Portfolio Analytics AI 📈

⚠️ Project Status

This project is currently a Proof of Concept (POC) and is in very early stages of development.

Important Disclaimers:

• 🚧 Early Development: This is a POC implementation and should be considered experimental
• 📊 Unvalidated: The algorithms and calculations have not been validated with real-world financial data
• 👨‍💼 No Professional Review: The financial models and risk calculations have not been reviewed by finance professionals
• 🚫 Not Production Ready: This package is NOT ready for production use or real investment decisions

Use at your own risk. This software is intended for educational and research purposes only.

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A comprehensive Python package for AI-powered portfolio analytics, optimization, and risk management. Built on modern portfolio theory and advanced statistical methods, this package provides professional-grade tools for investment analysis, portfolio construction, and risk assessment.

🌍 Where It Can Be Used

Portfolio Analytics AI is designed for a wide range of financial applications:

Investment Management

• Asset Managers: Portfolio construction, optimization, and performance monitoring
• Wealth Management: Client portfolio analysis and risk assessment
• Hedge Funds: Quantitative trading strategies and risk management
• Pension Funds: Long-term portfolio optimization and liability matching

Financial Research & Academia

• Quantitative Research: Backtesting investment strategies and factor analysis
• Academic Research: Financial modeling and portfolio theory validation
• Risk Management: Institution-wide risk assessment and stress testing
• Financial Education: Teaching modern portfolio theory and investment concepts

Fintech & Investment Platforms

• Robo-Advisors: Automated portfolio allocation and rebalancing
• Investment Apps: Portfolio analytics and performance tracking
• Trading Platforms: Risk assessment and optimization tools
• Financial Advisory: Client portfolio recommendations and reporting

Personal Finance

• Individual Investors: Portfolio optimization and risk analysis
• Financial Advisors: Client portfolio construction and monitoring
• Retirement Planning: Long-term investment strategy optimization

🚀 Installation

From PyPI (Recommended)

pip install portfolio-analytics-ai

From Source

git clone https://github.com/shamitv/PortfolioAnalyticsAI.git
cd PortfolioAnalyticsAI
pip install -e .

📁 Project Structure

PortfolioAnalyticsAI/
├── src/portfolio_analytics/           # Main package directory
│   ├── __init__.py                   # Package initialization and exports
│   ├── portfolio.py                  # Core Portfolio class
│   ├── data_provider.py              # Data fetching and management
│   ├── optimization.py               # Portfolio optimization algorithms
│   ├── risk_models.py                # Risk modeling and VaR calculations
│   ├── performance.py                # Performance metrics and analysis
│   ├── visualization.py              # Plotting and visualization tools
│   ├── analyzer.py                   # Comprehensive portfolio analyzer
│   └── sample_data/                  # Sample datasets and market data
├── notebooks/                        # Jupyter notebook examples
│   ├── 01_getting_started.ipynb      # Basic usage and introduction
│   ├── 02_analyzer_demo.ipynb        # Portfolio analyzer demonstration
│   ├── 03_cache_population_demo.ipynb # Data caching examples
│   └── 04_random_portfolio_analysis.ipynb # Advanced analysis examples
├── tests/                            # Unit tests and integration tests
├── sample_data/                      # External sample data files
├── pyproject.toml                    # Package configuration
└── README.md                         # Project documentation

🏗️ Core Classes Overview

1. Portfolio (portfolio.py)

The central class for portfolio management and analysis.

Key Features:

• Portfolio construction with custom weights or equal weighting
• Performance metrics calculation (returns, volatility, Sharpe ratio)
• Integration with data providers for historical data loading
• Portfolio rebalancing and weight optimization
• Risk-adjusted performance analysis

Primary Methods:

• load_data(): Load historical price data
• portfolio_return(), annual_return(): Calculate returns
• portfolio_volatility(), annual_volatility(): Calculate risk metrics
• sharpe_ratio(): Risk-adjusted performance
• optimize_weights(): Portfolio optimization

2. DataProvider (data_provider.py)

Comprehensive data fetching and management system.

