Applied quantitative finance in Python

Type of study	Follow-up Master
Language of instruction	English
Code	154-0571/01
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) Project defense

Literature

BRUGIÈRE, Pierre. Quantitative portfolio management: with applications in Python. Cham, Switzerland: Springer, 2020. Springer texts in business and economics. ISBN 978-3-030-37739-7.
HILPISCH, Yves J. Financial theory with Python: a gentle introduction. Sebastopol, CA: O'Reilly, 2021. ISBN 978-1-098-10435-1.
HILPISCH, Yves J. Python for finance: mastering data-driven finance. Second edition. Sebastopol, CA: O'Reilly, 2018. ISBN 978-1-492-02433-0.

Advised literature

HILPISCH, Yves J. Python for algorithmic trading: from idea to cloud deployment. Sebastopol, CA: O'Reilly, 2020. ISBN 978-1-492-05335-4.
LAROSE, Chantal D. and Daniel T. LAROSE. Data science using Python and R. Hoboken: Wiley, 2019. Wiley series on methods and applications in data mining. .
UNPINGCO, José. Python programming for data analysis. Cham, Switzerland: Springer, 2021. ISBN 978-3-030-68951-3.

back to search page