论文标题
通过中国商品市场的加强学习市场建设
Market Making via Reinforcement Learning in China Commodity Market
论文作者
论文摘要
做市商在金融市场中起着至关重要的作用。成功的做市商应控制库存和不利选择风险，并为市场提供流动性。作为控制问题的一种重要方法，增强学习享有数据驱动和较少的假设的优势，自2018年以来就受到了市场制定领域的极大关注。但是，尽管中国商品市场对农产品，非有产性金属以及其他一些行业的贸易量最大，但在中国市场上的市场却仍然很少见。在本文中，我们试图填补空白。我们的贡献是三倍：我们开发自动交易系统，并验证在中国商品市场上应用强化学习的可行性。此外，我们通过分析其对不同环境条件的反应来探测代理的行为。
Market makers play an essential role in financial markets. A successful market maker should control inventory and adverse selection risks and provide liquidity to the market. As an important methodology in control problems, Reinforcement Learning enjoys the advantage of data-driven and less rigid assumptions, receiving great attention in the market-making field since 2018. However, although the China Commodity market has the biggest trading volume on agricultural products, nonferrous metals, and some other sectors, the study of applying RL to Market Making in China market is still rare. In this thesis, we try to fill the gap. Our contribution is threefold: We develop the Automatic Trading System and verify the feasibility of applying Reinforcement Learning in the China Commodity market. Also, we probe the agent's behavior by analyzing how it reacts to different environmental conditions.
