Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 608-615.doi: 10.11896/jsjkx.201100068

• Interdiscipline & Application • Previous Articles     Next Articles

Meta-learning Algorithm Based on Trend Promote Price Tracing Online Portfolio Strategy

DAI Hong-liang, LIANG Chu-xin   

  1. School of Economics and Statistics,Guangzhou University,Guangzhou 510006,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:DAI Hong-liang,born in 1978,Ph.D,professor,postdoctoral supervisor,is a member of China Computer Federation.His main research interests include machine learning,data mining,pattern recognition and bioinformatics.
  • Supported by:
    General Projects of National Social Science Foundation(18BTJ029).

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Abstract: With the rapid development of China's economy and the continuous increase of income available for distribution,people's investment demand has become more intense,and how to efficiently and rationally carry out investment portfolio has become a hot issue for investors.In response to the problem that online portfolio strategy predict stock price singly and is difficult to determine its accurate investment proportion,we propose a meta-learning algorithm based on Trend Promote Price Tracing (TPPT).Firstly,considering the influence of stock price anomalies,a three-state price prediction method which is based on the equal-weight slope value of the historical window period,is proposed to track the price changes.Secondly,the Error Back Propagation (BP) algorithm based on gradient projection is added to solve the investment ratio.Thus,TPPT strategy maximizes the cumulative wealth by feeding back the increasing capacity of assets to the investment ratio.Finally,the empirical analysis of five typical data shows that TPPT strategy has a great advantage in balancing risk and return,it is shown that TPPT strategy is a robust and effective online portfolio strategy.

Key words: Gradient projection, Investment proportion, Online portfolio investment, Price anomalies, Three-state price

CLC Number: 

  • TP391
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