The recommendation was simple: start with AI Factor right away

[Jrinne]

@pitmaster

I see no clear evidence that ML-based Designer Models have outperformed ranking systems (RS)

Some are using ML methods to optimize P123 classic ranking systems:

Genetic algorithm to replace manual optimizaton in P123 classic

These tools seem too complex to me and may increase the risk of overfitting to the data. If you prefer to focus on factor selection and economic intuition rather than factor weighting, you might consider a custom discrete optimizer. Such an optimizer can: Set factor weights on a fixed grid (e.g., min = 0.025, max = 0.10, step = 0.025). Accept a dictionary of fixed weights for core factors you always want in the model. The advantage is avoiding wasted computation on meaningless precision — …

ML Script Performance vs. Genetic Algorithms & CMA-ES**

I've been experimenting extensively with various approaches to build a machine learning model using offline data from the "factor download" feature, but for some reason, I'm struggling to achieve strong results. Below is my latest script, and I'm not quite sure what I'm doing that's preventing it from working well. So far, I'm getting decidedly better results—both in out-of-sample and sub-universe tests—by using a hybrid approach: Genetic Algorithms for factor selection and CMA-ES algorithms fo…

And most recently:

PCA Formula Exporter: Automatically turn Principal Components into Ranking Systems

@Scifospace came with a brilliant idea to break down the result from a Principal Component Analysis (PCA) Components to new factors for P123. While PCA is excellent for uncovering the "true" underlying drivers of stock returns by reducing multicollinearity and distilling correlated factors into independent components, translating the math into Portfolio123 formulas is incredibly tedious. To make this effortless, I built a small desktop application: PCA Formula Exporter. What it does: It takes…

P123 Classic has always been a linear GAM (Generalized Additive Model) by definition. We have moved from mostly manual optimization to what is clearly a gray area for everyone—including those most against machine learning.

Going forward, I think some non-linear GAMs hold promise, not just for their transparency, but for their ability to handle non-linear monotonic relationships. This would essentially be a 'non-linear P123 Classic.'

Glass Box or Black Box: Explainable boosting machines let you choose

Prefer a glass box over a black box? Microsoft has been working on that. Here is a video: The Science Behind InterpretML: Explainable Boosting Machine - Microsoft Research "InterpretML is essentially a high-performance Generalized Additive Model (GAM) that uses Gradient Boosting to learn the non-linear functions for each feature. For P123 each feature could be part of a GAM with how each feature contributes fully visualized. It is an open source Python Program.

More and more, the question shouldn't be 'P123 vs. Machine Learning.' Rather, the question is: 'How can I use machine learning to optimize P123 Classic?' Even the use of Mod() (or odd/even universes) can be seen as a form of cross-validation.

From a marketing perspective, P123 could do more to frame this less as a debate between opposing sides. Maybe P123’s AI 2.0 will incorporate apps from @AlgoMan and others to help bridge that gap.

As it is, whenever someone likes P123 Classic, there is often an implied dislike for P123’s AI. This implies a divide that isn't good for marketing or the community.

P123 would do well—marketing-wise—to facilitate more ML options specifically for optimizing P123 Classic. This would soften the debate that often ends in implicit criticism of the AI tools, while providing tools for everyone. As I understand it, ML has helped the bottom line despite the negative sentiment in the forum.