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 many of the 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 are interested in quantitative finance, financial modeling, and trading, or simply want to learn how Python and pandas can 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 using 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 using 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 focus on pandas data structures, you will learn to load and manipulate time-series financial data and then calculate common financial measures, leading into more advanced derivations using 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 will learn about more complex trading algorithms and implement them using open source back-testing 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.
Michael Heydt
Michael Heydt is an independent consultant, educator, and trainer with nearly 30 years of professional software development experience, during which time, he focused on Agile software design and implementation using advanced technologies in multiple verticals, including media, finance, energy, and healthcare. He holds an MS degree in mathematics and computer science from Drexel University and an executive master's of technology management degree from the University of Pennsylvania's School of Engineering and Wharton Business School. His studies and research have focused on technology management, software engineering, entrepreneurship, information retrieval, data sciences, and computational finance. Since 2005, he has specialized in building energy and financial trading systems for major investment banks on Wall Street and for several global energy-trading companies, utilizing .NET, C#, WPF, TPL, DataFlow, Python, R, Mono, iOS, and Android. His current interests include creating seamless applications using desktop, mobile, and wearable technologies, which utilize high-concurrency, high-availability, and real-time data analytics; augmented and virtual reality; cloud services; messaging; computer vision; natural user interfaces; and software-defined networks. He is the author of numerous technology articles, papers, and books. He is a frequent speaker at .NET user groups and various mobile and cloud conferences, and he regularly delivers webinars and conducts training courses on emerging and advanced technologies. To know more about Michael, visit his website at http://bseamless.com/.
