The last post was about making a number trustworthy. Leakage geometry, purge widths, de-overlap, a baseline that could not cheat. It ended with a minute-scale ceiling that held at 52% across seven configurations and a model family swap.
This one is about what happens after you trust the number. Because a probability you are going to bet on is a different object from a probability you are going to report.
The probabilities are not decorative
The path-passage classifier is a three-class LightGBM. It returns p_up, p_down, p_none. Those go straight into the expected-value score that decides whether to take a trade and how big:
long_score = p_up * (B - C) + p_down * (-B - C) + p_none * (-C)
