Financial Data Science with Python

Name: Financial Data Science with Python | An Integrated Approach to Analysis, Modeling, and Machine Learning
Brand: Business Expert Press
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An Integrated Approach to Analysis, Modeling, and Machine Learning

Haojun Chen(Autor*in)

Business Expert Press

Erschienen am 16. Mai 2025

276 Seiten

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Inhalt

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
Conclusion
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
Adding Elements
Removing 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)
Modifying Elements
3.4.4 Set Operations
Union
Difference (- Operator)
Intersection
Symmetric Difference
3.5 Built-in Methods for Iterables
TypeError
Conclusion
Exercises
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
Conclusion
Moving Forward
Exercises
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)
Conclusion
Exercises
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
Broadcasting
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
Conclusion
Exercises
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
Conclusion
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
Conclusion
Exercise
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
Conclusion
Exercise
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
Conclusion
Exercise
CHAPTER 11 Introduction to Machine Learning
Introduction
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
11.4 Model Selection
11.5 Hyperparameter Tuning
11.6 Performance Evaluation
11.6.1 Evaluation Metrics
For Regression ML Models
For Classification ML Models
11.6.2 Other Evaluation Criteria
Overfitting and Underfitting
Model Interpretability
Conclusion
CHAPTER 12 Regression Machine Learning Models in Finance
Introduction
12.1 Importing and Preparing Data
12.1.1 Data Description
12.1.2 Defining the Target Variable and Features
Target Variable
Features
12.1.3 Data Preparation
12.2 Linear Models
12.2.1 Linear Regression
Parameters Trained
Hyperparameter
12.2.2 Ridge Lasso, Elastic Net Regressions
Elastic Net Regularization
12.3 Nonlinear Models
12.3.1 K-Nearest Neighbors Regression
Implementation
12.3.2 Random Forest Regression
Model Configuration
12.3.3 Interpretation of Feature Effects
Feature Importance
Conclusion
Exercise
CHAPTER 13 Classification Machine Learning Models for Finance
Introduction
13.1 Introduction of ML Classification and Applications
How Classification Differs From Other Machine Learning Problems
Examples of Classification Applications in Finance
13.2 Data Preparation for Classification ML
Loading Data
Data Set Features
Target Variables
13.2.1 Handling Categorical and Continuous Variables
13.2.2 Data Partitioning
13.2.3 Descriptive Statistics and Preliminary Analysis
13.2.4 Correlation Analysis
13.3 Overview of Classification ML Models
13.3.1 Logistic Regression
Principle Behind Logistic Regression
Advantages of Logistic Regression
Limitations of Logistic Regression
Interpretation of the Coefficients in Logistic Regression
Log-Odds Interpretation
Hyperparameter Tuning of Logistic Regression
13.3.2 Decision Trees
Principle Behind Decision Trees
Advantages of Decision Trees
Limitations of Decision Trees
Key Hyperparameters
13.3.3 Support Vector Machines (SVM)
Principle Behind SVM
Advantages of SVM
Limitations of SVM
Key Hyperparameters
RBF vs Sigmoid
Sigmoid Kernel
RBF Kernel
Sigmoid Kernel
13.4 Evaluation of ML Classifiers
Precision
Recall (Sensitivity or True Positive Rate)
F1 Score
Accuracy
Confusion Matrix
Risk Management Consideration
13.5 Training Implementation for Binary Classification
13.5.1 Training Logistic Regression for Binary Classification
13.5.2 N-Fold Cross-Validation and Grid-Searching
13.5.3 Training Decision Trees for Binary Classification
13.5.4 Training SVM for Binary Classification
13.6 Muti-Class Classification
13.7 Ensemble Methods
13.7.1 Bagging
Procedure
13.7.2 Boosting
Types of Boosting
Procedure
13.7.3 Random Forest Classification
13.8 Class Imbalance: Issues and Solutions
13.8.1 Issues With Class Imbalance
Bias Toward Majority Class
Poor Generalization
Misleading Performance Metrics
13.8.2 Addressing Class Imbalance
Reassigning Class Weight
Automatic and Manual Class Weighting
Limitation
Resampling Techniques
13.9 Performance Optimization for ML Classifiers
Parallelism and Resource Utilization
13.9.1 Strategies to Mitigate Computational Costs
Early Stopping and Depth Control
Randomized Search
13.10 Evaluating Classification Models With ROC Curve and AUC
Receiver Operating Characteristic Curve (ROC Curve)
Area Under the Curve (AUC)
Comparing Logistic Regression With Random Forest Using ROC Curve and AUC
13.11 Feature Importance
Feature Importance in Binary Classifiers
Permutation Feature Importance (PFI)
Feature Importance in Multiclass Classifiers
Tree-Based Feature Importance
Conclusion
Exercise
CHAPTER 14 Unsupervised Learning in Finance
Introduction
14.1 Financial Applications of Unsupervised Learning
Market Segmentation
Portfolio Diversification
Customer Retention
Trend Analysis
Sentiment Analysis
14.2 Clustering Techniques
14.2.1 K-Means Clustering
Loading Data
Data Cleaning and Preprocessing for K-Means Clustering
Elbow Method
14.2.2 Hierarchical Clustering
Principle of Hierarchical Clustering
Agglomerative (Bottom-Up) Clustering
Divisive (Top-Down) Clustering
Problems Hierarchical Clustering Is Good for
Problems Hierarchical Clustering Is not Ideal for
14.3 Dimensionality Reduction
Data Preparation
14.3.1 Principal Component Analysis (PCA)
Feature Loadings
PCA-Based Clustering
Biplot
14.3.2 t-SNE (t-Distributed Stochastic Neighbor Embedding)
Understanding t-SNE
Conclusion
Exercise
Appendix
How to Install External Python Packages/Libraries
What Is a Python Environment?
Where Are Packages Saved After Installation?
Different Ways to Install Packages
Installing Packages via Command-Line
Installing Packages With Anaconda
Installing Packages in Jupyter Notebook
External Python Libraries/Packages Used in This book
arch
Imblearn
matplotlib
mplfinance
numpy
numpy_financial
pandas
plotly
scipy
seaborn
sklearn
statsmodels
yfinance
References
About the Author
Index
Back cover

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