Benchmarked my SMC zones against random zones over three years. Random won.

Status first: demo account, three pairs, no proven edge, nothing for sale. Posting the negative result because I don't see many of them here. Deterministic backtest, Aug 2023 to Aug 2026, roughly 6,900 opportunities, with a random-zone floor placed next to every measurement rather than computed once at the end. Result. Entry at touch came out at +0.101 R. Random zones came out at +0.116 R. The gap is inside the noise, so the selection rules carry no information over random. Then it got worse in a way I didn't expect. Adding the confirmation sequence took it to −0.016 R, so confirmation actively subtracted, and it cost about two thirds of the opportunities to do it. The bottleneck turned out to be arithmetic rather than conceptual: the large majority of missed confirmations were "no CHoCH within 16 candles", and the real median wait after a sweep is 37 candles. The window was catching about a fifth of what it was supposed to catch. I ran 17 definition variants, preregistered with a dated file, holdout pairs I hadn't looked at. Three results cleared the detectability threshold and all three were negative. Costs closed it: median risk 21.5 pips, median cost 0.07 R, and net of that all 17 cells go negative, best arm from +0.099 to −0.066. One effect had the right shape across every cell, and it died on the holdout. The holdout is spent now. On these pairs there's no independent check left, which I think is the part people underestimate about running many variants. Two things I'd flag for anyone doing the same thing. Put the random floor next to every measurement instead of at the end, because it's the only thing that told me my rules were doing nothing. And write the cost model in before you look at results, since at 21.5 pips of risk the spread is the same order of magnitude as the largest gross edge I ever measured, which means half the variants were never really positive. What this doesn't answer. The backtest traded every zone. The live system picks one, and the picking is done by an LLM writing a falsifiable thesis before the fact, with a deterministic engine deciding whether the conditions actually held. That selection has never been measured against the same random floor, and it's the only open question I have left. Genuine question for the sub: has anyone here isolated a selection layer and benchmarked it against random on the same universe? I have the deterministic side answered and no idea how to get a clean read on the selection without burning a fresh holdout on it.