AQuA: Recursively Self-Improving Quantitative Trading Research Agents

Excerpt

arXiv:2608.12841v2 Announce Type: replace Abstract: We study recursive self-improvement at the level of quantitative-investment research: whether an autonomous system can use evidence from earlier experiments to improve the hypotheses and candidates proposed in later iterations. We present AQuA, which comprises two separate language-model-driven research systems: one for symbolic factor discovery and one for trainable model development. The two systems do not share agents, memories, candidate sp