Methodology

1. What “paper trading” means here

The paper block on this page comes from the official account's paper trading: when a signal fires, the system simulates the trade with a deterministic fill model — not real on-chain execution. The model includes price impact (slippage scales with the square root of order size over pool liquidity; the model version is archived with every fill), a liquidity penalty on exits (during liquidity collapses, fills are simulated against the degraded book, not a clean spread), and a flat fee per fill.

Real-money trading has already begun (first real close on 2026-07-31). This page therefore shows two bases side by side: paper, and the founder account's actual on-chain fills. They are never added together and never replace one another — simulated fills are usually better than real ones (no MEV, no failed transactions), so merging them would let the simulation flatter the real record. Other users' strategy results stay private to their owner and never appear here.

2. The iron rule: research labels ≠ performance

Every scanned candidate — including rejected ones — gets 6h / 24h / 72h forward returns: how far the market itself moved after the card price. They include no slippage, no fees, and don't imply fillability; they exist purely to study signal quality. Every research label on this site carries its own badge and is structurally separated from paper PnL — the two are never mixed.

Dead coins: if a token's pair disappears (rug / delisting), the forward return is recorded as −100% and flagged “presumed dead” rather than “no data” — otherwise rejected candidates' returns would be systematically overstated.

3. Win rate and profit factor

Win rate = closed positions with positive realized PnL ÷ all closed positions (partial-TP-then-stop trades count by net result). Profit factor = gross profits ÷ absolute gross losses. Open positions are excluded from win rate and are not marked to market — equity only changes on close.

4. Shadow benchmark: does the AI have alpha?

Three cohorts on the same basis (24h research labels): A = everything that passed the hard filters (a naive equal-weight shadow portfolio); B = the subset the AI proposed to buy; C = the subset the AI explicitly passed on. B−A is the AI's selection alpha. If any cohort has fewer than 10 samples, the page honestly says “no conclusion”. If B≈A persists long-term, the value is all in the filter layer and the AI is expensive decoration — we'll publish that conclusion either way.

5. Signal latency

End-to-end latency = candidate data collection completed → Telegram card sent, recorded per signal; the page shows trailing 7-day P50 / P95. Note the clock starts at “data collection completed”, slightly after the token first appears on-chain, and the card-sent timestamp differs from actual delivery by sub-second amounts.

6. Publish audit: nothing gets deleted

When a signal is first published, a hash of its decision content (card time, card price, thesis, TP/SL plan) and the publish timestamp are written to an append-only publish log. Once published, a signal stays on this page forever — the losers, the ugly ones, the presumed-dead ones, all of them. Card prices can be independently verified on-chain via the token address.

7. Approval-mode disclosure

The public track record on this page comes from fully automatic approval: any proposal passing the hard filters executes with zero human intervention. It is therefore the system's own record — reproducible, with no human timing bias.

User strategies differ: each instance chooses automatic or manual confirmation on its own. In a semi-automatic instance every proposal waits for your confirmation in Telegram or on the web, and expires if you don't act. User-instance results are visible only to their owner and never enter this page.

8. Risk disclosure

Newly launched meme coins are among the riskiest assets on-chain; most go to zero. Past performance does not predict future results. Nothing on this page is investment advice.