1 Actionable AI Prompt

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Amazon reported earnings on October 30th after the bell.

The next day, the stock ripped +10%.

I made money. And here's the kicker - with the help of AI to structure complex options around earnings, I would've made money if it tanked too.

My thesis going in:

I'd been talking to AI engineers building on AWS. Demand was insane. Amazon had system-wide outages from usage spikes. The AI infrastructure buildout was real.

Meanwhile, the market was pricing Amazon like cloud growth was over. Too bearish given these tailwinds.

I had high conviction Amazon would beat. But I'm not an idiot - earnings are binary events and anything can happen.

So I needed a structure where I win if I'm right AND I win if I'm wrong.

Most retail traders buy naked calls into earnings. They pay inflated premiums. They fight time decay (every day the option loses value). Then implied volatility crush reduces returns the second results print - even if they're directionally right.

I did the opposite.

I used AI to engineer an options backspread with defined payouts across multiple scenarios.

Here's what that structure delivered:

→ Made a lot more $$ when Amazon rips from convexity structure
→ Made money if Amazon tanked on earnings from selling "expensive" in the money options
→ Maximum loss capped and defined before entry
→ Protected from IV crush through selling calls
→ Capital efficient (small risk for large notional exposure)

This is how professionals express conviction: asymmetric structure, not hope.

The problem? Designing ratio spreads, calculating optimal strike selection, and mapping scenario payoffs used to require years of options expertise.

I'm going to show you how AI helped me structure this trade in minutes - and why this is the edge retail finally has access to.

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The Prompt

Use ChatGPT Thinking Mode. And fill in your own variables around portfolio size and maximum risk per trade here.

Prompt 1:

Role: You are my options strategist and risk manager.

Task: Help me structure, size, and analyze an earnings trade on Amazon. My view is the company will meaningfully beat expectations and the stock could gap higher post-print.

Inputs:

• Ticker: AMZN

• Event: Earnings (Oct 31 weekly)

• Account size: $1,000,000

• Max risk per trade: 1%

• Thesis: large upside surprise, >8–10% potential move

• Target structure: consider verticals, ratio spreads, backspreads

What to produce:

Recommend 2–3 structures and explain why

For each, show strikes, expiries, quantity, expected debit/credit

Map payoff at expiration: −5%, flat, +5%, +10%, +15%, +20% move

Show max loss and upside scenarios

Explain Greek profile in simple terms (delta, gamma, theta, vega)

Give a plain-English risk checklist and exit plan

Format:

• Table for payoff and sizing

• Bullet points for logic and risk controls

• Short summary at the end

Tone rules:

• Direct, hedge-fund style clarity

• No fluff, no hype

• Explain like you're teaching a smart investor

The Result

Full output here: Link

AI gave me three structure options. I went with the backspread play (option B in the table below).

Here’s what the backspread delivered:

The Setup:

- 1×2 Call Backspread (short 220 C, long 2× 230 C, Oct 31 expiry)

- Received credit on entry (you get paid to put the trade on)

- Maximum loss defined and capped before I clicked submit

- Two breakeven points: one on the downside, one on the upside

Why this structure wins:

If Amazon tanks on earnings → I keep the credit. Small profit.

If Amazon stays flat → I keep the credit. Small profit.

If Amazon gaps up 5-8% → I take a small loss (the worst-case zone, near $230).

If Amazon rips 10%+ → The trade goes convex. Unlimited upside as the two long calls accelerate.

This is the edge most retail traders never access.

They buy naked calls and pray for a gap up. If it doesn’t happen, they lose everything.

I engineered a structure (using AI!) where multiple outcomes pay me. The only lose scenario was a moderate 5-8% gap - and even that loss was capped and defined before entry.

Amazon gapped +10%. The long calls printed.

But here’s what matters more: the structure worked exactly as designed.

AI didn’t just spit out strikes. It modeled the Greeks. It showed me scenario payoffs. It explained why theta was slightly positive (the short call funded the longs). It showed me how vega exposure would behave post-earnings.

Most importantly - it forced discipline.

Max loss capped. Risk budget respected. No guessing. No emotional entries.

This is how professionals think. Thesis first. Structure second. Risk budget third. Then execute.

AI compressed what used to take hours of scenario modeling into a 5-minute conversation. I got payoff tables. Greek exposure. Breakeven points. Entry instructions.

The takeaway:

If you trade options into earnings and you’re not using AI to structure trades, you’re fighting professionals with your bare hands.

You don’t need to be an expert. But you do need process.

AI gives you that process instantly.

Taking Community Requests:

I am going to spend time this week crafting custom AI workflows and prompts from our community here.

If there are any workflows or techniques you have in mind, I'll do my best to make it possible. You might even get featured in the "more reads & resources" section of the newsletter :)

Submit your request at the below link....

Survey Link: Link

If you liked this, more reads & resources:

How to climb the AI ladder in finance: A 4-level framework

Peter Lynch ChatGPT Prompts

AI + Options: Beating Amazon Earnings