Isbn: 9781803246970 - machine learning for trading: a disciplined workflow from research to live execution, with nine case studies and ai agents (14 resultados)

Condición: New.

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

Paperback. Condición: New. 3rd. Build and deploy AI-driven trading systems using the 7-Stage workflow with pandas, Polars, LightGBM, PyTorch, Optuna, zipline-reloaded, MLflow, Feast, and SHAPKey FeaturesBuild point-in-time pipelines, integrate alternative data, and ensure data integrityBuild and validate predictive models using GBMs, Transformers, and causal inference frameworks to create robust, interpretable alpha signalsDeploy RAG systems, autonomous financial agents, and diffusion-based synthetic data generatorsBook DescriptionThe rapid rise of AI and the growing complexity of financial markets have transformed quantitative trading into a data-driven, process-oriented discipline. This third edition provides a comprehensive blueprint for designing, validating, and deploying systematic trading strategies powered by modern machine learning. It introduces the 7 stage ML4T Workflow, a professional framework that unites data engineering, model development, validation, and live deployment into one cohesive process. It demonstrates how to turn raw market, fundamental, and alternative data into predictive signals and robust, production-ready trading systems. You'll learn to build advanced pipelines for feature engineering, model evaluation, and portfolio optimization using libraries such as Polars, LightGBM, PyTorch, and Optuna. Practical notebooks illustrate every stage of the workflow, from factor testing and backtesting with zipline reloaded to live deployment with MLOps tools such as MLflow, Feast, and Prometheus. Additional coverage of synthetic data generation, Graph Neural Networks, and Reinforcement Learning extends the toolkit for building resilient, adaptive strategies that thrive in dynamic markets. By the end of this book, you'll be proficient to build your own industrial-grade "alpha factory".What you will learnTransform raw data into predictive alpha factors, validated with leak-proof cross-validationMaster advanced models, from Gradient Boosting Machines to Transformers, Graph Neural Networks, and Reinforcement Learning agentsHarness Generative AI, Retrieval Augmented Generation, and Causal Inference to make models interpretable, auditable, and compliant with regulatory standardsBuild production-ready trading infrastructure using MLOps, feature stores, and model monitoring to transition research into live capital deployment safelyWho this book is forIf you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.…

Paperback. Condición: New. 3rd. Build and deploy AI-driven trading systems using the 7-Stage workflow with pandas, Polars, LightGBM, PyTorch, Optuna, zipline-reloaded, MLflow, Feast, and SHAPKey FeaturesBuild point-in-time pipelines, integrate alternative data, and ensure data integrityBuild and validate predictive models using GBMs, Transformers, and causal inference frameworks to create robust, interpretable alpha signalsDeploy RAG systems, autonomous financial agents, and diffusion-based synthetic data generatorsBook DescriptionThe rapid rise of AI and the growing complexity of financial markets have transformed quantitative trading into a data-driven, process-oriented discipline. This third edition provides a comprehensive blueprint for designing, validating, and deploying systematic trading strategies powered by modern machine learning. It introduces the 7 stage ML4T Workflow, a professional framework that unites data engineering, model development, validation, and live deployment into one cohesive process. It demonstrates how to turn raw market, fundamental, and alternative data into predictive signals and robust, production-ready trading systems. You'll learn to build advanced pipelines for feature engineering, model evaluation, and portfolio optimization using libraries such as Polars, LightGBM, PyTorch, and Optuna. Practical notebooks illustrate every stage of the workflow, from factor testing and backtesting with zipline reloaded to live deployment with MLOps tools such as MLflow, Feast, and Prometheus. Additional coverage of synthetic data generation, Graph Neural Networks, and Reinforcement Learning extends the toolkit for building resilient, adaptive strategies that thrive in dynamic markets. By the end of this book, you'll be proficient to build your own industrial-grade "alpha factory".What you will learnTransform raw data into predictive alpha factors, validated with leak-proof cross-validationMaster advanced models, from Gradient Boosting Machines to Transformers, Graph Neural Networks, and Reinforcement Learning agentsHarness Generative AI, Retrieval Augmented Generation, and Causal Inference to make models interpretable, auditable, and compliant with regulatory standardsBuild production-ready trading infrastructure using MLOps, feature stores, and model monitoring to transition research into live capital deployment safelyWho this book is forIf you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.…

Condición: New.

