Learn algorithmic trading with Python by building one complete strategy, from market data to automated execution. Explore machine learning, AI signals, and rigorous backtesting

Key Features

• Follow one volatility tail-hedge strategy from market data to cloud deployment, and build more that show a real edge
• Explore time-series models, machine learning, and AI-driven alpha signal generation
• Challenge backtests with walk-forward analysis and detect lookahead bias and overfitting

Book Description

Go beyond learning Python libraries in isolation. This book teaches you the complete workflow professionals use to build systematic trading strategies.

You start by acquiring, exploring, and cleaning market data, handling missing values, misaligned time series, and noise. From there, you form trading hypotheses, generate signals, and define entry and exit rules. You evaluate risk-adjusted performance and challenge your backtests through out-of-sample testing and walk-forward analysis. Next, you explore time-series models, machine learning for trading, and AI-driven alpha signal generation. Finally, you build a deployable strategy repository and automate execution in the cloud. One volatility tail-hedge strategy runs from raw data to cloud deployment, and you build five further strategies from published research, covering momentum, regime detection, economic indicators, and sector rotation, each tested to confirm that its signal does real work.

Drawing on 25 years of institutional trading experience, the book shows you how to spot lookahead bias and overfitting and distinguish promising signals from noise. You work with VectorBT and QuantStats, with PythonAnywhere for deployment. The book includes all the code, and by the end you have working strategies and a repeatable framework for algorithmic trading with Python

What you will learn

• Acquire, clean, and denoise financial time-series data
• Use exploratory analysis to develop trading hypotheses
• Build momentum, mean-reversion, and time-series strategies
• Apply machine learning and AI to alpha signal generation
• Validate backtests with out-of-sample and walk-forward testing
• Detect lookahead bias, survivorship bias, and overfitting
• Evaluate strategies using risk-adjusted performance metrics
• Build a deployable repository and automate cloud execution

Who this book is for

This book is for traders going systematic, data scientists entering quantitative finance, and portfolio managers automating their workflows. Whether you are building your first complete strategy or moving beyond isolated Python scripts, you share one frustration: books that teach tools but not how to put them together. This book gives you that missing piece.

You should be comfortable with basic Python, including functions, loops, and working in Jupyter notebooks, and with finance fundamentals such as returns, volatility, and risk.

Table of Contents

Part 1. Foundations and Data

Chapter 1. Introducing Algorithmic Trading

Chapter 2. Setting up a Python Quantitative Workflow

Chapter 3. Data Retrieval

Chapter 4. Understanding Exploratory Data Analysis

Chapter 5. Data Preparation

Chapter 6. Removing Noise From Financial Data

Part 2. Evaluating and Validating Strategies

Chapter 7. Evaluating Strategy Performance

Chapter 8. Understanding Backtesting

Chapter 9. Out-of-Sample Testing

Chapter 10. Walk-Forward Analysis

Chapter 11. How to Spot a Bad Backtest

Part 3. Forecasting and Rules-Based Strategies

Chapter 12. Time-Series Models

Chapter 13. Algorithmic Trading Strategies

Part 4. Machine Learning and AI Signals

Chapter 14. Climbing the Machine Learning Ladder

Chapter 15. Machine Learning in Practice

Chapter 16. AI as an Alpha Signal Generator

Chapter 17. Building a Deployable Strategy Repository

Chapter 18. Running Your Strategy Without You

Chapter 19. Unlock the Code Bundle and the PDF Version

Appendix A. Asset Class Overview

Appendix B. Cheat Sheets and Checklists

Appendix C. Data Provider Resources

About the Author

Cordell Tanny possesses over 25 years of expertise in designing and deploying algorithmic trading systems for institutional markets. Leveraging a B.Sc. in Biology from McGill University, he applies a scientific, hypothesis-driven methodology to finance. He held senior roles at Standard Life Investments and RBC, overseeing a $2 billion CAD multi-asset program. Cordell is a CFA Charterholder, Certified FRM, and holds the FDP charter. Currently a quantitative consultant, he architects bespoke Python-based trading infrastructures and machine learning models for investment firms. He lectures at Concordia University and develops comprehensive online curricula on systematic trading. He frequently addresses CFA Societies on the technical implementation of AI strategies.