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Haskell Financial Data Modeling and Predictive Analytics, by Pavel Ryzhov
Ebook Download Haskell Financial Data Modeling and Predictive Analytics, by Pavel Ryzhov
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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 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.
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 Haskell's 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.
- Sales Rank: #2613836 in Books
- Published on: 2013-10-25
- Released on: 2013-10-25
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .26" w x 7.50" l, .45 pounds
- Binding: Paperback
- 112 pages
About the Author
Pavel Ryzhov
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.
Most helpful customer reviews
8 of 9 people found the following review helpful.
Good overview, if you have the necessary math
By Andrew Gibiansky
The book itself is relatively well-written and pleasant to read. There is plenty of supplementary code and it covers much interesting material, from basics of Haskell to maximum likelihood estimation to autoregressive models and the Kalman filter. If you're a beginner with Haskell and would like to read a bit of Haskell code and also happen to be pretty familiar with the math behind time series, statistical estimation, etc, you will probably enjoy the book.
However, there are some caveats - the audience for this book is pretty specific. If you're a Haskell developer and have a good amount of experience with Haskell, I would not recommend this book as you'll find the first third pretty tedious: it's effectively Learn You A Haskell summarized quickly with a few extra bits thrown in about parsing and attoparsec and databases. Also, if you don't have a background in financial and/or statistical math, this book may not be for you - it will often state equations as truths that, unless you have seen them (or their ilk) before, will not be the least bit obvious.
Overall, the book is quite good - if you happen to be in the exact target audience of "beginner at Haskell, or wanting to learn" and "knows a good bit about financial math or statistics".
3 of 3 people found the following review helpful.
Very densely packed with quality information!
By Steve G. Bjorg
First off, the samples can be readily downloaded from the publisher's site and absolutely required to make sense with the text. This book is best read on a regular computer so that you can easily switch between samples and the book.
Now on to the contents of the book. This is NOT an introduction to Haskell or financial modeling, but it does provide all the stepping stones to get there. At least for me, this book will require multiple readings, followed by many sessions of trial-and-error. I think it will be extremely valuable in learning a new set of skills, but it will undoubtedly require me to read other books on Haskell and statistical analysis. That's ok though since it's often annoying to have authors regurgitate and fill chapters with 101 stuff and then run out of steam on the juicy bits. This book is not like that: it's light on basics and dense on domain knowledge. Highly recommended for the serious self-learner!
0 of 0 people found the following review helpful.
overpriced
By Steven E.
It's overpriced for you get.
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