Polymarket XRP Trader Makes $233K Overnight: Details

A trader operating on Polymarket generated approximately $233,000 in profit within a single day after identifying and exploiting unusually low liquidity conditions affecting short-term XRP and Bitcoin markets. The activity occurred during a weekend trading session, a period typically characterized by reduced participation and thinner order books across both prediction markets and centralized exchanges. Available data shows that the trader, identified on Polymarket as “a4385,” focused on a narrow time-based prediction market tied to XRP’s short-term price movement on January 17. By coordinating positions across Polymarket and Binance, the trader was able to influence settlement outcomes at a relatively low cost.

Use of Short-Term Prediction Markets

The strategy centered on a 15-minute Polymarket contract that paid out depending on whether XRP’s price closed higher or lower during a specific window from 12:45 PM to 1:00 PM Eastern Time. During this interval, trading activity on Polymarket was limited, creating an environment where a single participant could absorb most of the available liquidity. The trader began purchasing contracts that would pay out if XRP moved higher by the end of the window. Rather than placing selective orders, he bought nearly every “up” contract available, regardless of price, steadily increasing his exposure. This accumulation continued while broader market activity remained subdued, allowing him to build a dominant position without immediate resistance.

Interaction With Automated Trading Systems

At the time, many of the opposing positions on Polymarket were held by automated trading systems rather than discretionary traders. These systems interpreted the rising price of the “up” contracts as a signal of demand and continued offering additional contracts instead of reducing exposure. As a result, the trader accumulated roughly 77,000 contracts at an average cost significantly below the final payout value. Despite a brief dip in XRP’s spot price early in the trading window, the contract pricing continued to move in favor of the trader’s position. By the midpoint of the session, the imbalance between available liquidity and demand had become more pronounced.

With minutes remaining before settlement, the trader executed the second phase of the strategy. A separate wallet placed a spot buy on Binance valued at approximately $1 million in USDT. Due to thin liquidity at the time, this order was sufficient to move XRP’s price upward by roughly 0.5%, ensuring that the Polymarket contract would settle in favor of the “up” outcome. Immediately after the prediction market resolved, the same wallet reversed the spot position, selling the acquired XRP and minimizing directional exposure. This sequence locked in the Polymarket gains while limiting prolonged market risk.

Cost Structure and Profitability

Estimated transaction costs associated with the strategy were relatively small compared to the resulting profit. Combined fees and slippage on Binance were estimated at around 0.25% per transaction, with additional platform fees applied to the prediction market trades. Given the trader’s fee tier on Binance, total costs were estimated near $6,200, though actual expenses may have been slightly lower. The disparity between execution costs and net profit highlights how low-liquidity conditions can magnify the impact of coordinated trading activity across interconnected markets.

Broader Impact and Repetition of the Strategy

After confirming the effectiveness of the approach, the trader reportedly repeated similar trades across other short-duration markets during the same weekend, including contracts linked to Bitcoin. Because overall trading volume did not recover, automated systems continued to provide liquidity under unfavorable conditions. While some participants recognized the pattern and withdrew from trading, others were slower to react. Public data suggests that at least one trader experienced losses exceeding $30,000, effectively erasing prior gains accumulated earlier in the year.

The episode has renewed discussion around liquidity risks, automated trading behavior, and the vulnerabilities that can arise during low-volume trading periods, particularly in short-term prediction markets tied closely to spot price movements.