Curve fitting happens when a trading strategy is shaped too perfectly around past market data. Instead of finding a real trading edge, the system is tuned to match historical price movements at an unrealistic level. It usually starts with good intentions. Traders run a backtest, see a few weak spots, then add more rules, filters or indicators. Over time, the strategy begins to mirror the past so closely that it loses its ability to handle new conditions. The results look great on paper, but the logic behind the system becomes fragile.

The danger shows up when the strategy goes live. Markets never repeat in the same way. A curve-fitted system struggles the moment price action shifts. The drawdowns become bigger, the win rate drops, and the trader realises the strong backtest was an illusion. Since the strategy was built on noise instead of real patterns, it reacts poorly to volatility, unexpected news and changing market trends.

Curve fitting also creates false confidence. Traders think they have found something powerful, so they increase risk or allocate more capital. When losses start, it becomes difficult to understand what went wrong because the system was never based on solid logic in the first place. To avoid this trap, traders focus on simple rules, clean data, and out-of-sample testing. A strategy that performs reasonably well across different conditions is far more reliable than one that only shines in a perfect backtest.