RI: Small: When Algorithms Trade: Dynamics, Limits, and Economic Implications

RI：小：算法交易时：动态、限制和经济影响

• 批准号: 1421391
• 负责人: Michael Wellman
• 金额: $ 50万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421391&HistoricalAwards=false
• 关键词: RI; Small; When; Algorithms; Trade

Recent years have seen a dramatic increase in algorithmic trading, to the point that the majority of orders in major equity exchanges today are generated by machines without direct human control. Experience has shown that this automation--particularly at the extremes of high-frequency trading (HFT)--makes a qualitative difference, due to the unprecedented speed and lack of direct human control. The emergence of HFT raises fundamental issues for the efficiency, fairness, and stability of financial markets. The practice is highly controversial, yet the lack of scientific understanding of HFT's implications impedes informed public debate bearing on the question. Algorithmic trading can also be viewed as herald of a wave of automated behavior with far-reaching effects in a plethora of domains. Methodological improvements from this project advance our ability to anticipate effects of autonomous agents in other areas of major economic and societal impact.This project conducts a systematic computational study of algorithmic trading. The investigation combines online learning and optimization techniques from the point of view of theoretical machine learning and agent-based modeling (ABM) approaches to develop models of financial trading substantially more comprehensive and robust than heretofore possible. Modeling financial markets as multiagent systems affords heterogeneity: traders differing in objectives, information (access to data and observability of the environment), and response capability (processing and execution speed). Learning and decision-theoretic methods provide a principled basis for defining adaptive strategies that are effective across a broad range of operating conditions and possess guarantees in adversarial environments. Evidence on algorithmic trading implications is derived through systematic computational experimentation.The project contributes both to scientific knowledge about algorithmic trading, and to agent-based methodology for analyzing complex strategic domains. One particularly novel feature of this study is its emphasis on the effect of temporal structure (e.g., communication latency patterns, adaptive strategies) on the dynamics of algorithm interaction. The agent-based methodology developed here provides a unifying framework for selecting among candidate behaviors based on specified solution concepts, such as game-theoretic or evolutionary equilibria. It exploits ideas from several fields, including simulation modeling, stochastic search, statistical analysis, and machine learning.

近年来，算法交易急剧增加，以至于今天主要股票交易所的大多数订单都是由机器生成的，而没有直接的人类控制。 经验表明，这种自动化-特别是在高频交易（HFT）的极端情况下-由于前所未有的速度和缺乏直接的人为控制而产生了质的差异。 高频交易的出现对金融市场的效率、公平和稳定提出了根本性的问题。 这种做法极具争议性，但缺乏对高频交易影响的科学理解阻碍了对该问题的知情公众辩论。电子交易也可以被视为自动化行为浪潮的先驱，在众多领域产生深远影响。 该项目的方法改进提高了我们预测自主代理在其他主要经济和社会影响领域的影响的能力。该项目对算法交易进行了系统的计算研究。 该调查结合了在线学习和优化技术，从理论机器学习和基于代理的建模（ABM）方法的角度来开发比迄今为止更全面和强大的金融交易模型。 将金融市场建模为多智能体系统提供了异质性：交易者在目标、信息（对数据的访问和环境的可观察性）和响应能力（处理和执行速度）方面存在差异。 学习和决策理论方法为定义适应性策略提供了原则性基础，这些策略在广泛的操作条件下有效，并在对抗性环境中具有保证。 通过系统的计算实验得出算法交易影响的证据。该项目有助于有关算法交易的科学知识，以及分析复杂战略领域的基于代理的方法。 这项研究的一个特别新颖的特点是它强调时间结构的影响（例如，通信延迟模式、自适应策略）对算法交互的动态性的影响。 在这里开发的基于代理的方法提供了一个统一的框架，根据指定的解决方案的概念，如博弈论或进化均衡的候选行为之间进行选择。 它利用了几个领域的想法，包括模拟建模，随机搜索，统计分析和机器学习。

项目成果

Iterated Deep Reinforcement Learning in Games: History-Aware Training for Improved Stability

游戏中的迭代深度强化学习：历史感知训练以提高稳定性

• DOI: 10.1145/3328526.3329634
• 发表时间: 2019
• 期刊: 20th ACM Conference on Economics and Computation
• 作者: Wright, Mason;Wang, Yongzhao;Wellman, Michael P.
• 通讯作者: Wellman, Michael P.