  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.
