AI Prompts for Options Traders: 7 Templates Worth Saving

The best AI prompts for options traders are not the ones that ask a model what to trade. They are the ones that make a model do structured work on information you supply: decoding a position you are considering, comparing two structures built on the same thesis, arguing the bear case against your own idea, or reading a stack of closed trades back to you without flattery.

Seven prompts cover most of what a general-purpose assistant is genuinely good at inside an options workflow. Each follows the same shape: a role, the data you paste in, a constraint on what the model may not assume, a fixed output format, and a verification step that forces the arithmetic into view. That last slot is what separates a prompt worth saving from one worth running once.

What AI Prompts for Options Traders Actually Do

A prompt is the full set of instructions you give an AI assistant before it answers. A general-purpose assistant here means a chat model with no connection to market data, which describes most of the tools traders reach for first. It reads what you paste, predicts a plausible continuation, and returns it in whatever register you asked for.

That last detail is the one that matters. The model is optimizing for a plausible answer, not a correct one, and in options work those two come apart fast. Ask for the breakeven on a spread and supply the legs, and it will usually get there. Ask for the breakeven on a spread without supplying the legs, and it will invent legs rather than tell you it cannot answer.

The model has no chain. Everything it tells you about strikes, expirations, and premiums is either something you pasted in or something it invented.

So a saved prompt is doing two jobs at once. It defines the analytical task, and it fences off the space where the model would otherwise fill gaps with invention. The fence is the part most prompt collections skip, and it is why so many of them produce confident output that falls apart the moment you check a number against your broker.

This is a narrower claim than the marketing around AI trading tools usually makes. FINRA's own survey of the securities industry describes firms conducting upfront and ongoing testing of AI tools rather than trusting them out of the box, which is the same discipline applied at a larger scale. Our guide to using AI in options trading covers the wider tooling picture, and a separate look at whether ChatGPT belongs in your process at all works through the case for and against.

How a Saved Prompt Works

The five slots: role, data, constraints, output shape, verification. A prompt that fills all five behaves the same way every time you run it. A prompt that fills two of them behaves differently depending on the mood of the sentence you happened to type.

The verification slot deserves the most attention, because it is the only one that catches the model being wrong. Asking for a number gets you a number. Asking for the number and the arithmetic that produced it gets you something you can audit in about fifteen seconds.

Work through a debit spread to see why that matters. Suppose XYZ trades at $100, and you are pricing a bull call spread: buy the $100 call at $6.00, sell the $110 call at $2.50. The net debit is $6.00 minus $2.50, or $3.50 per share. FINRA states that a standard-size options contract is equal to 100 shares of the underlying security, so the cash outlay is $3.50 times 100, or $350 per spread.

From there the rest follows in three steps:

1. Maximum loss is the debit paid, $350, which happens if XYZ finishes at or below $100 and both calls expire worthless.
2. Maximum gain is the distance between strikes minus the debit: $110 minus $100 is $10.00, minus the $3.50 debit is $6.50 per share, or $650 per spread.
3. Breakeven is the long strike plus the debit: $100 plus $3.50, so XYZ must finish above $103.50 at expiration.

Now the payoff. Run those example inputs through a prompt that demands the arithmetic, and the two most common model errors become visible instantly. Suppose it returns $1,000 as the maximum gain: it used the strike width and forgot to subtract the debit. Suppose instead it returns $6.50: it did the per-share math correctly and forgot the multiplier. Both are wrong by an amount that would matter, and both stay invisible if you ask only for the total.

The Seven Prompts Worth Saving

Each prompt below is written to be pasted as is, with the bracketed parts replaced. They are deliberately blunt, because politeness in a prompt tends to invite hedging, and hedging is what you are trying to eliminate.

Prompt 1: The Position Decoder

Use it before you open anything, to check your own understanding of the structure against a second reading.

Act as an options analyst. I am pasting one position below, exactly as my broker shows it. Do not add legs, change strikes, or assume any price I have not given you. Return a table with five rows: net debit or credit per spread, maximum loss, maximum gain, breakeven price or prices, and the assignment risk on any short leg. Show the arithmetic for every row on its own line, including the contract multiplier. If an input you need is missing, list what is missing and stop rather than guessing. Position:

Check the multiplier line first. If it is absent, the rest of the table is unverified.

Prompt 2: The Structure Comparison

Use it when you have a view and are unsure which structure expresses it most cheaply.

I have one thesis and I want to see it expressed three ways. Thesis: [state it in one sentence, including the move you expect and over what period]. Underlying price: [price]. Compare a [structure A], a [structure B], and a [structure C] built on that thesis. Return one table with a row per structure and these columns: maximum loss, maximum gain, breakeven, and the single market condition that hurts it most. Use only the prices I supply. Do not tell me which one to take and do not rank them. Finish with one sentence naming the assumption all three share.

The closing sentence is the useful part. Three structures that share one hidden assumption are one bet, not three.

Prompt 3: The Red Team

Use it on any position you feel confident about, which is exactly when the check is worth the most.

