Agent system uses past trading memory to improve financial decisions
Agent Memory with Episodic Retrieval for Financial Decision-Making
Artificial Intelligence
Summary
Financial trading is complicated and requires quick decisions based on lots of information. The authors created META, a system that remembers past trading experiences and uses that memory to make better decisions today. META combines several specialized expert agents with a decision maker and a memory module that recalls similar past market situations. This helps the system adapt to changing market conditions and make more accurate short-term predictions.
What this means in practice
- •For financial trading teams: Improve short-term trading decisions by using a memory-aware agent system that recalls past market conditions and adapts indicator signals accordingly.$Commercial implications: Enables trading platforms to offer smarter, real-time decision support tools that combine multiple indicators and past experiences for better trades.
- •For algorithmic trading developers: Integrate episodic-memory-augmented multi-agent frameworks to build more adaptable and interpretable trading algorithms sensitive to market regimes.
Authors
Nuoyue Xu, Jiang Liu, Wenxuan Huang, Xiang Zhang, Juntai Cao, Jiaqi Wei
Abstract
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.