Systematic trading research with backtesting, alpha validation, and statistical rigor to separate real edges from overfit signals.

• Covers backtesting methodology, alpha signal research, factor investing, statistical arbitrage, and regime detection with emphasis on avoiding common pitfalls like look-ahead bias and overfitting
• Includes walk-forward analysis, out-of-sample testing, and transaction cost modeling to validate strategies beyond in-sample performance
• Grounded in skepticism toward machine learning for alpha and focus on simple, robust approaches validated across multiple regimes and time periods
• Draws on institutional quant research experience to identify when apparent alpha is actually hidden beta or factor exposure