Coiled and trapped supply: reading who still has to sell

A memecoin chart tells you where price has been. It does not tell you the thing that actually decides where price goes next: how many people are sitting on positions they intend to exit, and what price they are waiting for.

That information exists. Every unit of float was bought by someone at some price, and that price is a strong predictor of what they do next. Reconstructing the distribution of those cost bases is the entire idea behind how mEEme reads a token.

Two kinds of overhang

Split the float at the current price and you get two populations that behave in opposite ways.

Coiled supply — below spot, in profit

Holders who bought lower than the current price. They are up, they can leave whenever they like, and nothing is stopping them. This is the overhang you are racing: the more of the float that sits in profit beneath you, the more supply can hit the market on any green candle without anyone needing a reason.

Coiled supply is not automatically bearish. Early holders who have held through several drawdowns have demonstrated they are not quick sellers. But it is potential energy, and when it releases it releases fast — which is where the name comes from.

Trapped supply — above spot, underwater

Holders who bought higher and are down. They are not choosing to hold; they are waiting to get out. Each cluster of them forms a ceiling, because as price climbs back toward their entry, a wave of “just let me exit flat” selling appears.

This is why recoveries stall at oddly specific prices. It is not resistance in the technical analysis sense. It is a crowd of people who bought there, have been waiting months or hours to be made whole, and take the first chance they get.

Coiled supply is what can leave. Trapped supply is what you have to eat through. A token with heavy coiled supply and a thin ceiling behaves nothing like one with the reverse, even if the charts look identical.

How you get the distribution without wallet data

The obvious approach is to walk every holder’s transaction history on-chain and compute their actual cost basis. It is accurate, and it is expensive — thousands of wallets, each needing full history, through a paid RPC provider. For a token that launched four hours ago with 9,000 holders, that is not a request you can make on demand.

The alternative is to infer it from the traded volume profile. Every candle in a token’s history has a price range and a volume, so it tells you how much was bought near that price. Weight each price bucket by the volume that traded there, apply a decay for turnover — supply bought early gets partly resold as the token trades, so old buckets should not be counted at full weight forever — and you get an estimated distribution of where the float currently sits.

This is a statistical model of a crowd, not a ledger of individuals, and it should be treated that way. It is wrong most reliably on very young tokens with thin history, where there is not enough traded volume to infer much of anything.

What the distribution actually tells you

• Where a rally stalls. The first meaningful trapped shelf above spot is the most likely place a move runs out of buyers.
• Where support is real. A dense band of coiled supply just below spot is a level people defended by buying. Losing it means the people who bought there are now underwater — the band flips from support to ceiling.
• Whether a “dip” is a dip. If price falls through the largest coiled band, everyone in it just became trapped supply. The structure above you got heavier, not cheaper.
• How much room a recovery has. Thin ceiling and price can move quickly. Stacked shelves and every one is a place it can die.

Why insider supply is counted separately

Not all coiled supply is equal. Float held by wallets that were funded by the same source shortly before launch behaves as one decision, not many — it does not trickle out, it leaves together. Treating a cluster like that as ordinary retail float understates the risk badly, so it is scored on its own rather than averaged into the crowd.

Identifying those clusters is heuristic and produces both false positives and false negatives. Anyone sophisticated enough to matter can break the funding pattern deliberately, and some do.