Abstract
Financial markets pose fundamental challenges for asset return prediction due to their high dimensionality, non-stationarity, and persistent volatility. Despite advances in large language models and multi-agent systems, current quantitative research pipelines suffer from limited automation, weak interpretability, and fragmented coordination across key components such as factor mining and model innovation. In this paper, we propose R&D-Agent for Quantitative Finance, in short RD-Agent(Q), the first data-centric multi-agent framework designed to automate the full-stack research and development of quantitative strategies via coordinated factor-model co-optimization. RD-Agent(Q) decomposes the quant process into two iterative stages: a Research stage that dynamically sets goal-aligned prompts, formulates hypotheses based on domain priors, and maps them to concrete tasks, and a Development stage that employs a code-generation agent, Co-STEER, to implement task-specific code, which is then executed in real-market backtests. The two stages are connected through a feedback stage that thoroughly evaluates experimental outcomes and informs subsequent iterations, with a multi-armed bandit scheduler for adaptive direction selection. Empirically, RD-Agent(Q) achieves up to 2X higher annualized returns than classical factor libraries using 70% fewer factors, and outperforms state-of-the-art deep time-series models on real markets. Its joint factor-model optimization delivers a strong balance between predictive accuracy and strategy robustness. Our code is available at: this https URL.
Abstract (translated)
金融市场在资产回报预测方面提出了根本性的挑战，这些挑战源于市场的高维度、非平稳性和持续的波动性。尽管大型语言模型和多代理系统有所进步，但目前的数量化研究流程仍然存在自动化程度有限、解释能力弱以及关键组成部分（如因子挖掘和模型创新）之间的协调碎片化等问题。在这篇论文中，我们提出了“定量金融研发代理”(R&D-Agent for Quantitative Finance)，简称RD-Agent(Q)，这是首个以数据为中心的多代理框架，旨在通过协同优化因子-模型来自动完成数量化策略的全流程研究与开发。 RD-Agent(Q)将量化过程分解为两个迭代阶段：**研究阶段（Research stage）**，该阶段动态地设置目标对齐提示、基于领域先验构建假设并将其映射到具体任务；以及 **开发阶段（Development stage）**，这一阶段利用代码生成代理Co-STEER来实现特定的任务代码，并在实际市场回测中执行这些代码。两个阶段通过一个反馈阶段连接起来，在这个阶段里对实验结果进行全面评估，并为后续迭代提供信息，同时使用多臂赌博机调度器进行适应性方向选择。 从经验上讲，RD-Agent(Q)实现了比经典因子库高出2倍的年化回报率，且只用了70%的因素数量。此外，它在实际市场上超过了现有的最先进的深度时间序列模型性能。其联合优化因子-模型的方法能够提供预测准确性和策略稳健性之间的良好平衡。 我们的代码可以在以下链接找到：[此URL](this https URL)。
URL
https://arxiv.org/abs/2505.15155
