QuantaMine v2 — AI-Powered Stock Analyzer

Combines fundamental metrics, technical indicators, news sentiment, and Gemini LLM reasoning into a composite investment score.

Architecture

QuantaMine/
├── backend/          FastAPI (Python 3.11)
│   └── app/
│       ├── main.py       SSE streaming API
│       ├── analyzer.py   Scoring engine
│       └── models.py     Pydantic models
├── frontend/         Next.js 15 + TailwindCSS
│   └── app/
│       ├── page.tsx      Main dashboard
│       └── components/   StockCard, ScoreBar, etc.
├── docker-compose.yml
└── .env.example

Scoring Model

Component	Weight	Source
Algo Score	50%	Value + Quality + Risk + Momentum
Sentiment	20%	FinBERT on NewsAPI headlines
LLM Confidence	30%	Gemini reasoning

Ratings: Strong Buy (≥80) · Buy (≥65) · Hold (≥45) · Avoid (<45)

Quick Start

1. Configure environment

cp .env.example .env
# Fill in GEMINI_API_KEY, NEWSAPI_KEY, HUGGINGFACE_API_KEY

2. Run with Docker

docker compose up --build

Open http://localhost:3000

3. Run locally (development)

Backend:

cd backend
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
uvicorn app.main:app --reload --port 8000

Frontend:

cd frontend
npm install
npm run dev

API

Endpoint	Method	Description
/health	GET	Health check
/api/validate	POST	Validate ticker symbols
/api/analyze/stream/live	POST	SSE streaming analysis

Example

curl -X POST http://localhost:8000/api/analyze/stream/live \
  -H "Content-Type: application/json" \
  -d '{"tickers": ["AAPL", "MSFT", "NVDA"]}'

Deployment

Vercel (frontend) + Railway/Render (backend)

1. Deploy backend to Railway or Render
2. Set NEXT_PUBLIC_API_URL to your backend URL
3. Deploy frontend to Vercel

Features

• Real-time streaming — results appear as each ticker is analyzed via SSE
• 6-pillar scoring — Value, Quality, Risk, Momentum, Sentiment, AI
• Gemini reasoning — natural language explanation per stock
• Interactive UI — expandable cards, sortable table, CSV export
• Docker ready — single docker compose up to run everything