EvolveTrade is a self‑evolving framework that treats the system prompt of a tool‑using LLM trading agent as a text‑parameterized policy. After each update interval, a Policy Agent revises this policy using accumulated decision traces and portfolio feedback while keeping the backbone LLM fixed, allowing the agent to refine its information‑acquisition and portfolio‑construction procedures over time. Experiments across multiple market regimes and two LLM backbones show that EvolveTrade often improves Sharpe Ratio and Cumulative Return over fixed‑policy baselines, with behavioral analyses indicating increased code‑mediated analysis and regime‑relevant computations.
By Sehee Kim, Yumin Choi, Minki Kang, Sung Ju Hwang
arXiv:2607. 28127v1 Announce Type: cross Abstract: Recent advances in Generative AI have substantially improved financial sentiment analysis through post-trained financial large language models (LLMs).
By Giorgos Iacovides, Wuyang Zhou, Danilo Mandic
arXiv:2607. 11141v1 Announce Type: new Abstract: Large language models (LLMs) based agents are beginning to participate in portfolio construction and market analysis, where decisions must be justified under evolving information and risk constraints.
By Changlun Li, Peixian Ma, Qiqi Duan, Zhenyu Lin, Peineng Wu
arXiv:2605. 05580v2 Announce Type: replace Abstract: Quantitative trading agents have demonstrated substantial promise in automating factor discovery, signal aggregation, and portfolio execution.
By Yishuo Yuan, Jiayi Sheng, Sirui Zeng, Jiaqi Wang, Jiaheng Liu
arXiv:2608.29372v1 Announce Type: new
Abstract: Retrospective backtests provide a limited test of adaptive trading agents: they cannot rule out historical contamination, expose sensitivity to a singl...
By Xiangxin Luo, Chengtian Hong, Haohua Li, Yongyi Xie
arXiv:2606. 00143v1 Announce Type: cross Abstract: Financial markets are inherently non-stationary, exhibiting frequent regime shifts and structural changes that render traditional Portfolio Management (PM) approaches ineffective.
By Chaofan Pan, Lingfei Ren, Linbo Xiong, Yonghao Li, Wei Wei, Xin Yang