Qlib: An AI-oriented Quantitative Investment Platform
This work addresses infrastructure gaps for AI-driven quantitative investment, which is incremental as it builds on existing AI methodologies in finance.
The authors tackled the challenge of integrating AI technologies into quantitative investment by developing Qlib, a platform designed to upgrade infrastructure and handle data-driven workflows, resulting in a tool that aims to enhance research and practical investment efficiency.
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.