MasterFaculty of FinTech, Quant Systems & Digital Commerce

Master in Algorithmic Trading & Quantitative Systems

Welcome, future architects of financial algorithms. In this program, we will not just learn to predict the future, but to create the systems that can truly understand the world. Let's build the algorithms that will shape the future of finance.

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Level: Master
Learning model: Professor + Mentor
Named list: See the named lists · 12 months recommended

NXAcademic
Edition

The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

Academic focus

Algorithmic Trading, Predictive Models, Automated Execution, Financial Engineering

Practical focus

Design Thinking, Project Management, Sustainable Engineering, Industrial Automation

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

Deutsche Bank
Internships in quantitative finance and risk management
The European Central Bank
Internships in financial stability and economic analysis

Career opportunities

Senior Data Scientist
In a tech, finance, or government
Business Intelligence Architect
Designing data systems for businesses
Policy Analyst
Using data to inform government or NGO decisions
Research Scientist
In academia or industry

Jobs and projects

Problem-Solving
Applying a systematic approach to data-driven challenges
Programming
Competency in statistical languages like R or Python
Attention to Detail
Meticulousness in data cleaning and analysis
Ethical Reasoning
Understanding the ethical implications of data use

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

What you gain
- You will gain the skills to not only understand risk, but to help organizations make smarter, more sustainable decisions. You will become a trusted advisor in the world of finance and insurance.
Skills you build
- Statistical Learning: Proficiency in advanced machine learning and statistical models.
- Causal Inference: Understanding the cause-and-effect relationships in data.
- Decision Theory: Applying statistical principles to make optimal decisions.
- Big Data Analytics: Skills in extracting valuable patterns from large datasets.

Listed courses

Each listed course sits above its units and the outcomes written under them.

Master in Algorithmic Trading & Quantitative Systems

01Foundations of Quantitative Trading
1. FoundationsFoundations of Foundations of Quantitative Trading
  The learner can you will gain the skills to not only understand risk, but to help organizations make smarter, more sustainable decisions, as applied to Foundations of Quantitative Trading.
  The learner can you will become a trusted advisor in the world of finance and insurance, as applied to Foundations of Quantitative Trading.
2. MethodsMethods in Foundations of Quantitative Trading
  The learner can apply a method from Foundations of Quantitative Trading to a documented case.
  The learner can select an appropriate method from Foundations of Quantitative Trading for a stated problem.
3. ApplicationApplication of Foundations of Quantitative Trading
  The learner can evaluate a practice of Foundations of Quantitative Trading against a stated criterion.
  The learner can transfer Foundations of Quantitative Trading to a new documented context.
02Algorithmic Trading and Automated Execution
1. FoundationsFoundations of Algorithmic Trading and Automated Execution
  The learner can explain the core terms of Algorithmic Trading and Automated Execution.
  The learner can distinguish related ideas inside Algorithmic Trading and Automated Execution.
2. MethodsMethods in Algorithmic Trading and Automated Execution
  The learner can apply a method from Algorithmic Trading and Automated Execution to a documented case.
  The learner can select an appropriate method from Algorithmic Trading and Automated Execution for a stated problem.
3. ApplicationApplication of Algorithmic Trading and Automated Execution
  The learner can evaluate a practice of Algorithmic Trading and Automated Execution against a stated criterion.
  The learner can transfer Algorithmic Trading and Automated Execution to a new documented context.
03Predictive Models and Stochastic Processes
1. FoundationsFoundations of Predictive Models and Stochastic Processes
  The learner can explain the core terms of Predictive Models and Stochastic Processes.
  The learner can distinguish related ideas inside Predictive Models and Stochastic Processes.
2. MethodsMethods in Predictive Models and Stochastic Processes
  The learner can apply a method from Predictive Models and Stochastic Processes to a documented case.
  The learner can select an appropriate method from Predictive Models and Stochastic Processes for a stated problem.
3. ApplicationApplication of Predictive Models and Stochastic Processes
  The learner can evaluate a practice of Predictive Models and Stochastic Processes against a stated criterion.
  The learner can transfer Predictive Models and Stochastic Processes to a new documented context.
04Research Seminar in Quantitative Trading
1. FoundationsFoundations of Research Seminar in Quantitative Trading
  The learner can explain the core terms of Research Seminar in Quantitative Trading.
  The learner can distinguish related ideas inside Research Seminar in Quantitative Trading.
2. MethodsMethods in Research Seminar in Quantitative Trading
  The learner can apply a method from Research Seminar in Quantitative Trading to a documented case.
  The learner can select an appropriate method from Research Seminar in Quantitative Trading for a stated problem.
3. ApplicationApplication of Research Seminar in Quantitative Trading
  The learner can evaluate a practice of Research Seminar in Quantitative Trading against a stated criterion.
  The learner can transfer Research Seminar in Quantitative Trading to a new documented context.
05The Ethics of AI in Finance
1. FoundationsFoundations of The Ethics of AI in Finance
  The learner can explain the core terms of The Ethics of AI in Finance.
  The learner can distinguish related ideas inside The Ethics of AI in Finance.
2. MethodsMethods in The Ethics of AI in Finance
  The learner can apply a method from The Ethics of AI in Finance to a documented case.
  The learner can select an appropriate method from The Ethics of AI in Finance for a stated problem.
3. ApplicationApplication of The Ethics of AI in Finance
  The learner can evaluate a practice of The Ethics of AI in Finance against a stated criterion.
  The learner can transfer The Ethics of AI in Finance to a new documented context.
06Design Thinking Workshops
1. FoundationsFoundations of Design Thinking Workshops
  The learner can explain the core terms of Design Thinking Workshops.
  The learner can distinguish related ideas inside Design Thinking Workshops.
2. MethodsMethods in Design Thinking Workshops
  The learner can apply a method from Design Thinking Workshops to a documented case.
  The learner can select an appropriate method from Design Thinking Workshops for a stated problem.
3. ApplicationApplication of Design Thinking Workshops
  The learner can evaluate a practice of Design Thinking Workshops against a stated criterion.
  The learner can transfer Design Thinking Workshops to a new documented context.
07Project Management for Engineers
1. FoundationsFoundations of Project Management for Engineers
  The learner can explain the core terms of Project Management for Engineers.
  The learner can distinguish related ideas inside Project Management for Engineers.
2. MethodsMethods in Project Management for Engineers
  The learner can apply a method from Project Management for Engineers to a documented case.
  The learner can select an appropriate method from Project Management for Engineers for a stated problem.
3. ApplicationApplication of Project Management for Engineers
  The learner can evaluate a practice of Project Management for Engineers against a stated criterion.
  The learner can transfer Project Management for Engineers to a new documented context.
08Professional Skills for Engineers
1. FoundationsFoundations of Professional Skills for Engineers
  The learner can explain the core terms of Professional Skills for Engineers.
  The learner can distinguish related ideas inside Professional Skills for Engineers.
2. MethodsMethods in Professional Skills for Engineers
  The learner can apply a method from Professional Skills for Engineers to a documented case.
  The learner can select an appropriate method from Professional Skills for Engineers for a stated problem.
3. ApplicationApplication of Professional Skills for Engineers
  The learner can evaluate a practice of Professional Skills for Engineers against a stated criterion.
  The learner can transfer Professional Skills for Engineers to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

