I published a small theoretical project on GitHub and would appreciate feedback from anyone working with real quant models.

RIMC (Recursive Intelligence Market Cycle Hypothesis) is not a trading system or a full asset-pricing model. It’s a hypothesis for treating alpha as structural drift that arises from finite-speed learning and observation delay—rather than as a leftover regression residual. The goal is to write that structure explicitly in equations.

The repo is mostly text with a small sample simulation. Think of it as a dynamical-systems-meets-finance thought experiment.

I’d be interested in reactions such as:

• Does this framing of persistent alpha make sense in practical quant terms? • Are there existing models/papers that do something similar? • How does this compare with your experience handling delay or learning effects?

Any feedback—critical or supportive—is welcome.

GitHub: https://github.com/rimc-lab/RIMC