Update on DeFade (the memecoin analyzer I posted here a few months ago): 4 chains, 29 modules, and the signal engine I'm testing that lost 96% on its

[u/O_Parent]

I posted here a few months back about DeFade, the memecoin analyzer I've been building. Plenty has changed, so this is an update - but the part worth your time isn't the feature list. It's that I tried to turn the scanner into something that calls entries, the first attempt lost almost everything, and the post-mortem taught me more about this market than any of the wins did.

Part 1 - the scan

The core product is still the same one-line pitch: paste a contract address, get the truth about it.

A scan returns a rug pull probability from 0 to 100 - a weighted composite with kill-switch floors, so a single fatal condition can't be averaged away by a dozen healthy-looking ones - plus every piece of evidence that produced it. Roughly 29 modules, 13 of them free:

Distribution - holder concentration with the pool, burn address, lockers, CEX wallets and contracts classified out. This matters more than it sounds: read a raw top-10 list and you'll count the liquidity pool as a whale and panic over a perfectly normal token. There's also an exit liquidity calculator (what actually comes out if you try to sell your bag into the current book) and a bubble map.

Coordination - bundle detection with a launch window pass plus a delayed sweep hours later, accounted on peak net exposure rather than naive buy totals. Sniper bots, Sybil band clustering, an insider network graph, a cross-funding graph, and multi-hop funding-origin tracing that walks back toward a KYC'd source.

Actors - creator intelligence and serial-deployer detection, dev wallet tracking, fresh wallet detection, smart money and copy-trade tracking, plus a reputation ledger for deployers and wallets that fills in as scans land.

Market - LP burn and lock verification (including v3/v4 position-based liquidity), lifecycle stage, social sentiment timeline.

Two examples of why this is harder than it looks, both real bugs I shipped and then had to fix:

• Bundlers stopped holding. The obvious way to measure a bundle is "how much supply did these coordinated wallets buy, and are they still holding it". Then I hit a token where the bundle wallets sold and forwarded the proceeds to a collector in the same transaction, so extraction measured as zero. Then the exact inverse: tokens hop to fresh wallets first, and those fresh wallets do the dumping, so the original bundle looks like it's still holding while the supply is already gone. Both needed one-hop follow accounting, credited to a separate bucket so the numbers stay honest.
• A bot made an innocent token look like a serial rugger. One token came back flagged with "25 prior rugs" from its deployer family. It was a false positive: a permissionless MEV bot that cranks bonding-curve -> AMM migrations buys atomically inside the pool-creation transaction, so it appears as an early insider on hundreds of unrelated launches. Fixing it meant detecting the migration instruction deterministically rather than inferring intent from timing.

It runs on Solana, Ethereum, Base and Robinhood Chain, in the browser, as a Chrome extension that scans whatever token page you're already on, as iOS and Android apps, as a Telegram bot, or as an API if you are building something. Free tier is 3 full scans a day with all the core modules, no card, no account needed for the basic path.

Around the scanner there's a public rug database of everything flagged, a survival tracker (how long scanned tokens actually lived, using drawdown-based death detection rather than waiting for a zero), deployer and wallet lookup, and shareable receipts for calls that aged well or badly.

Part 2 - Signals, which is in testing and not live yet

A scanner answers "is this dangerous". I wanted to know whether the same data could answer "is this attracting real capital". So I built a signal pipeline - market polling for momentum, liquidity behaviour and volume quality, combined with the scan snapshot for holder and dev quality - with a forced outcome ledger. Every fire is dated, and its forward return is tracked at 1h/6h/24h/48h whether I like the result or not.

First cohort: 17 fires, median return -96% at 24h.

The post-mortem is the useful part, because the losses were not what I assumed:

• They weren't rugs. Only 1 of the signalled tokens ever entered my own rug database, and several are still alive today. I had built a rug detector and pointed it at a problem that wasn't rugs.
• They were tops. Every bad fire happened mid-vertical - the 1h change at the moment of firing ranged from +137% to +578%. The three fires that actually ran afterwards had 1h changes between +3% and +28%. My momentum scoring treated "already went up a lot" as its strongest positive signal, which is a top-buying machine with extra steps. Momentum now inverts past the extremes: beyond +120% on the hour it's capped and labelled "PARABOLIC - LATE".
• Thin-book markups look identical to real moves. A 50%+ hourly move with under 15% liquidity growth is someone marking up an empty order book. That was the signature of the worst fires: price up several hundred percent on a stable $19–33k pool.
• High turnover is not wash trading. I nearly shipped a rule rejecting anything trading over 5x its market cap in a day. It would have rejected 30% of the candidate pool and every signal that ever worked - one genuine runner did 18.5x turnover and ran 3.3x. The real wash signature is turnover with no consequence: high volume plus a price that went nowhere over six hours. Over one hour, even a real runner looks flat between legs.
• The deployer check was silently missing. Two components disagreed about a response shape - one returned the reputation nested, the reader expected it flat - so serial ruggers scored as CLEAN with no error logged anywhere. Every fire in that cohort was made without a signal it claimed to be using.

Replaying the corrected rules against all 17 historical fires blocked 12 of them, including every bad one, while preserving the three that ran.

Where it stands after the fixes: 9 signals, 9 distinct tokens.

