FEATURE REQUEST #762
FOMO Radar — calibrated probability output with a labeler and backtester
Original creator
@expire76
2
Score
+2
Upvotes
-0
Downvotes
0
Comments
Implementation Progress
1
Submitted
2
Reviewing
3
Approved
4
Building
5
QA & Testing
6
Shipped!
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20.0%
Payout Cap
3.00x principal
Delivery target
—
Revenue to date
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Escrow
0x191a04fe…8dC3Ed
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Description
From ET-25011 (expire76): replace the 0-100 conviction with a checkable probability ('x% chance of 3x within 72h') via isotonic regression on historical outcomes. Prerequisite: a labeler + backtester over past signals (MemeTrans-style public data, rug detection from the first 5 minutes, graduation survival). Nobody has measured the score->outcome link yet; this builds it.
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Acceptance criteria (from expire76's design note "The State Layer", 13 Sep 2026, §02/§11/§14 — ET-25104/25106):
1. Labeling universe includes every launch, including tokens that died within a minute (no survivorship filter).
2. Label = return under a named exit policy (trailing stop / time exit / partial sells), not the price maximum.
3. Labels are liquidity-adjusted: simulate selling the real size against real pool depth at each future timestamp.
4. Walk-forward only, with an embargo longer than the longest label horizon.
5. L1 gate metric is MCC >= 0.30 (class-balance-invariant), never AUCPRC on an 84%-positive set.
6. Persist window_complete into the point-in-time store beside every feature vector; the labeler excludes or flags restart-biased rows.
7. Test by regime window (graduation-rate break Mar-Jun 2026), and account for own price impact in thin pools.
8. A negative-EV result at every horizon is a valid, publishable outcome of this item — it gates 760/761/763.
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