Mean-Field Games for Algorithmic Trading and Market Equilibrium explores the application of mean-field game theory to modern financial markets. This book presents a rigorous framework for modeling large populations of interacting agents, price formation, and strategic behavior in high-frequency and algorithmic trading environments.

Readers will learn how to formulate and solve mean-field games, analyze Nash equilibria in competitive market settings, and implement scalable simulations using Python and JAX. The text bridges advanced mathematical theory with practical computational methods, covering topics such as differential games, optimal control in finance, and large-scale agent-based modeling.

Key Features:

• Mathematical foundations of mean-field games and their relevance to market microstructure
• Numerical methods for solving high-dimensional game systems with JAX
• Implementation of interacting agent models and equilibrium computation
• Applications to algorithmic trading strategies and market equilibrium analysis

Written for quantitative researchers, financial engineers, and graduate students in applied mathematics, operations research, or computational finance, this book provides both theoretical insights and working code examples for building sophisticated market simulation models.

This is a technical reference focused on clarity, mathematical precision, and reproducible computational approaches.