Introduction to Quantitative Finance
Lecturers
Robert Yang
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 2025
Lecture time and location will be announced soon. The first lecture is open to all. Subsequent lectures will be open to enrolled and auditing students only.
For Spring 2025, materials will be posted in the EdStem instead of the website for ease of use. Anyone (including those not taking the course) is welcome to join the Ed to view the materials.
(The link just brings you to Gradescope, please enter the code J65X82 to join)
Fall 2025 Content is Subject to Change.
Communication will be done primarily through Ed, contact staff there!
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