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  • Book Overview & Buying Learning Quantitative Finance with R
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Learning Quantitative Finance with R

Learning Quantitative Finance with R

By : Jeet, PRASHANT VATS
3.7 (3)
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Learning Quantitative Finance with R

Learning Quantitative Finance with R

3.7 (3)
By: 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 (10 chapters)
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K means algorithm


The K means algorithm is an unsupervised machine learning algorithm. Unsupervised learning is another way of classifying the data as it does not require labeling of the data. In reality, there are many instances where labeling of the data is not possible, so we require them to classify data based on unsupervised learning. Unsupervised learning uses the similarity between data elements and assigns each data point to its relevant cluster. Each cluster has a set of data points which are similar in nature. The K means algorithm is the most basic unsupervised learning algorithm and it just requires data to plug into the algorithm along with the number of clusters we would like it to cluster returning the vector of cluster labeling for each data point. I used normalized data along with the number of clusters. I used the in-sample data which was used during logistic regression, to be divided into three clusters.

set.seed() is used to have the same output in every iteration; without...

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Learning Quantitative Finance with R
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