DEVELOPMENT OF AN OPTIMIZED INTELLIGENT MACHINE LEARNING APPROACH IN FOREX TRADING USING MOVING AVERAGE INDICATORS

O. E. Aru; C. C. Okechukwu

O. E. Aru Department of Computer Engineering, College of Engineering and Engineering Technology, Michael Okpara University of Agriculture, Umudike.
C. C. Okechukwu Department of Computer Engineering, College of Engineering and Engineering Technology, Michael Okpara University of Agriculture, Umudike

Keywords: Expert Advisor, Forex Trading, Genetic algorithm, Machine Learning, Moving Averages, Meta Trader

Abstract

This research presents the development of an optimized intelligent machine learning approach in Forex trading using two variants of Moving Average indicators. The main aim of the Expert Advisor (EA) development is to introduce a new intelligent model for automated execution of trades in the Forex market, reducing potential losses due to human errors and sentimental factors in trading Forex. In developing this trading model, Momentum strategy was used since it takes advantage of market swings, along with Machine Learning - Genetic algorithm, being a type of supervised learning used in training the past historical data based on selected trading parameters in a Meta Trader 4 (MT4) platform. The new Expert Advisor –Exponential Moving Average (ESMA) was built using the MQL4 language which is based on C++ for programming specific trading strategies and easily facilitates automated trading. The result is an optimized intelligent trading system that implements the intersection of the two moving averages at various periods, to execute trades autonomously with a profit pass rate of 75% visible from the Optimization chart of the MetaTrader 4 (MT4) platform