ML-TEA: A set of quantitative investment algorithms based on machine learning and technical analysis

LI Bin, LIN Yan, TANG Wenxuan

Systems Engineering - Theory & Practice ›› 2017, Vol. 37 ›› Issue (5) : 1089-1100.

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Systems Engineering - Theory & Practice ›› 2017, Vol. 37 ›› Issue (5) : 1089-1100. DOI: 10.12011/1000-6788(2017)05-1089-12

ML-TEA: A set of quantitative investment algorithms based on machine learning and technical analysis

  • LI Bin1, LIN Yan1, TANG Wenxuan2
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Abstract

Quantitative investment is an important application of system engineering in the financial investment area. Quantitative investment tries to automatically invest on securities using computational algorithms, and to obtain excess return. This paper proposes a novel quantitative trading algorithm based on machine learning and technical analysis, named "ML-TEA" (machine learning and technical analysis). ML-TEA predicts the stock's movements using the technical indicators calculated by prices and volumes. The empirical results show that firstly, three strategies can obtain an annual return of 25%, which outperforms the index's 10.60% and buy and hold's 3%, and the state of the art algorithms. The three algorithms also significantly outperform the benchmarks and the state of the art in terms of risk adjusted return, i.e., Sharpe ratio, Treynor ratio, and Jensen's alpha. Secondly, Ada-TEA and SVM-TEA can resist reasonable transaction costs that are much higher than the actual transaction costs.

Key words

quantitative investments / machine learning / technical analysis

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LI Bin , LIN Yan , TANG Wenxuan. ML-TEA: A set of quantitative investment algorithms based on machine learning and technical analysis. Systems Engineering - Theory & Practice, 2017, 37(5): 1089-1100 https://doi.org/10.12011/1000-6788(2017)05-1089-12

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Funding

National Natural Science Foundation of China (71401128, 91646206);Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry;Academic Team Building Plan for Young Scholars from Wuhan University (16WSKTD008)
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