توضیحاتی در مورد کتاب Haskell Financial Data Modeling and Predictive Analytics
نام کتاب : Haskell Financial Data Modeling and Predictive Analytics
عنوان ترجمه شده به فارسی : مدل سازی داده های مالی Haskell و تجزیه و تحلیل پیش بینی کننده
سری :
نویسندگان : Pavel Ryzhov
ناشر :
سال نشر : 2013
تعداد صفحات : 112
ISBN (شابک) : 1782169431 , 9781782169437
زبان کتاب : English
فرمت کتاب : pdf
حجم کتاب : 3 مگابایت
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فهرست مطالب :
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 GADTs
Persistent 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 modifiers
Summary
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 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
Chapter 6: Advanced Cabal
Common usage
Packaging with Cabal
Cabal in sandbox
Summary
Appendix: References
Index