Abstract
In this paper we describe an infrastructure for implementing hybrid intelligent agents with the ability to trade in the Forex Market without requiring human supervision. This infrastructure is composed of three modules. The “Intuition Module”, implemented using an Ensemble Model, is responsible for performing pattern recognition and predicting the direction of the exchange rate. The “A Posteriori Knowledge Module”, implemented using a Case-Based Reasoning System, enables the agents to learn from empirical experience and is responsible for suggesting how much to invest in each trade. The “A Priori Knowledge Module”, implemented using a Rule-Based Expert System, enables the agents to incorporate non-experiential knowledge in their trading decisions. This infrastructure was used to develop an agent capable of trading the USD/JPY currency pair with a 6 hours timeframe. The agent’s simulated and live trading results lead us to believe our infrastructure can be of practical interest to the traditional trading community.
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© 2008 Springer-Verlag Berlin Heidelberg
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Barbosa, R.P., Belo, O. (2008). Autonomous Forex Trading Agents. In: Perner, P. (eds) Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects. ICDM 2008. Lecture Notes in Computer Science(), vol 5077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70720-2_30
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DOI: https://doi.org/10.1007/978-3-540-70720-2_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70717-2
Online ISBN: 978-3-540-70720-2
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