TOTO-SPY-Strategy

A QuantConnect algorithmic trading strategy leveraging TOTO (Time Series Optimized Transformer for Observability) models for SPY trading.

Overview

This project implements a sophisticated trading strategy that combines:

• TOTO Models: State-of-the-art transformer-based time series forecasting
• SPY Focus: Specialized for S&P 500 ETF trading
• QuantConnect Integration: Ready for backtesting and live trading

Features

• 🔮 TOTO Forecasting: Advanced transformer models for price prediction
• 📊 Technical Analysis: Comprehensive indicator integration
• ⚡ Real-time Processing: Optimized for live trading environments
• 🎯 Risk Management: Built-in position sizing and risk controls
• 📈 Performance Analytics: Detailed backtesting and performance metrics

Project Structure

TOTO-SPY-Strategy/
├── algorithms/          # QuantConnect algorithm implementations
├── models/             # TOTO model configurations and utilities
├── data/               # Historical data and preprocessing
├── backtests/          # Backtest results and analysis
├── research/           # Jupyter notebooks for strategy development
├── utils/              # Shared utilities and helpers
└── docs/               # Documentation and strategy notes

Getting Started

Prerequisites

• Python 3.11+
• QuantConnect CLI or LEAN engine
• Required Python packages (see requirements.txt)

Installation

1. Clone the repository:

git clone https://github.com/[your-username]/TOTO-SPY-Strategy.git
cd TOTO-SPY-Strategy

2. Install dependencies:

pip install -r requirements.txt

3. Configure QuantConnect credentials (if using cloud):

lean login

Usage

1. Local Development:

lean research

2. Backtesting:

lean backtest

3. Live Trading (paper/live):

lean live deploy

Strategy Components

TOTO Integration

• Model training pipeline
• Real-time inference
• Feature engineering for financial data

Trading Logic

• Signal generation from TOTO predictions
• Entry/exit rules
• Position sizing algorithms

Risk Management

• Drawdown controls
• Volatility-based position sizing
• Stop-loss mechanisms

Performance Metrics

Track key performance indicators:

• Sharpe Ratio
• Maximum Drawdown
• Alpha/Beta vs SPY
• Win Rate
• Profit Factor

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

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

Disclaimer

This software is for educational and research purposes only. Past performance does not guarantee future results. Trading involves risk of loss.