Mastering Pandas for Finance

Amazon.com Price: $44.99 (as of 22/04/2019 23:33 PST- Details)

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Description

Master pandas, an open source Python Data Analysis Library, for financial data analysis

About This Book

  • A single source for learning how to use the features of pandas for financial and quantitative analysis.
  • Explains the various financial concepts including market risk, options valuation, futures calculation, and algorithmic trading strategies.
  • Step-by-step demonstration with interactive and incremental examples to apply pandas to finance

Who This Book Is For

If you have an interest in quantitative finance, financial modeling, and trading, or simply need to find out how Python and pandas can also be applied to finance, then this book is ideal for you. Some knowledge of Python and pandas is assumed. Interest in financial concepts is helpful, but no prior knowledge is expected.

What You Will Learn

  • Modeling and manipulating financial data the use of the pandas DataFrame
  • Indexing, grouping, and calculating statistical results on financial information
  • Time-series 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 the use of momentum and mean reversion strategies
  • Option pricing and calculation of Value at Risk
  • Modeling and optimization of financial portfolios

In Detail

This book will teach you to use Python and the Python Data Analysis Library (pandas) to solve real-world financial problems.

Starting with a focal point on pandas data structures, you’ll discover ways to load and manipulate time-series financial data and then calculate common financial measures, leading into more advanced derivations the use of fixed- and moving-windows. This leads into correlating time-series data to both index and social data to build simple trading algorithms. From there, you’ll learn about more complex trading algorithms and implement them the use of open source back-testing tools. Then, you’ll 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 steadily through progressive examples the use of interactive Python and IPython Notebook.

By the end of the book, you’ll be familiar with applying pandas to many financial problems, giving you the knowledge needed to leverage pandas in the real world of finance.

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