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 R&D-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. R&D-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, R&D-Agent(Q) achieves up to 2× 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.

Financial time series prediction is an important and challenging data mining task for quantitative investment. The inherent non-linearity, high noise, and susceptibility to various factors, such as macroeconomic conditions and market sentiment in the stock market, increase the difficulty of prediction. Existing financial industries mainly employ time series models or fundamental analysis methods for prediction. However, these methods fail to effectively capture the complex interrelationships between equity. In recent years, graph neural networks (GNNs), due to their powerful relational modeling capabilities, have been applied to stock prediction. However, with the advances of recent digital power, such as widely-used high-frequency trading techniques, existing graph-based methods still have shortcomings in effectively learning multi-granularity temporal relations as they cannot effectively learn the patterns in different frequencies, e.g., minute-level, daily, weekly, etc. Therefore, in this paper, we propose a multi-granularity graph augmented learning framework for interrelated financial time series forecasting. We first construct a temporal return relationship graph with multi-granularity financial time series, including weekly, daily, and minute-level, to comprehensively capture the dynamic relations of equities, including both medium-term trends and short-term fluctuations. Then, to further augment the node relations, we devise an attentional graph augment module to improve the graph learning with fundamental data, which are jointly optimized in the prediction layer. We conduct extensive empirical studies on multiple datasets from both the Chinese and U.S. stock markets. The results demonstrate that our proposed model consistently outperforms existing baseline methods across four key financial metrics, including ARR, ASR, CR, and IR, thereby validating its effectiveness and superiority. The model has been applied and empirically tested in commercial-grade trading platforms, further demonstrating its efficiency and robustness in real-world trading environments.

Stock price prediction is a challenging problem in the field of finance and receives widespread attention. In recent years, with the rapid development of technologies such as deep learning and graph neural networks, more research methods have begun to focus on exploring the interrelationships between stocks. However, existing methods mostly focus on the short-term dynamic relationships of stocks and directly integrating relationship information with temporal information. They often overlook the complex nonlinear dynamic characteristics and potential higher-order interaction relationships among stocks in the stock market. Therefore, we propose a stock price trend prediction model named LSR-IGRU in this paper, which is based on long short-term stock relationships and an improved GRU input. Firstly, we construct a long short-term relationship matrix between stocks, where secondary industry information is employed for the first time to capture long-term relationships of stocks, and overnight price information is utilized to establish short-term relationships. Next, we improve the inputs of the GRU model at each step, enabling the model to more effectively integrate temporal information and long short-term relationship information, thereby significantly improving the accuracy of predicting stock trend changes. Finally, through extensive experiments on multiple datasets from stock markets in China and the United States, we validate the superiority of the proposed LSR-IGRU model over the current state-of-the-art baseline models. We also apply the proposed model to the algorithmic trading system of a financial company, achieving significantly higher cumulative portfolio returns compared to other baseline methods. Our sources are released at https://github.com/ZP1481616577/Baselines\_LSR-IGRU.