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Master of Science in Financial Engineering

Length: 2 years (42 semester credit hours)
Award: Master of Science

The Master of Science in Financial Engineering is composed of fourteen courses and is designed to be completed in two years. All courses are taught sequentially, delivered online, and focus on applied projects.

Designed by industry experts, WQU's program integrates mathematical, statistical, and computer science tools with finance theory to help students master the skills needed for a career in financial engineering. Throughout the program, students will engage with advanced technologies, such as Python, Matlab, and WebSim. WQU's program provides a global perspective on the financial engineering in both developing and established markets. Each and every student engages in activities and simulations, such as a trading competition, research projects, and forum discussions. Additionally, students are encouraged to participate in group projects, guest lectures, and mentoring sessions.

WorldQuant University Catalog

Detailed information about WQU, the program, requirements for admission, academic policies, and other considerations are available in the WorldQuant University Catalog. You can view and download a copy here.

Program objectives

  • Demonstrate an understanding of global financial markets
  • Evaluate the strengths and weaknesses of statistics in analyzing financial data
  • Identify risks related to financial business and develop mitigation strategies
  • Design and evaluate the efficacy of Python algorithms
  • Describe Econometric modeling and forecasting of financial markets
  • Analyze regression, inference, and time-series models
  • Apply statistical and machine learning to the financial markets
  • Design and evaluate alphas
  • Evaluate the current trends of the global financial landscape


There are no prerequisites beyond WorldQuant University’s admissions requirements.


There are fourteen courses in the Master of Science in Financial Engineering program. Course Descriptions

Course Descriptions

Course Descriptions

Our specialized curriculum integrates mathematical, statistical, and computer-science tools with finance theory as applied in institutional settings. WQU students are challenged with a curriculum that provides them with the technical and business knowledge and skills to succeed across industries.
An applied project serves as a capstone to the WorldQuant University curriculum. The curriculum was designed to fit the needs of the current financial-employment environment based on input from industry practitioners, education experts, and faculty.

There are fourteen courses in the M.S. in Financial Engineering. The course descriptions are provided below.

WQU 601 Financial Markets I

The Financial Markets I course is intended as an introduction to Financial Markets. The course discusses the instruments traded in the markets, the institutions that support and frame the markets, the trading mechanisms and the regulatory structure. It is intended to be descriptive and conceptual. The aim is to familiarize students with the breadth and scope of equity, debt, commodity, and derivative markets. The course will incorporate discussions on recent developments such as High Frequency Trading and the Dodd-Frank Act.

3 Semester Credit Hours

WQU 603 Statistics

The Statistics course expounds on basic statistical concepts that are important in portfolio management. The goal is to understand the strengths and weaknesses of statistics in interpreting and analyzing data. There will be hands-on exercises that will give students a better feel for the subject.

3 Semester Credit Hours

WQU 605 Programming in Python I

The Programming in Python I course covers the basics of Python Programming as it relates to Financial Computing. Students will learn about Python, object-oriented programming concepts, build simple numerical programs, create functions, explore scoping, recursion, variables, modules, files, tuples, lists and higher-order functions. Students will build programs and learn testing and debugging techniques and handle exceptions. Students will also master abstract data types and classes, inheritance and encapsulation. Finally, they will learn and use tools like PyLab and build stochastic programs, explore random walks, and Experimental Data.

3 Semester Credit Hours

WQU 607 Algorithms I

The Algorithms I course covers the basic concepts of Algorithms. Students will learn about algorithms and their role in computing. They will examine data structures, recursion, sorting, and searching. They will then look at different algorithms like tree algorithms, graph algorithms, greedy algorithms and numerical algorithms.

3 Semester Credit Hours

WQU 609 Financial Markets II

The Financial Markets II course builds upon the foundation course Financial Markets I to demonstrate how the various instruments that were previously introduced are assembled to build portfolios. The course takes the perspective of the asset management industry and is intended to be descriptive and conceptual. The aim is to familiarize students with the breadth and scope of the full array of markets in equity, debt, commodity, and alternative investments.

3 Semester Credit Hours

WQU 611 Programming in Python II

The Programming in Python II course covers advanced Python concepts related to Financial Computing. Students will learn how to create financial calculators, calculate interest rates, examine closing price and trading volume, use Python to calculate comparisons among stocks and analyze high-frequency data, compare return versus volatility, write and debug Python code, and use modules. They will explore NumPy and SciPy and use Matplotlib to explore visual finance. They will explore stats, the Black-Scholes-Merton Option Model, and the Monte Carlo Simulation, explore volatility measures and GARCH and explore applications like the 52-week high and low trading strategy, Roll’s model to estimate spread (1984), Amihud’s model for illiquidity (2002), Pastor and Stambaugh liquidity measure, Fama-French three-factor model, and Fama-MacBeth regression.

3 Semester Credit Hours

WQU 613 Econometrics

The Econometrics course covers econometrics as statistical methods as applied to finance, building on the concepts students were introduced to in Financial Markets I and II, Statistics and Python I courses. This course provides an introduction to the modeling and forecasting of financial markets, with a thorough grounding in basic regression and inference, and moving on to more advanced time-series models like GARCH and cointegration.

