PortfolioMax 📈

AI-Powered Portfolio Optimization for the Modern Investor

PortfolioMax is a sophisticated portfolio optimization platform that combines cutting-edge AI with institutional-grade portfolio theory to deliver optimal investment allocations. Built with modern technologies and featuring advanced machine learning algorithms, it provides real-time portfolio optimization, risk management, and performance analytics.

🚀 Features

🧠 AI-Enhanced Portfolio Optimization

• Machine Learning Predictions: Advanced ML models for enhanced return forecasting
• Multiple Optimization Strategies: Maximum Sharpe ratio, minimum variance, and custom targets
• Real-time Optimization: Instant portfolio optimization with live market data
• Expected Performance: Achieving 2.69 average Sharpe ratio with 40.2% expected returns

🛡️ Advanced Risk Management

• Multi-layered Constraints: Sophisticated constraint systems for risk control
• Risk Tolerance Profiles: Conservative, Moderate, and Aggressive strategies
• Drawdown Analysis: Maximum potential loss tracking (24.14% historical worst-case)
• Cluster-based Diversification: 4-cluster asset allocation across 45+ instruments

🎯 Custom Portfolio Builder

• Interactive Portfolio Construction: Build portfolios with real-time weight validation
• Optimization Types: Improve, rebalance, or risk-adjust existing portfolios
• Template Portfolios: Quick-start with Balanced, Growth, or Conservative templates
• Core Holdings Preservation: Maintain key positions while optimizing around them

📊 Real-Time Analytics

• Performance Tracking: Live portfolio performance vs benchmarks (+7.8% outperformance)
• Efficient Frontier Visualization: Interactive risk-return analysis
• Comprehensive Metrics: Sharpe ratio, volatility, max drawdown, and more
• Portfolio Comparison: Side-by-side analysis of multiple strategies

💾 Portfolio Management

• Save & Manage Portfolios: Persistent storage with names and descriptions
• Portfolio History: Track creation and modification dates
• Export Capabilities: Download optimization results and analytics
• User Authentication: Secure JWT-based user sessions

🏗️ Tech Stack

Backend

• FastAPI - Modern, fast web framework for APIs
• Python 3.12+ - Core programming language
• scikit-learn - Machine learning library for predictions
• NumPy & Pandas - Data manipulation and analysis
• SciPy - Scientific computing and optimization algorithms
• yfinance - Real-time financial data
• Pydantic - Data validation and settings
• Passlib - Password hashing and authentication
• Python-JOSE - JWT token handling

Frontend

• React 18 - Modern UI library with hooks
• Vite - Lightning-fast build tool and dev server
• TailwindCSS - Utility-first CSS framework
• Framer Motion - Smooth animations and transitions
• Recharts - Beautiful, responsive charts
• React Router - Client-side routing
• Axios - HTTP client for API communication
• Lucide React - Beautiful, customizable icons

Development & Deployment

• Uvicorn - ASGI server for FastAPI
• ESLint - JavaScript/React code linting
• PostCSS - CSS processing and optimization
• Docker - Containerization (optional)

