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Curriculum

An integrative curriculum - finance, code, AI as one discipline.

An intensive Bootcamp followed by core building blocks and electives. Designed for working professionals and international candidates.

Hybrid format

How classes work.

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Classroom + live online

Classes at RAF (Knez Mihailova 6) simulcast live to remote participants. Pick the mode that fits your week.

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Recorded for async catch-up

Every lecture is recorded. Travel for work or miss a session - replay at your pace, no penalty.

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Evening schedule, max 3× weekly

18:00–21:00 CET. Compatible with full-time employment and international time zones.

Admissions

Application and Entry Requirements

Application and Entry Requirements

We are looking for talented and creative individuals interested in developing world-class expertise in the field of computational finance and becoming active players in the regional and global marketplace. While strong quantitative aptitude is beneficial, there are no formal requirements in terms of the background or a major. What is crucial is curiosity and motivation.

We are seeking ambitious individuals eager to develop cutting-edge expertise in computational finance. Our program is designed for those who aspire to become leaders in the evolving global and regional financial landscapes, equipped with skills that extend beyond traditional finance into programming, data science, and AI applications.

While a strong quantitative aptitude is an advantage, we welcome candidates from diverse academic and professional backgrounds from around the world. No specific major or prior experience in finance is required. What matters most is a passion for learning, curiosity to explore new technologies, and the motivation to tackle complex real-world challenges.

If you're ready to embrace the integration of finance with Python and R programming, machine learning, and generative AI systems-and unlock their potential in solving real-world business challenges-this program is for you.

Core modules

Six building blocks across two semesters.

About The Bootcamp

A two-month preparatory course covering the fundamentals of finance, programming (Python and R), mathematics, and artificial intelligence - with no prior knowledge required. Students develop practical skills in financial data analysis and visualization, alongside solid foundations in calculus, linear algebra, and statistics. The course carries no ECTS credits but serves as essential preparation for the main program.

Dec–Feb
Bootcamp

About The Bootcamp

Before the program commences we offer a comprehensive two-month preparation course designed to build foundational skills in finance, programming, mathematics, probability, and applications of AI technologies – all essential for success in computational finance.

During the Bootcamp, students:

  • Develop confidence in coding with Python and R while solving real-world problems in finance and economics.
  • Understand essential financial concepts, learn to efficiently process and analyze financial data and create advanced data visualizations.
  • Gain a solid grasp of essential concepts in calculus, linear algebra, and probability, with a focus on their practical applications in finance and economics.
  • Explore generative AI systems and learn to apply these technologies in problem-solving, technical writing, code debugging, and creating innovative solutions to financial and economic challenges.

No prior finance or programming knowledge is required, making this course an excellent starting point for individuals from diverse academic backgrounds. Please note that the Bootcamp does not carry ECTS credits but serves as an invaluable foundation for the main program.
01
6 ECTS · REQUIRED · SEMESTER I

Financial Computing and Quantitative Investments

The course equips students with the theoretical foundations and practical Python skills needed to understand and implement core concepts in investment science and quantitative strategies. It culminates in a group competition where teams design, implement, and defend their own data-driven investment strategies.

Feb–Jul
Required

Financial Computing and Quantitative Investments

The aim of the course is to enable participants to understand and implement in Python key concepts in investment science and quantitative investment strategies.

By the end of the course, participants should acquire the tools required for making sound investment decisions. They should be able to understand both the foundational theory and underlying concepts, as well as learn how to practically apply these concepts in Python in all stages of the work flow.

6 ECTS · Required · Feb–Jul
02
6 ECTS · REQUIRED · SEMESTER I

Financial Derivatives

The course builds a strong understanding of the models and methods used for valuing, hedging, and trading financial derivatives in both discrete and continuous time. Students apply their knowledge in Python and test their strategies using a realistic trading simulator.

Feb–Jul
Required

Financial Derivatives

The goal of the course is to develop firm understanding of the principal ideas and models that underpin modern financial practice and theory and to build hands-on experience in valuation, hedging and trading of financial derivatives using Python.

At the end of the course, students will understand the institutional aspects and methods of valuation and hedging of derivative securities in discrete and continuous time, effectively utilize data on financial derivatives and implement derivative valuation and hedging methods in Python.

