Financial Data Analysis Using Python

2025-01-24 分类：电子书 评论(0)

Financial Data Analysis Using Python

Financial Data Analysis Using Python

Author: Dmytro Zherlitsyn (Author)

Publisher finelybook 出版社:‏ ‎Mercury Learning and Information

Edition 版本:‏ ‎ N/A

Publication Date 出版日期:‏ ‎ 2024-12-19

Language 语言: ‎ English

Print Length 页数: ‎ 480 pages

ISBN-10: ‎ 1501523864

ISBN-13: ‎ 9781501523861

Book Description

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 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.

FEATURES

  • Illustrates financial data analysis using Python data science libraries and techniques
  • Uses Python visualization tools to justify investment and trading strategies
  • Covers asset pricing and portfolio management methods with Python

About the Author

Dmytro Zherlitsyn, PhD, has dedicated over 20 years to university teaching, business training, financial consulting, scientific research, and data analysis. He has authored over 250 academic publications (e-learning courses, textbooks, scientific papers, and monographs) in Economics, Finance, Data Science, System Analysis, and Software Engineering. His work encompasses the development of predictive models for business and market analysis, including advanced regression, simulation, and machine learning methods for financial sectors, and the cryptocurrency market.

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