Question 1

What is overfitting in a trading backtest?

Accepted Answer

Overfitting is when a strategy is tuned so tightly to past data that it captures noise and coincidences instead of a repeatable pattern. It looks excellent on the data it was built from and then falls apart on data it has never seen.

Question 2

What is the difference between overfitting and curve-fitting?

Accepted Answer

They are closely related. Curve-fitting is shaping a strategy's rules and settings to match the exact twists of a historical chart; overfitting is the general result, a model that has memorized the past instead of learning something that generalizes. In practice people use the terms almost interchangeably.

Question 3

Why is a near-perfect backtest a warning sign?

Accepted Answer

Real edges are messy and survive losing stretches. A backtest with almost no drawdown and a near-perfect equity curve usually means the rules were bent to fit every wiggle of one specific stretch of history, which rarely repeats.

Question 4

How do you guard against overfitting?

Accepted Answer

Keep the rules simple, use few parameters, and check the result on out-of-sample data the rules were never tuned on. If performance collapses on unseen data, the edge was probably fitted to noise.