[R-SIG-Finance] Simulate the stock market for back testing strategy ---R bootstrap function

[Brian G. Peterson]

Dirk Eddelbuettel wrote:
> On 9 February 2008 at 07:05, elton wang wrote:
> | Thanks for Brian's reply.
> | to make this  more relevant to this list, what
> | functions in R can do bootstrap resampling while
> | keeping the autocorrelation in the original data? (I
> | only know function of sample()). Would this resmapled
> | data do any good on back testing? 
> 
> No. 
> 
> But any decent book on bootstrapping mentions the problem, and many theses
> and papers were (are ?) written on the issue. I haven't looked in a while, 
> but 'block bootstrap' once was a popular idea for this. And an ad-hoc method
> I used five or six years ago for low-frequency (monthly) data was to sample
> in two stages 
> 	first sample an integer (say between 1 and 6) to determine how 'large'
> 		a chunk I would fetch
> 	then sample an integer between 1 and N to determine where I pick the
> 		chunk from
> and re-constitute resample series this way.  As I said, 'ad-hoc'.  There are
> many other ways.   But don't do just sample() as it is guaranteed to break
> any possible structure in the correlation your data.

A block bootstrap for time series is implemented in a slightly more 
robust manner than that described by Dirk above in the function 
tsbootstrap(tseries)

There are a number of other bootstrap methods available in package 
"boot" and corresponding function "boot", but I haven't examined these 
in detail for their tuning or applicability in time series.

I think I laid out some basic steps of building a trading model on 
actual historical data in my prior email.  Simulated data (via 
resampling or any other method) after the point where you have a target 
model is only a validator of the model, not the starting point, or 
you're almost certain to get worthless results.

Regards,

   - Brian