Those are some cool ideas, thanks for sharing.
For factor combination, I wonder if you could achieve something similar by using Alphalens in research? I did something similar in another thread where I conducted a search over a lookback window space for a particular factor. Of course, alphalens isn't the exact same as a backtest, but it's a good test for the predictive power of a factor, or particular combination of factors. The backtest is more of a simulation of how the signal holds up with some real world conditions like commission and slippage.
The computing power available on Quantopian would limit the space you can look over, but you could certainly perform some form of a coarse search.
I still want to add a word of warning against overfitting. You sound like you know what you're doing, but for someone who is a bit newer to the concept, it's important to understand that the more you search a space for combining factors or fitting models, the more likely it is that the system is overfit.
The idea of doing an on-the-fly parameter search is an interesting one. I can see why this might have some value, but I would also think you'd have to have a good sense of the decay rate of a particular signal. If you wait too long to re-fit the model, the signal might be gone, and if you do it too quickly, I imagine it would be overfit. Either way, it sounds interesting. While it might not work on a large number of factors, you might be able to do a course search on a small group of factors, maybe just to verify that there's something that could work.
My recommendation would be to start with Alphalens, and maybe try rolling the simulation window as well as a parameter going into the factor.
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