Python for Quantitative Finance and Algorithmic Trading

Financial markets generate massive amounts of data, and analyzing it efficiently requires both solid theoretical knowledge and the right programming skills. This text-based course bridges the gap between financial theory and practical Python implementation. You will transition from understanding basic market concepts to writing structured Python scripts that model stock prices, optimize asset allocation, and evaluate financial risk. What you'll learn: - Understand foundational financial instruments, including stocks, bonds, and market mechanics. - Apply modern Python libraries to clean, manipulate, and analyze historical market data. - Implement Modern Portfolio Theory and the Capital Asset Pricing Model (CAPM) using vectorized calculations. - Calculate option pricing using the Black-Scholes formula and run Monte Carlo simulations to model future price paths. - Measure financial risk by calculating Value at Risk (VaR) using historical and parametric methods. - Write clean, maintainable code using Python type hints to structure financial models and algorithmic logic. The course begins with core financial definitions and market fundamentals before introducing Python-based data analysis. You will progress through asset pricing models, portfolio optimization, and risk management simulations, concluding with the principles of algorithmic strategy design. This course is designed for beginners interested in quantitative finance, finance professionals looking to add Python to their toolkit, and programmers wanting to understand financial modeling. No prior experience in finance or advanced programming is required. Start reading today to build your quantitative analysis skills from the ground up.