Principal Investigators

Project Summary

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.

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