Zipline is a Python library for trading applications that powers the Quantopian service mentioned above. It is an event-driven system that supports both backtesting and live-trading.
In this article we will learn how to install Zipline and then how to implement Moving Average Crossover strategy and calculate P&L, Portfolio value etc.
This article is divided into the following four sections:
- Benefits of Zipline
- Installation (how to install Zipline on local)
- Structure (format to write code in Zipline),
- Coding Moving average crossover strategy with Zipline
Benefits of Zipline
- Ease of use
- Zipline comes “batteries included” as many common statistics like moving average and linear regression can be readily accessed from within a user-written algorithm.
- Input of historical data and output of performance statistics are based on Pandas DataFrames to integrate nicely into the existing PyData ecosystem
- Statistic and machine learning libraries like matplotlib, scipy, statsmodels, and sklearn support development, analysis, and visualization of state-of-the-art trading systems