Can AI Predict Crypto Prices? How to Test the Claim

For price or return forecasts, common error measures include mean absolute error and root mean squared error. The first averages the size of the miss. The second gives large misses more weight. Percentage errors need care near zero and when comparing assets with very different volatility.

For up-or-down predictions, accuracy can be misleading if one class dominates. Precision, recall, balanced accuracy and a confusion matrix show which kinds of calls the model gets right. A strategy test also needs executable prices, fees, spread and position rules. The role of market liquidity helps explain why a paper forecast can be hard to trade at the quoted price.

Probability forecasts need calibration. If a model assigns 70% probability to many comparable events, roughly 70% should occur over a large sample if the estimates are calibrated. A probability can be calibrated without being useful, and useful without being perfectly calibrated, so compare it with a baseline and a scoring rule such as the Brier score, which measures the accuracy of stated probabilities.

Report a distribution, not only one average. Show median error and the large-loss tail alongside the average. Then break the results down by year, bull and bear periods, liquid and thin assets, and forecast horizon to reveal failure hidden by the total.