Market Data Engineering for Quants

<p>Market Data Engineering for Quants: Normalizing Replaying and Aligning Time<br /><br />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.<br /><br />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.<br /><br />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.</p>