Quantitative Trading Machine Learning Using Differential Evolution Algorithm
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Author list: Vinitnantharat N., Inchan N., Sakkumjorn T., Doungjitjaroen K., Worasucheep C.
Publisher: Hindawi
Publication year: 2019
Start page: 230
End page: 235
Number of pages: 6
ISBN: 9781728107196
ISSN: 0146-9428
eISSN: 1745-4557
Languages: English-Great Britain (EN-GB)
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Abstract
Technical analysis, which is commonly used world-wide, employs trading price data to calculate technical indicators. These indicators assist investors in making decision on buy/sell timing for the highest profit. However, there are no such golden rules to guarantee the highest profit from using technical indicators despite hundreds of research in this field. In this research, a quantitative trading machine learning (QTML) was proposed to suggest buying or selling time for a stock. QTML employs Differential Evolution algorithm to analyze trading signals from a set of widely-used technical indicators. Daily stock trading information from different industries during 2014-2019 from Stock Exchange of Thailand (SET) are used for evaluating its performance compared with the buy-And-hold strategy. The experiment is with 0.2% commission fee for all buying and selling transactions. The result shows that the proposed QTML model can assist investors in making a higher profit, particularly in stocks with fluctuated or downward trends. ฉ 2019 IEEE.
Keywords
quantitative trading, technical analysis
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