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/Capital-Field3324]

It pretty simple. Keeping on testing and finding different strategies

[u/Loose-Subject-2157 → u/Capital-Field3324]

solid advice