Overfitting

Building a model that describes the noise in a sample rather than the signal, so it performs well historically and poorly afterwards.

The tell is complexity that earns its way in only on the training data: many parameters, thresholds tuned to specific values, rules with exceptions.

The defences are a held-out period, preference for simpler specifications, and honest accounting of how many variations were tried before the good one appeared.

The more strategies tested, the more likely the best one is luck.