Options Data in ChatGPT: Build an AI Options Analysis Workflow in Excel

Options data in ChatGPT is what you are really after when you want an AI assistant to do more than describe options and instead run the same disciplined workflow a professional runs in a spreadsheet. You do not want the model to guess at an implied volatility figure, invent a put-call ratio, or hand you a strike ladder that looks plausible but was never verified. You want the AI to screen a watchlist, size the move the options market is pricing, pick a strike by probability, and score an income idea, with every number coming from a licensed source you can audit. This guide shows how to turn options data in ChatGPT into a repeatable, four-step workflow, and it includes two free Excel templates so you can see every formula working.

The single idea to take away: the value of asking ChatGPT about options is not "AI plus a data feed." It is one source of truth. When the same MarketXLS Model Context Protocol (MCP) functions power both the chat answer and your Excel cell, the number the assistant reads back to you and the number in your workbook are not two estimates that happen to agree. They are one calculation surfaced in two places.

Options data in ChatGPT: raw model vs. a licensed workflow

Here is the gap this workflow closes. A general model is excellent at language and poor at being the system of record for a number that changes every second. The table below shows what you typically get from a raw model against what you get when ChatGPT calls the MarketXLS MCP primitives that also refresh the attached templates.

The pattern is consistent. The model handles the conversation; MarketXLS handles the math and the data. That division of labor is the whole point of connecting options data to an AI assistant.

What "options data in ChatGPT" actually means

Model Context Protocol is an open standard that lets an AI assistant call external tools in a structured, predictable way. The MarketXLS MCP server publishes a catalog of options functions as MCP tools. When you connect it to ChatGPT or any MCP-aware assistant, the model can call those tools by name, pass a ticker, an expiry, and a strike, and receive a clean, typed result it can reason about.

That means a request like "screen my watchlist for the highest IV rank and show me the expected move on the top name" no longer produces a creative-writing exercise. The assistant recognizes the intent, calls real functions such as ImpliedVolatilityRank1y("NVDA") and ImpliedVolatility30d("NVDA"), and reports the licensed values. The important part is the source: those functions are the exact ones that power MarketXLS in Excel, fed by enterprise-grade market data. So the analysis ChatGPT gives you and the analysis your workbook calculates are the same calculation.

Why this is different from a generic finance plugin

Most "AI plus finance" setups bolt a model onto a raw quote feed and ask the model to do the analytics. That is exactly where errors creep in. A model improvising an expected move will silently use the wrong volatility input or the wrong day-count. A model estimating a probability of profit will use whatever assumption it reached for in that conversation. The MarketXLS approach keeps the analytics on the licensed side. The model never computes delta or implied volatility itself; it requests them from functions that always use the same convention. The unique capability is not that an AI can talk about options. It is that the options data never leaves the deterministic pipeline traders already trust in their spreadsheets.

The four-step AI options workflow

You do not need hundreds of functions to do serious options work with an assistant. You need a tight sequence: screen, size, select, and score. Each step below maps to a sheet in the attached templates and to a small set of real MarketXLS functions the assistant can call through MCP.

Step 1: Screen the watchlist

Start broad. Ask the assistant to rank your optionable names by how rich their options are and how the flow is leaning. The screener sheet in the template pulls these fields for every ticker:

=QM_Last("AAPL")                    ' live underlying price
=ImpliedVolatility30d("AAPL")       ' 30-day implied volatility
=ImpliedVolatilityRank1y("AAPL")    ' where IV sits in its 1-year range (0-100)
=opt_TotalVolumeOptions("AAPL")     ' total options volume today
=opt_TotalOpenInterestOptions("AAPL") ' total open interest
=opt_PutCallVolRatio("AAPL")        ' put-call volume ratio (sentiment)
=opt_PutCallOIRatio("AAPL")         ' put-call open-interest ratio

IV rank is the anchor. A high IV rank means options are relatively expensive versus the last year, which favors premium-selling ideas; a low IV rank favors premium-buying ideas. The put-call volume ratio adds a directional tilt: below 1.0 is call-heavy, above 1.0 is put-heavy. The template blends IV rank, the volume-to-open-interest ratio, and put-call positioning into a single 0 to 100 Signal Score so you can sort the list the way an assistant would rank candidates for you. The Signal Score is a convenience ranking, not a trade signal.

