Getting Started¶

Learn-Quant is a collection of 109 self-contained modules. There is no package to install and no build step — you clone the repo and run whichever lesson you want to learn from.

1. Install¶

git clone https://github.com/MeridianAlgo/Learn-Quant
cd Learn-Quant
python -m venv .venv
# Windows:  .venv\Scripts\activate
# macOS/Linux: source .venv/bin/activate
pip install -r requirements.txt

A few JavaScript modules also need Node.js (v18+).

Requirements

The core stack is numpy, pandas, scipy, scikit-learn and matplotlib. Everything is pinned in requirements.txt; development tools (ruff, pytest) live in requirements-dev.txt.

2. Run a module¶

Every folder is independent. Change into it and run the main script:

cd "Black-Scholes Option Pricing"
python black_scholes.py

cd "Options Pricing - JavaScript"
node blackScholes.js

Modules whose file name ends in _tutorial.py are interactive — they walk you through the concept with worked examples and quizzes.

3. How a module is laid out¶

Quantitative Methods - GARCH/
├── README.md      ← the lesson: theory, formulas, usage, pitfalls
└── garch.py       ← the implementation with a runnable __main__ demo

Read the README.md for the why, then open the .py file for the how. The two are written to be read side by side.

4. Suggested order¶

If you are working through the whole curriculum, follow the learning paths. In short:

Python Fundamentals → Data Structures & Algorithms
Advanced Python for production patterns
Quantitative Methods for the maths
Options & Finance → Risk & Portfolio → Strategies
AI / ML and Market Microstructure to specialise

5. Run the tests (optional)¶

pip install -r requirements-dev.txt
pytest z_tests -q
ruff check .

Ready? Head to the module index or pick a learning path.