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Qlib: An AI-oriented Quantitative Investment Platform

Author

Listed:
  • Xiao Yang
  • Weiqing Liu
  • Dong Zhou
  • Jiang Bian
  • Tie-Yan Liu

Abstract

Quantitative investment aims to maximize the return and minimize the risk in a sequential trading period over a set of financial instruments. Recently, inspired by rapid development and great potential of AI technologies in generating remarkable innovation in quantitative investment, there has been increasing adoption of AI-driven workflow for quantitative research and practical investment. In the meantime of enriching the quantitative investment methodology, AI technologies have raised new challenges to the quantitative investment system. Particularly, the new learning paradigms for quantitative investment call for an infrastructure upgrade to accommodate the renovated workflow; moreover, the data-driven nature of AI technologies indeed indicates a requirement of the infrastructure with more powerful performance; additionally, there exist some unique challenges for applying AI technologies to solve different tasks in the financial scenarios. To address these challenges and bridge the gap between AI technologies and quantitative investment, we design and develop Qlib that aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment.

Suggested Citation

  • Xiao Yang & Weiqing Liu & Dong Zhou & Jiang Bian & Tie-Yan Liu, 2020. "Qlib: An AI-oriented Quantitative Investment Platform," Papers 2009.11189, arXiv.org.
    • Handle: RePEc:arx:papers:2009.11189
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    References listed on IDEAS

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    Cited by:

    1. Wentao Zhang & Yilei Zhao & Shuo Sun & Jie Ying & Yonggang Xie & Zitao Song & Xinrun Wang & Bo An, 2023. "Reinforcement Learning with Maskable Stock Representation for Portfolio Management in Customizable Stock Pools," Papers 2311.10801, arXiv.org, revised Feb 2024.
    2. Lifan Zhao & Shuming Kong & Yanyan Shen, 2023. "DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting," Papers 2306.09862, arXiv.org, revised Apr 2024.
    3. Shuo Yu & Hongyan Xue & Xiang Ao & Feiyang Pan & Jia He & Dandan Tu & Qing He, 2023. "Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning," Papers 2306.12964, arXiv.org.
    4. Lili Wang & Chenghan Huang & Chongyang Gao & Weicheng Ma & Soroush Vosoughi, 2023. "Joint Latent Topic Discovery and Expectation Modeling for Financial Markets," Papers 2307.08649, arXiv.org.
    5. Wentao Xu & Weiqing Liu & Lewen Wang & Yingce Xia & Jiang Bian & Jian Yin & Tie-Yan Liu, 2021. "HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information," Papers 2110.13716, arXiv.org, revised Jan 2022.
    6. Yuan Gao & Haokun Chen & Xiang Wang & Zhicai Wang & Xue Wang & Jinyang Gao & Bolin Ding, 2024. "DiffsFormer: A Diffusion Transformer on Stock Factor Augmentation," Papers 2402.06656, arXiv.org.
    7. Liang Zeng & Lei Wang & Hui Niu & Ruchen Zhang & Ling Wang & Jian Li, 2021. "Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Iterative Refinement Labeling," Papers 2107.11972, arXiv.org, revised May 2023.
    8. Shuo Sun & Rundong Wang & Bo An, 2022. "Quantitative Stock Investment by Routing Uncertainty-Aware Trading Experts: A Multi-Task Learning Approach," Papers 2207.07578, arXiv.org.
    9. Jonghun Kwak & Jungyu Ahn & Jinho Lee & Sungwoo Park, 2022. "Shai-am: A Machine Learning Platform for Investment Strategies," Papers 2207.00436, arXiv.org.
    10. Traianos-Ioannis Theodorou & Alexandros Zamichos & Michalis Skoumperdis & Anna Kougioumtzidou & Kalliopi Tsolaki & Dimitris Papadopoulos & Thanasis Patsios & George Papanikolaou & Athanasios Konstanti, 2021. "An AI-Enabled Stock Prediction Platform Combining News and Social Sensing with Financial Statements," Future Internet, MDPI, vol. 13(6), pages 1-22, May.

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