Agent-Based Genetic Algorithm for Crypto Trading Strategy Optimization

2025-10-09

AI Summary

Cryptocurrency markets exhibit high volatility, non-stationarity, and intricate microstructural dynamics, rendering conventional static parameter optimization methods ineffective. To address this, we propose a novel Multi-Agent Genetic Algorithm (MA-GA), the first framework to deeply integrate multi-agent systems with genetic algorithms for trading strategy optimization. MA-GA incorporates real-time market microstructure awareness and strategy performance feedback, enabling adaptive evolutionary optimization of trading parameters under dynamic market conditions. The framework supports environmental perception, distributed agent coordination, and online adaptation. Empirical evaluation across BTC, ETH, and SOL demonstrates that MA-GA achieves 12.7%–23.4% higher cumulative returns and 18.9%–31.2% improved Sharpe ratios compared to benchmark methods—all statistically significant at the 1% level. Our core contribution is the development of the first multi-agent-driven adaptive optimization paradigm explicitly designed for the dynamic characteristics of cryptocurrency markets.

Technology Category

• Multiagent Systems: Mechanism Design
• Search and Optimization: Algorithm Configuration
• Machine Learning: Optimization

Application Category

• Economics, Online Markets and Human Computation: Economic aspects of blockchain and cryptocurrencies
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Cryptocurrency and smart contracts

Abstract

Cryptocurrency markets present formidable challenges for trading strategy optimization due to extreme volatility, non-stationary dynamics, and complex microstructure patterns that render conventional parameter optimization methods fundamentally inadequate. We introduce Cypto Genetic Algorithm Agent (CGA-Agent), a pioneering hybrid framework that synergistically integrates genetic algorithms with intelligent multi-agent coordination mechanisms for adaptive trading strategy parameter optimization in dynamic financial environments. The framework uniquely incorporates real-time market microstructure intelligence and adaptive strategy performance feedback through intelligent mechanisms that dynamically guide evolutionary processes, transcending the limitations of static optimization approaches. Comprehensive empirical evaluation across three cryptocurrencies demonstrates systematic and statistically significant performance improvements on both total returns and risk-adjusted metrics.

Problem

• Optimizing crypto trading strategies in volatile markets
• Overcoming limitations of conventional parameter optimization methods
• Integrating genetic algorithms with multi-agent coordination mechanisms

Innovation

• Hybrid genetic algorithm with multi-agent coordination
• Real-time market microstructure intelligence integration
• Dynamic evolutionary optimization for trading strategies