VATS / Jeet Learning Quantitative Finance with R

Implement machine learning, time-series analysis, algorithmic trading and more
1. Auflage 2025
ISBN: 978-1-78646-525-2
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 284 Seiten

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.

Autoren/Hrsg.

VATS, PRASHANT

Jeet, Dr. Param

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken

Leseproben

Importing and exporting different data types

In R, we can read the files stored from outside the R environment. We can also write the data into files which can be stored and accessed by the operating system. In R, we can read and write different formats of files, such as CSV, Excel, TXT, and so on. In this section, we are going to discuss how to read and write different formats of files.

The required files should be present in the current directory to read them. Otherwise, the directory should be changed to the required destination.

The first step for reading/writing files is to know the working directory. You can find the path of the working directory by running the following code:

>print (getwd())

This will give the paths for the current working directory. If it is not your desired directory, then please set your own desired directory by using the following code:

>setwd("")

For instance, the following code makes the folder the working directory:

>setwd("C:/Users")

How to read and write a CSV format file

A CSV format file is a text file in which values are comma separated. Let us consider a CSV file with the following content from stock-market data:

14-10-2016

2139.68

2149.19

2132.98

2132.98

3.23E+09

2132.98

13-10-2016

2130.26

2138.19

2114.72

2132.55

3.58E+09

2132.55

12-10-2016

2137.67

2145.36

2132.77

2139.18

2.98E+09

2139.18

11-10-2016

2161.35

2161.56

2128.84

2136.73

3.44E+09

2136.73

10-10-2016

2160.39

2169.6

2160.39

2163.66

2.92E+09

2163.66

To read the preceding file in R, first save this file in the working directory, and then read it (the name of the file is ) using the following code:

>data<-read.csv("Sample.csv") >print(data)

When the preceding code gets executed, it will give the following output:

Date Open High Low Close Volume Adj.Close 1 14-10-2016 2139.68 2149.19 2132.98 2132.98 3228150000 2132.98 2 13-10-2016 2130.26 2138.19 2114.72 2132.55 3580450000 2132.55 3 12-10-2016 2137.67 2145.36 2132.77 2139.18 2977100000 2139.18 4 11-10-2016 2161.35 2161.56 2128.84 2136.73 3438270000 2136.73 5 10-10-2016 2160.39 2169.60 2160.39 2163.66 2916550000 2163.66

by default produces the file in DataFrame format; this can be checked by running the following code:

>print(is.data.frame(data))

Now, whatever analysis you want to do, you can perform it by applying various functions on the DataFrame in R, and once you have done the analysis, you can write your desired output file using the following code:

>write.csv(data,"result.csv") >output <- read.csv("result.csv") >print(output)

When the preceding code gets executed, it writes the output file in the working directory folder in CSV format.

XLSX

Excel is the most common format of file for storing data, and it ends with extension or .

The package will be used to read or write files in the R environment.

Installing the package has dependency on Java, so Java needs to be installed on the system. The package can be installed using the following command:

>install.packages("xlsx")

When the previous command gets executed, it will ask for the nearest CRAN mirror, which the user has to select to install the package. We can verify that the package has been installed or not by executing the following command:

>any(grepl("xlsx",installed.packages()))

If it has been installed successfully, it will show the following output:

[1] TRUE Loading required package: rJava Loading required package: methods Loading required package: xlsxjars

We can load the library by running the following script:

>library("xlsx")

Now let us save the previous sample file in format and read it in the R environment, which can be done by executing the following code:

>data <- read.xlsx("Sample.xlsx", sheetIndex = 1) >print(data)

This gives a DataFrame output with the following content:

Date Open High Low Close Volume Adj.Close 1 2016-10-14 2139.68 2149.19 2132.98 2132.98 3228150000 2132.98 2 2016-10-13 2130.26 2138.19 2114.72 2132.55 3580450000 2132.55 3 2016-10-12 2137.67 2145.36 2132.77 2139.18 2977100000 2139.18 4 2016-10-11 2161.35 2161.56 2128.84 2136.73 3438270000 2136.73 5 2016-10-10 2160.39 2169.60 2160.39 2163.66 2916550000 2163.66

