Deep Learning for Finance

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Autor:: Sofien Kaabar

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Wydawnictwo:: O'Reilly Media (Z chęcią przeczytam książkę w języku polskim)
Ocena:: Bądź pierwszym, który oceni tę książkę
Stron:: 362
Dostępne formaty:: ePub

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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

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Oceny i opinie klientów: Deep Learning for Finance Sofien Kaabar (0) Weryfikacja opinii następuję na podstawie historii zamówień na koncie Użytkownika umieszczającego opinię. Użytkownik mógł otrzymać punkty za opublikowanie opinii uprawniające do uzyskania rabatu w ramach Programu Punktowego.

Szczegóły książki

ISBN Ebooka:: 978-10-981-4835-5, 9781098148355
Data wydania ebooka :: 2024-01-08 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@helion.pl.
Język publikacji:: angielski
Rozmiar pliku ePub:: 7.3MB
Rozmiar pliku Mobi:: 7.3MB

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Spis treści książki

Preface
- Why This Book?
- Who Should Read It?
- Conventions Used in This Book
- Using Code Examples
- OReilly Online Learning
- How to Contact Us
- Acknowledgments
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
- 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
5. Introducing Technical Analysis
- Charting Analysis
- Indicator Analysis
  - Moving Averages
  - The Relative Strength Index
- Pattern Recognition
- Summary
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
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
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
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
10. Deep Reinforcement Learning for Time Series Prediction
- Intuition of Reinforcement Learning
- Deep Reinforcement Learning
- Summary
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 Bitcoins Volatility Using Deep Learning
- Real-Time Visualization of Training
- Summary
12. Market Drivers and Risk Management
- Market Drivers
  - Market Drivers and Economic Intuition
  - News Interpretation
- Risk Management
  - Basics of Risk Management
    - Stops and targets
    - Trailing stops
    - Economic calendar
  - Behavioral Finance: The Power of Biases
    - Cognitive biases
    - Emotional biases
- Summary
Index