Options Playbook
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Methodology
How market structure, expiration, volatility pricing, chain quality, and payoff precision shape a validated strategy comparison.
An options structure cannot be evaluated in the abstract. Its usefulness depends on the underlying, the exact expiration, the market's volatility pricing, the quality of the available contracts, and the job the payoff is expected to perform.
Exact-expiration analysis
The same ticker can support different structures at different dates. A near-term expiration emphasizes the immediate price path and rapid time decay. A longer horizon gives the thesis more time, shifts attention toward weekly structure, and can carry a different volatility regime. Earnings may be central to one date and irrelevant to the next.
Liquidity changes too. Quotes, spreads, volume, open interest, and the availability of useful strikes belong to a particular option series. The methodology therefore treats ticker plus exact expiration as the unit of analysis. It does not calculate a generic ranking for the stock and reuse it across the calendar.
The evidence model
The final fit value summarizes five bounded components. It is a comparison inside the current setup—not a probability of profit, expected return, or promise that a trade will work.
Daily and weekly bars describe trend, strength, extension, recent range, RSI pressure, and any prepared reversal pattern. The selected horizon determines whether daily or weekly structure should carry more weight.
This dimension asks whether the payoff shape agrees with what price is doing. A continuation structure, a mean-reversion target, and an open-ended movement trade should not receive the same interpretation of the chart.
Calendar days to expiration establish the strategy horizon, while known earnings context determines whether a major company event occurs before the selected date. For ETFs, company earnings are omitted rather than represented as missing.
This dimension asks whether the structure has enough time for its thesis and whether its time-decay or event exposure is coherent. A far-dated expiration is not treated as a stretched version of a short trade.
Internally calculated implied volatility is compared with relevant realized volatility. Expected move frames what the option market is pricing, and skew shows whether downside or upside options carry the richer relative premium.
This dimension asks what volatility exposure the structure buys or sells and whether that exposure is expensive, cheap, or inconclusive in the available facts. Missing volatility data is never converted into favorable evidence.
Bid/ask width, quoted contracts, volume, open interest, and coverage across the working strike zone show whether the selected series offers credible building blocks. Values are tied to timestamps and freshness metadata.
This dimension asks whether the structure can be researched on a usable chain. Thin or incomplete data lowers confidence; it does not become a false zero or a silent assumption of liquidity.
Every catalog entry has defined legs, profit and loss zones, volatility bias, time bias, and account requirements. These structural facts are fixed before the AI sees the market context.
This dimension asks whether the payoff directly expresses the thesis. A broad range, a narrow target, convex movement, directional income, stock replacement, and protection solve different problems even when their market labels overlap.
1 / Prepare
Market data is normalized first. Implied volatility and Greeks are calculated internally from the selected-expiration quotes, underlying price, time, rates, and dividend assumptions. Daily and weekly context is assembled before the ranking request begins.
2 / Compare
The AI receives the prepared facts and the curated single-expiration strategy library. It compares structures only inside that contract and returns five candidates with component evidence and concise explanations.
3 / Validate
The response must contain five unique known strategies, valid component bounds, and evidence references that exist in the prepared input. The server computes the visible total and rejects an incomplete result.
How to read the result
A higher fit value means the structure aligns more closely with the current evidence and product methodology than the alternatives shown below it. It does not predict the future, estimate the chance of profit, or tell the user how much to risk.
The explanation is intentionally short, but the Fit view exposes the existing component breakdown and cited facts. Opening that view is local: it does not trigger another market-data request or another AI call.
Facts carry source and freshness information. Missing values remain missing. If the required market context cannot be prepared or the AI response violates the output contract, the analysis fails rather than presenting an improvised fallback ranking.
The methodology stops at structure comparison. It does not choose exact contracts, calculate a personal position size, account for an individual's portfolio or taxes, route an order, or monitor a live position. Those decisions require separate judgment and tools.