Includes Index.

1. Getting Started with Python for Finance
2. Python Tools for Data Analysis: Primer to Pandas and NumPy
3. Financial Data Manipulation with Python
4. Exploratory Data Analysis for Finance
5. Investment and Trading Strategies
6. Asset Pricing and Portfolio Management
7. Time Series Analysis and Financial Data Forecasting
8. Risk Assessment and Volatility Modelling
9. Machine Learning and Deep Learning in Finance
10. Time Series Analysis and Forecasting with FB Prophet Library
Appendix A: Python Code Examples for Finance
Appendix B: Glossary
Appendix C: Valuable Resources

Python's intuitive syntax and beginner-friendly nature makes it an ideal programming language for financial professionals. It acts as a bridge between the world of finance and data analysis.
This book will introduce essential concepts in financial analysis methods and 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 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 Python programming language to understand the basics of data preparation, visualization, and manipulation in the world of financial data.