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News Sentiment Dashboard - Backend

A FastAPI-based backend service for analyzing stock market sentiment from news articles using FinBERT and AI-powered rationales.

🏗️ Architecture

backend/app/
├── main.py                 # FastAPI application and routes
├── config.py              # Configuration management
├── models.py              # Data models
├── schemas.py             # Pydantic schemas for API
├── utils.py               # Shared utility functions
├── core/
│   └── sentiment.py       # FinBERT sentiment analysis
├── services/
│   └── rationales.py      # AI rationale generation
└── sources/
    ├── common.py          # Shared source utilities
    ├── collector.py       # News collection coordinator
    ├── google_news.py     # Google News fetcher
    └── yfinance.py        # Yahoo Finance fetcher

🚀 Features

  • Multi-Source News Collection: Yahoo Finance and Google News RSS feeds
  • Financial Sentiment Analysis: FinBERT model for domain-specific analysis
  • Intelligent Weighting: Multi-factor weighting (recency, source credibility, engagement)
  • AI-Powered Rationales: ChatGPT explanations for sentiment classifications
  • Deduplication: Smart removal of duplicate news articles
  • Source Filtering: Only trusted news sources included
  • Async Processing: Non-blocking news collection and analysis

📦 Dependencies

Core Dependencies

pip install fastapi uvicorn pydantic feedparser python-dateutil

Optional Dependencies

# For sentiment analysis
pip install transformers torch

# For AI rationales
pip install openai

# For domain extraction
pip install tldextract

⚙️ Configuration

Environment variables (all optional):

# API Keys
OPENAI_API_KEY=your_openai_key_here

# News Collection
LOOKBACK_DAYS=5
MAX_ITEMS=40

# Sentiment Analysis
HALF_LIFE_HOURS=24.0
DEFAULT_SOURCE_WEIGHT=0.75

# CORS
CORS_ALLOW_ORIGINS=*

🏃‍♂️ Running the Application

Development

cd backend
python -m uvicorn app.main:app --reload --reload-dir app

Production

cd backend
python -m uvicorn app.main:app --host 0.0.0.0 --port 8000

📡 API Endpoints

Health Check

GET /health

Sentiment Analysis

GET /sentiment?ticker=TSLA&lookback_days=7&include_rationales=true&limit=20

Parameters:

  • ticker (required): Stock ticker symbol
  • lookback_days (optional): Days to look back (1-14, default: 5)
  • include_rationales (optional): Include AI explanations (default: true)
  • limit (optional): Max news items (1-50, default: 10)

Response:

{
  "ticker": "TSLA",
  "as_of": "2024-01-15T10:30:00Z",
  "lookback_days": 7,
  "overall_score": 0.2345,
  "n_items": 15,
  "items": [
    {
      "id": "a1b2c3d4e5f6g7h8",
      "source": "reuters.com",
      "title": "Tesla reports record Q4 deliveries",
      "url": "https://reuters.com/...",
      "published_at": "2024-01-15T09:00:00Z",
      "text": "Tesla delivered 484,507 vehicles...",
      "label": "positive",
      "prob_positive": 0.85,
      "prob_neutral": 0.10,
      "prob_negative": 0.05,
      "score": 0.8,
      "weight": 0.9,
      "weighted_score": 0.72,
      "rationale": "Positive for TSLA: Tesla reports record Q4 deliveries..."
    }
  ]
}

🔧 Code Quality Improvements

✅ Completed Cleanup Tasks

  1. Import Organization: Cleaned up imports and removed unused dependencies
  2. Documentation: Added comprehensive docstrings to all functions
  3. Code Duplication: Consolidated duplicate utility functions
  4. Error Handling: Improved error handling with proper logging
  5. Type Hints: Added complete type annotations
  6. Configuration: Centralized configuration management
  7. Logging: Added proper logging throughout the application
  8. Code Formatting: Standardized code formatting and structure

🏗️ Architecture Improvements

  • Separation of Concerns: Clear separation between data models, business logic, and API layer
  • Modular Design: Each module has a single responsibility
  • Error Resilience: Graceful handling of missing dependencies and API failures
  • Performance: Lazy loading of heavy ML models
  • Maintainability: Clean, readable code with comprehensive documentation

📊 Sentiment Analysis Pipeline

  1. News Collection: Fetch from multiple sources concurrently
  2. Source Filtering: Keep only trusted sources (weight ≥ 0.6)
  3. Deduplication: Remove duplicate articles
  4. Sentiment Analysis: FinBERT model with financial domain expertise
  5. Weighting: Multi-factor importance scoring
  6. Rationale Generation: AI explanations for sentiment classifications
  7. Response Building: Structured API response

🎯 Weighting System

  • Recency Weight (50%): Exponential decay based on article age
  • Source Weight (30%): Credibility based on news source
  • Engagement Weight (20%): User interaction (Reddit upvotes/comments)

🧪 Testing

# Test health endpoint
curl http://localhost:8000/health

# Test sentiment analysis
curl "http://localhost:8000/sentiment?ticker=TSLA&limit=5"

📈 Performance

  • Startup Time: < 1 second (with lazy loading)
  • Model Loading: 5-15 seconds (first sentiment call)
  • API Response: 1-3 seconds (depending on news volume)
  • Memory Usage: ~500MB (with loaded model)

🔒 Security

  • Input Validation: Pydantic schemas validate all inputs
  • Rate Limiting: Built-in FastAPI rate limiting
  • CORS: Configurable CORS settings
  • Error Handling: No sensitive information in error messages

🚀 Deployment

The application is ready for deployment on:

  • Docker: Use the provided Dockerfile
  • Cloud Platforms: AWS, GCP, Azure
  • Container Orchestration: Kubernetes, Docker Swarm
  • Serverless: AWS Lambda, Google Cloud Functions

📝 License

This project is licensed under the MIT License.