Introduction to quantitative strategies 2015​

This course introduces students to quantitative trading. A trader usually starts with an intuition or a vague trading idea. Using mathematics, s/he turns the intuition into a quantitative trading model for analysis, back testing and refinement. When the quantitative trading model proves to be likely profitable after passing rigorous statistical tests, the trader implements the model on a computer system for automatic execution. In short, quantitative trading is the process where ideas are turned into mathematical models and then coded into computer programs for systematic trading. It is a science where mathematics and computer science meet. In this course, students study trading strategies from popular academic literature and learn the fundamental mathematics and IT aspects of this emerging field. By working on the class projects, they will gain hands-on experience. After satisfactorily completing this course, the students will have the necessary quantitative, computing, and programming skills in quantitative trading. They are therefore well prepared for a front office role in hedge funds or banks.

This course covers some popular trading strategies such as technical analysis, trend following, mean reversion, factor model, portfolio optimization as well as risk management. We examine these strategies from the perspective of quantitative mathematics. This course has been taught in many universities and organizations including National University of Singapore, Nanyang Technological University, Hong Kong University of Science and Technology and Fudan University.



Lesson 1Technical Analysis: A Scientific Perspective
Lesson 2Hidden Markov Trading Model
Lesson 3Trend Following
Lesson 4Pairs Trading by Cointegration
Lesson 5Optimal Pairs Trading by Stochastic Control
Lesson 6Pairs Trading by Stochastic Spread Methods
Lesson 7Small Mean Reverting Portfolio
Lesson 8Performance Measures
Lesson 9Quantitative Equity Portfolio Management
Lesson 10Risk Management


Numerical Methods in Quantitative Trading, by Dr. Haksun Li, Dr. Waipun Ken Yiu, Dr. Kevin Haoyu Sun


Prof. Haksun Li is a co-founder of NM LTD., NM FinTech LTD., NM Optim LTD. and Atomus Asset Management.

The group of NM companies has the single mission of “Making the World Better using Mathematics”. Under his leadership, the company group serves security houses, hedge funds, high-net-worth individuals, multinational corporations, factories and plants all over the world.

NM LTD. is dedicated to the research and development of innovative computing technologies for business operations as well as quantitative wealth management. Using state-of-the-art computer science, Haksun has developed a suite of programming libraries of fundamental and advanced mathematics. The suite has made the otherwise very-hard-to-understand and inaccessible mathematical theories at the fingertip of business users.

NM Optim LTD helps factories and plants to streamline operations, improve productivity and increase revenue by applying operations research and optimization theories. It is the bridge between industries and academia. Haksun leads a team to revolutionize the workflow of factories by making more efficient use of resources, better scheduling, better management, better pricing, etc., all based on mathematical models, bringing companies to Industry 4.0.

NM FinTech LTD. develops financial analytic systems for quantitative portfolio managers worldwide. For the U.S. equity market, Haksun created a Risk Management System, together with a factor library and tools for portfolio construction. For the China bond market, Haksun created SuperCurve which is the only accurate yield curve publicly available for pricing Chinese bonds, together with a suite of fixed income analytics.

Atomus Asset Management is a hedge fund in Shanghai, China. Haksun leads the research team to use advanced technologies, such as AI and Big Data, to develop quantitative trading strategies. The fund has achieved excellent stable investment performance.

Haksun is the associate dean and a professor of the Big Data Finance and Investment Institute of Fudan University, China. Prior to that, Haksun was a quantitative analyst and a quantitative trader at UBS and BNP Paribas.

Haksun holds a bachelor’s degree in mathematics and a master’s degree in financial mathematics from the University of Chicago, as well as a master’s and a doctoral degree in computer science and engineering (with specialization in A.I.) from the University of Michigan, Ann Arbor.

Introduction to Data Science


Course Description

Data science is the fastest growing field in this generation. Data scientist has been named the number one job in the U.S. NM is a team of mathematicians and computer scientists. We use data to build decision-making system to help managers make data-driven business decisions based on mathematics and science. We create this course to help aspiring young people to learn data science.

This course is different from many other “standard” data science curriculums which are by-and-large Python programming in statistics and machine learning for adults. First, our target readers are high school to junior college students who may not have been exposed to the necessary mathematics for data science. As data science is an interdisciplinary field of mathematics, statistics and computer science, we start with linear algebra, which is fundamental to all numerical computing, and gradually build up the more advanced mathematics to regression and machine learning. We want to pave a solid theoretical foundation for young readers. Unlike a college textbook, the mathematics concepts are intentionally made not rigorous so that junior students can understand the intuition behind them. Many examples are provided and illustrated with charts, graphs and pictures for easy understanding.

Second, our choice of programming language is Kotlin, a modern open-source Java Virtual Machine (JVM) based language designed to make people happy. Kotlin is the next-generation data science language. JetBrains develops Kotlin and Google officially makes it the preferred language for Android. Unlike Python which is notoriously difficult to share code with your friends, the data science applications developed in Kotlin can be easily deployed to any 13 billion devices that support Java, mostly likely including your mobile phones. Kotlin applications can be easily integrated with the dozens of Java application frameworks such as Spring Boot. Moreover, Kotlin, complied to JVM bytecode, has superior performance compared to Python interpretation.

Happy coding!

After this course you will learn

  1. Mathematics needed for data science
  2. Statistics
  3. Kotlin and Java application development
  4. Developing data-driven solutions to business and scientific problems
All the code examples in this online course can be copied-and-pasted in the S2 IDE for running. Go to:
S2 data science
If you would like to learn in more detail about the algorithms and numerical methods, there are textbooks that we recommend. You will be able to purchase them from Amazon and other major bookstores.
As we continue to develop and improve this course, we welcome your feedback and comments. To leave a comment or ask a question, visit our forum: