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The Use of Dynamically Optimised High Frequency Moving Average Strategies for Intraday Trading

Authors: Abdalla Kablan, Joseph Falzon


This paper is motivated by the aspect of uncertainty in financial decision making, and how artificial intelligence and soft computing, with its uncertainty reducing aspects can be used for algorithmic trading applications that trade in high frequency. This paper presents an optimized high frequency trading system that has been combined with various moving averages to produce a hybrid system that outperforms trading systems that rely solely on moving averages. The paper optimizes an adaptive neuro-fuzzy inference system that takes both the price and its moving average as input, learns to predict price movements from training data consisting of intraday data, dynamically switches between the best performing moving averages, and performs decision making of when to buy or sell a certain currency in high frequency.

Keywords: financial decision making, high frequency trading, Adaprive neuro-fuzzy systems, moving average strategy

Digital Object Identifier (DOI):

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