Advances in financial machine learning / Marcos Lopez de Prado.  (Text) (Text)

Lopez de Prado, Marcos Mailoc
Call no.: HG104 .L674 2018Publication: New Jersey : Wiley, c2018Description: xxi, 366 p. : illISBN: 9781119482086; 1119482089Subject(s): Finance -- Data processingFinance -- Mathematical modelsMachine learningLOC classification: HG104 | .L674 2018
Contents:Financial Machine Learning as a Distinct Subject -- Part 1 Data Analysis. Financial Data Structures -- Labeling -- Sample Weights -- Fractionally Differentiated Features -- Part 2 Modelling. Ensemble Methods -- Cross-validation in Finance -- Feature Importance -- Hyper-parameter Tuning with Cross-Validation -- Part 3 Backtesting. Bet Sizing -- The Dangers of Backtesting -- Backtesting through Cross-Validation -- Backtesting on Synthetic Data -- Backtest Statistics -- Understanding Strategy Risk -- Machine Learning Asset Allocation -- Part 4 Useful Financial Features. Structural Breaks -- Entropy Features -- Microstructural Features -- Part 5 High-Performance Computing Recipes. Multiprocessing and Vectorization -- Brute Force and Quantum Computers -- High-Performance Computational Intelligence and Forecasting Technologies.
Summary: "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"--Summary: "This book begins by structuring financial data in a way that is amenable to machine learning (ML) algorithms. Then, the author discusses how to conduct research with ML algorithms on that data and how to backtest your discoveries. Most of the problems and solutions are explained using math, supported by code. This makes the book very practical and hands-on. Readers become active users who can test the solutions proposed in their work. Readers will learn how to structure, label, weight, and backtest data. Machine learning is the future, and this book will equip investment professionals with the tools to utilize it moving forward"--
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Includes bibliographical references and index.

Financial Machine Learning as a Distinct Subject -- Part 1 Data Analysis. Financial Data Structures -- Labeling -- Sample Weights -- Fractionally Differentiated Features -- Part 2 Modelling. Ensemble Methods -- Cross-validation in Finance -- Feature Importance -- Hyper-parameter Tuning with Cross-Validation -- Part 3 Backtesting. Bet Sizing -- The Dangers of Backtesting -- Backtesting through Cross-Validation -- Backtesting on Synthetic Data -- Backtest Statistics -- Understanding Strategy Risk -- Machine Learning Asset Allocation -- Part 4 Useful Financial Features. Structural Breaks -- Entropy Features -- Microstructural Features -- Part 5 High-Performance Computing Recipes. Multiprocessing and Vectorization -- Brute Force and Quantum Computers -- High-Performance Computational Intelligence and Forecasting Technologies.

"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"--

"This book begins by structuring financial data in a way that is amenable to machine learning (ML) algorithms. Then, the author discusses how to conduct research with ML algorithms on that data and how to backtest your discoveries. Most of the problems and solutions are explained using math, supported by code. This makes the book very practical and hands-on. Readers become active users who can test the solutions proposed in their work. Readers will learn how to structure, label, weight, and backtest data. Machine learning is the future, and this book will equip investment professionals with the tools to utilize it moving forward"--

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