Abstract
In the study of financial phenomena, agent-based artificial markets are efficient tools for testing economic assumptions of market regulation. While it is easy to populate these virtual worlds with “basic” chartist agents using price history (increase or decrease of prices, moving averages...), it is nevertheless necessary, in order to study rationality phenomena and influence between agents, to add some kind of learning agents. Several authors have of course already been interested in adaptive techniques but they mainly take into account price history. But prices are only consequences of orders and therefore reasoning about orders provides a head start in the deductive process. In this paper we show how to take into account all of the information about the market, including how to leverage the information from order books such as the best limits, size of bid-ask spread or cash at hand waiting to accommodate more effectively to market offerings. Like B. Arthur we focus here on the use of LCS agents.
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Mathieu, P., Gaciarz, M. (2013). Improving Classifier Agents with Order Book Information. In: Demazeau, Y., Ishida, T., Corchado, J.M., Bajo, J. (eds) Advances on Practical Applications of Agents and Multi-Agent Systems. PAAMS 2013. Lecture Notes in Computer Science(), vol 7879. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38073-0_18
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