Alpha-R1 Enhancing Investment Strategies with LLMs and RL

📝 Original Paper Info

- Title: Alpha-R1 Alpha Screening with LLM Reasoning via Reinforcement Learning
- ArXiv ID: 2512.23515
- Date: 2025-12-29
- Authors: Zuoyou Jiang, Li Zhao, Rui Sun, Ruohan Sun, Zhongjian Li, Jing Li, Daxin Jiang, Zuo Bai, Cheng Hua

📝 Abstract

Signal decay and regime shifts pose recurring challenges for data-driven investment strategies in non-stationary markets. Conventional time-series and machine learning approaches, which rely primarily on historical correlations, often struggle to generalize when the economic environment changes. While large language models (LLMs) offer strong capabilities for processing unstructured information, their potential to support quantitative factor screening through explicit economic reasoning remains underexplored. Existing factor-based methods typically reduce alphas to numerical time series, overlooking the semantic rationale that determines when a factor is economically relevant. We propose Alpha-R1, an 8B-parameter reasoning model trained via reinforcement learning for context-aware alpha screening. Alpha-R1 reasons over factor logic and real-time news to evaluate alpha relevance under changing market conditions, selectively activating or deactivating factors based on contextual consistency. Empirical results across multiple asset pools show that Alpha-R1 consistently outperforms benchmark strategies and exhibits improved robustness to alpha decay. The full implementation and resources are available at https://github.com/FinStep-AI/Alpha-R1.

💡 Summary & Analysis

1. **Machine Learning in Healthcare**: Machine learning excels at processing large-scale data and is useful for identifying important patterns in medical data. Think of it as the doctor in a hospital. 2. **Supervised vs Unsupervised Learning**: Supervised learning uses datasets with known outcomes, while unsupervised learning finds patterns without predefined labels. It’s like teaching a machine to solve problems on its own. 3. **Future Potential of Reinforcement Learning**: Effective at finding optimal treatment plans through iterative feedback loops, reinforcement learning shows promise for future applications.

📄 Full Paper Content (ArXiv Source)

📊 논문 시각자료 (Figures)

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