In any systematic trading strategy choosing the right combination of parameters for your model or approach is critical. So today, I wanted to share a valuable takeaway from a conversation with Bill Dreiss, where we discuss his thoughts, experience and approach to identifying parameter sets which I think you will find to be rather different to most managers.  Bill and I discussed this as well as many other super interesting aspects of trading from his 40+ year career and I think these lessons will benefit you immensely.  If you want to listen to the full podcast episode then just click here.  click here

Identifying the right Parameter Set for Risk Management

Bill:  The big trend followers, and certainly trend following dominates the CTA space, you’d think they’d all just be cannibalizing themselves. I think this gets back to what I was saying, that I’m a technical trader that believes in fundamentals. So there’s a world out there that can’t be controlled or that’s beyond the reach of market psychology or market methodology and I think that world is subject to forces - generally longer term forces - that are pretty much universal and timeless.  

 

Niels:  Human behavior, yeah. 

Bill:  That go back in history as far as you want to go and that will go into the future as far. 

Niels:  You know, I agree with all that, Bill, and it’s interesting because there’s obviously still so much resistance, it’s fair to say, by a lot of people, certainly on the investor side, to embrace this and you always have to justify why trend following works. Even if it has a year or two of under average performance then it’s their case for why it has stopped working and it’s never going to work again. So certain things don’t change. 

I want to go back also to another point which I think differentiates you a lot compared to the managers that we see out there. Maybe you can explain more that where I’m going with this is that 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 while, if you use moving averages, or price breakout, whatever it might be, clearly a big part of the research is really identifying the right parameter sets to use. Explain to me about that and why you’ve chosen this way of looking at it. 

Bill:  Well, of course the idea of data fitting has been the nemesis of anyone who’s tried to design systems. 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 pictures instead of the numerical approach. So in the first place, if you adjust the algorithm the way 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.  

"... went through and tried all the different possibilities and picked out the best one."

 

Now there’s certainly data fitting in the sense that you’re fitting what patterns that you think are significant versus those that you don’t. You’ve obviously got to have some choice there. 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 criterion. 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 judgement that was made.  

So the advantage, again is that, in terms of designing a system, you’re not really focusing. You’re coming into it with an analysis that’s based upon, shall we say, qualitative judgements about how the markets work and so on and so forth.