This course introduces students to quantitative trading. A “quant” portfolio manager or a trader usually starts with an intuition or a vague trading idea. Using mathematics, s/he turns the intuition into a mathematical trading model for analysis, back testing and refinement. When the quantitative investment model proves to be likely profitable after passing rigorous statistical tests, the portfolio manager 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 investment strategies from the popular academic literature and learn the fundamental mathematics and IT aspects of this emerging field. After satisfactorily completing this course, the students will have an overview of the necessary quantitative, computing, and programming skills in quantitative trading.



Markowitz’s celebrated mean-variance portfolio optimization theory assumes that the means and covariances of the underlying asset returns are known. In practice, they are unknown and have to be estimated from historical data. Plugging the estimates into the efficient frontier that assumes known parameters has led to portfolios that may perform poorly and have counter-intuitive asset allocation weights; this has been referred to as the “Markowitz optimization enigma.” [Lai, Xing and Chen, 2010] explains the root cause of the enigma and propose a new approach to resolve it. Specificially, it assumes that the mean and the covariances are unknown. The classical quadratic optimization problem therefore becomes a stochastic optimization problem. Not only is the new approach shown to provide substantial improvements over previous methods, but it also allows flexible modeling to incorporate dynamic features and fundamental analysis of the training sample of historical data, as illustrated in simulation and empirical studies.



AlgoQuant greatly streamlines our modeling and backtesting process. Our family office builds our research IT infrastructure leveraging the AlgoQuant API. Stella Xing

Owner, Chorys Limited

I am impressed with NM’s innovative mathematical approach to quantitative/algorithmic trading and their trading research technologies. Glad to work with them. Allen Yan

Deputy CEO, Rongtong Fund Management

As a head quant trader in a prop trading house, I found in the libraries proposed by Numerical Method Inc. an impressive set of algorithms and tools that I can use as a foundation to my in-house framework. These libraries enabled me to quickly implement research ideas by using standard tools, but also having access to more sophisticated up-to-date algorithms, as companions to our in-house algorithms.

The fact that these libraries are reliable, robust and efficiently implemented turned out to be an important gain of time for us, so we could really focus on the elaboration of our intellectual property, and the implementation of our own tool and strategies.

Finally, the support and developing teams are really prompt at responding to questions, which is a real plus.


Head Quant, Hedge Fund, Switzerland