Agent Memory with Episodic Retrieval for Financial Decision-Making
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 ...
| Authors | Nuoyue Xu, Jiang Liu, Wenxuan Huang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 23 September 2026 |
| Last updated at source | 23 September 2026 |
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