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
Author(s): Sofien Kaabar
Publisher: O'Reilly Media, Inc.
Year: 2024
Language: English
Commentary: Early Release
Pages: 206
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
