Optimizing Algorithmic Trading Strategies via Multi Agent Reinforcement Learning Architectures Integrating Market Microstructure Dynamics and Competitive Game Logic
Abstract
The evolution of financial markets into high-frequency, algorithmically driven ecosystems has necessitated a shift in how trading strategies are designed, evaluated, and deployed. Conventional single-agent optimization models frequently fail to account for the reflexive nature of modern markets, where the actions of one participant directly influence the state space of others. This research explores the integration of Multi-Agent Reinforcement Learning (MARL) architectures with granular market microstructure dynamics and competitive game logic to enhance the robustness and efficiency of algorithmic trading systems. By conceptualizing the market as a decentralized, non-stationary environment, this study examines how distributed agents can learn to navigate complex liquidity landscapes, manage adverse selection risks, and optimize execution through cooperative and competitive interactions. The paper emphasizes the systemic implications of MARL deployment, focusing on the structural trade-offs between computational overhead and execution latency, the governance of autonomous financial systems, and the broader socio-technical infrastructure required to sustain fair and stable market environments. Through a comprehensive analysis of multi-agent coordination and competitive equilibrium, the study provides a framework for understanding how algorithmic intelligence can be aligned with long-term market integrity and regulatory compliance.
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