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ECTS Course Overview



Applied quantitative finance in Python

* Exchange students do not have to consider this information when selecting suitable courses for an exchange stay.

Course Unit Code154-0571/01
Number of ECTS Credits Allocated5 ECTS credits
Type of Course Unit *Choice-compulsory
Level of Course Unit *Second Cycle
Year of Study *
Semester when the Course Unit is deliveredWinter Semester
Mode of DeliveryFace-to-face
Language of InstructionEnglish
Prerequisites and Co-Requisites Course succeeds to compulsory courses of previous semester
Name of Lecturer(s)Personal IDName
NEM191Ing. Radek Němec, Ph.D.
KRE330doc. 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.
Learning Outcomes of the Course Unit
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.
Course Contents
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
Recommended or Required Reading
Required Reading:
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.
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.
Recommended Reading:
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. ISBN 978-1-119-52681-0.
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.
LAROSE, Chantal D. a Daniel T. LAROSE. Data science using Python and R. Hoboken: Wiley, 2019. Wiley series on methods and applications in data mining. ISBN 978-1-119-52681-0.
UNPINGCO, José. Python programming for data analysis. Cham, Switzerland: Springer, 2021. ISBN 978-3-030-68951-3.
Planned learning activities and teaching methods
Lectures, Tutorials
Assesment methods and criteria
Tasks are not Defined