Every market site shows you prices, candles, market caps, and volume. What I wanted to know was something different: when does an asset start behaving unusually relative to itself? That became Tickerz. Sigma-1 produces Heat™, a 0 to 100 score based on how abnormal an asset’s recent price and volume behavior is compared with its own trailing baseline. It does not compare Bitcoin to Solana or NVIDIA to Tesla. Each asset is measured against its own history. So a normally quiet asset suddenly moving can score hotter than a large asset having a fairly normal day. When Heat reaches 70, Tickerz opens an event and freezes the price. Then it records what happened +24h, +7d, and +30d later. Wins and losses are treated exactly the same. The record stays there. That part matters to me. A lot of market products are very good at showing what is interesting right now, but once the list changes, there is no real way to go back and judge whether the signal meant anything. Tickerz keeps the receipts. I also chose not to score assets until there is enough baseline data to make the comparison meaningful. Sigma-1 requires at least 14 days of history before an asset gets a Heat score. At launch, Tickerz covers the top 100 crypto assets plus a curated group of major US equities. The methodology is public, the Sandbox lets you run the model over historical data, and the Board is available through a free JSON API with no key or signup. And the name is pretty literal: z-scores, per ticker. Tickerz is not trying to predict the market or tell anyone what to buy. Heat measures attention, not merit. If you check it out, I’d especially like feedback on the methodology, the receipts, and what you think Tickerz should measure next.

Tickerz

Real-time market attention, measured with receipts.

Real-time market attention, measured with receipts.