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Pythonic event-driven backtesting library for trading algorithms
What it is
A Pythonic algorithmic trading library providing an event-driven system for backtesting. Users write algorithms in Python and can access common statistics like moving averages and linear regression directly from within their code, with input and output based on Pandas DataFrames for integration into the PyData ecosystem. Libraries such as matplotlib, scipy, statsmodels, and sklearn can be used for analysis and visualization. Installation is somewhat more involved than a typical Python package, so consulting the install documentation is recommended.
At a glance
Research onlyOur rating, based on popularity, maintenance and how ready it is for real use.
| Best for | Professional quants |
|---|---|
| Used for | Backtesting, Strategy research |
| Markets | US equities, Multi-market |
| Stack | Python |
| Learning curve | Steep learning curve |
| Practical value | Medium practical value |
| Cost | Free and open source |
| Hardware | No GPU needed |
| Maintenance | No commits in over six months |
GitHub stars, last 30 days
Daily snapshots since 2026-09-12 (up to 30 days): +56 over the period, now 20,146. Gaps mean no snapshot was taken that day.
Pythonic algorithmic trading library.
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