This function will run applyStrategy on portfolio.st,
once for each parameter combination as specified by the parameter
distributions and constraints in the paramset. Results are gathered and
returned as a list containing a slot for each parameter combination.
apply.paramset(strategy.st, paramset.label, portfolio.st, ..., account.st,
mktdata = NULL, nsamples = 0, user.func = NULL, user.args = NULL,
calc = "slave", audit = NULL, packages = NULL, verbose = FALSE,
paramsets, rule.subset = NULL, perf.subset = NULL, psgc = TRUE,
store = TRUE)the name of the strategy object
a label uniquely identifying the paramset within the strategy
the name of the portfolio
any other passthru parameters
the name of the account
optional xts mktdata object, will be passed unchanged to applyStrategy
if > 0 then take a sample of only size nsamples from the paramset
an optional user-supplied function to be run for each param.combo at the end, either on the slave or on the master (see calc)
user-supplied list of arguments for user.func
'slave' to run updatePortf and tradeStats on the slave and return all portfolios and orderbooks as a list: higher parallelization but more data transfer between master and slave; 'master' to have updatePortf and tradeStats run at the master and return all portfolios and orderbooks in the .blotter and .strategy environments resp: less parallelization but also less data transfer between slave and master; default is 'slave'
a user-specified environment to store a copy of all portfolios, orderbooks and other data from the tests, or NULL to trash this information
a vector specifying names of R packages to be loaded by the slave, default NULL
return full information, in particular the .blotter environment, default FALSE
a user-sepcified (sub)set of paramsets to run
ISO-8601 subset for period to execute rules over, default NULL (will use all dates)
ISO-8601 subset for period to examine performance over, default NULL (will use all dates)
boolean, if TRUE, the default, will force gc garbage collection periodically in workers to conserve RAM
indicates whether to store the strategy in the .strategy environment
apply.paramset uses the foreach package to start the runs for each parameter combination, and as such allows for parallel processing. It is up to the caller to load and register an appropriate backend, eg. doMC, doParallel or doRedis.
Note that we will attempt to pass dots through to most other called functions.
This could include arguments such as the tradeDef argument for
tradeStats, or additional arguments to be passed to
applyStrategy
It is also worth discussing the nsamples argument. This option will
randomly sample from the total parameter space. It is quite useful for testing
your parameterization distributions. It is not always terribly useful for
real tests,even for a large parameter space. Use of this sampling methodology,
if there are not enough samples, will make parameter surface analysis
challenging, for example, because there may be 'voids' in any randomly chosen
parameter space. Also, if apply.paramset is called via walk.forward
with nsamples, then the sampled parameter sets will be different for
each training period, as the sampling methodology is independent. This
latter issue could be addressed by passing paramsets instead, so if
you must use a parameter subset (e.g. one generated via some optimization
algorithm, or to use a constand sample for all training periods), then
passing paramsets should be preferred to passing nsamples.
add.distribution,
add.distribution.constraint,
delete.paramset