Financial Engineering With Python

This book provides a rigorous and highly practical introduction to financial engineering through the unifying lens of portfolio construction. Rather than treating quantitative finance as a collection of elegant but isolated models, it shows how probability, stochastic processes, stochastic calculus, optimization, arbitrage, and option pricing come together in implementable trading and hedging decisions. The book develops the mathematical foundations needed for serious work in the field, then moves to market structure, no-arbitrage pricing, portfolio optimization, static and dynamic hedging, and both non-path-dependent and path-dependent options. A distinctive feature is its continuous integration of Python: chapter-level Python with AI sections, computational laboratories, and an accompanying code repository help readers verify formulas, simulate processes, solve linear programs, and turn theory into executable workflows. With exercises and examples spanning combinatorics to Black-Scholes, binomial models, model-free pricing, and robust portfolio design, the book is suited both to advanced students and to practitioners who want mathematically sound finance presented with computational clarity and real implementation value.