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Mastering R for Quantitative Finance

You're reading from   Mastering R for Quantitative Finance Use R to optimize your trading strategy and build up your own risk management system

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Product type Paperback
Published in Mar 2015
Publisher
ISBN-13 9781783552078
Length 362 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (15) Chapters Close

Preface 1. Time Series Analysis FREE CHAPTER 2. Factor Models 3. Forecasting Volume 4. Big Data – Advanced Analytics 5. FX Derivatives 6. Interest Rate Derivatives and Models 7. Exotic Options 8. Optimal Hedging 9. Fundamental Analysis 10. Technical Analysis, Neural Networks, and Logoptimal Portfolios 11. Asset and Liability Management 12. Capital Adequacy 13. Systemic Risks Index

Chapter 4. Big Data – Advanced Analytics

In this chapter, we will deal with one of the biggest challenges of high-performance financial analytics and data management; that is, how to handle large datasets efficiently and flawlessly in R.

Our main objective is to give a practical introduction on how to access and manage large datasets in R. This chapter does not focus on any particular financial theorem, but it aims to give practical, hands-on examples to researchers and professionals on how to implement computationally - intensive analyses and models that leverage large datasets in the R environment.

In the first part of this chapter, we explained how to access data directly for multiple open sources. R offers various tools and options to load data into the R environment without any prior data-management requirements. This part of the chapter will guide you through practical examples on how to access data using the Quandl and qualtmod packages. The examples presented here...

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