Introduction to Algorithmic Trading Strategies

 
Note: this course is superseded by the CQI introductory course: Introduction to Quantitative Investment

 

Course Description

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 the 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.

 

Course Outline

Session Topics Readings Homeworks
1 Overview of Algorithmic Trading Lecturer handout hw1
2 Hidden Markov Trading Model Lecturer handout hw2
lab programming a hidden Markov chain
3 Pairs Trading by Cointegration Lecturer handout hw3
lab programming a cointegration model; parameter sensitivity analysis
4 Optimal Pairs Trading by Stochastic Control Lecturer handout hw4
5 Pairs Trading by Stochastic Spread Methods Lecturer handout
lab programming a Kalman filter; parameter calibration
6 Technical Analysis: Linear Trading Rules Lecturer handout
7 Portfolio Optimization Lecturer handout hw5
lab strategy & portfolio optimization
8 Risk Management Lecturer handout

 

Recommended Readings

Numerical Method’s collection of Quantitative/Algorithmic Trading Literature

 

Student Projects

 

Student Feedback