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Show HN: I built an AI agent that helps me invest (github.com)

32 points by haniehz ↗ HN
A while back, I built a simple app to track stocks. It pulled market data and generated daily reports based on my risk tolerance. Basically a personal investment assistant. It worked well enough that I kept going.

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!

12 comments

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axezing121321
Happy to elaborate on how ARC OS works — It parses subjective input into logic trees with assumptions, conflict checks, bias flags, and reasoning trails.

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.

pizzathyme
I think others are asking more about the real-world investment value of this, not the technical implementation.

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.

[dead] axezing121321
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axezing121321
Sorry, I think I replied in the wrong thread. My comment was meant for a different topic — feel free to ignore!
btbuildem
Sounds interesting -- can you share some real examples?

In context of investing, what does "It worked well enough" translate to?

poulpy123
Well the most important information is missing: what was your ROI ? :D
haniehz
I didn’t build this to beat the market...it started as a way to reduce my own decision fatigue and make sure I wasn’t missing obvious signals. That said, it’s more of a research agent than a trading bot. For ROI, it’s not auto-trading, but it has helped me avoid a few bad calls and made my reports more consistent and thorough.
65
Hate to be that guy but I'm not sure how this is any better or more reliable than something like FinViz or NeighborhoodScout.
haniehz
Great question and those are solid tools! Where this differs is the orchestration. Instead of switching tabs or manually checking five sources, this bundles everything into one interactive, LLM-assisted report, plus it remembers your preferences, investment style, and context. You can even swap out GPT for a local model if you’re privacy-conscious or budget-sensitive.
astrange
The ideal design for such an agent is that it would tell you to put all your money in a Betterment account, and then if you try to do anything other than that it should give you electric shocks until you stop.

(Vanguard if you don't benefit from tax loss harvesting.)

thomasrp
Is it possible to try your real estate AI agent? I've built a real estate data API (stream.estate) and would be very much interested to see how it works; maybe we can add some features to make that kind of project better.