Some call OpenClaw nothing more than a toy lobster, while others aim to turn it into a profit machine. Sending the lobster to Polymarket marks a new trend many are eager to try.
On Xiaohongshu, users have offered 1,000 yuan for help deploying OpenClaw. A primary use case is quantitative trading on Polymarket—an idea that’s far from random.
On February 13 (UTC), the official OpenClaw blog revealed that a bot powered by OpenClaw demonstrated the remarkable potential of autonomous agents in prediction markets—raking in $115,000 in just one week.
In late January (UTC), Polymarket posted that agents were actively trading on the platform, seeking to offset their token costs.
It sounds almost unreal. Some lobsters drain their owners’ wallets, while others not only support themselves but also generate returns for their owners.
Bots Strike Gold on Polymarket
While human traders remain captive to fear and greed, a bot account named “0x8dxd” quietly executed more than 20,000 trades on Polymarket, amassing over $1.7 million in profits.
First, a quick introduction to Polymarket—a marketplace where virtually anything is tradable.
As the world’s largest decentralized prediction market platform, Polymarket allows users to trade Yes or No contracts on verifiable future events. Contract prices fluctuate between $0 and $1, directly reflecting market consensus probabilities. Users earn rewards based on the accuracy of their predictions.
For example:
Between 2024 and 2025, fans and investors worldwide are closely following Taylor Swift and NFL star Travis Kelce’s relationship. Polymarket launched a contract: “Will the two announce their engagement by the end of 2025?” While the market consensus leaned toward “NO,” some users bought large amounts of “YES” and ultimately made significant profits.
In short, sharper insights into an event can mean profit on Polymarket. For bots like 0x8dxd, however, prediction accuracy isn’t the cornerstone. Their playbook revolves around exploiting bugs with lightning-fast, automated strategies that humans simply can’t match.
In summary, bots rely on several core tactics.
First, mathematical parity arbitrage. This takes advantage of prediction market bugs. In Polymarket’s binary options, the winning contract always settles at $1, regardless of whether it’s “Yes” or “No.” When market sentiment shifts or liquidity dries up, the total cost for both sides (Yes and No) can drop below $1. Bots instantly buy both sides, locking in risk-free arbitrage.
Second, they focus on ultra-short-term crypto volatility markets. Five- and fifteen-minute prediction markets for BTC, ETH, and others are highly volatile, especially during forced liquidation events on exchanges—creating price dislocations that are perfect for high-frequency bot intervention.
Third, bots serve as digital market makers, placing high-frequency two-sided orders to capture the spread. For example, if a fair price hovers around 80 cents, the bot buys at $0.80 and quickly sells at $0.81 or $0.82. Each trade produces a tiny profit, but the cumulative returns can be substantial.
Bottom line: Bots ruthlessly harvest Polymarket thanks to their speed advantage and unwavering discipline. This highlights the drawbacks of humans as carbon-based traders: slower reactions, less rationality, and the need for sleep. OpenClaw dramatically lowers the barrier to deploying automated trading bots, fueling the expansion of machine-driven trading.
Unlike traditional Python bots, OpenClaw lets traders configure trading agents and automate trading without deep coding skills. OpenClaw’s built-in capabilities make it versatile for trading scenarios—its “lobsters” can monitor market prices and volumes around the clock, ensuring traders never miss an opportunity and can react quickly to risks.
In fact, many have already associated 0x8dxd with OpenClaw. Though there’s no direct evidence that 0x8dxd is built on OpenClaw, its activity coincides with OpenClaw’s launch. As stories spread of 0x8dxd turning Polymarket into a cash machine, the OpenClaw community rushed to develop Polymarket trading skills.
Recently, OpenClaw has become a buzzword in automated trading discussions on Polymarket. Still, relying solely on generic strategies is no guarantee of success.
Is This Really Profitable?
The simple truth: When a formula for stable arbitrage becomes public, it stops working. If everyone uses the same strategy, that strategy breaks down. So be wary of any “how-to” guides promising easy profits.
