Designing Adaptive Trading Agents DAVID PARDOE Yahoo! Labs and PETER STONE The University of Texas at Austin This extended abstract summarizes the research presented in Dr. Pardoe s recently-completed Ph.D. thesis [Pardoe 2011]. The thesis considers how adaptive trading agents can take advantage of previous experience (real or simulated) in other markets while remaining robust in the face of novel situations in a new market. Its contributions are at the intersection of machine learning and electronic commerce, with particular focus on transfer learning and fully autonomous trading agents. Categories and Subject Descriptors: I.2.6 [Computing Methods]: Arti cial Intelligence General Terms: Algorithms, Design, Economics, Experimentation Additional Key Words and Phrases: agents, auctions, machine learning, Trading Agent Competition Along with the growth of electronic commerce has come an interest in developing autonomous trading agents. In many situations, such agents must interact directly with other (human or automated) market participants, and so the behavior of these participants must be taken into account when designing agent strategies. There are two commonly used approaches to doing so: the game-theoretic approach of nding an equilibrium, and the empirical approach of using historical market data to create a complete model of the market. These approaches
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Designing adaptive trading agents
Pardoe, David; Stone, Peter
Follow ACM SIGecom Exchanges
, Volume 10 (2)
Association for Computing Machinery – Jun 1, 2011
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