Agent-Based Genetic Algorithm for Crypto Trading Strategy Optimization (2510.07943v1)

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.

• The paper presents CGA-Agent, a novel hybrid framework that integrates genetic algorithms with multi-agent systems for adaptive crypto trading optimization.
• By continuously re-optimizing strategy parameters in volatile markets, the approach significantly improves performance metrics such as Sharpe and Sortino ratios.
• Experimental results across major cryptocurrencies, including a 550% increase in ETH profitability, highlight the framework's robust adaptive capabilities.

Agent-Based Genetic Algorithm for Crypto Trading Strategy Optimization

The paper presents a novel hybrid framework combining Genetic Algorithms (GAs) with intelligent multi-agent systems to optimize trading strategies in highly volatile cryptocurrency markets. The proposed system, termed Crypto Genetic Algorithm Agent (CGA-Agent), dynamically adjusts strategy parameters by integrating real-time market intelligence and performance feedback, thus addressing the shortcomings of static optimization methods.

Introduction

Cryptocurrency markets exhibit extreme volatility, pronounced tail risks, and intricate microstructure patterns, challenging conventional trading strategy optimization techniques. The paper introduces CGA-Agent, which leverages the capabilities of GAs and MAS to provide adaptive optimization in these dynamic environments. By incorporating multi-agent coordination mechanisms into genetic algorithm operations, CGA-Agent achieves improved convergence properties and evolutionary dynamics, optimizing trading strategies adaptively without manual intervention. Experimentation across major cryptocurrencies demonstrates substantial enhancements in total returns and risk-adjusted performance metrics such as Sharpe and Sortino ratios.

Related Work

Evolutionary Algorithms in Financial Optimization

GAs have been effectively applied to optimize financial parameters in complex, non-linear, and multi-modal search spaces. These algorithms thrive where traditional gradient-based and heuristic methods often fall short due to the unique characteristics of cryptocurrency markets, including volatility and sentiment-driven dynamics. The paper builds on this foundation by extending GAs' applications through sophisticated integration with multi-agent systems for real-time market analysis and adaptive optimization.

Multi-Agent Systems in Quantitative Finance

MAS have a significant impact on financial decision-making by enabling coordinated analysis and distributed decision-making capabilities. The paper leverages MAS principles to enhance backtesting outcomes and adapt strategies to varying market regimes through agent interactions. Previous systems, such as FLAG-TRADER and GA-LLM, demonstrate MAS benefits in trading decisions, setting a precedent for integrating MAS in parameter optimization.

Methodology

Problem Formulation

The paper formalizes the optimization problem as finding an optimal strategy parameter vector $\boldsymbol{\theta}^*$ that maximizes a fitness function $\mathcal{F}$. This optimization problem adapts to evolving market conditions by continuously re-optimizing parameters using a rolling window of market data.

CGA-Agent Architecture

The CGA-Agent framework comprises specialized agents: Analysis Agent, Generate Agent, Evaluate Agent, Choose Agent, Crossover Agent, and Mutation Agent, each performing distinct tasks related to strategy optimization. From initializing parameter genes to continuously optimizing them within a loop, these agents collaborate to adaptively improve trading strategies.

Mechanisms of CGA-Agent

The agents execute various processes, from generating initial parameter genes and evaluating strategy performance to selecting, crossing, and mutating genes based on quantitative market prior knowledge and predefined templates. This coordinated approach ensures adaptability and optimization efficiency in rapidly evolving markets.

Experimental Evaluation

Baseline and Dataset Specification

The experiments utilize a self-designed Scalping Strategy as a baseline combined with the CGA-Agent framework across three prominent cryptocurrencies: Bitcoin, Ethereum, and Binance Coin. Backtesting employs a rolling-window framework, optimizing parameters every 30 trading days.

Comprehensive Results Analysis

The CGA-Agent demonstrates significant improvements compared to the baseline strategy across all tested cryptocurrencies. For ETH, the strategy optimization increased PnL by 550%, showcasing the framework's capacity to enhance profitability amidst volatile market conditions. Consistent findings across BTC and BNB further reinforce the framework's robustness.

Conclusion

CGA-Agent signifies an advancement in dynamic crypto trading strategy optimization, addressing the limitations of conventional methods by integrating genetic algorithms with multi-agent systems. The research substantiates CGA-Agent's efficacy in adapting to dynamic market environments, establishing it as a valuable tool in quantitative finance. Future research could explore extending these techniques further into other financial domains, capitalizing on both theoretical and empirical enhancements.