Building a Moving Average Crossover Backtest with Zipline

Summary

The document introduces Zipline as an event-driven Python library for running trading algorithms and backtests. It outlines the algorithm structure: an initialization step stores the selected security, while a handler processes each market bar, places orders, and records values. The example applies this structure to SPY using a short and a long moving average. It buys when the short average is above the long average and there is no existing position, sizing the purchase with available cash; it exits when the short average falls below the long average. Recorded fields include price, moving averages, position, cash, portfolio value, and P&L.

The article describes plotting recorded results and mentions historical data loading and performance analysis, but it provides no backtest performance figures or comparison with a benchmark. Its example uses one asset and a simple crossover rule; it does not discuss transaction costs, slippage, risk controls, or out-of-sample validation. It also notes that market data source availability can be a practical issue.

Key ideas

• Zipline runs algorithms by initializing context and then processing market data through a handler for each event.
• A moving average crossover compares a shorter window with a longer window to generate entry and exit signals.
• The example sizes purchases from available cash and closes the position when the short average drops below the long average.
• Recorded prices, signals, portfolio values, cash, and P&L can be used to inspect a backtest.
• The document gives no performance evidence or treatment of trading costs and risk controls.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.