Multiple strategies for trading short-term stock index futures based on visual trend bands

• Published in: Multimedia tools and applications 2021-11, Vol.80 (28-29), p.35481-35494
• Main Authors: Chou, Hsien-Ming, Hung, Chihli
• Format: Article
• Language: English
• Subjects: 1166: Advances of machine learning in data analytics and visual information processing; Algorithms; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Employment; Futures; Investment strategy; Machine learning; Multimedia Information Systems; Optimization; Special Purpose and Application-Based Systems
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-020-10496-2

Many day traders focus on forecasts of stock index futures. These securities are suitable for frequent and time-sensitive trading as well as for short-term investments. However, most day traders’ strategies are based on their experiences or news headlines. Combined with a pool trading policy, this may lead to unsatisfactory average monthly profit, particularly when compared to the opportunity cost of the traders’ full-time employment in other non-trading jobs. This paper represents multiple investment strategies for day traders based on visual trend bands on short-term stock index futures. This study uses sequential minimal optimization and other machine learning algorithms to evaluate the performance of visual trend bands and derive strategies for better predictions. This study also applies empirical methods on short-term stock index futures datasets to explore the impact of visual trend bands on short-term stock index trading. The accuracy of our proposed visual trend bands reaches 82%, which is not only an objectively high forecasting accuracy rate but also substantially higher than other visual trend bands. The proposed visual trend bands can support day traders in realizing higher profits in their day trades and short-term investments.