We build Ohyolo, an AI options trading assistant. Our whole pitch is that the AI is allowed to say NO\_TRADE, sitting out is a valid outcome.

So when it evaluated 1,151 setups over two weeks and entered exactly one, it looked like the system working as designed. Choppy market, high bar, no forced trades.

It wasn't. It was a regression we shipped ourselves.

The system prompt asked the model to do two jobs at once: grade the setup A–F, *and* apply the trading policy, "your default decision is NO\_TRADE," "missing a trade is always better than taking a weak one," "only trade A or B setups."

Stating the policy inside the grading instruction contaminated the grade. The model stopped reporting what it saw and started reporting what it thought it was allowed to act on. In 400 sampled decisions after the change, grades A and B disappeared **entirely,** 0 of 400, against A=5/B=3 in the matched window before. Our risk engine then raised the bar to A-grade during elevated-volatility periods (\~60% of the time), demanding a grade the model had stopped issuing at all.

The fix was architectural, not a prompt tweak. The model now only assesses — direction and a grade. The code decides what grade is tradeable. Market-risk escalation tightens a number in config instead of leaning on the grader.

Two things I'd flag for anyone building on LLM decisions:

**Never state your execution policy inside your assessment prompt.** The model will optimize the assessment to satisfy the policy, and you'll never see it happen, English output looks perfectly reasonable the whole time.

**Log the threshold, not just the decision.** "Skipped" was ambiguous between "the setup was weak" and "the bar was raised." Those are unrelated problems and we couldn't tell them apart, which is most of why this took two weeks to find.

Our AI trading system said "no" 1,150 times in two weeks. We thought it was being disciplined. It was broken.