The course covers advanced quantitative portfolio management techniques with hands-on computing using Python. Topics will include portfolio theories, factor models, smart beta strategies, dynamic hedging, dynamic conditional covariance modeling, active portfolio management and performance evaluation etc. Programming and numerical algorithm will be a focus and students are expected to learn and apply Python to real practice problems encountered widely in the industry.