stockholm-finance 1.0.7

Advanced financial sentiment analysis tool with policy impact assessment

📊 Stockholm Finance

Note: Pre-commit hooks are now active! 🎉

Advanced financial sentiment analysis tool with policy impact assessment, intelligent caching, and professional dashboard interface.

🚀 Quick Start

Installation

# From PyPI
pip install stockholm-finance

# From source
git clone https://github.com/aykaym/stockholm.git
cd stockholm
pip install -e .

Docker

# Run with Docker
docker run -it ghcr.io/aykaym/stockholm:latest

# Development environment
docker-compose up dev

Command Line Usage

# Run the main financial analyzer
stockholm-finance

# Launch the interactive dashboard
stockholm-dashboard

Python API Usage

from src.core.financial_analyzer import main

# Run analysis
main()

# Or use the dashboard
from src.ui.textual_dashboard import StockholmDashboard
dashboard = StockholmDashboard()
dashboard.run()

✨ Features

🎯 Core Analysis

• Sentiment Analysis: Advanced NLP for market sentiment from news articles
• Policy Analysis: Government policy impact assessment on markets
• Multi-Ticker Support: Analyze sentiment across multiple stocks simultaneously
• Real-time Data: Live market data integration with yfinance

💾 Smart Caching

• TTL-based Caching: Intelligent cache expiration (15min news, 5min prices)
• Performance Optimization: 50+ cache reads/second, 20+ writes/second
• Storage Efficiency: Compressed JSON storage with automatic cleanup

📊 Interactive Dashboard

• Professional TUI: Rich terminal interface with real-time updates
• Multiple Views: Ticker info, earnings data, price charts, sentiment analysis
• Responsive Design: Adaptive layouts for different terminal sizes

🧪 Enterprise Testing

• 37+ Tests: Comprehensive test suite with 100% pass rate
• Coverage: 80% cache manager, 68% policy analyzer, 39% sentiment analyzer
• Performance: Benchmarked at 10+ articles/second processing

📋 Requirements

• Python: 3.8 or higher
• Dependencies: See requirements.txt
• Optional: Docker for containerized deployment

🏗️ Architecture

stockholm/
├── src/
│   ├── core/           # Core analysis engines
│   ├── data/           # Data fetching and caching
│   ├── ui/             # User interface components
│   └── config/         # Configuration management
├── tests/              # Comprehensive test suite
├── docs/               # Documentation
├── scripts/            # DevOps and utility scripts
└── .github/            # CI/CD workflows

🧪 Testing

# Run all tests
python -m pytest tests/ -v

# With coverage
python -m pytest tests/ --cov=src --cov-report=html

# Performance tests
python -m pytest tests/test_performance.py -v

# Use the coverage checker
python check_coverage.py

🚀 Development

Local Setup

# Clone repository
git clone https://github.com/yourusername/stockholm.git
cd stockholm

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Linux/Mac
venv\Scripts\activate     # Windows

# Install dependencies
pip install -r requirements.txt
pip install -e ".[dev]"

# Run tests
python -m pytest tests/ -v

Docker Development

# Development environment
docker-compose up dev

# Run tests in container
docker-compose up test

# Code quality checks
docker-compose up quality

📦 Releases

Automated Releases

The project uses automated versioning and releases:

# Create new release
python scripts/release.py

# Choose release type:
# - patch: Bug fixes (1.0.0 → 1.0.1)
# - minor: New features (1.0.0 → 1.1.0)  
# - major: Breaking changes (1.0.0 → 2.0.0)

CI/CD Pipeline

• ✅ Automated Testing: All Python versions (3.8-3.12)
• ✅ Security Scanning: Bandit + Safety vulnerability checks
• ✅ Code Quality: Black, isort, flake8, mypy
• ✅ Docker Images: Multi-platform builds
• ✅ GitHub Releases: Automatic changelog generation

📊 Performance

Benchmarks

• Sentiment Analysis: 10+ articles/second
• Cache Performance: 50+ reads/second, 20+ writes/second
• Memory Usage: <100MB for 1000 articles
• Test Suite: 37 tests in <1 second

Scalability

• Concurrent Processing: Multi-threaded analysis support
• Batch Operations: Efficient bulk data processing
• Memory Efficient: Optimized for large datasets

🔒 Security

• Dependency Scanning: Automated vulnerability checks
• Code Analysis: Static security analysis with Bandit
• Input Validation: Sanitized data processing
• No Secrets: No hardcoded API keys or credentials

📖 Documentation

• Complete Documentation: Comprehensive user guide
• Testing Guide: Testing strategy and coverage
• DevOps Guide: CI/CD and deployment
• Coverage Report: Code coverage analysis

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes with tests
4. Run quality checks (black src/ tests/ && pytest)
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• TextBlob: Natural language processing
• yfinance: Financial data API
• Textual: Modern terminal UI framework
• Rich: Beautiful terminal formatting
• pytest: Testing framework

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: Project Docs

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