Advanced Algorithmic Trading

“Advanced Algorithmic Trading” by Michael Halls-Moore, founder of QuantStart.com, is an authoritative and practical guide for building professional-grade trading algorithms using quantitative finance principles and Python programming.

This book picks up where basic trading guides leave off. It takes you deep into the core of how hedge funds, prop firms, and quant teams build, test, and execute systematic strategies. You’ll learn everything from data ingestion and strategy architecture to live execution and performance tracking — all based on real-world development pipelines.

Halls-Moore covers:

• Strategy development using momentum, mean reversion, and machine learning
• Data acquisition from APIs, web scraping, and financial databases
• Portfolio optimization and risk-adjusted metrics
• Backtesting engines, walk-forward analysis, and slippage modeling
• Live trading with brokers and cloud deployment

If you’re serious about building a career in quantitative trading or managing your own algorithmic portfolio, this book is essential. It teaches you how to think, code, and optimize like a quant — not a hobbyist.

✅ What You’ll Learn:

• How to structure and code professional algorithmic trading systems
• The complete lifecycle: data collection → backtesting → execution
• Using Python, Pandas, NumPy, and object-oriented programming for strategy design
• How to analyze performance using Sharpe, CAGR, max drawdown, and more
• Portfolio management, position sizing, and rebalancing
• Integrating machine learning into trading models

💡 Key Benefits:

• Real-world code examples and scalable strategy templates
• Helps traders bridge the gap between academic theory and practical systems
• Offers production-ready solutions for backtesting and execution
• Explains risks, pitfalls, and debugging techniques for live environments
• Equips readers for institutional roles or independent quant trading

👤 Who This Book Is For:

• Intermediate to advanced traders with a programming background
• Quantitative analysts and data scientists moving into trading
• Developers building systematic trading strategies
• Finance professionals wanting to automate portfolio strategies
• Traders ready to build, test, and deploy scalable algorithms

📚 Table of Contents:

1. Introduction to Algorithmic Trading
2. The Quant Trading Workflow
3. Data Acquisition and Storage
4. Strategy Design and Factor Modeling
5. Backtesting Infrastructure
6. Performance Metrics and Portfolio Evaluation
7. Position Sizing and Portfolio Rebalancing
8. Execution Logic and Market Microstructure
9. Machine Learning in Finance
10. Live Trading Architecture and Deployment
11. Debugging, Logging, and System Monitoring
12. Advanced Topics and Future Research