Alpha-R1: Alpha Screening with LLM Reasoning via Reinforcement Learning
This addresses the challenge of generalizing investment strategies in changing economic environments for quantitative finance practitioners, representing a novel method for a known bottleneck.
The paper tackles the problem of signal decay and regime shifts in non-stationary markets by developing Alpha-R1, an 8B-parameter reasoning model that uses LLM reasoning via reinforcement learning for context-aware alpha screening, resulting in consistent outperformance of benchmark strategies and improved robustness to alpha decay across multiple asset pools.
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.