154-0571/01 – Applied quantitative finance in Python (AQFP)
| Gurantor department | Department of Finance | Credits | 5 |
| Subject guarantor | doc. Ing. Aleš Kresta, Ph.D. | Subject version guarantor | doc. Ing. Aleš Kresta, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Choice-compulsory type B |
| Year | 2 | Semester | winter |
| | Study language | English |
| Year of introduction | 2022/2023 | Year of cancellation | |
| Intended for the faculties | EKF | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
Students of the course will learn how to code in Python. They will be familiar with the conditional statements, functions, loops, basic data types and structures. They will understand the principles of working with libraries, packages and classes. They will be able to work with scientific packages such as NumPy and Pandas.
Graduates of the course will have the following skills and competencies. In Python, they will be able to calculate risk and return of individual securities and portfolios, calculate the investment portfolios, back-test the investment portfolio strategies, create and back-test algorithmic trading strategies, perform Monte Carlo simulations, price options and calculate the Greeks and implied volatility.
Teaching methods
Lectures
Tutorials
Summary
The course is aimed at expanding students' ability to formulate, solve and subsequently interpret practical problems in the field of quantitative finance with the support of the Python programming language. Attention is paid especially to practical applications of individual models and approaches, in which students are expected to have at least basic theoretical knowledge and orientation.
Compulsory literature:
Recommended literature:
Additional study materials
Way of continuous check of knowledge in the course of semester
Elaboration and defense of the project. For students with an ISP, the same requirements apply.
E-learning
Other requirements
no additional requirements
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1) Introduction to Python: core concepts and syntax, basic data types and working with variables, control structures
2) Structured data types (data structures), shorthand syntax for optimized use of control structures
3) Basic principles of software project organization, use within Python program: functions and classes, scope and visibility of variables, working with libraries and packages
4) Libraries NumPy and Pandas: uses, examples
5) Input/Output Operations
6) Handling of financial time series in Python, calculation of basic statistics and visualization
7) Stochastics: random numbers generation, simulation of stochastic processes
8) Portfolio optimization problem, portfolio performance measures, back-testing of portfolio investment strategies
9) Technical analysis and algorithmic trading, back-testing of trading strategies
10) Risk management: risk measures, risk estimation and its back-testing
11) Valuation of derivatives, calculation of Greeks and implied volatility
12) Project defense
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction