arXiv AI

Agent Memory with Episodic Retrieval for Financial Decision-Making

META (Memory Enhanced Trading Agent) is a new agent-based trading framework that augments large language models with episodic memory. It combines specialized indicator agents—such as Trend, MACD, Stochastic, RSI, SMA, AVWAP, and Heikin‑Ashi—with a Decision Agent that fuses their reports, while a Memory module retrieves and updates past trading episodes encoded as market state embeddings with outcomes and reflections. By recalling relevant experiences and adaptively reweighting signals under similar market regimes, META improves directional accuracy and robustness in short‑horizon evaluations, offering regime‑aware, interpretable, and low‑latency decision‑making for financial trading.

arXiv AI
Sep 17

EvolveTrade: Experience-Driven Policy Refinement for Self-Evolving LLM Trading Agents

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 AI
Jun 24

When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments

arXiv:2407. 18957v5 Announce Type: replace-cross Abstract: Can AI Agents simulate real-world trading environments to investigate the impact of external factors on stock trading activities (e.

By Chong Zhang, Xinyi Liu, Zhongmou Zhang, Mingyu Jin, Lingyao Li, Zhenting Wang, Wenyue Hua, Dong Shu, Suiyuan Zhu, Xiaobo Jin, Sujian Li, Mengnan Du, Yongfeng Zhang