我用 ChatGPT 回测了 SPY 0DTE 策略，并用 Codex 构建了 IBKR 自动化机器人

I highly suggest paper trading. You have so much complexity to your system that there is real risk of overfitting.

How did it fare in backtesting, or on a paper account?

If you're trusting codex to write something without error and willing to put money on the line for a first test, I'd say you're gambling in more ways than one.

If the question is 'what do you think of these rules, will thia be profitable? ' the answer is perform backtesting.

That being said, way to vibe code, must have had a lot of work put in, sounds like you put a lot of work Into it.

Worst case scenerio you taught yourself a lot of stock trading mechanics details, which even if the program flops, will be useful some way or another.

This looks great, what could possibly go wrong. Try out on paper first, please.

I listened to those that mentioned a stop loss, and I changed the strategy- i posted my new one. the first one didn't run, partly because of me not being able to leave my laptop running and partly due to the trades not filling the requirements. I also looked at QQQ as well, and made a similar strategy. have a look at my new post on here.

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Paper trade it first. The weak point is not that AI wrote code, it is that 0DTE plus IBKR automation gives tiny bugs real money consequences fast. Log every decision, order, fill, rejection, and skipped trade before letting it touch size.

The rules are the part everyone backtests and the part that matters least here. On a same day SPY breakout system your edge lives or dies on execution, and that is the one thing a historical bar backtest cannot see. You are almost certainly filling at the mid in the test. Live, you cross a bid ask that on 0DTE can be 10 to 20 percent of the premium, and an opening range breakout puts your entries in the widest spread window of the whole session. Round trip that on every signal and a system that shows positive expectancy on paper can sit underwater before the rules ever get a chance to work. Before you risk real capital, rerun it charging yourself the full spread plus a tick of slippage on every fill and see if the edge survives. If it only works at the mid, it does not work.

this is genuinely helpful, not just the usual fluff. bookmarking this thread.

You didn't explain your data source, but you should backtest whatever you come up with on historical tick by tick data to get any confidence on how it would perform on a live feed. You won't get the same result as backtesting on aggregated bars, and even tick data is not identical to what you would have received in real time because of data errors and latency.

Where did you get the data for option premium

Good luck, please post results of testing.

For backtesting, it may be worth exploring the parameter set and seeing how various parameter changes (i.e. chosen range level plus +.90 instead of .80, etc) affect profit factor or PnL. A robust strategy exists when the hyperparameter changes but what you're optimizing (i.e. profit factor or PnL or whatever metric) is still roughly consistent and doesn't drastically change.

A good metaphor is a mountain range: if your strategy results in a continuous set of elevation across parameter changes, that's good. But if you have a few defined peaks of your metric and a set of valleys where a slight parameter change results in drastic performance changes, it's something to be more skeptical of