The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. 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 Python and FinanceChapter 1 Why Python for Finance?What Is Python?Technology in FinancePython for FinanceConclusionsFurther ReadingChapter 2 Infrastructure and ToolsPython DeploymentToolsConclusionsFurther ReadingChapter 3 Introductory ExamplesImplied VolatilitiesMonte Carlo SimulationTechnical AnalysisConclusionsFurther ReadingFinancial Analytics and DevelopmentChapter 4 Data Types and StructuresBasic Data TypesBasic Data StructuresNumPy Data StructuresVectorization of CodeConclusionsFurther ReadingChapter 5 Data VisualizationTwo-Dimensional PlottingFinancial Plots3D PlottingConclusionsFurther ReadingChapter 6 Financial Time Seriespandas BasicsFinancial DataRegression AnalysisHigh-Frequency DataConclusionsFurther ReadingChapter 7 Input/Output OperationsBasic I/O with PythonI/O with pandasFast I/O with PyTablesConclusionsFurther ReadingChapter 8 Performance PythonPython Paradigms and PerformanceMemory Layout and PerformanceParallel ComputingmultiprocessingDynamic CompilingStatic Compiling with CythonGeneration of Random Numbers on GPUsConclusionsFurther ReadingChapter 9 Mathematical ToolsApproximationConvex OptimizationIntegrationSymbolic ComputationConclusionsFurther ReadingChapter 10 StochasticsRandom NumbersSimulationValuationRisk MeasuresConclusionsFurther ReadingChapter 11 StatisticsNormality TestsPortfolio OptimizationPrincipal Component AnalysisBayesian RegressionConclusionsFurther ReadingChapter 12 Excel IntegrationBasic Spreadsheet InteractionScripting Excel with PythonxlwingsConclusionsFurther ReadingChapter 13 Object Orientation and Graphical User InterfacesObject OrientationGraphical User InterfacesConclusionsFurther ReadingChapter 14 Web IntegrationWeb BasicsWeb PlottingRapid Web ApplicationsWeb ServicesConclusionsFurther ReadingDerivatives Analytics LibraryChapter 15 Valuation FrameworkFundamental Theorem of Asset PricingRisk-Neutral DiscountingMarket EnvironmentsConclusionsFurther ReadingChapter 16 Simulation of Financial ModelsRandom Number GenerationGeneric Simulation ClassGeometric Brownian MotionJump DiffusionSquare-Root DiffusionConclusionsFurther ReadingChapter 17 Derivatives ValuationGeneric Valuation ClassEuropean ExerciseAmerican ExerciseConclusionsFurther ReadingChapter 18 Portfolio ValuationDerivatives PositionsDerivatives PortfoliosConclusionsFurther ReadingChapter 19 Volatility OptionsThe VSTOXX DataModel CalibrationAmerican Options on the VSTOXXConclusionsFurther ReadingAppendix Selected Best PracticesAppendix Call Option ClassAppendix Dates and Times