面向金融决策的具有情景检索的智能体记忆
Agent Memory with Episodic Retrieval for Financial Decision-Making
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中文总结 AI 辅助
提出META，首个RAG式情景记忆增强多智能体交易框架，通过检索历史交易情景并自适应重加权信号，提升短期方向准确性与稳健性。
中文摘要 AI 辅助
大型语言模型（LLMs）在金融分析和推理方面展现出强大能力，推动了基于智能体的交易框架的最新进展。尽管这些系统显示出潜力，但先前的方法要么强调长期预测，要么作为无状态分析器运行，限制了它们在复杂环境下交易需求中的适用性。为解决这些不足，我们引入了META（记忆增强交易智能体），这是首个用于金融决策的类似RAG的情景记忆增强多智能体框架。META整合了一组专门的指标智能体（如趋势、MACD、随机指标、RSI、SMA、AVWAP、Heikin-Ashi），以及一个融合其报告的决策智能体，还有一个记忆模块，该模块检索并更新以市场状态嵌入形式编码的过去交易情景及其结果和反思。通过回忆相关经验并在相似市场制度下自适应地重新加权信号，META在短期评估中实现了改进的方向准确性和稳健性。我们的结果表明，情景记忆为交易和决策中提供了一种强大的机制，用于实现制度感知、可解释和低延迟的决策。该项目的代码已在GitHub上发布。
英文摘要
Large language models (LLMs) have demonstrated strong capabilities in financial analysis and reasoning, inspiring recent advances in agent-based trading frameworks. While these systems show promise, prior approaches either emphasize long-horizon forecasting or operate as stateless analyzers, limiting their applicability to the demands of trading in complicated settings. To address these gaps, we introduce META (Memory Enhanced Trading Agent), the first RAG-like episodic-memory-augmented multi-agent framework for financial decision making. META integrates a family of specialized indicator agents (e.g., Trend, MACD, Stochastic, RSI, SMA, AVWAP, Heikin-Ashi) with a Decision Agent that fuses their reports, and a Memory module that 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 achieves improved directional accuracy and robustness under short-horizon evaluation. Our results demonstrate that episodic memory provides a powerful mechanism for regime-aware, interpretable, and low-latency decision-making in trading and decision making. The code of this project is released on GitHub.
发表机构
- University of British Columbia（不列颠哥伦比亚大学）
- University of Science and Technology of China（中国科学技术大学）
- Fudan University（复旦大学）
- Zhejiang University（浙江大学）
机构由 AI 辅助整理，请以论文原文为准。
