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Computational learning techniques for intraday FX trading using popular technical indicators
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Learning to trade via direct reinforcement
IEEE Transactions on Neural Networks
Review: Expert systems and evolutionary computing for financial investing: A review
Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
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IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
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| Hi-index | 12.05 |
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 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 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.