Derivatives Intelligence
Options force an autonomous agent to reason about direction, magnitude, time, and volatility simultaneously. The architecture must represent nonlinear consequences before it can select a structure intelligently.
Eight components define an advanced options-agent system, from volatility interpretation through scenario-based risk. Their broader design principles can be studied through stateful simulations and inspectable decision records.
Classify implied and realized volatility as low, elevated, expanding, or contracting before selecting a structure. The model should expose its lookback, term structure, and event assumptions. QuantLib provides open-source derivatives analytics that can support the calculations. The language agent uses the typed result as evidence and should not infer a volatility regime from option names alone.
Represent expected price ranges, alternative outcomes, probabilities, and catalysts rather than reducing every view to up or down. The thesis should state which part of the distribution creates value and what evidence invalidates it. QuantLib offers pricing and risk foundations, while the agent layer explains why a particular distributional assumption fits the available market evidence.
Maintain delta, gamma, theta, vega, and relevant cross-position exposure as authoritative portfolio fields. Refresh them when price, volatility, or time changes instead of asking the model to remember prior values. NautilusTrader includes options and Greeks-related capabilities in its trading architecture. The final agent rationale should describe how the proposed structure changes total portfolio sensitivities.
Map the directional, volatility, and time thesis to a bounded list of candidate structures and compare payoff, maximum loss, break-even points, capital use, and assignment risk. Deterministic code should calculate the geometry. QuantLib can support valuation. The language agent chooses among validated candidates and must explain why the structure improves upon a simpler underlying position.
Align expiration with the expected catalyst window, time needed for the thesis to develop, and the decay profile of the structure. Reject contracts whose remaining life leaves no margin for timing error. Cboe's options education materials document core expiration concepts. The agent should state the planned review and exit dates before entering the position.
Reject structures when bid-ask spreads, depth, open interest, leg count, or market conditions make the theoretical payoff unrealistic to capture. Execution assumptions should be explicit and conservative. NautilusTrader offers detailed event-driven order and market models for advanced implementation. The agent cannot override this filter merely because its thesis has high confidence.
Revalue the complete options portfolio across price, volatility, and time shocks, including adverse combinations rather than one expected path. Report maximum loss, nonlinear exposure changes, and concentration by catalyst. QuantLib supplies pricing and scenario-analysis building blocks. Preserve the shock grid and outputs with the decision so later risk review does not depend on the model's prose summary.
Give a separate reviewer the thesis, candidate structure, Greeks, liquidity, scenario losses, and a simpler alternative. Ask whether complexity materially improves the payoff or only adds hidden assumptions. The reviewer returns one bounded objection and cannot place orders. Record whether the critique changed the approved structure, size, or expiration so its contribution remains measurable.
An options agent should make every assumption about direction, timing, volatility, and loss structure inspectable. BotTrade's benchmark philosophy offers the right foundation: define the environment, preserve state, compare policies, and study the complete decision record.