# Agent-Based Genetic Algorithm for Crypto Trading Strategy Optimization

arXiv: https://arxiv.org/abs/2510.07943 · HTML: https://mlsanity.com/paper/2510.07943/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Builds on (most cited first, 7 of 7)

- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024)
- [TradingAgents: Multi-Agents LLM Financial Trading Framework](https://mlsanity.com/paper/2412.20138/) (2024)
- [FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making](https://mlsanity.com/paper/2407.06567/) (2024)
- [FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading](https://mlsanity.com/paper/2502.11433/) (2025)
- [Optimizing Trading Strategies in Quantitative Markets using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2303.11959/) (2023)
- [Blending Ensemble for Classification with Genetic-algorithm generated Alpha factors and Sentiments (GAS)](https://mlsanity.com/paper/2411.03035/) (2024)
- [A Hybrid GA LLM Framework for Structured Task Optimization](https://mlsanity.com/paper/2506.07483/) (2025)

## Built on by (most cited first, 1 of 1)

- [EVOQUANT: Self-Evolving Verifier-Guided Strategy Optimization for Robust Quantitative Trading](https://mlsanity.com/paper/2607.12455/) (2026)

## Abstract (arXiv)

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.