Quantitative Finance with Python¶

Welcome! This book is written in Markdown and published using MkDocs with the Material for MkDocs theme.

Much of the foundational material in this book was developed through conversations with AI assistants, based on my lecture notes and additional resources, particularly GitHub. While I have reviewed and revised parts of the content, time constraints have prevented a complete line-by-line verification. I sincerely welcome contributions -- especially corrections and suggestions -- via GitHub pull requests or email.

GitHub: https://github.com/SungchulLee/financial_math_book_writing

Email: sungchul@yonsei.ac.kr

1. Running the Python Code Examples¶

To run the code examples in this book, install the following packages that are not included in the standard Anaconda distribution:

bash pip install beautifulsoup4 cvxpy lxml mplfinance pandas_datareader requests selenium webdriver-manager yfinance

2. Installing MkDocs (For Building the Book Locally)¶

If you would like to build or modify this book locally, install MkDocs and the Material theme:

bash pip install mkdocs mkdocs-material

If your project uses additional MkDocs plugins, install them as well:

bash pip install mkdocs-mermaid2-plugin mkdocs-jupyter mkdocs-git-revision-date-localized-plugin

3. Serving the Book Locally¶

After installation, navigate to the root directory of the repository:

bash cd financial_math_book_writing

Start the local development server:

bash mkdocs serve

Then open your browser at http://127.0.0.1:8000. The page will automatically reload when you modify Markdown files.

4. Building the Static Site¶

To generate the static site files:

bash mkdocs build

The compiled site will be created in the site/ directory.

5. Recommended: Using requirements.txt¶

For convenience, you may install all required packages at once using:

bash pip install -r requirements.txt

6. Supplementary Python Implementations¶

Several Python examples in this book are adapted from Financial-Models-Numerical-Methods by Nicola Cantarutti (cantaro86). That project provides Jupyter notebooks and a Python library (FMNM) covering PDE methods, Fourier inversion, Levy processes, Kalman filtering, and portfolio optimization. The files prefixed with cantaro86_ throughout this book are self-contained, educational adaptations of that library, mapped to the corresponding chapters:

Sungchul Lee

Department of Mathematics, College of Science

Yonsei University

Seoul, South Korea