[sunnynagra]

Thanks! I use several key prompting techniques:

1. Role + Goal Setting: The AI acts as a creative market analyst focused on discovering overlooked opportunities and emerging trends.

2. Structured Analysis Framework: - Detailed evaluation criteria (innovation, moat, management, growth potential) - Sector diversity requirements - Focus on finding hidden gems vs obvious mega-cap tech stocks

3. Time-Bound Precision: Instead of vague "3-6 months" holding periods, I require exact hour calculations tied to specific catalysts like: - FDA approval dates - Earnings releases - Product launches - Conference presentations

4. Quality Controls: - Must be valid NYSE/NASDAQ symbols - Diverse across sectors/market caps - Conviction level scoring (1-10) - Each pick needs unique thesis + catalyst - JSON output format for consistency

The key is combining structured analysis with creative discovery - pushing the AI to look beyond obvious choices while maintaining some analytical rigor.

[thevilledev]

What’s the investment horizon for these daily decisions? Does it have a maximum hold time? How long will you run the experiment and is it enough to cover all the catalysts that are expected?

[sunnynagra]

I don't have a hard set maximum hold date, but planning on running at least buys for a year. I will re-evaluate consistently to see if it is still useful to keep up and running.

[datadrivenangel]

Makes sense. Any thoughts on expanding scope to have multiple 'analyst' roles per LLM model? Could be interesting to see if changing roles/prompts yields better results.

[tedd4u]

Sunny, given this investment objective, what would you consider a good (and transparent) benchmark? Thanks for sharing this.