Offers a review of the examination that includes test-taking strategies, discussion of relevant concepts, practice questions, and detailed answers.

    The Fourth Edition maintains the dual focus on the concepts and techniques from both the traditional, structured approach and the object-oriented approach to systems development. Instructors have the flexibility to emphasize one approach over the other, or both, while referring to one integrated case study that runs through every chapter.

    Intuition and computational abilities are stressed. Original material on DE and multiple integrals has been expanded.

    He has the bankwide oversight on all quantitative research and the development of new products and applications supporting the trading and structuring businesses. Dan Galai, Ph.D., is the Abe Gray Professor of Banking and Finance at The Hebrew University. He is a co-CEO of Sigma PCM, an investment banking firm. Galai has consulted for the Chicago Board Options Exchange and the American Stock Exchange, and for many major banks and corporations. Robert M. Mark Ph.D., is the Chief Executive Officer of Black Diamond, which provides corporate governance, risk management consulting, and transaction services. He is the chairperson of The Professional Risk Managers' International Association's (PRMIA) Blue Ribbon Panel. He was awarded the Financial Risk Manager of the Year by the Global Association of Risk Professionals (GARP).

    This hands-on guide helps both developers and quantitative analysts get started with Python, and 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 and 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, NumPy array handling, time series analysis with pandas, visualization with matplotlib, high performance I/O operations with PyTables, date/time information handling, and selected best practicesFinancial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regressionSpecial topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies

    The constituent technologies discussed comprise neural networks, fuzzy logic, genetic algorithms, and a number of hybrid systems which include classes such as neuro-fuzzy, fuzzy-genetic, and neuro-genetic systems. The hybridization of the technologies is demonstrated on architectures such as Fuzzy-Back-propagation Networks (NN-FL), Simplified Fuzzy ARTMAP (NN-FL), and Fuzzy Associative Memories. The book also gives an exhaustive discussion of FL-GA hybridization. This book with a wealth of information that is clearly presented and illustrated by many examples and applications is designed for use as a text for courses in soft computing at both the senior undergraduate and first-year postgraduate engineering levels.