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Haskell Financial Data Modeling and Predictive Analytics电子书

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15人正在读 | 0人评论 9.8

作       者:Pavel Ryzhov

出  版  社:Packt Publishing

出版时间:2013-10-25

字       数:52.7万

所属分类: 进口书 > 外文原版书 > 电脑/网络

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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 programming is not required but will be useful. An interest in high frequency finance is essential.
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Haskell Financial Data Modeling and Predictive Analytics

Table of Contents

Haskell Financial Data Modeling and Predictive Analytics

Credits

About the Author

About the Reviewers

www.PacktPub.com

Support files, eBooks, discount offers and more

Why Subscribe?

Free Access for Packt account holders

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

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