Advances in Financial Machine Learning

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

• Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
• Python for Finance: Analyze Big Financial Data
• Introduction To Algo Trading: How Retail Traders Can Successfully Compete With Professional Traders
• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
• Building Winning Algorithmic Trading Systems: A Trader's Journey From Data Mining to Monte Carlo Simulation to Live Trading (Wiley Trading)
• Big Debt Crises
• Algorithmic Trading: Winning Strategies and Their Rationale (Wiley Trading)
• Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more
• The Unrules: Man, Machines and the Quest to Master Markets
• Systematic Trading: A unique new method for designing trading and investing systems