Key Features:

• Yahoo Finance integration for real-time and historical data
• SQLite caching for improved performance
• Risk-free rate data (Treasury yields)
• Market calendars and trading day handling
• Support for stocks, ETFs, and sector data

Primary Methods:

• get_price_data(): Fetch historical price data
• get_risk_free_rate(): Retrieve Treasury yield data
• cache_stock_data(): Cache data for offline use
• get_sector_etfs(): Access sector ETF information
• get_sp500_companies(): S&P 500 constituent data

3. PortfolioOptimizer (optimization.py)

Advanced portfolio optimization using modern portfolio theory.

Key Features:

• Mean-variance optimization (Markowitz)
• Maximum Sharpe ratio optimization
• Minimum variance portfolios
• Target return optimization
• Efficient frontier calculation
• Custom constraints and bounds support

Primary Methods:

• optimize(): Main optimization function
• calculate_efficient_frontier(): Generate efficient frontier
• max_sharpe_optimization(): Find maximum Sharpe ratio portfolio
• min_variance_optimization(): Find minimum variance portfolio

4. RiskModel (risk_models.py)

Comprehensive risk modeling and Value-at-Risk calculations.

Key Features:

• Multiple VaR calculation methods (Historical, Parametric, Monte Carlo)
• Expected Shortfall (Conditional VaR)
• Maximum Drawdown analysis
• Downside risk metrics
• Portfolio-level and component-level risk attribution

Primary Methods:

• calculate_var(): Value at Risk calculation
• calculate_expected_shortfall(): Expected Shortfall
• calculate_maximum_drawdown(): Maximum drawdown analysis
• calculate_downside_deviation(): Downside risk metrics
• calculate_component_var(): Risk decomposition

5. PerformanceAnalyzer (performance.py)

Portfolio performance analysis and benchmarking.

Key Features:

• Comprehensive performance metrics
• Benchmark comparison and relative performance
• Risk-adjusted return measures
• Rolling performance analysis
• Attribution analysis

Primary Methods:

• calculate_metrics(): Comprehensive performance metrics
• alpha_beta_analysis(): Market risk analysis
• tracking_error(): Benchmark tracking metrics
• information_ratio(): Risk-adjusted excess returns
• rolling_metrics(): Time-varying performance analysis

6. PortfolioVisualizer (visualization.py)

Professional-grade visualization tools for portfolio analysis.

Key Features:

• Static and interactive charts
• Efficient frontier visualization
• Performance dashboards
• Risk analysis plots
• Customizable styling and themes

Primary Methods:

• plot_efficient_frontier(): Interactive efficient frontier
• plot_cumulative_returns(): Performance over time
• plot_correlation_matrix(): Asset correlation heatmap
• plot_drawdown(): Drawdown analysis
• create_dashboard(): Comprehensive dashboard

7. Analyzer (analyzer.py)

AI-powered comprehensive portfolio analysis system.

Key Features:

• Automated portfolio analysis and insights
• LLM-ready visualizations and metrics
• Greeks calculation for options portfolios
• Multi-dimensional risk assessment
• Integrated reporting system

Primary Methods:

• generate_comprehensive_analysis(): Complete portfolio analysis
• generate_metrics(): All performance and risk metrics
• generate_visualizations(): Chart generation for analysis
• generate_insights(): AI-powered insights and recommendations

📚 Sample Notebooks

The notebooks/ directory contains comprehensive examples and tutorials:

01_getting_started.ipynb

• Purpose: Introduction to basic portfolio construction and analysis
• Content: Creating portfolios, loading data, basic metrics calculation
• Audience: Beginners to portfolio analytics
• Key Concepts: Portfolio class usage, data loading, simple optimization

