About this course
• Theory and practices in quantitative finance
• Python in implementation and analysis
• Hands-on experience
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Python 3.9
Basic programming features
Data structures and types Classes and objects
Error and exception handling Data handling and visualization Data I/O operations
Standard Python libraries: numpy,pandas,scikit-learn,etc
Valuation of financial instruments
Monte Carlo simulation
Portfolio risk measurements
Historical volatilities
Value-at-Risk, Expected Shortfall Time series forecasting using machine learning models
and more…
Pre-requisites
Quantitative finance
GR5010 Introduction to Math Finance or equivalent
Mathematics
Linear algebra (matrices, eigenvalues and eigenvectors), Calculus (Taylor’s expansion, integration and multivariable calculus), elementary analysis (e.g. limits), probability theory (distribution, conditional probabilities), stochastic calculus (e.g. ito’s lemma) and statistics (mean, variance, moment generating functions, linear regression)
Computing skills
Familiarity with programming concepts. Prior programming experience is preferred but not required
Technical Preparations
– Windows 10 or MacOS
– 2.0 GB RAM, 2.6 GHz (minimum)
• Python installation
– https://www.anaconda.com/products/individual – Jupyter Notebook
References
Python for Finance: Mastering Data- Driven Finance
Yves Hilpisch
Options, Futures and Other Derivatives
Monte Carlo Methods in Financial Engineering
Machine Learning in Finance: From Theory to Practice
M. Dixon, I. Halperin, P.
No standard textbook for this course
Course work & Grading scheme
Requirements
Percentage
Class participation
• 4-5 assignments in total
• Include theory and practice
• Submit homework via
Courseworks by due date
• Late homework: no points
Written Exams
• Open book, in-class
• Theory (~30%) and practice (~70%) Class participation
• May have polls, quizzes, breakout room • Stimulating discussions and insights
Course policies
• If you have any questions about homework, please ask your TA in advance, eg. 24 hours before the due time
• Late homework: no points
• If students are caught copying, all participants would receive zero for
that assignment
• Unless you have a good reason, make up means 20% off, and you need to get a TA to proctor
• Subject line starts with “GR5260”
• Email will typically be replied by the end of the next business day
• If you don’t get a reply, please come to see me before the next class
Assignment: Theory
Example: Kelly bet
Assignment: Practice
Example: Kelly bet
Important dates
Change of program ends
Last day to drop class
Spring break
Mar 14 – 18
Python vs C++
• Python as an interpreted language
• An interpreter reads the python program and executes it. It executes
the program instructions as it reads them.
• C++ as a compiled language
• A compiler first reads the entire program and translates it into a different form before running the compiled form. The compiled code can be run repeatedly without repeated translation
Installing Python and Jupyter
https://www.anaconda.com/products/individual
• Python 3.7 or higher
Jupyter Notebook
• web-based application for interactive computing
• generates notebook documents that can be shared with others
• can be saved in other formats
• uses IPython kernel for execution of code
• IPython kernel is integrated with matplotlib
• can be run locally without internet access or run remotely but accessed via internet
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