BeeTrade A Multi-Agent Orchestration Framework for Algorithmic Trading

Traditional algorithmic trading systems face three documented challenges: rigid architectures requiring weeks to modify (Chan, 2021), opaque decision processes failing regulatory requirements (Narang, 2013; ESMA 2018), and inability to leverage recent advances in large language models for reasoning (Cartea et al., 2015). We present BeeTrade, a multi-agent orchestration framework that addresses these limitations through three principled innovations: (1) hierarchical task decomposition via directed acyclic graphs (DAGs) enabling parallel execution and dynamic replanning, (2) dual-protocol communication combining Model Context Protocol (MCP) for agent-tool interaction and Agent-to-Agent (A2A) protocol for peer collaboration with versioned JSON schemas, authentication, and rate limiting, and (3) a hybrid memory architecture that enables natural language explainability queries with 94% accuracy (p