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

lgli/Financial Data Analysis Using Python.pdf

Data analysis, financial modeling, and portfolio management (English Edition)

Python for Finance

De Gruyter Mouton

De Gruyter, Inc.

Manish Jain

DEG Press

United States, United States of America

India, India

Half title
Title
Copyright
Dedication
Contents
Preface
Acknowledgments
About the Author
Reviewers
Chapter 1: Getting Started with Python for Finance
Introduction
Structure
Objectives
Finance Principles and Contemporary Trends in Data Analysis
Financial Investor
Financial Market Institutions
Two Critical Finance Categories
Financial Data Analysis
Comparing Analytical Tools for Various Programming Languages
Python Programming Language Advantages
The Role of Python in Finance
Python Instruments Are Ready for Data Analysis
Installing Python on a Local PC
Installation Procedure
Post-Installation Setup and Configuration
Managing Non-Standard Packages
Python IDEs
Spyder
Jupyter Notebook
Easy Start with Python in the Cloud
Python Libraries for Finance
Installation of Essential Libraries for Data Manipulation and Data Analysis
Python Essentials
Syntax Principles and Python Code Style
Basic Operators
Control Flow and Simple Output
Python Basic Data Structures
Types for Data Analysis
Conclusion
Questions
Key Terms
References
Chapter 2: Python Tools for Data Analysis: Primer to Pandas and NumPy
Introduction
Structure
Objectives
The Creation and Manipulation of Python Data Structures
Basic Data Manipulations and Computations Within Python Built-In Data Structures
Calculating Operations for Data Analysis and Rate of Returns
Defining Custom Functions for Data Analysis
NumPy for Data Analysis
Creating NumPy Arrays
Sorting and Sorting by Arguments
Adding and Removing Data
Array Shape Manipulation
Find Values and Filtering Operations
Arithmetical and Statistical Operations
Working with pandas for Data Analysis
Creating a Series and DataFrame
Indexing, Finding, and Filtering Data
Data Manipulation
Calculated Values and Creating New Features
Conclusion
Questions
Key Terms
References
Chapter 3: Financial Data Manipulation with Python
Introduction
Structure
Objectives
Financial Data World: Sources and Valuation Aspects
Yahoo Finance to Access Financial Market Data
Working with Diverse File Formats
Excel Data File Format with pandas
CSV Data File Format with pandas
Open Data Sources and Python Library for Getting Data
Low-Level APIs and Web Scraping
Binance Cryptocurrency Exchange Example
Web Scraping and the Beautiful Soup Python Library
Conclusion
Questions
Key Terms
References
Chapter 4: Exploratory Data Analysis for Finance
Introduction
Structure
Objectives
Basic Patterns for Processing Raw Financial Data
Importing Data and Structuring DataFrames
Elementary Data Clearing Patterns
Data Transformation and the Creation of New Features
EDA Essentials for Financial Analysis
Descriptive Statistics
Basic Statistical Data Visualization
Moving Averages in Financial Analysis
Basics of Correlation Analysis
Conclusion
Questions
Key Terms
References
Chapter 5: Investment and Trading Strategies
Introduction
Structure
Objectives
Investment Strategies in the Financial Assets Markets
Fundamental Analysis
Graphical Analysis with Python
Basic Stock Graphics Tools
Graphical Analysis Patterns
Technical Analysis Metrics and Tools
Conclusion
Questions
Key Terms
References
Chapter 6: Asset Pricing and Portfolio Management
Introduction
Structure
Objectives
Allocation of Financial Assets and Core Metrics with Python
Portfolio Theory and Diversification
Markowitz’s Portfolio Theory and Its Modifications
Modifications of the Original Markowitz’s Portfolio Theory
Simulation Method for Estimated Optimal Asset Allocation
Mathematical Optimization Method with Python
Regressions and Capital Asset Pricing Model Fundamentals
Python Libraries for Regression Analysis
Python Tools for CAPM Assessment
Conclusion
Questions
Key Terms
References
Chapter 7: Time-Series Analysis and Financial Data Forecasting
Introduction
Structure
Objectives
TSA: Core Principles and Concepts
Pandas Toolkits for Time-Series Data Analysis
Traditional Forecasting Methods and Models
The statsmodels Toolkits for TSA
Error Statistical Metrics: MAPE, MSE, and MAE
Exponential Smoothing and the HW Model
Seasonality Decomposition in Python
The HW Method: Multiplicative Trend and Seasonal Components
ARIMA Approach: From MA to Seasonality and External Variables
Stationarity Test
Autocorrelation and Partial Autocorrelation Functions
Custom ARIMA Model Estimation
SARIMA and SARIMAX Models
Conclusion
Questions
Key Terms
References
Chapter 8: Risk Assessment and Volatility Modeling
Introduction
Structure
Objectives
Probability Theory Basics
Normal Distribution
VaR Metric for Risk Assessment in Finance
Monte Carlo Method in Finance
Geometric Brownian Motion Method for Price Prediction
Option Pricing: The Black-Scholes Formula
ARCH and GARCH Models
Model Fitting and Diagnostic Checking
Conclusion
Questions
Key Terms
References
Chapter 9: Machine Learning and Deep Learning in Finance
Introduction
Structure
Objectives
ML Concepts
ML Models
The Universal Algorithm of ML
Python ML Libraries and Tools
Scikit-Learn Python Library
XGBoost and LightGBM Libraries
ML Models for Financial Data
Clustering Analysis of Financial Data
Forecasting Stock Prices
Conclusion
Questions
Key Terms
References
Chapter 10: Time-Series Analysis and Forecasting with the FB Prophet Library
Introduction
Structure
Objectives
Prophet Essentials
Forecasting with Prophet
Seasonality Parameters of Prophet’s Models
Changepoints Adjusting
Additional Regressors
Cross-Validation and Hyperparameter Tuning
Cross-Validation and Preventing Overfitting
Hyperparameter Tuning
Conclusion
Questions
Key Terms
References
Appendix A: Python Code Examples for Finance
Creating a PythonFinance Virtual Environment
Importing Libraries
Fetching Stock Price Data from Yahoo Finance
Fetching Other Data from Yahoo Finance
Technique Analyses Indices (TA-Lib Library)
Graphical Analyses (Core Libraries)
Graphical Analyses (mplfinance Library)
Portfolio Optimization (scipy Library)
Statsmodels Regression
Time-Series Data Featuring
pmdarima Parameters Tuning (pmdarima Library)
VAR
GARCH Models (arch Library)
Hyperparameters Tuning with Cross-Validation
Appendix B: Glossary
Appendix C: Valuable Resources
Index

