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

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

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

**Edition**: **1st Edition****ISBN**: **1782169431****Posted on**: **12/13/2016****Format**: **Pdf****Page Count**: **112 Pages****Author**: **Pavel Ryzhov,**: *-- ----------------*

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