Building a Vectorbt Event-Driven Loop:
What if your backtesting loop could evaluate 100,000 parameter combinations in under 10 seconds? That is the reality when you build a vectorbt event-dri...
What if your backtesting loop could evaluate 100,000 parameter combinations in under 10 seconds? That is the reality when you build a vectorbt event-driven loop using Numba-compiled kernels instead of standard Python iteration. The Python backtesting landscape has split into two distinct camps: vectorized engines for speed and event-driven engines for execution fidelity. According to python.financial, the industry has moved from monolithic event-driven frameworks to a hybrid approach where vectorized engines handle research velocity while event-driven systems ensure execution realism. This article explores how to bridge that gap by building a high-performance loop in VectorBT that mimics event-driven behavior without sacrificing speed. The core difference between these frameworks lies in how they process time and data. Vectorized backtesting applies logic to entire arrays of historical data simultaneously, leveraging the linear algebra capabilities of modern processors. In contrast, event-driven backtesting simulates the chronological passage of time bar by bar, processing market data as a sequence of discrete events.