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Hands-On Data Science with Anaconda

You're reading from   Hands-On Data Science with Anaconda Utilize the right mix of tools to create high-performance data science applications

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788831192
Length 364 pages
Edition 1st Edition
Languages
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Authors (2):
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James Yan James Yan
Author Profile Icon James Yan
James Yan
Yuxing Yan Yuxing Yan
Author Profile Icon Yuxing Yan
Yuxing Yan
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Toc

Table of Contents (15) Chapters Close

Preface 1. Ecosystem of Anaconda 2. Anaconda Installation FREE CHAPTER 3. Data Basics 4. Data Visualization 5. Statistical Modeling in Anaconda 6. Managing Packages 7. Optimization in Anaconda 8. Unsupervised Learning in Anaconda 9. Supervised Learning in Anaconda 10. Predictive Data Analytics – Modeling and Validation 11. Anaconda Cloud 12. Distributed Computing, Parallel Computing, and HPCC 13. References 14. Other Books You May Enjoy

Example #1 – stock portfolio optimization

Sometimes we refer to single-period portfolio optimization as Markowitz portfolio optimization. Our input datasets include the expected returns, the standard deviations, and the correlation matrix between financial assets, and our output will be an efficient frontier formed by those assets. In the rest of the chapter, we will use historical returns to represent expected returns and use the historical correlation in place of expected correlation.

In the following examples, we use an R package called fPortfolio. We use the following code to install the package:

install.packages("fPortfolio") 

To load various embedded datasets, we use the data() function (see the following example code):

library(fPortfolio)
data(GCCINDEX.RET)
dim(GCCINDEX.RET) [1] 824 11

The following table lists the embedded datasets:

#
Name
Dimension...
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