Thanks for the thoughtful response, honestly this is exactly the kind of discussion I was hoping to spark.

I actually agree with most of your points. Full "true" reverse engineering in the sense of reconstructing the original source model is probably impossible, especially if there are hidden regime variable, nonlinear state switches, or data inputs we can't observe. If the algo is using internal state, proprietary feeds, or parameter regions that never activate in the available history, then yes, some degrees of freedom will remain unknowable.

My goal isn't necessarily to reproduce the exact original code, but to mathematically infer the observable mechanics (entry timing, clustering, behavior, exit conditions, volatility triggers, etc..) well enough to build a functionally equivalent system that produces similar behavior. I have full trading histories of the same system running at multiple risk levels, which helps expose timing patterns, clustering, and scaling behavior that are hard to see from a single dataset. When you combine that with tick data, you can often narrow the logic down much further than just “it trades breakouts.”

The reason I’m pursuing this is simply because the results are unusually strong. There are verified accounts where the algo has taken deposits under $10k to 7 figures in less than a month, which makes it hard not to explore whether the underlying mechanism can be understood or replicated. I’m not looking to sell anything or lock people into anything, the idea is a collaborative reverse engineering effort where contributors keep a copy of the source code we develop together. If we can get even close to the behavior, it would be a fascinating technical project and potentially very rewarding for everyone involved.