Introduction to Quantitative Finance
IEOR 198: Thursdays 6-8pm @ Latimer 120
Lecturers
Peter Zhang
Neeraj Rattehalli
Sandeep Mukherjee
Aaron Janse
Anish Muthali
Chiraag Balu
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.
This DeCal was previously known as STAT 198 and CS 198-134.
Fall 2023
Decisions have been released. If you don't see an email, message decal [at] traders.berkeley.edu
Lectures are Thursdays 6pm - 8pm at Latimer 120. The first lecture will be 9/7 and is open to all. Subsequent lectures will be open to enrolled and auditing students only.
Fall 2023 Syllabus
Fall 2023 Content is Subject to Change.
Communication will be done primarily through Ed, contact staff there!
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 |
|---|---|---|---|---|
| 9/7 | Introduction to Quantitative Finance (open to public) | DeCal Application | Peter Zhang | Slides |
| 9/14 | What is Fair? | Homework 1 (Now due 9/23 11:59pm) Options Reading | Peter Zhang | Slides |
| 9/21 | Markets and Risk | Homework 2 (due 9/27 11:59pm) Voleon Lecture Interest/Availability Form: due 9/23 11:59pm | Peter Zhang | Slides |
| 9/28 | Execution Strategies | Voleon Lecture 9/27 from 8-10PM at Dwinelle 88! | Peter Zhang | Slides |
| 10/5 | Execution Strategies + Citadel Guest | TBD | Citadel Securities, Peter Zhang | Slides |
| 10/12 | Discovering Trade Ideas | Lab 1 (due 10/18 11:59pm) | Chiraag Balu | Slides |
| 10/19 | Statistical Arbitrage | TBD | Sandeep Mukherjee | Slides |
| 10/27 | Algorithm Optimization | TBD | Anish Muthali | Slides |
| 11/2 | Trading Technology | TBD | Aaron Janse | Slides |
| 11/9 | Guest Lecture: Head of Trading at Optiver | TBD | John Zhu | |
| 11/16 | Optimization and Latency in Execution | TBD | Aaron Janse | |
| 11/23 | Thanksgiving | TBD | TBD | |
| 11/30 | TBD | TBD | TBD |
Prerequisites
We require a strong interest in learning about Finance and Technology and basic coding ability in Python at or above the level of CS61A and.
Overview
The course will start by covering important ideas and intuitions for successful quantitative trading. This will lead into techniques for discovering trading strategies, and quantifying them. Finally, students will be guided through writing their own trading execution engine.
Desired Outcome
By the end of the term, students who have successfully completed Introduction to Quantitative Finance will be able to:
- Understand vital discretionary trading intuitions
- Navigate the modeling problems in quantitative finance
- Build systematic trading software
Methods of Instruction
The course will meet once a week for two hours. One hour of office hours will be available to assist students' understanding of the material. The class time will be roughly half lecture, half hands on assignments and activities - so be sure to attend!
Grading
This course will be graded on attendance, participation, weekly assignments, and a final project.
Participation and Attendance: 40%
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 in 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. Assignments will be a mix of content quizzes and applications of classroom concepts in Jupyter Notebooks.
Assignments will be assigned during each class, and due the following week (Thursday) on Gradescope. Students will receive feedback on their assignments by Sunday midnight.
Coding Project: 30%
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.
Sponsoring Professor
Thank you Professor Mastrolia from IEOR for sponsoring us! For questions about the course, please do not contact the sponsoring professor. Instead, email decal [at] traders.berkeley.edu.
Thibaut Mastrolia
mastrolia [at] eecs.berkeley.edu