Financial Data Science with Python : An Integrated Approach to Analysis, Modeling, and Machine Learning.

Format:

Book

Author/Creator:

Chen, Haojun.

Language:

English

Physical Description:

1 online resource (278 pages)

Edition:

1st ed.

Place of Publication:

New York : Business Expert Press, 2025.

Summary:

In today's finance industry, data-driven decision-making is essential. Financial Data Science with Python: An Integrated Approach to Analysis, Modeling, and Machine Learning bridges the gap between traditional finance and modern data science, offering a comprehensive guide for students, analysts, and professionals. This book equips readers with the tools to analyze complex financial data, build predictive models, and apply machine learning techniques to real-world financial challenges. Beginning with foundational Python concepts, the author covers essential topics like data structures, object-oriented programming, and key libraries such as NumPy and Pandas. The book advances into more complex areas, including financial data processing, time series analysis with ARIMA and GARCH models, and both supervised and unsupervised machine learning methods tailored to finance. Practical techniques like regression, classification, and clustering are explored in a financial context. A key feature is the hands-on approach. Through real-world examples, projects, and exercises, readers will apply Python to tasks like risk assessment, market forecasting, and financial pattern recognition. All code examples are provided in Jupyter Notebooks, enhancing interactivity. Whether you're a student building foundational skills, a financial analyst enhancing technical expertise, or a professional staying competitive in a data-driven industry, this book offers the knowledge and tools to succeed in financial data science.

Contents:

Front cover

Half title

Title

Copyright

Description

Contents

Preface

Key Features of This Book

Guide for Readers

Code Reference

Steps to use the Notebook

Structure of the Book

End-of-Chapter Exercises

CHAPTER 1 Introduction to Python Programming

1.1 Why Python for Finance?

1.1.1 Real-World Applications

1.2 Setting up Your Python Environment

What Does It Mean to "Install Python"?

What Is a Python Version?

Why Focus on Python 3.12?

What to Be Aware of When Using Other Versions

1.2.1 Using Jupyter Notebook as Your Python IDE

Different Types of Cells in Jupyter Notebook

1.3 Basic Syntax and Commands

Python Commands

Conclusion

CHAPTER 2 Python Programming Fundamentals

2.1 Data Types and Variables

2.1.1 Understanding Variables as Pointers in Python

2.1.2 Variable Creation

2.2.1 Numbers, Strings, and Booleans

Numbers

F-Strings: Formatted String Literals

2.2.2 Type Conversion

Dynamically Typed Versus Statically Typed Languages

2.3 Control Structures: Conditionals and Loops

2.3.1 If Statements

2.3.2 For and While Loops

Infinite Loops

2.4 Functions and Modules

2.4.1 Defining and Calling Functions

Default Parameters in a Function

Function Scope

Types of Scope

2.4.2 Importing and Using Modules

Installing New Modules Through Anaconda

Installing New Modules Through pip

Importing and Using Modules in Python

How Python Finds Modules

2.5 Python Exceptions

2.5.1 Exception Handling With try and Except Blocks

Exercises

CHAPTER 3 Data Structures in Python

3.1 Lists

3.1.1 Creating List Objects

3.1.2 Accessing List Elements

3.1.3 Altering List Elements

Adding Elements

Removing Elements

3.2 Tuples

3.2.1 Creating Tuple Objects

Using the Tuple() Constructor.

3.2.2 Accessing Tuple Elements

Indexing and Slicing

3.2.3 Altering Tuple Elements (Concatenation, Exclusion)

Concatenation

Exclusion

Using Tuples for Functions With Multiple Return Values

3.3 Dictionaries

3.3.1 Creating Dictionary Objects

3.3.2 Accessing Dictionary Elements

3.3.3 Altering Dictionary Elements (Modifying, Adding, Removing)

Modifying Elements

3.4 Sets

3.4.1 Creating Set Objects

3.4.2 Accessing Set Elements

3.4.3 Altering Set Elements (Modifying, Adding, Removing)

3.4.4 Set Operations

Union

Difference (- Operator)

Intersection

Symmetric Difference

3.5 Built-in Methods for Iterables

TypeError

CHAPTER 4 Objects and Classes in Python

4.1 Introduction to OOP

4.1.1 The Basic Structure of OOP

Classes and Objects

4.1.2 The Basic Principle of OOP

The Self Parameter

4.2 Class Instances

Public Attributes

Private Attributes

4.3 Class Inheritance

Basic Syntax of Inheritance

Polymorphism in Action

4.4 Examining Built-in Classes

Using Built-in Functions to Examine Classes

4.5 Examining External Classes

Moving Forward

Part 1: Define the Base Class

Part 2: Create a Subclass

Part 3: Test your Code

CHAPTER 5 NumPy for Financial Computation

5.1 Introduction to NumPy

Introduction to Array

Comparing NumPy Arrays With Lists

5.2 Creating and Manipulating Arrays

Creating Arrays

Reshaping Arrays

Accessing Elements and Slicing

5.3 Mathematical Operations With NumPy

5.3.1 Basic Arithmetic Operations

Vectorized Operations

Broadcasting

Aggregation Functions

Example: Calculating Weighted Returns

5.4 Using NumPy for Financial Calculations

5.4.1 Financial Functions in NumPy.

Net Present Value (NPV)

Calculate IRR

Future Value (FV)

Payment (PMT)