Here is a position I am considering. Argue against it. List the five most likely ways it loses money, ordered by how likely each is rather than how severe. For each, name the specific market condition that triggers it and roughly where it starts to hurt. Do not reassure me, do not restate the bull case, and do not suggest an alternative trade. Finish with the one question I should be able to answer before opening it. Position:

Prompt 4: The Greeks Translator

Use it when you know the options greeks as definitions but not yet as consequences.

Translate the following position greeks into plain English consequences. For each of delta, gamma, theta, and vega, give me one sentence in the form "if X changes by Y, this position gains or loses roughly Z", using the values I supply and stating the units. Flag any greek whose effect accelerates or changes sign as expiration approaches. Do not explain what the greeks are in general terms. Position greeks:

Forcing the "if X then Z" form is what stops the answer collapsing into a textbook definition you already had.

Prompt 5: The Event Decomposition

Use it ahead of a scheduled event, to see how much of what you are paying is for the event itself.

I am looking at a position around a scheduled event. Separate the two bets I am making: the bet on direction, and the bet on the change in implied volatility. Using the implied volatility figures I supply for the front expiration and a later expiration, estimate how much of the current premium is attributable to the event, and state what happens to that portion once the event passes regardless of which way the underlying moves. State every assumption you make. Do not predict the outcome of the event. Inputs:

Prompt 6: The Journal Debrief

Use it monthly, against a real export from your trading journal.

Below are my last [n] closed trades with entry reason, structure, holding period, and result. Find the patterns, not the story. Report: the setup appearing most often in losses, the setup appearing most often in wins, the average holding period of winners versus losers, and any rule I appear to state but not follow. Use only what is in the data. Do not encourage me, do not congratulate me, and do not suggest new strategies. If the sample is too small to support a claim, say so. Trades:

The "rule I state but do not follow" line surfaces more than the win rate does, because it compares your stated process against your actual fills.

Prompt 7: The Rule Formalizer

Use it when a rule lives in your head but has never survived being written down.

I am going to describe a trading rule in vague language. Rewrite it as a specification precise enough that two people applying it to the same chart would take the same action. Ask me for any threshold I left undefined instead of choosing one for me. Return the rule as numbered conditions, then list every ambiguity you had to resolve and how you resolved it. Do not evaluate whether the rule is any good. My rule:

How Prompting Differs From Screening

Traders most often confuse a saved prompt with a screener, because both feel like asking software a question. They are different instruments, and mixing them up is where most of the damage happens.

A screener queries a live database and returns rows that satisfy your filters. A prompt asks a language model to reason over text you provided. The distinction shows up across four dimensions:

In practice this means the two belong at different points in the process. Screening comes first and narrows the universe. Prompting comes second, once you already have a specific structure in front of you and want it stress tested. Running it the other way around, asking a model to surface candidates, is where invented tickers and stale prices enter the workflow. If you want the model closer to real data, the fix is to paste the data: learning to read an options chain well enough to excerpt the relevant rows is more useful here than any prompt.

Why It Matters to Traders

The concrete gain is that a structured prompt makes your own reasoning inspectable. Most losing options trades are not the result of a bad forecast, they are the result of a structure the trader did not fully understand at the moment of entry. A prompt that forces max loss, breakeven, and assignment risk into a table catches that gap before the fill rather than after it.

The second gain is comparability. Because the output shape is fixed, two positions analysed a month apart produce answers you can lay side by side. Free-form questions produce free-form answers, and free-form answers cannot be compared, which quietly removes any chance of learning from the sequence.

A saved prompt is not a question you ask once. It is a fixed procedure you run against changing inputs, which is why the structure matters more than the wording.

There is also a defensive reason to keep the model on the analysis side of the line. FINRA's Regulatory Notice 24-09, published in June 2024, reminds member firms that existing rules apply unchanged when they adopt generative AI, and points to accuracy and reliability as live concerns to be addressed through testing and governance. That is guidance aimed at firms rather than individuals, but the underlying logic transfers cleanly: the tool does not inherit authority from being called AI.

Edge Cases and Gotchas

It invents chains. Ask about strikes you did not supply and you will usually get strikes that look right and do not exist. The failure is quiet, because invented strikes are formatted exactly like real ones.

Multi-leg arithmetic drifts. Single-leg math is usually reliable. Four-leg structures with unequal widths are where the multiplier gets dropped or a credit gets counted twice, which is the specific reason the verification slot exists.

Adjusted contracts break the default assumption. After a split, merger, or special dividend, an options contract may no longer deliver 100 shares of a single security. A model reasoning from the standard multiplier will produce confidently wrong numbers on an adjusted contract, and nothing in its answer will signal that it made an assumption at all.

Specifications go stale. Training data has a cutoff, and exchange hours, product specs, and tax treatment all change. Anything the model states as a rule rather than as arithmetic should be confirmed against the primary source before you rely on it. FINRA maintains that its rules are technology neutral and continue to apply when firms use these tools, so the obligation to check does not move because the answer arrived from a model.

The label is not a credential. In March 2024 the SEC charged two investment advisers with making false and misleading statements about their use of artificial intelligence, and both were censured and ordered to cease and desist. Whatever a tool is called, it is not registered, it does not know your circumstances, and it cannot assess suitability. Treat every output as a draft to verify, never as advice to act on.

Frequently Asked Questions

Sources

• Artificial Intelligence (AI) in the Securities Industry, FINRA