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After the tumultuous period marked by the 2007-2008 Financial Crisis and the Great Recession of 2009, the _nancial industry has entered a new era. Quantitative strategies, together with statistical models and methods, knowledge representation and data analytics, and algorithms and informatics for their development and implementation, are of increasing importance in this new era. The onset of this era is marked by two \revolutions” that have transformed modern life and business. One is technological, dubbed \the FinTech revolution” for _nancial services by the May 9, 2015, issue of The Economist which says: \In the years since the crash of 2007-08, policymakers have concentrated on making _nance safer. . . . Away from the regulator spotlight, another revolution is under way. . . . From payments to wealth management, from peerto- peer lending to crowdfunding, a new generation of startups is taking aim at the heart of the industry { and a pot of revenues that Goldman Sachs estimates is worth $4.7 trillion. Like other disrupters from Silicon Valley, _ntech _rms are growing fast.” The other is called \big data revolution”. In August 2014, the UN Secretary General commissioned an Independent Advisory Group to make recommendations on \bringing about a data revolution” in sustainable development. The October 2012 issue of Harvard Business Review features an article on \Big Data: The Management Revolution”. On August 20, 2015, the Premier of the People’s Republic of China asked di_erent government departments to share their data and implement a big data action plan. Soon afterward, on September 5, 2015, the country’s State Council issued an action plan to develop and promote big data applications in economic planning, _nance, homeland security, transportation, agriculture, environment, and health care. To respond to the opportunities and challenges of this new era and the big data and FinTech revolutions that have fascinated their students, the two academics (Guo and Lai) on the author team, who happen to be teaching students in the greater Silicon Valley, developed and taught new courses in the Financial Engineering/Mathematics Curriculum at Berkeley and Stanford in the past three years and exchanged their course material. They also invited practitioners from industry, in particular the other two co-authors (Shek and Wong), to give guest lectures and seminars for these courses. This informal collaboration quickly blossomed into an intense concerted e_ort to write up the material into the present book that can be used not only to teach these courses more e_ectively but also to give short courses and training programs elsewhere, as we have done at Shanghai Advanced Institute of Finance, Fudan University Tsinghua University, Chinese University of Hong Kong, Hong Kong University of Science & Technology, National University of Singapore, National Taiwan University, and Seoul National University. A prerequisite or co-requisite of these courses is a course at the level of STATS 240 (Statistical Methods in Finance) at Stanford, which covers the _rst six chapters of Lai and Xing (2008). We will therefore make ample references to the relevant sections of these six chapters, summarizing their main results without repeating the details. The website for this book can be found at http://lait.web.stanford. edu/quantstratbook/. The datasets for the exercises and examples can be downloaded from the website. We want to highlight in the book an interdisciplinary approach to the development and implementation of algorithmic trading and quantitative strategies. The interdisciplinary approach, which involves computer science and engineering, _nance and economics, mathematics and statistics, law and regulation, is reected not only in the research activities of the recently established Financial and Risk Modeling Institute (FARM) at Stanford, but also in the course o_erings of Berkeley’s Financial Engineering and Stanford’s Financial Mathematics that has currently been transformed to the broader Mathematical and Computational Finance program to reect the greater emphasis on data science, statistical modeling, advanced programming and high performance computing. Besides the interdisciplinary approach, another distinctive feature of the book is the e_ort to bridge the gap between academic research/education and the _nancial industry, which is also one of the missions of FARM. Di_erent parts of the book can be used in short thematic courses for practitioners, which are currently being developed at FARM.