Book description
Master pandas, an open source Python Data Analysis Library, for financial data analysis
In Detail
This book will teach you to use Python and the Python Data Analysis Library (pandas) to solve realworld financial problems.
Starting with a focus on pandas data structures, you will learn to load and manipulate timeseries financial data and then calculate common financial measures, leading into more advanced derivations using fixed and movingwindows. This leads into correlating timeseries data to both index and social data to build simple trading algorithms. From there, you will learn about more complex trading algorithms and implement them using open source backtesting tools. Then, you will examine the calculation of the value of options and Value at Risk. This then leads into the modeling of portfolios and calculation of optimal portfolios based upon risk. All concepts will be demonstrated continuously through progressive examples using interactive Python and IPython Notebook.
By the end of the book, you will be familiar with applying pandas to many financial problems, giving you the knowledge needed to leverage pandas in the real world of finance.
What You Will Learn
 Modeling and manipulating financial data using the pandas DataFrame
 Indexing, grouping, and calculating statistical results on financial information
 Timeseries modeling, frequency conversion, and deriving results on fixed and moving windows
 Calculating cumulative returns and performing correlations with index and social data
 Algorithmic trading and backtesting using momentum and mean reversion strategies
 Option pricing and calculation of Value at Risk
 Modeling and optimization of financial portfolios
Publisher resources
Table of contents

Mastering pandas for Finance
 Table of Contents
 Mastering pandas for Finance
 Credits
 About the Author
 About the Reviewers
 www.PacktPub.com
 Preface
 1. Getting Started with pandas Using Wakari.io

2. Introducing the Series and DataFrame
 Notebook setup
 The main pandas data structures – Series and DataFrame
 The basics of the Series and DataFrame objects
 Reindexing the Series and DataFrame objects
 Summary
 3. Reshaping, Reorganizing, and Aggregating
 4. Timeseries
 5. Timeseries Stock Data
 6. Trading Using Google Trends
 7. Algorithmic Trading
 8. Working with Options
 9. Portfolios and Risk
 Index
Product information
 Title: Mastering pandas for Finance
 Author(s):
 Release date: May 2015
 Publisher(s): Packt Publishing
 ISBN: 9781783985104
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