Applied Quantitative Finance in Python: Selected Theories and Examples

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dc.contributor.authorKresta, Aleš
dc.date.accessioned2024-11-04T07:26:56Z
dc.date.available2024-11-04T07:26:56Z
dc.date.issued2024cs
dc.description.abstractThe book Applied Quantitative Finance in Python provides a comprehensive guide to the intersection of finance, statistics, and computer science. Balancing theory and practical application, it uses Python to simplify complex financial models with real-world code snippets. It covers essential principles and advanced topics, guiding readers through real-world financial problems. Whether you are a novice or an experienced practitioner, this book aims to be a companion in exploring applied quantitative finance. Starting with the basics of time series, the book progresses to risk measurement, portfolio optimization, technical analysis, and option valuation. Each chapter offers hands-on Python examples, highlighting potential pitfalls and providing practical insights.cs
dc.description.placeofpublicationOstravacs
dc.description.sourceE-vyuka
dc.identifier.doi10.31490/9878024847481
dc.identifier.isbn978-80-248-4747-4 (print)
dc.identifier.isbn978-80-248-4748-1 (on-line)
dc.identifier.urihttp://hdl.handle.net/10084/155246
dc.language.isoencs
dc.publisherEkonomická fakulta VŠB-TUOcs
dc.relation.ispartofseriesSOET - Series of Economics Textbookscs
dc.relation.projectidCZ.10.03.01/00/22_003/0000048cs
dc.relation.projectnameEuropean Union Operational Programme Just Transitioncs
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights© VSB – Technical University of Ostrava
dc.rights.accessopenAccesscs
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectoptions valuation; portfolio optimization; python programming; quantitative finance; risk measurement and management; technical analysis; time series analysiscs
dc.titleApplied Quantitative Finance in Python: Selected Theories and Examplescs
dc.typebookcs
evyuka.subject.version154-0571/01cs

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