Ye, Ailun, Chinthalapati, V. L. Raju, Serguieva, Antoaneta and Tsang, Edward (2018) Developing sustainable trading strategies using directional changes with high frequency data. In: 2017 IEEE International Conference on Big Data (Big Data). IEEE, pp. 4265-4271. ISBN 978-1538627167 (doi:https://doi.org/10.1109/BigData.2017.8258453)

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Abstract

Market prices are traditionally recorded in fixed time intervals. Directional Change is an alternative approach to summarize price movements in financial markets that is consistent with across all time scales. Unlike time series, directional change summarizes the big data in finance by focusing on the intrinsic time of the data. This captures deeper intrinsic data qualities and thus trading strategies based on directional change are more sustainable and less disruptive. In this paper, we propose four trading strategies using the concept of directional change and explore the combination with technical analysis. The trading strategies are tested using EUR/USD and GBP/USD high frequency FX market data. Empirical results show good performance of our trading strategies based on thresholds, and that combining with technical analysis brings further improvement.

Item Type:	Conference Proceedings
Title of Proceedings:	2017 IEEE International Conference on Big Data (Big Data)
Additional Information:	Conference held from 11-14 December 2017, Boston, MA, USA.
Uncontrolled Keywords:	FX trading; directional changes; sustainable trading strategies
Subjects:	H Social Sciences > HF Commerce > HF5601 Accounting
Faculty / School / Research Centre / Research Group:	Faculty of Business Faculty of Business > Department of Accounting & Finance
Last Modified:	12 Mar 2020 15:42
URI:	http://gala.gre.ac.uk/id/eprint/19924

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