I gave seasonality the most weight in my model because it sounded smart. it earned about half of it

when i built my pre market scan i weighted seasonality heavily. everyone talks about it, there's a chart for every month and every day of the week, it felt like the kind of edge that's sitting there in plain sight and most people ignore. after 60 days of actually scoring my own calls it's the input i trust least. quick context so this makes sense. i run a scan before the bell that pulls positioning, liquidity levels, seasonality for that specific day, and the calendar. it gives me one directional lean on NQ, ES and GC with a level where the lean is dead. every call gets logged in the morning and scored at the close, and scored means the close finished on the side of the lean, nothing about whether a trade off it made money. when i went back and looked at which drivers were actually behind the good calls, seasonality kept showing up on the wrong side. not randomly wrong, which would be fine. wrong in a specific way. it was right on quiet days and wrong on the days that mattered. any session where the calendar had something real in it, seasonality was just noise sitting in my model adding confidence to a lean it hadn't earned. and those are exactly the days with the range worth trading. so the input was helping me most on days i shouldn't be sizing up and hurting me most on days i should. which in hindsight is obvious. seasonality is an average of a bunch of years where each of those years had its own reason for moving. cpi didn't care that the second week of march is historically bullish. the calendar was the opposite. it barely feels like an edge because everyone can see the same schedule, but weighting it properly did more for my hit rate than anything else i changed. positioning was second. i'm not saying throw seasonality out. i still have it in there, just with a much smaller say, and it gets muted entirely on days with real data. what changed my mind wasn't a theory, it was that i finally had 76 scored calls to look at instead of a feeling. the broader thing i took from this: i weighted my inputs by how clever they felt, not by how they performed. i'd guess most people building any kind of systematic process do the same thing and never check, because checking means finding out the smart sounding part of your model is dead weight. now i'm rebuilding it so the log sets the weights. if a driver hasn't earned its place over the last 60 days it gets less say next month. curious if anyone else has gone back and scored their individual inputs separately rather than the system as a whole. i suspect a lot of models are carrying one component that does all the work and three that are along for the ride.