smartportfolio 2.1.5

GNN + Prophet + DRL Portfolio Optimization TUI - A terminal-based intelligent asset allocation system

📊 SmartPortfolio

Intelligent Portfolio Optimization using Graph Neural Networks, Prophet Forecasting, and Deep Reinforcement Learning

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🚀 Overview

SmartPortfolio is a terminal-based portfolio optimization system that combines cutting-edge machine learning techniques to generate intelligent asset allocations:

• 🔗 Graph Neural Networks (GAT) — Model complex asset relationships and correlations
• 📈 Prophet Forecasting — Time series predictions with seasonality awareness
• 🤖 Deep Reinforcement Learning (PPO) — Dynamic allocation via hierarchical agents
• 💻 Neural Terminal TUI — Beautiful, Bloomberg-inspired terminal interface

✨ Features

Feature	Description
PyTorch GAT	Pure PyTorch Graph Attention Network for asset embeddings
Correlation Graphs	Dynamic asset relationship visualization
Live Prices	Real-time market data via yfinance
Portfolio Metrics	Expected return, volatility, Sharpe ratio
Plot Export	Save allocation charts (pie, bar, dashboard)
8GB RAM Optimized	Memory-efficient for consumer hardware

📦 Installation

Prerequisites

• Python 3.10 or higher
• pip package manager

Quick Install

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

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

# Install package
pip install -e .

Dependencies

Core dependencies are automatically installed:

• torch — PyTorch for neural networks
• networkx — Graph operations
• prophet — Time series forecasting
• stable-baselines3 — Reinforcement learning
• textual — Terminal user interface
• yfinance — Market data

🎮 Usage

Launch the TUI

# Start with default tickers
smartportfolio

# Start with custom ticker file
smartportfolio --tickers my_tickers.csv

TUI Commands

Command	Description
LOAD <file>	Load tickers from CSV/XLSX file
LOADWEIGHTS <file>	Load previous portfolio weights
RUN	Execute optimization pipeline
STATUS	Show current system status
EXPORT	Export weights to CSV
PLOT	Save allocation charts
GRAPH	Save correlation graph visualization
HELP	Display help information
CLEAR	Reset all state

Keyboard Shortcuts

Key	Action
q	Quit application
r	Run optimization
l	Load tickers
e	Export results
h	Show help
Esc	Focus command bar

Ticker File Format

ticker
AAPL
MSFT
GOOGL
AMZN
META

📊 Output Files

All outputs are saved to the outputs/ directory with timestamp-UUID naming:

File Pattern	Description
*_portfolio_weights.csv	Asset weights allocation
*_allocation_pie.png	Pie chart visualization
*_allocation_bar.png	Bar chart visualization
*_correlation_graph.png	Asset relationship graph
*_dashboard.png	Combined metrics dashboard

🏗️ Architecture

smartportfolio/
├── __init__.py          # Package initialization
├── __main__.py          # CLI entry point
├── app.py               # Main TUI application
├── config.py            # Configuration management
├── visualization.py     # Matplotlib plotting
├── data/                # Data handling modules
│   ├── fetcher.py       # Yahoo Finance data fetcher
│   ├── features.py      # Feature engineering
│   └── storage.py       # Local file storage
├── graph/               # Graph neural network modules
│   ├── builder.py       # Dynamic graph construction
│   ├── gat.py           # NumPy GAT implementation
│   └── torch_gat.py     # PyTorch GAT implementation
├── forecasting/         # Time series forecasting
│   └── prophet_model.py # Prophet integration
├── rl/                  # Reinforcement learning
│   ├── environment.py   # Gymnasium environment
│   └── agent.py         # PPO agent
└── components/          # TUI widgets
    ├── header.py        # App header
    ├── sidebar.py       # Watchlist sidebar
    └── main_content.py  # Portfolio overview

🔧 Configuration

Configuration is managed via smartportfolio/config.py:

# Model parameters
gat_embedding_dim = 32
gat_num_heads = 4
gat_dropout = 0.1

# Data parameters
default_period = "2y"
correlation_threshold = 0.3

# RL parameters
rl_learning_rate = 0.0003
train_episodes = 100

🧪 Development

Setup Development Environment

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Format code
black smartportfolio/

# Lint code
ruff check smartportfolio/

Running Tests

pytest tests/ -v --cov=smartportfolio

📈 Performance

Tested on consumer hardware (8GB RAM):

Operation	Time	Memory
Data fetch (10 tickers)	~5s	~200MB
Graph construction	<1s	~50MB
GAT embedding	~2s	~300MB
Full pipeline	~15s	~500MB

🤝 Contributing

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

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.

🔐 Security

For security concerns, please see our Security Policy.

📞 Support

• 📖 Documentation
• 🐛 Issue Tracker
• 💬 Discussions

🙏 Acknowledgments

• Textual — Terminal UI framework
• Prophet — Time series forecasting
• Stable-Baselines3 — RL algorithms
• yfinance — Market data

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