Most computer-vision demos start with a dataset someone else curated. Mine started with screen recordings I was not allowed to redistribute - which turned out to be the most instructive constraint in the whole project.

This is the story of trade-state-classifier : a fine-tuned YOLO26n-cls model that looks at a screenshot from a trading livestream and answers one binary question - is there a filled position visible on screen right now? - plus the labeling pipeline that made it possible without anybody's data leaving my machine.

Why binary classification (and not object detection)

The upstream goal is an event pipeline: watch a livestream, detect the moment a streamer's position state changes (flat -> open -> flat), extract the displayed entry/stop/target levels at that moment, and hand a validated event to the execution layer.

Full UI-element detection (order tickets, entry lines, price badges) is the endgame - but it needs far more labeled variety than I could curate responsibly.…