Deep Learning for Finance: Creating Machine & Deep Learning Models for Trading in Python

Cover
Copyright
Table of Contents
Preface
	Why This Book?
	Who Should Read It?
	Conventions Used in This Book
	Using Code Examples
	O’Reilly Online Learning
	How to Contact Us
	Acknowledgments
Chapter 1. Introducing Data Science and Trading
	Understanding Data
	Understanding Data Science
	Introduction to Financial Markets and Trading
	Applications of Data Science in Finance
	Summary
Chapter 2. Essential Probabilistic Methods for Deep Learning
	A Primer on Probability
	Introduction to Probabilistic Concepts
	Sampling and Hypothesis Testing
	A Primer on Information Theory
	Summary
Chapter 3. Descriptive Statistics and Data Analysis
	Measures of Central Tendency
	Measures of Variability
	Measures of Shape
	Visualizing Data
	Correlation
	The Concept of Stationarity
	Regression Analysis and Statistical Inference
	Summary
Chapter 4. Linear Algebra and Calculus for Deep Learning
	Linear Algebra
		Vectors and Matrices
		Introduction to Linear Equations
		Systems of Equations
		Trigonometry
	Calculus
		Limits and Continuity
		Derivatives
		Integrals and the Fundamental Theorem of Calculus
		Optimization
	Summary
Chapter 5. Introducing Technical Analysis
	Charting Analysis
	Indicator Analysis
		Moving Averages
		The Relative Strength Index
	Pattern Recognition
	Summary
Chapter 6. Introductory Python for Data Science
	Downloading Python
	Basic Operations and Syntax
	Control Flow
	Libraries and Functions
	Exception Handling and Errors
	Data Structures in numpy and pandas
	Importing Financial Time Series in Python
	Summary
Chapter 7. Machine Learning Models for Time Series Prediction
	The Framework
	Machine Learning Models
		Linear Regression
		Support Vector Regression
		Stochastic Gradient Descent Regression
		Nearest Neighbors Regression
		Decision Tree Regression
		Random Forest Regression
		AdaBoost Regression
		XGBoost Regression
	Overfitting and Underfitting
	Summary
Chapter 8. Deep Learning for Time Series Prediction I
	A Walk Through Neural Networks
		Activation Functions
		Backpropagation
		Optimization Algorithms
		Regularization Techniques
		Multilayer Perceptrons
	Recurrent Neural Networks
	Long Short-Term Memory
	Temporal Convolutional Neural Networks
	Summary
Chapter 9. Deep Learning for Time Series Prediction II
	Fractional Differentiation
	Forecasting Threshold
	Continuous Retraining
	Time Series Cross Validation
	Multiperiod Forecasting
	Applying Regularization to MLPs
	Summary
Chapter 10. Deep Reinforcement Learning for Time Series Prediction
	Intuition of Reinforcement Learning
	Deep Reinforcement Learning
	Summary
Chapter 11. Advanced Techniques and Strategies
	Using COT Data to Predict Long-Term Trends
		Algorithm 1: Indirect One-Step COT Model
		Algorithm 2: MPF COT Direct Model
		Algorithm 3: MPF COT Recursive Model
		Putting It All Together
	Using Technical Indicators as Inputs
	Predicting Bitcoin’s Volatility Using Deep Learning
	Real-Time Visualization of Training
	Summary
Chapter 12. Market Drivers and Risk Management
	Market Drivers
		Market Drivers and Economic Intuition
		News Interpretation
	Risk Management
		Basics of Risk Management
		Behavioral Finance: The Power of Biases
	Summary
Index
About the Author
Colophon