The Penn-Lehman Automated Trading Project
IEEE Intelligent Systems
Reinforcement learning for optimized trade execution
ICML '06 Proceedings of the 23rd international conference on Machine learning
Three automated stock-trading agents: a comparative study
AAMAS'04 Proceedings of the 6th AAMAS international conference on Agent-Mediated Electronic Commerce: theories for and Engineering of Distributed Mechanisms and Systems
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Autonomous trading is often seen as artificial intelligence applied to finance by AI researchers, but it may also be a way to motivate the development of autonomous agents, just like robot soccer competitions are used to motivate the research in mobile robots. In fact, some initiatives could be observed in recent years, for instance [1] and [2]. In this paper, we present a multiagent system composed by several autonomous analysts that use fundamentalist information in their reasoning process. These fundamentalist information are composed by company profit, dividends, data related to the company economic sector among others. This kind of information is rarely used on autonomous trading, because most of the agents deal only with technical information, which is composed by price and volume time series. Furthermore, we do not find a open source stock market simulator with support to fundamentalist trader agents. We then created a significantly extended version of the open source financial market simulation tool, called AgEx. This designed version provides also fundamentalist information about the trader's assets. As well as, makes more efficient the exchange of messages within AgEx. This efficiency allows traders that may submit orders in very short intervals of just some seconds or even some fraction of second, to use AgEx as a test platform. Using this new version of AgEx, we implemented and tested the multiagent system based on fundamentalist agents, that we call FAS. The achieved results are presented and analyzed.