Ask AI to suggest a covered-call strike and it invents one. Paste in real data from your broker and ask the right questions, and it becomes a useful second-opinion reviewer. Seven prompts that work because they start from what's actually in your account.
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Most “AI prompts for covered calls” lists ask the model to do the one thing it can’t: read your broker screen. They want it to name a strike, guess a premium, work out a yield, all from numbers it never had. The answer comes back sounding sure of itself. It’s also made up. (A covered call is just selling someone the right to buy shares you already own at a set price, in return for cash up front.)
These seven prompts do the opposite. Each one sits on a real decision in the trade: is this stock even worth writing calls on, are the premiums rich or cheap right now, which strike fits what I’m trying to do, what could blow up before the contract ends, do I roll the call out or let the shares go. Every one makes me bring the real numbers. The AI doesn’t find the trade. It checks the one I found.
I run these on my own holdings, mostly on BMNR and a few others where I’m running the wheel: buy shares, sell calls against them, and if the shares get sold, sell puts to buy them back, round and round. Some I run every trade. Some only when something changes. None of them replaces the broker; they sit on top of it.
The seven, in plain terms: (1) is this stock worth it, (2) are the premiums rich or cheap, (3) which strike off my shortlist fits, (4) what could blow up before expiry, (5) roll, close, or let the shares go, (6) what did this trade teach me, (7) is the whole thing actually paying.
The discipline
Two rules sit underneath every prompt below.
One. The AI can’t see the live prices on your broker screen. Ask it for a strike and a premium and it will invent numbers that sound right but don’t exist on anything you can trade. (Two things have changed since I wrote this. ChatGPT Plus users who switch on a broker link, Alpaca, as of mid-2026, can now pull real prices inside ChatGPT. And ChatGPT with web access will sometimes quote a price it scraped off a data site, which is worse in a way: not invented, just stale, a number that was true at some moment you can’t pin down.) Neither changes the rule. Your broker is the source of truth for what you can trade right now; everything else is a starting point you check. Every prompt below either makes you paste the real numbers in, or tells the model not to invent any. If you take one thing from this post, take that.
Two. Every prompt uses the Prompt Stack: SCOPE, FILTER, RISK, VERDICT. SCOPE fences the model to the numbers you give it and lets it say when it can’t verify something. FILTER makes you bring real numbers and splits fact from guess. RISK is the part most people skip. VERDICT stops the “it depends” non-answer that makes AI useless for a real decision.
Drop either rule and you’re back with the prompt lists that tell you to ask AI for “the best covered call to sell today”.
Should I write covered calls on this stock at all?
When to use it: Before you pick any strike, the real first question: is this a stock you should be selling calls on at all? It catches the most common mistake: chasing a bit of income on a stock you actually want to keep, where the cash you collect isn’t worth the upside you cap.
Why it earns its place: Every other prompt list skips this and jumps straight to picking a strike. That’s backwards. If you wouldn’t be happy to lose the shares at a sensible price, you shouldn’t be writing calls. This is the one I reach for first on a stock I haven’t written calls on before.
SCOPE: Work only from what I tell you about this holding below; don’t fill in prices, figures or events from memory, and if something you’d need isn’t here, say so rather than guess. You are checking whether selling covered calls on [COMPANY NAME] makes sense given why I hold it. Do not suggest strikes or premiums. That comes later.
FILTER: Why I hold [COMPANY NAME]: [ONE-SENTENCE REASON]. How long I plan to hold: [HORIZON, e.g. 6 months / years]. How I’d feel if the shares got sold: [HAPPY TO SELL / WOULD BE ANNOYED / WOULD HAVE TO BUY THEM BACK]. Split the plain facts about this stock (sector, how much it usually moves in a month, whether it pays a dividend if you know) from the parts of my reasoning that are just a guess.
RISK: Name the three situations where selling calls on this stock would actively work against me, for example capping a big run, forcing a tax bill by selling the shares, or trapping me in an endless chase as the stock keeps climbing. Be concrete.
VERDICT: One of three answers: “Fine to write calls on”, “Only worth it when the stock is running hot”, or “Don’t write calls on this”. State your confidence (low / medium / high) and the one thing that would change your mind.
