Quant research & backtesting · Open-source community
vectorbt
Open-source Python library for vectorized backtesting, using NumPy/pandas array operations to test thousands of parameter combinations fast.
vectorbt is a Python backtesting library that evaluates trading strategies using vectorized NumPy and pandas operations instead of an event-by-event simulation loop, which lets it sweep large parameter grids and portfolios of assets far faster than traditional loop-based backtesters. It includes portfolio simulation, performance metrics, interactive Plotly-based visualizations, and integrates with common technical-indicator libraries. The open-source version covers single- and multi-asset backtesting and parameter optimization; a separate commercial vectorbt PRO exists with additional performance and feature extensions, but the open-source core itself carries no license fee.
At a glance
| Vendor | Open-source community |
|---|---|
| Pricing model | Open source + paid options |
| Free tier | Yes |
| Deployment | Self-hosted |
| Open source | Yes (Apache-2.0) |
| Best for | Quant researchers who need to sweep large parameter or asset grids faster than event-driven backtesters allow. |
Pricing
Free, open-source Python library; a separate paid vectorbt PRO product exists but is not covered here.
Pricing has not been verified yet — see the vendor's site.
Features
- Vectorized backtesting for fast parameter sweeps
- Multi-asset and multi-parameter portfolio simulation
- Built-in performance and risk metrics
- Interactive Plotly-based visualizations
- Integration with TA-Lib and other indicator libraries
- Numba-accelerated computation
Integrations
Profile last reviewed September 21, 2026