期刊名称：Finance Publications / Centre for Financial Research, Cambridge University
出版年度：2004
卷号：2004
出版社：Cambridge University
摘要：This paper introduces adaptive reinforcement learning (ARL) as the basis for a fully
automated trading system application. The system is designed to trade FX markets and
relies on a layered structure consisting of a machine learning algorithm, a risk management
overlay and a dynamic utility optimization 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 ¯xed choice of model tuning parameters unnecessary. It also allows for a risk-return
trade-o® 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.
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