Backtesting Prediction Market Strategies: Order Books, Fees, Slippage, and Fills
Backtesting prediction market strategies is the discipline of testing a rule against historical or simulated market conditions before live automation. The goal is not to prove the future. The goal is to find weak assumptions before money is at risk.
This guide is not financial advice. Prediction market trading carries risk, and backtests can be wrong.
Backtesting prediction market strategies: what matters
Good backtests should account for:
| Factor | Why it matters |
|---|---|
| Order book depth | A displayed price may not fill your whole size |
| Fees | A small edge can disappear after costs |
| Slippage | Real execution can be worse than the signal price |
| Timing | Signals cannot trade before the data exists |
| Drawdown | A profitable rule can still be hard to hold |
TurbineFi puts backtesting in front of its prediction market trading bot workflow so users can reject weak strategies before connecting automation.
Backtests are decision tools, not promises
The best use of a backtest is comparative. Does one rule behave better than another? Does performance survive conservative fill assumptions? Does the bot trade too often? Does it break when market conditions change?
If the answer is uncomfortable, the backtest did its job.
FAQ
Why backtest prediction market strategies?
Backtesting helps identify weak assumptions, fee drag, unrealistic fills, and unacceptable drawdowns before live trading.
Can a good backtest predict live profits?
No. A good backtest can improve decision quality, but it cannot guarantee live results.
What should I review after a backtest?
Review PnL, drawdown, trade count, fees, fills, market selection, and whether the rule makes economic sense.
Should backtesting come before automation?
Yes. Backtesting should happen before prediction market automation, not after.