HILPISCH, Yves J. Financial theory with Python: a gentle introduction. Sebastopol, CA: O'Reilly, 2022. ISBN 978-1-098-10435-1.
KELLIHER, Chris. Quantitative finance with Python: a practical guide to investment management, trading and financial engineering. Boca Raton, FL: Chapman & Hall/CRC, 2022. ISBN 978-1-032-01443-2.
KRESTA, Aleš. Applied quantitative finance in Python: selected theories and examples. Ostrava: VSB - Technical University of Ostrava, 2024. ISBN 978-80-248-4748-1 .
LEWINSON, Eryk. Python for finance cookbook: over 80 powerful recipes for effective financial data analysis. Second edition. Birmingham, UK: Packt Publishing, 2022. ISBN 978-1-80324-319-1 .
Applied quantitative finance in Python
| Language of instruction | angličtina |
|---|---|
| Code | 154-0571 |
| Abbreviation | AQFP |
| Course title | Applied quantitative finance in Python |
| Coordinating department | Department of Finance |
| Course coordinator | doc. Ing. Aleš Kresta, Ph.D. |
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.
Students of the course will learn how to code in Python. They will be familiar with 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 investment portfolios, back-test investment portfolio strategies, create and back-test algorithmic trading strategies, perform Monte Carlo simulations, price options and calculate the Greeks and implied volatility.
Graduates will independently and critically evaluate financial data and the results of quantitative analyses, assess the appropriateness and limitations of the methods used, and justify investment and trading decisions based on the results obtained. They will be able to independently address complex financial problems using Python, interpret model results, and respond to new situations and changing conditions in financial markets.
Students of the course will learn how to code in Python. They will be familiar with 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 investment portfolios, back-test investment portfolio strategies, create and back-test algorithmic trading strategies, perform Monte Carlo simulations, price options and calculate the Greeks and implied volatility.
Graduates will independently and critically evaluate financial data and the results of quantitative analyses, assess the appropriateness and limitations of the methods used, and justify investment and trading decisions based on the results obtained. They will be able to independently address complex financial problems using Python, interpret model results, and respond to new situations and changing conditions in financial markets.
Literature
Doporučená literatura
BRUGIÈRE, Pierre. Quantitative portfolio management: with applications in Python. Cham, Switzerland: Springer, 2020. ISBN 978-3-030-37739-7.
HILPISCH, Yves J. Python for algorithmic trading: from idea to cloud deployment. Sebastopol, CA: O'Reilly, 2020. ISBN 978-1-492-05335-4.
LIU, Peng. Quantitative trading strategies using Python: technical analysis, statistical testing, and machine learning. Berkeley, CA: Apress, 2023. ISBN 978-1-4842-9674-5 .
UNPINGCO, José. Python programming for data analysis. Cham, Switzerland: Springer, 2021. ISBN 978-3-030-68951-3.
HILPISCH, Yves J. Python for algorithmic trading: from idea to cloud deployment. Sebastopol, CA: O'Reilly, 2020. ISBN 978-1-492-05335-4.
LIU, Peng. Quantitative trading strategies using Python: technical analysis, statistical testing, and machine learning. Berkeley, CA: Apress, 2023. ISBN 978-1-4842-9674-5 .
UNPINGCO, José. Python programming for data analysis. Cham, Switzerland: Springer, 2021. ISBN 978-3-030-68951-3.