my backtests look great and i still don't trust them, help me think

ok so i've been at this maybe 7 months. background is i did a couple of ML courses (the usual ones) and i thought "ok, prediction problem, i know how to do prediction problems." turns out no. my current process, and i'm posting it partly to see it written down and go "oh no": 1) pull daily bars for a basket of ~40 large cap names 2) build features: a few return lookbacks, realized vol, volume z-score, distance from a moving average, day of week (yes really) 3) label = next day return, sign of it 4) gradient boosting, train on 2015-2021, test 2022-onward 5) if the test accuracy looks decent i wire the signal into a small automated rule and paper it the part that confuses me is the gap between accuracy and money. i've had models sitting around 53-54% directional on holdout which sounds like a real edge to me, and then the equity curve of the same thing is flat or slightly down before i even think about costs. i *think* what's happening is it's right on lots of tiny days and wrong on the few big ones, so the accuracy metric is basically lying to me. is that the standard beginner trap? should i just be regressing on returns instead of classifying sign, or weighting samples by magnitude, or is the whole framing off? other thing i genuinely don't know: how many times am i allowed to look at my holdout. because i have definitely looked at 2022-onward more than once. more like thirty times. every time i change a feature i re-check it. i'm aware this is probably contaminating everything but i don't know what the alternative is in practice - do people keep a third slice they literally never touch until the end? and then what, if it fails do you throw away 4 months of work? not asking anyone to hand me a strategy, i'm asking how you structure the loop so you're not fooling yourself. because right now i suspect i'm fooling myself constantly and just don't know where.