A modeling error where an automated strategy or model is too closely fitted to historical data (including noise), resulting in poor generalization to new data. An overfitted forex robot shows great backtest results but fails in live trading. Techniques like cross-validation, walk-forward testing, and simplicity help mitigate overfitting.

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