This repository documents my self study journey to pursue a role as a junior quantitative trader and to prepare for a master's degree in Quantitative finance / finance.

Welcome to my GitHub profile ! I'm a former entrepreuneur with a bachelor degree in physics, now transitioning into quantitative finance. My current goal is to pursue graduate studies in Quantitative Finance at a master level and work in financial market. I am passionate about trading and quant research and I am exploring how mathematical models and programming can be applied to financial decision making and strategies design.

This GitHub is my way of documenting that journey, building projects, and connecting with like minded individuals.

LinkedIn: linkedin.com/in/paul-c-94349036b

My goal is to review and strengthen my foundations in mathematics and programming and selected finance topics with a focus on the skills needed for a career in quantitative trading.

Here are the certifications I’m currently working on as part of my learning path.
You can follow my progress in the dedicated repositories linked on my profile.

Tip: Clicking a repository below opens its homepage, which includes a “Quick access” section. There you’ll find links to all certificates and their detailed folders (modules, notes, progress, proof).

Finance & Python Learning Portfolio

• Investments: Financial Markets,Options and Derivatives - Summer University — Copenhagen Business School ✅
• Financial Markets — Yale University (Coursera) ✅
• Executive Programme in Algorithmic Trading (EPAT®) — QuantInsti
• Introduction to Portfolio Construction and Analysis with Python — EDHEC (Coursera)
• Advanced Portfolio Construction and Analysis with Python — EDHEC (Coursera)
• Python and Machine Learning for Asset Management — EDHEC (Coursera)
• Python and Machine-Learning for Asset Management with Alternative Data Sets — EDHEC (Coursera)
• Bloomberg Market Concepts (BMC) — Bloomberg

Quantitative Math Learning Portfolio

• Linear Algebra — Imperial College London (Coursera)
• Multivariate Calculus — Imperial College London (Coursera)
• Mathematics for Machine Learning – PCA — Imperial College London (Coursera)
• Probability & Statistics: To p or not to p? — University of London (Coursera)
• Statistics with Python — University of Michigan (Coursera)
• Advanced Probability and Statistical Methods — Johns Hopkins University (Coursera)
• Stochastic Calculus for Finance — edX / FutureLearn

This section outlines the books I’m currently studying some Chapter summaries, solved exercises, and personal notes are available in the corresponding repositories.

• Heard on the Street – Timothy Crack
• Linear Algebra Done Right (4th ed.) — Sheldon Axler
• Calculus, Vol. 1 — Tom M. Apostol
• Calculus, Vol. 2 — Tom M. Apostol
• Introduction to Probability — Joseph K. Blitzstein & Jessica Hwang
• Probability and Statistics — Morris H. DeGroot & Mark J. Schervish
• Statistical Inference (2nd ed.) — George Casella & Roger L. Berger (optional/advanced)
• Stochastic Calculus for Finance I & II — Steven E. Shreve
• Understanding Analysis (2nd ed.) — Stephen Abbott
• Discrete Mathematics and Its Applications — Kenneth H. Rosen
• Convex Optimization — Stephen Boyd & Lieven Vandenberghe
• Principal Component Analysis (2nd ed.) — I.T. Jolliffe

This GitHub showcases my long term commitment I’m always open to collaborations, projects, or discussions related to trading, quantitative finance, algorithmic trading, or financial modeling.
Feel free to follow my journey, explore my work and collaborate. Feedback and suggestions are welcome!