"Designing Backtest Engines: Foundations of Reproducible Quant Simulation"

In modern systematic trading, ideas are plentiful but credible evidence is rare. Designing Backtest Engines: Foundations of Reproducible Quant Simulation is written for quantitative researchers, trading technologists, and data scientists who need more than a quick PnL chart—they need simulation results that can withstand internal review, model risk committees, and regulator scrutiny. This book shows how to turn fragile research scripts into robust, auditable infrastructure that produces the same answer tomorrow as it did today.

You will learn how to construct deterministic workflows spanning data ingestion, feature engineering, order generation, execution modeling, and portfolio accounting. Core chapters cover market data and calendar modeling, event-driven backtest architecture, realistic execution and cost models, and precise PnL and return calculation. The book then develops a rigorous validation toolkit, including walk-forward design, bias detection, bootstrap uncertainty estimation, and multiple-testing control, before concluding with reporting, governance, and provenance practices that make simulation outputs explainable and comparable over time.

Assuming familiarity with basic markets, statistics, and programming, the text focuses on design choices and trade-offs rather than any single language or tech stack. Presented in LaTeX with clear structure and implementation-oriented guidance, it serves as both a blueprint for building new engines and a diagnostic manual for strengthening existing

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