04-800-AF Advanced Quantitative Financial Analytics and Algorithmic Trading
Location: Africa
Units: 12
Semester Offered: Spring
04-800-AF Advanced Quantitative Financial Analytics and Algorithmic Trading
Location: Africa
Units: 12
Semester Offered: Spring
Algorithmic trading serves as a practical application of software engineering and data science methodologies and quantitative analysis techniques within the context of financial markets. This project-based course offers an introduction to algorithmic trading and the principles behind it, while emphasizing universally applicable engineering concepts and data-driven methodologies.
Students will gain an understanding of the fundamentals of financial markets and trading systems, learn how to manage data, generate signals, backtest strategies, and use APIs to execute trades. Additionally, they will apply risk management principles, position sizing, and software development best practices such as unit testing in Python. Most importantly, the course will teach students specific thinking patterns and data science methodologies that can be applied across various engineering and data analysis fields. Students will be equipped with a toolbox needed to continue researching trading strategies, predictive analytics, or other data science-related topics independently.
Following condensed lecture videos, the course will emulate a professional environment through a series of individual assignments culminating in a functional project. Delivery of the project will be guided by direct instruction, Q&A calls, and an online chat group with the lecturers, similar to a real workplace. Students will deliver a functional project in Python, according to a specification, while also taking exams on the theoretical materials covered in the lectures.
Student progress is assessed through the delivery of practical projects according to a specification and evaluation criteria. While there are no prerequisites for this course, an understanding of statistics, probabilities, hypothesis testing, measures of spread, confidence intervals, and related topics is assumed.