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

Qlib: An AI-oriented Quantitative Investment Platform