trade-prediction-markets

To backtest a YES/NO trading strategy on Polymarket, use this skill to generate Python strategy code and evaluate its performance against historical prediction market data. You can simulate experiments across politics, economics, sports, and crypto markets.

Yes, you can browse and filter Polymarket prediction events by category. The skill provides market discovery tools to fetch events across crypto, politics, economics, and sports, allowing you to retrieve data availability and recent results before generating a strategy.

The best way to generate a systematic trading strategy for prediction markets is to define rules like buying YES when implied probability is below a threshold. This skill builds that Python strategy code and backtests it across multiple Polymarket events.

Yes, this Polymarket backtesting tool supports eventual live deployment. While primarily designed for simulated experiments and backtesting performance on historical data, the generated strategy code can be transitioned for live trading execution.

To start analyzing Polymarket YES/NO markets, you need to load the skill's browsing tools to fetch market data and check data availability. The skill retrieves recent results and historical market data required for generating and backtesting strategies.

Backtesting prediction market strategies with Python is limited by historical data availability for specific events. The skill retrieves data availability to check constraints, ensuring simulated experiments only run on Polymarket events with sufficient historical records.