On the World Cup stage, as teams fiercely compete for the prized trophy, a hidden whale quietly made a fortune on the prediction markets.
On Polymarket, an account nicknamed swisstony has a total historical profit of $18.62 million, with recent monthly earnings soaring to $10.33 million. The account, registered as early as July 2025, has quickly gained popularity due to its astonishing profits, now with 922,000 profile visits.
As of July 13th, this account has made a total of 139,617 predictions, with a current position value of approximately $606,100. It is worth noting that almost all of its current positions are focused on the FIFA World Cup semi-final match between France and Spain on July 14, 2026. This includes bets on France losing the game, with around $160,000 wagered. Furthermore, it has predominantly bet on NO for various specific score details, aiming for a 5%-10% return.
The account's win rate remains at 52.9%, with a total trading position of over 245,000, and a trading volume in the hundreds of millions of dollars. These numbers place it at the top tier within the Polymarket ecosystem. Public research indicates that most retail addresses experience long-term losses, while a few high-frequency, systematic accounts achieve significant positive returns through scaled execution. Although swisstony's win rate is not extremely high, combined with high trading frequency and position management, it amplifies the positive EV (Expected Value) advantage.
Since creating the account about a year ago, this whale has completed a total of 139,617 prediction operations. This translates to an average of about 380 trades executed per day, 16 per hour, non-stop 24/7. It is highly likely that this operation is driven by an API-powered high-frequency quant bot.
In its bio, it describes itself as a "trash panda." In North American culture, raccoons are known for rummaging through trash cans, showcasing their mastery of survival. This signature succinctly captures its core strategy: making money in Polymarket's vast data trash and small price differences, ultimately amassing a multi-million dollar empire.
Over 17 Instances of Earning Over $1 Million
Examining the profitability of this address reveals its astonishing nature: it has experienced 17 profits exceeding $1 million, with the largest one being a winning bet in Germany on June 25. The whale bet against NO, earning $2,221,241, a 111.67% profit.
The ROI displayed in the screenshot is generally high, indicating a strategic buying advantage. The investment scale is significant, with single transactions often in the $400,000-$1,000,000 range.
This whale favors large NO bets (against the win), targeting overvalued strong teams such as Germany, Paraguay (multiple occurrences), England, and Japan. The buy-in prices mostly ranged from 35.8¢-53.7¢, with the market implying these strong teams had a winning probability of around 46%-64% at the time. However, the actual results were losses or no wins, reflecting a typical anti-favorite strategy.
100x Return
In prediction markets, when the market deems something as highly unlikely and it eventually occurs, the profit return is disproportionately high. This whale excels not only in high-risk high-reward bets but also at a high level of low-risk high-reward.
For the match shown in the graph, the buy-in prices were extremely low: 0.2¢–1.2¢ (implying a market probability of only 0.2%-1.2%), with most funds invested being only around a few thousand dollars, yet each contribution yielding profits exceeding $100,000.
Events that were initially considered nearly impossible by the market indeed happened, with the account capitalizing on high returns at minimal cost.
By allocating small funds to ultra-low probability events, it is akin to a "lottery-style" but systematically executed approach. Once successful, these low-cost bets contribute profits of $100,000 or more each, serving as a significant supplement to overall profitability. While these high multiple trades carry low individual risk capital, their win rate is exceedingly low. Most of these bets are likely to end up losing.
Even though most of these bets may lose (due to the inherently low probabilities), hitting a few occasionally significantly boosts the total profits without heavily impacting the capital.
Overall, it is highly probable that this account's automated system covers a wide range of underdogs and low-liquidity markets, where serious mispricing is more common, enabling the execution of dual-track strategies to profit.
Large funds bet against popular strong teams, while small funds bet on extreme long shots. The combination of both ensures steady large profits while enhancing overall returns through high multiple trades.
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