多智能体模拟股票市场中的深度Q学习做市商
Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market
摘要：
做市商通过提供流动性在金融市场中发挥着关键作用。他们通常在订单簿中填写买入和卖出限价订单，以便为交易者提供可供选择的价格水平。本文的重点正是从基于代理的角度研究这些做市商的策略。特别是，我们提出了应用强化学习（RL）在模拟股票市场中创建智能市场标记。本研究分析了RL做市商代理在非竞争性（只有一个RL做市商同时学习）和竞争性场景（多个RL做市商同时学习）中的行为，以及他们如何在Sim2Real范围内调整策略，并获得有趣的结果。此外，它涵盖了不同实验之间的政策转移的应用，描述了竞争环境对RL代理性能的影响。强化学习和深度强化学习技术被证明是有利可图的做市商方法，从而更好地了解他们在股票市场中的行为。
英文摘要：
Market makers play a key role in financial markets by providing liquidity. They usually fill order books with buy and sell limit orders in order to provide traders alternative price levels to operate. This paper focuses precisely on the study of these markets makers strategies from an agent-based perspective. In particular, we propose the application of Reinforcement Learning (RL) for the creation of intelligent market markers in simulated stock markets. This research analyzes how RL market maker agents behaves in non-competitive (only one RL market maker learning at the same time) and competitive scenarios (multiple RL market markers learning at the same time), and how they adapt their strategies in a Sim2Real scope with interesting results. Furthermore, it covers the application of policy transfer between different experiments, describing the impact of competing environments on RL agents performance. RL and deep RL techniques are proven as profitable market maker approaches, leading to a better understanding of their behavior in stock markets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
