Scholarlink

This study proposes and evaluates a hybrid framework that integrates multi-agent systems and functional architecture for automated financial trading in Vietnam. The framework assigns specialized roles - fundamental, technical, news, market, trader and risk agents - operating over multimodal stock market data (OHLCV, technical indicators, firm fundamentals, and textual news). Agents interact via structured horizontal debates and vertical risk gating to produce auditable trading proposals. Experimental results demonstrate improved risk-adjusted returns and enhanced explainability, while modular design supports deployment for back-office automation (e.g., report generation and opportunity identification). The study contributes a methodology for combining agentic large language model (LLMs) with disciplined functional pipelines and provides practical guidance for deploying such systems in emerging markets characterized by high volatility and limited liquidity. Implications for FinTech adoption and regulatory alignment in Vietnam are discussed. VinTradeAgent is available at https://github.com/thanhENC/Vin-Trade-Agent .

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Optimizing Multi-agent and Functional Architectures for Enhanced Financial Trading Performance: VinTradeAgent Case Study

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • An Dinh Van,
  • Anh Nguyen Thi Linh,
  • Phuong Pham Nguyen Hien,
  • Hung Nguyen Gia,
  • Trinh Tran Thi Kieu,
  • Hung Nguyen Quang

摘要

This study proposes and evaluates a hybrid framework that integrates multi-agent systems and functional architecture for automated financial trading in Vietnam. The framework assigns specialized roles - fundamental, technical, news, market, trader and risk agents - operating over multimodal stock market data (OHLCV, technical indicators, firm fundamentals, and textual news). Agents interact via structured horizontal debates and vertical risk gating to produce auditable trading proposals. Experimental results demonstrate improved risk-adjusted returns and enhanced explainability, while modular design supports deployment for back-office automation (e.g., report generation and opportunity identification). The study contributes a methodology for combining agentic large language model (LLMs) with disciplined functional pipelines and provides practical guidance for deploying such systems in emerging markets characterized by high volatility and limited liquidity. Implications for FinTech adoption and regulatory alignment in Vietnam are discussed. VinTradeAgent is available at https://github.com/thanhENC/Vin-Trade-Agent .