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Hi Richard --
It depends a little on what is being tested, but the questions to ask are: "Is this result probably because the system is good? What is the probability that these results came from random results? Are these results significantly better than some standard I want to use?" Those questions certainly make sense when the result being tested is whether expectancy is greater than 0, or profit per trade is greater than 0. With a function like CAR/MDD, the test is not so clear. Try running some tests using your system and your objective function. See what the critical level of the objective function seems to be -- that is, what level produces results you would trade. This part does not have to be out-of-sample, you are just trying to calibrate your metric. Once you have done that, then the t-test is ((mean-criticallevel)/stdev)*sqrt(N).
Thanks,
Howard
On Sun, Oct 11, 2009 at 10:31 PM, Richard <richpach2@xxxxxxxxx> wrote:
Hi Howard,
I follow this discussion with interest and I thank you for your invaluable comments. I am still confused about the statement .."look for systems that have truly out-of-sample t-test scores of 2.0".
In AB terms, what is the objective function we need to compare it to "t-test"? Are we looking for CAR/ADD to be greater than 2.0?
Kind Regards
Richard
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