CHAPTER 6 Financial Data Processing With Pandas

6.1 Introduction to Pandas

6.2 Introduction to Series

The Roles of Indexes of Pandas Data Object

6.2.1 Creating and Accessing Pandas Series

Specifying the Index and Data Type

6.2.2 Data Type Specification in Pandas

Common Data Types in Pandas

6.2.3 Accessing Series

Using Label/Index

List of Labels

Boolean Array

6.2.4 Reassigning or Adding Entries to a Series

Reassigning Entries

Adding New Entries

6.3 Introduction to DataFrame

DataFrame Structure Diagram

6.3.1 Creating DataFrame

From a Dictionary

Setting Row, Column, and Data Type

Import From a File

6.3.2 Accessing DataFrame

Accessing Columns

Accessing Rows

Accessors With Indexes and Slices

6.3.3 Modifying Series and DataFrame

Changing Index and Column Labels

Changing Data Entries

Removing and Adding Data Entries

Dropping Entries From a DataFrame

Adding Entries to DataFrame

Adding New Rows and Columns With Concat (Pandas.concat)

6.4 Operations With Series and DataFrame

Element-Wise Operations

Aggregation Methods

Element-Wise Operations: 'Apply' and 'Map'

The Update Function

6.5 Data Cleaning and Filtering

Converting Data Using to_numeric Function (Pandas.to_numeric)

Marking Invalid Entries

Dropping NA Entries

Conditional Filtering

CHAPTER 7 Principle of Statistics for Financial Data Science

7.1 Introduction to Financial Statistics

Introduction to Computer Simulation

7.2 Probability Distributions

7.2.1 Review of Common Probability Distribution in Finance

Uniform Distribution

Binomial Distribution

Lognormal Distribution

F Distribution.

7.2.2 Probability Distributions Using Python

7.3 Descriptive Statistics

7.3.1 Review of Common Descriptive Statistics

Sample Mean

Sample Median

Sample Standard Deviation (std)

Sample Skewness

Sample Kurtosis

7.3.2 Calculating Descriptive Statistics

7.4 Statistical Estimators

Randomness

Accuracy (Unbiasness)

Preciseness (Reliability)

Consistency

Selecting Estimators

7.4.1 Evaluating Estimators

7.4.2 Eye-Balling Analysis

7.4.3 Asymptotic Analysis

Complex Estimators

7.5 Hypothesis Testing

Major Steps

Test Statistic

The p-Value

7.5.1 One-Sample and Two-Sample t-Test

Type I and Type II Errors and Power of Hypothesis Testing

7.5.2 Power Analysis

7.5.3 Testing the Relationship

Formulating Hypothesis

Perform Hypothesis Testing

7.5.4 Power Plot

7.6 Nonparametric Statistics

Applications and Benefits

7.6.1 Nonparametric Versions of Analysis

Wilcoxon Signed-Rank Test

Two-Sample Wilcoxon Signed-Rank Test

Spearman Rank-Order Test

Further Exploration

Exercise

CHAPTER 8 Financial Time Series Analysis

8.1 Financial Time Series Data

8.2 Manipulating Time Serie Data With Pandas

pd.DatetimeIndex

Time Series DataFrame

8.3 Handling and Manipulating Time Series Data

Resampling and Frequency Conversion

Time Series Indexing and Slicing

Reindexing Time Series Data

Time-Shifting in Time Series Data

Time-Rolling and Expanding in Time Series Data

Time-Expanding (Expanding Window Statistics)

Managing Missing Values in Time Series Data

Filling the NA Values

8.4 Time Series Analysis

8.4.1 Components of Time Series Data

How These Components Aid Financial Decision Making

8.4.2 Stationarity Analysis

Mathematical Definition of Stationarity

Test for Stationarity

Converting Nonstationary Series.

8.4.3 Seasonal Decomposition

8.4.4 Autoregressive Analysis

ACF Versus PACF

Identifying Significant Lags

ACF/PACF Plot

8.4.5 ARIMA Model

Prediction With ARIMA

8.4.6 GRACH Model

Implementation of GARCH Model

Predicting Variance With GARCH

8.4.7 Cointegration Model for Lead-lag Relationship

Steps to Perform Cointegration Analysis

CHAPTER 9 Data Visualization

9.1 Plotting With Matplotlib

9.1.1 Basic Plotting

9.1.2 Building Subplot

9.1.3 Customizing Plots

9.1.4 Other Commonly Used Plots

Histogram

Scatter Plot

Box Plot

9.2 Advanced Visualization With Seaborn

Distribution Plot

Kernel Density Estimate

Pair Plot

Heatmap

9.3 Interactive Visualizations

9.3.1 Building Interactable Plots With plotly

Basic Line Plot With plotly

9.3.2 Animated Plot

CHAPTER 10 Financial Modeling With OOP

Introduction

10.1 Object-Oriented Modeling for Fixed-Income Securities

10.1.1 Introduction to Fixed-Income Securities

Interest Rate, Yield Rate and Discount Rate

Yield Curve

10.1.2 Model Construction Under OOP Framework

Step 1: Creating the Parent Class

Basic Attributes

Methods

Step 2: Creating the Bond Subclass

Fixed-Rate Bond

Year Conventions in Finance

Accrued Interest Calculation

Clean and Dirty Prices

Net Present Value

Duration

10.2 Model Testing and Analysis

Testing Zero-Bond Model

Testing Fixed-Rate Bond Model I: Constant Yield

Testing Fixed-Rate Bond Model II: Nonconstant Yield

Testing Float-Rate Bond Model

CHAPTER 11 Introduction to Machine Learning

What Is Machine Learning?

11.1 Comparing Machine Learning With Traditional Quantitative Methods

11.2 Overview of Model Training

Loss Function

11.3 Data Preparation.

Training, Validation, and Test Sets.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

1-63742-826-X

OCLC:

1518284443