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. Praise for ADVANCES in FINANCIAL MACHINE LEARNING Dr. López de Prado has written the first comprehensive book describing the application of modern ML to financial modeling. The book blends the latest technological developments in ML with critical life lessons learned from the author's decades of financial experience in leading academic and industrial institutions. I highly recommend this exciting book to both prospective students of financial ML and the professors and supervisors who teach and guide them. --PROF. PETER CARR, Chair of the Finance and Risk Engineering Department, NYU Tandon School of Engineering Financial problems require very distinct machine learning solutions. Dr. López de Prado's book is the first one to characterize what makes standard machine learning tools fail when applied to the field of finance, and the first one to provide practical solutions to unique challenges faced by asset managers. Everyone who wants to understand the future of finance should read this book. --PROF. FRANK FABOZZI, EDHEC Business School; Editor of The Journal of Portfolio Management Marcos has assembled in one place an invaluable set of lessons and techniques for practitioners seeking to deploy machine learning methods in finance. Marcos's insightful book is laden with useful advice to help keep a curious practitioner from going down any number of blind alleys, or shooting oneself in the foot. --ROSS GARON, Head of Cubist Systematic Strategies; Managing Director, Point72 Asset Management The first wave of quantitative innovation in finance was led by Markowitz optimization. Machine learning is the second wave and it will touch every aspect of finance. López de Prado's Advances in Financial Machine Learning is essential for readers who want to be ahead of the technology rather than being replaced by it. --PROF. CAMPBELL HARVEY, Duke University; Former President of the American Finance Association The author's academic and professional first-rate credentials shine through the pages of this book-- indeed, I could think of few, if any, authors better suited to explaining both the theoretical and the practical aspects of this new and (for most)unfamiliar subject. Destined to become a classic in this rapidly burgeoning field. --PROF. RICCARDO REBONATO, EDHEC Business School; Former Global Head of Rates and FX Analytics at PIMCO Preamble Foreword by Paul Tudor Jones, II,. Tudor Investment Corporation. Foreword by Professor. Horst Simon,. Lawrence Berkeley National Laboratory. 1. Financial ML as a distinct subject. Section 1: Data Analysis 2. Financial data structures. 3. Labeling. 4. Sample weights. 5. Fractional differentiation. CE: Continue deleting punctuation from TOC Section 2: Modelling 6. Ensemble methods. 7. Cross validation in finance. 8. Feature importance. 9. Hyper-parameter tuning with CV. Section 3: Backtesting 10. Bet sizing. 11. The dangers of backtesting. 12. Backtesting through CV. 13. Backtesting on synthetic data. 14. Backtest statistics. 15. Understanding strategy risk. 16. ML asset allocation. Section 4: Useful Financial Features 17. Structural breaks. 18. Entropy features. 19. Microstructural features. Section 5: HPC Recipes 20. Multiprocessing and vectorization. 21. Brute force and quantum computers. Epilogue 22. Partnering with Berkeley Lab (by Dr. John Wu). 23. ML at University of California, Berkeley (by TBD).