What good output looks like: A clear verdict, risks tied to this stock rather than options in general, and the model owning up to which parts it’s guessing on. If it starts naming strike prices, the prompt has failed. Start again.
Where it falls short: The AI doesn’t know your tax position, your other holdings, or what you actually paid for the shares. This checks the stock, not your whole portfolio.
Are the premiums on this name rich or compressed?
When to use it: Are the premiums on this stock dear or cheap against their own history? That’s what decides whether you’re being paid enough to risk having the shares sold out from under you.
Why it earns its place: This is the input most people skip. The price of a call depends on how much the market expects the stock to swing, what’s called implied volatility, the market’s guess at the size of the next move. When that’s high, premiums are fat; when it’s low, they’re thin. Most prompt lists either ignore this or assume the AI can look it up. It can’t, reliably. So this prompt makes you bring the numbers and asks the model to do the part it’s good at: reading them. My own rule is to sell when the expected swing is high and sit out when it’s low, so this read is what decides whether I open a trade at all.
SCOPE: You cannot see live data. Work only from the numbers I paste below, and don’t fill any gap from memory. If something you’d need isn’t in what I give you, say so rather than estimate it.
FILTER: Here are the volatility figures for [COMPANY NAME], pulled from [SOURCE, e.g. broker / Barchart / Market Chameleon] on [DATE]:
- IV30, the expected swing over the next 30 days: [VALUE]%
- IV Rank, where that sits versus the past year, 0 to 100: [VALUE]
- IV Percentile, how often the past year was lower than today: [VALUE]
- Realised 30-day vol, how much it actually moved recently: [VALUE]% (if available)
- Next known event: [EARNINGS DATE / DIVIDEND / NONE]
Explain in plain English what this combination tells me about how richly the premiums are priced right now. Rank and Percentile can disagree, so say which one is more telling for this stock and why.
RISK: Give me two reasons the expected swing might be high because the market knows something real (a coming event), rather than just being temporarily overpriced. What would I look at to tell the two apart?
VERDICT: Call the current premiums RICH / NORMAL / CHEAP for selling calls on this stock, with one sentence on whether that changes how close to the current price I should be selling.
What good output looks like: A real difference drawn between rank and percentile (rank reacts more to one wild spike; percentile to how often), a rich-or-cheap label, and a flag if there’s an event coming up inside the month or so the contract would run. If the model makes up a volatility number instead of using the one you pasted, stop and start again.
Where it falls short: Only as good as the numbers you paste. It can’t tell you why the expected swing is where it is. That’s research on the company, not a prompt.
Which strike on my shortlist actually fits my intent?
When to use it: You’ve narrowed it down to two or three possible strikes off your broker screen. Which one fits what you’re actually trying to do, and where is your gut leading you wrong?
Why it earns its place: This is where people most often chase the bigger premium and end up selling a call so close to the current price that they lose the shares they wanted to keep. Asking AI to invent strikes invites it to make numbers up. Asking it to weigh up options you already pulled off your broker is the opposite: useful, fast, and it makes you commit to your goal before you see its answer. The shortlist I paste comes straight off my broker screen, and the prompt’s job is to stop me talking myself into the richer one.
SCOPE: Work only from the figures I paste below; I pulled these off my broker screen. Don’t invent or adjust any of them from memory, and if something’s missing, say so rather than fill it in. You are not picking strikes. You are checking my shortlist.
FILTER: [COMPANY NAME] last traded at [PRICE]. What I paid for the shares: [BASIS]. What I want: [MOSTLY INCOME / KEEP THE SHARES / HAPPY TO SELL]. Candidate strikes for [EXPIRY DATE], copied from my broker (delta = rough chance the shares get sold; OI = how many of these contracts are open, a quick read on how easy it is to trade):
- Strike A: [STRIKE], delta [VALUE], premium [PREMIUM], OI [OI]
- Strike B: [STRIKE], delta [VALUE], premium [PREMIUM], OI [OI]
- Strike C: [STRIKE], delta [VALUE], premium [PREMIUM], OI [OI]
For each, work out: (1) what I make if the stock goes nowhere, (2) what I make if the shares get sold at the strike, (3) that second figure scaled to a yearly rate, using the real days left. Show the maths. Don’t fill in anything I left blank. If I haven’t given you something, say so.