I am a quantitative analyst who believes that the markets are a complex system of probabilities. My work focuses on developing predictive models and algorithmic systems to trade financial assets with precision and efficiency. I'll teach you to see the patterns in the noise and build the algorithms that can capitalize on them.

Applied mentorship

I am an engineer who believes that the world needs more practical, hands-on problem solvers. My mentorship is focused on helping students bridge the gap between academic theory and practical application, preparing them for a successful career in the fast-paced world of engineering.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

• Blog Post: 'Beyond Prediction: The Dawn of Algorithmic Trading' - An accessible essay on the growing field of algorithmic trading and its importance for the future of finance. · • Blog Post: 'The Statistical Fallacies of A/B Testing' - Discusses the common pitfalls of A/B testing and how causal inference can lead to better business decisions. · • Conference Paper: 'A New Method for Causal Inference in High-Dimensional Data' - Presented at the International Conference on Machine Learning, detailing a breakthrough in understanding causality in complex systems. · • Journal Article: 'Causal Inference for Fair and Transparent Machine Learning' - Published in the Journal of Statistical Science, this paper provides a framework for designing AI systems that are both powerful and ethical. · • Standard Article: 'A Look at the Future of Causal Analytics in Public Policy' - A multi-part series for a policy magazine on how causal models are transforming public policy. · • Book: 'The Causal Inference Handbook' - A comprehensive textbook on the theoretical and practical applications of causal inference. · • Total Score: 29/30 · • Kairos Badge: 🥇

R / 02

Mentor practice lens

• Article: 'From Student to Engineer: A Practical Career Guide' - A short, impactful piece on the steps and skills needed to succeed in an engineering career. · • Guide: 'A Beginner's Guide to Using R for Historical Data Analysis' - A practical tutorial for new students on the basics of a key bioinformatics programming language. · • Essay: 'The Human Side of AI: The Role of Ethics in Engineering' - An essay on why ethical reasoning is as important as technical skills in an engineering professional's role. · • Total Score: 28/30 · • Kairos Badge: 🥇

Adaptive capability

Professor superpower

GAF-powered Causal Pathway Visualization

Adaptive capability

Mentor superpower

GAF-powered Career Pathway Optimization

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

AI Super Professor

Prof. Dr. Tobias Keller

Algorithmic Trading, Predictive Models, Automated Execution, Financial Engineering

Meet your professor Open the classroom

AI Super Mentor

Dr. Sofia Mendez

Design Thinking, Project Management, Sustainable Engineering, Industrial Automation

Meet your mentor Open the classroom

Same faculty and level

Related programs

Named lists

Named lists for this house

Core. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

Duration	Bachelor	Master This programme	Doctorate
9 months · Fast track	15000 EUR	12000 EUR	15000 EUR
12 months · Recommended	18000 EUR	15000 EUR	18000 EUR
15 months · Standard	21000 EUR	18000 EUR	21000 EUR
18 months · Flexible	24000 EUR	21000 EUR	24000 EUR
21 months · Extended	27000 EUR	24000 EUR	27000 EUR
24 months · Part-time	30000 EUR	27000 EUR	30000 EUR

These are the owner lists. Enrolment is not open. Nothing here is a sale.

Named tuition lists Add-on services

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Master in Algorithmic Trading & Quantitative Systems

Academic focus

Practical focus

Deutsche Bank

The European Central Bank

Senior Data Scientist

Business Intelligence Architect

Policy Analyst

Research Scientist

Problem-Solving

Programming

Attention to Detail

Ethical Reasoning

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Tobias Keller

Dr. Sofia Mendez

Find the program that expands your universe.