OptionsMath AI is a thesis-to-options solver: a language model turns your market thesis into explicit, editable scenario assumptions, then a deterministic options engine prices every candidate structure across those scenarios and ranks them by modeled expected value under the assumptions you can see and change.
The ones worth asking first
Is this ChatGPT with an options prompt?
No. A general chat model can discuss a thesis and describe strategies in prose, but it produces option numbers by generating text that resembles option numbers. Here the language model is confined to one job: converting your thesis into a constrained scenario schema. Every strike, price, P&L, and ranking after that comes from a compiled options engine running calibrated stochastic pricing models over the live chain — the same code, the same way, every run.
What does the language model actually do?
It reads your thesis along with the ticker's live spot and as-of date, and emits a structured scenario tree: an objective plus one branch per outcome, each with a horizon, a move, a volatility shift, a skew shift, and a probability. Probabilities are normalized and clamped before the engine runs. If the model fails, a deterministic parser builds the tree instead — the analysis still completes.
Where do the option prices come from?
From the live option chain plus calibrated models. The engine fits an SSVI volatility surface and the structural models — Bates, Heston, Merton jump-diffusion, and Variance Gamma — to the chain, keeps only the models whose calibration error is within 4x the best fit, and prices each leg in each scenario from those. Where the surviving models disagree, the spread between them is recorded and charged against the candidate's score rather than hidden.
What does "best" mean in the ranking?
Highest-ranked under the scenarios and constraints shown — nothing more. The ranking is conditional on your assumptions: change a probability or a horizon and the order changes. It is not a claim that a structure is objectively superior, and it is not a forecast that any scenario will occur.
Can the math be right and the answer still be wrong?
Yes, and this is the honest limitation of the whole design. The engine calculates precisely under the assumptions it is given. If the scenario tree is wrong, you get a precisely calculated analysis of a world that does not happen. That is why the branches are shown and editable rather than buried — reviewing them is the part of the work that stays yours.
Does it place trades or tell me what to buy?
No. There is no brokerage connection and no order routing. Outputs are model-derived, hypothetical research artifacts for your own analysis — not investment advice, not personalized recommendations, and not a promise of any outcome.
Why doesn't it backtest, screen, or execute?
OptionsMath AI is deliberately narrow. It does one job — turning a market thesis into ranked option structures — and leaves the rest of your stack alone. It does not backtest, screen flow, chart, or execute. You already have tools you trust for those, and your existing brokerage — Robinhood, IBKR, Schwab, wherever you execute — is where orders belong. The narrow scope is what lets the solver do its one job properly.
Rankings are model-derived under the assumptions shown. They are hypothetical research outputs, not investment advice, recommendations, or promises of return. Longer treatments live in how the solver works and the methodology; the full research disclosures are on the disclaimer page.
Try it on your own view
Describe a move in one sentence. You will see the scenario tree before anything is priced.