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Mastering Python for finance /
Annotation
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2015.
|
| Colección: | Community experience distilled.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Python for Financial Applications; Is Python for me?; Free and open source; High-level, powerful, and flexible; A wealth of standard libraries; Objected-oriented versus functional programming; The object-oriented approach; The functional approach; Which approach should I use?; Which Python version should I use?; Introducing IPython; Getting IPython; Using pip; The IPython Notebook; Notebook documents; Running the IPython Notebook; Creating a new notebook; Notebook cells
- Code cellMarkdown cell; Raw NBConvert cell; Heading cells; Simple exercises with IPython Notebook; Creating a notebook with heading and Markdown cells; Saving notebooks; Mathematical operations in cells; Displaying graphs; Inserting equations; Displaying images; Inserting YouTube videos; Working with HTML; The pandas DataFrame object as an HTML table; Notebook for finance; Summary; Chapter 2: The Importance of Linearity in Finance; The capital asset pricing model and the security market line; The Arbitrage Pricing Theory model; Multivariate linear regression of factor models
- Linear optimizationGetting PuLP; A simple linear optimization problem; Outcomes of linear programs; Integer programming; An example of an integer programming model with binary conditions; A different approach with binary conditions; Solving linear equations using matrices; The LU decomposition; The Cholesky decomposition; The QR decomposition; Solving with other matrix algebra methods; The Jacobi method; The Gauss-Seidel method; Summary; Chapter 3: Nonlinearity in Finance; Nonlinearity modeling; Examples of nonlinear models; The implied volatility model; The Markov regime-switching model
- The threshold autoregressive modelSmooth transition models; An introduction to root-finding; Incremental search; The bisection method; Newton's method; The secant method; Combining root-finding methods; SciPy implementations; Root-finding scalar functions; General nonlinear solvers; Summary; Chapter 4: Numerical Procedures; Introduction to options; Binomial trees in options pricing; Pricing European options; Are these formulas relevant to stocks? What about futures?; Writing the StockOption class; Writing the BinomialEuropeanOption class
- Pricing American options with the BinomialTreeOption classThe Cox-Ross-Rubinstein model; Writing the BinomialCRROption class; Using a Leisen-Reimer tree; Writing the BinomialLROption class; The Greeks for free; Writing the BinomialLRWithGreeks class; Trinomial trees in options pricing; Writing the TrinomialTreeOption class; Lattices in options pricing; Using a binomial lattice; Writing the BinomialCRROption class; Using the trinomial lattice; Writing the TrinomialLattice class; Finite differences in options pricing; The explicit method; Writing the FiniteDifferences class