portfolio-analytics-ai 0.2.5

AI-powered portfolio analytics and optimization tools

Portfolio Analytics AI 📈

A comprehensive Python package for portfolio analytics, optimization, and risk management using modern portfolio theory and advanced statistical methods.

🚀 Features

• Portfolio Construction & Management: Create and manage multi-asset portfolios with flexible weighting schemes
• Modern Portfolio Theory: Implement mean-variance optimization with efficient frontier calculation
• Risk Analysis: Value at Risk (VaR), Expected Shortfall, Maximum Drawdown, and more
• Performance Analytics: Comprehensive performance metrics with benchmark comparison
• Data Integration: Seamless integration with Yahoo Finance and extensible data provider architecture
• Visualization: Interactive and static charts for portfolio analysis
• Risk Models: Multiple VaR calculation methods (historical, parametric, Monte Carlo)

📦 Installation

Prerequisites

• Python 3.8 or higher
• pip package manager

Setup Virtual Environment

# Clone or navigate to the project directory
cd PortfolioAnalyticsAI

# Create virtual environment
python -m venv .venv

# Activate virtual environment
# On macOS/Linux:
source .venv/bin/activate
# On Windows:
.venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Install the package in development mode
pip install -e .

🎯 Quick Start

Basic Usage

from portfolio_analytics import Portfolio, DataProvider

# Create a data provider
data_provider = DataProvider()

# Define your portfolio
symbols = ['AAPL', 'GOOGL', 'MSFT', 'AMZN', 'TSLA']
weights = {'AAPL': 0.3, 'GOOGL': 0.2, 'MSFT': 0.2, 'AMZN': 0.15, 'TSLA': 0.15}

# Create portfolio
portfolio = Portfolio(
    name="Tech Portfolio",
    symbols=symbols,
    weights=weights,
    data_provider=data_provider
)

# Load historical data
portfolio.load_data(start_date='2020-01-01', end_date='2023-01-01')

# Get basic metrics
print(f"Annual Return: {portfolio.annual_return():.2%}")
print(f"Annual Volatility: {portfolio.annual_volatility():.2%}")
print(f"Sharpe Ratio: {portfolio.sharpe_ratio():.3f}")

Portfolio Optimization

from portfolio_analytics import PortfolioOptimizer

# Create optimizer
optimizer = PortfolioOptimizer(portfolio)

# Optimize for maximum Sharpe ratio
optimal_weights = optimizer.optimize('max_sharpe')

# Update portfolio with optimal weights
portfolio.update_weights(optimal_weights)

print("Optimized Portfolio Metrics:")
print(f"Annual Return: {portfolio.annual_return():.2%}")
print(f"Annual Volatility: {portfolio.annual_volatility():.2%}")
print(f"Sharpe Ratio: {portfolio.sharpe_ratio():.3f}")

Risk Analysis

from portfolio_analytics import RiskModel

# Create risk model
risk_model = RiskModel(portfolio)

# Calculate Value at Risk
var_95 = risk_model.value_at_risk(confidence_level=0.95)
var_99 = risk_model.value_at_risk(confidence_level=0.99)

# Calculate Expected Shortfall
es_95 = risk_model.expected_shortfall(confidence_level=0.95)

# Calculate Maximum Drawdown
max_dd = risk_model.maximum_drawdown()

print(f"VaR (95%): {var_95:.2%}")
print(f"VaR (99%): {var_99:.2%}")
print(f"Expected Shortfall (95%): {es_95:.2%}")
print(f"Maximum Drawdown: {max_dd:.2%}")

📊 Visualization

from portfolio_analytics import PortfolioVisualizer

# Create visualizer
visualizer = PortfolioVisualizer(portfolio)

# Plot price history
visualizer.plot_price_history()

# Plot correlation matrix
visualizer.plot_correlation_matrix()

# Plot efficient frontier
visualizer.plot_efficient_frontier()

# Create interactive dashboard
visualizer.create_dashboard()

🧪 Running Examples

Console Example

python example.py

Jupyter Notebook

jupyter notebook notebooks/01_getting_started.ipynb

🧪 Testing

Run the test suite to ensure everything is working correctly:

# Run all tests
pytest tests/

# Run with verbose output
pytest tests/ -v

# Run specific test file
pytest tests/test_portfolio.py -v

📁 Project Structure

PortfolioAnalyticsAI/
├── src/
│   └── portfolio_analytics/
│       ├── __init__.py           # Package initialization
│       ├── portfolio.py          # Core Portfolio class
│       ├── data_provider.py      # Data fetching utilities
│       ├── optimization.py       # Portfolio optimization
│       ├── risk_models.py        # Risk analysis tools
│       ├── performance.py        # Performance analytics
│       └── visualization.py      # Charting and visualization
├── tests/
│   └── test_portfolio.py         # Unit tests
├── notebooks/
│   └── 01_getting_started.ipynb  # Interactive examples
├── example.py                    # Complete usage example
├── requirements.txt              # Package dependencies
├── pyproject.toml               # Project configuration
└── README.md                    # This file

📈 Key Classes

Portfolio

Core class for portfolio management and basic analytics.

DataProvider

Handles data fetching from multiple sources (Yahoo Finance, etc.).

PortfolioOptimizer

Implements modern portfolio theory optimization techniques.

RiskModel

Provides comprehensive risk analysis capabilities.

PerformanceAnalyzer

Calculates performance metrics and benchmark comparisons.

PortfolioVisualizer

Creates static and interactive visualizations.

🔧 Dependencies

• numpy: Numerical computations
• pandas: Data manipulation and analysis
• matplotlib: Static plotting
• seaborn: Statistical visualizations
• plotly: Interactive charts
• scikit-learn: Machine learning utilities
• yfinance: Yahoo Finance data
• scipy: Scientific computing
• jupyter: Interactive notebooks

📋 Development

Code Style

This project uses Black for code formatting:

black src/ tests/

Type Checking

MyPy is used for static type checking:

mypy src/

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

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

🆘 Support

• Create an issue for bug reports or feature requests
• Check the examples and documentation for common use cases
• Review the test suite for implementation details

🎯 Future Enhancements

• Additional data sources (Alpha Vantage, IEX Cloud)
• Machine learning-based risk models
• Factor model implementation
• Real-time portfolio monitoring
• Advanced optimization algorithms
• ESG scoring integration
• Options strategies analysis

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Happy Portfolio Analyzing! 📊💼