This Polymarket Trader Made $2.2M in 60 Days Using AI – Here’s What That Means for Prediction Markets

Here’s how an AI-powered trader earned $2.2 million on Polymarket using data models, automation and probability-based trading strategies.

A trader used artificial intelligence to shock Polymarket after earning $2.2 million in about two months. 

The account has the pseudonym ilovecircle and reportedly used data models rather than instinct to make trades. 

The story now shows how prediction markets reward automation and speed, rather than an ability to “guess” future outcomes.

How the AI Trading Strategy Worked on Polymarket

For context, Polymarket allows users to trade on future outcomes and each market represents a question with a yes or no answer. 

The shares pay one dollar if the outcome happens and zero if it fails. This way, prices show market belief.

The trader in question treated Polymarket like a quant trading venue and used little to no human judgment. Instead, algorithms handled nearly every step.

The trader used artificial intelligence to write code, track data and place trades in order to find events where market prices failed to reflect real odds.

The system focused on mispriced markets. When prices drifted from reality, the bot acted and took advantage of the gaps.

Related Reading: Polymarket Eyes $12B Valuation as Crypto Expansion Accelerates

Claude Helped Build the Trading System

The trader used Anthropic’s Claude AI as a coding partner, and this choice changed the scale of the operation.

Claude helped generate Python scripts that connected to the Polymarket API. These scripts handled authentication, pricing data and trade execution.

Debugging happened faster as the AI helped fix errors in real time. The model also improved its execution logic through constant iteration.

Building such a system once required a full engineering team. However, one person could now manage it using AI tools alone.

The trader also built a dashboard to monitor large accounts. This allowed them quick reactions to whale activity.

Data Sources Powered the Decision Engine

The bot relied on more than Polymarket odds and pulled in data from many channels.

The trader used news feeds and social media sentiment to update the system as events unfolded, and on-chain activity showed how large traders behaved.

They also used legislative trackers to monitor bill progress alongside sports data streams that provided updated scores and injuries. 

Each source fed into a single model, which compared real-world signals against market prices.

Probability Modelling Replaced Gut Feelings

The trader also relied on probability math that compared two numbers.

The first number came from Polymarket prices, with a share price at 0.60 implying a 60% chance.

The second number came from the AI model, which calculated probability based on live data.

If the model estimated a 75% chance while the market showed 60%, the trade made sense and was likely to be positive.

This logic was repeated thousands of times, and individual losses mattered less than aggregate results.

Reports also indicate that the system achieved about 74% accuracy across trades across markets like sports, crypto events and political outcomes.

Overall, the story shows how tools that were once reserved for institutional use are now available to individuals. AI is reducing barriers to entry, and coding skills may now matter more than intuition.

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