📊 Financial News Impact Analyzer

A real-time financial news analysis platform that tracks global financial institutions and their impact on Indian markets using AI-powered sentiment analysis.

🌟 Features

• Real-time News Aggregation: Fetches latest news from RSS feeds and AI-powered search
• Sentiment Analysis: Multi-layered sentiment analysis using VADER, TextBlob, and keyword matching
• Impact Scoring: Calculates institution impact scores based on multiple factors
• Interactive Dashboard: Beautiful, responsive UI with real-time charts
• AI Chatbot: Ask questions about institutions, trends, and market sentiment
• WebSocket Updates: Live data updates without page refresh

🏗️ Tech Stack

Backend:

• Python 3.9+
• Flask & Flask-SocketIO
• SQLite
• Anthropic Claude AI
• NLTK, TextBlob, VADER

Frontend:

• HTML5, CSS3, JavaScript (ES6+)
• Chart.js
• Socket.IO Client

📋 Prerequisites

• Python 3.9 or higher
• Anthropic API Key (Get one here)
• Windows/Linux/macOS

🚀 Installation & Setup

Step 1: Clone/Extract the Project

cd financial-news-bot

Step 2: Create Virtual Environment

Windows:

python -m venv venv
venv\Scripts\activate

Linux/macOS:

python3 -m venv venv
source venv/bin/activate

Step 3: Install Dependencies

pip install -r requirements.txt

Step 4: Download NLTK Data (Required for sentiment analysis)

Run Python and execute:

python -c "import nltk; nltk.download('punkt'); nltk.download('stopwords'); nltk.download('vader_lexicon')"

Step 5: Configure Environment Variables

1. Copy .env.example to .env:

   copy .env.example .env    # Windows
   cp .env.example .env      # Linux/macOS

2. Edit .env and add your Anthropic API key:

   ANTHROPIC_API_KEY=sk-ant-your-actual-key-here
   SECRET_KEY=your-secret-key-change-this

3. Generate a secure secret key (optional but recommended):

   python -c "import secrets; print(secrets.token_hex(32))"

Step 6: Create Required Directories

The application will auto-create these, but you can create them manually:

mkdir data logs

▶️ Running the Application

Method 1: Using run.py (Recommended)

python run.py

Method 2: Using Flask directly

cd backend
python app.py

Expected Output:

============================================================
  FINANCIAL NEWS IMPACT ANALYZER
============================================================

🚀 Server starting...
📍 URL: http://127.0.0.1:5000
🔧 Environment: development
🐛 Debug Mode: True

⏰ Auto-refresh interval: 1800 seconds

💡 Open your browser and navigate to: http://localhost:5000

============================================================

Performing initial news fetch...
==================================================
Starting news analysis at 2024-12-25 10:30:00
==================================================
...

🌐 Accessing the Application

Open your browser and navigate to:

http://localhost:5000

Or:

http://127.0.0.1:5000

🎮 How to Use

1. Initial Load

• The app automatically fetches news on startup
• Wait 30-60 seconds for initial data processing

2. View Dashboard

• See market summary and key statistics
• View impact score charts
• Check institution rankings table

3. Refresh Data

• Click "Refresh Data" button for latest news
• Enable "Auto-Refresh" for automatic updates every 5 minutes

4. Ask Questions

• Use the AI chatbot to ask about:
  • Specific institutions: "Tell me about JPMorgan"
  • Top performers: "Show me the top institutions"
  • Sentiment: "Which institutions are positive?"
  • India focus: "Which institutions focus on India?"
  • Overview: "Give me a summary"

⚙️ Configuration

Edit .env file to customize:

# API Configuration
ANTHROPIC_API_KEY=your-key-here

# Server Settings
HOST=127.0.0.1              # Use 0.0.0.0 for network access
PORT=5000

# Refresh Intervals
NEWS_REFRESH_INTERVAL=1800  # 30 minutes (in seconds)
QUICK_REFRESH_INTERVAL=300  # 5 minutes

# Logging
LOG_LEVEL=INFO              # DEBUG, INFO, WARNING, ERROR

📁 Project Structure

financial-news-bot/
├── backend/
│   ├── __init__.py
│   ├── app.py                  # Main Flask application
│   ├── config.py               # Configuration
│   ├── database.py             # Database operations
│   ├── news_scraper.py         # News fetching
│   ├── sentiment_analyzer.py   # Sentiment analysis
│   └── impact_calculator.py    # Impact scoring
├── frontend/
│   ├── index.html              # Main HTML
│   ├── css/
│   │   └── style.css          # Styles
│   └── js/
│       ├── main.js            # Main logic
│       ├── chart.js           # Charts
│       └── websocket.js       # WebSocket
├── data/                       # Database storage
├── logs/                       # Log files
├── venv/                       # Virtual environment
├── .env                        # Environment variables
├── .env.example               # Environment template
├── requirements.txt           # Dependencies
├── run.py                     # Entry point
└── README.md                  # readme.md file -- these file 

🐛 Troubleshooting

Issue: "ANTHROPIC_API_KEY is required"

Solution: Make sure you've set your API key in the .env file

Issue: "Module not found"

Solution:

pip install -r requirements.txt

Issue: "Port 5000 already in use"

Solution: Change PORT in .env file:

PORT=8000

Issue: "Database locked"

Solution:

# Delete the database and restart
rm data/news_cache.db
python run.py

Issue: NLTK download errors

Solution:

import nltk
nltk.download('all')

Issue: Can't access from other devices

Solution: Change HOST in .env:

HOST=0.0.0.0

Then access via: http://YOUR_IP:5000

📊 API Endpoints

GET /api/news

Get latest news analysis (cached for 5 minutes)

POST /api/refresh

Force refresh news data

GET /api/status

Get system status

POST /api/chat

Send message to AI chatbot

GET /api/health

Health check endpoint

🔒 Security Notes

For Production Deployment:

1. Change SECRET_KEY to a secure random value
2. Restrict CORS origins in app.py
3. Use HTTPS with proper SSL certificates
4. Set DEBUG=False in production
5. Use environment variables for all secrets
6. Implement rate limiting on API endpoints
7. Add authentication for admin features

🚀 Production Deployment

Using Gunicorn (Recommended):

pip install gunicorn

gunicorn --worker-class eventlet -w 1 --bind 0.0.0.0:5000 backend.app:app

Using Docker:

Create Dockerfile:

FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["python", "run.py"]

Build and run:

docker build -t financial-news-bot .
docker run -p 5000:5000 --env-file .env financial-news-bot

📝 License

This project is for educational purposes.

🤝 Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

📧 Support

For issues and questions:

• Check the troubleshooting section
• Review logs in logs/app.log
• Check console output for errors

🎯 Roadmap

• Add more news sources
• Implement user authentication
• Add historical data visualization
• Email alerts for significant changes
• Mobile app
• Multi-language support

⚠️ Disclaimer

This tool is for informational purposes only. Not financial advice. Always do your own research before making investment decisions.

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Made by ❤️ Om Kapale