سيقدم هذا الكتاب مفاهيم أساسية في أساليب ونماذج التحليل المالي، ويغطي تحليل السلاسل الزمنية، والتحليل الرسومي، والتحليل الفني والأساسي، وتسعير الأصول ونظرية المحفظة، واستراتيجيات الاستثمار والتجارة، وتقييم المخاطر والتنبؤ بها، وممارسات التعلم الآلي المالية. ستوضح لغة برمجة بايثون ومكتبات نظامها البيئي، مثل Pandas وNumPy وSciPy وstatsmodels وMatplotlib وSeaborn وScikit-learn وProphet، وأدوات علوم البيانات الأخرى، هذه المفاهيم المالية المتجذرة في أمثلة عملية. سيساعدك هذا الكتاب أيضًا على فهم مفاهيم ديناميكيات السوق المالية، وتقدير مقاييس ربحية الأصول المالية، والتنبؤ بالاتجاهات، وتقييم الاستراتيجيات، وتحسين المحافظ، وإدارة المخاطر المالية. ستتعلم أيضًا تقنيات تحليل البيانات باستخدام لغة برمجة بايثون لفهم أساسيات إعداد البيانات وتصورها والتلاعب بها في عالم البيانات المالية.سماتيوضح تحليل البيانات المالية باستخدام مكتبات وتقنيات علوم البيانات في Pythonيستخدم أدوات تصور Python لتبرير استراتيجيات الاستثمار والتداوليغطي أساليب تسعير الأصول وإدارة المحافظ باستخدام بايثون

2025-01-23