Agent-Based Genetic Algorithm for Crypto Trading Strategy Optimization
Qiushi Tian, Churong Liang, Kairan Hong, Runnan Li
TL;DR
The paper tackles parameter optimization for crypto trading strategies amid volatile and regime-shifting markets by introducing the CGA-Agent, a hybrid framework that fuses genetic algorithms with multi-agent coordination to leverage real-time market microstructure signals. By formalizing a rolling-window optimization problem and deploying six specialized agents, the approach dynamically evolves strategy parameters based on live performance feedback. Empirical results on BTC, ETH, and BNB show substantial improvements in total returns and risk-adjusted metrics, with ETH demonstrating the largest gains under reoptimization. The work advances adaptive parameter optimization in quantitative finance and highlights the value of integrating market intelligence into evolutionary search for robust performance in volatile crypto environments.
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.