Quantitative Trading(II)
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.
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
Qing Fu Liu
Date
10th March ~ 23rd June, 2023
Location
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| Friday | 09:50 - 12:15 | ZOOM 07 | 559 700 6085 | BIMSA |
Prerequisite
Investments, Financial Mathematics, Applied Stochastic Processes, Linear Regression, Applied Time Series Analysis, Machine Learning
Reference
一、因子投资
（1）石川，因子投资：方法与实践，电子工业出版社，2020年9月
（2）理查德C.格林诺德, 雷诺德N.卡恩等，主动投资组合管理：创造高收益并控制风险的量化投资方法，机械工业出版社，2014年9月
（3）路德维希B.钦塞瑞尼，金大焕，量化股票组合管理：积极型投资组合构建和管理的方法，机械工业出版社，2018年9月
二、量化交易策略
（4）丁鹏，量化投资——策略与技术，电子工业出版社，2014年9月
（5）拉瑟·海耶·佩德森，高效的无效：行家如何投资与市场如何定价，中国人民大学出版社，2021年5月
（6）弗朗索瓦·塞尔·拉比唐，对冲基金手册，上海交通大学出版社，2014年1月
三、高频交易
（7）Xin Guo, Tze Leung Lai, Howard Shek, Samuel Po-Shing Wong，量化交易：算法、分析、数据、模型和优化，高等教育出版社，2020年2月
（8）Álvaro Cartea, Sebastian Jaimungal, José Penalva，算法和高频交易，科学出版社，2021年2月
（9) Jean-Philippe Bouchaud, Julius Bonart, Jonathan Donier, Martin Gould, Trades, Quotes and Prices: Financial Markets Under the Microscope, Cambridge University Press, March 2018
四、人工智能在量化交易中的应用
（10）马科斯·洛佩斯·德普拉多，金融机器学习，中信出版集团，2021年5月
（11）Stefan Nagel，机器学习与资产定价，电子工业出版社，2022年7月
（12）斯蒂芬·詹森，机器学习在算法交易中的应用，中国水利水电出版社，2023年1月
（1）石川，因子投资：方法与实践，电子工业出版社，2020年9月
（2）理查德C.格林诺德, 雷诺德N.卡恩等，主动投资组合管理：创造高收益并控制风险的量化投资方法，机械工业出版社，2014年9月
（3）路德维希B.钦塞瑞尼，金大焕，量化股票组合管理：积极型投资组合构建和管理的方法，机械工业出版社，2018年9月
二、量化交易策略
（4）丁鹏，量化投资——策略与技术，电子工业出版社，2014年9月
（5）拉瑟·海耶·佩德森，高效的无效：行家如何投资与市场如何定价，中国人民大学出版社，2021年5月
（6）弗朗索瓦·塞尔·拉比唐，对冲基金手册，上海交通大学出版社，2014年1月
三、高频交易
（7）Xin Guo, Tze Leung Lai, Howard Shek, Samuel Po-Shing Wong，量化交易：算法、分析、数据、模型和优化，高等教育出版社，2020年2月
（8）Álvaro Cartea, Sebastian Jaimungal, José Penalva，算法和高频交易，科学出版社，2021年2月
（9) Jean-Philippe Bouchaud, Julius Bonart, Jonathan Donier, Martin Gould, Trades, Quotes and Prices: Financial Markets Under the Microscope, Cambridge University Press, March 2018
四、人工智能在量化交易中的应用
（10）马科斯·洛佩斯·德普拉多，金融机器学习，中信出版集团，2021年5月
（11）Stefan Nagel，机器学习与资产定价，电子工业出版社，2022年7月
（12）斯蒂芬·詹森，机器学习在算法交易中的应用，中国水利水电出版社，2023年1月
Audience
Graduate
Video Public
No
Notes Public
No
Language
Chinese
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 national and provincial research projects. He is currently an associate editor at Digital Finance and an editor at World Economic Papers.