Vectorbt is a backtesting engine that evaluates thousands of trading strategies simultaneously by vectorizing computations across NumPy arrays rather than looping through individual strategies sequentially.
The tool solves the problem of slow strategy evaluation by taking a matrix-oriented approach to backtesting. Instead of testing one strategy at a time across historical bars, it packs multiple strategy configurations into NumPy arrays and accelerates computation using Numba and Rust, enabling grid searches that would take hours to complete in seconds. This allows traders and researchers to explore thousands of trading ideas across different assets and timeframes in a single run.
Vectorbt suits quantitative traders, algorithmic researchers, and data scientists who need to rapidly prototype and evaluate multiple strategy variations. It works well for portfolio optimization, parameter sweeps, and large-scale experimentation where traditional sequential backtesting becomes a bottleneck. The tool is designed for both human researchers conducting manual analysis and AI agents performing automated strategy discovery. It provides detailed analysis down to individual trades and includes interactive visualization capabilities for exploring results.
The project maintains automated test coverage and publishes releases through continuous integration workflows. A Rust engine is available as a separate package to further accelerate performance-critical operations. The tool is distributed through standard Python packaging channels and also offered as a Docker image for containerized deployment.
