LATIDIA · Investigación

Memoria del agente con recuperación episódica para la toma de decisiones financieras

arXiv: 2609.28771v1Tipo de anuncio: nuevo Resumen: Los modelos de lenguaje grandes (LLM) han demostrado fuertes capacidades en el análisis financiero y el razonamiento, inspirando avances recientes en los marcos de negociación basados en agentes. Mientras que t

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arXiv:2609.28771v1 Announce Type: new 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