R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization

Paper: arXiv 2505.15155

Authors: Yuante Li, Xu Yang, Xiao Yang, Minrui Xu, Xisen Wang, Weiqing Liu, Jiang Bian

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: https://github.com/microsoft/RD-Agent.

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper focuses on a multi-agent framework with architectural details and experimental results (real-market backtests, specific metrics like annualized returns), indicating strong empirical rigor, while the math is largely algorithmic and systems-oriented rather than dense theoretical derivations.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Automate Quant Strategy R&D"] --> B["Data Inputs<br>Financial Markets & Historical Data"]
  B --> C{"Research Stage"}
  C --> D["Dynamic Prompting &<br>Hypothesis Formulation"]
  D --> E["Task Mapping<br>to Domain Prior"]
  E --> F{"Development Stage"}
  F --> G["Co-STEER Agent<br>Code Generation"]
  G --> H["Real-Market<br>Backtest Execution"]
  H --> I["Evaluation & Feedback"]
  I --> J["Multi-Armed Bandit<br>Adaptive Scheduler"]
  J -->|Iterate| C
  I --> K["Key Findings<br>2X Higher Returns, 70% Fewer Factors"]

Cited References (104)

1. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (2022)
2. RD2Bench : Toward Data-Centric Automatic R & D (2024)
3. Language Models Are Few-Shot Learners (2020)
4. Reflexion: Language Agents with Verbal Reinforcement Learning (2023)
5. Teaching Large Language Models to Self-Debug (2024)
6. Self-Planning Code Generation with Large Language Model (2023) — link
7. 101 formulaic alphas (2016)
8. Qlib: An AI-Oriented Quantitative Investment Platform (2020) — link

6 of 104 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.