The AI Options Strategist: How OptionsDeck Reads the Market and Structures a Trade

Generic chatbots can't trade. Ask one "should I buy NVDA calls" and you'll get a hedged non-answer with a 60% confidence number it pulled from nowhere. What's missing is context — the model has no idea where NVDA is in its trend, what IV is doing, where the dealer gamma sits, or whether unusual flow is going one way.

Step 1: Gather the operator's view

When you ask the OptionsDeck Strategist for an idea on a ticker, we don't pass the ticker symbol to the model and hope. We assemble a context bundle:

• Technicals: current spot, 1d/5d/20d returns, 52-week high/low position, RSI(14), MACD line/signal/histogram, ATR%, SMA(20/50/200) stack, trend classification, pattern flags (overbought, breakout momentum, near 52w high, etc.), and the top 5 pivot-cluster support and resistance levels.
• IV regime: IV rank, IV percentile, HV30, HV60.
• Dealer GEX: total dollar gamma, regime, gamma flip level, top 3 call walls and put walls.
• Recent flow: last 20 unusual options prints with aggressor classification.
• Your preferences: account size, risk per trade, allowed strategies, DTE bounds, directional bias.

Step 2: Compute the directional view ourselves

Before the model sees the context, we compute our own directional classifier — a weighted vote of trend, RSI position, MACD direction, SMA stack, GEX skew, and pattern flags. We pass both the raw context and our classifier output to the model, and the system prompt instructs it to usually agree with the classifier unless it has a strong contrary read. This anchors the model to real data instead of letting it freelance.

Step 3: Generate one structure

The model is given a list of allowed strategies (from your preferences), a regime-weighted prior over those strategies (long debits favored in low IV, credit spreads favored in high IV, neutrals like iron condors only when both IV and rangebound), and a strict JSON output schema. It returns:

• Direction (bullish / bearish / neutral / no_trade)
• Strategy (one of the allowed set)
• Thesis (4 sentences max, must cite specific levels)
• Legs (BTO/STO with strike + expiration)
• Entry / target / stop / timeframe
• Confidence (own estimate)
• Risk notes (what invalidates the trade)

Step 4: Snap to greek-optimal contracts

The model's strikes are suggestions, not orders. After parsing, our contract picker scans the full OptionsDeck Direct Feed chain at the suggested expiration and selects the contract that:

• Closest matches the target absolute delta for the leg role (0.40 for long debit, 0.25 for short premium)
• Has bid/ask spread below 30% of mid
• Has at least 25 open interest
• Maximizes a composite score weighting delta-fit, liquidity, and spread quality

The result: every leg is a real, fillable contract with attached greeks. We then aggregate position-level delta/gamma/theta/vega and surface it for the risk dashboard and portfolio greeks.

Step 5: Confidence and position sizing

We recompute confidence from the technical/flow/GEX alignment and blend it with the model's stated confidence. Anything that smells like a default (0.68 exactly) gets overwritten. Then the position sizer takes confidence + max-risk-per-trade and returns suggested contracts, Kelly fraction, and risk-of-ruin estimate.

The result: a trade you can read, defend, and act on

You get one idea. With a specific structure, specific contracts, a thesis that references real numbers, and a stop that's anchored to a real level. You can save it as a tracked play — which auto-arms price alerts at the target and stop — or paper-trade it first.

Try it on your favorite ticker. 7-day trial, no card. The AI Strategist runs on every plan from day one.