Are LLMs used in early-stage alpha research beyond coding assistance?
I’ve been experimenting with using LLMs as a research assistant for alpha exploration, not just for coding.
More specifically, I use them to help refine alpha hypotheses and structure ideas around signal design and market state representations before anything is formally implemented or backtested.
In some cases, the model helps “reframe” raw intuitions into more structured research objects (e.g., possible microstructure signals, regime-dependent behavior, or alternative feature representations).
I’m curious whether this is something seen at all in professional HFT / systematic trading environments:
- Are AI tools used beyond implementation support, i.e. in early-stage research such as alpha hypothesis generation or signal design exploration?
- Or is the current usage still mostly limited to coding, feature engineering, and analysis tooling rather than conceptual research?
From a practical standpoint, I’m also wondering how firms prevent over-reliance on “plausible but non-tradable” ideas generated at this abstraction layer.