URL: https://quantcodedenny.com/posts/ai-prompts-covered-options

This master prompt covers all scenarios: Covered Call, Cash-Secured Put, and Weekly Reflection. Use slash commands to select the workflow.

Principles

1. Respect long-term ownership as a guiding principle when suggesting adjustments.
2. Avoid trades triggered by earnings, major news, or correlated sector risk.
3. Minimize assignment probability while capturing reasonable premium.
4. Make reflection and tracking spreadsheet-friendly.

/covered_call

You are my covered call co-pilot. Minimal Input:

• Ticker: [STOCK_TICKER]
• Strike: optional (AI will only suggest if missing or violating rules; otherwise keep user strike)
• Goal: “income”
• Current price: inferred automatically
• Shares: full 100-share lot
• Expiry: next-next Friday (7–14d)

Covered Call Rules

URL: https://quantcodedenny.com/posts/llm-trade-common-scenarios/

Use AI to plan smarter trading decisions — systematically, calmly, and without emotional bias.

This post shows structured prompts to improve weekly options strategies, day trading checks, and long-term signal monitoring.

• /cashsecuredput Plan a weekly cash-secured put on RKLB with strikes balancing premium and assignment risk
• /coveredcall Plan a weekly covered call on RKLB with strikes minimizing assignment risk
• /guardrail I plan to buy QQQ at $550
• /tradeanalysis MSFT dip this week / Day trading QQQ today
• /earlyalerts Reddit in tech/social sector

Commands & Workflows

/coveredcall → Weekly Covered Call Planning

Plan a weekly covered call to generate income while controlling assignment risk.

URL: https://quantcodedenny.com/posts/llm-stock-sentiment/

0 Intro

This tool empowers engineers to automate stock sentiment analysis with precision and speed.

It combines two core capabilities: parsing recent news headlines to extract market sentiment and insights, and parsing SEC filings to surface key financial and risk information. Both streams are fed into a configurable LLM pipeline, allowing you to run fast local tests with lightweight models or perform high-accuracy production analysis. Designed for modularity and reuse, it integrates seamlessly into your workflows—turning raw data into actionable insights without manual reading.