Description
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.
Key Features
● Comprehensive guide to Python for financial data analysis and modeling.
● Practical examples and real-world applications for immediate implementation.
● Covers advanced topics like regression, Machine Learning and time series forecasting.
What you will learn
● Learn financial data analysis using Python data science libraries and techniques.
● Learn Python visualization tools to justify investment and trading strategies.
● Learn asset pricing and portfolio management methods with Python.
● Learn advanced regression and time series models for financial forecasting.
● Learn risk assessment and volatility modeling methods with Python.
Who this book is for
This book is designed for financial analysts and other professionals interested in the financial industry with a basic understanding of Python programming and statistical analysis. It is also suitable for students in finance and data science who wish to apply Python tools to financial data analysis and decision-making.
Table of Contents
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
Dmytro Zherlitsyn, a Professor and Doctor of Science, has dedicated over 20 years to university teaching, business training, financial consulting, scientific research and data analysis.
