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.