Why Goodhart’s Law Matters Deeply to Traders
“When a measure becomes a target, it ceases to be a good measure.” – Charles Goodhart
In trading, precision is both a tool and a trap. The pursuit of measurable improvement—through backtested systems, metrics, and indicators—often masks a deeper vulnerability: the corruption of signals once they become goals in themselves. This is the heart of Goodhart’s Law, and it applies to traders at every level of experience.
Goodhart’s Law warns that when a particular metric becomes the focus of decision-making, its reliability as an indicator deteriorates.
Initially a critique of monetary policy, it finds fertile ground in financial markets, where traders are constantly searching for patterns to exploit. The problem arises when those patterns are no longer observed but pursued. When traders fixate on win rate, profit factor, Sharpe ratio, or drawdown control as ultimate goals, the behaviour that once produced those outcomes begins to distort.
Take, for example, a system with a historically high win rate. A trader, seeing this, may begin to ignore trades that slightly deviate from their optimal setup or exit early to “lock in” profits to preserve that win rate. But in doing so, they erode the statistical edge the system was designed to exploit. The measure—win rate has become a target, and its meaning is lost.
The same occurs when traders optimise strategies solely to maximise backtested results—overfitting to past data results in fragile systems that collapse in real-world conditions.
Optimisation is retrospective, not prospective.
Another manifestation is psychological. Traders may begin to measure their competence by how often they are right, rather than by the quality of their decision-making. This cognitive shift leads to an aversion to accepting valid losses, overtrading to recoup money, or abandoning systems altogether in pursuit of metrics that make them feel in control. Ironically, this need to control erodes the very performance they seek.
Process gives way to ego.
Goodhart’s Law also has implications for risk management. Some traders, eager to show low drawdowns or consistent equity curves, may reduce position sizes to negligible levels or stop trading after a few losses. This makes their metrics look better, but may sabotage their long-term expectations. The trader ends up optimising for the appearance of safety rather than the reality of risk-reward.
Gaming returns is often a function of fund managers and I have written many times before about how the use of simple metrics can be manipulated to make a losing system look like a winning system.
Ultimately, Goodhart’s Law reminds traders to stay focused on process over metrics.
A sound trading system is not one that merely looks good on a spreadsheet—it remains robust, adaptable, and psychologically sustainable in the real world. Traders must avoid mistaking the map for the territory. Indicators, systems, and statistics are tools for navigation, not destinations themselves.
