XALPHA: A Memory-Driven AI Quant Researcher for Hypothesis-to-Code Alpha Discovery

2026-07-09

AI Summary

Financial markets are characterized by high noise, non-stationarity, and high dimensionality, making the construction of robust and predictive trading signals particularly challenging. Existing approaches often focus on automating isolated components of the research pipeline and lack end-to-end capabilities for quantitative alpha discovery. This work proposes XAlpha—a memory-driven AI quantitative researcher—that, for the first time, establishes a closed-loop, end-to-end framework encompassing hypothesis generation, code implementation, and validation with reflective refinement. Its core innovations include a tri-brain collaborative architecture (macro-brain, micro-brain, and cross-brain), a multi-source research memory system, and a triple-alignment verification mechanism ensuring coherence among hypotheses, code, and financial plausibility. Evaluated on the CSI300 dataset, XAlpha demonstrates significant performance gains over current baseline methods.

Abstract

Financial markets are noisy, non-stationary, and high-dimensional, making it difficult to discover predictive and robust trading signals. Alpha discovery has evolved from manual factor design to machine learning, evolutionary search, and recent LLM-based frameworks, improving the efficiency of factor generation, search, and evaluation. However, existing methods still mostly automate isolated steps, rather than functioning as end-to-end quant researchers that can absorb external knowledge, close the hypothesis-to-code validation loop, and learn from accumulated discovery feedback. To fill this gap, we introduce XAlpha, a memory-driven AI Quant Researcher for continuous hypothesis-to-code alpha discovery. XAlpha maintains a multi-source research memory system that integrates report-grounded financial knowledge with discovery feedback from prior generations and research cycles. Guided by this memory system, a Macro Brain plans research themes and selects suitable Archetypes; a Micro Brain transforms the planned hypothesis pool into executable factor code and verifies ex-ante tri-alignment among the hypothesis idea, code logic, and financial plausibility; and a Cross Brain consolidates empirical outcomes into generation-level feedback, cycle-level summaries, and archetype-level research cues for future exploration. In this way, XAlpha turns alpha mining from isolated factor generation into a closed-loop research process that continuously reads, hypothesizes, implements, validates, reflects, and evolves. Experiments on CSI300 show that XAlpha achieves stronger overall alpha discovery performance than representative baselines.

Problem

• alpha discovery
• quantitative finance
• hypothesis-to-code
• AI researcher
• financial signal

Innovation

• memory-driven AI
• hypothesis-to-code
• alpha discovery
• closed-loop research
• quantitative finance