Building Robust Trading Systems

A strategy that works brilliantly in backtests but fails in live trading is worthless. The goal is not to optimize for the past, but to build systems that remain effective across an uncertain future.

Simplicity Over Complexity

Complex models with many parameters are fragile. Simple models with few parameters are robust. When in doubt, choose the simpler approach. A strategy you can explain in one sentence is more likely to persist than one requiring a dissertation.

Multiple Timeframes, Multiple Markets

A pattern that appears in one market on one timeframe might be spurious. A pattern that appears across multiple markets and timeframes is more likely to reflect something fundamental about market structure.

Regime Awareness

Markets behave differently in different regimes: trending vs. mean-reverting, high volatility vs. low volatility, risk-on vs. risk-off. A robust system either adapts to these regimes or is designed to be regime-agnostic.

Stress Testing

Every strategy should be tested against historical stress periods: 2008, 2020, and others. If a strategy would have blown up during these periods, it probably will blow up during the next crisis—which will inevitably come.

Continuous Monitoring

Even robust strategies can decay. Continuous monitoring of performance metrics, factor exposures, and execution quality is essential. The goal is to detect degradation early, before it becomes catastrophic.

Building robust systems is harder than building optimal ones, but robust systems are the only ones that matter.