High-Performance Quantitative Finance

Reactive Publishing

High-Performance Quantitative Finance provides a practical, hands-on framework for financial engineers, quantitative analysts, and data scientists building modern analytical pipelines. As financial datasets continue to scale, legacy tools like Pandas and Excel often become primary bottlenecks in execution speed and memory management. This book demonstrates how to leverage Polars and DuckDB to process multi-gigabyte market datasets efficiently.

What You Will Learn:

• High-Speed In-Memory Processing: Utilize Polars' lazy evaluation, query optimization, and native multi-threading for backtesting and signal generation.

• Embedded Analytics with DuckDB: Execute fast, SQL-native analytical queries directly on local vector storage, Parquet files, and market data archives.

• Memory-Efficient Data Architecture: Eliminate memory overhead and Out-Of-Memory (OOM) errors when handling tick data, order books, and time-series records.

• Hybrid Workflows: Combine vector-based operations with relational queries to build resilient, maintainable quantitative software.

Whether you are optimizing risk metrics, executing high-throughput simulations, or modernizing legacy analytical scripts, this guide delivers the practical code patterns required for high-performance financial data engineering.