Original Title: "Mosquito Meat, Roll Out 100,000 Dollars Profit"
Original Author: Maher, Foresight News
A miracle has occurred again on Polymarket.
With extensive monitoring, crazy betting, and over the course of a year, a certain address managed to net a profit of 100,000 dollars just through small margins, compounding.
The address we are analyzing, planktonXD (0x4ffe49ba2a4cae123536a8af4fda48faeb609f71), is an extremely typical high-frequency quant player. Since joining in February 2025, in just one year, it has achieved a net profit of 106,000 dollars through over 61,000 predictions.
In the prediction market, most people are betting on "black swans" or chasing big news, but planktonXD has taken a completely different path: extreme certainty and terrifying execution frequency.
Looking at planktonXD's historical trading data, the most shocking aspect is its 61,000 predictions. From February 2025 to February 2026, it averaged about 170 trades per day.
This frequency far exceeds the limits of human manual operation, making it clear that this player is using automated trading scripts (Bots). It is not "predicting" outcomes, but "harvesting" price differences.
An interesting phenomenon is that planktonXD's "Biggest Win" is only 2,527.4 dollars. Compared to its total profit of 100,000 dollars, this single maximum profit seems very "small" (only about 2% of total profit).
Some retail players always hope to win big, betting all their chips based on their confident judgments.
If they win, that’s great; if they lose, it becomes very difficult to return to the table.
If we take a step back, even if every time they go ALL IN they win, as long as they lose once, they would incur a huge loss.
Looking at its trading history, it never goes all-in on single extreme events, nor does it bet on high odds. Its profit curve exhibits a perfect 45-degree smooth upward trend, with almost no significant pullbacks. This indicates that it employs a market maker strategy: placing orders on both sides of the order book to earn the buy-sell price spread, or performing micro-arbitrage through price fluctuations across different markets.
It does not always hold positions long-term (Buy and Hold), but frequently enters and exits the market. This "light position, fast turnover" approach significantly reduces single-point risk. Even if an unexpected situation occurs in a prediction market (such as a sudden change in election results), its overall capital pool is minimally affected.
This quant robot does not specialize in trading vertical tracks, such as weather, but chooses to bet across multiple tracks, including sports, weather, coin prices, and politics. It continuously monitors thousands of prediction markets across the platform, seeking moments of price inefficiency.
The VALORANT Challengers is a classic practical case for this trader.
You can understand it as "minor leagues" or "regional leagues" in the eSports circle. Fuego and LYON are professional teams from the Latin American region. Due to the small audience and extremely high information asymmetry of such competitions, it becomes a "paradise for arbitrage" for quant robots.
It purchased 3,664.9 shares of Fuego winning at a unit price of 0.1¢, and ultimately this transaction yielded a return of a staggering 874.09 dollars, resulting in a phenomenal return rate of 23,750%!
This is a typical "small position betting on high odds". In a long-tail market with extremely poor liquidity or where the public strongly pessimistic about a certain option (like the results of eSports matches), it utilizes Bots to monitor those options that are mispriced to almost "zero". It does not need to predict who will win; it only needs to know that the probability of Fuego winning is definitely not just 0.1%. Essentially, it is harvesting the market's "extreme sentiment" and "lack of liquidity".
Speaking of sentiment, coin prices reflect it most vividly.
Will the price of SOL drop to 130 dollars between January 12-18?
It invested about 16 dollars at a price of 0.7¢ (where the market believed the winning probability was below 1%) and ultimately took home 1,574 dollars, achieving an astonishing return rate of 9,285%.
Why could this "almost impossible" prediction make it a lot of money at that time?
During significant fluctuations in the cryptocurrency market, mainstream predictions tend to be bullish or range-bound. planktonXD captures those "extremely bearish" options that are priced at 0.1¢ - 1¢ around the clock. These options are seen as worthless by ordinary people, but are viewed by quants as extremely cheap insurance. As soon as a deep spike or sudden negative news occurs in the market, these "worthless papers" can skyrocket by thousands of times instantly. Furthermore, in certain price ranges (like SOL $40), due to the current price being far from the predicted price, order books are often very thin. planktonXD uses automated scripts to place orders in these "no-man's lands", eating up those cheap shares that are thrown away due to panic or mistakes, essentially acting as a carrier of probabilities.
planktonXD's SOL strategy indicates that in Polymarket, buying the "impossible" does not mean it believes it will happen, but because the "probability of occurrence" is underestimated by the market. It bought a fraction of a dollar's worth of market panic probability, which is typical of "antifragile" trading.
planktonXD's success offers ordinary retail investors three core insights:
The power of compound interest should not be underestimated; earning 0.5% daily through high-frequency trading will yield far more stable returns a year later than betting on a coin with a tenfold return. Technology is the killer skill; in the Crypto era, quant tools and API access capabilities are standard for top players. Lastly, certainty outweighs odds. In prediction markets, seeking small profit opportunities with very high probabilities (such as over 90% certainty) is easier to survive than betting on big events with 50/50 odds.
After all, the highest level of play in prediction markets is not predicting the future, but managing probabilities and liquidity.
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