用分层策略对齐让大模型更稳地炒股，兼顾实时多源数据。
Strat-LLM: Stratified Strategy Alignment for LLM-based Stock Trading with Real-time Multi-Source Signals
- 分层策略对齐框架，结合实时行情、新闻和财报数据。
- 350亿参数模型在严格约束下表现最佳，1220亿参数模型需灵活模式。
- 避免小赢陷阱，适合追求长期稳健收益的量化交易者。
大语言模型正演变为自主交易代理，但现有基准常忽略架构推理与策略一致性之间的相互作用。我们提出Strat-LLM框架，基于分层策略对齐，在2025年全程实时前向环境中运行，整合序列价格、实时新闻和年报等异构数据，消除前瞻偏差。对A股与美股的大量压力测试显示：(1) 推理型模型在自由模式下依赖内部逻辑达峰值效用，而标准模型需严格模式作为关键风险锚点；(2) 对齐效用具有市场状态依赖性，自由与引导模式在上升市捕捉动量，严格模式则在下行市降低回撤；(3) 中等规模模型（350亿参数）在严格约束下表现最优，超大规模模型（1220亿参数）在严苛规则下出现对齐损耗，但在引导模式中获得性能溢价；(4) 标准LLM常陷入高胜率陷阱，为小幅盈利牺牲总回报，唯有通过深度推理或外部严格约束才能缓解。项目详情见 https://Strat-LLM.github.io。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are evolving into autonomous trading agents, yet existing benchmarks often overlook the interplay between architectural reasoning and strategy consistency. We propose Strat-LLM, a framework grounded in Stratified Strategy Alignment. Operating in a live-forward setting throughout 2025, it integrates heterogeneous data including sequential prices, real-time news, and annual reports to eliminate look-ahead bias. Extensive stress tests on A-share and U.S. markets reveal: (1) reasoning-heavy models achieve peak utility in Free Mode via internal logic, whereas standard models require Strict Mode as a vital risk anchor; (2) alignment utility is regime-dependent, with Free and Guided modes capturing momentum in uptrending markets, while Strict Mode mitigates drawdowns in downtrends; (3) mid-scale models (35B) show optimal fidelity under strict constraints, whereas ultra-large models (122B) suffer an alignment tax under rigid rules but gain a performance premium in Guided Mode; (4) standard LLMs often fall into a high win-rate trap, optimizing for small gains at the expense of total returns, which can only be mitigated through deep reasoning or strict external guardrails. Project details are available at https://Strat-LLM.github.io.
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