Who Made a Fortune on FOMO? Analyzing the Top 20 Profit Rankings

What are the positions and strategies of the top 20?

Written by: KarenZ, Foresight News

If we view the FOMO profit rankings as a scoreboard, looking solely at the final numbers reveals who made the most money, but it doesn't show whether these profits came from frequent trading or a few large positions.

By comparing the number of trades, average positions, and profits by token of the top 20, we can see three different paths: some relied on early purchases for hundredfold returns, some exchanged large capital for absolute gains when tokens already had a high market value, and others sought opportunities through high-frequency trading, but ultimately, account rankings were determined by one or two core positions.

As of September 4, the total PnL of the top 20 was $82.3781 million, with an average of $4.1189 million per person and a median of $2.9120 million.

The top spot, Unipcs (Bonk Guy), made a profit of $14.26 million, nearly 100 million RMB, accounting for 17.3% of the total PnL of the top 20.

FOMO Profit Rankings Top 20

The 20 accounts collectively completed about 24,500 trades, averaging about 1,225 trades per account, with a median of about 746 trades. The simple average of the "average holding period" across accounts is approximately 4 days and 9 hours, with a median of 4 days. This statistic reflects account-level averages without weighting by the capital scale of each trade.

Commonality 1: The Rankings Are Driven by a Few "Super Positions"

The most typical case is Unipcs (Bonk Guy).

From July 15 to 17, Unipcs bought about $67,495 worth of PONS in three transactions, with an average purchase market value of about $6.1 million. As of the writing, this portion of PONS is valued at about $7.43 million, yielding a paper profit of approximately 108 times. Just this core position is enough to explain why he has consistently held the top spot.

Besides PONS, Unipcs has paper profits exceeding 10 times on DELTA and microduck; positions in USELESS, MarsCoin, Basecat, and BOW (Longbow) recorded paper profits of about 1 to 3 times. His strategy is not to bet on just one token but to continue seeking secondary opportunities that can amplify profits outside of a super position.

Unipcs' average holding period still reaches 7 days and 2 hours, indicating that "frequent trading" and "holding core positions for a long time" can coexist.

DumbCrayonEater is closer to the idea of "one position changing fate," with total profits of $8.93 million. Among these, the AI position yielded about 345 times, or $7.35 million in profit; the account's average holding period reached 11 days and 21 hours, the longest among the top 20. Although the total number of trades also reached about 2,200, the true determinant of account scale remains AI, rather than spreading profits across thousands of trades.

This concentration of profits is not an isolated case: the third-ranked Salem's AI profit contribution is about $6 million; the fourth-ranked Nate (co-founder of LONG) contributed about $4.56 million from AI; Burgz's AI contribution is about $3.9 million; Blockworks Research analyst AJC's PONS position achieved a profit multiple of 128 times, contributing $3.03 million in paper profits; Wood's AI contribution is about $2.57 million; RugDalio's PONS contribution is about $2.23 million; and LP 1's AI contribution is about $1.9 million. Roughly comparing these core positions to the total PnL on the list, they typically explain over 80% to 90% of the profits.

Salem's total PnL is $6.2969 million. He invested $9,913 when AI's market value was about $370,000 and traded multiple times early on; even after AI's market value rose to millions, tens of millions, and even about $200 million, he continued to increase his position. After subsequent increases, his average purchase market value was raised to about $16.2 million, but AI still contributed about $6 million in profit, accounting for about 95% of his total PnL.

Nate's main profits also come from AI. Nate first bought about $773 when AI's market value was less than $100,000; later, he made multiple purchases as AI's market value rose to millions and tens of millions. FOMO account data shows that he aggregated an investment of about $50,000, with an average purchase market value of about $2.7 million, and the AI position's floating profit is about $4.56 million, accounting for about 92% of his total PnL.

WLFI advisor ogle ranks seventh. On July 14, he first bought $4,974 when PONS' market value was about $470,000, followed by transfers, receptions, increases, and decreases in position. Due to subsequent trading volumes far exceeding the initial purchase, his aggregated investment is about $3.72 million, with an average purchase market value of about $15.7 million; as of the writing, PONS has contributed about $3.77 million in profit, accounting for over 99% of his total PnL.

Among the top 20, there are also those who accumulated results relying on multiple medium-multiple positions. Frogman's CASHCAT and MarsCoin contributed about $1.03 million (doubling) and $1.12 million in paper profits (3 times), respectively; Avast's MarsCoin and CASHCAT contributed about $2.45 million and $1.17 million (4 times); change's profits came from VVV (124%), MOLT (155%), STONKBROKER (69%), and contract positions.

This set of differences illustrates that "super positions" do not necessarily mean buying only one token. Its more accurate meaning is that the majority of profits in an account ultimately concentrate in one to three positions that significantly outperform other trades, rather than being averaged across all transactions.

Commonality 2: They Bet on More Than Just Tokens, But on Ecological Launch Windows

According to the large result positions in the table, at least 15 of the top 20 have significant profits involving AI or PONS: AI appears in the large result positions of 9 accounts, while PONS appears in 7 accounts.

