Published on Tue Sep 22 2020

Qlib: An AI-oriented Quantitative Investment Platform

Xiao Yang, Weiqing Liu, Dong Zhou, Jiang Bian, Tie-Yan Liu

Quantitative investment aims to maximize the return and minimize the risk in a set of financial instruments. Qlib aims to realize the potential, power the research, and create the value of AI technologies in quantitative investment.

0
0
0
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.

Wed May 19 2021
Machine Learning
Robo-Advising: Enhancing Investment with Inverse Optimization and Deep Reinforcement Learning
Machine Learning (ML) has been embraced as a powerful tool by the financial industry. We propose a full-cycle data-driven investment robo-advising framework. The proposed investment pipeline is applied on real market data from April 1, 2016 to February 1, 2021.
12
1
0
Sun Aug 26 2018
Artificial Intelligence
FinBrain: When Finance Meets AI 2.0
Financial intelligence has elicited much attention from the AI 2.0 era. In our current dynamic capital market, financial intelligence demonstrates a fast and accurate machine learning capability.
0
0
0
Fri Jul 10 2020
Artificial Intelligence
Data science and AI in FinTech: An overview
Financial technology (FinTech) has been playing an increasingly critical role in driving modern economies, society, technology, and many other areas. Smart FinTech is the new-generation FinTech, largely inspired and empowered by data science and new- generation AI and (DSAI) techniques.
2
0
0
Thu Nov 19 2020
Machine Learning
FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance
The FinRL library allows users to streamline their own developments and compare with existing schemes easily. Within FinRL, virtual environments are configured with stock market datasets, trading agents are trained with neural networks, and extensive backtesting is analyzed via trading performance.
3
11
16
Wed Jul 24 2019
Machine Learning
AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks
AlphaStock is a novel reinforcement learning (RL) based investment strategy enhanced by interpretable deep attention networks. The experiments on long-periodic U.S. and Chinese markets demonstrate the effectiveness and robustness of AlphaStock over diverse market states.
0
0
0
Mon Jul 08 2019
Artificial Intelligence
An intelligent financial portfolio trading strategy using deep Q-learning
An intelligent agent is trained to identify an optimal trading action by using deep Q-learning. We formulate a Markov decision process model for the portfolio trading process. The model adopts a discrete combinatorial action space, determining the trading direction at prespecified trading size.
0
0
0