This paper documents the development of three autonomous stock-trading agents within the framework of the Penn Exchange Simulator (PXS), a novel stock-trading simulator that takes advantage of electronic crossing networks to realistically mix agent bids with bids from the real stock market [1]. The three approaches presented take inspiration from reinforcement learning, myopic trading using regression-based price prediction, and market making, These approaches are fully implemented and tested with results reported here, including individual evaluations using a fixed opponent strategy and a comparative analysis of the strategies in a joint simulation, The market-making strategy described in this paper was the winner in the fall 2003 PLAT live competition and the runner-up in the spring 2004 live competition, exhibiting consistent profitability. The strategy's performance in the live competitions is presented and analyzed. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Sherstov, A. A., & Stone, P. (2005). Three automated stock-trading agents: A comparative study. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3435 LNAI, pp. 173–187). https://doi.org/10.1007/11575726_13
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