A machine learning approach to intraday trading on foreign exchange markets

Authors:
Andrei Hryshko;Tom Downs
Affiliations:
School of ITEE, University of Queensland, St. Lucia, QLD, Australia;School of ITEE, University of Queensland, St. Lucia, QLD, Australia
Venue:
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Year:
2005

Citing 5
Cited 0

Technical Note: \cal Q-Learning

Machine Learning
Neuro-Dynamic Programming

Neuro-Dynamic Programming
Intraday FX Trading: An Evolutionary Reinforcement Learning Approach

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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

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Abstract

Foreign Exchange trading has emerged in recent times as a significant activity in many countries. As with most forms of trading, the activity is influenced by many random parameters so that the creation of a system that effectively emulates the trading process will be very helpful. In this paper we try to create such a system using Machine learning approach to emulate trader behaviour on the Foreign Exchange market and to find the most profitable trading strategy.