Introduction to Quantitative Finance
Mondays 5 - 7pm Hearst Mining 390
Lecturers
Robert Yang
Neeraj Rattehalli
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 2024
Lectures will occur weekly on Mondays from 5pm - 7pm at Hearst Mining 390, beginning on September 9th.
Fall 2023 Syllabus
- Join Gradescope! (DKXP22)
(The link just brings you to Gradescope, please enter the code DKXP22 to join)
Fall 2024 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 |
|---|---|---|---|---|
| 1/31 | Introduction to Quantitative Finance (open to public) | Homework 1 (Now due 2/15 11:59pm) | Peter Zhang | Slides |
| 2/7 | What is Fair? | Homework 2 (due 2/15 11:59pm) Options Reading | Peter Zhang | Slides |
| 2/14 | Markets and Risk | Homework 3 (due 2/21 4:59pm) | Peter Zhang | Slides |
| 2/21 | Execution Strategies | Homework 4 (due 2/28 4:59pm) | Peter Zhang | Slides |
| 2/28 | Options | TBD | Neeraj Rattehalli | Slides |
| 3/6 | Guest Lecture: John Zhu, Head of Trading at Optiver | TBD | John Zhu | All below links are outdated, but may be used for reference. Slides |
| 3/13 | Discovering Trade Ideas | TBD | Anish Muthali | Slides |
| 3/20 | Guest Lecture: Max Dama | TBD | Max Dama | Slides |
| 4/3 | Testing Performance | TBD | Anish Muthali | |
| 4/10 | Statistical Arbitrage | TBD | Anish Multhali | Slides |
| 4/17 | Trading Technology | TBD | Aaron Janse | |
| 4/24 | Optimization and Latency in Execution | TBD | Aaron Janse |
Prerequisites
We require a strong interest in learning about Finance and Technology and basic coding ability in Python at or above the level of CS 61A. Students should also have a basic understanding of probabilty and statistics at the level of an introductory course.
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] berkeley.edu