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High-Performance Quantitative Finance: Building Fast Data Pipelines with Polars and DuckDB - Couverture souple
Synopsis
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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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High-Performance Quantitative Finance
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PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. N° de réf. du vendeur L0-9798190129922
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High-Performance Quantitative Finance (Paperback)
impression à la demandeVendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
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Paperback. Etat : new. Paperback. Reactive PublishingHigh-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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798190129922
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High-Performance Quantitative Finance: Building Fast Data Pipelines with Polars and DuckDB
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High-Performance Quantitative Finance
impression à la demandeVendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni
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PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. N° de réf. du vendeur L0-9798190129922
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High-Performance Quantitative Finance (Paperback)
impression à la demandeVendeur : CitiRetail, Stevenage, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : new. Paperback. Reactive PublishingHigh-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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798190129922
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High-Performance Quantitative Finance : Building Fast Data Pipelines with Polars and DuckDB
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Taschenbuch. Etat : Neu. Neuware - Reactive PublishingHigh-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. N° de réf. du vendeur 9798190129922
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