PONS, AI, and narratives related to Robinhood Chain, launchpads, tokenized assets pairing, and trading fee backflows are closely intertwined. Positions like CASHCAT, MarsCoin, and "Bull Coming" represent traders' bets on the heat of new narratives.

They may not belong to the same chain, but they share similar temporal characteristics: they are all in a phase where new ecosystems rapidly attract capital and attention.

The rankings include those who gained hundredfold returns from very early purchases, as well as those who made large investments only after tokens reached medium or even high market values. Unipcs, DumbCrayonEater, AJC, Wood, and Cardinal Saint highlight the "high multiples brought by early prices"; Frogman, Avast, cosby, and "230" emphasize "exchanging large capital for absolute gains after certainty increases."

Thus, being "early" does not necessarily mean buying in the first minute or the first day. More importantly, it involves completing research and establishing positions that match judgments while ecological liquidity, users, and attention have not yet been fully released. Early purchases increase potential multiples, while later heavy positions increase absolute profits; both may enter the rankings, but the risk structures are entirely different.

Commonality 3: High Frequency and Long Holding Are Not Contradictory

When looking at the top 20 by trade count and average holding time, it becomes evident that "trading frequency" and "holding patience" are not on the same axis.

frank is the most typical high-frequency account: about 4,400 trades, with an average holding time of only 1 day and 7 hours; change has about 2,700 trades, with an average holding time of 2 days and 9 hours; Burgz has about 2,400 trades, with an average holding time of 1 day and 17 hours. These accounts indeed exhibit rapid rotation characteristics.

However, having many trades does not mean core positions are necessarily held for a short time. Unipcs has about 2,600 trades, but the average holding time reaches 7 days and 2 hours; DumbCrayonEater has about 2,200 trades, with an average holding time of 11 days and 21 hours; Nate has about 1,900 trades, with an average holding time of 7 days and 10 hours. They likely retain truly confident main positions for a longer time outside of a large number of peripheral trades.

On the other end are accounts that choose to act selectively. "230" has only 80 trades, while cosby, LP 1, Frogman, RugDalio, and ogle have about 220, 203, 235, 236, and 252 trades, respectively. Low frequency does not necessarily mean long-term holding: RugDalio has an average holding time of only 2 days and 9 hours, while ogle reaches 7 days and 5 hours. Trade count, average holding time, and position concentration must be viewed together.

Therefore, existing data can support descriptions like "high-frequency rotation," "low-frequency concentration," and "core positions held long," but cannot directly prove that someone can consistently buy high and sell low.

High frequency is a tool for them to seek opportunities or manage risks, while large result positions are the core that determines rankings.

Commonality 4: Most Large Results Remain on Paper

From the currently confirmed position statuses, most large results on the rankings still include unrealized gains and have not been fully cashed out.

Ethermonk is one of the few cases where cashing actions can be clearly observed. His CASHCAT has realized profits of about $1.45 million, with a return rate of about 55%; "Bull Coming" has realized profits of about $794,000, with a return rate of about 122%, both positions have been fully closed. Meanwhile, he still holds a floating position in microduck, with paper profits of about $420,000, or about 1.3 times.

This represents a more complete position management: closed positions are responsible for locking in profits, while open positions retain the possibility of further increases. Compared to merely looking at total PnL, this split better reflects how much price risk the account currently bears.

What Does This Ranking Really Teach Us?

First, look at whether the ecosystem can continue to generate new value before examining specific tokens. When a new ecosystem launches, narratives and attention can bring in the first batch of capital, but whether the heat can continue depends on observing actual revenue, trading volume, liquidity, and user growth. Only when these indicators persist can the value capture logic of tokens be further validated.

Second, valuations can be compared among similar projects. It is difficult to judge whether a token is expensive or cheap by looking at whether it has a market value of $10 million or $100 million. A more effective method is to compare similar launchpads, meme leaders, or ecological tokens on other chains to see if there is any undervaluation.

Third, large results require both low costs and sufficient positions. High multiples usually come from earlier buying positions, while high profits also depend on the scale of investment.

Fourth, being optimistic does not mean never selling. One can retain core positions that determine account limits while also taking profits in stages during the rise to recover capital. Ethermonk's fully closed CASHCAT and "Bull Coming" positions, along with the still-held microduck, bear two different types of risks.

Fifth, the concentration of top accounts reflects the formation of basic consensus. The top 20 often build large positions on the same two tokens, indicating that narratives and attention are indeed important clues for discovering opportunities; however, when this concentration has already appeared on the rankings, the buying costs, potential multiples, and exit liquidity for later entrants may be completely different.

Finally, it is essential to understand the survivor bias behind the rankings. Early buying, concentrated holdings, and long-term holding can yield hundredfold returns but may also lead to losses close to zero. The profit rankings only showcase successful accounts that remain at the forefront and cannot be used to infer that those employing the same strategies are generally profitable.