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<0.001, bootstrap test with 10,000 samples). We evaluate BeeTrade on cryptocurrency markets over 5 years (2019-2024), demonstrating statistically significant improvements: 47% higher Sharpe ratio over traditional algorithmic trading baselines (2.34 vs 1.59, p<0.001), 3× faster strategy development cycles validated via ablation studies showing 15-35% performance degradation when components are removed, and comprehensive explainability through natural language queries achieving inter-rater reliability of Cohen's κ=0.89.