Scaling Cointegration Analysis with DuckDB and SQLite

Summary

This final installment in a retail statistical arbitrage series explains why larger cointegrated stock portfolios can strain a workflow built around SQLite. It distinguishes SQLite’s role in transactional tasks from an OLAP database’s role in analytical workloads, then proposes using DuckDB for historical data analysis while retaining SQLite for trading operations. The broader workflow screens and scores cointegrated baskets, monitors portfolio weight stability and mean-reversion behavior, and checks for structural breaks.

The article compares SQLite and DuckDB on price aggregation, historical quote joins, and Rolling Windows Eigenvector Comparison calculations. It reports DuckDB running 2 to 23 times faster on a low-end machine, with the advantage growing as sample size increases. These are workload-specific benchmarks, not evidence of strategy returns or universal database performance. The proposed system is motivated by growth to larger datasets, and its effectiveness depends on the data, queries, and hardware used.

Key ideas

• Cointegrated baskets require more data and computation than simple two-asset pairs.
• The workflow assigns transactional processing to SQLite and analytical queries to DuckDB.
• Portfolio monitoring combines weight stability analysis with tests for structural breaks.
• The article reports DuckDB speed advantages in three specific database benchmarks.
• Benchmark performance does not establish trading profitability or guarantee the same speedup on other workloads.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.