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Financial Data Engineering with Python: Market, Accounting, and Forecasting Pipeline Design

by Danny Munrow , James Preston
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Current price ₹2,949.00
Original price ₹3,274.00
Original price ₹3,274.00
Original price ₹3,274.00
(-10%)
₹2,949.00
Current price ₹2,949.00

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Book cover type: Paperback
  • ISBN13: 9798247274483
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Publication Date:
  • Pages: 428
  • Original Price: GBP 25.88
  • Language: English
  • Edition: N/A
  • Item Weight: 567 grams
  • BISAC Subject(s): Finance / Financial Engineering

Reactive Publishing

Financial data is no longer just stored. It is engineered, validated, versioned, and deployed like production software.

Financial Data Engineering with Python is a practical, system-level guide for building robust financial data pipelines that support market analytics, accounting infrastructure, and forward-looking forecasting models. Designed for financial analysts, data engineers, quant researchers, and technical finance professionals, this book bridges the gap between traditional financial data handling and modern production-grade data architecture.

Instead of focusing on theory alone, this book shows how real financial data systems are structured in high-performance environments where data latency, accuracy, auditability, and reproducibility directly impact decision-making and risk exposure.

Inside, you will learn how to:

- Design resilient market data pipelines for pricing, trading, and risk systems
- Engineer accounting data flows that support reconciliation, audit trails, and reporting integrity
- Build forecasting data layers that integrate historical, real-time, and external macro datasets
- Implement Python-based ETL, validation, and monitoring frameworks for financial workloads
- Structure financial data models for scalability across research, reporting, and production systems
- Reduce data fragility using schema controls, versioning, and automated quality checks

The book emphasizes production reality: messy source data, regulatory constraints, system interoperability, and the need for repeatable, testable data processes across financial organizations.

Whether you are modernizing legacy finance workflows, building institutional-grade analytics infrastructure, or developing next-generation financial data platforms, this guide provides a clear, implementation-focused blueprint grounded in real-world financial data engineering practice.

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