Deep Learning for Finance

✍ Scribed by Sofien Kaabar

• Publisher: O'Reilly Media, Inc.
• Year: 2024
• Tongue: English
• Leaves: 206
• Category: Library

✦ Synopsis

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

✦ Table of Contents

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