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Thanks! I use several key prompting techniques: 1. Role + Goal Setting: The AI acts as a creative market analyst focused on discovering overlooked opportunitie
by sunnynagra 2y ago
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 2y agoWhat’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 2y agoI 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 2y agoMakes 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 2y agoSunny, given this investment objective, what would you consider a good (and transparent) benchmark? Thanks for sharing this.