Step 2: Size the expected move

Once you have a candidate, ask "how far can this move by expiry?" The expected move is one standard deviation of the range the options market is pricing between now and expiration. The math is straightforward and uses two live inputs:

Expected 1-sigma move ($) = QM_Last("AAPL") * ImpliedVolatility30d("AAPL") * SQRT(DTE/365)

If AAPL trades at 223.80 with 30-day IV of 22.4% and 43 days to expiration, the one-sigma move is roughly plus or minus 18 dollars. That gives you an upper and lower one-sigma band (about a 68% chance of finishing inside it) and a two-sigma band (about 95%). This is the single most useful number an options trader can ask an AI for, because it frames every strike decision that follows. The Expected Move sheet builds the bands automatically and updates the moment IV or price changes.

Step 3: Select a strike by probability

Now translate the move into a strike. The strike-selector ladder shows, for each strike, its distance in dollars, its distance in sigmas, an approximate probability of finishing in the money, and the live contract delta:

=OptionSymbol("AAPL",DATE(2026,8,21),"Call",245) ' build the contract symbol
=opt_Delta(QM_Last("AAPL"),QM_Last(OptionSymbol("AAPL",DATE(2026,8,21),"Call",245)),DATE(2026,8,21),"Call",245)

Delta is doing double duty here. It measures how much the option price changes for a one-dollar move in the underlying, and it also serves as a rough market-implied probability that the option finishes in the money. A 0.30-delta call sits near the one-sigma out-of-the-money strike, which is why so many covered-call and cash-secured-put frameworks target it. Asking ChatGPT for "the 30-delta call on AAPL for the August expiry" now returns a real strike from the licensed chain rather than a guess.

Step 4: Score the income idea

Finally, turn the selected strike into dollars and cents. The Income Strategy sheet takes your account size and estimates the premium yield of a covered call or cash-secured put per name, annualized, so you can compare candidates on one screen:

=QM_Last(OptionSymbol("AAPL",DATE(2026,8,21),"Call",245)) ' premium collected
Premium yield = Premium / Share price
Annualized    = Premium yield * 365 / DTE

This is educational analysis, not a recommendation. It is pre-commission, assumes the position is held to expiry, and ignores early assignment and margin nuances. But it lets you and the assistant have a grounded conversation about which idea offers the best risk-adjusted premium, using one licensed source for every input.

The options primitives that matter

The whole workflow rests on a compact set of MarketXLS functions. Each one below is verified in the MarketXLS function catalog, and each is exposed as an MCP tool the assistant can call directly.

The last function is worth calling out. QM_GetOptionQuotesAndGreeks returns a spill-array table of the entire chain with strikes, quotes, implied volatility, and the full Greek set in one call. It is the assistant's one-shot primitive when you ask for "the whole chain," and it is the same function that populates the Live Option Chain sheet in the template.

The templates: what is inside

Both workbooks share an eight-sheet structure. The Sample workbook is pre-filled with an illustrative snapshot captured on the data date so you can read the layout offline. The Template workbook is entirely live MarketXLS formulas that refresh on open and recalculation.

• Cover and How To Use orient you and explain each sheet.
• Inputs holds the only cells you edit (highlighted yellow with a gold border): focus ticker, expiry date, days to expiration, risk-free rate, account size, minimum IV-rank filter, and a target delta. Every other sheet reads from here.
• AI Options Screener ranks the watchlist by 30-day IV, IV rank, volume, open interest, put-call ratios, and the blended Signal Score, with color scales and charts.
• Expected Move & Strike Selector turns 30-day IV into a one-sigma dollar move, plots the one and two-sigma bands, and builds a strike ladder with distance in sigmas, an approximate probability of finishing in the money, and live deltas.
• Live Option Chain builds an at-the-money ladder for the focus ticker: calls on the left, puts on the right, sharing a center strike column, each cell a live MarketXLS option formula.
• Income Strategy estimates covered-call or cash-secured-put premium yield, annualized and sized to your account.
• Methodology & Glossary documents the data sources, the MCP model, the assumptions behind the illustrative Greeks, the glossary, and the disclaimer.

