Introduction to Quantitative Finance
CS 198-134: Thursdays 8-10 PM PT at Mulford 159 (Gradescope)
Lecturers
Prakash Srivastava
asrivastava [at] berkeley.edu
Sandeep Mukherjee
sandeep.m [at] berkeley.edu
Akul Arora
akularora [at] berkeley.edu
Course Description
Quantitative Finance has a high barrier of entry with expertise in quantitative subjects required for a career. We hope to bridge the gap between industry expectations and the student’s possible career choices by exposing them to a basic understanding of quantitative finance. Through units in economics, machine learning, and quantitative investing, students will learn the necessary skills to be familiarized with the industry, and we hope that this is an opportunity for students to develop their own quantitative intuition about the market.
Spring 2023
STAT 198 is now CS 198-134!
Spring 2023 Syllabus
Schedule
Slides are only available for UC Berkeley students and staff using a berkeley.edu account. Guest lecture slides are generally not made available.
| Date | Lecture | Homework | Lecturers | Resources |
|---|---|---|---|---|
| 1/26 | Introduction to Quantitative Finance (open to public) | DeCal App | Akul, Sandeep, Prakash | Slides, Recording |
| 2/2 | The Financial Landscape | Quiz 1, Coding HW0 | Prakash | Slides, Recording |
| 2/9 | Risk | Quiz 2 | Prakash, Akul | Slides, Recording |
| 2/16 | Market Microstructure | Quiz 3 | Sandeep, Akul, Prakash | Slides, Recording |
| 2/23 | Market Making | Quiz 4, Coding HW1 | Anish, Dominic, Prakash | Slides, Recording |
| 3/2 | ETF Theory and International ETFs | Quiz 5 | Alex Kwon - Citadel Securities | Slides |
| 3/9 | Options | Quiz 6, Coding HW2 | Alberto, Aekus | Slides, Recording |
| 3/16 | Quantitative Strategies | Quiz 7 | Akul, Prakash | Slides, Recording |
| 3/23 | Guest Lecture | None | Max Dama - Headlands | |
| 3/30 | Spring Break | None | None | |
| 4/6 | Stat Arb and Modeling | Quiz 8 | Sandeep | Slides, Recording |
| 4/13 | Guest Speaker | Coding HW3 | John Zhu - Optiver | |
| 4/20 | Crypto | Quiz 9 | Chiraag | Slides |
| 4/27 | High Frequency Trading | TBD | Akul, Prakash | Slides, Recording |
Prerequisites
We require a strong interest in Finance and Technology and basic coding ability in Python at or above the level of CS61A and CS61B.
It is also recommended that students have some statistics background at the level of an introductory statistics or data science course.
Overview
The course will start by introducing the fundamentals of the Capital Markets, including Market Microstructure and Securities Pricing. Then, we will look at basic data processing skills to scrape, extract, and process open data from the web to build predictive models using modern Machine Learning techniques. We will tie the course together by introducing basic portfolio optimization and the popular methods used by various quantitative firms. Finally, we will test the student’s understanding of the material by having each student build their own trading strategy from selected tickers, analyze, and present the results.
Desired Outcome
By the end of the term, students who have taken Introduction to Quantitative Finance are expected to be able to:
- Understand the basic concepts of market dynamics
- Understand basic data processing skills to scrape, extract, and process data
- Be aware of wide-ranging applications of modeling techniques
- Be able to use prototype software and packages to develop their own projects
Methods of Instruction
The course will meet once a week for two hours. One hour of office hours is designated after each lecture to assist students' understanding of the material.
Grading
This course will be graded on attendance, participation, weekly assignments, and a final project.
Participation and Attendance: 30%
Students will have one excused absence for the semester. Attendance is extremely important to understand the course material and to stay on track. Every unexcused absence will result as a 10% reduction in the final grade.
Weekly assignments: 30%
There will be weekly assignments to test on practical applications of the course material for each week. These will be helpful in implementing students' final projects. Assignments will be a mix of weekly reading summaries and applying theoretical concepts in Jupyter Notebook.
Assignments will be assigned during each class, and due the following week (Thursday) before class via email to the instructors. Students will receive feedback on their assignments by Sunday midnight.
Final Project: 40%
This will be a cumulative final project that the student will work on for the entire semester. It will test students' knowledge and understanding by applying the course material to a real-live trading environment.
Pass/No Pass
In order to pass the course, you need at least 70%.
Policies
This course will be graded on attendance, participation, weekly assignments, and a final project.
Late Assignments
Assignments are due in class with a 50% penalty per day until the score reaches 0%.
Academic Dishonesty
Students must cite any external sources used in their work. Failure to do so will be considered academic dishonesty.
Professor
Thank you Professor Ranade for sponsoring us!
Gireeja Ranade
ranade [at] eecs.berkeley.edu