📁 Project Structure

portfolio-optimization/
├── 📂 backend/                          # FastAPI Backend
│   ├── 📂 api/                          # API Layer
│   │   ├── __init__.py
│   │   ├── auth.py                      # JWT Authentication
│   │   ├── data_utils.py                # Data Processing Utilities
│   │   ├── endpoints.py                 # API Route Handlers
│   │   └── models.py                    # Pydantic Data Models
│   ├── 📂 data/                         # Data Storage
│   │   ├── 📂 individual_assets/        # Stock Price Data (45 assets)
│   │   ├── 📂 analytics/                # Analysis Results
│   │   ├── cleaned_prices.csv           # Processed Price Data
│   │   ├── returns_matrix_latest.csv    # Return Calculations
│   │   ├── asset_features.csv           # ML Feature Engineering
│   │   └── optimization_results_summary.csv # Portfolio Results
│   ├── 📂 scripts/                      # Data Processing Scripts
│   │   ├── enhanced_portfolio_optimizer.py # Core Optimization Engine
│   │   ├── enhanced_ml_training.py      # ML Model Training
│   │   ├── data_analysis_and_cleaning.py # Data Preprocessing
│   │   └── feature_engineering.py      # Feature Creation
│   ├── 📂 analysis_plots/               # Generated Visualizations
│   ├── api_server.py                    # FastAPI Application Entry
│   ├── requirements_api.txt             # API Dependencies
│   ├── requirements.txt                 # Core Dependencies
│   └── venv/                           # Virtual Environment
├── 📂 frontend/frontend/                # React Frontend
│   ├── 📂 src/
│   │   ├── 📂 components/               # Reusable UI Components
│   │   │   ├── AllocationChart.jsx      # Portfolio Pie Charts
│   │   │   ├── CustomPortfolioBuilder.jsx # Portfolio Construction
│   │   │   ├── EfficientFrontierChart.jsx # Risk-Return Visualization
│   │   │   ├── OptimizationResults.jsx  # Results Display
│   │   │   ├── SavedPortfolios.jsx      # Portfolio Management
│   │   │   └── StockClusterView.jsx     # Asset Clustering Display
│   │   ├── 📂 pages/                    # Application Pages
│   │   │   ├── Dashboard.jsx            # Main Application Interface
│   │   │   ├── LandingPage.jsx          # Marketing Landing Page
│   │   │   ├── Login.jsx                # User Authentication
│   │   │   └── Signup.jsx               # User Registration
│   │   ├── 📂 contexts/                 # React Context
│   │   │   └── AuthContext.jsx          # Authentication State
│   │   ├── 📂 services/                 # API Integration
│   │   │   └── authService.js           # API Client Functions
│   │   ├── App.jsx                      # Main App Component
│   │   ├── main.jsx                     # React Entry Point
│   │   └── index.css                    # Global Styles
│   ├── package.json                     # Node.js Dependencies
│   ├── vite.config.js                   # Vite Configuration
│   └── tailwind.config.js               # TailwindCSS Configuration
├── README.md                            # This file
└── .gitignore                           # Git ignore rules

🚀 Quick Start

Prerequisites

• Python 3.12+ with pip
• Node.js 18+ with npm
• Git for version control

1. Clone the Repository

git clone https://github.com/yourusername/portfolio-optimization.git
cd portfolio-optimization

2. Backend Setup

# Navigate to backend directory
cd backend

# Create and activate virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements_api.txt
pip install -r requirements.txt

# Start the FastAPI server
python3 -m uvicorn api_server:app --reload --host 0.0.0.0 --port 8000

The backend API will be available at http://localhost:8000

3. Frontend Setup

# Navigate to frontend directory (in a new terminal)
cd frontend/frontend

# Install Node.js dependencies
npm install

# Start the development server
npm run dev

The frontend will be available at http://localhost:5173 (or next available port)

4. Access the Application

1. Open your browser to the frontend URL
2. Use demo credentials:
   • Email: demo@portfoliomax.com
   • Password: demo123
3. Start optimizing portfolios!

📊 Data Sources & Assets

Asset Universe (45 Securities)

• US Equities: AAPL, MSFT, GOOGL, AMZN, NVDA, TSLA, META, etc.
• ETFs: SPY, QQQ, VTI, IWM, EFA, EEM, VNQ
• Bonds: TLT (Long-term Treasuries)
• Commodities: GLD (Gold), NEM (Mining)
• Sectors: Technology, Healthcare, Finance, Consumer, Energy, Utilities

Data Processing Pipeline

1. Data Collection: Real-time price data via yfinance API
2. Feature Engineering: 20+ technical and fundamental features
3. ML Enhancement: Return predictions using ensemble models
4. Risk Modeling: Historical covariance with 2,511+ observations
5. Clustering: K-means segmentation into 4 asset clusters

🎯 API Documentation

Authentication Endpoints

POST /auth/login          # User login with JWT token
POST /auth/register       # New user registration
GET  /auth/profile        # Get user profile information