02_analyzer_demo.ipynb

• Purpose: Demonstrates the comprehensive Analyzer class
• Content: Advanced analytics, visualization generation, AI-powered insights
• Audience: Intermediate users seeking comprehensive analysis
• Key Concepts: Multi-dimensional analysis, automated reporting, visualization

03_cache_population_demo.ipynb

• Purpose: Data caching and performance optimization
• Content: Setting up data caches, offline analysis, performance improvements
• Audience: Users working with large datasets or limited internet
• Key Concepts: Data caching, SQLite integration, performance optimization

04_random_portfolio_analysis.ipynb

• Purpose: Advanced portfolio analysis with random portfolios and optimization
• Content: Monte Carlo portfolio generation, efficient frontier analysis, risk decomposition
• Audience: Advanced users and researchers
• Key Concepts: Portfolio simulation, optimization comparison, risk analysis

📊 Risk Metrics Calculation

Value at Risk (VaR)

Measures the potential loss in portfolio value over a specific time period at a given confidence level.

Calculation Methods:

1. Historical VaR: Uses historical return distribution

   VaR = Percentile of historical returns at (1 - confidence_level)
   

2. Parametric VaR: Assumes normal distribution of returns

   VaR = μ - (σ × Z_α)
   where μ = mean return, σ = standard deviation, Z_α = critical value
   

3. Monte Carlo VaR: Uses simulated return paths

   VaR = Percentile of simulated returns at (1 - confidence_level)
   

Expected Shortfall (ES)

Average loss exceeding the VaR threshold, providing tail risk measurement.

ES = E[Loss | Loss > VaR]

Maximum Drawdown

Maximum peak-to-trough decline in portfolio value.

Drawdown_t = (Peak_value - Current_value) / Peak_value
Max_Drawdown = max(Drawdown_t) for all t

Sharpe Ratio

Risk-adjusted return measure.

Sharpe_Ratio = (Portfolio_Return - Risk_Free_Rate) / Portfolio_Volatility

Sortino Ratio

Downside risk-adjusted return measure.

Sortino_Ratio = (Portfolio_Return - Risk_Free_Rate) / Downside_Deviation

Beta

Systematic risk relative to market benchmark.

Beta = Covariance(Portfolio_Returns, Market_Returns) / Variance(Market_Returns)

🎨 Visualization Gallery

1. Efficient Frontier Plot

Description: Interactive plot showing the risk-return trade-off for optimal portfolios.

Usage & Interpretation:

• X-axis: Portfolio volatility (risk)
• Y-axis: Expected return
• Curve: Represents optimal portfolios at each risk level
• Points: Individual assets and current portfolio position
• Interpretation: Portfolios on the frontier are optimal; points below are sub-optimal

2. Cumulative Returns Chart

Description: Time series plot comparing portfolio performance against benchmarks.

Usage & Interpretation:

• X-axis: Time period
• Y-axis: Cumulative return (starting from 1.0 or 100%)
• Lines: Portfolio vs benchmark performance
• Interpretation: Upward slope indicates positive returns; steeper slope shows better performance

3. Correlation Matrix Heatmap

Description: Color-coded matrix showing correlations between portfolio assets.

Usage & Interpretation:

• Color Scale: Red (negative correlation) to Blue (positive correlation)
• Values: Range from -1 (perfect negative) to +1 (perfect positive)
• Interpretation: Lower correlations indicate better diversification benefits

4. Portfolio Composition Pie Chart

Description: Visual representation of portfolio weights and asset allocation.

Usage & Interpretation:

• Sectors: Different colors represent different assets
• Size: Proportional to portfolio weight
• Interpretation: Shows concentration risk and diversification level

5. Drawdown Analysis

Description: Time series showing portfolio drawdowns from peak values.

