zipline
Python developers can use Zipline’s event-driven library to backtest algorithmic trading strategies.
About zipline
Zipline is an open-source, event-driven Python library for backtesting algorithmic trading strategies. It provides built-in statistics such as moving average and linear regression, uses Pandas DataFrames for historical data input and performance output, and can be used alongside matplotlib, scipy, statsmodels, and sklearn. Algorithms are run through the Zipline command-line interface over a specified date range, with results saved for analysis in Python. It supports Python 2.7, 3.5, and 3.6 and can be installed via pip or conda. Installation is described as more involved than the average Python package, and sample pricing and asset data must be downloaded before running an algorithm.
Key features
- Event-driven backtesting system for trading algorithms
- Built-in statistics including moving average and linear regression
- Historical data input and performance output using Pandas DataFrames
- Works alongside matplotlib, scipy, statsmodels, and sklearn
- Run algorithms via CLI over specified start and end dates
- Ingest sample pricing and asset data before backtesting
Best for: Python developers building and backtesting algorithmic trading strategies.
Good for
- Open-source codebase with published contribution guidelines
- Uses Pandas DataFrames for data input and performance output
- Includes built-in statistics and example algorithms
Watch out for
- Supports only Python 2.7, 3.5, and 3.6
- Installation is more involved than the average Python package
zipline at a glance
- Category
- Development › Developer Tools
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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zipline FAQ
Which Python versions does Zipline support?
Python 2.7, 3.5, and 3.6, with installation via pip or conda.
How do I run a backtest?
Run 'zipline ingest' to download sample pricing and asset data, then use 'zipline run' with an algorithm file, start and end dates, and output file to save the performance DataFrame.
Can I use data analysis libraries with Zipline?
Yes. Input and output use Pandas DataFrames, and it can be used with matplotlib, scipy, statsmodels, and sklearn.
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