AI bots for finding trading opportunities on Polymarket

Challenge

Prediction markets move quickly. Monitoring many events, estimating probabilities, identifying gaps between price and calculated likelihood, and executing at the right moment is difficult to do consistently by hand.

Constraints

• Positive expected value does not guarantee profit on every trade or over any particular period.
• The strategy must account for liquidity, fees, slippage and position-size limits.
• An AI signal cannot be the sole basis for execution without explicit risk rules.

Solution

• Built bots that collect market data, analyse events through a defined AI framework and estimate potential EV.
• Formalised entry and exit conditions, liquidity filters and position-management rules.
• Automated trade execution scenarios and decision logging for retrospective analysis.
• Separated signal generation from risk control so the strategy can be tested and adjusted independently.

Outcome

• The workflow from opportunity discovery to execution and trade logging runs automatically.
• Bots systematically filter for estimated positive-EV scenarios instead of relying on emotional decisions.
• The team can review signal, trade and error history to improve the strategy over time.
• Financial performance depends on market conditions and risk parameters; this case study is not a promise of guaranteed returns.