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Claude AI powered arbitrage bot turns $600 into $10,000 in 48 hours

Claude AI powered arbitrage bot turns $600 into $10,000 in 48 hours

A Claude AI-powered arbitrage bot made a profit of $9,400 in two days on Polymarket, a blockchain-based prediction market.

Finbold analyzed a 25-second clip of live orders on April 7, showing a bot trading dozens of contracts that made a net profit of 1,567% in 48 hours. Some of the visible trades range from Federal Reserve policy decisions, Bitcoin (BTC) price milestones, Apple AI, and Starship launch dates.

Claude AI-powered arbitrage bot. Source: AdiiX

During the visible terminal, the bot moves the account balance from about $6,000.56 to over $6,225 in real time. The trading algorithm heavily leveraged small edges, ranging between $2 to $20 per trade, but stacked up fast through high-frequency execution.

To facilitate trading, the developer prompted Claude to identify mispriced Polymarket contracts and to track wallets that copy elite traders. During this period, the AI bot scanned more than 1,000 wallets, with one address moving its balance from $120 to $1.4 million via 1,947 trades and achieving a 55% win rate.

Claude-powered bots used to make generational wealth on the rise

This incident is part of a broader trend. Cases of AI agents being used to build profitable trading bots on Polymarket have been recorded in recent months.  In other instances, a trader used a bot for copy trading on Polymarket and turned $5 into more than $500,000 at press time, through 100% win rate of 17,000 predictions.

Last week, Finbold reported that a Claude-powered trading bot had grown an account balance from $1 to $3.3 million on Polymarket. The rising number of AI trading bots on Polymarket is bolstered by traders instantly reverse-engineering strategies from GitHub repositories.

As such, traders have been converting relatively modest stakes into substantial returns through high-frequency, model-assisted execution. Nonetheless, erroneous bot configurations have also led to significant losses, underscoring the need for a cautious approach, as Finbold noted earlier.

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