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PyTorch for Quantitative Finance: Applying Deep Learning and Neural SDEs to Algorithmic Trading - Softcover
Inhaltsangabe
Reactive Publishing
Bridge the gap between modern deep learning and quantitative finance using PyTorch.
PyTorch for Quantitative Finance provides a rigorous, hands-on guide to building, training, and deploying neural architectures across financial markets. Designed for quantitative analysts, developers, and data scientists, this book moves beyond toy datasets to address the real-world complexities of market microstructure, non-stationary time series, and continuous-time stochastic modeling.
Rather than relying on black-box heuristics, you will learn how to integrate deep learning directly with mathematical finance. Discover how to leverage PyTorch to solve high-dimensional partial differential equations (PDEs), construct generative models for market simulation, and design robust algorithmic trading strategies.
What You Will Learn:Neural Stochastic Differential Equations (Neural SDEs): Model continuous-time asset dynamics and latent market trajectories using differentiable SDE solvers in PyTorch.
Deep Factor Models & Risk Management: Construct non-linear factor models to capture complex multi-asset dependencies and tail-risk exposures.
Market Simulation with Generative Models: Use GANs and Variational Autoencoders (VAEs) to generate realistic synthetic financial time series for backtesting.
Algorithmic Trading & Execution: Implement deep reinforcement learning algorithms for optimal execution, portfolio rebalancing, and dynamic hedging.
Production-Grade PyTorch Pipelines: Optimize model performance with custom C++ extensions, GPU acceleration, and efficient data loaders tailored for high-frequency time series.
Whether you are implementing continuous-time models or deploying end-to-end algorithmic execution engines, this book delivers the code, theory, and architecture required to build state-of-the-art quantitative systems.
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PyTorch for Quantitative Finance: Applying Deep Learning and Neural SDEs to Algorithmic Trading
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PyTorch for Quantitative Finance (Paperback)
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Paperback. Zustand: new. Paperback. Reactive PublishingBridge the gap between modern deep learning and quantitative finance using PyTorch.PyTorch for Quantitative Finance provides a rigorous, hands-on guide to building, training, and deploying neural architectures across financial markets. Designed for quantitative analysts, developers, and data scientists, this book moves beyond toy datasets to address the real-world complexities of market microstructure, non-stationary time series, and continuous-time stochastic modeling.Rather than relying on black-box heuristics, you will learn how to integrate deep learning directly with mathematical finance. Discover how to leverage PyTorch to solve high-dimensional partial differential equations (PDEs), construct generative models for market simulation, and design robust algorithmic trading strategies.What You Will Learn: Neural Stochastic Differential Equations (Neural SDEs): Model continuous-time asset dynamics and latent market trajectories using differentiable SDE solvers in PyTorch.Deep Factor Models & Risk Management: Construct non-linear factor models to capture complex multi-asset dependencies and tail-risk exposures.Market Simulation with Generative Models: Use GANs and Variational Autoencoders (VAEs) to generate realistic synthetic financial time series for backtesting.Algorithmic Trading & Execution: Implement deep reinforcement learning algorithms for optimal execution, portfolio rebalancing, and dynamic hedging.Production-Grade PyTorch Pipelines: Optimize model performance with custom C++ extensions, GPU acceleration, and efficient data loaders tailored for high-frequency time series.Whether you are implementing continuous-time models or deploying end-to-end algorithmic execution engines, this book delivers the code, theory, and architecture required to build state-of-the-art quantitative systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798188445232
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PyTorch for Quantitative Finance : Applying Deep Learning and Neural SDEs to Algorithmic Trading
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Taschenbuch. Zustand: Neu. Neuware - Reactive PublishingBridge the gap between modern deep learning and quantitative finance using PyTorch.PyTorch for Quantitative Finance provides a rigorous, hands-on guide to building, training, and deploying neural architectures across financial markets. Designed for quantitative analysts, developers, and data scientists, this book moves beyond toy datasets to address the real-world complexities of market microstructure, non-stationary time series, and continuous-time stochastic modeling.Rather than relying on black-box heuristics, you will learn how to integrate deep learning directly with mathematical finance. Discover how to leverage PyTorch to solve high-dimensional partial differential equations (PDEs), construct generative models for market simulation, and design robust algorithmic trading strategies.What You Will Learn: - Neural Stochastic Differential Equations (Neural SDEs): Model continuous-time asset dynamics and latent market trajectories using differentiable SDE solvers in PyTorch.- Deep Factor Models & Risk Management: Construct non-linear factor models to capture complex multi-asset dependencies and tail-risk exposures.- Market Simulation with Generative Models: Use GANs and Variational Autoencoders (VAEs) to generate realistic synthetic financial time series for backtesting.- Algorithmic Trading & Execution: Implement deep reinforcement learning algorithms for optimal execution, portfolio rebalancing, and dynamic hedging.- Production-Grade PyTorch Pipelines: Optimize model performance with custom C++ extensions, GPU acceleration, and efficient data loaders tailored for high-frequency time series.Whether you are implementing continuous-time models or deploying end-to-end algorithmic execution engines, this book delivers the code, theory, and architecture required to build state-of-the-art quantitative systems. Bestandsnummer des Verkäufers 9798188445232
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