Traders at Berkeley

Introduction to Quantitative Finance

CS 198-134: Thursdays 8-10 PM PT at Mulford 159 (Gradescope)


Prakash Srivastava

asrivastava [at]

Sandeep Mukherjee

sandeep.m [at]

Akul Arora

akularora [at]

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


Slides are only available for UC Berkeley students and staff using a 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


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.


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:

  1. Understand the basic concepts of market dynamics
  2. Understand basic data processing skills to scrape, extract, and process data
  3. Be aware of wide-ranging applications of modeling techniques
  4. 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.


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%.


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.


Thank you Professor Ranade for sponsoring us!

Gireeja Ranade

ranade [at]