The financial industry has adopted Python at a tremendous rate recently with some of the largest investment banks & hedge funds using it to build core trading & risk management systems This hands-on guide helps both developers & quantitative analysts get started with Python & guides you through the most important aspects of using Python for quantitative finance Using practical examples through the book author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives & risk analytics based on a large realistic case study Much of the book uses interactive IPython Notebooks with topics that include Fundamentals Python data structures Num Py array handling time series analysis with pandas visualization with matplotlib high performance IO operations with Py Tables datetime information handling & selected best practices Financial topics mathematical techniques with Num Py Sci Py & Sym Py such as regression & optimization; stochastics for Monte Carlo simulation value-at-Risk & Credit-value-at-Risk calculations; statistics for normality tests mean-variance portfolio optimization principal component analysis (PCA) & Bayesian regression Special topics performance Python for financial algorithms such as vectorization & parallelization integrating Python with Excel & building financial applications based on Web technologies