"Market Data Engineering for Quants: Normalizing, Replaying, and Aligning Time"

Modern quantitative research lives or dies on the quality of its market data. This book is written for quantitative developers, researchers, and data engineers who must turn chaotic exchange feeds into precise, reproducible inputs for trading models and backtests. Rather than treating data as an afterthought, it approaches market data engineering as a first-class quantitative discipline, where time, precision, and topology are as important as alpha signals.

Readers will learn how to model time down to nanoseconds, choose safe numeric types, decode binary exchange protocols, and design storage layouts that can sustain petabyte-scale tick archives. The book walks through normalization, validation, and corporate-action handling, then develops the algorithms needed for point-in-time queries, as-of joins, and cross-stream synchronization. It culminates in the construction of deterministic replay and simulation engines that reproduce historical market states with audit-ready fidelity.

The focus is deeply practical yet theoretically grounded, assuming comfort with basic programming (C++/Java/Python), SQL, and introductory quantitative finance. All concepts are presented in a system-oriented, implementation-level style suitable for LaTeX-based technical documentation, emphasizing reproducibility, temporal correctness, and engineering trade-offs often glossed over in trading literature.

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