Deep Learning for Finance

Table of contents

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