Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using Python

Copyright
Table of Contents
Preface
	Who This Book Is For
	How This Book Is Organized
		Part I: The Framework
		Part II: Supervised Learning
		Part III: Unsupervised Learning
		Part IV: Reinforcement Learning and Natural Language Processing
	Conventions Used in This Book
	Using Code Presented in the Book
	Python Libraries
	O’Reilly Online Learning
	How to Contact Us
	Acknowledgments
		Special Thanks from Hariom
		Special Thanks from Sahil
		Special Thanks from Brad
Part I. The Framework
	Chapter 1. Machine Learning in Finance: The Landscape
		Current and Future Machine Learning Applications in Finance
			Algorithmic Trading
			Portfolio Management and Robo-Advisors
			Fraud Detection
			Loans/Credit Card/Insurance Underwriting
			Automation and Chatbots
			Risk Management
			Asset Price Prediction
			Derivative Pricing
			Sentiment Analysis
			Trade Settlement
			Money Laundering
		Machine Learning, Deep Learning, Artificial Intelligence, and Data Science
		Machine Learning Types
			Supervised
			Unsupervised
			Reinforcement Learning
		Natural Language Processing
		Chapter Summary
			Next Steps
	Chapter 2. Developing a Machine Learning Model in Python
		Why Python?
		Python Packages for Machine Learning
			Python and Package Installation
		Steps for Model Development in Python Ecosystem
			Model Development Blueprint
		Chapter Summary
			Next Steps
	Chapter 3. Artificial Neural Networks
		ANNs: Architecture, Training, and Hyperparameters
			Architecture
			Training
			Hyperparameters
		Creating an Artificial Neural Network Model in Python
			Installing Keras and Machine Learning Packages
			Running an ANN Model Faster: GPU and Cloud Services
		Chapter Summary
			Next Steps
Part II. Supervised Learning
	Chapter 4. Supervised Learning: Models and Concepts
		Supervised Learning Models: An Overview
			Linear Regression (Ordinary Least Squares)
			Regularized Regression
			Logistic Regression
			Support Vector Machine
			K-Nearest Neighbors
			Linear Discriminant Analysis
			Classification and Regression Trees
			Ensemble Models
			ANN-Based Models
		Model Performance
			Overfitting and Underfitting
			Cross Validation
			Evaluation Metrics
		Model Selection
			Factors for Model Selection
			Model Trade-off
		Chapter Summary
	Chapter 5. Supervised Learning: Regression (Including Time Series Models)
		Time Series Models
			Time Series Breakdown
			Autocorrelation and Stationarity
			Traditional Time Series Models (Including the ARIMA Model)
			Deep Learning Approach to Time Series Modeling
			Modifying Time Series Data for Supervised Learning Models
		Case Study 1: Stock Price Prediction
			Blueprint for Using Supervised Learning Models to Predict a Stock Price
		Case Study 2: Derivative Pricing
			Blueprint for Developing a Machine Learning Model for Derivative Pricing
		Case Study 3: Investor Risk Tolerance and Robo-Advisors
			Blueprint for Modeling Investor Risk Tolerance and Enabling a Machine Learning–Based Robo-Advisor
		Case Study 4: Yield Curve Prediction
			Blueprint for Using Supervised Learning Models to Predict the Yield Curve
		Chapter Summary
		Exercises
	Chapter 6. Supervised Learning: Classification
		Case Study 1: Fraud Detection
			Blueprint for Using Classification Models to Determine Whether a Transaction Is Fraudulent
		Case Study 2: Loan Default Probability
			Blueprint for Creating a Machine Learning Model for Predicting Loan Default Probability
		Case Study 3: Bitcoin Trading Strategy
			Blueprint for Using Classification-Based Models to Predict Whether to Buy or Sell in the Bitcoin Market
		Chapter Summary
		Exercises
Part III. Unsupervised Learning
	Chapter 7. Unsupervised Learning: Dimensionality Reduction
		Dimensionality Reduction Techniques
			Principal Component Analysis
			Kernel Principal Component Analysis
			t-distributed Stochastic Neighbor Embedding
		Case Study 1: Portfolio Management: Finding an Eigen Portfolio
			Blueprint for Using Dimensionality Reduction for Asset Allocation
		Case Study 2: Yield Curve Construction and Interest Rate Modeling
			Blueprint for Using Dimensionality Reduction to Generate a Yield Curve
		Case Study 3: Bitcoin Trading: Enhancing Speed and Accuracy
			Blueprint for Using Dimensionality Reduction to Enhance a Trading Strategy
		Chapter Summary
		Exercises
	Chapter 8. Unsupervised Learning: Clustering
		Clustering Techniques
			k-means Clustering
			Hierarchical Clustering
			Affinity Propagation Clustering
		Case Study 1: Clustering for Pairs Trading
			Blueprint for Using Clustering to Select Pairs
		Case Study 2: Portfolio Management: Clustering Investors
			Blueprint for Using Clustering for Grouping Investors
		Case Study 3: Hierarchical Risk Parity
			Blueprint for Using Clustering to Implement Hierarchical Risk Parity
		Chapter Summary
		Exercises
Part IV. Reinforcement Learning and Natural Language Processing
	Chapter 9. Reinforcement Learning
		Reinforcement Learning—Theory and Concepts
			RL Components
			RL Modeling Framework
			Reinforcement Learning Models
			Key Challenges in Reinforcement Learning
		Case Study 1: Reinforcement Learning–Based Trading Strategy
			Blueprint for Creating a Reinforcement Learning–Based Trading Strategy
		Case Study 2: Derivatives Hedging
			Blueprint for Implementing a Reinforcement Learning–Based Hedging Strategy
		Case Study 3: Portfolio Allocation
			Blueprint for Creating a Reinforcement Learning–Based Algorithm for Portfolio Allocation
		Chapter Summary
		Exercises
	Chapter 10. Natural Language Processing
		Natural Language Processing: Python Packages
			NLTK
			TextBlob
			spaCy
		Natural Language Processing: Theory and Concepts
			1. Preprocessing
			2. Feature Representation
			3. Inference
		Case Study 1: NLP and Sentiment Analysis–Based Trading Strategies
			Blueprint for Building a Trading Strategy Based on Sentiment Analysis
		Case Study 2: Chatbot Digital Assistant
			Blueprint for Creating a Custom Chatbot Using NLP
		Case Study 3: Document Summarization
			Blueprint for Using NLP for Document Summarization
		Chapter Summary
		Exercises
Index
About the Authors
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