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vectorbt
Vectorized Python backtesting and strategy research toolkit
What it is
A Python library for backtesting, algorithmic trading, and research that packs thousands of strategy configurations into NumPy arrays, accelerated with Numba and an optional Rust engine, so grid searches run all at once rather than bar by bar. It covers portfolio backtesting with trade and drawdown analytics, a rich indicator ecosystem, walk-forward optimization, and interactive Plotly visualization. The design suits human researchers working at scale as well as AI agents driving experimentation in code. Some advanced capabilities—parallelization, limit orders, leverage, and portfolio optimization—live in the separate paid VectorBT PRO edition rather than this open-source version.
At a glance
RecommendedOur rating, based on popularity, maintenance and how ready it is for real use.
| Best for | Professional quants |
|---|---|
| Used for | Backtesting, Strategy research |
| Markets | Multi-market |
| Stack | Python |
| Learning curve | Steep learning curve |
| Practical value | High practical value |
| Cost | Free and open source |
| Hardware | No GPU needed |
| Maintenance | Last commit 2 days ago |
GitHub stars, last 30 days
Daily snapshots since 2026-09-12 (up to 30 days): +270 over the period, now 9,332. Gaps mean no snapshot was taken that day.
Find your trading edge, using a powerful toolkit for backtesting, algorithmic trading, and research.
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