基于价格趋势驱动的元学习算法在线投资组合策略

doi:10.11896/jsjkx.201100068

摘要/Abstract

摘要： 随着我国经济的迅速发展和国民可供分配收入的不断增加,人们投资需求变得更为强烈,如何高效合理地进行投资组合俨然成为投资者关注的热点问题。针对在线投资组合策略过于单一的价格预测和难以确定精准投资比例的问题,提出了基于价格趋势驱动的元学习算法在线投资组合策略(TPPT)。首先,考虑到股票价格异象的影响,提出了根据历史窗口期的等权重斜率值的三状态价格预测方法来追踪价格变化。其次,加入基于梯度投影的快速误差反向传播(BP)算法来求解投资比例。于是TPPT策略就将资产的增加能力反馈到投资比例上,以此来最大化累积财富。最后,5个典型数据的实证分析表明了TPPT策略在平衡风险与收益上占据较大的优势,是一种稳健且行之有效的在线投资组合策略。

关键词: 价格异象, 三状态价格, 梯度投影, 投资比例, 在线组合投资

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

中图分类号:

TP391

戴宏亮, 梁楚欣. 基于价格趋势驱动的元学习算法在线投资组合策略[J]. 计算机科学, 2021, 48(11A): 608-615. https://doi.org/10.11896/jsjkx.201100068

DAI Hong-liang, LIANG Chu-xin. Meta-learning Algorithm Based on Trend Promote Price Tracing Online Portfolio Strategy[J]. Computer Science, 2021, 48(11A): 608-615. https://doi.org/10.11896/jsjkx.201100068

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