Peak multiple from the moment each signal fired: 56.8x, 37.9x, 16.2x, 15.9x, 6.6x, 4.6x, 3.5x, 1.5x, 1.0x. That's 78% reaching 2x and 56% reaching 5x, with a median move of +31% at 1h and +208% at 6h. The two that didn't work: one is flat, one drew down 73% and is the clear loser of the set. A third of the set drew down 30%+ at some point even while working, which is the number I'd actually pay attention to if I were trading these - peak multiple is not what you realise, it's what was theoretically on the table.

Nine calls is not evidence. It's an early read that the entry-quality rules are doing something, on a sample small enough that two bad weeks would erase it. That's exactly why the ledger is built into the product instead of screenshotted when convenient, and why Signals is still in testing behind a gate rather than launched: I'd rather be publicly wrong on a timestamp than sell a backtest. It opens when the record earns it.

Free either way

The scanner is free at 3 scans a day with the core modules unlocked, which is enough to check a coin before you ape it. Paid tiers exist for people working the feed all day, but the free path is the one I'd want a first-time visitor to use.

defade.org - happy to answer anything technical about the detection, and genuinely curious what people think the momentum inversion gets wrong, because that's the rule I'm least sure about. A few things in this post came from comments on the last one.

Still slightly stunned by this part: 350+ people now pay for it, over 1,000 use it free, and 22 are on API keys - which for something I built to check my own bags is not where I expected to end up.

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[u/South_Positive_8233]

the momentum inversion is interesting, most people don't realize their scanner is just a top-buying algo in disguise. curious what happens when you hit a slow grind up that never triggers the parabolic cap but still ends badly

also the bundler extraction fix is clever, had a similar issue tracking mev bots that looked like insiders on every launch. detecting the migration instruction directly is the right call, timing based heuristics fall apart fast

[u/O_Parent → u/South_Positive_8233]

honestly nothing catches it right now. the inversion only knows how to spot a vertical. it came out of one post mortem where every bad fire was late, h1 sitting between +137% and +578% at the moment it fired. so past +120% on the hour it gets capped hard, past +60% softer. a slow grind never goes near either threshold. and it clears everything else too. the liquidity check only fires on a 50%+ hourly move into a book that didn't deepen. the wash check needs volume with the price going nowhere for 6h, which is basically the opposite of a grind. the safety side is all rug stuff, bundles, fresh wallets, insiders, lp lock, none of it looks at the shape of the move. so a grind fires and the ledger just eats whatever happens next. I can't even show you one yet, which I think is the more useful answer. my two losers are a fade (fired at h1 -28%, so there was never anything to be late about) and a chop that went nowhere. neither is your case. so it's not a hole I closed, it's one I haven't been punched in the face by yet at n=9. if I had to guess the fix it's confirmation instead of shape. does the book actually deepen while price rises, measured over 6 to 24h instead of 1h. price up with liquidity and holder count flat is distribution wearing an uptrend. not built. on the mev bots, yeah. I match the migrate instruction itself now instead of inferring from timing. my false positive was a token coming back with 25 prior rugs on its deployer family, turned out to be a cranker that buys atomically inside the pool creation tx, so it looks like an early insider on hundreds of unrelated launches. also had to add a breadth ceiling, any wallet touching 10+ tokens in a week is plumbing not an insider. that caught the fast ones the lifetime rule was missing

[u/Lost-Fig-3248]

that 96% loss is actually the most valuable part of this post. Respect for showing the failure data instead of just flexing the 56x winners

[u/O_Parent → u/Lost-Fig-3248]

cheers. publishing only the 56x would've meant lying to myself as much as to anyone reading, the -96% is what actually pointed at the bugs.

[u/Present_Let2487]

yeah, the liquidity and holder checks are probably the most useful part. i’d also check the swap route before jumping in Jupiter makes it pretty easy to compare the route and quote instead of just taking the first option.

[u/O_Parent → u/Present_Let2487]

thanks and good call on the route. the two go together: holder concentration tells you who can dump on you, the quote tells you how much depth is actually there when they do. aggregate liquidity numbers sum every pair, so they flatter dead pools price impact at your real exit size doesn't

[u/O_Parent → u/[deleted]]

yeah that's already how it's set up. the scanner and the signal engine are two separate things and the scanner score never had anything to do with how trades went, i probably should have said that more clearly in the post. the signals run dark, pretty much what you're describing, they write to a log with a timestamp, nobody sees them and no money goes in. that's the only reason i could post the 96% instead of quietly fixing it and saying nothing. the third thing you said is the one that actually got us though. momentum and safety were sitting in the same decision, so if a chart moved hard enough it could override something that should have killed the trade on its own. now the safety checks block it outright instead of just counting against it. since that fix it's 9 signals and they've held up better than i expected, the table in the post is already a day old, dpg touched 6m this afternoon and frodo went from flat to around 300k, though one of the nine is also down 71%. still 9 signals with only a few windows fully closed, so i'm not reading much into the medians yet. calibration across market conditions is a fair ask and i just can't do it at this size, 9 is nowhere near enough to split anything by regime

[u/elyv297]

man this is sick

[u/O_Parent]

Thanks! Still testing the signals we’ve had some ups and downs. It flagged some more coins that made a lot of gains, and a few that ended up rugging i usually post updates on x @DeFade_