Golden Standard

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  Landing Page

Video: https://www.loom.com/share/d6c22fc6d0134f8a80bbe58f0085d163?sid=b7c35296-d42c-407e-b3e9-6e6c3a391a02

Inspiration

Golden Standard is a real-time sentiment dashboard for individual stocks. It pulls:

Reddit comments from r/wallstreetbets

News headlines via Google News

(Coming soon) Twitter/X discussions

Then, it uses Gemini AI to:

Summarize what people are saying

Extract bullish vs bearish perspectives

Classify overall sentiment as positive, neutral, or negative

All this is displayed in a clean, interactive React frontend, allowing users to get a sentiment report for any ticker in seconds.

What it does

Python Backend using FastAPI for scraping, AI processing, and APIs

PRAW for Reddit comment parsing

SerpAPI for news data

Polygon.io API for latest market data

Google Gemini API for AI-generated summaries and formatting

React Frontend using TypeScript and Tailwind to render news, forums, and live analysis

Dynamic sentiment endpoint that updates analysis based on the requested ticker in real time

How we built it

Managing real-time scraping without being rate-limited or blocked

Getting Gemini to reliably return clean, structured JSON

Mapping unstructured Reddit data into something AI could reason with

Dealing with SSL/cert issues and Python environment conflicts during setup

Designing a frontend flexible enough to render AI-driven content without breaking layout

Challenges we ran into

Creating a seamless, full-stack pipeline from live internet data → AI processing → visual display

Built a modular backend that can scale to other sources like YouTube, Twitter, or Discord

Developed custom prompts that extract meaningful insights from chaotic, noisy Reddit threads

It works — and it feels smart. That’s the best win.

Accomplishments that we're proud of

Prompt engineering is everything when working with LLMs

Reddit comments are messy — structuring them is a project in itself

FastAPI is incredibly powerful for building real-time AI-backed APIs

Frontend and backend integration for AI systems takes careful planning to avoid breaking the user experience

You don't need a giant team to build something that feels enterprise-grade

What we learned

What we learned We learned how to integrate routes into a frontend system.

What's next for Golden Standard

dd Twitter/X data via Playwright or a scraping proxy

Integrate LLM-based alerts for unusual sentiment spikes or shifts

Train a lightweight custom model on past sentiment + stock movement to predict short-term moves

Add multi-ticker comparisons and industry-wide heatmaps

Deploy publicly and offer a free tier + pro API for fintech devs

Built With

• api
• css
• fastapi
• gemini
• javascript
• node.js
• python
• react
• tailwind