Book Image

F# for Quantitative Finance

By : Johan Astborg
Book Image

F# for Quantitative Finance

By: Johan Astborg

Overview of this book

F# is a functional programming language that allows you to write simple code for complex problems. Currently, it is most commonly used in the financial sector. Quantitative finance makes heavy use of mathematics to model various parts of finance in the real world. If you are interested in using F# for your day-to-day work or research in quantitative finance, this book is a must-have.This book will cover everything you need to know about using functional programming for quantitative finance. Using a functional programming language will enable you to concentrate more on the problem itself rather than implementation details. Tutorials and snippets are summarized into an automated trading system throughout the book.This book will introduce you to F#, using Visual Studio, and provide examples with functional programming and finance combined. The book also covers topics such as downloading, visualizing and calculating statistics from data. F# is a first class programming language for the financial domain.
Table of Contents (17 chapters)
F# for Quantitative Finance
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Adding support for downloading the data


The market data will be pulled from Yahoo! Finance on a daily basis; we'll use closing prices and from them calculate the data needed. The data will be downloaded once the Download data button in the GUI is clicked on. The following is the code to illustrate how downloading can be handled by a background thread:

let fetchOne(url:string) =
  let uri = new System.Uri(url)
let client = new WebClient()
let html = client.DownloadString(uri)
html

let downloadNewData(url1:string, url2:string) =
  let worker = new BackgroundWorker()
  worker.DoWork.Add(fun args ->
  printfn("starting background thread")
  let data = fetchOne(url)
  printfn "%A" data)
  worker.RunWorkerAsync()

The trading system will follow these steps from the downloading process until the data is parsed:

  1. Download the data from Yahoo! Finance.

  2. Parse the data and perform the calculations.

  3. Store the data in the model.