Python for Finance Cookbook: Over 80 powerful recipes for effective financial data analysis, 2nd Edition

📚📚 I. THÔNG TIN SẢN PHẨM
📒 Mã sản phẩm :  STT675
📒 Nhà xuất bản :  Packt Publishing; 2nd ed. edition (December 30, 2022)
📒 Tác giả :  Eryk Lewinson
📒 Ngôn ngữ :   Tiếng Anh
📒 Số trang :  740 trang
📒 Hình thức : Bìa Mềm, IN ĐEN TRẮNG
📒 Loại : Sách gia công đóng gáy keo chắc chắn chất lượng cao
📒 Giấy in : Giấy ngoại định lượng 70msg, viết vẽ và hightlight thoải mái.
📒 Chất lượng : Bản in rõ nét, giá rất tốt cho mọi người.

📚📚 II. MÔ TẢ SẢN PHẨM
📒 1.Mô tả sản phẩm
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, 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 learn

• Preprocess, analyze, and visualize financial data
• Explore time series modeling with statistical (exponential smoothing, ARIMA) and machine learning models
• Uncover advanced time series forecasting algorithms such as Meta's Prophet
• Use Monte Carlo simulations for derivatives valuation and risk assessment
• Explore volatility modeling using univariate and multivariate GARCH models
• Investigate various approaches to asset allocation
• Learn how to approach ML-projects using an example of default prediction
• Explore modern deep learning models such as Google's TabNet, Amazon's DeepAR and NeuralProphet

Key Features

• Explore unique recipes for financial data processing and analysis with Python
• Apply classical and machine learning approaches to financial time series analysis
• Calculate various technical analysis indicators and backtest trading strategies

        
📒 2. Tác giả

Eryk Lewinson received his master's degree in Quantitative Finance from Erasmus University Rotterdam. In his professional career, he has gained experience in the practical application of data science methods while working in risk management and data science departments of two ""big 4"" companies, a Dutch neo-broker and most recently the Netherlands' largest online retailer.