Name: Deep Learning for Finance: Creating Machine & Deep Learning Models for Trading in Python (en Inglés)
Price: 208.51 PEN
Availability: InStock
Author: Kaabar Sofien
ISBN: 9781098148393

portada Deep Learning for Finance: Creating Machine & Deep Learning Models for Trading in Python (en Inglés)

Deep Learning for Finance: Creating Machine & Deep Learning Models for Trading in Python (en Inglés)

Formato

Libro Físico

Editorial

Oreilly & Associates Inc

Autor

Kaabar Sofien

Idioma

Inglés

N° páginas

350

Encuadernación

Tapa Blanda

ISBN13

9781098148393

N° edición

Deep Learning for Finance: Creating Machine & Deep Learning Models for Trading in Python (en Inglés)

Kaabar Sofien (Autor) · Oreilly & Associates Inc · Tapa Blanda

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Reseña del libro "Deep Learning for Finance: Creating Machine & Deep Learning Models for Trading in Python (en Inglés)"

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