Магистратура
2026/2027
Прикладные модели с использованием языка Python
Статус:
Курс по выбору (Инвестиции на финансовых рынках)
Где читается:
Факультет экономических наук
Когда читается:
2-й курс, 3 модуль
Онлайн-часы:
26
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
3
Контактные часы:
12
Course Syllabus
Abstract
The goal of this course is to provide students with practical skills in modeling financial instruments using Python libraries. Successful work in financial markets requires specialists to possess knowledge and skills in analyzing large amounts of diverse data. Building effective trading strategies today relies heavily on software tools. Knowledge of financial instrument pricing models and the ability to apply them in practice is an integral part of a financial engineer's training.
Learning Objectives
- The course "Applied Financial Engineering Models Using Python" aims to introduce students to modern technologies for analyzing financial instruments. This course emphasizes the practical application of quantitative finance models. The theoretical component is presented to the extent necessary for understanding the models' key characteristics and properties. Conceptually, the course is based on the approach adopted by international universities and business schools, which focuses on the applied aspects of financial engineering. This requires the mandatory study of software tools necessary for solving practical problems. Python libraries are proposed as such a tool. This language has become the de facto standard in financial data processing. A distinctive feature of Python is its relative accessibility and ease of learning. The language's computational libraries allow for the implementation of a wide variety of quantitative finance algorithms. Students are introduced to classic option pricing models. Exotic financial products are also considered, in particular the barrier option model, which is very popular among investors. The course focuses on issues related to the modeling of interest rate instruments. Quantitative equity portfolio management and financial econometrics are also emphasized. The material in this course can be used in preparing a final thesis.
Expected Learning Outcomes
- Understand the main pricing models for financial instruments and the prerequisites for their practical application
- Develop investment strategies and model an optimal portfolio using Python numerical libraries
- Identify the properties of random processes necessary for modeling financial assets
- Display numerical and statistical Python libraries used for financial market analysis
- Building Financial Instrument Models Using Python Libraries
Course Contents
- 1. Mathematical and Software Tools for Financial Engineering
- 2. Option Modeling. Basic Approaches
- 3. Option Modeling. Strategies and Tools
- 4. Developing Trading Strategies
- 5. Building an Optimal Portfolio
- 6.Credit Risk and Credit Derivatives
- 7. Financial Econometric Models
Interim Assessment
- 2026/2027 3rd module0.7 * Домашнее задание + 0.1 * Активность + 0.2 * Защита работы
Bibliography
Recommended Core Bibliography
- Taieb, D. (2018). Data Analysis with Python : A Modern Approach. Birmingham, UK: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1993344
- Теория вероятностей и математическая статистика : учеб. пособие, Мхитарян, В. С., 2013
- Финансовый менеджмент : учебник, Берзон Н.И., 2020
Recommended Additional Bibliography
- Álvaro Scrivano. (2019). Coding with Python. Minneapolis: Lerner Publications ™. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1947372
- Beysolow, T. (2018). Applied Natural Language Processing with Python : Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing. [Berkeley, CA]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1892182
- Анализ данных в MS Excel : основные сведения о MS Excel, статистические таблицы и графики, статистические функции, пакет анализа (анализ данных) : учеб. пособие для вузов, Мхитарян, В. С., 2018
- Форварды, фьючерсы, опционы, экзотические и погодные производные, Буренин, А. Н., 2008