论文标题
市场影响对交易员的影响：向自动交易系统添加多级订单不平衡 - 敏感性
Market Impact in Trader-Agents: Adding Multi-Level Order-Flow Imbalance-Sensitivity to Automated Trading Systems
论文作者
论文摘要
Financial markets populated by human traders often exhibit "market impact", where the traders' quote-prices move in the direction of anticipated change, before any transaction has taken place, as an immediate reaction to the arrival of a large (i.e., "block") buy or sell order in the market: e.g., traders in the market know that a block buy order will push the price up, and so they immediately adjust their quote-prices upwards.现在，大多数主要的金融市场都涉及许多“机器人交易者”，自主的自适应软件代理，而不是人类。本文探讨了如何使此类交易者对阻止订单的可靠预期敏感性，以使机器人交易员完全填充的市场也表现出对市场影响的影响。在2019年的出版物中，教会和悬崖在一个简单的确定性机器人交易员ISHV中提出了最初的结果，ISHV通过监视市场供求之间的不平衡度量，从而表现出这种市场影响效应。我们论文的新颖贡献是：（a）我们批评教会和悬崖使用的方法，表明它们是虚弱的，并认为需要更强大的失衡度量； （b）我们主张使用多级订单流量不平衡（Mlofi：Xu等，2019）作为对不平衡敏感的机器人交易者的更好基础； （c）我们证明了更强大的MLOFI度量在扩展ISHV中的使用，以及众所周知的AA和ZIP交易代理算法（这两者先前均被证明始终如一地胜过人类交易者）。我们证明，此处介绍的新的对不平衡的交易者确实具有市场影响影响，因此非常适合在影响的市场中运营，而影响是引起关注或兴趣的因素，但不会遭受教会和悬崖使用方法的弱点。我们在此报告的工作的源代码可在Github上免费获得。
Financial markets populated by human traders often exhibit "market impact", where the traders' quote-prices move in the direction of anticipated change, before any transaction has taken place, as an immediate reaction to the arrival of a large (i.e., "block") buy or sell order in the market: e.g., traders in the market know that a block buy order will push the price up, and so they immediately adjust their quote-prices upwards. Most major financial markets now involve many "robot traders", autonomous adaptive software agents, rather than humans. This paper explores how to give such trader-agents a reliable anticipatory sensitivity to block orders, such that markets populated entirely by robot traders also show market-impact effects. In a 2019 publication Church & Cliff presented initial results from a simple deterministic robot trader, ISHV, which exhibits this market impact effect via monitoring a metric of imbalance between supply and demand in the market. The novel contributions of our paper are: (a) we critique the methods used by Church & Cliff, revealing them to be weak, and argue that a more robust measure of imbalance is required; (b) we argue for the use of multi-level order-flow imbalance (MLOFI: Xu et al., 2019) as a better basis for imbalance-sensitive robot trader-agents; and (c) we demonstrate the use of the more robust MLOFI measure in extending ISHV, and also the well-known AA and ZIP trading-agent algorithms (which have both been previously shown to consistently outperform human traders). We demonstrate that the new imbalance-sensitive trader-agents introduced here do exhibit market impact effects, and hence are better-suited to operating in markets where impact is a factor of concern or interest, but do not suffer the weaknesses of the methods used by Church & Cliff. The source-code for our work reported here is freely available on GitHub.
