Building technical strategies with VectorBT

This recipe introduces you to the powerful vector-based backtesting library VectorBT. One of the most compelling advantages of using VectorBT is its speed in running simulations. Whether you are testing a single strategy or optimizing across a multi-dimensional parameter space, VectorBT’s performance is optimized to deliver results in a fraction of the time traditional methods would require.

Built on top of well-established libraries such as pandas, NumPy, and Numba, VectorBT seamlessly integrates into the data science ecosystem. It leverages pandas for its DataFrame structure, which is familiar to most quants. NumPy’s numerical computing abilities provide the mathematical backbone, ensuring that heavy calculations are performed efficiently. However, the real game-changer is Numba, a Just-In-Time (JIT) compiler that translates Python functions to optimized machine code at runtime. Thanks to Numba, VectorBT can execute loops...