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Haskell Financial Data Modeling and Predictive Analytics.
This book is a hands-on guide that teaches readers how to use Haskell's tools and libraries to analyze data from real-world sources in an easy-to-understand manner. This book is great for developers who are new to financial data modeling using Haskell. A basic knowledge of functional programmin...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Packt Publishing,
2013.
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| 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: Getting Started with Haskell Platform; The Haskell platform; Quick tour of Haskell; Laziness; Functions as first-class citizens; Datatypes; Type classes; Pattern matching; Monads; The IO monad; Summary; Chapter 2: Getting Your Hands Dirty; The domain model; The Attoparsec library; Parsing plain text files; Parsing files in applicative style; Outlier detection; Essential mathematical packages; Grubb's test for outliers.
- Template Haskell, quasiquotes, type families and GADTsPersistent ORM framework; Declaring entities; Inserting and updating data; Fetching data; Summary; Chapter 3: Measuring Tick Intervals; Point process; Counting process; Durations; Experimental durations; Maximum likelihood estimation; Generic MLE implementation; Poisson process calibration; MLE estimation; Akaike information criterion; Haskell implementation; Renewal process calibration; MLE estimation; Cox process calibration; MLE estimation; Model selection; The secant root finding algorithm; The QuickCheck test framework.
- QuickCheck test data modifiersSummary; Chapter 4: Going Autoregressive; The ARMA model definition; The Kalman filter; Matrix manipulation libraries in Haskell; HMatrix basics; The Kalman filter in Haskell; The state space model for ARMA; ARMA in Haskell; ACD model extension; Experimental conditional durations; The Autocorrelation function; Stream fusion; Autocorrelation plot; QML estimation; State space model for ACD; Summary; Chapter 5: Volatility; Historic volatility estimators; Volatility estimator framework; Alternative volatility estimators; The Parkinson's number.
- The Garman-Klass estimatorThe Rogers-Satchel estimator; The Yang-Zhang estimator; Choosing a volatility estimator; The variation ratio method; Forecasting volatility; The GARCH (1,1) model; Maximum likelihood estimation of parameters; Implementation details; Parallel computations; Code benchmarking; Haskell Run-Time System; The divide and conquer approach; GARCH code in parallel; Evaluation strategy; Summary; Chapter 6: Advanced Cabal; Common usage; Packaging with Cabal; Cabal in sandbox; Summary; Appendix: References; Index.