Deep learning for finance
creating machine & deep learning models for trading in Python
Seiten
2024
|
1. Auflage
O'Reilly Media (Verlag)
978-1-0981-4839-3 (ISBN)
O'Reilly Media (Verlag)
978-1-0981-4839-3 (ISBN)
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.
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
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
Sofien Kaabar is a financial author, trading consultant, and institutional market strategist specializing in the currencies market with a focus on Technical & Quantitative topics. Sofien's goal is to make Technical Analysis objective by incorporating clear conditions that can be analyzed and created with the use of technical indicators that rival existing ones. Having elaborated many successful trading algorithms, Sofien is now sharing back the knowledge he has acquired over the years to make it accessible to everyone.
| Erscheinungsdatum | 17.01.2024 |
|---|---|
| Verlagsort | Sebastopol |
| Sprache | englisch |
| Maße | 178 x 233 mm |
| Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Wirtschaft ► Betriebswirtschaft / Management ► Finanzierung | |
| ISBN-10 | 1-0981-4839-8 / 1098148398 |
| ISBN-13 | 978-1-0981-4839-3 / 9781098148393 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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