Usage & Interpretation:

• X-axis: Time period
• Y-axis: Drawdown percentage (negative values)
• Shaded areas: Periods of loss from peak
• Interpretation: Deeper/longer drawdowns indicate higher risk periods

6. Risk-Adjusted Metrics Dashboard

Description: Comprehensive dashboard showing multiple risk and performance metrics.

Usage & Interpretation:

• Multiple Panels: Various risk metrics in organized layout
• Gauges/Bars: Visual representation of metric values
• Benchmarks: Comparison against market standards
• Interpretation: Provides holistic view of portfolio risk profile

7. Returns Distribution Histogram

Description: Histogram showing the distribution of portfolio returns with normal distribution overlay.

Usage & Interpretation:

• Bars: Frequency of returns in each range
• Curve: Normal distribution overlay
• Tail Areas: Extreme loss/gain probabilities
• Interpretation: Shows return distribution characteristics and tail risks

8. Performance Attribution Chart

Description: Breakdown of portfolio performance by asset or sector contributions.

Usage & Interpretation:

• Bars: Contribution of each holding to total return
• Colors: Positive (green) vs negative (red) contributions
• Interpretation: Identifies which holdings drove performance

🔧 Quick Start Example

import pandas as pd
from portfolio_analytics import Portfolio, DataProvider, PortfolioOptimizer, PortfolioVisualizer

# Initialize components
data_provider = DataProvider()
visualizer = PortfolioVisualizer()

# Create a technology portfolio
tech_stocks = ['AAPL', 'GOOGL', 'MSFT', 'AMZN', 'TSLA']
portfolio = Portfolio(tech_stocks, name="Tech Portfolio")

# Load historical data
portfolio.load_data(data_provider, start_date="2020-01-01", end_date="2023-12-31")

# Calculate basic metrics
annual_return = portfolio.annual_return()
annual_vol = portfolio.annual_volatility()
sharpe = portfolio.sharpe_ratio()

print(f"Annual Return: {annual_return:.2%}")
print(f"Annual Volatility: {annual_vol:.2%}")
print(f"Sharpe Ratio: {sharpe:.2f}")

# Optimize portfolio
optimizer = PortfolioOptimizer()
optimal_weights = optimizer.optimize(portfolio.returns, method="max_sharpe")

# Create visualizations
fig = visualizer.plot_efficient_frontier(portfolio.returns)
fig.show()

# Generate comprehensive analysis
from portfolio_analytics import Analyzer

analyzer = Analyzer(portfolio)
analysis = analyzer.generate_comprehensive_analysis()

📈 Advanced Features

Risk Management

• Multi-method VaR calculation: Historical, Parametric, Monte Carlo
• Stress testing: Scenario analysis and sensitivity testing
• Risk decomposition: Component and marginal risk contributions
• Tail risk analysis: Expected Shortfall and extreme value analysis

Portfolio Optimization

• Multiple objectives: Return maximization, risk minimization, Sharpe optimization
• Constraints support: Weight bounds, sector limits, turnover constraints
• Robust optimization: Handling parameter uncertainty
• Multi-period optimization: Dynamic rebalancing strategies

Performance Analysis

• Attribution analysis: Performance decomposition by factors
• Style analysis: Return-based style analysis
• Benchmark comparison: Relative performance metrics
• Risk-adjusted returns: Multiple Sharpe-like ratios

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

📄 License

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

📞 Support

• GitHub Issues: Report bugs or request features
• Documentation: Full documentation
• PyPI Package: portfolio-analytics-ai

🎯 Roadmap

Upcoming Features

• Machine Learning Integration: ML-based return predictions and risk modeling
• Alternative Data Sources: Integration with additional financial data providers
• Options Analytics: Options pricing and Greeks calculation
• ESG Integration: Environmental, Social, and Governance factor analysis
• Real-time Analytics: Live portfolio monitoring and alerts
• API Development: RESTful API for portfolio analytics services

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Portfolio Analytics AI - Empowering investment decisions through advanced analytics and AI-driven insights.