作者位次 #2/4ACL 2026Findings· Findings of ACL
A lpha Q uanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading
Zheye Deng · Weixiang Yan · Changlong Yu · Jiashu Wang
摘要
While Large Language Model (LLM) agents show promise in automated trading, they still face critical limitations. Prominent multi-agent frameworks often suffer from inefficiency, produce inconsistent signals, and lack the end-to-end optimization required to learn a coherent strategy from market fe...