Da: HPB-Red, Dallas, TX, U.S.A.
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Aggiungi al carrellopaperback. Condizione: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Editore: Packt Publishing December 2022, 2022
ISBN 10: 1803243198 ISBN 13: 9781803243191
Lingua: Inglese
Da: Eagle Eye Books, Decatur, GA, U.S.A.
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Da: California Books, Miami, FL, U.S.A.
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Da: Best Price, Torrance, CA, U.S.A.
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Da: California Books, Miami, FL, U.S.A.
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Editore: Packt Publishing 12/30/2022, 2022
ISBN 10: 1803243198 ISBN 13: 9781803243191
Lingua: Inglese
Da: BargainBookStores, Grand Rapids, MI, U.S.A.
EUR 48,16
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Aggiungi al carrelloPaperback or Softback. Condizione: New. Python for Finance Cookbook - Second Edition: Over 80 powerful recipes for effective financial data analysis 2.75. Book.
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 49,50
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 42,96
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Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
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Da: medimops, Berlin, Germania
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Da: Books Puddle, New York, NY, U.S.A.
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
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EUR 48,18
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Da: Majestic Books, Hounslow, Regno Unito
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Da: Biblios, Frankfurt am main, HESSE, Germania
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Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 75,90
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Use modern Python libraries such as pandas, NumPy, and scikit-learn and popular machine learning and deep learning methods to solve financial modeling problemsPurchase of the print or Kindle book includes a free Elektronisches Buch in the PDF formatKey FeaturesExplore unique recipes for financial data processing and analysis with PythonApply classical and machine learning approaches to financial time series analysisCalculate various technical analysis indicators and backtest trading strategiesBook DescriptionPython is one of the most popular programming languages in the financial industry, with a huge collection of accompanying libraries. In this new edition of the Python for Finance Cookbook, you will explore classical quantitative finance approaches to data modeling, such as GARCH, CAPM, factor models, as well as modern machine learning and deep learning solutions.You will use popular Python libraries that, in a few lines of code, provide the means to quickly process, analyze, and draw conclusions from financial data. In this new edition, more emphasis was put on exploratory data analysis to help you visualize and better understand financial data. While doing so, you will also learn how to use Streamlit to create elegant, interactive web applications to present the results of technical analyses.Using the recipes in this book, you will become proficient in financial data analysis, be it for personal or professional projects. You will also understand which potential issues to expect with such analyses and, more importantly, how to overcome them.What you will learnPreprocess, analyze, and visualize financial dataExplore time series modeling with statistical (exponential smoothing, ARIMA) and machine learning modelsUncover advanced time series forecasting algorithms such as Meta's ProphetUse Monte Carlo simulations for derivatives valuation and risk assessmentExplore volatility modeling using univariate and multivariate GARCH modelsInvestigate various approaches to asset allocationLearn how to approach ML-projects using an example of default predictionExplore modern deep learning models such as Google's TabNet, Amazon's DeepAR and NeuralProphetWho this book is forThis book is intended for financial analysts, data analysts and scientists, and Python developers with a familiarity with financial concepts. You'll learn how to correctly use advanced approaches for analysis, avoid potential pitfalls and common mistakes, and reach correct conclusions for a broad range of finance problems.Working knowledge of the Python programming language (particularly libraries such as pandas and NumPy) is necessary.Table of ContentsAcquiring Financial DataData PreprocessingVisualizing Financial Time SeriesExploring Financial Time Series DataTechnical Analysis and Building Interactive DashboardsTime Series Analysis and ForecastingMachine Learning-Based Approaches to Time Series ForecastingMulti-Factor ModelsModelling Volatility with GARCH Class ModelsMonte Carlo Simulations in FinanceAsset AllocationBacktesting Trading StrategiesApplied Machine Learning: Identifying Credit DefaultAdvanced Concepts for Machine Learning ProjectsDeep Learning in Finance.