Noisereductionvs.informationloss
Every system built to interpret markets in real time has to filter — there's simply too much happening, across too many sources, for anything to be surfaced unfiltered. But every filter carries a risk that's easy to underestimate: a real signal can look identical to noise right up until the context that would explain it arrives. A minor personnel change at a regulator, an unusual but small shift in bond issuance, a quiet change in language buried in a routine filing — each of these can look like nothing worth flagging, until it turns out to be the first thread of something significant.
This is the real difficulty in building interpretive systems, and it's not a problem that gets solved once. It's not building the filter that's hard — that part is almost mechanical. It's calibrating how aggressively to apply it, continuously, without quietly deleting the small, easy-to-miss signals that matter most precisely because almost nothing else is paying attention to them yet. A filter tuned too loose drowns the person using it in noise. A filter tuned too tight creates a false sense of clarity by simply removing anything that doesn't fit the pattern it already expects. The honest position is that this tradeoff never fully resolves — it has to be revisited constantly, informed by what the system missed last time, not assumed solved once and left alone.