HETEROGENEOUS TRADING STRATEGY ENSEMBLING FOR INTRADAY TRADING ALGORITHMS

• Published in: South African journal of industrial engineering Vol. 34; no. 3; pp. 156 - 169
• Main Authors: Koegelenberg, Coetzee, van Vuuren, Jan
• Format: Journal Article
• Language: English
• Published: Bedfordview South African Institute for Industrial Engineering 01-11-2023; The Southern African Institute for Industrial Engineering
• Subjects: Algorithms; Automation; Competitive advantage; Decision making; Engineering, Industrial; Machine learning; Methods; Program trading; Securities markets; Strategy; Time series
• ISSN: 2224-7890; 1012-277X; 2224-7890
• DOI: 10.7166/34-3-2951

Since the inception of algorithmic trading during the mid-1970s, considerable resources and time have been committed by the financial sector to the development of trading algorithms in the hope of obtaining a competitive advantage over human contenders. A plethora of trading algorithms has been proposed in the literature; each algorithm is unique in its design, but little emphasis has been placed on heterogeneous trading strategy ensembling. In this paper we propose a trading strategy ensemble method for combining three different domain-specific trading strategies: a deterministic strategy, a probabilistic strategy, and a machine-learning strategy. The objective of the trading strategy ensemble is to find an appropriate trade-off between the levels of return and the risk exposure of a trader. We implement our strategy across different historical forex currency pair data in a bid to validate the trading strategy ensemble, and we analyse the results by invoking appropriate return and risk performance measures.