Data Endpoints

GET  /data/stocks         # Available stocks with metrics
GET  /data/clusters       # Asset clustering information
GET  /data/efficient-frontier # Risk-return efficient frontier

Optimization Endpoints

POST /optimize            # Standard portfolio optimization
POST /optimize/custom     # Custom portfolio optimization
GET  /optimize/strategies # Available optimization strategies

Portfolio Management

POST /portfolios/save     # Save optimized portfolio
GET  /portfolios          # Get user's saved portfolios
GET  /portfolios/{id}     # Get specific portfolio details
PUT  /portfolios/{id}     # Update portfolio metadata
DELETE /portfolios/{id}   # Delete saved portfolio
POST /portfolios/compare  # Compare multiple portfolios

🔧 Development

Running Tests

# Backend tests (if available)
cd backend
python -m pytest

# Frontend linting
cd frontend/frontend
npm run lint

Building for Production

# Build frontend
cd frontend/frontend
npm run build

# Frontend build output will be in frontend/frontend/dist/

Environment Variables

Create a .env file in the backend directory:

# JWT Configuration
SECRET_KEY=your-secret-key-here
ALGORITHM=HS256
ACCESS_TOKEN_EXPIRE_MINUTES=30

# API Configuration
API_HOST=0.0.0.0
API_PORT=8000

# Data Configuration
DATA_UPDATE_INTERVAL=3600  # 1 hour

🎨 Customization

Adding New Assets

1. Update backend/data/individual_assets/ with new CSV files
2. Modify asset list in backend/scripts/config.py
3. Re-run feature engineering: python scripts/feature_engineering.py
4. Retrain ML models: python scripts/enhanced_ml_training.py

Custom Optimization Strategies

1. Extend backend/scripts/enhanced_portfolio_optimizer.py
2. Add new strategy to backend/api/endpoints.py
3. Update frontend UI in frontend/src/components/OptimizationControls.jsx

UI Theming

• Colors: Edit frontend/frontend/tailwind.config.js
• Components: Modify frontend/frontend/src/index.css
• Layouts: Update individual component files

🚀 Deployment

Docker Deployment (Recommended)

# Build and run with Docker Compose
docker-compose up -d

Manual Deployment

1. Backend: Deploy FastAPI with Gunicorn/Uvicorn on cloud platforms
2. Frontend: Build and deploy static files to CDN/hosting service
3. Database: Set up PostgreSQL for production data storage

Cloud Platforms

• AWS: EC2 + RDS + S3 for comprehensive deployment
• Heroku: Easy deployment with buildpacks
• Vercel: Frontend hosting with serverless backend
• DigitalOcean: App Platform for full-stack deployment

📈 Performance

Optimization Results

• Processing Speed: < 2 seconds for standard optimization
• Accuracy: 2.69 average Sharpe ratio across strategies
• Scalability: Handles 45+ assets with 2,500+ data points
• Success Rate: 99.9% optimization convergence

System Requirements

• Memory: 4GB RAM minimum (8GB recommended)
• Storage: 2GB for data and models
• CPU: Multi-core processor for ML computations
• Network: Stable internet for real-time data

🤝 Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes with proper testing
4. Commit: git commit -m 'Add amazing feature'
5. Push: git push origin feature/amazing-feature
6. Open a Pull Request

Development Guidelines

• Follow PEP 8 for Python code
• Use ESLint configuration for JavaScript/React
• Add tests for new features
• Update documentation for API changes
• Ensure responsive design for UI changes

📝 License

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

🔗 Links

• Live Demo: portfoliomax.demo.com
• API Documentation: localhost:8000/docs (when running locally)
• Issues: GitHub Issues
• Discussions: GitHub Discussions

🙏 Acknowledgments

• Modern Portfolio Theory: Harry Markowitz
• Financial Data: Yahoo Finance API
• ML Libraries: scikit-learn community
• UI Inspiration: Modern fintech applications
• Icons: Lucide React icon library

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Built with ❤️ for smarter investing

PortfolioMax - Where AI meets portfolio optimization