6 ECTS · Required · Feb–Jul
03
6 ECTS · REQUIRED · SEMESTER I

Statistics and Financial Data Analysis

The course introduces students to key statistical methods for financial data analysis, with an emphasis on both the underlying theory and the economic intuition behind the results. Participants learn traditional statistical approaches outside the machine learning framework and implement them in R.

Feb–Jul
Required

Statistics and Financial Data Analysis

The course provides a comprehensive introduction to key concepts used in applied statistical work with financial data. The emphasis is both on the key principles of the underlying statistical theory as well as on the economic intuition behind the estimates.

At the end of the course, the students will have a good understanding of the traditional statistical methods for financial data analysis outside the machine learning framework, their merits and disadvantages, and will be well equipped to conduct individual data-based research or industry projects. In addition, they will be able to implement these concepts in R.

6 ECTS · Required · Feb–Jul
04
3 ECTS · REQUIRED · SEMESTER I

Fixed Income and Credit

The course develops an understanding of fixed income securities, interest rate derivatives, and the valuation and hedging methods used in practice. Students learn to construct yield curves and implement the relevant models in Python.

Feb–Jul
Required

Fixed Income and Credit

The objective of the course is to develop understanding of fixed income securities and markets as well as interest rate derivatives. We study valuation and hedging using these instruments and discuss how these methods are used in practice.

At the end of the course, students will understand the institutional aspects and methods of valuation of fixed income securities such as bonds and related instruments, construction of yield curves, valuation and hedging using interest rate derivatives. In addition, they will be able to implement these models in Python.

3 ECTS · Required · Feb–Jul
05
6 ECTS · REQUIRED · SEMESTER II

Machine Learning

The course covers the most important supervised and unsupervised machine learning algorithms and their applications in finance, with a focus on model validation, interpretability, and implementation in Python. Students learn how to select the right model, evaluate its quality, and interpret results using real financial datasets.

Sep–Dec
Required

Machine Learning

The course provides a comprehensive introduction to the most important machine learning models and algorithms and their applications in finance, with an emphasis on model performance, validation, and interpretability and their implementation in Python.

At the end of the course, the students will have a good understanding of the most important supervised and unsupervised machine learning algorithms and their applications. They will know which models are suitable for a given problem and data set, how to evaluate the model quality, and how to interpret the results.

6 ECTS · Required · Sep–Dec

Elective courses

Select 2 from the list.
01
6 ECTS · ELECTIVE · SEMESTER II

Topics in Financial Technologies

The course explores the intersection of new technologies and finance, focusing on the digital transformation of key financial functions - payments, lending, investment, and market aggregation. Students gain familiarity with the Fintech ecosystem, the technological foundations of the industry, and the key business and regulatory challenges involved.

Sep–Dec
Elective

Topics in Financial Technologies

The aim of the course is to study interconnection between new technology and finance and provide fundamental understanding of digital innovation of financial intermediation functions (money, payments, capital raising, market aggregation, price discovery) with emphasis on scalable business models and consumer products.

Upon the completion of the course participants should be familiar with principal varieties of Fintech ecosystem and what and how is disrupted in traditional money, payment, lending, banking and investment industries.

6 ECTS · Elective · Sep–Dec
02
6 ECTS · ELECTIVE · SEMESTER II

Algorithmic Trading, Blockchain, and Decentralized Finance (DeFi)

The course introduces students to blockchain technology, cryptocurrencies, and algorithmic trading, with practical application of technical analysis across various markets and asset classes. Participants learn the basics of the Solidity programming language and how to create ERC20 and ERC721 tokens on the Ethereum network.

Sep–Dec
Elective

Algorithmic Trading, Blockchain, and Decentralized Finance (DeFi)

This course provides an introduction to the blockchain technology, cryptocurrencies, retail trading and algorithmic trading. A Technical Analysis and Trading Systems will be introduced as a necessary basis for retail and algorithmic trading on cryptocurrency and other markets.

At the end of the course, participants will learn how to apply Technical Analysis on cryptocurrencies, but also on many other asset classes. They will be able to create and use Technical Indicators, to assess the risk involved in trading, and to create profitable trading systems for manual and algorithmic trading.

