walk.forward: Rolling Walk Forward Analysis
In naturalsmen/quantstrat: Quantitative Strategy Model Framework

Description Usage Arguments Details Value Author(s) See Also

A wrapper for apply.paramset() and applyStrategy(), implementing a Rolling Walk Forward Analysis (WFA).

walk.forward(strategy.st, paramset.label, portfolio.st, account.st, period,
  k.training, nsamples = 0, audit.prefix = NULL, k.testing,
  obj.func = function(x) {     which(x == max(x)) }, obj.args = list(x =
  quote(tradeStats.list$Net.Trading.PL)), anchored = FALSE,
  include.insamples = TRUE, ..., verbose = FALSE)

`strategy.st`	the name of the strategy object
`paramset.label`	a label uniquely identifying within the strategy the paramset to be tested
`portfolio.st`	the name of the portfolio object
`account.st`	the name of the account object
`period`	the period unit, as a character string, eg. 'days' or 'months'
`k.training`	the number of periods to use for training, eg. '3' months
`nsamples`	the number of sample param.combos to draw from the paramset for training; 0 means all samples (see also apply.paramset)
`audit.prefix`	prefix to generate filenames for storage of audit data. For each training set, a separate file is created, containing an enviroment called .audit, with all in-sample portfolios and orderbooks as well as information as to which param.combos were evaluated, and the result of the objective function. In addition, a special file is generated that contains portfolio and orderbook for the concatenated testing param.combos as selected by the objective function, plus (optionally) complete in-sample portfolios and orderbooks for reference (see include.insamples)
`k.testing`	the number of periods to use for testing, eg. '1 month'
`obj.func`	a user provided function returning the best param.combo from the paramset, based on training results; defaults to 'max'
`obj.args`	a user provided argument to obj.func, defaults to quote(tradeStats.list$Net.Trading.PL)
`anchored`	whether to use a fixed start for the training window (TRUE), or a sliding start (FALSE); defaults to FALSE
`include.insamples`	will optionally run a full backtest for each param.combo in the paramset, and add the resulting in-sample portfolios and orderbooks to the file '<prefix>.results.RData'; default TRUE
`...`	optional parameters to pass to apply.paramset()
`verbose`	dumps a lot of info during the run if set to TRUE, defaults to FALSE

walk.forward executes a strategy on a portfolio, while rolling a re-optimization of one of the strategies parameter sets during a specified time period (training window), then selecting an optimal parameter combination from the parameter set using an obj function, then applying the selected parameter combo to the next out-of-sample time period immediately following the training window (testing window). Once completed, the training window is shifted forward by a time period equal to the testing window size, and the process is repeated. WFA stops when there are insufficient data left for a full testing window.

For a complete description, see Jaekle&Tomasini chapter 6.

a list consisting of a slot containing detailed results for each training + testing period, as well as the portfolio and the tradeStats() for the portfolio

Jan Humme

applyStrategy apply.paramset endpoints tradeStats

naturalsmen/quantstrat documentation built on May 23, 2019, 12:22 p.m.