On-Chart AI indicators: The model is grading its own homeworkMicro E-mini Nasdaq-100 Index FuturesCME_MINI:MNQ1!MoTradeSystemsAI indicators that train directly on your chart are getting genuinely impressive — real neural networks, modern optimizers, careful anti-repaint discipline. The engineering deserves respect. The promise doesn't. An on-chart model learns on the exact bars where it displays its "performance." No holdout, no out-of-sample test, no validation against data it hasn't seen. In machine learning that's the first disqualifier: in-sample fit is not evidence. The equity curve it shows you isn't a backtest — it's a training log. Even clean implementation details (training on confirmed bars only, scalers fitted on training data) don't repair this. They make the overfitting reproducible, not valid. The deeper issue is economic. An edge needs a risk premium and an identifiable payer — someone on the other side with a reason to pay you. An optimizer fitting patterns to price data produces neither. It will find "structure" in a random walk if you let it. I ran 50+ pre-registered backtests looking for timing edges. Zero survived sealed out-of-sample data with realistic costs. What survived instead: measuring the environment and sizing for the risk you're carrying. Context over prediction. Education/analysis only, not investment advice. Backtest ≠ Live.