Multi-agent AI stock analyzer

Details

External ID

45946056

Source

HN

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Product

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Website domain

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Launched

Nov. 16, 2025

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Upvotes

5

Upvotes percentile

0.0982532751091703

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Fetched at

Sept. 7, 2026, 9:25 p.m.

Updated at

Sept. 7, 2026, 9:25 p.m.

Description

Hey HN! I built PRISM-INSIGHT, a multi-agent system where 13 specialized AI agents collaborate to analyze Korean stocks (KOSPI/KOSDAQ). It's completely open source and has been running live since March 2025.[What it does] The system automatically detects surging stocks twice daily, generates analyst-level reports, and executes trading strategies. Each agent specializes in something different – technical analysis, trading flows, financials, news, market conditions, etc. They work together like a real research team.[Why I built this] I wanted to see if GPT-4 and GPT-5 could genuinely replicate what human analysts do, but without the typical single-agent limitations. So I split the work across multiple specialized agents that collaborate. The trading simulation has been running for 8 months now with real Korean market data.[How to try it]Join the live Telegram channel! https://t.me/prism_insight_global_en (gets daily alerts and reports)Check the real-time dashboard! https://analysis.stocksimulation.kr (all trades, performance, AI reasoning)Clone and run it yourself! https://github.com/dragon1086/prism-insight[The interesting parts] The system uses MCP (Model Context Protocol) servers to give agents access to live market data, web search, and financial APIs. I'm using GPT-4.1 for analysis, GPT-5 for trading decisions, and Claude Sonnet 4.5 for the conversational bot.The first trading simulation (Season 1, Mar-Sep 2025) returned 408% across 51 trades. Current season(2) is at +11% realized returns vs KOSPI's +16%. Also running it with real money now ($10k account, up 9.35% since late September).[Tech stack] Python 3.10+, async/await throughout, SQLite for trade history, Playwright for PDF reports, matplotlib for charts. The whole thing is about 8,400 lines of Python across 56 files.[What makes it different] Most AI trading projects are either single-agent or black boxes. This one uses a multi-agent architecture where you can see exactly what each agent is analyzing and why. Everything is transparent – the dashboard shows every trade, every decision, and all the reasoning.It's MIT licensed and runs entirely on your machine if you want. I'm covering the API costs (~$200/month) to keep the public Telegram channel free for 450+ users(Korean channel + Global channel).Would love feedback on the multi-agent approach or questions about running AI agents in production!

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