Print Course syllabus
Course syllabus - Python in Financial Engineering
Scope
15.0 credits
Course code
MAA711
Valid from
Autumn semester 2018
Education level
Second cycle
Progressive Specialisation
A1F (Second cycle, has second-cycle course/s as entry requirements)
Main area(s)
Mathematics/Applied Mathematics
School
School of Education, Culture and Communication
Ratified
2017-12-12
Literature lists
Course literature is preliminary up to 8 weeks before course start. Course literature can be valid over several semesters.
-
Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calib
ISBN: 9781119037996
Options, Futures, and Other Derivatives
Latest edition
Mathematical modeling and computation in finance: with exercises and Python and Matlab computer codes
ISBN: 9781786348050
Reference Literature
Options, Futures, and Other Derivatives
Latest edition
Mathematical modeling and computation in finance: with exercises and Python and Matlab computer codes
ISBN: 9781786348050
Objectives
The objective of the course is to give a fundamental and applicable knowledge for use of the programming language Python to solve problems in financial mathematics.
Learning outcomes
At the end of a passed course, the student is expected to be able to
1.manage and qualitatively compare development environments like Spider and Jupiter to produce their own financial mathematical applications
2. apply the principles of Python programming and running as well as write well-structured and well-documented programs
3. implement advanced models for financial instruments, simulate stochastic processes, solve stochastic differential equations and apply finite difference methods
Course content
- introduction to and installation of Python/Anaconda
- Python object-oriented programming, the basis of the programming language, data types, structures, modules, functions, classes and objects, lists, tuples, dictionaries and file management
- processing and visualization of data, financial time series, mathematical tools such as stochastic processes and statistics, integration with Excel and the Web
- problem solving: basic numerical methods related to mathematics and finance, simulation, valuation of derivatives, portfolio calculations
Specific requirements
Analytical Finance 1, 7.5 credits or Stochastic Processes, 7.5 credits or equivalent.
In additional Swedish course B/Swedish course 3 and English course A/English course 6 are required. For courses given entirely in English exemption is made from the requirement in Swedish course B/Swedish course 3.
Examination
PRO1, Project, 9 credits, oral and written presentation concerning learning outcomes 2-3, grade Fail (U), Pass (G) or Pass with distinction (VG).
SEM1, Seminar, 6 credits, computer exercises concerning learning outcomes 1-3, grade Fail (U), Pass (G) or Pass with distinction (VG).
For Pass with distinction (VG) on the course as a whole, the student must have earned that grade for PRO1 and SEM1.
A student who has a certificate from MDU regarding a disability has the opportunity to submit a request for supportive measures during written examinations or other forms of examination, in accordance with the Rules and Regulations for Examinations at First-cycle and Second-cycle Level at Mälardalen University (2020/1655). It is the examiner who takes decisions on any supportive measures, based on what kind of certificate is issued, and in that case which measures are to be applied.
Suspicions of attempting to deceive in examinations (cheating) are reported to the Vice-Chancellor, in accordance with the Higher Education Ordinance, and are examined by the University’s Disciplinary Board. If the Disciplinary Board considers the student to be guilty of a disciplinary offence, the Board will take a decision on disciplinary action, which will be a warning or suspension.
Grade
Three-grade scale