Every sheet includes a "MarketXLS Functions Used" box so you always know which formula powers which value, whether you are reading it in Excel or asking for it in ChatGPT.

Download the templates:

• - Pre-filled with an illustrative snapshot so you can read the structure offline
• - Live-updating formulas that refresh with MarketXLS

How the AI and the spreadsheet stay in sync

The reason the chat answer and the cell agree is that neither one improvises. When you ask ChatGPT for the expected move on AAPL, the MCP server calls QM_Last and ImpliedVolatility30d and does the same square-root-of-time arithmetic the workbook does. When you open the workbook, those same functions recalculate. There is no reconciliation step and no "which number is right" moment, because there is only one number.

This matters most when you move between surfaces during a single decision. You might screen on your phone by asking the assistant for the three highest IV-rank names, then open the workbook at your desk to build the strike ladder, then go back to the assistant to sanity-check the covered-call yield. Because every step reads the same licensed feed, the analysis is continuous rather than a series of disconnected estimates. For a deeper look at the underlying functions, the MarketXLS options tools page catalogs the full set, and the documentation walks through connecting the MCP server to an assistant.

A worked example, end to end

Suppose your assistant screens the watchlist and flags NVDA with a 1-year IV rank in the mid-50s and heavy call flow. You ask for the expected move: with the stock near 161 and 30-day IV around 44%, the one-sigma move to the August expiry is meaningful, so you know any strike inside roughly plus or minus one sigma carries real assignment risk. You ask for the 0.30-delta call, and the strike selector returns a specific out-of-the-money strike from the live chain. You ask what a covered call at that strike would yield, and the Income Strategy sheet prices the contract via QM_Last(OptionSymbol(...)), divides by the share price, and annualizes it. In four questions you have gone from a watchlist to a sized, probability-aware idea, and every number is auditable in the workbook. That is the difference between an AI that talks about options and an AI that runs an options workflow.

FAQ

Can ChatGPT really pull live options data? On its own, a language model cannot fetch live market data reliably. Connected to the MarketXLS MCP server, it can call licensed functions such as QM_GetOptionQuotesAndGreeks and ImpliedVolatility30d and return real values. The MCP server is what turns "options data in ChatGPT" from a description into a data call.

What is the difference between IV and IV rank? ImpliedVolatility30d returns the raw 30-day implied volatility as a decimal. ImpliedVolatilityRank1y returns where that figure sits within its own one-year range on a 0 to 100 scale. A stock can have a high absolute IV but a low IV rank if its volatility is usually even higher. IV rank is the better screen for whether options are relatively rich or cheap.

How is the expected move calculated? Expected 1-sigma move equals the underlying price times 30-day implied volatility times the square root of time to expiry in years. It represents about a 68% probability that the stock finishes within the plus or minus one-sigma band. The template computes it live from QM_Last and ImpliedVolatility30d.

Why use delta to pick a strike? Delta measures the option's price sensitivity to the underlying and doubles as a rough market-implied probability that the option finishes in the money. A 0.30-delta option sits near the one-sigma out-of-the-money strike, which is why many income frameworks target it. The template reads delta from opt_Delta on the live chain.

Do the sample numbers update? The Sample workbook holds a fixed illustrative snapshot so it opens cleanly without a data connection. The Template workbook is all live MarketXLS formulas and refreshes when you open and recalculate it with MarketXLS installed.

Is this investment advice? No. The workbook and this article are educational. They show how to structure an options workflow and where the numbers come from. They are not recommendations to buy or sell any security or option, and options carry substantial risk. Verify all data independently and consult a licensed professional before trading.

The bottom line

Options data in ChatGPT is genuinely useful only when the AI stops improvising and starts calling a licensed, deterministic source. The MarketXLS MCP server does exactly that: it lets an assistant screen a watchlist by IV rank, size the expected move, select a strike by probability, and score an income idea, using the same functions that fill your Excel cells. The result is one analysis, not two, whether you read it in a chat window or a spreadsheet. Download the two templates above to see every formula working, and when you are ready to connect the workflow to your own assistant, explore MarketXLS or book a demo to see the options tools in action.