Axiom memecoin filters: settings, ownership and execution risks

I find the screening process in Radi8's Axiom explanation more useful than the profit claims attached to it. It gives readers a sequence to examine: narrow the watchlist, check ownership and activity, then review the settings that affect an order. The creator says the additional filters help avoid weaker tokens and rug pulls. I would still inspect each candidate rather than treat a passing result as evidence of safety.

What Axiom memecoin filters do

Memecoins often draw their appeal from attention, community enthusiasm and a shared joke or theme. Their market depends heavily on whether other participants stay interested. A busy chart may attract buyers without giving those buyers a lasting reason to hold the token.

The screening process uses Axiom's New Pairs, Final Stretch and Migrated categories to narrow the candidates. It then calls for a closer look at ownership and trading activity. A numerical filter can remove tokens that fall outside selected conditions, but it cannot answer every question about the tokens left on the list.

I would pay particular attention to what each metric measures. Liquidity is the money available to support trading, so it matters when entering or leaving a position. Volume measures how much has traded over the platform's chosen period. Market capitalization estimates total token value from price and supply; it does not tell you how much money a sale could recover. A high figure in one field cannot replace a useful reading in another.

The additional Advanced Filters interface targets developer history, bot activity, fake transactions and inactive tokens. These labels identify what the filters are intended to screen. Their usefulness still depends on how accurately the tool detects those conditions, including whether it mistakes ordinary activity for suspicious behavior.

Put the profit claims in context

Radi8 introduces the strategy by claiming to be up 500k over the last year and about 32k for the month after correcting the spoken figure. Those are the creator's performance claims. They are not returns a reader should expect from copying the preset.

The portfolio screen displays Realized PNL of +$490.2K, Total PNL of +$487K and Unrealized PNL of -$3.27K across 28876 transactions. PNL means profit and loss. Realized PNL concerns completed positions; unrealized PNL concerns gains or losses attached to positions that remain open. Keeping those categories separate explains how profitable closed trades can coexist with losses on current holdings.

I would also consider what an aggregate result leaves unresolved. The transaction count reflects many decisions, not an isolated use of a filter. The total does not describe the path taken to reach it or connect each result to a particular screening rule.

The visible calendar is labeled August 2025. It shows a 25-day win streak and total realized profit of +$31.4K. That date prevents the calendar from confirming the creator's spoken claim about the current month, even though the screen is presented as evidence of profitable trading.

The creator suggests starting with 5 to 10 Solana, mentions three as an alternative and describes 10 to 15 as the best starting range. These are their capital preferences, not requirements established by the filters. I would not read them as evidence that committing more money makes a speculative token safer or the process easier to learn.

A screening rule can be worth examining without accepting the performance pitch around it. The relevant question is whether the rule addresses the risk it claims to address. A profit screenshot cannot answer that on its own, and a capital suggestion does not explain how liquidity or ownership will affect a particular trade.

Set up the screening sequence

The creator recommends Final Stretch and Migrated for newer traders rather than New Pairs. They briefly misspeak before correcting that recommendation. Settings are supplied for all the categories, so the presence of a New Pairs preset should not be mistaken for their beginner preference.

I would read the following as a record of the creator's process, not an instruction to submit a trade. Follow the checks in order, using the category values in the table and the ownership and activity thresholds explained below.

1. Open Advanced Filters in the additional filter interface.
2. Select "Filter Developer with Migrated Tokens", "Filter Bot Activity", "Filter Fake Transactions" and "Filter Inactive Tokens".
3. Configure New Pairs with the Age, Dev Holding %, Liquidity, Volume and Market Cap values in the table.
4. Configure Final Stretch with the listed Market Cap and Global Fees Paid values.
5. Configure Migrated with the listed Liquidity, Market Cap and Global Fees Paid values.
6. Inspect the top 10 holders' combined share against the creator's ceiling.
7. Inspect the transaction display for the bubbles the creator uses to assess chart activity.
8. Compare the pro traders figure with the creator's activity threshold.
9. Check DEX paid status when the token exceeds the creator's market-cap threshold.
10. Read the holder count alongside the token's market capitalization.
11. Open the bubble maps view and compare individual holder shares with the creator's ceiling.
12. Check Global Fees Paid in the token view.
13. Review Buy Settings and Sell Settings before adopting an execution preset.

The video leaves the Age, Priority and Bribe units and the advanced filters' detection methods unexplained.

Check the relevant tool documentation before relying on those fields. I would also verify how each input applies its value: the New Pairs instructions explicitly identify maximums and minimums, while the creator does not verbally specify every input's direction for the other categories. Guessing could produce a different screen from the one intended.

Global Fees Paid belongs to the token-screening process here. It is separate from the transaction fee settings used for your own order. Keeping those fields distinct avoids confusing a token's accumulated activity with the cost or configuration of buying or selling it.

Read holder concentration carefully

The creator says the top 10 holders should not collectively exceed 25% of a token. They also reject a token when an individual holder owns more than 10%. Both checks matter because a group can hold a substantial share without any member crossing the individual ceiling. An individual wallet can also exceed its limit even when the rest of the supply looks dispersed.

