CmpQTS: Comparative Visual Analysis of Quantitative Timing Strategies

Mengqin He,Chaojian Zhang, Jingwei Lin,Jin Xu,Wenmin Lin,Zhigeng Pan

2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)(2022)

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摘要
Quantitative timing strategies play a significate role in quantitative investment. However, as various quantitative timing strategies have been proposed, the main challenge lies in the selection of effective quantitative timing strategies to achieve excess market returns. Even for experienced investors, it is still hard to hand-pick the right one. In this paper, we propose CmpQTS, which, to our best knowledge, is the first visualization system that attempts to assist users in comprehensively exploring the performances of different quantitative timing strategies. In particular, CmpQTS compares and evaluates different quantitative timing strategies at two levels: a Multi-Strategy level, for comparing different quantitative timing strategies; and a Single-Strategy level, for evaluating a strategy in different aspects. The effectiveness of the system is demonstrated by two case studies and a user experiment using the China A-Shares of Shanghai dataset consisting of 486 stocks for 22 years (2000-2021).
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Financial Data Visualization,Visual Comparison,Visual Analytics,Intelligent Visualization
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