AUC-ROC

AUC-ROC has become standard metric for evaluating cryptocurrency trading prediction models. Unlike simple accuracy (which treats all misclassifications equally), AUC-ROC measures discrimination across thresholds—critical because traders can adjust position sizing and risk based on model confidence. A model predicting 0.95 probability (near-certain upside) justifies larger position than 0.55 probability (slight edge). AUC-ROC quantifies whether the model ranks high-probability predictions more reliably than low-probability ones. In practical crypto trading, imbalanced datasets create accuracy blindness. If price increases 52% of the time and a model predicts increase 100% of the time, accuracy reaches 52% despite uselessness. AUC-ROC avoids this trap by evaluating performance across all thresholds. Traders can examine the ROC curve determining optimal thresholds: accepting lower win rates for higher average profits (moving threshold to right), or maximizing win rate accepting lower profit per trade (moving left). This threshold flexibility directly translates to position sizing and risk management decisions. Professional crypto traders use AUC-ROC alongside other metrics. A model achieving 0.7 AUC has significant discriminative ability but isn't guaranteed profitable—profitability requires win size exceeding loss size. AUC-ROC reveals whether the model has genuine edge; combining with magnitude metrics (average winning vs losing trade size) determines expected profitability.