Similarly, you can write R files in format by executing the following code:

>output<-write.xlsx(data,"result.xlsx") >output<- read.csv("result.csv") >print(output)

Web data or online sources of data

The Web is one main source of data these days, and we want to directly bring the data from web form to the R environment. R supports this:

URL <- "http://ichart.finance.yahoo.com/table.csv?s=^GSPC" snp <- as.data.frame(read.csv(URL)) head(snp)

When the preceding code is executed, it directly brings the data for the index into R in DataFrame format. A portion of the data has been displayed by using the function here:

Date Open High Low Close Volume Adj.Close 1 2016-10-14 2139.68 2149.19 2132.98 2132.98 3228150000 2132.98 2 2016-10-13 2130.26 2138.19 2114.72 2132.55 3580450000 2132.55 3 2016-10-12 2137.67 2145.36 2132.77 2139.18 2977100000 2139.18 4 2016-10-11 2161.35 2161.56 2128.84 2136.73 3438270000 2136.73 5 2016-10-10 2160.39 2169.60 2160.39 2163.66 2916550000 2163.66 6 2016-10-07 2164.19 2165.86 2144.85 2153.74 3619890000 2153.74

Similarly, if we execute the following code, it brings the DJI index data into the R environment: its sample is displayed here:

>URL <- "http://ichart.finance.yahoo.com/table.csv?s=^DJI" >dji <- as.data.frame(read.csv(URL)) >head(dji)

This gives the following output:

Date Open High Low Close Volume Adj.Close 1 2016-10-14 18177.35 18261.11 18138.38 18138.38 87050000 18138.38 2 2016-10-13 18088.32 18137.70 17959.95 18098.94 83160000 18098.94 3 2016-10-12 18132.63 18193.96 18082.09 18144.20 72230000 18144.20 4 2016-10-11 18308.43 18312.33 18061.96 18128.66 88610000 18128.66 5 2016-10-10 18282.95 18399.96 18282.95 18329.04 72110000 18329.04 6 2016-10-07 18295.35 18319.73 18149.35 18240.49 82680000 18240.49

Please note that we will be mostly using the and indexes for example illustrations in the rest of the book and these will be referred to as and .

Databases

A relational database stores data in normalized format, and to perform statistical analysis, we need to write complex and advance queries. But R can connect to various relational databases such as MySQL Oracle, and SQL Server, easily and convert the data tables into DataFrames. Once the data is in DataFrame format, doing statistical analysis is easy to perform using all the available functions and packages.

In this section, we will take the example of MySQL as reference.

R has a built-in package, , which provides connectivity with the database; it can be installed using the following command:

>install.packages("RMySQL")

Once the package is installed, we can create a connection object to create a connection with the database. It takes username, password, database name, and localhost name as input. We can give our inputs and use the following command to connect with the required database:

>mysqlconnection = dbConnect(MySQL(), user = '...', password = '...', dbname = '..',host = '.....')

When the database is connected, we can list the table that is present in the database by executing the following command:

>dbListTables(mysqlconnection)

We can query the database using the function , and the result is returned to R by using function . Then the output is stored in DataFrame format:

>result = dbSendQuery(mysqlconnection, "select * from ") >data.frame = fetch(result) >print(data.fame)

When the previous code gets executed, it returns the required...

Über Autor(innen)

VATS PRASHANT :

Prashant Vats is a masters in mathematics from one of India's leading technological institute, IIT Mumbai. Prashant has been into analytics industry for more than 10 years and has worked with various leading multinational companies as well as consulted few of companies as data scientist across several domain.Jeet Dr. Param :

Dr. Param Jeet is a Ph.D. in mathematics from one of India's leading technological institute in Madras (IITM), India. Dr. Param Jeet has a couple of mathematical research papers published in various international journals. Dr. Param Jeet has been into the analytics industry for the last few years and has worked with various leading multinational companies as well as consulted few of companies as a data scientist.