paperback. Condizione: Very Good. Tsang, Edward (illustratore). Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority!
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PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
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Aggiungi al carrelloPAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Lingua: Inglese
Editore: Taylor and Francis Ltd, GB, 2023
ISBN 10: 1032384433 ISBN 13: 9781032384436
Da: Rarewaves.com USA, London, LONDO, Regno Unito
Prima edizione
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Aggiungi al carrelloPaperback. Condizione: New. 1st. Finance students and practitioners may ask: can machines learn everything? Could AI help me? Computing students or practitioners may ask: which of my skills could contribute to finance? Where in finance should I pay attention? This book aims to answer these questions. No prior knowledge is expected in AI or finance.Including original research, the book explains the impact of ignoring computation in classical economics; examines the relationship between computing and finance and points out potential misunderstandings between economists and computer scientists; and introduces Directional Change and explains how this can be used.To finance students and practitioners, this book will explain the promise of AI, as well as its limitations. It will cover knowledge representation, modelling, simulation and machine learning, explaining the principles of how they work. To computing students and practitioners, this book will introduce the financial applications in which AI has made an impact. This includes algorithmic trading, forecasting, risk analysis portfolio optimization and other less well-known areas in finance. Trading depth for readability, AI for Finance will help readers decide whether to invest more time into the subject.
Lingua: Inglese
Editore: H N H International Limited, 2023
ISBN 10: 1032384433 ISBN 13: 9781032384436
Da: Majestic Books, Hounslow, Regno Unito
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Lingua: Inglese
Editore: H N H International Limited, 2023
ISBN 10: 1032384433 ISBN 13: 9781032384436
Da: Books Puddle, New York, NY, U.S.A.
Condizione: New.
EUR 34,79
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Aggiungi al carrelloPaperback / softback. Condizione: New. New copy - Usually dispatched within 4 working days.
Da: Ria Christie Collections, Uxbridge, Regno Unito
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Da: Chiron Media, Wallingford, Regno Unito
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Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
Prima edizione
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Lingua: Inglese
Editore: H N H International Limited, 2023
ISBN 10: 1032384433 ISBN 13: 9781032384436
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 42,95
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Da: GreatBookPrices, Columbia, MD, U.S.A.
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EUR 47,65
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Aggiungi al carrelloPaperback. Condizione: Brand New. 122 pages. 7.81x5.06x0.47 inches. In Stock.
Condizione: New. 2023. 1st Edition. Paperback. . . . . . Books ship from the US and Ireland.
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Condizione: New. pages cm Fourth edition Includes bibliographical references and index.
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Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 60,73
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Condizione: New. 1st edition NO-PA16APR2015-KAP.
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Lingua: Inglese
Editore: Taylor and Francis Ltd, GB, 2022
ISBN 10: 0367540959 ISBN 13: 9780367540951
Da: Rarewaves.com USA, London, LONDO, Regno Unito
EUR 68,14
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Aggiungi al carrelloPaperback. Condizione: New. Based on interdisciplinary research into "Directional Change", a new data-driven approach to financial data analysis, Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading applies machine learning to financial market monitoring and algorithmic trading. Directional Change is a new way of summarising price changes in the market. Instead of sampling prices at fixed intervals (such as daily closing in time series), it samples prices when the market changes direction ("zigzags"). By sampling data in a different way, this book lays out concepts which enable the extraction of information that other market participants may not be able to see. The book includes a Foreword by Richard Olsen and explores the following topics: Data science: as an alternative to time series, price movements in a market can be summarised as directional changes Machine learning for regime change detection: historical regime changes in a market can be discovered by a Hidden Markov Model Regime characterisation: normal and abnormal regimes in historical data can be characterised using indicators defined under Directional Change Market Monitoring: by using historical characteristics of normal and abnormal regimes, one can monitor the market to detect whether the market regime has changed Algorithmic trading: regime tracking information can help us to design trading algorithmsIt will be of great interest to researchers in computational finance, machine learning and data science.About the AuthorsJun Chen received his PhD in computational finance from the Centre for Computational Finance and Economic Agents, University of Essex in 2019.Edward P K Tsang is an Emeritus Professor at the University of Essex, where he co-founded the Centre for Computational Finance and Economic Agents in 2002.