MSc Financial Technology & Quantitative Analytics

Overview

The financial sector is driven by quantitative modeling and computational speed. Durham University's MSc in FinTech & Quantitative Analytics combines rigorous mathematical finance with machine learning algorithms, high-frequency data structures, and decentralized finance technologies. MSc Financial Technology & Quantitative Analytics is taught as one award, not a list of unrelated modules. The published length is 24 months, and the credit total is 36. Students move from Mathematical Methods in Finance & Econometrics through Mathematical Methods in Finance & Econometrics; Financial Programming with Python & C++; Derivatives Pricing & Stochastic Calculus; Algorithmic Trading & High-Frequency Strategies; Machine Learning & NLP in Financial Markets; Decentralized Finance (DeFi) & Blockchain Protocols, and finish on MSc FinTech Capstone Dissertation. Each course has a question, a method, and a submission. In this field, students define a measurable question, fit a method that can be checked, and report where it fails. Reading is a dataset description, a methods note, and one published evaluation. The artefact a marker expects is a project note with the question, the method, the result, and the limitation. The award is built so that a graduate can do the following in practice: Develop and backtest automated algorithmic trading models using Python and C++ Model quantitative financial risk, Value at Risk (VaR), and extreme event stress testing Evaluate distributed ledger protocols, automated market makers (AMMs), and smart contracts Teaching assumes the student can read a source, attempt a problem before the seminar, and revise after feedback. Attendance at live seminars is part of the design. The capstone or final course must use the methods of the earlier courses; a project that ignores them does not pass. The pages for each course name the topics that are examined. Those topics are the syllabus. A brochure line is not a substitute for them.

Programme Structure & Delivery

Learning Outcomes

• ✦Develop and backtest automated algorithmic trading models using Python and C++
• ✦Model quantitative financial risk, Value at Risk (VaR), and extreme event stress testing
• ✦Evaluate distributed ledger protocols, automated market makers (AMMs), and smart contracts
• ✦Apply natural language processing (NLP) to financial sentiment and earnings analysis

Career Pathways

• ✦Quantitative Analyst (Quant)
• ✦FinTech Solutions Engineer
• ✦Algorithmic Trader
• ✦Financial Risk Modeler
• ✦Investment Tech Strategist

Curriculum