Polymarket has already taken steps to curb bot arbitrage, including trading fees, increased transaction friction, and changes to order execution delays to limit sniping strategies.
This pushes traders to tap deeper AI potential and search for more hidden opportunities. Some combine generic strategies with unique scenarios to unearth unexpected ways to play—like weather trading.
Weather prediction is one of Polymarket’s most popular use cases, with some bots dedicated to trading weather data.
For instance, an account named “automatedAItradingbot” joined Polymarket in January 2025, specializing in weather bets and earning over $70,000 in profit. Another bot trading only the London weather market turned $1,000 into $24,000 in under a year.
The core logic: prediction markets often lag in reacting to sudden weather changes. Theoretically, with a sensitive and reliable AI agent—say, an OpenClaw equipped with a weather plugin—you could bet on lines before the odds adjust after official weather updates.
But that’s not smart enough. As large language models evolve, bots shouldn’t just spot obvious signals like weather forecasts—they should, at minimum, operate on a level of intelligence beyond human capabilities.
In fact, AI is already showing even more compelling predictive market potential.
A study on “LiveTradeBench” ran simulated trades using real-world, live data. On Polymarket’s “2025 Russia-Ukraine ceasefire” contract, a large model’s own reasoning and prediction created an opportunity for significant profit.
Here’s how:
In October last year, Zelensky visited the White House and proposed a “drone for Tomahawk missiles” deal. Grok-3 used “belief-based reasoning,” dynamically raising its internal ceasefire probability from 0.15 to 0.22. It also noticed the “YES” contract price spiked to $0.18. This cross-checking led Grok-3 to conclude the contract was undervalued, so it took a strong long position. As the market price rose, Grok-3 realized a profit.
But Grok wasn’t the top performer.
The same study tested 21 leading large language models in financial markets, including U.S. equities and Polymarket. Claude-Sonnet-3.7 was the clear winner on Polymarket, delivering a 20.54% cumulative return over 50 trading days with a maximum drawdown of just 10.65%, far outperforming the market average.
Beyond “Money for Nothing” Stories
These experiments deserve more attention than typical bot arbitrage tales—they point to new horizons. If bots like 0x8dxd thrive on speed and sniping, large models have brought reasoning itself into play as a weapon.
In the future, large models may provide the decision-making—distilling scattered information into probability estimates—while tools like OpenClaw execute, turning those insights into orders and position management. What was once the domain of quant funds is now accessible to individual developers.
This signals a shift in prediction market competition.
Traditionally, humans relied on experience and intuition. In the high-frequency era, machines dominated with speed and discipline. Now, as reasoning becomes programmable, the true edge lies in transforming complex information into accurate probabilities.
So, a new hope emerges: with a smart, reliable lobster, maybe you could turn Polymarket into your personal money printer.
Unfortunately, theory and practice still diverge. Prophet Arena, a platform for evaluating AI predictive power, highlights critical risks.
First, large models’ predictive abilities are inconsistent. Top models can rival or even beat the market consensus in open-domain prediction, but “being right” and “making money” are not the same. Better accuracy doesn’t guarantee sustained outsized returns.
Second, timing is a real-world challenge. As an event nears resolution, sudden information shocks become more frequent. During these periods, models tend to be conservative and slow to update, while human traders can react faster.
Third, large models are easily swayed by noise. Emotional news or social media spikes can greatly shift their probability estimates. By contrast, experienced human traders are more anchored and less likely to be rattled by short-term noise.
Additionally, frameworks like OpenClaw usually require importing private keys and granting trading permissions—introducing security risks that could quietly drain your account.
So, instead of expecting AI plus OpenClaw to dominate prediction markets, focus on their deeper impacts. As more AI-driven agents enter, price reactions to information will accelerate, likely erasing the dream of easy arbitrage.
As bots and lobsters flood the market, arbitrage windows will only shrink. Sustained profitability won’t hinge on having a smarter lobster, but on understanding and managing your real risk.
AI can place the bets, but humans are still responsible for the consequences.
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