Deep learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and it also helps you create and backtest trading algorithms based on machine learning and reinforcement learning. Sofien Kaabar—financial author, trading consultant, and institutional market strategist—introduces deep learning strategies that combine technical and quantitative analyses. By fusing deep learning concepts with technical analysis, this unique book presents outside-the-box ideas in the world of financial trading. This A-Z guide also includes a full introduction to technical analysis, evaluating machine learning algorithms, and algorithm optimization. Understand and create machine learning and deep learning models Explore the details behind reinforcement learning and see how it's used in time series Understand how to interpret performance evaluation metrics Examine technical analysis and learn how it works in financial markets Create technical indicators in Python and combine them with ML models for optimization Evaluate the models' profitability and predictability to understand their limitations and potential 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 Deep learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and it also helps you create, trade, and back-test trading algorithms based on machine learning and reinforcement learning. Sofien Kaabarfinancial author, trading consultant, and institutional market strategistintroduces deep learning strategies that combine technical and quantitative analyses. By fusing deep learning concepts with technical analysis, this unique book presents out-of-the-box ideas in the world of financial trading. This A-Z guide also includes a full introduction to technical analysis, evaluating machine learning algorithms, and algorithm optimization.
