Strategic sequential bidding in auctions using dynamic programming
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Performance analysis of a counter-intuitive automated stock-trading agent
ICEC '03 Proceedings of the 5th international conference on Electronic commerce
The Penn-Lehman Automated Trading Project
IEEE Intelligent Systems
Learning to trade via direct reinforcement
IEEE Transactions on Neural Networks
Designing safe, profitable automated stock trading agents using evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Rational Bidding Using Reinforcement Learning
GECON '08 Proceedings of the 5th international workshop on Grid Economics and Business Models
Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services
OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part I on On the Move to Meaningful Internet Systems:
A framework for autonomic networked auctions
Proceedings of the 2007 Workshop on INnovative SERvice Technologies
Stronger CDA strategies through empirical game-theoretic analysis and reinforcement learning
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Autonomous Agents and Multi-Agent Systems
Towards automated trading based on fundamentalist and technical data
SBIA'10 Proceedings of the 20th Brazilian conference on Advances in artificial intelligence
Annals of Mathematics and Artificial Intelligence
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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.