Turn a stock trading idea into explicit rules in Premiss. Inspect the generated Python, the historical trades and the assumptions behind the result.
Featured product: Premiss · Reviewed 8 November 2026
Premiss is an AI-assisted stock strategy tester for creating readable strategy code and running historical tests on supported stock and ETF markets. A useful stock backtest still needs explicit timing, data assumptions, costs, position sizing and a matched benchmark. Verify coverage before testing.
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Opening Premiss does not automatically run this plan. Copy the specification, review it in the app, and confirm the choices there.
“Buy when the trend improves” leaves the signal, timeframe and execution undefined. A testable alternative names two lookback windows, describes a crossover on completed candles, and chooses when the resulting order can fill. The specification should be clear enough for two people to implement the same rule.
Ask how the data treats splits, dividends, trading sessions and delisted securities. Adjusted and unadjusted prices answer different questions. Selecting only today’s successful stocks can introduce survivorship bias. A single-stock example is not evidence of a broad market edge.
Choose several entries and exits and check the rule against the information available at the time. Look for trades before indicator warm-up, fills on unavailable prices and inconsistent position sizes. Only then interpret the summary and benchmark comparison.
Practical reading for the choices behind the tool.
Questions to resolve before interpreting a historical strategy result.
Read the guide Field guide / Stock strategy testingAn explicit rule example with timing, warm-up and execution decisions.
Read the guideThe local builder creates a precise strategy specification. Historical simulations run in Premiss after you open the app, select a supported market and confirm the test.
Premiss lets you describe a strategy in plain English and inspect the generated Python. You still need to review the rules and assumptions rather than treating generated code as automatically correct.
This page does not fetch market data. Before running a test, verify the selected dataset’s adjustment and corporate-action handling in the actual product.
No. It is an explicit educational example for learning how to specify and inspect a test. No profitability claim is made.
Product claims are bounded by the linked sources. The practical checklists and local tools are original editorial material.
Reviewed 8 November 2026. Read the editorial method.
Build a strategy, inspect its code and review the evidence. Check current plans and supported workflows.