This podcast episode delves into the nuances of overfitting in trading strategies, illustrating how strategies optimized for past market performance can lead to significant discrepancies in live trading results. Rob and Andrew explore various forms of overfitting—explicit, implicit, and tacit—highlighting the importance of model complexity and the dangers of relying on outdated knowledge. They discuss the critical role of backtesting not just as a tool for evaluation but for discovery, while emphasizing the necessity of rigorous data quality and risk management. Through a blend of shared experiences and analytical insights, the episode calls for a balanced approach to strategy development and a heightened awareness of the inherent biases that can undermine trading performance.
