Haskell Financial Data Modeling and Predictive Analytics

187 Pages·2013·1.57 MB·English

by  Pavel Ryzhov

Haskell Financial Data Modeling and
Predictive Analytics
Table of Contents
Haskell Financial Data Modeling and Predictive Analytics
Credits
About the Author
About the Reviewers
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Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
1. Getting Started with the 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
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 GADTs
Persistent ORM framework
Declaring entities
Inserting and updating data
Fetching data
Summary
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 modifiers
Summary
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
The Autocorrelation plot
QML estimation
State-space model for ACD
Summary
5. Volatility
Historic volatility estimators
Volatility estimator framework
Alternative volatility estimators
The Parkinson's number
The Garman-Klass estimator
The 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
6. Advanced Cabal
Common usage
Packaging with Cabal
Cabal in sandbox
Summary
A. References
Index
Haskell Financial Data Modeling and
Predictive Analytics
Haskell Financial Data Modeling and
Predictive Analytics
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First published: October 2013
Production Reference: 1221013
Published by Packt Publishing Ltd.
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ISBN 978-1-78216-943-7
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Cover Image by Duraid Fatouhi (<[e-mail]>)
Credits
Author
Pavel Ryzhov
Reviewers
Gregory Collins
Ivan Perez
Acquisition Editor
Sam Birch
Commissioning Editor
Harsha Bharwani
Technical Editors
Krishnaveni Haridas
Chandni Maishery
Project Coordinator
Joel Goveya
Proofreader
Clyde Jenkns
Indexer
Tejal Soni
Graphics
Ronak Dhruv
Production Coordinator
Nilesh R. Mohite
Cover Work
Nilesh R. Mohite