I've been running an experiment for months: asking 8 different AI models (GPT-4, Claude, Gemini, DeepSeek, Kimi, Grok, Mistral, Perplexity) the exact same investing questions.
One pattern kept emerging: every model, regardless of architecture, fell into one of 4 investing philosophies:
- Index school — diversify, minimize fees, let the market work
- Value school — find underpriced assets with a margin of safety
- Growth school — bet on high-growth compounders
- Trend school — follow price momentum and manage risk tightly
This got me thinking: most people using AI for investing are prompting blindly — their prompts don't match their actual philosophy. So they get generic answers.
What I built
A free browser-based toolkit: My AI Investment Operating System
Main tool: 5-question diagnostic that identifies your investing school and generates a personalized Prompt Library — prompts you can drop into Claude, GPT-4, or any AI today.
6 companion tools:
- DCA Simulator — model dollar-cost averaging scenarios with compound projections
- Recovery Navigator — calculate breakeven points and map exit strategies for losing positions
- Dividend Engine — screen dividend stocks through the lens of your investing school
- Kelly Master — apply Kelly Criterion to size positions correctly
- Pyramid Builder — plan staged entry positions at multiple price levels
- Portfolio Clarity — audit your current holdings against your school's criteria
Tech stack
Pure HTML/CSS/JS, no framework, no backend. Everything runs client-side. Hosted as static files on a WordPress site's /tools/ directory.
The AI committee logic uses a scoring matrix built from patterns across all 7 experiments — no live API calls, all logic is embedded.
Link
All free, no login: https://ordinarymantrying.com/tools/ai-invest-os.html
Would love feedback from developers who also invest — especially on the UX of the diagnostic flow.
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