Financial Data Analysis Using Python

by Dmytro Zherlitsyn

Overview

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.
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

Product Details

ISBN-13:	9781501521881
Publisher:	De Gruyter
Publication date:	12/26/2024
Sold by:	Barnes & Noble
Format:	eBook
Pages:	505
File size:	35 MB
Note:	This product may take a few minutes to download.
Age Range:	18 Years

About the Author

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

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 & Trading Strategies
6: Asset Pricing & Portfolio Management
7: Time Series Analysis & Financial Data Forecasting
8: Risk Assessment & Volatility Modelling
9: Machine Learning & Deep Learning in Finance
10: Time Series Analysis & Forecasting with the FB Prophet Library
Appendices:
A: Python Code Examples for Finance
B: Glossary
C: Valuable Resources