Book Image

Learning Quantitative Finance with R

By : Dr. Param Jeet, PRASHANT VATS
Book Image

Learning Quantitative Finance with R

By: Dr. Param Jeet, PRASHANT VATS

Overview of this book

The role of a quantitative analyst is very challenging, yet lucrative, so there is a lot of competition for the role in top-tier organizations and investment banks. This book is your go-to resource if you want to equip yourself with the skills required to tackle any real-world problem in quantitative finance using the popular R programming language. You'll start by getting an understanding of the basics of R and its relevance in the field of quantitative finance. Once you've built this foundation, we'll dive into the practicalities of building financial models in R. This will help you have a fair understanding of the topics as well as their implementation, as the authors have presented some use cases along with examples that are easy to understand and correlate. We'll also look at risk management and optimization techniques for algorithmic trading. Finally, the book will explain some advanced concepts, such as trading using machine learning, optimizations, exotic options, and hedging. By the end of this book, you will have a firm grasp of the techniques required to implement basic quantitative finance models in R.
Table of Contents (16 chapters)
Learning Quantitative Finance with R
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Linear filters


The first step in time series analysis is to decompose the time series in trend, seasonality, and so on.

One of the methods of extracting trend from the time series is linear filters.

One of the basic examples of linear filters is moving average with equal weights.

Examples of linear filters are weekly average, monthly average, and so on.

The function used for finding filters is given as follows:

Filter(x,filter)

Here, x is the time series data and filter is the coefficients needed to be given to find the moving average.

Now let us convert the Adj.Close of our StockData in time series and find the weekly and monthly moving average and plot it. This can be done by executing the following code:

> StockData <- read.zoo("DataChap4.csv",header = TRUE, sep = ",",format="%m/%d/%Y") 
>PriceData<-ts(StockData$Adj.Close, frequency = 5)
> plot(PriceData,type="l")
> WeeklyMAPrice <- filter(PriceData,filter=rep(1/5,5))
> monthlyMAPrice <- filter(PriceData,filter=rep...