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Building a Polymarket Backtesting Framework in Python

Structure

1. Data layer — pull orderbook + trade history via the Python client and cache locally
2. Execution simulator — fill orders against historical depth, not mid-price
3. Strategy layer — entry/exit logic, kept separate from execution so it can be swapped
4. Evaluation — win rate against resolution, slippage, drawdown

Related

• How to backtest a Polymarket strategy
• Execution simulation with prediction market data

Related guides

• Prediction Market Data for Academic Research
• Common Backtesting Mistakes in Prediction Markets
• Data Cleaning for Prediction Market Backtests
• Execution Simulation With Prediction Market Data

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