Deep Reinforcement Learning for Futures Trading : Adaptive Algorithms for Volatility, Signals, and Market Regimes
Language: English
Published by Independently Published Dez 2025, 2025
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 46.83
Quantity: 2 available
Add to basketItem description from seller
Neuware - Reactive PublishingThis book is your definitive guide to building, training, and deploying deep reinforcement learning systems for modern futures markets. Designed for quantitative analysts, systematic traders, and advanced practitioners, it bridges cutting-edge academic research with real-world execution logic.The futures market is a shifting landscape of volatility, liquidity, and structural regime changes. Traditional models fail when conditions move too quickly. Reinforcement learning thrives in this environment. By learning directly from reward structures, market transitions, and agent-environment feedback loops, RL becomes a powerful engine for signal discovery and autonomous strategy optimization.This book delivers a complete, end-to-end framework for RL-powered trading: - How to design an RL environment that reflects market reality- State representation: volatility tensors, price transforms, microstructure features- Reward engineering that aligns the agent with real P&L- Policy networks, deep Q-learning, PPO, and actor-critic architectures- Training pipelines that prevent overfitting and mode collapse- Detecting and adapting to structural market regimes- Execution-aware RL: slippage, latency, and position sizing- Building an RL trading engine that can operate in productionYou also receive practical blueprints, including walkthrough Python examples, environment templates, and agent-training workflows tailored for futures contracts across equities, commodities, FX, and rates.James Preston presents a direct, technical, and actionable guide built for traders who demand measurable edge. If you are ready to integrate reinforcement learning into your systematic futures strategies, this book gives you the tools, architecture, and methodology required to compete at the frontier of AI-driven markets.…
Seller Inventory # 9798277014097
- Title
- Deep Reinforcement Learning for Futures Trading : Adaptive Algorithms for Volatility, Signals, and Market Regimes
- Author
- James Preston
- Publisher
- Independently Published Dez 2025
- Publication year
- 2025
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798277014097
- Item weight
- 476 grams
- Dimensions
- 229x152x25 mm
- Series
- Book 1 of 9: Reinforcement Learning Applied
This book is your definitive guide to building, training, and deploying deep reinforcement learning systems for modern futures markets. Designed for quantitative analysts, systematic traders, and advanced practitioners, it bridges cutting-edge academic research with real-world execution logic.
The futures market is a shifting landscape of volatility, liquidity, and structural regime changes. Traditional models fail when conditions move too quickly. Reinforcement learning thrives in this environment. By learning directly from reward structures, market transitions, and agent-environment feedback loops, RL becomes a powerful engine for signal discovery and autonomous strategy optimization.
This book delivers a complete, end-to-end framework for RL-powered trading:
• How to design an RL environment that reflects market reality
• State representation: volatility tensors, price transforms, microstructure features
• Reward engineering that aligns the agent with real P&L
• Policy networks, deep Q-learning, PPO, and actor–critic architectures
• Training pipelines that prevent overfitting and mode collapse
• Detecting and adapting to structural market regimes
• Execution-aware RL: slippage, latency, and position sizing
• Building an RL trading engine that can operate in production
You also receive practical blueprints, including walkthrough Python examples, environment templates, and agent-training workflows tailored for futures contracts across equities, commodities, FX, and rates.
James Preston presents a direct, technical, and actionable guide built for traders who demand measurable edge. If you are ready to integrate reinforcement learning into your systematic futures strategies, this book gives you the tools, architecture, and methodology required to compete at the frontier of AI-driven markets.
"Synopsis" may belong to another edition of this title.
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