quant-examples

A collection of examples and tutorials on quantitative finance, numerical methods, stochastic processes, and simulation.

Contents (Jupyter notebooks)

• brownian_bridge.ipynb: Brownian Bridge construction and applications.
• cos_method.ipynb: COS method for option pricing.
• dynamic_beta_kalman_filter.ipynb: Dynamic Beta model using Kalman filtering.
• euler_milstein.ipynb: Euler and Milstein schemes for SDEs.
• fractional_brownian_motion.ipynb: Fractional Brownian Motion construction.
• gaussian_mixture_models.ipynb: Gaussian Mixture Model and Expectation-Maximization algorithm.
• gaussian_pca.ipynb: Principal Component Analysis of two-dimensional Gaussian data.
• levy_construction_brownian_motion.ipynb: Lévy's construction of Brownian Motion.
• ica_pca.ipynb: Independent Component Analysis (ICA).
• inverse_gaussian.ipynb: Inverse Gaussian distribution and applications.
• pandas_datareader.ipynb: Examples using the Pandas Datareader package.
• yield_curve_pca.ipynb: PCA on yield curve data.

Python Installation Guide

Option 1: Standard Virtual Environment

1. Clone the repository:

   git clone https://github.com/fbourgey/quant-examples.git

2. Navigate to the project directory:

   cd quant-examples

3. Create a virtual environment:

   python3 -m venv .venv

4. Activate the environment:

   source .venv/bin/activate

5. Install dependencies:

   pip install .

6. Launch Jupyter Lab (optional):

   jupyter lab

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Option 2: Using uv (Recommended)

If you have uv installed, setup is simpler and faster.
After cloning the repository (steps 1–2 above), run:

uv sync

This will automatically create a virtual environment and install all dependencies.