{
  "id": 4844,
  "url": "https://arxiv.org/abs/2605.06024v1",
  "title": "Strat-LLM: Stratified Strategy Alignment for LLM-based Stock Trading with Real-time Multi-Source Signals",
  "summary": "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:",
  "authors": "Wenliang Huang, Zengyi Yu",
  "category": "research",
  "topics": "bias-fairness,safety-alignment,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-07T11:17:23.000Z",
  "fetched_at": "2026-07-14T16:31:17.583Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4844",
  "original_url": "https://arxiv.org/abs/2605.06024v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}