Raw model outputs rarely arrive perfectly calibrated; “70%” might behave like 55% over hundreds of trades. MarketXED’s learning loop tracks labeled outcomes over time and adjusts how probabilities map to reality.
Triple-barrier style labeling ties forecasts to practical paths—what actually happened to price—so calibration improves where it hurts: position sizing and expectation.
That is why confidence moves as data accumulates: the system is designed to get less wrong about being wrong, not to chase a static hype score.