RISK: For each strike, name the one way it goes wrong: what stock move makes this the wrong pick looking back? Rank the three by which one hurts what I said I want the least.
VERDICT: Pick one. Say which of my inputs would have to change for your pick to flip to another.
What good output looks like: Visible arithmetic, three different ways it could go wrong, and a pick that matches what you said you want rather than just the biggest premium. If you said “keep the shares” and the model picks the 0.45-delta strike, the one most likely to get them sold, for the headline yield, push back.
Where it falls short: Delta is only a rough guide to the odds. The model will treat 0.30 delta as about a 30% chance the shares get sold, which is broadly true but not exact, and it gets less reliable on jumpy stocks. For the trade itself, the odds figure on your broker screen beats the model’s.
What’s about to blow up inside my contract window?
When to use it: Is there a results day, a dividend date, a central-bank meeting, or some other known event inside the life of the contract that you’ve forgotten about? It’s the cheapest mistake to avoid and the one most often missed. Nobody sells a call planning to be caught out by results; they just forget to look.
Why it earns its place: This is a checklist, not analysis, and AI is good at thorough, boring checklists. It also catches a quieter risk: a dividend can make the buyer take your shares early, which people often miss. The version I paste in starts from my real end date and strike, so the model is checking my actual trade, not a made-up one.
SCOPE: Work only from the trade details I give you below; don’t assume dates or events you can’t see, and flag anything you’d need to check rather than guess at it. Your job is to list everything I might have forgotten about the life of this contract, not to say whether the trade is good.
FILTER: Trade: selling [N] [COMPANY NAME] [EXPIRY] [STRIKE] calls. Today: [DATE]. The contract runs [DAYS] days. What I know about the company: [SECTOR, ANY EVENTS YOU KNOW ABOUT].
Walk through this checklist and flag anything I should check before placing the trade:
- Results day inside the window
- A dividend inside the window, and whether it’s big enough that the buyer might take my shares early to collect it
- Known product, legal, or economic events
- Anything unusual clustered around my end date
- My tax position, if I tell you when I bought (how long I’ve held can change how the income is taxed)
RISK: For anything flagged “check”, tell me exactly what to look up and where, and what would make it bad enough to call off the trade.
VERDICT: GREEN (go), AMBER (check X, Y), or RED (don’t, with a specific reason). One line.
What good output looks like: Specific things to check in specific places. “Check the dividend date on the company’s investor page or the Nasdaq calendar; if the dividend is big enough the day before, expect the buyer to take your shares early.” Not vague warnings.
Where it falls short: AI doesn’t have today’s calendar of results dates. It knows roughly when a company tends to report from past years, but don’t trust it on the exact day. Any specific date it gives you is a thing to go and check, not a fact.
My short call is in the money: roll, close, or let it assign?
When to use it: Mid-trade. The stock has run. It’s now above your strike, or close to it, which means the call is on track to cost you the shares. What do you actually do?
Why it earns its place: This is the biggest recurring decision in the strategy. Most prompts treat rolling the call out as a topic to explain, not a decision to make. Done right, this one makes you spell out your updated view before the AI answers, which is the actual discipline. I keep a hard rule that I close or roll by 21 days before the end rather than nurse a trade to the wire, and the prompt is built to hold me to that rather than let me wing it.
SCOPE: Work only from the trade and the figures I give you below; don’t invent a roll price or any number I haven’t supplied, and if something you’d need isn’t here, say so. You will help me choose between three actions: ROLL (move the call out to a later date), CLOSE AT A LOSS (buy the call back now), or LET THE SHARES GO (let them be sold at the strike).
FILTER: Trade: sold [N] [COMPANY NAME] [EXPIRY] [STRIKE] calls. Premium I took: [CREDIT]. What it costs to buy back now: [DEBIT TO CLOSE]. Stock now: [PRICE]. Days left: [DTE]. Why I sold the call in the first place: [REASON]. What’s changed in my view of the stock since: [UPDATED VIEW, or “nothing’s changed, it just ran”]. Tax and cost context: [E.G. “held long enough for the lower tax rate”, “I paid far less than today’s price”, “I’d rather not trigger a tax bill this year”].
