 Название: Financial Data Analysis Using Python
Автор: Dmytro Zherlitsyn
Издательство: Mercury Learning and Information
Год: 2025
Страниц: 505
Язык: английский
Формат: pdf (true), epub
Размер: 38.9 MB
This book will introduce essential concepts in financial analysis methods & models, covering time-series analysis, graphical analysis, technical and fundamental analysis, asset pricing and portfolio theory, investment and trade strategies, risk assessment and prediction, and financial ML practices. The Python programming language and its ecosystem libraries, such as Pandas, NumPy, SciPy, Statsmodels, Matplotlib, Seaborn, Scikit-learn, Prophet, and other Data Science tools will demonstrate these rooted financial concepts in practice examples. This book will also help you understand the concepts of financial market dynamics, estimate the metrics of financial asset profitability, predict trends, evaluate strategies, optimize portfolios, and manage financial risks. You will also learn data analysis techniques using the Python programming language to understand the basics of data preparation, visualization, and manipulation in the world of financial data.
This book introduces fundamental concepts for analyzing financial markets and supporting investment decisions. These concepts, including time-series analysis, graphical analysis, technical and fundamental analysis, asset pricing, portfolio theory, investment and trading strategies, risk assessment, and the basics of financial Machine Learning, are more than just theoretical. We bring them to life with real-world examples of analyzing financial market dynamics, forecasting future trends, optimizing investment portfolios, assessing strategies, and managing financial risks, making the content engaging and applicable to your work.
With this book, you will gain Python programming basics, its primary libraries for data analysis, and their integration with the core financial concepts.
Chapter 1: Getting Started with Python for Finance - explains foundational knowledge of Python’s role in finance and its advantages over other programming languages. The installation and configuration of Python on local computers or using the Google Colab cloud platform are described. This chapter provides an overview of the top libraries for solving financial problems with Python. It also illustrates the fundamentals of the Python programming language, including syntax, operators, and basic data structures, including those related to financial data analysis.
Features:
Illustrates financial data analysis using Python data science libraries & techniques
Uses Python visualization tools to justify investment and trading strategies
Covers asset pricing & portfolio management methods with Python
Contents:
Chapter 1: Getting Started with Python for Finance
Chapter 2: Python Tools for Data Analysis: Primer to Pandas and NumPy
Chapter 3: Financial Data Manipulation with Python
Chapter 4: Exploratory Data Analysis for Finance
Chapter 5: Investment and Trading Strategies
Chapter 6: Asset Pricing and Portfolio Management
Chapter 7: Time-Series Analysis and Financial Data Forecasting
Chapter 8: Risk Assessment and Volatility Modeling
Chapter 9: Machine Learning and Deep Learning in Finance
Chapter 10: Time-Series Analysis and Forecasting with the FB Prophet Library
Appendix A: Python Code Examples for Finance
Appendix B: Glossary
Appendix C: Valuable Resources
Index
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