Financial Technology

2026/2027

Учебный год

ENG

Обучение ведется на английском языке

3

Кредиты

Кто читает:

Департамент финансов

Статус:

Курс по выбору

Когда читается:

2-й курс, 3 модуль

Преподаватель

Антипов Артем Игоревич

Course Syllabus

Abstract

This course offers a practice-oriented introduction to the technologies and data infrastructure that underpin modern financial markets, with a particular focus on the Russian market environment. Starting from the premise that data is the foundation of any financial analysis, the course progressively covers the full practical workflow: how market and reference data are accessed (file exports, API requests, web scraping), how financial firms architect their data storage, how exchange trading is organized technically using the Moscow Exchange (MOEX) as the primary example, and which datasets the exchange provides and how to obtain them. Students gain hands-on familiarity with the professional toolset of the industry, including trading terminals (QUIK, MetaTrader), information terminals (Bloomberg, Refinitiv, Andromeda), and public analytical resources (TradingView, DOHOD, InvestIdei, Smart-Lab). The course concludes with the core data-analysis instruments, Excel and Python, and an introduction to algorithmic trading, strategy development, and backtesting. Upon completion, students are equipped to navigate real-world financial data sources, operate industry-standard platforms, and build the data pipelines required for trading and investment analysis. The course is designed for graduate students who want to understand what they will actually work with in practice when collecting and analyzing data, trading, and conducting investment analysis. It bridges the gap between academic finance and the day-to-day reality of the profession, emphasizing the practical questions that academic curricula often leave unanswered: where data comes from, how it is accessed and stored, how an exchange functions under the hood, and which software and information systems professionals rely on. Students develop both an applied understanding of market infrastructure and concrete technical competencies in data access, processing, and analysis. Successful completion prepares students for careers in trading, investment analysis, financial data engineering, and quantitative research, giving them a realistic and operational view of the financial technology landscape they will enter.

Learning Objectives

• The students will gain a practical understanding of the data sources, infrastructure, and software tools used in financial markets, and will be able to apply them when collecting and analyzing data, trading, and performing investment analysis. Successful completion of the course will make them ready for the job market.

Expected Learning Outcomes

• Understand how financial data is sourced, accessed, and stored, and be able to retrieve it through file exports, API requests, and web scraping
• Understand the technical organization of exchange trading and the structure of financial market infrastructure, using the Moscow Exchange (MOEX) as the primary example
• Know the professional tools of financial technology — trading terminals, information terminals, public data resources, Excel and Python — and be able to apply them for data analysis and algorithmic trading

Course Contents

• Financial Data: The Foundation of Analysis
• Accessing and Acquiring Data
• Data Storage Architecture in Financial Firms
• Exchange Trading Infrastructure and MOEX Market Data
• Professional Software: Trading and Information Systems
• Data Analysis Tools: Excel and Python
• Algorithmic Trading: From Strategy to Backtest

Assessment Elements

• Project work
• Exam
• In-Class Activity

Interim Assessment

• 2026/2027 3rd module
  0.3 * Project work + 0.5 * Exam + 0.2 * In-Class Activity

Bibliography

Recommended Core Bibliography

• Hilpisch, Y. (2014). Python for Finance : Analyze Big Financial Data (Vol. First edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=925360
• Kleppmann, M. (2017). Designing Data-Intensive Applications : The Big Ideas Behind Reliable, Scalable, and Maintainable Systems. Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1487643
• McKinney, W. (2018). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython (Vol. Second edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1605925

Recommended Additional Bibliography

• Harris, L. (2002). Trading and Exchanges : Market Microstructure for Practitioners. Oxford: Oxford University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2096842
• Irene Aldridge. (2013). High-Frequency Trading : A Practical Guide to Algorithmic Strategies and Trading Systems: Vol. 2nd edition. Wiley.

Authors

• Soloveva Ekaterina Evgenevna