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  • Book Overview & Buying F# for Quantitative Finance
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F# for Quantitative Finance

F# for Quantitative Finance

By : Johan Astborg
3.8 (5)
Buy this Book
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F# for Quantitative Finance

F# for Quantitative Finance

3.8 (5)
By: Johan Astborg
Buy this Book

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)
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F# for Quantitative Finance
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F# for Quantitative Finance
Credits
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Credits
About the Author
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About the Author
About the Reviewers
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About the Reviewers
www.PacktPub.com
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www.PacktPub.com
Preface
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Preface
Lock Free Chapter
1
Introducing F# Using Visual Studio
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Introducing F# Using Visual Studio
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Introduction
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Getting started with Visual Studio
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Understanding F# Interactive
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Language overview
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Explaining mutability and immutability
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Primitive types
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Explaining type inference
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Explaining functions
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Documenting your code
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Your first application
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The whole program
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The power of prototyping
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Functional languages in quantitative finance
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Understanding the imperative code and interoperability
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Summary
2
Learning More About F#
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Learning More About F#
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Structuring your F# program
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Looking deeper inside data structures
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Choosing data structures
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More on functional programming
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Asynchronous and parallel programming
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A brief look at imperative programming
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Using XML documentation
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Summary
3
Financial Mathematics and Numerical Analysis
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Financial Mathematics and Numerical Analysis
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Understanding the number representation
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Learning about numerical types in F#
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Arithmetic operators
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Learning about arithmetic comparisons
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Math operators
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Conversion functions
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Introducing statistics
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Using the Math.NET library
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Learning about root-finding algorithms
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Summary
4
Getting Started with Data Visualization
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Getting Started with Data Visualization
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Making your first GUI in F#
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Composing interfaces
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Displaying data
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Extending the application to use Bollinger bands
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Using FSharp.Charting
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Creating a bar chart
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Summary
5
Learning Option Pricing
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Learning Option Pricing
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Introduction to options
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Learning the Black-Scholes formula
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The Monte Carlo method
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Summary
6
Exploring Volatility
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Exploring Volatility
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Introduction to volatility
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Learning about implied volatility
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Solving for implied volatility
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Delta hedging using Black-Scholes
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Exploring the volatility smile
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Summary
7
Getting Started with Order Types and Market Data
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Getting Started with Order Types and Market Data
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Introducing orders
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Understanding order execution
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Introducing market data
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Implementing simple pretrade risk analysis
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Introducing FIX and QuickFIX/N
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Summary
8
Setting Up the Trading System Project
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Setting Up the Trading System Project
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Explaining automated trading
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Understanding software testing and test-driven development
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Understanding NUnit and FsUnit
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Connecting to Microsoft SQL Server
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Introducing type providers
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Writing test cases
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Summary
9
Trading Volatility for Profit
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Trading Volatility for Profit
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Trading the volatility
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Plotting payoff diagrams with FSharpCharts
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Learning directional trading strategies
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Deriving the mathematics
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Learning relative value trading strategies
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Defining the trading strategy
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Summary
10
Putting the Pieces Together
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Putting the Pieces Together
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Understanding the requirements
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Revisiting the structure of the system
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Understanding the Model-View-Controller pattern
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Executing the trading strategy using a framework
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Building the GUI
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Presenting information in the GUI
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Adding support for downloading the data
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Looking at possible additions to the system
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Summary
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Index
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Index

Learning about implied volatility

Here we'll use the bisection method introduced in Chapter 3, Financial Mathematics and Numerical Analysis. This is a numerical method for finding roots. The implied volatility is the root where the function value is zero for the Black-Scholes function for different input parameters. The volatility of an underlying instrument is the input to Black-Scholes which gives the same price as the current price of the option.

Vega tells us about the sensitivity in the option price for the changes in the volatility of the underlying asset. Look at Yahoo! Finance and find the option data. Take that option data into the following solve function:

Figure 2: The VIX-index for the S&P500 index options from 2000-01-01 to 2013-11-01

The VIX-index, as seen in the preceding screenshot, is an index which combines the implied volatility of S&P 500 index options. This can be interpreted as an indication of future volatility.

CONTINUE READING
83
Tech Concepts
36
Programming languages
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F# for Quantitative Finance
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