What 'AI backtesting' actually means
It doesn't mean an AI predicting the market. It means using AI to translate an idea you simply describe into a correct, runnable backtest - writing the code, wiring up the data, and handling the mechanics that used to require a programmer.
The intelligence is in the translation and the rigor, not in fortune-telling. The backtest itself is still a straightforward simulation on historical data.
How it works, step by step
A well-built AI backtester follows the same disciplined path a careful coder would:
- Read your intent: parse 'hold bitcoin above its 100-day average' into precise rules.
- Generate the code: write the actual backtest logic for those rules.
- Run it on real data: execute against years of real candles, stepping through time without lookahead.
- Show the result: return the trades, the equity curve, and summary metrics - ideally with the code visible.
Keeping AI backtesting honest
AI makes it effortless to generate a backtest - which also makes it effortless to generate a misleading one. The defences are the classic ones: insist on realistic costs, check the data spans varied conditions, confirm there's no future-peeking, and vary the inputs to test for overfitting.
The single best safeguard is transparency. If you can see the generated code and the exact trades, you can audit the claim. A backtest you can't inspect is just a number.
How Premiss does it
Premiss turns an idea you simply describe into a real Python backtest on years of real market data, and shows you the code, the trades, and a verified result. The AI does the building; you keep the ability to check every number - which is the whole point.