Now, the same framework helps me with real estate: comparing neighborhoods, checking flood risk, weather patterns, school zones, old vs. new builds, etc. It’s a messy, multi-variable decision—which turns out to be a great use case for AI agents.
Instead of ChatGPT or Grok 4, I use mcp-agent, which lets me build a persistent, multi-agent system that pulls live data, remembers my preferences, and improves over time.
Key pieces: • Orchestrator: picks the right agent or tool for the job • EvaluatorOptimizer: rates and refines the results until they’re high quality • Elicitation: adds a human-in-the-loop when needed • MCP server: exposes everything via API so I can use it in Streamlit, CLI, or anywhere • Memory: stores preferences and outcomes for personalization
It’s modular, model-agnostic (works with GPT-4 or local models via Ollama), and shareable.
Let me know what you all think!
Backtesting would be more useful. Of course, LLMs cannot be backtested since they know the past.
This system is impossible to test. I would be hesitant to trust it.
Best metric here would be a Sharpe Ratio and drawdown details. https://en.wikipedia.org/wiki/Sharpe_ratio
> This system is impossible to test. I would be hesitant to trust it.
Disagree. Best test would be a paper test going forward, audited by a common platform.
In context of investing, what does "It worked well enough" translate to?
(Vanguard if you don't benefit from tax loss harvesting.)
Fidelity has no expense funds or low expense funds, but if they change their mind or the company becomes bad for you, you can't move it to another provider without being taxed.
If you will never leave Fidelity or if it's not taxable anyways (retirement) then it's fine, but if you think you might have to move assets then it's a potential risk unless other providers also support them.
Being trapped is not worth the slightly lower expenses IMO.
It’s symbolic only (no LLMs), designed for alignment auditing, law/policy frameworks, and decision explainability.
If anyone wants an example, I can post a breakdown here.
What actual trades were made by the user/creator? What was the ROI? How did profitability compare to their returns before using this tool?
With today's LLM's it's easy for anyone to generate a 20-page "report" with a analysis about investments. But a report that, when followed, actually gives you above-average returns? No one has shown evidence of that yet.