vectorbt

Why we included this project

VectorBT tackles backtesting with matrices rather than bar-by-bar loops: it pushes thousands of parameter combinations into NumPy arrays, with Numba and a Rust engine handling the compute-heavy parts. That design lets a quant or trading researcher sweep a large strategy grid across a market in seconds, where a conventional for-loop engine would churn much longer. Vectorized portfolio analytics sit on top, so traders working in equities or crypto can test many ideas across configurations without writing their own speed hacks. It is a focused quantitative tool, not a general machine-learning library, and it pays off most for people who already know the trading domain and want fast strategy research at scale.