9798277014097 - deep reinforcement learning for futures trading: adaptive algorithms for volatility, signals, and market regimes: 2 (reinforcement learning applied) de preston, james (6 resultados)
Idioma: Inglés
Editorial: Amazon Digital Services LLC - Kdp, 2025
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Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de AmericaPBShop.store US
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EUR 37,48
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PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
Idioma: Inglés
Editorial: Amazon Digital Services LLC - Kdp, 2025
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Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK
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EUR 34,40
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PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
Idioma: Inglés
Editorial: Independently Published Dez 2025, 2025
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 46,00
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Taschenbuch. Condición: Neu. 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.
Idioma: Inglés
Editorial: Independently published, 2025
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Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books
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EUR 33,07
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Condición: New. Print on Demand.
Idioma: Inglés
Editorial: Independently Published, 2025
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Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
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EUR 37,83
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Paperback. Condición: new. Paperback. 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-w…orld 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Idioma: Inglés
Editorial: Independently Published, 2025
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Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail
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EUR 38,49
Envío por EUR 43,22Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: new. Paperback. 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-w…orld 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
