library(PortfolioAnalytics)
data(edhec)
R <- edhec[,1:6]
colnames(R) <- c("CA", "CTAG", "DS", "EM", "EMN", "ED")
funds <- colnames(R)
# create an xts object of regimes
# Here I just randomly samples values to create regime 1 or regime 2. In
# practice, this could based on volatility or other regime switching models
set.seed(123)
regime <- xts(sample(1:2, nrow(R), replace=TRUE, prob=c(0.3, 0.7)), index(R))
# Portfolio for regime 1
port1 <- portfolio.spec(funds)
port1 <- add.constraint(port1, "weight_sum", min_sum=0.99, max_sum=1.01)
port1 <- add.constraint(port1, "box", min=0.1, max=0.5)
port1 <- add.objective(port1, type="risk", name="ES", arguments=list(p=0.9))
port1 <- add.objective(port1, type="risk_budget", name="ES",
arguments=list(p=0.9), max_prisk=0.5)
# Portfolio for regime 2
port2 <- portfolio.spec(funds)
port2 <- add.constraint(port2, "weight_sum", min_sum=0.99, max_sum=1.01)
port2 <- add.constraint(port2, "box", min=0, max=0.6)
port2 <- add.objective(port2, type="risk", name="StdDev")
port2 <- add.objective(port2, type="risk_budget", name="StdDev", max_prisk=0.5)
# Combine the portfolios
portfolios <- combine.portfolios(list(port1, port2))
# with the regime and portfolios
regime.port <- regime.portfolios(regime, portfolios)
regime.port
# Should result in portfolio for regime 2
opt1 <- optimize.portfolio(R, regime.port,
optimize_method="random",
search_size=2000,
trace=TRUE)
opt1
opt1$regime
# Should result in portfolio for regime 1
opt2 <- optimize.portfolio(R[1:(nrow(R)-1)], regime.port,
optimize_method="DEoptim",
search_size=2000,
trace=TRUE)
opt2
opt2$regime
# Run optimization with rebalancing for regime switching
opt.rebal <- optimize.portfolio.rebalancing(R, regime.port,
optimize_method="random",
rebalance_on="quarters",
training_period=130,
search_size=2000,
trace=TRUE)
# The print and summary methods work the same as they do for optimizations
# without regime switching
opt.rebal
summary(opt.rebal)
# Get the regime at each rebalance date
lapply(opt.rebal$opt_rebalancing, function(x) x$regime)
# Extract the weights
wt <- extractWeights(opt.rebal)
wt
# Extract the objective measures*
obj <- extractObjectiveMeasures(opt.rebal)
str(obj)
obj
# Extract the stats*
xt <- extractStats(opt.rebal)
str(xt)
# *
# Note that this returns a list of N elements for N regimes. We may have
# different objectives and/or a different number of objectives which makes
# returning a single xts object difficult
chart.Weights(opt.rebal, colorset=bluemono)
# Chart the risk contribution for regime 1
chart.RiskBudget(opt.rebal, match.col="ES", risk.type="percentage",
regime=1, colorset=bluemono)
# Chart the risk contribution for regime 2
chart.RiskBudget(opt.rebal, match.col="StdDev", risk.type="percentage",
regime=2, colorset=bluemono)
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