How memecoin filters work, and where they can fail

I find EducationalPasha's memecoin filters useful for narrowing a crowded trading screen. I am less convinced by the creator's promise of profit. Their argument is that trading fees, wallet activity and social links help distinguish genuine interest from coordinated trading. Yet their examples include a losing trade, suspicious tokens that pass the filters and a migration prediction they later revise.

What memecoin filters can screen

Memecoins often attract buyers through a joke, a cultural reference or an online community. Attention matters to that appeal, but a busy chart is a poor measure of who is interested. Independent buyers and coordinated wallets can produce activity on the same token.

Axiom is the interface used here. The creator prefers its interface and available controls, a personal judgement rather than evidence that it produces better trades. Its New Pairs, Final Stretch and Migrated screens present different pools of tokens to inspect.

I would choose the screen based on the stage of trading I wanted to examine. New Pairs contains newly appearing opportunities. Final Stretch is used to look for tokens approaching migration, while Migrated contains tokens that have passed that stage. In token-launch systems, migration generally means moving into another trading or liquidity arrangement.

A filter checks whether a token meets selected conditions. It cannot tell you whether a large holder plans to sell or whether another buyer will arrive. Those questions require a closer look at the transactions behind the displayed activity.

The creator discourages trading New Pairs and calls it gambling. Their concern is that an early buyer can sell into people copying that wallet. Copy traders follow another wallet's transactions, but may enter at a different price and end up buying while the original trader exits.

I see that as a reason to inspect the transaction sequence, rather than assume every early buyer has the same motive. The creator's broader division between profitable and unprofitable traders is more certain than these examples support. Choosing a better-looking token still leaves position size and the eventual exit unresolved.

Set the screens in Axiom

These settings reproduce the creator's configuration; they are chosen thresholds, not established safety standards. One field deserves particular care: the creator initially puts the Final Stretch fee threshold in the maximum field, then corrects it to the minimum field.

1. Open New Pairs and select Metrics.
2. Under Protocol, select Pump.fun and Stonk.fun, leaving Mayhem mode out of this configuration.
3. Select Apply to update the results.
4. Require Twitter under socials, without requiring a website.
5. Open Final Stretch and select Pump.fun and Stonk.fun under Protocol.
6. Set the maximum Age to 1 hour.
7. Set the minimum Global Fees Paid to 0.5, then select Apply.
8. Open Migrated and select Pump.fun and Stonk.fun under Protocol.
9. Set the minimum Global Fees Paid to 3.
10. Require Twitter under socials.
11. Leave Age unchanged for this screen.
12. Set the minimum holders requirement to 100 if you want to include the creator's suggested additional filter.

I would check the field labels before applying a copied setup. A minimum removes results below a threshold; a maximum removes results above it. Mixing them up reverses the intended screen, even if the remaining tokens look plausible.

The protocol selection limits which launch ecosystems appear. It does not establish that the selected ecosystems are safer than those left out. Age also narrows the pool rather than measuring token quality: this setup restricts the early-stage screen but leaves age unchanged on Migrated.

The video does not establish the full costs of entering and exiting these positions. Check the platform's fees and transaction settings to understand those costs separately from the screening thresholds.

What Global Fees Paid tells you

The creator describes Global Fees Paid as fees generated when people trade a token through trading platforms. They use it to question apparently busy markets with very little recorded fee activity.

That comparison depends on what the metric counts and which transactions it captures. Considerable visible trading interest paired with little associated fee activity may need an explanation, but the figures must describe comparable activity before the mismatch means much.

I read the total as evidence of fee-generating transactions, not a count of investors. One participant can trade repeatedly, and coordinated traders can spread transactions across several wallets. Paying fees makes that activity cost money; it does not make the buyers independent.

The creator warns that some software can generate the fees used in this screen. Someone trying to attract buyers may accept that expense to make a token appear active. A higher reading therefore does not automatically resolve the concern raised by a low one.

Before migration, the creator suggests around 6 SOL in Global Fees and no more than 200 viewers as a plausible profile. They describe much higher fees or viewer counts as signs of fabricated activity. These are their rules of thumb, not demonstrated boundaries between genuine and fake markets.

I would keep that judgement separate from the minimum setting. The minimum decides which tokens appear; the later assessment asks whether their activity looks credible. Without examining the transactions behind the total, neither an unusually low nor an unusually high reading proves who is trading or why.

Compare signals instead of trusting a total

The creator considers GTA6 suspicious and swordcat organic. Their displayed figures allow a comparison, but are not a performance ranking.

Market cap uses token price and supply to describe valuation. Liquidity describes the trading capital available in a market or pool, which matters when selling: a quoted price does not guarantee that a sizeable position can exit at that price. Neither figure measures the same thing as fees paid.

I find the relationship between those figures more useful than any one total. The GTA6 screen also records 405 active viewers and an all-time-high market cap of $41.1K. The creator calls its chart and viewer interest fake because they consider the fee activity implausible relative to that peak valuation. That accusation is their interpretation, not something the figures alone prove.

For swordcat, the creator describes trading as organic and says ordinary participants are profiting. Market cap, liquidity and fee totals cannot establish that claim: they do not reveal each participant's purchase price, sale price or remaining exposure.

The comparison also shows why a universal fee cutoff would be misleading. The creator's high-fee warning concerns tokens before migration, while they accept the much larger swordcat reading. The token's trading stage changes their interpretation.

