A probabilistic decision layer that scores every incoming trade before execution. It estimates trade quality from live market features, compares the setup against model thresholds, and helps route capital toward higher-conviction opportunities.
The ML Scoring Method sits inside the live trading path and evaluates each new signal using a trained model family built from historical trade behavior.
Real-time cumulative performance of the ML Scoring Method. Every data point reflects the model's live impact on trade selection quality and downstream outcomes.
The ML Scoring Method improves through accumulated trade history, model monitoring, and live policy control. It is built to evolve without becoming opaque, so every score can still be traced back to model outputs, thresholds, and decision state.
The model can learn from momentum, structure, volatility, macro signals, projection edges, and other live context features that are difficult to express cleanly in static rule form.
Scoring alone is not enough. Each output is judged against active thresholds, precision floors, margin rules, and policy states so the system remains operationally disciplined.
As more validated trade data becomes available, the method can transition from broad fallback coverage toward more specialized model decisions with stronger confidence.
Every live decision can be logged with pwin, threshold, margin, final score, policy state, and backoff level, making it easier to monitor real behavior and tighten the system over time.