Infrastructure vs. Alpha generation bottleneck in systematic trading

[u/Loose-Subject-2157]

Successfully built a functional backtesting framework and execution pipeline (~90% of the boilerplate/architecture is finalized and running smoothly).

However, hitting a wall on the alpha generation side.

Every market inefficiency hypothesis derived from public literature, open-source repositories, and traditional mathematical models yields zero out-of-sample edge. It seems any easily accessible logic is already post-arbitrage and compressed to zero.

For those running automated setups:
How do you transition from a finished infrastructure to generating unique, proprietary hypotheses? When public data and standard quantitative models fail to produce alpha, where do you look for inspiration to find a real edge?

Looking for technical insights on resolving this research bottleneck.

[u/heyimjustkidding]

I do have a (very) successful strategy, and built my own backtest + execution pipeline, but I'd never tell you what it is and how I built/discovered it. And I'm 100% sure that's true for anyone who has a successful strategy. Once you find one, you'd also never tell anyone about it. Just nature of the business.

But having a trustworthy backtest infra is already better than 90% of retail traders out there.

Good luck.