Haskell Financial Data Modeling and Predictive Analytics

Get an in-depth analysis of financial time series from the perspective of a functional programmer

Haskell Financial Data Modeling and Predictive Analytics

Progressing
Pavel Ryzhov

Get an in-depth analysis of financial time series from the perspective of a functional programmer
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Book Details

ISBN 139781782169437
Paperback112 pages

About This Book

  • Understand the foundations of financial stochastic processes
  • Build robust models quickly and efficiently
  • Tackle the complexity of parallel programming

Who This Book Is For

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.

 

Table of Contents

Chapter 1: Getting Started with the Haskell Platform
The Haskell platform
Quick tour of Haskell
Summary
Chapter 2: Getting your Hands Dirty
The domain model
The Attoparsec library
Parsing plain text files
Parsing files in applicative style
Outlier detection
Template Haskell, quasiquotes, type families, and GADTs
Persistent ORM framework
Summary
Chapter 3: Measuring Tick Intervals
Point process
Counting process
Durations
Poisson process calibration
Renewal process calibration
Cox process calibration
Model selection
The secant root-finding algorithm
Summary
Chapter 4: Going Autoregressive
The ARMA model definition
The Kalman filter
Matrix manipulation libraries in Haskell
The Kalman filter in Haskell
The state-space model for ARMA
ARMA in Haskell
ACD model extension
Experimental conditional durations
Summary
Chapter 5: Volatility
Historic volatility estimators
Volatility estimator framework
Alternative volatility estimators
Forecasting volatility
Summary
Chapter 6: Advanced Cabal
Common usage
Packaging with Cabal
Cabal in sandbox
Summary

What You Will Learn

  • Learn how to build a FIX protocol parser
  • Calibrate counting processes on real data
  • Estimate model parameters using the Maximum Likelihood Estimation method
  • Use Akaike criterion to choose the best-fit model
  • Learn how to perform property-based testing on a generated set of input data
  • Calibrate ACD models with the Kalman filter
  • Understand parallel programming in Haskell
  • Learn more about volatility prediction

In Detail

Haskell is one of the three most influential functional programming languages available today along with Lisp and Standard ML. When used for financial analysis, you can achieve a much-improved level of prediction and clear problem descriptions.

Haskell Financial Data Modeling and Predictive Analytics is a hands-on guide that employs a mix of theory and practice. Starting with the basics of Haskell, this book walks you through the mathematics involved and how this is implemented in Haskell.

The book starts with an introduction to the Haskell platform and the Glasgow Haskell Compiler (GHC). You will then learn about the basics of high frequency financial data mathematics as well as how to implement these mathematical algorithms in Haskell.

You will also learn about the most popular Haskell libraries and frameworks like Attoparsec, QuickCheck, and HMatrix. You will also become familiar with database access using Yesod’s Persistence library, allowing you to keep your data organized. The book then moves on to discuss the mathematics of counting processes and autoregressive conditional duration models, which are quite common modeling tools for high frequency tick data. At the end of the book, you will also learn about the volatility prediction technique.

With Haskell Financial Data Modeling and Predictive Analytics, you will learn everything you need to know about financial data modeling and predictive analytics using functional programming in Haskell.

Authors

Table of Contents

Chapter 1: Getting Started with the Haskell Platform
The Haskell platform
Quick tour of Haskell
Summary
Chapter 2: Getting your Hands Dirty
The domain model
The Attoparsec library
Parsing plain text files
Parsing files in applicative style
Outlier detection
Template Haskell, quasiquotes, type families, and GADTs
Persistent ORM framework
Summary
Chapter 3: Measuring Tick Intervals
Point process
Counting process
Durations
Poisson process calibration
Renewal process calibration
Cox process calibration
Model selection
The secant root-finding algorithm
Summary
Chapter 4: Going Autoregressive
The ARMA model definition
The Kalman filter
Matrix manipulation libraries in Haskell
The Kalman filter in Haskell
The state-space model for ARMA
ARMA in Haskell
ACD model extension
Experimental conditional durations
Summary
Chapter 5: Volatility
Historic volatility estimators
Volatility estimator framework
Alternative volatility estimators
Forecasting volatility
Summary
Chapter 6: Advanced Cabal
Common usage
Packaging with Cabal
Cabal in sandbox
Summary

Book Details

ISBN 139781782169437
Paperback112 pages
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