opt)
##### Solution 2: loop over returns and change the constraints #####
library(foreach)
library(iterators)
ep.i <- endpoints(R, on = rebal)[which(endpoints(R, on = rebal) >= train)]
out_list<-foreach::foreach(ep=iterators::iter(ep.i), .errorhandling='pass',
.packages='PortfolioAnalytics') %dopar% {
tmpR <- R[1:ep,]
idx <- index(last(tmpR))
# this will use the last observation before idx of weights in the
benchmark
# if the index of the returns does not match the index of
benchmark_weights
# e.g. annual benchmark weights and quarterly rebalancing period
# should do some other checks in here to make sure you get valid weights
bw <- as.numeric(last(bweights[paste("/",idx,sep="")]))
init.portf$constraints[[2]]$min <- bw - 0.02
init.portf$constraints[[2]]$max <- bw + 0.02
optimize.portfolio(R[1:ep,], portfolio=init.portf,
optimize_method="random", trace=TRUE,
rp=rp, parallel=FALSE)
}
# extract the optimal weights
xts(do.call(rbind, lapply(out_list, function(x) x$weights)), index(R)[ep.i])
# optimal weights for solution 1 and 2 should be equal
all.equal(xts(do.call(rbind, lapply(out_list, function(x) x$weights)),
index(R)[ep.i]),
extractWeights(opt), check.attributes = FALSE)
##########
Regards,
Ross
On Fri, Apr 17, 2015 at 12:02 PM, Peter <peter.michaels at gmail.com> wrote:
> Hi,
>
> I am just starting to use PortfolioAnalytics and I am trying to set up a
> group constraint that dynamically adjusts the min/max based on the date.
>
> I want to constrain the portfolio sector weights to always be +/- 2% from
> the benchmark sector weights. Is it possible to set the constraint so that
> I can provide a function which resets the min/max parameters? I don't see
> how the optimize.portfolio.rebalancing call can be used for a benchmark-ed
> portfolio without changing the group constraint for every rebalance period.
>
> Thank you for your help.
> Peter
>
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>
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