Generating and Optimizing Human-Readable Quantitative Program Trading Strategies through a Genetic Programming Framework

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@Article{TENG:2021:PCS,

• author = "Bin Teng and Yufeng Shi and Xin Wang and Yunchuan Sun",
• title = "Generating and Optimizing Human-Readable Quantitative Program Trading Strategies through a Genetic Programming Framework",
• journal = "Procedia Computer Science",
• volume = "187",
• pages = "613--617",
• year = "2021",
• note = "2020 International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI2020",
• keywords = "genetic algorithms, genetic programming, Quantitative program trading, Expression tree, Regularization",
• ISSN = "1877-0509",
• URL = "https://www.sciencedirect.com/science/article/pii/S1877050921009200",
• DOI = "10.1016/j.procs.2021.04.112",
• size = "5 pages",
• abstract = "we provide a highly flexible genetic programming framework for automatic generation and optimization of program trading strategies. We propose the input/output modules and their implementation methods, decoupled from the GP kernel, making it a priori-posteriori framework for trading practitioners. For human-readable purposes, we also give various empirical regularization methods, including NSGA-II multi-objective selection, as well as experimentally effective performance measures",
• notes = "Institute for Financial Studies, Shandong University, Jinan 250100, China",

}

Genetic Programming entries for Bin Teng Yufeng Shi Xin Wang Yunchuan Sun

Citations