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

Defining the trading strategy


The trading strategy for our system will be based on relative value volatility arbitrage as described earlier. This will enable us to trade exclusively with options, to be more precise, in-the-money call options.

First, we define the slope between the two "edges" of the moneyness: the upper and lower bounds of the moneyness. We have to look at a graph for doing this. For the preceding graph, that would typically be [0.5, 1.0].

To get a more mathematical expression for the slope, we look at two points and calculate the slope from these:

Here, m is the moneyness and σ (sigma) is the implied volatility from the option prices. The slope can either rise or fall, which means β will increase, decrease, or of course, neither will happen. Let's look at the two cases more closely.

Case 1 – increasing the slope

In the case of a slope that is lower than the regression (average), we can assume that the slope will eventually revert. In the case of a rising slope, the slope is...