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Provides comprehensive guidance and implementation patterns for developing quantitative trading backtests that produce reliable performance estimates. It covers essential concepts such as handling look-ahead and survivorship biases, implementing proper data splits (training, validation, and test sets), and performing walk-forward analysis to ensure strategy robustness. By offering both event-driven and high-performance vectorized backtesting templates, it enables developers to create sophisticated infrastructure that accounts for realistic transaction costs, slippage, and market impact, making it an indispensable resource for anyone building or validating automated trading algorithms.
