book

Python for Finance

by Yves Hilpisch

December 2014

Intermediate to advanced

606 pages

13h 46m

English

O'Reilly Media, Inc.

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Conventions Used in This BookUsing Code ExamplesSafari® Books OnlineHow to Contact UsAcknowledgments
What Is Python?Brief History of PythonThe Python EcosystemPython User SpectrumThe Scientific StackTechnology in FinanceTechnology SpendingTechnology as EnablerTechnology and Talent as Barriers to EntryEver-Increasing Speeds, Frequencies, Data VolumesThe Rise of Real-Time AnalyticsPython for FinanceFinance and Python SyntaxEfficiency and Productivity Through PythonShorter time-to-resultsEnsuring high performanceFrom Prototyping to ProductionConclusionsFurther Reading
Python DeploymentAnacondaPython Quant PlatformToolsPythonIPythonFrom shell to browserBasic usageMarkdown and LaTeXMagic commandsSystem shell commandsSpyderConclusionsFurther Reading
Implied VolatilitiesMonte Carlo SimulationPure PythonVectorization with NumPyFull Vectorization with Log Euler SchemeGraphical AnalysisTechnical AnalysisConclusionsFurther Reading
Basic Data TypesIntegersFloatsStringsBasic Data StructuresTuplesListsExcursion: Control StructuresExcursion: Functional ProgrammingDictsSetsNumPy Data StructuresArrays with Python ListsRegular NumPy ArraysStructured ArraysVectorization of CodeBasic VectorizationMemory LayoutConclusionsFurther Reading
Two-Dimensional PlottingOne-Dimensional Data SetTwo-Dimensional Data SetOther Plot StylesFinancial Plots3D PlottingConclusionsFurther Reading
pandas BasicsFirst Steps with DataFrame ClassSecond Steps with DataFrame ClassBasic AnalyticsSeries ClassGroupBy OperationsFinancial DataRegression AnalysisHigh-Frequency DataConclusionsFurther Reading
Basic I/O with PythonWriting Objects to DiskReading and Writing Text FilesSQL DatabasesWriting and Reading NumPy ArraysI/O with pandasSQL DatabaseFrom SQL to pandasData as CSV FileData as Excel FileFast I/O with PyTablesWorking with TablesWorking with Compressed TablesWorking with ArraysOut-of-Memory ComputationsConclusionsFurther Reading

Python Paradigms and PerformanceMemory Layout and PerformanceParallel ComputingThe Monte Carlo AlgorithmThe Sequential CalculationThe Parallel CalculationPerformance ComparisonmultiprocessingDynamic CompilingIntroductory ExampleBinomial Option PricingStatic Compiling with CythonGeneration of Random Numbers on GPUsConclusionsFurther Reading
ApproximationRegressionMonomials as basis functionsIndividual basis functionsNoisy dataUnsorted dataMultiple dimensionsInterpolationConvex OptimizationGlobal OptimizationLocal OptimizationConstrained OptimizationIntegrationNumerical IntegrationIntegration by SimulationSymbolic ComputationBasicsEquationsIntegrationDifferentiationConclusionsFurther Reading
Random NumbersSimulationRandom VariablesStochastic ProcessesGeometric Brownian motionSquare-root diffusionStochastic volatilityJump diffusionVariance ReductionValuationEuropean OptionsAmerican OptionsRisk MeasuresValue-at-RiskCredit Value AdjustmentsConclusionsFurther Reading
Normality TestsBenchmark CaseReal-World DataPortfolio OptimizationThe DataThe Basic TheoryPortfolio OptimizationsEfficient FrontierCapital Market LinePrincipal Component AnalysisThe DAX Index and Its 30 StocksApplying PCAConstructing a PCA IndexBayesian RegressionBayes’s FormulaPyMC3Introductory ExampleReal DataConclusionsFurther Reading
Basic Spreadsheet InteractionGenerating Workbooks (.xls)Generating Workbooks (.xslx)Reading from WorkbooksUsing OpenPyxlUsing pandas for Reading and WritingScripting Excel with PythonInstalling DataNitroWorking with DataNitroScripting with DataNitroPlotting with DataNitroUser-defined functionsxlwingsConclusionsFurther Reading
Object OrientationBasics of Python ClassesSimple Short Rate ClassCash Flow Series ClassGraphical User InterfacesShort Rate Class with GUIUpdating of ValuesCash Flow Series Class with GUIConclusionsFurther Reading
Web BasicsftplibhttpliburllibWeb PlottingStatic PlotsInteractive PlotsReal-Time PlotsReal-time FX dataReal-time stock price quotesRapid Web ApplicationsTraders’ Chat RoomData ModelingThe Python CodeImports and database preliminariesCore functionalityTemplatingStylingWeb ServicesThe Financial ModelThe ImplementationConclusionsFurther Reading
Fundamental Theorem of Asset PricingA Simple ExampleThe General ResultsRisk-Neutral DiscountingModeling and Handling DatesConstant Short RateMarket EnvironmentsConclusionsFurther Reading
Random Number GenerationGeneric Simulation ClassGeometric Brownian MotionThe Simulation ClassA Use CaseJump DiffusionThe Simulation ClassA Use CaseSquare-Root DiffusionThe Simulation ClassA Use CaseConclusionsFurther Reading
Generic Valuation ClassEuropean ExerciseThe Valuation ClassA Use CaseAmerican ExerciseLeast-Squares Monte CarloThe Valuation ClassA Use CaseConclusionsFurther Reading
Derivatives PositionsThe ClassA Use CaseDerivatives PortfoliosThe ClassA Use CaseConclusionsFurther Reading
The VSTOXX DataVSTOXX Index DataVSTOXX Futures DataVSTOXX Options DataModel CalibrationRelevant Market DataOption ModelingCalibration ProcedureAmerican Options on the VSTOXXModeling Option PositionsThe Options PortfolioConclusionsFurther Reading
Python SyntaxDocumentationUnit Testing
PythonNumPypandas

Overview

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.