Reinforcement Learning for Algorithmic Trading: From Theory to Real-World Implementation (Advanced Topics in Quantitative Trading)

Trex, Nova

/ ISBN 13: 9798314017210

Verlag: Independently published, 2025

In. Bestandsnummer des Verkäufers ria9798314017210_new

Inhaltsangabe:

Unlock the Future of Trading with Reinforcement Learning

Dive into the cutting-edge world of algorithmic trading with Reinforcement Learning for Algorithmic Trading: From Theory to Real-World Implementation by Nova Trex. This groundbreaking book bridges the gap between theoretical innovation and practical application, offering a comprehensive guide to harnessing the power of reinforcement learning (RL) to revolutionize financial markets.

From the fundamentals of global market dynamics and statistical modeling to the intricacies of designing adaptive RL-based trading strategies, this book equips readers with the knowledge and tools to thrive in today’s fast-paced trading landscape. Explore foundational concepts like Markov Decision Processes and deep learning integrations such as Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), then master advanced techniques for backtesting, risk management, and live deployment. With step-by-step system development, real-world code examples, and insights into feature engineering and data processing, Trex takes you from novice to expert with clarity and precision.

Perfect for quant traders, data scientists, and financial engineers, this book offers:

• A deep dive into the evolution, ethics, and regulatory considerations of algorithmic trading.
• Practical frameworks for building robust, scalable RL trading systems.

Reinforcement Learning for Algorithmic Trading is your essential companion to mastering the art and science of modern trading. Whether you're conceptualizing strategies, optimizing performance, or deploying live systems, Nova Trex delivers the roadmap to success in this dynamic and lucrative field.

Ready to trade smarter? Your journey starts here.

Bibliografische Details

Titel: Reinforcement Learning for Algorithmic ...
Verlag: Independently published
Erscheinungsdatum: 2025
Einband: Softcover
Zustand: New