From Installation to Strategy Execution Using Python’s Powerful Libraries
In the financial industry, algorithmic trading enables traders and investors to automate buying and selling decisions. Visualizing and analyzing financial data is a critical component of developing and testing these strategies. Our focus in this chapter is Zipline, a powerful Python library for backtesting trading algorithms, as well as its combination with Matplotlib and Pandas.
This guide walks you through setting up Zipline seamlessly with your existing Python setup. In addition to importing data, tracking stock performance, and implementing a simple moving average strategy, Zipline demonstrates how trading strategies can be developed and analyzed in real time. Here are tools and techniques that will streamline data analysis and trading workflows, no matter where you are in your algorithmic trading journey.
For smooth installation and to avoid conflicts with your global Python libraries, it’s advisable to set up Zipline in a dedicated conda environment. This method ensures that your main Python environment and site-packages remain untouched.
To install Zipline along with its dependencies, use the environment.yml file provided in the directory. Run the following command to create the environment:
conda env create -f environment.yml