Haskell Financial Data Modeling and Predictive Analytics

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.

從功能程式設計師的角度深入分析金融時間序列

概述:
- 瞭解金融隨機過程的基礎
- 快速高效地建立強大的模型
- 解決並行程式設計的複雜性

詳細內容:
Haskell是當今三個最具影響力的功能程式設計語言之一，與Lisp和Standard ML並列。在金融分析中使用Haskell，您可以實現更高水準的預測和清晰的問題描述。

《Haskell金融數據建模與預測分析》是一本理論與實踐相結合的實用指南。從Haskell的基礎知識開始，本書將引導您了解相關的數學知識以及如何在Haskell中實現。

本書首先介紹了Haskell平台和Glasgow Haskell Compiler（GHC）。然後，您將學習高頻金融數據數學的基礎知識，以及如何在Haskell中實現這些數學算法。

您還將學習最受歡迎的Haskell庫和框架，如Attoparsec、QuickCheck和HMatrix。您還將熟悉使用Yesod的Persistence庫進行數據庫訪問，以使您的數據有組織。本書還討論了計數過程和自回歸條件持續時間模型的數學，這些模型是高頻tick數據的常用建模工具。在本書的最後，您還將學習關於波動率預測技術。

通過《Haskell金融數據建模與預測分析》，您將學習使用Haskell進行金融數據建模和預測分析所需的一切知識。

本書的學習重點:
- 學習如何構建FIX協議解析器
- 在真實數據上校準計數過程
- 使用最大概似估計法估計模型參數
- 使用Akaike準則選擇最佳擬合模型
- 學習如何對生成的輸入數據進行基於屬性的測試
- 使用卡爾曼濾波器校準ACD模型
- 瞭解Haskell中的並行程式設計
- 了解更多關於波動率預測的知識

這本書的特點:
本書是一本實用指南，以易於理解的方式教讀者如何使用Haskell的工具和庫來分析來自現實世界的數據。

本書的讀者對象:
本書適合初次使用Haskell進行金融數據建模的開發人員。不需要基本的功能程式設計知識，但具備這方面的知識將會有所幫助。對高頻金融感興趣是必要的。