Prediction Market AI Backtesting
Prediction market AI backtesting combines agent-generated strategies with historical order book replay and settlement-aware results.
Prediction market AI backtesting is the process of using an AI assistant or agent to generate strategy rules, then testing those rules on historical prediction-market data. For Polymarket, the important part is not the prompt; it is whether the replay respects order book mechanics.
Why Prediction Markets Are Different
Prediction-market prices are probabilities, contracts settle to fixed outcomes, and liquidity can disappear near resolution. Backtests need settlement metadata, market lifecycle context, and execution-aware fills.
AI Agent Use Cases
- Generate first-draft strategy rules from prompts.
- Compare mean reversion, momentum, and scalp variants.
- Run the same idea across many resolved markets.
- Summarize where the strategy works and where it fails.
Evaluation Checklist
Use total PnL, win rate, average PnL per market, drawdown, slippage, and market failure count. A good AI-assisted workflow makes these metrics inspectable instead of hiding them behind a single score.