Chapter 1. Getting Started with pandas Using Wakari.io
In Mastering pandas for Finance, we will examine the use of pandas to manage financial data and perform various financial analyses with a specific focus on financial processes that can be facilitated using the capabilities provided within pandas, along with an occasional quantitative financial technique. I have made an assumption that you have basic knowledge of Python programming and have used IPython and IPython Notebooks. Knowledge of pandas is preferred, but we will cover enough information on pandas for any reader to be able to understand the technique being used. We will occasionally and briefly touch upon areas of quantitative finance, but those times will be mostly for information purposes and will have implementations that are provided in the code of the text.
During this voyage of discovery, we will begin with an overview/review of concepts and data structures in pandas that are of importance to financial analysis. We will then move into various concepts, techniques, tools, and examples of specific financial analysis problems as solved with Python, pandas, and several other Python libraries and tools, including Wakari, matplotlib, SciPy, Quandl, Zipline, and Mibian. These will be varied in nature, and topics ranging from analysis of historical stock data, correlating search data with trends in stock prices, algorithmic trading and backtesting, options modeling and pricing, and portfolio and risk analysis will be covered.
In this first chapter, we will walk through creating an account and environment in Wakari.io and installing the code samples into that environment. I have chosen Wakari.io as a basis for a pandas-based financial environment because it is relatively painless to get up and running with all of the tools we will utilize, and also the samples provided in the code bundle of this book are in the IPython Notebook format, which is simple to use within Wakari.io.
The use of Wakari, however, does not prevent you from using your own Python environment. The examples in the text will run in any Python environment and were originally built using the Anaconda and IPython Notebook formats with all of the mentioned tools installed within the environment. Just in case you don't want to use Wakari, all the code examples in the text are presented as IPython and will run in a properly configured IPython environment.
So, let's get started. In this chapter, we will cover the following topics:
- What is Wakari.io?
- Creating a Wakari account
- Updating the default Wakari environment to run all our examples
- Installing and running the code samples in Wakari
