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Evolving Trading Strategies Using Directional Changes

Kampouridis, Michael, Otero, Fernando E.B. (2017) Evolving Trading Strategies Using Directional Changes. Expert Systems with Applications, 73 . pp. 145-160. ISSN 0957-4174. (doi:10.1016/j.eswa.2016.12.032) (KAR id:59758)

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The majority of forecasting methods use a physical time scale for studying price fluctuations of financial markets, making the flow of physical time discontinuous. Therefore, using a physical time scale may expose companies to risks, due to ignorance of some significant activities. In this paper, an alternative and original approach is explored to capture important activities in the market. The main idea is to use an event-based time scale based on a new way of summarising data, called Directional Changes. Combined with a genetic algorithm, the proposed approach aims to find a trading strategy that maximises profitability in foreign exchange markets. In order to evaluate its efficiency and robustness, we run rigorous experiments on 255 datasets from six different currency pairs, consisting of intra-day data from the foreign exchange spot market. The results from these experiments indicate that our proposed approach is able to generate new and profitable trading strategies, significantly outperforming other traditional types of trading strategies, such as technical analysis and buy and hold.

Item Type: Article
DOI/Identification number: 10.1016/j.eswa.2016.12.032
Uncontrolled keywords: Directional changes; Financial forecasting; Algorithmic trading; Genetic algorithm
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Michael Kampouridis
Date Deposited: 28 Dec 2016 22:05 UTC
Last Modified: 16 Feb 2021 13:41 UTC
Resource URI: (The current URI for this page, for reference purposes)
Kampouridis, Michael:
Otero, Fernando E.B.:
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