For each of the three actions, lay out:
- What it does to my cash today
- What would have to be true for it to be the right call in 30 days
- What it says about where I think the stock goes next
RISK: If I roll, how many times would you roll this before the right move is just to let the shares go? Why that number?
VERDICT: Recommend one action. State the single new fact that would change your recommendation.
If you tell it "nothing changed, the stock just ran", the answer is usually "let the shares go, you got what you wanted".
What good output looks like: A clean three-way comparison, a firm cap on rolling (one or two times is plenty; rolling forever is how a winning strategy quietly turns into a losing one), and a recommendation that matches the view you typed in.
Where it falls short: AI can’t price a roll. It doesn’t know what the later call trades for. It can frame the decision; you still have to get the price from your broker.
Did the process work, separate from the outcome?
When to use it: The trade closed: the shares got sold, the call expired worthless, or you bought it back. Did the process work, separately from whether the result was good?
Why it earns its place: This is the prompt nobody writes and everybody needs. This strategy lives or dies on doing the same sensible thing over and over, which means most of the edge comes from keeping an honest record. AI is good at structured reflection if you give it the facts and tell it not to flatter you. I run this after a trade closes, when the temptation is to file a winner as “good call” and move on without checking whether the thinking behind it actually held.
SCOPE: Work only from the closed-trade facts I give you below; don’t fill in numbers I haven’t supplied, and if something’s missing, say so rather than guess. Score the process and the outcome separately. Do not be encouraging. Be useful.
FILTER: Trade closed: sold [N] [COMPANY NAME] [STRIKE] calls, opened [OPEN DATE] for [CREDIT], closed [CLOSE DATE] for [DEBIT or “expired worthless” or “shares sold at the strike”]. Profit or loss on the call: [VALUE]. Profit or loss on the shares over the same time: [VALUE if relevant]. Why I sold this strike at the time: [ORIGINAL REASONING]. What I’d do differently knowing what I now know: [HONEST ANSWER, or “I’m not sure, that’s why I’m asking”].
Walk me through:
- Did the strike I picked match what I said I wanted?
- Was the timing (versus how richly premiums were priced, versus events) defensible at the time, or only with hindsight?
- Did I manage the trade by a rule I’d set, or did I make it up as I went?
- What’s the one repeatable thing, good or bad, to take from this trade?
RISK: Name the thinking trap most likely at play if I treat this one trade as proof my approach works (or doesn’t). For example: too small a sample to tell, judging the decision by the result, or building a tidy story after the fact.
VERDICT: Score the process (1–5) and the result (1–5), separately. One sentence on the gap between them.
What good output looks like: Two scores that don’t match. A good result off a sloppy process is the most useful thing the model can flag. One concrete rule to repeat, not vague advice about “staying disciplined”.
Where it falls short: This is the prompt where AI is most likely to flatter you. The “do not be encouraging” line matters; without it, the model will find a way to call a losing trade a valuable lesson. If you read the output and feel good about everything, run it again and ask what it went easy on.
Is the wheel paying for itself, or just feeling productive?
When to use it: Every few months, or after the market mood shifts. Is the whole covered-call habit still doing what it was meant to? This is the prompt for anyone who’s been at it for six months and isn’t sure it’s working.
Why it earns its place: This isn’t the per-trade review. It looks at the strategy as a whole. The honest test is simple: would you have made more just holding the shares and doing nothing? Selling calls earns you a steady trickle of income, but every call you sell caps how much you make if the stock takes off, and that cap can quietly cost you more than the income brings in. It’s the question people most often dodge. A few cycles into running the wheel on BMNR, this is the one I make myself run, because by then the per-trade wins feel like proof, and comparing against “just held it” is the only thing that tells you whether they are.
SCOPE: Work only from the numbers I give you below; don’t fill in any figure from memory, and if something you’d need to judge this isn’t here, say so rather than guess. Start from the assumption that just holding the shares beats selling calls on most stocks; I have to talk you out of it with the numbers.
