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#' ---
#' title: "Regime Switching Demo"
#' author: Ross Bennett
#' date: "7/17/2014"
#' ---
#' Load package and data.
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))
#' Construct 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)
#' Construct 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))
#' Now we construct the regime model and corresponding portfolios to use for
#' each regime.
regime.port <- regime.portfolios(regime, portfolios)
regime.port
#' This optimization should result in out portfolio for regime 2.
opt1 <- optimize.portfolio(R, regime.port,
optimize_method="random",
search_size=2000,
trace=TRUE)
opt1
opt1$regime
#' This optimization should result in out portfolio for regime 1.
opt2 <- optimize.portfolio(R[1:(nrow(R)-1)], regime.port,
optimize_method="DEoptim",
search_size=2000,
trace=TRUE)
opt2
opt2$regime
#' Run optimization with rebalancing using our regime switching portfolio.
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)
#' We can extract which regime portfolio we optimized with at each rebalance date.
lapply(opt.rebal$opt_rebalancing, function(x) x$regime)
#' Extract the optimal weights at each rebalance date.
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/
#' Extract the optimal weights at each rebalance date.
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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