# Full Stack Quantitative Finance

**By Jeremy Bejarano**

Last updated: {sub-ref}`today` 


This set of notes is designed to accompany _FINM 32900: Full Stack Quantitative Finance_. 

**Course Description**
"Full Stack Quantitative Finance" is a hands-on course centered on key data science tools in quantitative finance. Acknowledging the field's wide scope, the course focuses on a common skill set across various data science subfields. 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, bond transactions from FINRA TRACE, options data from OptionMetrics, 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.


## Table of Contents

```{toctree}
:maxdepth: 1
README.md
acknowledgments.md
appendix.md
```

```{toctree}
:maxdepth: 1
:caption: Lectures 📖
overview_w1.md
overview_w2.md
overview_w3.md
overview_w4.md
overview_w5.md
overview_w6.md
overview_w7.md
overview_w8.md
overview_w9.md
```

```{toctree}
:maxdepth: 1
:caption: Homework 📝
HW0.md
HW1.md
HW2.md
HW3.md
HW4.md
HW5.md
final_project.md
```


<!-- 
# Table of contents
# Learn more at https://jupyterbook.org/customize/toc.html

format: jb-book
root: intro.md
chapters:
- file: README.md # This is the syllabus. It is designed this way 
# so that it can be viewed on GitHub as well.
- file: lectures/Week1/HW0.md
# ----------------------------
# Week 1: GitHub, GitHub Classroom, and Virtual Environments
# Data: IPUMS CPS
# HW: Wage growth during the recession
# TA Session: Setting up VS Code for HW
- file: lectures/Week1/overview_w1.md
  sections:
  - file: lectures/Week1/what_is_this_course_about.md
  - file: lectures/Week1/reproducible_analytical_pipelines.md
  - file: lectures/Week1/case_study_reproducibility_in_finance.md
  - file: lectures/Week1/virtual_environments.md
- file: lectures/Week1/HW1.md
  sections:
  - file: lectures/Week1/case_study_atlanta_fed_wage_growth_tracker.md
  - file: output/_01_wage_growth_during_the_recession.ipynb
# ----------------------------
# Week 2: Env Files, and Secrets
# Data: CRSP and yfinance Data
# Handling Jupyter Notebooks in PyDoit
# CRSP for historical data, yfinance for real-time data
# Tear sheet for individual stocks
# CRSP Market Portfolio and HW constructs SP 500 index
# TA Session: Pull requests on GitHub. GitHub skills page.
- file: lectures/Week2/overview_w2.md
  sections:
  - file: lectures/Week2/WRDS_intro_and_web_queries.md
  - file: output/_01_wrds_python_package.ipynb
  - file: lectures/Week2/env_files.md
- file: lectures/Week2/HW2.md
  sections:
  - file: output/_02_CRSP_market_index.ipynb
  - file: output/_03_SP500_constituents_and_index.ipynb
# ----------------------------
# Week 3: Task Runners, Automating Queries and the Basics of SQL
# Data: CRSP
# Pushing large computations to the cloud
# TA Session: No TA session because of the holiday. Lecture uses TA session as makeup day.
- file: lectures/Week3/overview_w3.md
  sections:
    - file: lectures/Week3/what_is_a_task_runner.md
    - file: lectures/Week2/project_structure.md
    - file: output/_05_basics_of_SQL.ipynb
- file: lectures/Week3/HW3.md
  sections:
  - file: output/_04_Fama_French_1993.ipynb
# ----------------------------
# Week 4: Publishing Reports: Markdown, LaTeX, and Github Pages
# Data: CRSP and Compustat Data, Merged
# Data: Datastream??
# Publishing charts to ChartBook. Individual pipeline page. Publishing to GitHub Pages
# Everybody contributes their own new chart to the ChartBook. Each student must contribute
# a unique chart to the ChartBook. Use GitHub Issue tracker to claim a chart BEFORE
# starting work on it. The issue must be approved by the instructor before you can start.
# Then, the repo that collects charts simply has a URL that points to the student's GitHub
# repo that contains the pipeline.
# TA Session: LaTeX essentials
- file: lectures/Week4/overview_w4.md
  sections:
  - file: lectures/Week4/reports_with_jupyter_notebooks.md
  - file: lectures/Week4/intro_to_LaTeX.md
  - file: lectures/Week4/latex_essentials.md
# ----------------------------
# Week 5: Medium Data
# Python Polars Package: Lazy evaluation, parallel execution, and streaming
# Data: Corporate TRACE Data
- file: lectures/Week5/overview_w5.md
  sections:
  - file: lectures/Week5/sphinx.md
  - file: lectures/Week5/unit_tests.md
# ----------------------------
# Week 6: Creating your own Python Package, plus CI/CD and GitHub Actions
# Hatch and Hatchling, Sphinx, Click CLI
# Yield Curve Estimation
# CRSP Treasury Data
- file: lectures/Week6/overview_w6.md
  sections:
  - file: lectures/Week6/GitHub_pull_requests.md
  - file: lectures/Week6/python_packaging_with_hatch.md
- file: lectures/Week6/HW4.md
  sections:
  - file: output/01_CRSP_treasury_overview_ipynb.ipynb
  - file: output/02_replicate_GSW2005_ipynb.ipynb
# ----------------------------
# Week 7: Working with Remotes, HPC Scheduler, GNU Parallel
# SSH, SCP, SFTP
# Data: SEC Filings
# Jupyter Notebooks running remotely, port forwarding. Connect traditional way and via VS Code
# Try Remote Tunnels: https://code.visualstudio.com/docs/remote/tunnels and https://www.youtube.com/watch?v=SyLHXdXhE1U
# TA Session: Bloomberg Terminal
- file: lectures/Week7/overview_w7.md
  sections:
  - file: lectures/Week7/bloomberg_terminal.md
  - file: lectures/Week7/LSEG_datastream.md
- file: lectures/subject_to_change_after_this_week.md
# ----------------------------
# Week 8: Big Data
# Data: NYSE Trade and Quote Data (TAQ)
# NBBO Calculation
# TA Session: SAS
- file: lectures/Week8/overview_w8.md
  sections:
  - file: lectures/Week8/github_actions_interactive_dashboard.md
  - file: output/_01_repo_spikes.ipynb
  - file: output/_corporate_hedging.ipynb
  - file: output/_spx_hedging.ipynb
- file: lectures/Misc/final_project.md
  sections:
  - file: lectures/Misc/final_project_rubric.md
  - file: lectures/Misc/potential_final_projects.md
- file: lectures/Misc/appendix.md
# ----------------------------
# Week 9: Publishing a live dashboard to the web
# IPUMS CPS Data  -->