Name: Applied DuckDB and Polars for Financial Data: Building In-Memory Analytics and Backtesting Pipelines in Python
SKU: 68912884
Price: 1414.00 TWD
Availability: InStock
Author: Van Der Post, Hayden;;;Schwartz, Alice
ISBN: 9798174121096

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

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

Type

Physical Book

Publisher

Independently published

Author

Van Der Post, Hayden;;;Schwartz, Alice

Format

Paperback

ISBN13

9798174121096

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

Van Der Post, Hayden;;;Schwartz, Alice (Author) · Independently published · Paperback

New Book Imported to Taiwan

Delivery: 27 Oct - 04 Nov

NT$ 1,414

NT$ 1,414

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

Reactive Publishing Master 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.