FILTER: I’ve been selling covered calls on [COMPANY NAME] for [PERIOD]. Numbers:
- Total income collected: [VALUE]
- Times the shares were sold off me: [N]
- Times I bought a call back at a loss: [N]
- Profit or loss from shares sold off me: [VALUE]
- Stock price at the start: [PRICE]
- Stock price now: [PRICE]
- What I’d have done with the shares otherwise: [HOLD / SELL EVENTUALLY / SELL SOME NOW]
Work out, with the maths visible:
- What I actually earned from the income, as a yearly rate on the money tied up
- What I’d have made just holding the shares over the same time
- What it cost me when shares were sold off me: the gain I’d have had versus the income I took
RISK: Two ways this comparison is unfair to selling calls, and two ways it’s unfair to just holding. Be even-handed.
VERDICT: One of: “It’s paying for itself”, “You’d have done better just holding, you’re paying for the feeling of income”, or “Too noisy to tell, keep a record for another [N] months”. Name the one thing I should be tracking that I’m not.
What good output looks like: Visible arithmetic, an honest comparison (the prompt should be willing to say you’d have done better just holding, which is the likely answer on a stock that’s been climbing, and the model needs permission to say so), and a specific thing to start tracking.
Where it falls short: Only as good as your records. If you haven’t been noting when shares were sold off you and what you made or lost, the prompt can’t help. The fix is to start a record now and run the prompt in three months.
Where AI prompts for covered calls help, and where to stop
| Stage | AI useful? | When to put the prompt down |
|---|---|---|
| Before the trade, is this stock suitable? | Yes, pressure-tests your reasoning | Never; this is the model’s strongest zone |
| Before the trade, are premiums rich or cheap? | Partial, reads the figures if you paste them | The moment you ask it for a live number |
| Before the trade, picking a strike | Yes, checks a shortlist you built | The moment you ask it to invent strikes |
| Before the trade, events to watch | Yes, builds the checklist | When it gives you a specific date, go and check it |
| In the trade, roll, close, or let the shares go | Yes, talks through the choice against your goal | When you need a real roll price, that’s the broker |
| After the trade, review | Yes, honest reflection | If the model starts flattering you |
| Strategy review, is it working? | Yes, spots patterns in your own record | If your record is patchy; finish it first |
The two hard “no” zones: anything needing the live prices on your broker screen (the model doesn’t have them and will invent strikes and premiums that don’t exist) and anything needing exact options maths (the model’s working is usually subtly wrong somewhere). Those are calculator jobs.
Field Report
What worked: Treating AI as a second opinion that holds what you’re trying to do and your real numbers in mind at the same time. The Prompt Stack keeps it from sliding into “supportive coach”.
What didn’t: Asking the model to read your broker screen, do the exact options maths, or pick a trade for you. Every one of those invites it to make numbers up.
Bottom line: Full confidence in the method. Rather less in whether you’ll resist asking for a strike recommendation anyway.
These are the seven I run. They’ll change as the tools change. In my testing, Claude has been the most willing to say “I don’t know, check the official guidance” on awkward tax questions, which is the right instinct for this work, though that gap will close. The discipline won’t. The AI can’t see your broker screen. Bring the numbers, or don’t bother running the prompt.
These seven cover getting into a trade. What to do after one closes, when the urge to sell another call straight away is loud, is at AI covered calls: when NOT to sell another one.
The made-up numbers I’ve caught running these prompts (what every tool gets wrong on options, and how) are at what AI gets right (and wrong) about options trading.
This method has run on my first wheel trade on BMNR and six covered-call cycles since I wrote it down. The closed trades (strikes, how long they ran, the return, the yearly-rate equivalent) are at /trades. Tickers named, sizes never.
Ben tests which AI assistants can be trusted with a real decision, the kind where being wrong costs real money. The verdicts here are what he found, including the times a tool simply wasn’t worth the trust. About Ben →
The site runs AI on real investing decisions. Start with the Prompt Stack for the four-stage framework, or the Field Guide PDF for the condensed version, free, no email.