Python for Finance (2nd Edition)

This book is the Chinese translation of the second edition of Mastering Python for Finance, aimed at readers who want to use Python for financial modeling and data analysis tasks. The book begins with Python fundamentals and the financial analysis environment, introducing common tools such as NumPy, pandas, SciPy, and Jupyter, and demonstrates the acquisition, organization, and statistical analysis of financial time series using public data sources such as Quandl. In the theoretical part, the book reviews financial mathematics foundations including the capital asset pricing model, arbitrage pricing theory, linear and nonlinear optimization, principal component analysis, and bond and interest rate models, and provides corresponding solution and implementation paths in Python. The practical part unfolds around numerical methods for option pricing, implied volatility characterization, building algorithmic trading platforms, and designing backtesting systems, while introducing machine learning and deep learning tools for price prediction and default risk analysis. The author emphasizes that most of the programming tools and data used in the book can be obtained through public channels, so readers can follow the examples hands-on, thereby forming an overall understanding of Python's ecosystem in finance and deepening their understanding of risk control. The book is clearly structured and balances principle explanation with code practice, making it suitable as an advanced reader for financial engineering and quantitative research directions.