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This course will be organised by Dirk Gerritsen of the Utrecht School of Economics, and can be taken as an elective within the Complex Systems Profile.
The course is organized as a BYOD course, which means that you have to bring own laptop. (BYOD stands for Bring Your Own Device)
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.
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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.
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 analyze 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. Both assignments count for 37.5% of the final grade, adding up to 75% of the total grade.
2. Written exam. The main component of the exam will be the lecture materials for all course weeks. In addition, there will be 1 exam question on a programming assignment. The minimum grade for the exam is 5. If a student scores lower than a 5, this automatically translates into a fail for the full course. If the final grade for the exam is a non-passing grade and if the student attended at least 80 percent of all lectures (attendance will be taken), the student qualifies for a retake. Only the exam part of this course can be done in the retake.
Course materials
To be announced on Blackboard
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