Made to Conquer

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 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 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. Create and understand machine learning and deep learning models Explore the details behind reinforcement learning and see how it's used in trading 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 profitability and the predictability of the models to understand their limitations and potential 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 [Heading to Come] Vectors and Matrices Introduction to Linear Equations Systems of Equations Trigonometry 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 Common Pitfalls of Technical Analysis Wanting to Get Rich Quickly Forcing the Patterns Hindsight Bias, the Dream Smasher Assuming That Past Events Have the Same Future Outcome Making Things More Complicated Than They Need to Be Summary 6. Introductory Python for Data Science Downloading Python Basic Operations and Syntax Control Flow Libraries and Functions Exceptions Handling and Errors Data Structures in Numpy and Pandas Importing Financial Time Series in Python Summary About the Author