Python for Finance Cookbook - Second Edition
Cód:
491_9781803243191
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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 eBook in the PDF formatKey Features:Explore 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 Description:Python 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, youll explore classical quantitative finance approaches to data modeling, such as GARCH, CAPM, factor models, and 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, youll also learn how to use Streamlit to create an elegant, interactive web applications to present the results of technical analyses. Finally, youll become familiar with modern machine learning and deep learning models which you can use for tasks such as credit default prediction, time series forecasting, and more.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 Learn:Preprocess, analyze, and visualize financial dataExplore time series modeling with statistical (exponential smoothing, ARIMA) and machine learning modelsUncover advanced time series forecasting algorithms such as Metas ProphetUse Monte Carlo si
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