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Introduction to Algorithmic Trading Strategies by Dr. Haksun Li

This course has been offered in the following venues.

  • Master of Science in Financial Mathematics, the Hong Kong University of Science and Technology, September – December, 2015.
  • Master of Science in Financial Mathematics, the Hong Kong University of Science and Technology, December 2011, January – May, 2013.
  • 复旦大学经济学院: 算法交易, November 2011November 2013.
  • the NTU-SGX program, July – August 2011.
  • the Nanyang Master of Science in Financial Engineering (MFE) programme, FE8827, NTU, March – April 2011, 2012.
  • the Master of Science in Quantitative Finance program, February – April 2011, 2012.


Since the publication of Markowitz’s seminar paper in 1952, there have been numerous research and papers that show that, though elegant and mathematically solid, his portfolio optimization theory is of little practical value. Markowitz’s theory makes two unrealistic assumptions – the availability of both expected means and expected covariance for the future which are known to be very difficult to estimate or even guess. In this presentation, we will survey the latest development in portfolio optimization technologies and how we can apply the new theories to generate positive profits in practice.

CFA, Beijing, Jul 4, 2017.
Beijing Wealth Management Association, Jul 5, 2017.
FuDan University summer seminar on quantitative finance, Jul 11, 2017.
QuantCon Singapore 2017. Nov 29, 2017.

Quantitative trading is distinguishable from other trading methodologies like technical analysis and analysts’ opinions because it uniquely provides justifications to trading strategies using mathematical reasoning. Put differently, quantitative trading is a science that trading strategies are proven statistically profitable or even optimal under certain assumptions. There are properties about strategies that we can deduce before betting the first $1, such as P&L distribution and risks. There are exact explanations to the success and failure of strategies, such as choice of parameters. There are ways to iteratively improve strategies based on experiences of live trading, such as making more realistic assumptions. These are all made possible only in quantitative trading because we have assumptions, models and rigorous mathematical analysis.

Quantitative trading has proved itself to be a significant driver of mathematical innovations, especially in the areas of stochastic analysis and PDE-theory. For instances, we can compute the optimal timings to follow the market by solving a pair of coupled Hamilton–Jacobi–Bellman equations; we can construct sparse mean reverting baskets by solving semi-definite optimization problems with cardinality constraints and can optimally trade these baskets by solving stochastic control problems; we can identify statistical arbitrage opportunities by analyzing the volatility process of a stochastic asset at different frequencies; we can compute the optimal placements of market and limit orders by solving combined singular and impulse control problems which leads to novel and difficult to solve quasi-variational inequalities.

Stanford-Tsinghua-PKU Conference on Quantitative Finance and Risk Management, March 31, 2017.
QuantCon Singapore 2016. Nov 11, 2016.

HKU-HKUST-Stanford Conference in Quantitative Finance. Dec 9, 2011.
Workshop On Recent Developments Of Financial Mathematics (REDFIN2011). Dec 13, 2011.

The Hong Kong University of Science and Technology. Apr 20, 2011.

The presentation outlines an approach to generate and analyze quantitative trading strategies in a scientific way. With examples, Dr. Li tries to differentiate scientifically justifiable models from the not so scientific ones. The conclusion is that mathematics is a useful tool to help make concrete and precise our market intuitions. Programming skill is what turns ideas and equations in reality that produces trading pnl.

NTU-SGX Centre for Financial Education, Nanyang Technological University. Feb 26, 2011.

The presentation summarizes his industrial experience. It outlines his approach of doing algo trading. The talk covers the IT infrastructure, the design of a trading system, the process of creating a trading strategy, as well as the technologies available to ease and automate strategy creation. Dr. Li will discuss some practical issues encountered in the past when building a trading system. Moreover, he will walk through a sample strategy. Throughout the talk, he emphasizes that technology is the deciding factor in this “arms race”. Computer science is as important as mathematics, if not more.

School of Information Systems, Singapore Management University. 2010
M.Sc. in Quantitative Finance programme, National University of Singapore. 2010

Conferences and Invitations