Prerequisite

Investments, Financial Mathematics, Applied Stochastic Processes, Linear Regression, Applied Time Series Analysis, Machine Learning

Abstract

In recent years, the theory and application of quantitative trading have made great progress. The application of quantitative trading in global financial markets is becoming more and more common. Artificial intelligence, alternative data and high-frequency trading are widely used by quantitative trading institutions. The purpose of this course is to enable graduate students to master the mathematical models, algorithms and optimization, trading strategies, market microstructure and high-frequency trading, artificial intelligence technology and other cutting-edge content of quantitative trading from the theoretical and technical level, and to cultivate the application and practical skills of quantitative trading for graduate students. The course covers the following four topics: 1.Factor Investment It includes introduction of factor investment, quantitative portfolio management, mainstream factors interpretation, anomaly factors interpretation, high frequency factors interpretation, alternative factors interpretation, advanced factor investment and so on. 2.Quantitative Trading Strategies It includes futures arbitrage strategies, CTA strategies, statistical arbitrage strategies, option strategies, fixed income strategies, macro strategies and other quantitative trading strategies and so on. 3.High-Frequency Trading It includes market microstructure, LOB modeling, high-frequency financial data modeling, optimal execution and allocation, HFT strategies, information technologies, regulation and risk management of HFT and so on. 4.Applications of Artificial Intelligence in Quantitative Trading Including the application of machine learning, deep learning, reinforcement learning, interpretable artificial intelligence, natural language processing and other technologies in quantitative trading. This course is suitable for master's and doctoral students with high mathematical and programming ability.

Lecturer Intro.

Qingfu Liu, the professor and doctoral supervisor at School of Economics, Fudan University, was awarded as Shanghai Pujiang Scholar. Prof. Liu obtained a doctorate in management science and engineering from Southeast University, was a postdoctoral fellow at Fudan University, and also a visiting scholar at Stanford University. Prof. Liu is now the executive dean of Fudan-Stanford Institute for China Financial Technology and Risk Analytics, the academic vice dean of Fudan-Zhongzhi Institute for Big Data Finance and Investment, and the vice dean of Shanghai Big Data Joint Innovation Lab. Prof. Liu's research interests mainly include financial derivatives, big data finance, quantitative investment, RegTech, green finance and non-performing asset disposal. He has published more than 80 papers in the Journal of economics, Journal of International Money and Finance, Journal of Management Sciences in China and other important journals at home and abroad, published three monographs, and presided over more than 20 nati