Applied DuckDB and Polars for Financial Data: Building In-Memory Analytics Backtesting Pipelines in Python

Reactive PublishingMaster high-throughput quantitative data processing with DuckDB and Polars, the modern Python stack for high-frequency financial analytics.As market data volumes grow, legacy tools like pandas and standard SQL databases struggle under memory limits and execution bottlenecks. Applied DuckDB and Polars for Financial Data provides a clear, practical guide to constructing high-performance data pipelines built to handle large-scale financial datasets using Python.This book delivers hands-on approaches to building fast, vectorised analytical workflows. Learn how to combine DuckDB's vectorized SQL query engine with Polars' lazy execution framework to query, transform, and analyze multi-gigabyte tick, order book, and trade feeds directly in memory without crashing your hardware environment.Inside, you will discover how to: - Process Tick and Trade Data: Leverage DuckDB for lightning-fast SQL queries on persistent and columnar data files like Parquet.- Optimize Dataframes with Polars: Master Polars' parallel processing, lazy evaluation, and memory-efficient streaming engine for high-frequency time series analysis.- Construct Backtesting Pipelines: Design backtesting systems capable of joining, filtering, and feature-engineering historical market data in seconds.- Handle In-Memory Analytics: Bridge DuckDB and Polars seamlessly zero-copy using Arrow arrays to avoid expensive serialization overhead.- Manage Out-of-Core Processing: Query datasets larger than RAM with disk-backed streaming strategies.Whether you are a quantitative developer, financial analyst, or data engineer, this practical blueprint gives you the tools to replace slow legacy pipelines with cutting-edge, high-speed Python architecture.