This is the code repository for Mastering pandas for Finance , published by Packt.
Master pandas, an open source Python Data Analysis Library, for financial data analysis
This book covers the following exciting features:
- 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 8 Modeling and optimization of financial portfolios
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
import pandas as pd
df = pd.DataFrame.from_items([('column1', [1, 2, 3])])
print (df)
Following is what you need for this book: 0
With the following software and hardware list you can run all code files present in the book (Chapter 1-).
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
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Learning IPython for Interactive Computing and Data Visualization [Video] [Packt] [Amazon]
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IPython Interactive Computing and Visualization Cookbook [Packt] [Amazon]
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, fnance, 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 fnance. Since 2005, he has specialized in building energy and fnancial 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-defned 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/.
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