Isbn: 9798274427647 - reinforcement learning for live market execution: building rl agents with action penalties, slippage modelling, market impact.: 2 (5 risultati)

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  • Lingua: Inglese

    Editore: Independently Published, 2025

    9798274427647

    Serie: Libro 2 di 6 - Algorithmic Alpha: Next-Gen Trading Systems for the Modern Market

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    Da: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US

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    PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

  • Lingua: Inglese

    Editore: Independently Published, 2025

    9798274427647

    Serie: Libro 2 di 6 - Algorithmic Alpha: Next-Gen Trading Systems for the Modern Market

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    Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK

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    PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

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  • Lingua: Inglese

    Editore: Independently Published, 2025

    9798274427647

    Serie: Libro 2 di 6 - Algorithmic Alpha: Next-Gen Trading Systems for the Modern Market

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    Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail

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    Paperback. Condizione: new. Paperback. Reactive PublishingExecution is where theories die and real trading begins.In today's markets, microseconds matter, order books shift without warning, and liquidity evaporates the moment a trader hesitates. Reinforcement learning, built on adaptive decision-making and continuous reward optimization, has become the most powerful framework for navigating this environment.Reinforcement Learning for Live Market Execution reveals how modern quant desks design agents that learn, react, and evolve inside real-time market conditions. This is not a theoretical tour. It is a practical, institutional-grade manual for building RL-driven execution systems capable of surviving and thriving in live markets.Inside, you'll learn how to: Construct RL agents that optimize entries, exits, sizing, and timing in dynamic environmentsModel slippage, spread, queue position, and market impact as penalties and rewardsTrain policies using volatility shocks, liquidity droughts, and regime shiftsIntegrate RL with microstructure signals: order flow imbalance, volatility bursts, and quote dynamicsBuild execution engines for futures, options, and crypto using constrained decision workflowsRun walk-forward simulations that mirror real-world stress conditionsDeploy agents to live trading while maintaining risk controls and fail-safe overridesEach chapter focuses on durability, how to engineer models that not only backtest well, but perform reliably when the market becomes chaotic, thin, or structurally hostile.For quantitative traders, algorithm designers, and researchers seeking an advanced but accessible pathway into reinforcement learning, this book offers a complete blueprint for turning RL into a true execution edge. This is the future of live market execution, built one decision at a time. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

  • Lingua: Inglese

    Editore: Independently Published, 2025

    9798274427647

    Serie: Libro 2 di 6 - Algorithmic Alpha: Next-Gen Trading Systems for the Modern Market

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    Da: CitiRetail, Stevenage, Regno UnitoCitiRetail

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    EUR 43,70

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    Quantità: 1 disponibile

    Paperback. Condizione: new. Paperback. Reactive PublishingExecution is where theories die and real trading begins.In today's markets, microseconds matter, order books shift without warning, and liquidity evaporates the moment a trader hesitates. Reinforcement learning, built on adaptive decision-making and continuous reward optimization, has become the most powerful framework for navigating this environment.Reinforcement Learning for Live Market Execution reveals how modern quant desks design agents that learn, react, and evolve inside real-time market conditions. This is not a theoretical tour. It is a practical, institutional-grade manual for building RL-driven execution systems capable of surviving and thriving in live markets.Inside, you'll learn how to: Construct RL agents that optimize entries, exits, sizing, and timing in dynamic environmentsModel slippage, spread, queue position, and market impact as penalties and rewardsTrain policies using volatility shocks, liquidity droughts, and regime shiftsIntegrate RL with microstructure signals: order flow imbalance, volatility bursts, and quote dynamicsBuild execution engines for futures, options, and crypto using constrained decision workflowsRun walk-forward simulations that mirror real-world stress conditionsDeploy agents to live trading while maintaining risk controls and fail-safe overridesEach chapter focuses on durability, how to engineer models that not only backtest well, but perform reliably when the market becomes chaotic, thin, or structurally hostile.For quantitative traders, algorithm designers, and researchers seeking an advanced but accessible pathway into reinforcement learning, this book offers a complete blueprint for turning RL into a true execution edge. This is the future of live market execution, built one decision at a time. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…