Course Syllabus: FINM 32900, Winter 2025

Course Syllabus: FINM 32900, Winter 2025#

FINM 32900, Full Stack Quantitative Finance

Summary#

Course Description “Full Stack Quantitative Finance” is a hands-on course centered on a core set of fundamental tools common across financial computing and data science. That is, this course examines elements of the analytical pipeline, from data extraction and cleaning to exploratory analysis, visualization, and modeling, and finally, publication and deployment. It does so with the aim of teaching the tools and principles behind creating reproducible and scalable workflows, including build automation, dependency management, unit testing, the command-line environment, shell scripting, Git for version control, and GitHub for team collaboration. These skills are taught through case studies, each of which will additionally give students practical experience with key financial data sets and sources such as CRSP and Compustat for pricing and financials, macroeconomic data from FRED and the BEA, bond transactions from FINRA TRACE, Treasury auction data from TreasuryDirect, textual data from EDGAR, and high-frequency trade and quote data from NYSE. Prior experience at an intermediate level with Python and the PyData stack is assumed.

  • Class: Mondays, 6 - 9 PM, in-person at the Stevanovich Center building, Room #112. (5727 S. University Ave.)

  • Lecturer: Jeremy Bejarano, jbejarano@uchicago.edu

  • Instructor Office Hours: Monday, 5 - 6 pm, in the FinMath library (first floor of the Stevanovich Center, 5727 S. University Ave.)

  • Teaching Assistants:

    • Viren Desai, vdd@uchicago.edu

    • Nick Lewis, nicklewis16@uchicago.edu

    • Note: Please include both TAs on all emails. However, students are strongly encouraged to post questions on the discussion page of the class GitHub repository here.

  • TA Session/TA Office Hours: Fridays from 12-1 pm CT on Zoom. See the Zoom link on the Canvas calendar.

  • Website: Canvas will be used for grades and for publishing Zoom links only. Homework and notes will be posted on the course GitHub repo: finm-32900/finm-32900-data-science. Questions and other class-related discussions should be posted here as well.

  • Textbook: The text for the course will be published incrementally here: https://finm-32900.github.io/

NOTE: Due to the holiday on January 20, a makeup class will be scheduled. This makeup class will be held on Friday, January 24 during the TA office hours. I will use the same Zoom link as the TA office hours, which you can find on the Canvas calendar.

Assignments#

  • Assignments must be submitted via GitHub before 6 pm on Fridays. Each assignment will be distributed on a Monday, and will be due the on the Friday of the following week (11 days later).

  • Assignments are automatically graded via the autograder on GitHub Classroom and solutions will be released shortly after. This means that the due date is strict. Late assignments will not be accepted.

  • Each student is to individually submit their assignment (unless otherwise specified). Students are encouraged to work in groups, but students are not allowed to copy each other’s code. Each student must write their own solutions individually.

  • After assignments are graded, solutions will be posted in separate GitHub repos, found here: finm-32900

Final Project#

In lieu of a final exam, students will be organized into groups of 2 (pairs) and will each complete a course project. Each group will present their completed project to the instructor at the end of the course. These presentations will be scheduled individually.

Assessment#

Grades will be based on coding assignments (70%), a final group project (25%), and participation (5%).

  • Assignments will be submitted individually and will be graded using GitHub’s automated testing tools.

  • The final project will be completed in groups. Students will choose the project from among a few options provided at the beginning of the quarter. The project will be graded not only on how well it accomplishes the assigned data cleaning and analysis task, but will be primarily graded on whether (1) the steps to reproduce it are fully automated and well documented, (2) the code is written in a clean and reusable fashion, and (3) the results are presented clearly and presented in a way that convinces the reader that the results are correct. A more specific rubric will be provided in class.

  • The participation grade will depend on the positive impacts that a student has on the class. These include participating in in-class discussions and/or answering questions on the class GitHub page (or on Canvas). Students are in no way penalized for giving wrong answers in these in-class discussions nor is there any penalty for asking for help—asking for help is often the best way to learn!

Schedule#

The schedule will follow the ordering of the chapters listed in the GitHub book found here: https://finm-32900.github.io/. Each week is it’s own chapter and the agenda is listed in the first sub-section of the chapter.

HW Due Dates#

References#

I will provide the lecture notes that we will use in class here: https://finm-32900.github.io/. As a prerequisite, you should have some prior familiarity with Python and the PyData stack (e.g., Numpy, Scipy, Pandas, Matplotlib). The following references may serve as useful refreshers:

A significant portion of this course is inspired by “The Missing Semester of Your CS Education”, a short course taught in the Computer Science department at MIT. I’ll rely on the material shown there for portions of this course.

Software to be used in class#

Lectures will feature live programming exercises in class, so students should have a WiFi-enabled laptop to bring to class.

Before the first class, please make sure to install the required software and sign up for the required services. Students will need to install the following software on their laptop. Each of these pieces of software are free:

Students should also sign up for an account with the following websites. We will use free versions of each of these services:

Quick Start#

To quickest way to run code in this repo is to use the following steps. First, you must have the conda
package manager installed (e.g., via Anaconda). However, I recommend using mamba, via [miniforge] (conda-forge/miniforge) as it is faster and more lightweight than conda. Second, you must have TexLive (or another LaTeX distribution) installed on your computer and available in your path. You can do this by downloading and installing it from here (windows and mac installers). Having done these things, open a terminal and navigate to the root directory of the project and create a conda environment using the following command:

conda create -n finm python=3.12
conda activate finm

and then install the dependencies with pip

pip install -r requirements.txt

Finally, you can then run

doit

And that’s it! The landing page of the textbook website will be available at ./_build/html/index.html.

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