A memetic-based technical indicator portfolio and parameters optimization approach for finding trading signals to construct transaction robot in smart city era

IF 0.9 4区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Intelligent Data Analysis Pub Date : 2023-08-10 DOI:10.3233/ida-220755

C.H. Chen, S. Hung, P.T. Chen, C.S. Wang, R.D. Chiang

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引用次数: 1

Abstract

With the development of smart cities, the demand for personal financial services is becoming more and more importance, and personal investment suggestion is one of them. A common way to reach the goal is using a technical indicator to form trading strategy to find trading signals as trading suggestion. However, using only a technical indicator has its limitations, a technical indicator portfolio is further utilized to generate trading signals for achieving risk aversion. To provide a more reliable trading signals, in this paper, we propose an optimization algorithm for obtaining a technical indicator portfolio and its parameters for predicting trends of target stock by using the memetic algorithm. In the proposed approach, the genetic algorithm (GA) and simulated annealing (SA) algorithm are utilized for global and local search. In global search, a technical indicator portfolio and its parameters are first encoded into a chromosome using a bit string and real numbers. Then, the initial population is generated based on the encoding scheme. Fitness value of a chromosome is evaluated by the return and risk according to the generated trading signals. In local search, SA is employed to tune parameters of indicators in chromosomes. After that, the genetic operators are continue employed to generate new offspring. Finally, the chromosome with the highest fitness value could be provided to construct transaction robot for making investment plans in smart city environment. Experiments on three real datasets with different trends were made to show the effectiveness of the proposed approach, including uptrend, consolidation, and downtrend. The total returns of them on testing datasets are 26.53% 33.48%, and 9.7% that indicate the proposed approach can not only reach risk aversion in downtrends but also have good returns in others.

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基于模因的技术指标组合和参数优化方法寻找交易信号构建智能城市时代的交易机器人

随着智慧城市的发展，对个人金融服务的需求越来越重要，个人投资建议就是其中之一。达到目标的一种常用方法是利用技术指标形成交易策略，寻找交易信号作为交易建议。然而，仅使用技术指标有其局限性，进一步利用技术指标组合来产生交易信号，以实现风险规避。为了提供更可靠的交易信号，本文提出了一种利用模因算法获得技术指标组合及其参数以预测目标股票走势的优化算法。该方法采用遗传算法(GA)和模拟退火算法(SA)进行全局和局部搜索。在全局搜索中，首先使用位串和实数将技术指标组合及其参数编码到染色体中。然后，根据编码方案生成初始种群。根据生成的交易信号，用收益和风险来评估染色体的适应度值。在局部搜索中，利用SA来调整染色体中指标的参数。之后，继续使用遗传算子来产生新的后代。最后，提供适应度值最高的染色体构建交易机器人，用于智慧城市环境下的投资规划。在三个具有不同趋势的真实数据集上进行了实验，验证了该方法的有效性，包括上升趋势、巩固趋势和下降趋势。它们在测试数据集上的总回报率分别为26.53%、33.48%和9.7%，表明本文提出的方法不仅可以在下行趋势中达到风险规避，而且在其他趋势中也有良好的回报。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Intelligent Data Analysis 工程技术-计算机：人工智能

CiteScore

2.20

自引率

5.90%

发文量

审稿时长

3.3 months

期刊介绍： Intelligent Data Analysis provides a forum for the examination of issues related to the research and applications of Artificial Intelligence techniques in data analysis across a variety of disciplines. These techniques include (but are not limited to): all areas of data visualization, data pre-processing (fusion, editing, transformation, filtering, sampling), data engineering, database mining techniques, tools and applications, use of domain knowledge in data analysis, big data applications, evolutionary algorithms, machine learning, neural nets, fuzzy logic, statistical pattern recognition, knowledge filtering, and post-processing. In particular, papers are preferred that discuss development of new AI related data analysis architectures, methodologies, and techniques and their applications to various domains.