This repository curates a comprehensive list of resources for individuals pursuing quantitative finance, particularly STEM graduates. It offers a structured collection of courses, books, articles, and papers covering mathematics, machine learning, and financial concepts, aiming to provide a deep understanding beyond introductory materials.

How It Works

The project acts as a curated knowledge base, prioritizing resources that offer in-depth understanding and practical application, often with a strong mathematical or coding foundation. It deliberately excludes widely accessible introductory courses, focusing instead on materials that enable users to leverage their existing STEM skills for advanced study in quantitative finance.

Quick Start & Requirements

• No installation required; it's a curated list of external resources.
• Prerequisites include a strong STEM background, particularly in mathematics and coding.
• Links to official resources are provided within the README for each listed item.

Highlighted Details

• Focus on "Math or Coding heavy" resources, suitable for STEM graduates.
• Explicitly avoids generic Coursera/YouTube courses in favor of deep-dive materials.
• Includes recommendations from Citadel and industry professionals (e.g., Yves Hilpisch, Marcos Lopez de Prado).
• Covers a broad spectrum from foundational mathematics to advanced topics like Reinforcement Learning in finance.

Maintenance & Community

• The project is maintained by PyPatel, an individual with experience as a Quant Trader in Indian High-Frequency Trading firms.
• The README mentions it's "open source for a reason," implying a community-driven aspect for contributions or improvements, though specific community channels are not listed.

Licensing & Compatibility

• The repository itself, as a collection of links and descriptions, does not appear to have a specific license.
• The linked external resources will have their own respective licenses, which users must adhere to. Compatibility for commercial use depends on the licenses of the individual linked materials.

Limitations & Caveats

The README states the author is "weak at grammar" and acknowledges potential mistakes, framing the repository as open source for community correction. Some resources may require significant prior knowledge or specific software environments not detailed here.