- author = "M. A. H. Dempster and V. Leemans",
- title = "An automated {FX} trading system using adaptive reinforcement learning",
- journal = "Expert Systems with Applications",
- year = "2006",
- volume = "30",
- number = "3",
- pages = "543--552",
- month = apr,
- note = "Special Issue on Financial Engineering",
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/2004/WP18.pdf",
-
DOI = "
doi:10.1016/j.eswa.2005.10.012",
- size = "10 pages",
- abstract = "This paper introduces adaptive reinforcement learning (ARL) as the basis for a fully automated trading system application. The system is designed to trade foreign exchange (FX) markets and relies on a layered structure consisting of a machine learning algorithm, a risk management overlay and a dynamic utility optimisation layer. An existing machine-learning method called recurrent reinforcement learning (RRL) was chosen as the underlying algorithm for ARL. One of the strengths of our approach is that the dynamic optimization layer makes a fixed choice of model tuning parameters unnecessary. It also allows for a risk-return trade-off to be made by the user within the system. The trading system is able to make consistent gains out-of-sample while avoiding large draw-downs.",
-
notes = "Centre for Financial Research, Judge Business School,
University of Cambridge & Cambridge Systems Associates
Limited, Cambridge, UK Also technical report
WP18/2004
",
