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Course module: ECMAF
ECMAF
Algorithms in Finance (for non-U.S.E. students) (7,5 ECTS)
Course infoSchedule
Course codeECMAF
ECTS Credits7.5
Category / LevelM (M (Master))
Course typeCourse
Language of instructionEnglish
Offered byFaculty of Law, Economics and Governance; Graduateschool REBO; Economics Master (for administratiev use only);
Contact personT. Walther
E-mailt.walther@uu.nl
Lecturers
Course contact
T. Walther
Other courses by this lecturer
Teaching period
3  (06/02/2023 to 23/04/2023)
Teaching period in which the course begins
3
Time slotB: B (TUE-morning, THU-afternoon)
Study mode
Full-time
RemarkElective for non-economics students.
Enrolment periodfrom 31/10/2022 up to and including 25/11/2022
Course application processOsiris Student
Enrolling through OSIRISYes
Enrolment open to students taking subsidiary coursesYes
Pre-enrolmentNo
Waiting listNo
Course goals
Learning goals
At the end of the course, the student is able to: 
  • understand the financial theory behind financial algorithms and analytics;
  • develop financial algorithms for trading;
  • understand the role of financial algorithms in modern financial markets and fintech developments.
Content
The course is organized as a BYOD course, which means that you have to bring own laptop. (BYOD stands for Bring Your Own Device)

Contents
With the rise of high frequency trading on stock markets during the past decade, algorithmic trading plays an increasingly important role on stock markets, for example to exploit mispricing. In the course students will learn to build financial algorithms based on clear and feasible valuation logic. During the sessions students will learn what valuation techniques can be applied to a range of financial products. Moreover, the role of financial algorithms in modern finance and fintech will be discussed. This course is given with the support of an internationally leading firm in the field of market making. This firm trades a wide range of products: listed derivatives, cash equities, exchange–traded funds, bonds and foreign currencies. This cooperation enables students to test their algorithms by applying them to actual price data of a wide range of financial products. The programming of algorithms will be done in Python, a programming language that is used more and more in firms that work with big data and comprehending it is therefore helpful for your future career. The students are expected to have some basic knowledge of Python; a small document discussing the expected level will be distributed via Blackboard prior to the start of the course. The first two weeks consist of two sessions per week where we discuss the valuation of stocks, bonds, and derivatives. In addition, there are two Python workshops which are optional to join. From course week three onwards, each course week will consist of a lecture and a workshop. The lectures discuss arbitrage setups used in financial markets, such as simple pairs trading using stocks, but also delta hedging using options. The workshops will be geared towards the development of algorithms to be built to exploit mispricing in stocks or derivatives.

Prerequisites
The students are expected to have some basic knowledge of Python; a small document discussing the expected level will be distributed via Blackboard prior to the start of the course.


Format
Lectures and workshops.

Assessment method
  1. This course comes with two assignments in small teams in which students design a trading strategy. The deliverable of both assignments is a report (3-6 pages) which contains at least the following four items: (i) the description of methods to analyse the data and develop the trading strategy, (ii) a detailed description of the logic behind each strategy, (iii) a visualization of the performance of the strategy, and (iv) discussion and possible aspects which can still be improved. In addition, also the notebook containing programming code has to handed in. The assignments count for 65% of the final grade.
  2. Written exams (midterm and final). The main component of the exams will be the lecture materials for all course weeks.

Course materials
To be announced on Blackboard.


Evaluation matrix.
  Midterm Exam 10% Final Exam 25% Assignments 65%
is able to understand the relations between financial markets and business economics x x x
is able to distinguish the main features of financial products and markets x x x
is able to value stocks based asset pricing models x x x
understands the mechanics of (electronic) trading floors x x x
is able to value the futures contracts and to profit from mispricing x x x
is able to understand the sensitivities of the Black Scholes model ('the greeks’)   x x
understands statistical arbitrage strategies and other elementary trading strategies   x x
has knowledge of the risks of trading financial instruments   x x
is able to contribute in teams and meet deadlines     x
is able to build increasingly functional computer trading algorithms     x
Competencies
-
Entry requirements
You must meet the following requirements
  • In Period 3 you may not be part of one of the following target groups
    • USE Students
Required materials
-
Recommended materials
Book
-
ISBN:978-0471460671
Title:Pairs Trading: Quantative Methods and Analysis
Author:Ganapathy Vidyamurthy
Publisher:Wiley
Edition:1
Instructional formats
Lecture

Tutorial

Tests
Midterm exam
Test weight10
Minimum grade1

Retake
Test weight1
Minimum grade1

Final test
Test weight25
Minimum grade1

Assignment
Test weight65
Minimum grade1

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