Apache Arrow 18 delivers targeted improvements for quantitative workloads. The headline features — SIMD aggregation kernels and faster Parquet reads — address the two most common bottlenecks in data-intensive backtesting pipelines.
SIMD Aggregation
Group-by aggregations (sum, mean, stddev) now use AVX2/NEON SIMD instructions, delivering 2-3x speedup on typical financial time series operations. For a backtest computing rolling statistics across 10,000 instruments, this reduces the data preparation phase from 45 seconds to 15 seconds.
Parquet Read Performance
Row group filtering with bloom filters now works across nested columns, enabling efficient filtering of Parquet files by date range, instrument, or any indexed column. Combined with predicate pushdown, selective reads of large datasets skip 80-90% of data on disk.
Python Integration
PyArrow 18 maintains zero-copy interop with NumPy and Pandas. The new compute kernels are automatically used by Pandas operations when Arrow-backed DataFrames are in use, providing speedups without code changes.
