Qlib: An AI-oriented Quantitative Investment Platform

TL;DR

Qlib is a comprehensive AI-oriented platform designed to advance quantitative investment by providing infrastructure and tools that address AI-related challenges and facilitate research and practical application in finance.

Contribution

The paper introduces Qlib, a new infrastructure platform that integrates AI technologies into quantitative investment workflows, addressing challenges and enabling innovation.

Findings

Enhanced research efficiency in quantitative investment

Improved infrastructure for AI-driven financial analysis

Facilitated adoption of AI in practical investment scenarios

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.