I Asked ChatGPT to Beat My Options Trade. It Picked a $11,000 Strategy. Mine Cost $828.

Key Concepts

• Options Trading: Financial derivatives that give the right to buy or sell an asset at a specific price.
• Implied Volatility (IV) Rank: A metric measuring the current level of implied volatility relative to its historical range; a high IV rank suggests options are relatively expensive.
• Short Put Spread: A credit strategy involving selling a put at a higher strike and buying a put at a lower strike to collect a premium.
• Call Debit Spread: A strategy involving buying a call at a lower strike and selling a call at a higher strike to profit from upward movement.
• LEAPS (Long-Term Equity Anticipation Securities): Options contracts with expiration dates longer than one year.
• Delta: A measure of an option's price sensitivity to changes in the underlying asset's price.
• Extrinsic Value: The portion of an option's premium that is not intrinsic value, representing time value and volatility.

1. The AI vs. Human Trading Challenge

The presenter, a veteran of the tastytrade platform, initiates a comparative study to determine if AI (specifically ChatGPT) can effectively formulate options trading strategies compared to an experienced human trader. The goal is to evaluate whether AI can provide actionable, cost-effective, and specific trade recommendations for Microsoft (MSFT).

2. Human Strategy: The "Super Bull" Approach

The presenter analyzes Microsoft, noting an IV Rank of 102 and an upcoming earnings event. His proposed strategy is a synthetic long position designed to be capital-efficient:

• Methodology: Selling a 420/410 put spread to finance the purchase of a 475/490 call debit spread.
• Financials:
  • Capital Required: $828.
  • Max Profit: $1,678.
  • Risk/Reward: Approximately 2:1 ratio.
  • Probability of Profit (POP): 53%.
• Rationale: This structure utilizes positive extrinsic value and provides a defined-risk way to get long on the stock while keeping capital outlay significantly lower than buying shares or deep-in-the-money (ITM) calls.

3. AI Strategy: ChatGPT Analysis

When prompted to provide a strategy to get long on Microsoft, ChatGPT’s performance was evaluated based on specificity and cost-efficiency:

• Initial Recommendations: The AI suggested deep ITM LEAPS (70–90 delta) and call debit spreads.
• Critique of AI Output:
  • Lack of Precision: The AI initially provided broad ranges (e.g., 70–90 delta) rather than specific strike prices.
  • Capital Intensity: The AI’s preferred strategy (deep ITM LEAPS) required between $11,000 and $21,000 in capital.
  • Actionability: While the AI correctly identified standard strategies (selling cash-secured puts, avoiding out-of-the-money "lottery ticket" calls), it failed to provide a nuanced, capital-efficient structure comparable to the presenter's spread-based approach.

4. Comparative Synthesis

The presenter concludes that for the current market environment, the human-led strategy is superior due to:

• Capital Efficiency: The human strategy required only $828 compared to the AI’s $11,000+ requirement.
• Strategic Specificity: The human trader provided exact strikes and a defined risk/reward profile, whereas the AI provided generalized textbook advice.

Notable Quote:

"I'm already cheaper... I'm going to say I win. And we're going to try this with more AI agents in the future. But for now, tastytrade won. Chat GPT zero."

Conclusion

The experiment highlights a significant gap between AI’s ability to retrieve general financial theory and a human trader's ability to synthesize market conditions (like high IV rank and earnings volatility) into a specific, cost-effective trade. While AI serves as a useful tool for identifying basic strategies, it currently lacks the tactical precision required to manage capital efficiency in complex options trading.