Pavel Ryzhov - Haskell Financial Data Modeling and Predictive Analytics
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- Book:Haskell Financial Data Modeling and Predictive Analytics
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Get an in-depth analysis of financial time series from the perspective of a functional programmer
Overview
- Understand the foundations of financial stochastic processes
- Build robust models quickly and efficiently
- Tackle the complexity of parallel programming
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 Yesods 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.
What you will learn from this book
- 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
Approach
This book is a hands-on guide that teaches readers how to use Haskells tools and libraries to analyze data from real-world sources in an easy-to-understand manner.
Who this book is written 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.
Pavel Ryzhov: author's other books
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Copyright 2013 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: October 2013
Production Reference: 1221013
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK..
ISBN 978-1-78216-943-7
www.packtpub.com
Cover Image by Duraid Fatouhi (<>)
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
Pavel Ryzhov has graduated from the Lomonosov Moscow State University in Russia in the field of mathematical physics, Toda equations and Lie algebras. In the past 10 years, he has worked as a Technical Lead and Senior Software Engineer. In the last three years, Pavel lead a startup company that mainly provided mathematical and web software development in Haskell. Also, he works on port of Quantlib, an HQuantLib project in his spare time.
I would like personally to thank my wife Marina and daughter Marta for supporting my beginnings, my parents for encouraging me, and the enormously helpful Haskell community for providing the best tools in the world.
Gregory Collins is a software engineer at Google Zrich, where he works on web search indexing. He has done M.Sc. in Computer Science from the Yale University, and has been programming in Haskell for over a decade.
Ivan Perez has been passionate about programming and mathematics since he was seven years old. He first learned Basic and Logo, which allowed him to experiment with basic programs and computer graphics. After using Visual Basic for several years, he went to a university, where he started programming in many other languages, including Ada, Prolog, and Haskell. This changed his view of software development forever, and he decided to focus on functional and logic programming in his career.
He obtained the degree of engineering in Computer Science from the Technical University of Madrid (UPM) in 2008, and a master's degree in Computational Logic in 2009 from the same university. He collaborated and worked with the Babel Research Group at UPM from 2003 to 2010, and worked for IMDEA Software from 2007 to 2009. In 2012, he also worked for the High Performance Computing Center ( HLRS ) at the University of Stuttgart, as part of a research project involving functional programming and supercomputing.
He is the founder of Keera Studios (now Keera Studios Ltd.), a UK-based company that uses Haskell, Scala, and other cool languages to create desktop, mobile, and web applications, and games.
I would like to thank the author, the editors, and the people at Packt for making this book possible. I would like to thank my wife, Natalia for her constant support and love, and my family and friends for always being there. I would also like to thank my associates and colleagues, who had the patience to listen to my crazy ideas and to embark with me on some of them. And last, but not the least, I would like to thank all the clients and companies who took a leap of faith with functional programming, and the whole Haskell community who made working with this language the most joyful experience.
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Welcome to Haskell Financial Data Modeling and Predictive Analytics . You will start with the most distinctive features of the language, then go through the data collection process with parsing, cleansing and archiving, and then come directly to data analysis and manipulation. You will learn a set of basic financial models that are commonly used in the industry and how they can be implemented in Haskell. At the end of the book you will learn deterministic parallelism and compiler-driven stream fusion optimization.
, Getting Started with the Haskell Platform , discusses a little bit of Haskell history, how to get the Haskell platform installed, and walks through a quick tour of the main features of the language.
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