Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Enhancing Q-learning for optimal asset allocation
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Computational learning techniques for intraday FX trading using popular technical indicators
IEEE Transactions on Neural Networks
System for foreign exchange trading using genetic algorithms and reinforcement learning
International Journal of Systems Science
Interday foreign exchange trading using linear genetic programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Genetic Programming and Evolvable Machines: ten years of reviews
Genetic Programming and Evolvable Machines
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
A machine learning approach to intraday trading on foreign exchange markets
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
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We have previously described trading systems based on unsupervised learning approaches such as reinforcement learning and genetic algorithms which take as input a collection of commonly used technical indicators and generate profitable trading decisions from them. This article demonstrates the advantages of applying evolutionary algorithms to the reinforcement learning problem using a hybrid credit assignment approach. In earlier work, the temporal difference reinforcement learning approach suffered from problems with overfitting the in-sample data. This motivated the present approach.Technical analysis has been shown previously to have predictive value regarding future movements of foreign exchange prices and this article presents methods for automated high-frequency FX trading based on evolutionary reinforcement learning about signals from a variety of technical indicators. These methods are applied to GBPUSD, USDCHF and USDJPY exchange rates at various frequencies. Statistically significant profits are made consistently at transaction costs of up to 4bp for the hybrid system while the standard RL is only able to trade profitably up to about 1bp slippage per trade.