| random.portfolio {PortfolioProbe} | R Documentation |
Returns a list of portfolios that satisfy the constraints imposed, but the utility is ignored.
random.portfolio(number.rand = 1, ..., out.trade = FALSE,
intermediate = NULL)
number.rand |
the number of random portfolios desired.
This can be zero (in which case NULL is returned), but
can not be negative.
|
... |
arguments of trade.optimizer need to be given that
specify the constraints.
Arguments concerning utility are ignored.
|
out.trade |
logical value stating if it should be the random trades (TRUE),
or the resulting random portfolios (FALSE) that are to be
returned.
The full name of this argument needs to be given.
|
intermediate |
you are unlikely to care about this argument.
Either NULL or a character string or a list.
See the Details section for more.
The full name of this argument needs to be given.
|
unless intermediate is given, a list of the random portfolios or trades.
This has attributes:
call |
an image of the call that created the object. |
timestamp |
a character string giving the time and date of creation. |
seed |
the random seed for the generator internal to the C code. |
version |
a vector stating the versions of C and S code used. This attribute is suppressed when the object is printed. |
checkinput |
a list with components prices, variance,
expected.return and existing that contains a few
values of each of these inputs.
This is used in randport.eval and update to make
sure that the data for these inputs are what you expect they are.
This attribute is suppressed when the object is printed.
|
funevals |
the number of portfolios that were examined during the search. This attribute is suppressed when the object is printed. |
iterhistory |
only present if the save.iterhistory control argument
is TRUE, and is seldom of any interest.
If present, it is a numeric vector giving the value of the objective
at each iteration of the algorithm for the last random portfolio
that is generated.
The value of the objective is a measure of how much a given portfolio
violates the constraints.
|
class |
"randportBurSt".
There are several methods for this class.
|
the S language random seed is created or changed (unless the seed
argument is given explicitly).
An error occurs if no portfolios were found that satisfy the constraints,
unless the throw.error control is set to FALSE.
This generally only happens if the constraints are inconsistent, or
at least very restrictive.
In the latter case, you can adjust control arguments to search more
thoroughly.
It is possible that fewer portfolios are generated than requested.
This happens if too many attempts fail.
There is a warning for this unless the do.warn item randport.fail
is set to FALSE.
If you want a bound on the utility, then you can use
random.portfolio.utility.
However, this is substantially slower than using random.portfolio.
There are randportBurSt methods for several generic functions.
When tracing is done, a message gives: the number of optimizations, the number of satisfactory portfolios found, and the number of failures.
The intermediate argument allows some computations to be avoided
in some circumstances.
The amount of computation per call is in the millisecond range, so is
unlikely to be important.
The way it would be used is to make one call with intermediate="Clist"
and then to pass a modification of that list as the intermediate
argument multiple times.
The list in question is a list of objects ready
to be passed into the C code (plus a few other things).
Tracing generally does not work under Windows.
This help was last revised 2009 November 27.
trade.optimizer, random.portfolio.control,
random.portfolio.utility, randport.eval,
deport.randportBurSt, summary.randportBurSt,
valuation.randportBurSt, head.randportBurSt,
update.randportBurSt, seed.BurSt.
randport1 <- random.portfolio(100, prices, varian, long.only=TRUE,
bench.constr=c(spx=.04^2/252), lin.constraints=cntrysect.conmat,
lin.bounds=cntrysect.bounds, gross.value=1e6)
randtrade1 <- random.portfolio(100, prices, varian, long.only=TRUE,
bench.constr=c(spx=.04^2/252), lin.constraints=cntrysect.conmat,
lin.bounds=cntrysect.bounds, gross.value=1e6, out.trade=TRUE)
# now use it
randalpha1 <- numeric(length(randtrade1))
for(i in 1:length(randtrade1)) {
randalpha1[i] <- trade.optimizer(prices, varian, long.only=TRUE,
bench.constr=c(spx=.04^2/252), lin.constraints=cntrysect.conmat,
lin.bounds=cntrysect.bounds, gross.value=1e6, funeval=0,
start=randtrade1[[i]])$alpha.values
}
# same thing again
randalpha1 <- unlist(randport.eval(randtrade1, keep="alpha.values"))
# and again
randalpha1 <- unlist(randport.eval(randport1, keep="alpha.values"))