Haskell Financial Data Modeling and Predictive Analytics

Haskell Financial Data Modeling and Predictive Analytics
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Table of Contents
Sample Chapters
  • Understand the foundations of financial stochastic processes
  • Build robust models quickly and efficiently
  • Tackle the complexity of parallel programming

Book Details

Language : English
Paperback : 112 pages [ 235mm x 191mm ]
Release Date : October 2013
ISBN : 1782169431
ISBN 13 : 9781782169437
Author(s) : Pavel Ryzhov
Topics and Technologies : All Books, Open Source

Table of Contents

Chapter 1: Getting Started with the Haskell Platform
Chapter 2: Getting Your Hands Dirty
Chapter 3: Measuring Tick Intervals
Chapter 4: Going Autoregressive
Chapter 5: Volatility
Chapter 6: Advanced Cabal
Appendix: References
    • 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
            • The 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

              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.
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              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

              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.


              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 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.


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