txnsim: Monte Carlo analysis of round turn trades
In braverock/blotter: Tools for Transaction-Oriented Trading Systems P&L

txnsim

R Documentation

Monte Carlo analysis of round turn trades

Description

Running simulations with similar properties as the backtest or production portfolio may allow the analyst to evaluate the distribution of returns possible with similar trading approaches and evaluate skill versus luck or overfitting.

Usage

txnsim(Portfolio, n = 10, replacement = TRUE,
  tradeDef = c("increased.to.reduced", "flat.to.flat", "flat.to.reduced"),
  ..., CI = 0.95)

Arguments

`Portfolio`	string identifying a portfolio
`n`	number of simulations, default = 100
`replacement`	sample with or without replacement, default TRUE
`tradeDef`	string to determine which definition of 'trade' to use. See `tradeStats`
`...`	any other passthrough parameters
`CI`	numeric specifying desired Confidence Interval used in hist.txnsim(), default 0.95

Details

Statisticians talk about the 'stylized facts' of a data set. If you consider the stylized facts of a series of transactions that are the output of a discretionary or systematic trading strategy, it should be clear that there is a lot of information available to work with. Initial analysis such as tradeStats and perTradeStats can describe the results of the series of transactions which resulted from the trading strategy. What else can we learn from these transactions regarding trading style or the skill of the trader? txnsim seeks to conduct a simulation over the properties of sampled round turn trades to help evaluate this.

With tradeDef='flat.to.flat', the samples are simply rearranging quantity and duration of round turns. This may be enough for a strategy that only puts on a single level per round turn.

With tradeDef='increased.to.reduced', typically used for more complex strategies, the simulation is also significantly more complicated, especially with replacement=TRUE. In this latter case, the simulation must try to retain stylized factos of the observed strategy, specifically:

percent time in market
percent time flat
ratio of long to short position taking
number of levels or layered trades observed

In order to do this, samples are taken and randomized for flat periods, short periods, and long periods, and then these samples are interleaved and layered to construct the random strategy. The overall goal is to construct a random strategy that preserves as many of the stylized facts (or style) of the observed strategy as possible, while demonstrating no skill. The round turn trades of the random replicate strategies, while outwardly resembling the original strategy in summary time series statistics, are the result of random combinations of observed features taking place at random times in the tested time period.

It should be noted that the first opened trade of the observed series and the replicates will take place at the same time. Quantity and duration may differ, but the trade will start at the same time, unless the first sampled period is a flat one. We may choose to relax this in the future and add or subtract a short amount of duration to the replicates to randomize the first entry more fully as well. Inclusion of flat periods should provide a fair amount of variation, so this may not be an issue.

The user may wish to pass Interval in dots to mark the portfolio at a different frequency than the market data, especially for intraday market data. Note that market data must be available to call updatePortf on.

We are including p-values for some sample statistics in the output as well. Some notes are in order on how this is calculatated, and how it may be interpreted. With small n, these p-values are meaningless. With large n, they should be fairly stable. Per North et. al. (2002) who use Davison & Hinkley (1997) as their source, the correct unbiased p-value for a simulation sample statistic is:

\frac{rank_bt+1}{n_samples+1}

where the rank of the observed statistic is compared against statistics calculated on the simulation. Interpretation of this result takes some care. The skeptical analyst would prefer to see a low p-value (e.g. the customary 0.05). The same analyst should be concerned about overfitting if an extraordinarily low p-value (e.g. 0.0001) is observed, or conclude that there may be room to improve the strategy is available if the p-value is low but not compelling (e.g. 0.15). Issues of multiple testing bias should also be considered. Interpretation of the p-value of the mean is most easily fit into the customary p<0.05 target. Appropriate critical values for other statistics may be lower or higher.

Value

a list object of class 'txnsim' containing:

replicates:a list by symbol containing all the resampled start,quantity, duration time series replicates
transactions:a list by symbol for each replicate of the Txn object passed to addTxns
backtest.trades:list by symbol containing trade start, quantity, duration from the original backtest
cumpl:an xts object containing the cumulative P&L of each replicate portfolio
initEq:a numeric variable containing the initEq of the portfolio, for starting portfolio value
seed: the value of .Random.seed for replication, if required
call:an object of type call that contains the txnsim call
Portfolio: string identifying a portfolio
n: number of simulations, default = 100
replacement: sample with or without replacement, default TRUE
samplestats:a numeric dataframe of various statistics for each replicate series
original:a numeric dataframe of the statistics for the original series
ranks:a numeric dataframe containing the ranking of the statistics
pvalues:a numeric dataframe containing the pvalues for the observed backtest compared to the sampled ranks
stderror:a numeric dataframe of the standard error of the statistics for the replicates
CI:numeric specifying desired Confidence Interval used in hist.txnsim(), default 0.95
CIdf:a numeric dataframe of the Confidence Intervals of the statistics for the bootstrapped replicates

Note that this object and its slots may change in the future. Slots replicates,transactions, and call are likely to exist in all future versions of this function, but other slots may be added and removed as S3method's are developed.

The backtest.trades object contains the stylized facts of the observed series, and consists of a list with one slot per instrument in the input portfolio. Each slot in that list contains a data.frame of

Start:timestamp of the start of the round turn, discarded later
duration:duration (difference from beginning ot end) of the observed round turn trade
quantity:quantity of the round turn trade, or 0 for flat periods

with additional attributes for the observed stylized facts:

calendar.duration:total length/duration of the observed series
trade.duration:total length/durtation used by round turn trades
flat.duration:aggregate length/duration of periods when observed series was flat
flat.stddev:standard deviation of the duration of individual flat periods
first.start:timestamp of the start of the first trade, to avoid starting simulations during a training period
period:periodicity of the observed series

Author(s)

Jasen Mackie, Brian G. Peterson

References

Burns, Patrick. 2006. Random Portfolios for Evaluating Trading Strategies. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=881735

North, B.V., D. Curtis, and P.C. Sham. Aug 2002. A Note on the Calculation of Empirical P Values from Monte Carlo Procedures. Am J Hum Genet. 2002 Aug; 71(2): 439-441. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC379178/

Davison & Hinkley. 1997. Bootstrap methods and their application.

Examples

## Not run: 

 n <- 10

 ex.txnsim <- function(Portfolio
                        ,n=10
                        ,replacement=FALSE
                        , tradeDef='increased.to.reduced'
                        , chart=FALSE
                        )
 {
    out <- txnsim(Portfolio,n,replacement, tradeDef = tradeDef)
    if(isTRUE(chart)) {
      portnames <- blotter:::txnsim.portnames(Portfolio, replacement, n)
      for (i in 1:n){
      p<- portnames[i]
        symbols<-names(getPortfolio(p)$symbols)
      for(symbol in symbols) {
        dev.new()
    	   chart.Posn(p,symbol)
      }
    }
  }
 invisible(out)
 } # end ex.txnsim

  demo('longtrend',ask=FALSE)
  lt.nr <- ex.txnsim('longtrend',n, replacement = FALSE)
  lt.wr <- ex.txnsim('longtrend',n, replacement = TRUE, chart = TRUE)
  plot(lt.wr)
  hist(lt.wr)

  require('quantstrat') #sorry for the circular dependency
  demo('bbands',ask=FALSE)
  bb.nr <- ex.txnsim('bbands',n, replacement = FALSE)
  bb.wr <- ex.txnsim('bbands',n, replacement = TRUE, chart = TRUE)
  plot(rsi.wr)
  hist(bb.wr)


## End(Not run) #end dontrun

braverock/blotter documentation built on Sept. 15, 2024, 8:45 p.m.