By the way, linear models are just one way to optimize P123 classic ranking system weights. There are clear advantages to non-linear methods for finding weights —particularly those that allow for interactions between features (which linear models can’t capture), or that directly optimize for simulation returns rather than something like mean squared error. These can be meaningful improvements.
Here’s a method Yuval found interesting in a previous discussion (with code even), though I ultimately can’t recommend it—it just didn’t work well in my hands:
Genetic Algorithm to Replace Manual Optimization in P123 Classic
In the thread @korr123 said he had used a genetic algorithm to automate and optimize P123 classic ranking system weights already.
That said, there are other paths worth exploring. I could share one or two of my approaches—under contract or for a price— as I just can't give away my hard work for free.
Also—and perhaps this is most important--anything posted in the forum is most likely to be used outside of P123 by retail traders who are not members or even P123 competitors before it becomes a P123 feature (if it ever does become a P123 feature).
But I also suspect you’d come up with equally effective, creative solutions independent of my methods, if you keep digging into this.
Also, I’m sure Azouz, Pitmaster, Korr, and others have some ideas on this topic. It’s a pretty obvious thing to do and I am sure I am not the first P123 member to explore this: As you (and Korr) ultimately did. What you are suggesting will work, I believe. I do not mean to diminish its effectiveness by introducing the possibility of other (additional) methods.