فهرست مطالب

• 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
• Calculus
• Summary
• Chapter 5: Introducing Technical Analysis
• Charting Analysis
• Indicator Analysis
• 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
• Overfitting and Underfitting
• Summary
• Chapter 8: Deep Learning for Time Series Prediction I
• A Walk Through Neural Networks
• 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
• 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
• Risk Management
• Summary
• Index
• About the Author