Natural Language Polymarket Backtesting
Backtest Polymarket strategies from plain-English prompts using generated rules, historical snapshots, and resolved market outcomes.
Natural language Polymarket backtesting lets you describe a strategy in normal trading language and convert it into a structured replay. This is useful for fast iteration before committing to code.
Example Natural Language Rules
- Buy UP if price breaks above 60c with less than 30% time remaining.
- Buy DOWN when BTC reference price drops quickly and UP price lags.
- Scalp tight-spread markets and exit after a 5c move.
- Stop trading a market after two losing entries.
What the Backtest Needs
A natural-language interface is only useful if the replay layer is strict. The backtest should use chronological snapshots, bid/ask execution, available depth, and resolved market outcomes.
Best Fit
This workflow is strongest for screening many strategy ideas quickly. Once an idea looks promising, you can inspect the generated rules, export the payload, and refine the conditions with more precise thresholds.