6 ECTS · Elective · Sep–Dec
03
6 ECTS · ELECTIVE · SEMESTER II

Investments

The course provides an in-depth understanding of investing in both conventional and alternative asset classes - private equity, venture capital, hedge funds - from the perspective of institutional investors. Students master investment valuation and full transaction lifecycle management.

Sep–Dec
Elective

Investments

The course provides in-depth contextual understanding of the financial investing in conventional (with the focus on equity) and in alternative asset classes (private equity, venture capital, hedge funds, etc.) from the point of view of institutional investors.

After the completion of the course participants should be able to understand how to value financial (equity) investments and implement best practice governance in a full transaction life cycle.

6 ECTS · Elective · Sep–Dec
04
6 ECTS · ELECTIVE · SEMESTER II

Quantitative Risk Management

The course offers hands-on training in risk management and prepares students for the internationally recognized FRM and PRM certification exams. Participants learn to independently model and verify risk across major risk classes, understand modern regulation, and implement models in R and/or Python.

Sep–Dec
Elective

Quantitative Risk Management

The aim of the course is to provide practical, hands-on training for those interested in working in risk management and complete their preparation for passing the FRM and PRM certificates, two globally recognized certificates for risk managers.

By the end of the class students should be able to perform independent risk modeling and verifications of risk models across major risk classes, understand contemporary risk regulation and the role that regulatory and economic capital play in modern financial institutions.

6 ECTS · Elective · Sep–Dec
05
6 ECTS · ELECTIVE · SEMESTER II

Stochastic Calculus

The course builds a solid working knowledge of stochastic differential equations and stochastic calculus, the fundamental tools for modeling and understanding financial asset behavior. Students learn to solve stochastic differential equations, apply Ito’s Lemma, and connect stochastic calculus to partial differential equations.

Sep–Dec
Elective

Stochastic Calculus

The aim of this course is to provide solid working knowledge of stochastic differential and stochastic calculus since stochastic differential equations are used to model the behaviour of financial assets and stochastic calculus is the fundamental tool for understanding and manipulating these models.

At the end of the course, participants should be able to solve different types of linear stochastic differential equations, understand and apply Ito’s Lemma for scalar and vector stochastic processes.

6 ECTS · Elective · Sep–Dec
06
6 ECTS · ELECTIVE · SEMESTER II

Numerical Methods

The course provides a mathematically rigorous introduction to Monte Carlo and finite difference methods for pricing financial options and analyzing their sensitivity to input parameters. Students develop a thorough understanding of the theory behind these methods and gain the ability to implement them independently.

Sep–Dec
Elective

Numerical Methods

The aim of the course is to provide a comprehensive and mathematically rigorous introduction to Monte Carlo and finite difference methods for pricing financial options and for evaluating their sensitivities to various input parameters.

At the end of the course, the student should have a thorough understanding of the basic theory behind Monte Carlo and finite difference methods, and be able to implement them in standard applications.

6 ECTS · Elective · Sep–Dec
Exam preparation

Unique preparation for CFA®, FRM® and PRMIA® (PRM™) exams

The MCF program provides uniquely high-quality education and comprehensive preparation for internationally recognized certifications - CFA®, FRM®, and PRMIA® (PRM™). None of our participants have ever failed these exams.

CFA®
FRM®
PRMIA®
Student voice
Trading lab Trading lab 3:42

Students working through the real-time trading assignment - graded live.

Compare

Master vs Short-Cycle.

Still unsure? Talk to us →
Master Track Master in Computational Finance Short-Cycle Track MCF Specialist
Length 12 months 12 months
Coursework All 5 modules All 5 modules
Thesis Required (16 ECTS) Not required
ECTS 60 ECTS 39 ECTS
Prior degree 4-year (240 ECTS) required Any degree, foreign-friendly
Nostrification Required for foreign degrees Not required
Outcome Master degree (CCA cert + diploma) Short-Cycle cert (CCA cert)
Next cohort · December 2026

Ready to talk?

Schedule a conversation with the Program Leadership to discuss your background, ambitions, and whether MCF is the right fit for you.

Talk to us → Apply for MCF