How to Backtest a Trading Strategy With ChatGPT

The workflow is: describe your strategy to ChatGPT, ask it to write a Python backtest, then copy that code into an environment (like a notebook), supply price data, fix whatever doesn't run, and read the output.

It works, and it's a great way to learn. But notice how many manual steps there are - and how each one is a chance for a subtle error to creep in.

If you go this route, here's the realistic sequence:

• Describe the strategy precisely - asset, entry, exit, costs - or ChatGPT will guess the gaps.
• Ask for a complete, runnable Python backtest using a known library.
• Get historical data yourself (an exchange export or a data API) and feed it in.
• Run it, then debug - missing imports, data-format mismatches, and edge cases are common.
• Scrutinise for lookahead bias and check costs are modelled, because the code will run even if it's subtly wrong.

The biggest risk is silent error. ChatGPT-written code can run perfectly and still be wrong - using a future price, omitting fees, or mishandling the data - producing a confident, beautiful, fake result.

You also own all the plumbing: data sourcing, environment setup, debugging. For a quick idea, that's a lot of overhead, and the parts most likely to introduce bias are exactly the parts you're handling manually.

Premiss is essentially the ChatGPT method, productised and made safe: you type the idea in plain English, and it writes the real backtest, sources the real data, runs it correctly, and shows you the code, the trades, and a verified result - without the copy-paste-and-debug loop. Same plain-English input; none of the manual plumbing or the silent-error risk.