Successful Algorithmic Trading

Are you ready to design, test, and automate your own trading strategies using Python? Successful Algorithmic Trading by Michael Halls Moore is a comprehensive, practical guide that bridges quantitative finance and real-world execution for traders eager to build algorithmic systems from the ground up.

Written by the founder of QuantStart.com, this book walks you step by step through designing and implementing robust algorithmic strategies. You’ll learn everything from data sourcing, strategy research, and backtesting to portfolio construction, performance analysis, and risk management—plus how to automate your system and connect it to a broker.

Perfect for both intermediate retail traders and professional quants, the book emphasizes practical coding with Python, offering real code examples, modeling strategies like mean reversion and momentum, and implementing event-driven backtesters. Whether you’re trading equities, futures, or forex, this book gives you a durable foundation to build a sustainable and automated trading operation.

What You’ll Learn

• How to design, backtest, and optimize trading strategies scientifically
• How to work with financial data, from sourcing to cleaning and storage
• Implementing a full event-driven backtester and execution engine in Python
• Performance and risk analysis techniques, including Sharpe ratio, drawdowns, VaR
• Using Python libraries (NumPy, Pandas, Matplotlib, scikit-learn, statsmodels) for quantitative analysis
• Real-world considerations for trading automation, data quality, and latency

Key Benefits

• Learn directly from a practitioner and QuantStart.com founder
• Built-in Python code to accelerate strategy development
• Covers both data science and trading system engineering
• Explains how to manage transaction costs, backtesting bias, and live execution
• Equips you with tools to compete in modern algorithmic markets

Who This Book Is For

• Intermediate to advanced retail traders who want to go beyond charting and signals
• Aspiring quantitative traders and developers
• Self-taught Python users seeking to apply their skills to trading
• Finance professionals exploring algorithmic execution and strategy automation

Table of Contents

1. Introducing Algorithmic Trading
2. What Is Algorithmic Trading?
3. Successful Backtesting
4. Automated Execution
5. Sourcing Strategy Ideas
6. Software Installation
7. Financial Data Storage
8. Processing Financial Data
9. Statistical Learning
10. Time Series Analysis
11. Forecasting
12. Performance Measurement
13. Risk and Money Management
14. Event-Driven Trading Engine
15. Strategy Implementation
16. Strategy Optimization