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
| Type of study | Follow-up Master |
|---|---|
| Language of instruction | English |
| Code | 154-0571/02 |
| Abbreviation | AQFP |
| Course title | Applied quantitative finance in Python |
| Credits | 5 |
| Coordinating department | Department of Finance |
| Course coordinator | doc. Ing. Aleš Kresta, Ph.D. |
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. Machine learning in quantitative finance: basic methods, prediction of financial variables and their applications in Python
13. Project defense
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. Machine learning in quantitative finance: basic methods, prediction of financial variables and their applications in Python
13. Project defense
Literature
Advised literature
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.