3 Semester Credit Hours

WQU 615 Alpha Design I

The Alpha Design I course will introduce the basic concepts related to statistical arbitrage within and across asset classes. Launching off this starting point, the course further delves into various aspects of the framework dealing with the intricacies involved in developing an alpha model. The course is broadly structured into four parts. The course starts with the industry terminology around statistical arbitrage. After laying this foundation, it covers various performance and risk measures to gauge alpha models and strategies. Then the course proceeds towards a deeper exposition of the pitfalls involved in alpha design, covering various biases, robustness and statistical considerations. Finally, it gives an overview of the basic operations and models, various online tools available for the student to practice his or her craft, culminating in the student’s first alpha.

3 Semester Credit Hours

WQU 617 Algorithms II

The Algorithms II course covers the core knowledge required to understand numerical algorithms for computational finance. Students will learn about advanced design and analysis techniques, examine Monte Carlo simulations, explore parallel algorithms and be introduced to machine learning. Students will design and test their own algorithms on the markets.

3 Semester Credit Hours

WQU 619 Risk Management

The Risk Management course is an introductory risk management course that seeks to present a comprehensive overview of risk management to the uninitiated students. It does so by first expounding on the need for risk management in the modern business environment. It then introduces the elementary concepts of risk and return that are fundamental to the concept of risk management. It then introduces the major risks faced by businesses that include credit, market, operational, strategic, reputation and enterprise wide management risk. It then puts special focus on market risk and elucidates each sub-component of market risk. It explains the inherent risk and its measurement techniques in derivative instruments like options, futures and derivatives. The concept of Value-at-Risk, which is at the core of market risk measurement, is further introduced. The course then explains the various modeling techniques used for credit, market and operational risk. The process of stress testing and the various stress testing methodologies employed in stress testing are discussed as well. The course puts special focus on the risk in derivative instruments like futures, forwards and options. Finally, it also talks about the regulatory prescription of risk management in Basel II & III guidelines.

3 Semester Credit Hours

WQU 621 Alpha Design II

A follow-up to the Alpha Design I course, the Alpha Design II course provide the students with a deeper understanding of Alpha development and demonstrates the application of these concepts in the real world of trading. It starts by introducing the various types of costs that are incorporated into the models to make them more realistic. The effect and importance of liquidity and capacity considerations are then illustrated. The course then delves deeper into the various classes of alpha models that exist in the world of quantitative trading. It then explores the intricacies of developing these models in an asset-sensitive framework and extends the concept to understand the synergy between these models across asset classes. The concepts learned in the previous courses on risk management and statistics are then leveraged to explain the theory of active portfolio management and transform these potential alpha models into realizable benefits for an organization. Finally, it touches upon the various available datasets, which could be employed as the building blocks into models to beat the financial markets.

3 Semester Credit Hours

WQU 623 Machine Learning

The Machine Learning course covers the basic concepts of machine learning. Students will learn about principles and applications of statistical learning, machine learning and tools therein. They will examine feasibility of learning, measures of fit and lift, supervised and unsupervised learning, and a handful of learning paradigms like logistic regression, neural networks, support vector machines, boosting, decision trees and more.

3 Semester Credit Hours

WQU 625 Alpha Design III, with Machine Learning

A follow-up course to Alpha Design II, the Alpha Design III with Machine Learning course will elaborate in even more detail and increasing complexity a variety of alpha strategies. This course will build on the Machine Learning course and its application to advanced alpha strategies. This course will rely on the knowledge from Statistics, Risk, Python programming and machine learning courses to develop a full understanding of alphas and trading algorithms. There will be a trading competition that will take advantage of the entire sequence of alpha courses.

3 Semester Credit Hours

WQU 690 Capstone Course

The Capstone course is designed to put the students’ knowledge of financial engineering to the test. Students will practically apply their knowledge through a capstone project.

3 Semester Credit Hours

Academic Calendar

Academic Calendar

At WorldQuant University, courses run for six weeks with a one-week break between course sessions, and a longer break at the end of the year. Students can begin the Master of Science in Financial Engineering program at any course session start date. (See Course Descriptions for course details.)


The 2017 academic calendar also offers seven start dates, as follows. Academic Year Begins: January 10, 2017 Academic Year Ends: December 10, 2017
Course Calendar:
Course Session January 10 February 19
Break February 20 February 27
Course Session February 28 April 9
Break April 10 April 17
Course Session April 18 May 28
Break May 29 June 5
Course Session June 6 July 16
Break July 17 July 24
Course Session July 25 September 3
Break September 4 September 11
Course Session September 12 October 22
Break October 23 October 30
Course Session October 31 December 10


The 2018 academic calendar has seven start dates, as follows. Academic Year Begins: January 9, 2018 Academic Year Ends: December 9, 2018
Course Calendar:
Course Session January 9 February 18
Break February 19 February 26
Course Session February 27 April 8
Break April 9 April 16
Course Session April 17 May 27
Break May 28 June 4
Course Session June 5 July 15
Break July 16 July 23
Course Session July 24 September 2
Break September 3 September 10
Course Session September 11 October 21
Break October 22 October 29
Course Session October 30 December 9


The 2019 academic calendar has seven start dates, as follows. Academic Year Begins: January 8, 2019 Academic Year Ends: December 8, 2019
Course Calendar:
Course Session January 8 February 17
Break February 18 February 25
Course Session February 26 April 7
Break April 8 April 15
Course Session April 16 May 26
Break May 27 June 3
Course Session June 4 July 14
Break July 15 July 22
Course Session July 23 September 1
Break September 2 September 9
Course Session September 10 October 20
Break October 21 October 28
Course Session October 29 December 8