We graded our own calls. ~24% green at day 7. Not good enough.
First changes: rug-rate scoring, a churn filter, and live price validation on every call. More coming.
I stopped shipping features on @smartflowradar this week and did something I'd been avoiding: checked how the calls actually performed
The good: it catches real moves. A real chunk of calls ran, some hit solid multiples — the signal detection is doing its job finding where money is flowing.
The honest gap: I'd been scoring calls by their PEAK price, so a token that pumped then dumped still counted as a "win" — the metric couldn't see a rug. When I measured where calls actually land by day 7: ~24% green, and most fade or nuke. Median call peaks ~1.2x. That's the real challenge, and I'd rather name it than hide it.
A few things digging in taught me:
• Bigger smart-money buys didn't mean better calls — the heaviest buys rugged more. Often I was catching the top of a coordinated pump, not the start.
• The cleanest rug tell was volume-vs-marketcap: tokens churning at ~4x their MC nuked; ones trading near 1x held. Simple filter I wasn't using.
• Almost all my calls came from a single trigger — not enough diversification.
What's next:
• score realized outcome + rug rate, not just peak
• filter the high-churn setups
• weight smart money smarter
• validate every call against live market data at post time
• diversify the signals
The engine for finding flow is there. The work now is making the calls survive — and measuring it honestly. Building it in the open; I'll post the before/after as the numbers move.
⚡ $DACITY — volume accelerating, price still flat
⚡ volume running 4.2× the hourly pace · 🧠 1 tracked wallets in · +$999 net in · 📣 called by 4 independent channels (24h)
⛓ Solana · MC $10.0M
@dacitygta
📡 $WORTH — passed every gate, smart money accumulating
🧠 3 tracked wallets in · +$2K net in · holders clean · liquidity healthy
⛓ Base · MC $427K
@WorthOnBase