AlphaSchema: Exploring the Space of Trading Semantics for LLM-Based Alpha Mining
Automated alpha mining has increasingly adopted large language model (LLM) agents for factor generation and iterative discovery. However, existing LLM-based systems often delegate both factor construction and search decisions to the agent itself, without an explicit exploration space or a principled mechanism for navigating that space. As a result, exploration remains largely implicit and difficult to control or optimize systematically. We introduce AlphaSchema, which constructs and explores a structured space of trading semantics for alpha mining. Each point in this space is a schema plan composed of Event, Context, Qualities, Direction, and Output, specifying the semantics of a candidat...
| Authors | Jingyang Yi, Jian Yang, Yifei Jin et al. |
|---|---|
| Primary category | cs.AI |
| Published | 29 July 2026 |
| Last updated at source | 29 July 2026 |
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