Quote from phattails:
From a practical stand point, you should have enough trades so that your historical sample test is a good approximation for the population. When applying statistics, which in your case is the sample error you must identify the assumptions-- one of which is independence of samples.
If they were independent then you would choose the sample error that suits your confidence requirement. =1/sqrt(n)). So, for a 10% error -- n=10... for a 5% error n=400..
Try to divide your data into subsets of data that you could classify as, for example, nonvolatilie trending, volatile trending, etc. Then you could weight the results of each sample test to reflect the actual results. Don't forget to rule out data that is no longer useful for representation, or create a work around.