Polymarket traders make bold moves over the weekend, revealing robot weaknesses with $233,000 in profits

A trader using a pseudonym recently staged an impressive “comeback” on the prediction market platform Polymarket—by carefully orchestrating operations during the thin liquidity weekend hours, successfully extracting $233,000 in profit from automated market maker bots. This trade not only shocked industry insiders but also once again exposed deep-seated issues within the current crypto market mechanisms, sparking strong calls from market participants for algorithm upgrades and stricter market rules.

A Carefully Designed “Hunting” Strategy—The Trader’s Three-Step Plan

The story’s protagonist is a trader operating under a pseudonym, who last weekend identified a specific time window—when crypto markets are quiet and small orders can disproportionately influence prices.

The trader’s strategy consisted of three steps. First, he began actively purchasing “Up” (UP) shares in the XRP prediction contract on Polymarket. The core question of this contract was: Will XRP’s price rise or fall between 12:45 PM and 1:00 PM Eastern Time on January 17? As the trader kept buying large quantities, the UP share price on Polymarket was pushed up to $0.70, while during the same period, XRP’s spot price on major exchanges actually fell by 0.3%—a divergence that the trader aimed to exploit.

Confused by this price divergence, the automated market maker bots on the platform began arbitraging—when they saw the UP share price on Polymarket far above the theoretical price based on the spot market, they were programmed to automatically sell more UP shares to profit. This was exactly what the trader anticipated. Ultimately, the trader successfully controlled up to 77,000 UP shares at an average price of $0.48.

In the second step, two minutes before the market closed, the trader launched a decisive attack. His Binance wallet executed a large purchase of XRP worth $1 million, instantly pushing XRP’s spot price up by about 0.5%. This critical price increase ensured that the contract on Polymarket settled in favor of the UP side, with UP shares paid out at $1 per share.

Finally, the trader sold the XRP he bought on Binance, causing the spot price to fall back. The cost of this operation was only about $6,200, but the bots lost years’ worth of average profits overnight.

The trader did not stop there; he replicated this strategy across multiple thinly traded weekend markets, systematically “harvesting” the automated market maker bots. Although some bots later adjusted and shut down operations, others still in operation were not spared.

The Bots’ Fatal Weakness—Why Weekends Become “Hunting Grounds”

This incident sharply reveals fundamental flaws in Polymarket’s existing automated market maker mechanism. The reason these bots are easily beaten lies in their “ignorance”—they treat every price tick as a completely identical market signal, lacking any contextual awareness.

Specifically, these bots cannot recognize key dynamics such as:

• Trade quality: the difference between large orders and normal-sized orders
• Liquidity conditions: market depth and available liquidity
• Adversarial tactics: whether facing malicious manipulation or organized trading
• Timing factors: how proximity to contract settlement affects risk

Weekends are especially dangerous because market participation is sparse, institutional investors have left, and trading depth significantly diminishes. In such an environment, small amounts of capital can move markets, and a carefully designed trading sequence can create false price signals, inducing bots to automatically execute loss-making trades based on preset logic.

Market Calls: Smarter Algorithms and Strict Rules Are Essential

This event immediately raised alarms among regulators and industry insiders. Chris Tremulis, Global Head of Commodity Compliance at Goldman Sachs, expressed concern on social media, emphasizing that maintaining market integrity is key to the future of prediction markets.

“To achieve large-scale adoption by institutional investors, strict market integrity rules are indispensable,” Tremulis said. This requires multiple levels of improvement:

• Stricter enforcement: real-time monitoring and intervention against obvious market manipulation
• Rapid exchange investigations: establishing efficient incident response mechanisms
• Public disciplinary actions: transparent penalties for violations to deter misconduct
• Regulatory coordination: submitting violation reports to agencies like the CFTC

He also pointed out that the industry needs to develop truly intelligent, context-aware automated market-making algorithms that can:

• Adapt in real-time: dynamically adjust strategies based on liquidity, volatility, participant types, etc.
• Identify counterparties: understand whether they are retail traders, institutions, or suspicious manipulators
• Make contextual decisions: judge whether to participate based on proximity to settlement, market depth, and other factors

Insights for Traders and Future Challenges

This incident serves as a wake-up call for the crypto market. On one hand, it showcases the innovation and strategic prowess of market participants; on the other, it exposes the fact that our market infrastructure remains in its infancy.

Currently, crypto prediction markets are experiencing rapid growth, with platforms like Polymarket attracting increasing numbers of participants. But without smarter mechanisms to prevent manipulation and protect liquidity providers, the credibility and institutional appeal of these markets will be severely threatened.

For traders, this operation might have been profitable, but from a long-term market health perspective, only by establishing genuine market integrity can prediction markets attract institutional capital and achieve true maturity. Looking ahead, we should expect to see smarter algorithms, stricter rules, and more transparent oversight—these are essential steps toward the institutionalization of prediction markets.