5arXiv cs.CL (Computation and Language)·
XALPHA: Memory-driven AI agent for end-to-end quantitative trading alpha discovery
Researchers introduce XAlpha, an LLM-based agentic system for continuous hypothesis-to-code alpha factor discovery in quantitative finance. The system uses a multi-source memory architecture with three specialized 'brain' modules (Macro, Micro, Cross) to close the loop from financial hypothesis generation through code implementation, validation, and iterative feedback. Unlike prior LLM-based quant frameworks that automate isolated steps, XAlpha operates as an end-to-end research agent that accumulates and reuses discovery knowledge across cycles. Experiments on the CSI300 index show improved alpha discovery performance over representative baselines.
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