Sólo hay que añadir el k=6 a la endpoints() y utilizar esa función en su lugar...
optimize.portfolio.rebalancing2 = function (R, portfolio = NULL, constraints = NULL, objectives = NULL,
optimize_method = c("DEoptim", "random", "ROI"), search_size = 20000,
trace = FALSE, ..., rp = NULL, rebalance_on = NULL, training_period = NULL,
rolling_window = NULL)
{
stopifnot("package:foreach" %in% search() || requireNamespace("foreach",
quietly = TRUE))
stopifnot("package:iterators" %in% search() || requireNamespace("iterators",
quietly = TRUE))
if (inherits(portfolio, "portfolio.list")) {
n.portf <- length(portfolio)
opt.list <- vector("list", n.portf)
for (i in 1:length(opt.list)) {
if (hasArg(message))
message = match.call(expand.dots = TRUE)$message
else message = FALSE
if (message)
cat("Starting optimization of portfolio ", i,
"\n")
opt.list[[i]] <- optimize.portfolio.rebalancing(R = R,
portfolio = portfolio[[i]], constraints = constraints,
objectives = objectives, optimize_method = optimize_method,
search_size = search_size, trace = trace, ... = ...,
rp = rp, rebalance_on = rebalance_on, training_period = training_period,
rolling_window = rolling_window)
}
out <- combine.optimizations(opt.list)
class(out) <- "opt.rebal.list"
return(out)
}
if (inherits(portfolio, "mult.portfolio.spec")) {
R <- proxy.mult.portfolio(R = R, mult.portfolio = portfolio)
portfolio <- portfolio$top.portfolio
}
call <- match.call()
start_t <- Sys.time()
if (!is.null(portfolio) & !is.portfolio(portfolio)) {
stop("you must pass in an object of class 'portfolio' to control the optimization")
}
if (hasArg(message))
message = match.call(expand.dots = TRUE)$message
else message = FALSE
if (hasArg(trailing_periods)) {
trailing_periods = match.call(expand.dots = TRUE)$trailing_periods
rolling_window <- trailing_periods
}
if (!is.null(constraints)) {
if (inherits(constraints, "v1_constraint")) {
if (is.null(portfolio)) {
tmp_portf <- portfolio.spec(assets = constraints$assets)
}
message("constraint object passed in is a 'v1_constraint' object, updating to v2 specification")
portfolio <- update_constraint_v1tov2(portfolio = tmp_portf,
v1_constraint = constraints)
}
if (!inherits(constraints, "v1_constraint")) {
portfolio <- insert_constraints(portfolio = portfolio,
constraints = constraints)
}
}
if (!is.null(objectives)) {
portfolio <- insert_objectives(portfolio = portfolio,
objectives = objectives)
}
call <- match.call()
if (optimize_method == "random") {
if (hasArg(rp_method))
rp_method = match.call(expand.dots = TRUE)$rp_method
else rp_method = "sample"
if (hasArg(eliminate))
eliminate = match.call(expand.dots = TRUE)$eliminate
else eliminate = TRUE
if (hasArg(fev))
fev = match.call(expand.dots = TRUE)$fev
else fev = 0:5
if (is.null(rp))
if (inherits(portfolio, "regime.portfolios")) {
rp <- rp.regime.portfolios(regime = portfolio,
permutations = search_size, rp_method = rp_method,
eliminate = eliminate, fev = fev)
}
else {
rp <- random_portfolios(portfolio = portfolio,
permutations = search_size, rp_method = rp_method,
eliminate = eliminate, fev = fev)
}
}
else {
rp = NULL
}
if (is.null(training_period) & !is.null(rolling_window))
training_period <- rolling_window
if (is.null(training_period)) {
if (nrow(R) < 36)
training_period = nrow(R)
else training_period = 36
}
if (is.null(rolling_window)) {
ep.i <- endpoints(R, on = rebalance_on,k=6)[which(endpoints(R,
on = rebalance_on) >= training_period)]
ep <- ep.i[1]
out_list <- foreach::foreach(ep = iterators::iter(ep.i),
.errorhandling = "pass", .packages = "PortfolioAnalytics") %dopar%
{
optimize.portfolio(R[1:ep, ], portfolio = portfolio,
optimize_method = optimize_method, search_size = search_size,
trace = trace, rp = rp, parallel = FALSE, ... = ...)
}
}
else {
ep.i <- endpoints(R, on = rebalance_on,k=6)[which(endpoints(R,
on = rebalance_on) >= training_period)]
out_list <- foreach::foreach(ep = iterators::iter(ep.i),
.errorhandling = "pass", .packages = "PortfolioAnalytics") %dopar%
{
optimize.portfolio(R[(ifelse(ep - rolling_window >=
1, ep - rolling_window, 1)):ep, ], portfolio = portfolio,
optimize_method = optimize_method, search_size = search_size,
trace = trace, rp = rp, parallel = FALSE, ... = ...)
}
}
names(out_list) <- index(R[ep.i])
end_t <- Sys.time()
elapsed_time <- end_t - start_t
if (message)
message(c("overall elapsed time:", end_t - start_t))
out <- list()
out$portfolio <- portfolio
out$R <- R
out$call <- call
out$elapsed_time <- elapsed_time
out$opt_rebalancing <- out_list
class(out) <- c("optimize.portfolio.rebalancing")
return(out)
}
Utilice el rebalance_on="months"
opt1 <- optimize.portfolio.rebalancing2(R=R, portfolio=port1,optimize_method="DEoptim",itermax=10,
search_size=2000,trace=TRUE,rebalance_on = "months",
training_period = 125,rolling_window = 125)
