I have some background in Python and AI engineering, some slight background in finance (UC berkeley executive education classes). AI engineering is more of my gig right now. I'm currently rag training and paper trading an open source system. "chunks" are the books and data i have used to train the system. I'm still building, I've only been on paper trade for 4 days, fixed a few bugs in the research phase last week.

For those of you building AI agent trading systems from scratch. What has worked? what has not worked? Just curious if i'm putting too much time, and energy into the wrong direction. If you're curious about the models i'm using, please ask; however they were chosen to run on my hardware, and i might try a few others as time goes on. Does anyone have better luck with C++, and Rust?

Edit: I made a new post with an updated high level overview.

Most AI tools today?

🧠 “Summarize this.”

💬 “Answer that.”

But someone quietly built an agent system that doesn’t just assist —

it thinks, argues, plans, and acts.

It’s called TradingAgents by Tauric Research.

And here’s what’s crazy:

It breaks trading down into roles, like a real hedge fund.

→ Market Analyst Agent scans prices, news, macro trends

→ Research Agent reads whitepapers, Twitter threads, reports

→ Sentiment Agent gauges social mood from Reddit/X

→ Bull vs Bear Agents argue for and against moves

→ Trader Agent listens, makes the call

→ Risk Manager Agent sets guardrails

→ Then it all gets executed in real time.

Not a fancy prompt chain.

Not another wrapper.

This is modular AI — with memory, roles, and goals.

And yeah, it runs with real trades.

Real stakes.

No human in the loop.

Why it matters?

This isn’t just about finance.

This is a glimpse at AI teams in action.

Now imagine this for:

✅ Support → triage agent, draft agent, review agent

✅ Marketing → ideation agent, content agent, performance agent

✅ Product ops → blocker agent, action agent, deploy agent

No bloated dashboards.

No busywork.

Just outcomes.

Have you guys used AI based tools where you can type your questions in natural language and get stocks? Like "Find me all large cap companies whose margins fall when oil prices go up". What has your experience been with such natural language screeners? or does the existing screeners such as one by yahoo finance and so on suffice? I have always felt like the manual screeners are inadequate to screen stocks based on more qualitative criteria's. Like say finding companies with significant revenue segment from AI, companies susceptible to copper prices or dependence on China and so on?

Many LLM trading bots die the moment you leave the US.

I built the opposite: a multi-agent system that screens small/mid-cap international value stocks (focusing on ones that are looking like they'll transition to growth). Motivation is personal worries over AI bubbles, US deficits and instability, and a desire to diversify more. The screener, in effect, incarnates my worries.

Hoping others try it out and help me refine it (link below).

Design:

• Bull/bear debate + validator agents (not just single prompts)

• Per-ticker memory isolation (vastly reduced cross-contamination)

• Fallback chain for the free/cheap data sources that randomly 404

• LangGraph + structured outputs + proper test suite

This is not an execution bot or a backtester. It's a research engine for evaluation tranches of ex-US equities (usually compiled into a screenable list, manually, using another AI).

MIT license, contribution-friendly, decent tests: https://github.com/rgoerwit/ai-investment-agent

Longer war-story (what broke and what worked):
https://medium.datadriveninvestor.com/building-an-open-source-agentic-ai-equity-research-tool-172783ed6961

I'd really like to know whether anyone else is looking for ways to identify and evaluate ex-US small and mid-cap GARP equities (ones that don't trigger PFIC reporting, aren't available via sponsored ADRs, and haven't been fully "discovered" by US analysts).

Has anybody implements LLM Agents for algorithmic trading? By providing information to the agents, you would have your own advisor type of thing.

I want to share my new open-source project, which I've been working on as part of my research. I previously posted about another open source project here that received huge success (see here), so I decided to share this one with you as well.

This concept follows a similar approach, but it utilizes a multi-agent system with LangGraph for agent orchestration. The system includes four agents:

• Data Collection Agent - gathers data from multiple sources

• Technical Analysis Agent - performs classical technical indicator calculations

• News Intelligence Agent - based on the PrimoGPT idea, creates seven custom NLP features

• Portfolio Manager Agent - takes everything into account and makes recommendations

I built the entire system to be easily extensible, whether adding new agents, new tools, or changing prompts.

Everything is open source with very simple instructions on how to run it, so you can easily test it and see the results.

GitHub repository: https://github.com/ivebotunac/PrimoAgent/

I know there will be both good and bad comments, but with this project, I wanted to give the community an idea and example of how such multi-agent AI systems can be used to help with financial analysis. This is intended exclusively for educational purposes.

If you find any bugs or have ideas on how to improve the system, feel free to contribute to the project.

Thanks, everyone, for the support!