Large holders can put pressure on the market when they sell. Ownership data helps identify positions capable of having that effect, but wallet addresses do not necessarily represent independent people. A single person can control multiple wallets, so an apparently broad distribution may still belong to a small group.

I would read the holder count as a count of addresses rather than a verified crowd of unrelated investors. The creator describes roughly 300 holders for a 40k token as good, while warning against giving the count too much weight. The share held by those addresses tells you more about who could move the market than the count alone.

Bubble maps help visualize ownership and, depending on the tool, relationships between wallets. The creator uses that view for the individual-holder check, but it loads poorly in the example. A failed or stale display leaves the ownership question unresolved; it is not a clean result. The same caution applies to other fields that have not updated or cannot be inspected.

Check activity without treating it as proof

The creator rejects the example token because the expected transaction bubbles are absent and describes the chart as potentially fake. That is their interpretation of the display, not a confirmed finding of fabricated trading.

Transaction markers can connect price movement with the trades behind it. They help reveal when a smooth or dramatic move rests on little activity. Missing markers, however, can also reflect an interface problem. Their meaning depends on how the tool works, so I would not turn a visual impression alone into an accusation.

For the "pro traders" count, the creator wants at least 100. The name is a platform label. It does not independently establish that the traders are skilled, profitable or acting separately. It identifies the participation the creator prefers to see, while leaving those other questions open.

The creator also requires DEX paid status when market capitalization is above 20k. DEX is short for decentralized exchange, where trades generally execute through blockchain-based systems. The displayed paid status is a screening condition, not evidence about the safety of the token's code or ownership.

I would use the token-level Global Fees Paid check as another activity observation. The creator does not claim that a higher figure guarantees better performance. Trades and fees can accumulate without creating durable demand, especially when automated activity does not reflect interest from independent buyers.

Understand the execution settings

The creator invites viewers to copy the displayed Buy Settings and Sell Settings. I would scrutinize these separately from the watchlist filters. They affect the terms on which a submitted order can execute, so a token that looks acceptable during screening may still be costly to enter or exit.

Buy Settings also show Auto Fee off and MEV Mode Off. Those switch positions describe the displayed buy configuration. They should not be assumed to apply to every transaction or every part of the platform.

Slippage is the difference between the expected trading price and the execution price. A tolerance generally sets how much adverse price movement an order can accept before failing its price condition. It permits a range of outcomes; it does not predict that the trade will suffer the full allowed difference.

I would pay close attention to the wider sell tolerance in this preset. It allows more room for an unfavorable execution price, particularly when liquidity is thin or prices move quickly. Whether an order uses that room depends on the conditions when it executes, not just the percentage entered in the settings.

Priority and Bribe are transaction-related fields. Transaction incentives can affect how an order competes for inclusion, although the effect depends on the system handling it. The creator's values do not establish what is appropriate for another trade size or market condition.

MEV means maximal extractable value: value that participants involved in transaction ordering can obtain from including or arranging transactions. Unfavorable ordering can affect traders, particularly in price-sensitive swaps. I would check the tool's explanation of its MEV Mode control rather than assume that the switch is a universal safety indicator.

Reviewing an order means checking whether the quoted trade reflects available liquidity, how much price variation it permits and what transaction costs it accepts. These conditions can change the result even after a token passes the screening process. A favorable-looking chart does not settle them.

Account for token and tool risks

The creator argues that normal Axiom filters are insufficient to avoid rug pulls and that the additional filters help. A rug pull generally involves insiders removing liquidity or otherwise abandoning or exploiting a token, leaving other holders exposed. I would describe the claimed benefit as reducing risk rather than preventing that outcome.

Screens capture current conditions. Ownership can change after inspection, liquidity can disappear and an active market can lose buyers. A token can also lose demand without fraud. Thin liquidity may then make an exit difficult even if trading remains possible, so suspicious-activity filters cannot remove the ordinary risk of losing money.

The browser tool adds a separate operational risk. The setup uses a third-party bookmarklet, a saved shortcut that can execute code on an open page. Unlike an ordinary bookmark, it may interact with the trading interface where it runs.

I would verify the source and permissions before allowing a browser tool that access. Check the site address before connecting a wallet, and inspect any signature or transaction request. Never give a filter tool a private key or recovery phrase. Those secrets control access to funds, and the apparent usefulness of an interface is no reason to disclose them.

Keep a record of the actual decision

I would use this process to keep a decision record rather than treat it as a promise of profitable trades. Record the category a candidate came from, the conditions it passed and the observations that remained uncertain. That preserves what was known at the time instead of reconstructing the decision after a price move.

Keep measured observations separate from assumptions about what comes next. A displayed holder share does not tell you whether that holder will sell. An activity count does not tell you whether demand will last. Writing down those assumptions can make it harder to overlook a warning just because the chart looks appealing.

A concentration ceiling has a clear connection to large-holder risk. A profitable screenshot has a much weaker connection to whether the same preset will work for another reader. I would ask what evidence connects each rule to its stated purpose, and leave an uncertain candidate outside the decision until the information can be inspected.

Preserve the execution configuration too. If a result disappoints, that record can help separate token selection from price tolerance or transaction handling. Otherwise, an outcome shaped by the order may be blamed on the filter. Alongside the screening observations, record the order's Slippage, Priority and Bribe settings.