Deep Learning for Finance: Creating Machine & Deep Learning Models for Trading in Python 9781098148393

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
1. Introducing Data Science and Trading
Understanding Data
Understanding Data Science
Introduction to Financial Markets and Trading
Applications of Data Science in Finance
Summary
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
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
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
5. Introducing Technical Analysis
Charting Analysis
Indicator Analysis
Moving Averages
The Relative Strength Index
Pattern Recognition
Summary
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
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
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
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
10. Deep Reinforcement Learning for Time Series Prediction
Intuition of Reinforcement Learning
Deep Reinforcement Learning
Summary
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
12. Market Drivers and Risk Management
Market Drivers
Market Drivers and Economic Intuition
News Interpretation
Risk Management
Basics of Risk Management
Stops and targets
Trailing stops
Economic calendar
Behavioral Finance: The Power of Biases
Cognitive biases
Emotional biases
Summary
Index