The.Hottest

Friday, January 31, 2020 , , , ,
Yves J. Hilpisch ... 720 pages - Publisher: O'Reilly Media; 2nd edition (January, 2019) ... Language: English - ISBN-10: 1492024333 - ISBN-13: 978-1492024330.

The financial industry has recently adopted Python at a tremendous rate, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. Updated for Python 3, the second edition of this hands on book helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics. Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full fledged framework for Monte Carlo simulation based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks.
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Thursday, January 30, 2020 , , ,
G. A. Vijayalakshmi Pai ... 316 pages - Publisher: Wiley-ISTE; (December 27, 2017) ... Language: English - ASIN: B078X252PR by Amazon.

The book is a monograph in the cross disciplinary area of Computational Intelligence in Finance and elucidates a collection of practical and strategic Portfolio Optimization models in Finance, that employ Metaheuristics for their effective solutions and demonstrates the results using MATLAB implementations, over live portfolios invested across global stock universes. The book has been structured in such a way that, even novices in finance or metaheuristics should be able to comprehend and work on the hybrid models discussed in the book.
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Wednesday, January 29, 2020 , , ,
Marcos Lopez de Prado ... 400 pages - Publisher: Wiley; (February, 2018) ... Language: English - ASIN: B079KLDW21 by Amazon.

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.
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