In this chapter, we will examine how to use pandas and a library known as Zipline to develop automated trading algorithms. Zipline (http://www.zipline.io/) is a Python-based algorithmic trading library. It provides event-driven approximations of live-trading systems. It is currently used in production as the trading engine that powers Quantopian (https://www.quantopian.com/), a free, community-centered platform for collaborating on the development of trading algorithms with a web browser.

We previously simulated trading based on a historical review of social and stock data, but these examples were naive in that they glossed over many facets of real trading, such as transaction fees, commissions, and slippage, among many others. Zipline provides robust capabilities to include these factors in the trading model.

Zipline also provides a facility referred to as backtesting. Backtesting is the ability to run an algorithm on historical data to determine the effectiveness...