I would use an unusual relationship between metrics to decide what to inspect next. A mismatch can point toward transactions or ownership worth examining, but it does not explain itself. Calling a token genuine on the strength of one total skips that work.

Follow the wallets behind the chart

A bundler automates a token launch and arranges purchases through multiple wallets. The on-screen definition describes buying a large portion of the supply in the same block, so those purchases fall within the same batch of blockchain transactions.

The creator's concern is that coordinated ownership can look like a crowd of unrelated buyers. Separate addresses show where tokens sit, not necessarily how many people control them. That matters when assessing whether buying pressure comes from many independent participants or a concentrated group.

I would examine how those addresses behave. Similar entry timing or a small set of wallets repeatedly buying and selling can justify closer inspection. These patterns can suggest coordination, though they do not identify who controls the wallets.

Quantum provides an example of an assessment changing after a closer look. The creator initially calls it potentially organic or semi-organic, then revises that view after noticing repeated wallet purchases and sales. They interpret those transactions as wallets extracting value from other participants.

The tracked YfxR wallet display records 0.331 SOL bought across 4 buys and 4.42 SOL sold across 13 sells. It also reports a profit of +$5.29 (+522.7%). Those are the display's figures, not a complete reconstruction of the wallet's finances or a result another trader could expect.

I pay attention to repeated selling because incoming demand must absorb it. A coordinated holder can distribute tokens into new purchases while the chart remains active. Looking only at the latest price candle can hide the fact that the holder is reducing exposure.

The creator also reacts positively when a recognisable tracked trader enters. One visible participant cannot establish the independence or motives of everyone else. Their arrival does not settle the concerns raised by the earlier wallet behaviour.

Read social links and holder counts carefully

Requiring Twitter excludes tokens without that selected link. It gives readers somewhere to inspect the stated idea and public discussion, but does not authenticate either. A linked account can still present attention that does not reflect independent demand.

Post Quantum Cockroach illustrates a meme narrative that connects quantum computing with cockroaches surviving destructive events. The referenced post is by laserr (@laserrXBT). The concept explains the joke; it is not technical evidence that the token protects against quantum-related threats.

I separate the story from the product claim when a meme borrows technical language. A memorable theme can attract attention without providing a corresponding technical function. Understanding why people notice the token does not explain why its market price should persist.

A website is optional in the New Pairs configuration above. That preference is not an endorsement of tokens without websites. Both websites and social accounts can provide context, and both can be copied or made to appear more credible than the project deserves.

Holder counts need the same care. They generally count addresses holding a token, rather than verified individuals. As the bundling example illustrates, one operator may control several addresses, so a higher count can coexist with concentrated ownership.

For me, ownership distribution is the more useful follow-up. Enough addresses to pass the filter may still leave the market dependent on a few large positions. Their share of the supply and selling behaviour matter more to potential selling pressure than the address count alone.

The creator also suggests that developers can use filtered tokens to understand the current meme theme, or meta. That offers a view of ideas attracting visible attention, but manufactured activity can distort the sample. Copying an apparently popular theme may mean copying a manufactured signal.

A passing token can still produce a loss

TALAFREAKO supplies the clearest realised trading outcome in these examples. The creator enters with 0.3 SOL, exits quickly and says they lost 28%. The position display gives the more precise result: -0.085 SOL (-28.32%).

That loss does not prove every New Pairs trade fails. It does put the creator's opening profitability promise in perspective: a selected token can move against a trader almost immediately, and these settings establish no dependable entry or exit advantage.

I give execution risk as much attention as token selection. In a fast-moving market, the price available when a transaction completes may differ from the price visible when it begins. That difference between the expected execution price and the price received is slippage; thin liquidity can make it worse.

Concentration creates another risk. A large holder's sale can overwhelm demand despite an active social account or a previously healthy-looking chart. A rug pull generally involves insiders removing support or exploiting control in a way that leaves other holders with severe losses. Screening conditions cannot eliminate that possibility.

The creator predicts that another suspicious-looking token will not migrate, regardless of what anyone does. Later, they express surprise that it has migrated. The person proposing the screening rules also makes a prediction that the subsequent example contradicts.

I take that reversal as a reason to avoid certainty, not evidence that the token was healthy. Migration alone does not establish fair ownership, sustainable demand or a profitable trade. The creator can be wrong about migration while the token remains risky.

Wallet security is separate from those market risks. Check the site address and requested permissions before connecting a wallet. Keep recovery phrases and private keys private. If learning an unfamiliar interface, limit the amount exposed to it; a market filter cannot protect funds from an unsafe wallet interaction.

Tie each judgement to observable evidence

Once a token passes the screen, I want a concrete reason for the next judgement. A transaction sequence can support a concern about coordinated selling. An ownership pattern can reveal concentration. An unexplained metric should remain unexplained until there is evidence behind an interpretation.

It also helps to decide what would change an assessment before becoming attached to it. Investigate the selling if coordinated selling is the concern. Inspect the basis for an audience claim if the concern is fabricated attention. A rising price does not answer either question.

The creator's profitability claim would require a consistent record of completed trades and their costs, including losses. A striking wallet result or an active chart answers a narrower question. Neither establishes that the settings produce a repeatable advantage.

I would keep a setting if it saves useful search time, while withholding conclusions it cannot support. The creator's own migration reversal is a concrete reason for that restraint: the token they said would never migrate later did.