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.

翻译：在非平稳市场中，信号衰减与机制转换对数据驱动的投资策略构成持续挑战。传统时间序列与机器学习方法主要依赖历史相关性，当经济环境变化时往往难以泛化。尽管大语言模型在处理非结构化信息方面展现出强大能力，但其通过显式经济推理支持量化因子筛选的潜力尚未得到充分探索。现有基于因子的方法通常将阿尔法因子简化为数值时间序列，忽略了决定因子经济相关性的语义逻辑。我们提出Alpha-R1——一个通过强化学习训练的80亿参数推理模型，用于情境感知的阿尔法因子筛选。Alpha-R1通过对因子逻辑与实时新闻进行推理，评估变化市场条件下阿尔法因子的相关性，并依据情境一致性选择性地激活或停用因子。跨多个资产池的实证结果表明，Alpha-R1持续优于基准策略，并对阿尔法衰减表现出更强的鲁棒性。完整实现与资源详见https://github.com/FinStep-AI/Alpha-R1。