This episode focuses on the crucial role of backtesting in algorithmic trading. It emphasizes the importance of meticulous backtesting to avoid pitfalls like look-ahead bias and data-snooping bias, which can inflate performance estimates. The text explores various methods for assessing the statistical significance of backtested results, including hypothesis testing and Monte Carlo simulations. Furthermore, it highlights practical challenges in backtesting, such as data quality issues (survivorship bias, stock splits, dividend adjustments), and the complexities of handling continuous futures contracts. Finally, it advocates for simple, linear models to mitigate data-snooping bias and suggests real-money testing as the ultimate validation step.
