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

近年来，算法交易大幅增加，以至于目前主要证券交易所的大多数订单都是由机器产生的，没有直接的人类控制。经验表明，这种自动化--尤其是在高频交易(HFT)的极端情况下--产生了质的不同，因为它的速度前所未有，而且缺乏直接的人类控制。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，但对高频交易的影响缺乏科学理解，阻碍了与这一问题有关的知情公众辩论。算法交易也可以被视为自动化行为浪潮的先兆，在太多的领域产生深远的影响。这个项目在方法上的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。这个项目对算法交易进行了系统的计算研究。这项研究从理论机器学习的角度结合了在线学习和优化技术，以及基于代理的建模(ABM)方法，以开发出比以往更全面和更稳健的金融交易模型。将金融市场建模为多主体系统提供了异质性：交易者在目标、信息(获取数据和环境的可观察性)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确定适应战略提供了原则性基础，这些战略在广泛的业务条件下有效，并在对抗性环境中具有保障。关于算法交易含义的证据是通过系统的计算实验得出的。该项目既有助于算法交易的科学知识，也有助于基于代理的方法来分析复杂的战略领域。这项研究的一个特别新颖的特点是它强调时间结构(例如，通信延迟模式、自适应策略)对算法交互动态的影响。这里开发的基于代理的方法提供了一个统一的框架，用于根据特定的解决方案概念(如博弈论或进化均衡)在候选行为中进行选择。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。

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.