Vectorbt vs Zipline: Vectorization Speed
What if your crypto backtest could evaluate 100,000 parameter combinations in under 10 seconds instead of waiting days? This speed gap defines the diffe...
What if your crypto backtest could evaluate 100,000 parameter combinations in under 10 seconds instead of waiting days? This speed gap defines the difference between vectorbt vectorization and traditional event-driven engines like Zipline. Key fact: VectorBT PRO can evaluate over 100,000 backtests in under 10 seconds when using chunked execution and parallelization via Pathos. Most traders start with event-driven backtesting because it feels intuitive. You write a loop, step through each bar, and make decisions. However, as your research expands to include multiple assets, longer histories, or wide parameter sweeps, Python-level loops become the limiting factor. This is where the architectural split becomes critical. On one side sits the vectorized paradigm, exemplified by the VectorBT family. These engines push work into NumPy-style array operations so you avoid the per-bar Python loop and can run simulations over large datasets very quickly. On the other side is the modern event-driven paradigm, epitomized by frameworks like Zipline. These systems optimize for realism: order types, fills, latency, and order book mechanics.