📈 Stock Pulse AI

AI-Powered Financial Market Analysis Platform

Stock Pulse AI is a comprehensive financial analysis platform that combines technical analysis, sentiment analysis, and real-time market data to provide intelligent insights for stock market investments. The platform uses advanced AI models to analyze news sentiment, social media trends, SEC filings, and technical indicators to generate actionable investment intelligence.

✨ Features

🎯 Core Analytics

Technical Analysis: 9-factor scoring system rating stocks from -9 to +9
Sentiment Analysis: Multi-source sentiment scoring using AI/NLP models
Real-time Market Data: Live stock prices, charts, and market indicators
Interactive Visualizations: Candlestick charts, line graphs, and technical indicators

🧠 AI-Powered Insights

News Sentiment: Analysis of financial news using FinBERT and VADER models
Social Media Sentiment: Reddit and social platform sentiment tracking
SEC Filings Analysis: Automated analysis of company filings and reports
Multi-timeframe Analysis: 1-day, 1-month, 1-year predictions and trends

🌐 Platform Features

RESTful API: FastAPI backend with automatic OpenAPI documentation
Modern Frontend: React-based dashboard with responsive design
Real-time Updates: Live market data and sentiment updates
Authentication: Secure user management and API access
Market Coverage: S&P 500, NASDAQ, and individual stock analysis

🛠 Technologies Used

Backend

FastAPI - Modern, fast web framework for building APIs
Python 3.9+ - Core programming language
PostgreSQL - Primary database for data storage
SQLAlchemy - Database ORM and query builder

AI/ML Stack

PyTorch - Deep learning framework for AI models
Transformers (Hugging Face) - Pre-trained NLP models (FinBERT)
VADER Sentiment - Rule-based sentiment analysis
scikit-learn - Machine learning algorithms and tools
pandas - Data manipulation and analysis

Data Sources & APIs

yfinance - Yahoo Finance API for stock data
NewsAPI - Financial news and sentiment data
Reddit API (PRAW) - Social sentiment analysis
SEC EDGAR - Official company filings and reports

Frontend

React 18 - Frontend framework
Plotly.js - Interactive data visualizations
React Router - Client-side routing
Recharts - Additional charting library

DevOps & Deployment

Docker - Containerization
Git - Version control
GitHub Actions - CI/CD pipelines

💻 System Requirements

Minimum Requirements

Python: 3.9 or higher
Node.js: 16.0 or higher
npm: 8.0 or higher
Memory: 4GB RAM (8GB recommended)
Storage: 2GB free space

Recommended Setup

Operating System: macOS, Linux, or Windows 10/11
Python: 3.10+
Memory: 8GB+ RAM for optimal AI model performance
GPU: Optional, for faster AI model inference

External Dependencies

NewsAPI Key: For financial news data (free tier available)
Internet Connection: For real-time market data and API access

🚀 Installation

1. Clone the Repository

git clone https://github.com/smundhra-git/Stock_PulseAI.git
cd Stock_PulseAI

2. Backend Setup

Create Virtual Environment

# Create and activate conda environment
conda create -n stockpulse python=3.9 -y
conda activate stockpulse

# OR using venv
python -m venv stockpulse_env
source stockpulse_env/bin/activate  # On Windows: stockpulse_env\Scripts\activate

Install Python Dependencies

pip install -r requirements.txt

Environment Configuration

Create a .env file in the root directory:

# Required API Keys
NEWSAPI=your_newsapi_key_here

# Optional: Database Configuration
DATABASE_URL=postgresql://username:password@localhost/stockpulse

# Optional: Reddit API (for enhanced social sentiment)
REDDIT_CLIENT_ID=your_reddit_client_id
REDDIT_CLIENT_SECRET=your_reddit_client_secret

Get your NewsAPI key:

Visit NewsAPI.org
Sign up for a free account
Copy your API key to the .env file

3. Frontend Setup

cd frontend
npm install

4. Start the Application

Start Backend (Terminal 1)

# From project root directory
conda activate stockpulse  # if using conda
uvicorn fastapi_service.main:app --reload --port 8080

Start Frontend (Terminal 2)

# From frontend directory
cd frontend
npm start

5. Access the Application

Frontend Dashboard: http://localhost:3000
API Documentation: http://localhost:8080/docs
Alternative API Docs: http://localhost:8080/redoc

📖 Usage

API Endpoints

Technical Analysis: GET /api/stock/{ticker}/technical
Sentiment Analysis: GET /api/stock/{ticker}/sentiment
Stock Charts: GET /api/stock/{ticker}/graph
Market Data: GET /api/sp500-realtime

Example API Call

curl -X 'GET' \
  'http://localhost:8080/api/stock/AAPL/sentiment' \
  -H 'accept: application/json'

📚 API Documentation

The FastAPI backend automatically generates interactive API documentation:

Swagger UI: http://localhost:8080/docs
ReDoc: http://localhost:8080/redoc

These interfaces allow you to:

Explore all available endpoints
Test API calls directly in the browser
View request/response schemas
Download OpenAPI specifications

📁 Project Structure

Stock_PulseAI/
├── src/                          # Core application logic
│   ├── api_handler.py           # API route handlers
│   ├── sentiment/               # Sentiment analysis modules
│   ├── technical/               # Technical analysis tools
│   └── database/                # Database operations
├── fastapi_service/             # FastAPI application
│   ├── main.py                  # FastAPI app configuration
│   └── routes.py                # API route definitions
├── frontend/                    # React frontend application
│   ├── src/                     # React source code
│   ├── public/                  # Static assets
│   └── package.json             # Frontend dependencies
├── requirements.txt             # Python dependencies
├── .env                         # Environment variables
└── README.md                    # Project documentation

🗺 Development Roadmap

✅ Completed

FastAPI backend with sentiment analysis
React frontend foundation
Technical analysis (9-factor scoring)
News and Reddit sentiment analysis
Real-time market data integration
Interactive API documentation

🚧 In Progress

Enhanced FinBERT model integration (PyTorch 2.6+ compatibility)
Advanced technical indicators
User authentication and portfolios

📋 Planned Features

Macroeconomic data integration (FRED API)
AI-powered price predictions
Portfolio optimization tools
Mobile-responsive enhancements
Real-time notifications and alerts
Advanced charting and analytics

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Fork the Project
Create your Feature Branch (git checkout -b feature/AmazingFeature)
Commit your Changes (git commit -m 'Add some AmazingFeature')
Push to the Branch (git push origin feature/AmazingFeature)
Open a Pull Request

📞 Support

If you have any questions or run into issues, please open an issue on GitHub.

Made with ❤️ for smarter financial analysis

Name		Name	Last commit message	Last commit date
Latest commit History 30 Commits
fastapi_service		fastapi_service
frontend		frontend
src		src
.DS_Store		.DS_Store
.gitignore		.gitignore
README.md		README.md
requirements.txt		requirements.txt

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