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This paper introduces AQuA, a framework for recursive self-improvement in quantitative trading research, utilizing two independent language-model-driven systems for symbolic factor discovery and model development. By retaining validated evidence from earlier experiments, each system independently enhances its hypotheses and candidate proposals, achieving notable performance metrics. The factor system achieves an information coefficient of approximately 0.190 in crypto markets, while the model system yields a Sharpe ratio of up to +2.50 in US equities, demonstrating consistent profitability across multiple years.
Recursive self-improvement in quantitative trading research leads to a groundbreaking Sharpe ratio of +2.50, showcasing the potential for autonomous systems to enhance investment strategies over time.
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 spaces, or research state. Instead, each independently closes its own research loop by retaining validated evidence and using it to guide subsequent proposals. In this bounded sense, both systems implement recursive self-improvement at the level of the research process. Each system also uses its own sealed sandbox, which fixes the data splits, feature and label definitions, and evaluator while allowing the model to act only through constrained factor expressions or configuration diffs. The factor system, a manager-mediated multi-agent pipeline, discovers and combines factors into a signal that reaches a combined information coefficient of about $0.190$ on a crypto universe. The model system, a config-driven loop over a hybrid time-series architecture, reaches a per-stock information coefficient of $+0.0843$ on US equities and converts it into a threshold long/short strategy with a held-out Sharpe of up to $+2.50$ at a two-leg cost. The strategy is positive in every year from 2021 to 2025.
