I know that you, or at least part of your system, is not looking at parameters, meaning

you're not trying to optimize a certain parameter set whilst, you know, if you use moving

averages or price breakout, whatever it might be, clearly a big part of the research is really identifying

the right parameters sets to use. Explain to me a little bit about that and why you, why have

you chosen sort of this way of looking at it?

Well, of course, the idea of data fitting has been the, you know, the nemesis of anyone who's tried to

design systems. And so one of the attractions to the fractal approach was that you're dealing,

again, with very fundamental patterns, but you're dealing with patterns as opposed to numbers.

You're dealing with sort of pictures instead of numerical

approach. So in the first place, just the algorithm, the way that I've described it, is not

a matter of optimizing on any kind of numerical parameters. It's a matter of setting up a certain

structure and then, in a sense,

graphically utilizing that structure to translate that into patterns. Now, there certainly is data

fitting in the sense that you're fitting what patterns that you think are significant, you know,

versus those that you don't. You've obviously got to have

some choice there. And for instance, trading weekly charts versus daily charts, that's obviously

a parameter that you've selected. But these might be numerical parameters, but they've been selected

on a qualitative criteria. They haven't been selected because I went through

and tried all the different possibilities

and picked out the best one.

It was a much broader type of judgment that was made.

So the advantage, again, is that in terms of designing a system,

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