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Softcover. Condition: Fair. Spuren von Feuchtigkeit / Nässe; Leichte Kratzer / Abnutzungen / Druckstellen. Utilize modern Python libraries like pandas, NumPy, and scikit-learn, along with machine learning and deep learning techniques, to tackle financial modeling challenges. This updated edition emphasizes classical quantitative finance methods, including GARCH, CAPM, and factor models, while integrating contemporary solutions. With just a few lines of code, you can efficiently process and analyze financial data. The new edition places greater focus on exploratory data analysis, enhancing your ability to visualize and comprehend financial information. Additionally, you will learn to use Streamlit for creating interactive web applications to showcase your technical analyses. The recipes provided will help you gain proficiency in financial data analysis for both personal and professional endeavors. You will also learn to anticipate potential issues in your analyses and, crucially, how to address them. This resource is designed for financial analysts, data analysts and scientists, and Python developers familiar with financial concepts. You will master advanced analytical techniques, avoid common pitfalls, and draw accurate conclusions across a variety of finance-related problems. A working knowledge of Python, particularly with libraries like pandas and NumPy, is essential.
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Taschenbuch. Condition: Neu. Python for Finance Cookbook - Second Edition | Over 80 powerful recipes for effective financial data analysis | Eryk Lewinson | Taschenbuch | Kartoniert / Broschiert | Englisch | 2022 | Packt Publishing | EAN 9781803243191 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Taschenbuch. Condition: 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.