Deep Learning for Finance

Spis treści ebooka

• Preface
  • Why This Book?
  • Who Should Read It?
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly 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 Bitcoins 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

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