Hello HN,

I’m a college student building a financial intelligence terminal for personal use. The goal is to capture "whisper numbers" and sentiment shifts in social media before they hit mainstream headlines.

I would love your feedback on the architecture and logic.

The Stack:

Backend: Python (PM2 managed)

LLM: OpenAI GPT 5.2 for high level analysis + GPT-5-mini for news filter and market data collection (via API)

Frontend: Next.js + Tailwind + Recharts

The Architecture (The interesting part):

1. The Collector

Instead of hitting the LLM immediately, I run a raw scraper every 30 minutes targeting:

- Nitter instances (for Twitter/X data without API limits).

- GNews RSS (for official headlines).

- DuckDuckGo (for general forum chatter).

- 1-min OHLC Data (via yfinance) to monitor price micro-structure.

2. The Agentic System

I split the analysis into two roles:

- Market Agent: Analyzes the 30-minute window of 1-min candle data. It looks for patterns like "V-shape recovery," "Flash crash," or "Volume exhaustion" (things a simple % change metric misses).

- News Agent: Analyzes the messages and events from the scrapper, and it can determine whether to wake up on-call senior agent for "emergency" during market open time.

- Senior Agent: Receives the cleaned news stream + the Market Agent's technical summary. It produces a Sentiment Score (-10 to +10) and a rationale. It creates a "red alert" if the price will probably be changing a lot in the next several hours.

- On-call Agent: Analyzes the news and search the internet for proofs and determine whether to send alert to users or not.

3. The Frontend

To better display information, I vibe coded a Next.js + Tailwindcss frontend. An updated version with i18n will soon be published.

My Questions for HN:

Latency vs. Depth: Currently, the AI analysis cycle takes ~3 minutes. For "swing trading" this is fine, but will it help if there's a better pattern to stream partial updates to the frontend without waiting for the full analysis to complete?

Hallucination Risks: I force the LLM to verify dates, but sometimes it still treats a "re-posted old news" as a new event. How do you guys architect verification layers for news agents?

Practicability: I just finished this agentic system, and it's my first day trading according to the info from the agentic system and it's currently +0.8% profit. I'd love advice from someone with more trading experience. Also, feel free to reach out to me if you want a preview of the system's output.

Some screenshots here: https://x.com/CLCKKKKK/status/2006085046269337799?s=20

My email: yiz29@illinois.edu

Thanks!

Ask HN: My multi-agent financial sentiment architecture