Regression trees for predicting trade success

Intuitively, why are the following 4 variables useful predicting?

var AutoCorrel = Correlation(Price,Price+1,30);
var Volatility = ATR(30)/ATR(1000);
var DomPeriod = DominantPeriod(100);
var FD = FractalDimension(Price,30);

 

They are not. They were just used for testing if the CART works at all. Only two of them had some predictive value for that strategy, that's why the profit was improved.

The CART inputs used in the last example were the fractal dimension, plus ATRs of 2 different time periods.

 

Intuitively, why do they have predictive power and can be helpful here?

 

This is hard to tell. You can see that an input has predictive value when it affects high-weight branches in the CART tree. That means that a certain range of this input, or input combination, is significantly linked to success or failure of the subsequent trade. But I can't say with certainty why a particular input is predictive for a certain strategy, and not for another strategy.

In this case, the predictive inputs had to do with volatility. This indicates probably that the lowpass filter gave false signals in situations outside the normal volatility range. At least that's the theory that comes to mind.

 

Then have you tried volatility directly?

 

• How good is the prediction fit out of sample? I've tried some random forest modeling and the r-squared is around 2% which isn't that great. Maybe for my particular data it's not a good algorithm to apply.
  
• If concentrating solely on volatility, options (implied volatility) are something to look at.

 

Yes. A simple volatility threshold indeed also improved the strategy, but less than the CART with the combination of inputs. Nevertheless, you can certainly get the same result as the CART with conventional methods, thresholds and optimizing, it just takes more experimenting and longer time.

The prediction fit of the example varies between 70% and 89% in the different WFO cycles. It heavily depends on the selection of the inputs.

I have not tried a random forest yet. My feeling is that it will probably generate a better fit, but it will make the strategy a black box where you don't see how it works. The single CART has the advantage that you can see the result in code, and it's simple enough for checking if it makes sense.

 

If you've got all these profitable systems, are you running any of them live now?

 

Yes. I'm meanwhile running about 20 systems on live accounts. But they are all conventional systems, I'm not yet live with a CART or perceptron system.

 

How are those 20 systems running live? Do they show similar performance as your back tests?