Paperback. Condición: New. 3rd. Build and deploy AI-driven trading systems using the 7-Stage workflow with pandas, Polars, LightGBM, PyTorch, Optuna, zipline-reloaded, MLflow, Feast, and SHAPKey FeaturesBuild point-in-time pipelines, integrate alternative data, and ensure data integrityBuild and validate predictive models using GBMs, Transformers, and causal inference frameworks to create robust, interpretable alpha signalsDeploy RAG systems, autonomous financial agents, and diffusion-based synthetic data generatorsBook DescriptionThe rapid rise of AI and the growing complexity of financial markets have transformed quantitative trading into a data-driven, process-oriented discipline. This third edition provides a comprehensive blueprint for designing, validating, and deploying systematic trading strategies powered by modern machine learning. It introduces the 7 stage ML4T Workflow, a professional framework that unites data engineering, model development, validation, and live deployment into one cohesive process. It demonstrates how to turn raw market, fundamental, and alternative data into predictive signals and robust, production-ready trading systems. You'll learn to build advanced pipelines for feature engineering, model evaluation, and portfolio optimization using libraries such as Polars, LightGBM, PyTorch, and Optuna. Practical notebooks illustrate every stage of the workflow, from factor testing and backtesting with zipline reloaded to live deployment with MLOps tools such as MLflow, Feast, and Prometheus. Additional coverage of synthetic data generation, Graph Neural Networks, and Reinforcement Learning extends the toolkit for building resilient, adaptive strategies that thrive in dynamic markets. By the end of this book, you'll be proficient to build your own industrial-grade "alpha factory".What you will learnTransform raw data into predictive alpha factors, validated with leak-proof cross-validationMaster advanced models, from Gradient Boosting Machines to Transformers, Graph Neural Networks, and Reinforcement Learning agentsHarness Generative AI, Retrieval Augmented Generation, and Causal Inference to make models interpretable, auditable, and compliant with regulatory standardsBuild production-ready trading infrastructure using MLOps, feature stores, and model monitoring to transition research into live capital deployment safelyWho this book is forIf you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.…

Paperback. Condición: New. 3rd. Build and deploy AI-driven trading systems using the 7-Stage workflow with pandas, Polars, LightGBM, PyTorch, Optuna, zipline-reloaded, MLflow, Feast, and SHAPKey FeaturesBuild point-in-time pipelines, integrate alternative data, and ensure data integrityBuild and validate predictive models using GBMs, Transformers, and causal inference frameworks to create robust, interpretable alpha signalsDeploy RAG systems, autonomous financial agents, and diffusion-based synthetic data generatorsBook DescriptionThe rapid rise of AI and the growing complexity of financial markets have transformed quantitative trading into a data-driven, process-oriented discipline. This third edition provides a comprehensive blueprint for designing, validating, and deploying systematic trading strategies powered by modern machine learning. It introduces the 7 stage ML4T Workflow, a professional framework that unites data engineering, model development, validation, and live deployment into one cohesive process. It demonstrates how to turn raw market, fundamental, and alternative data into predictive signals and robust, production-ready trading systems. You'll learn to build advanced pipelines for feature engineering, model evaluation, and portfolio optimization using libraries such as Polars, LightGBM, PyTorch, and Optuna. Practical notebooks illustrate every stage of the workflow, from factor testing and backtesting with zipline reloaded to live deployment with MLOps tools such as MLflow, Feast, and Prometheus. Additional coverage of synthetic data generation, Graph Neural Networks, and Reinforcement Learning extends the toolkit for building resilient, adaptive strategies that thrive in dynamic markets. By the end of this book, you'll be proficient to build your own industrial-grade "alpha factory".What you will learnTransform raw data into predictive alpha factors, validated with leak-proof cross-validationMaster advanced models, from Gradient Boosting Machines to Transformers, Graph Neural Networks, and Reinforcement Learning agentsHarness Generative AI, Retrieval Augmented Generation, and Causal Inference to make models interpretable, auditable, and compliant with regulatory standardsBuild production-ready trading infrastructure using MLOps, feature stores, and model monitoring to transition research into live capital deployment safelyWho this book is forIf you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.…

Paperback. Condición: new. Paperback. Build and deploy AI-driven trading systems using the 7-Stage workflow with pandas, Polars, LightGBM, PyTorch, Optuna, zipline-reloaded, MLflow, Feast, and SHAPKey FeaturesBuild point-in-time pipelines, integrate alternative data, and ensure data integrityBuild and validate predictive models using GBMs, Transformers, and causal inference frameworks to create robust, interpretable alpha signalsDeploy RAG systems, autonomous financial agents, and diffusion-based synthetic data generatorsBook DescriptionThe rapid rise of AI and the growing complexity of financial markets have transformed quantitative trading into a data-driven, process-oriented discipline. This third edition provides a comprehensive blueprint for designing, validating, and deploying systematic trading strategies powered by modern machine learning.It introduces the 7 stage ML4T Workflow, a professional framework that unites data engineering, model development, validation, and live deployment into one cohesive process. It demonstrates how to turn raw market, fundamental, and alternative data into predictive signals and robust, production-ready trading systems.Youll learn to build advanced pipelines for feature engineering, model evaluation, and portfolio optimization using libraries such as Polars, LightGBM, PyTorch, and Optuna.Practical notebooks illustrate every stage of the workflow, from factor testing and backtesting with zipline reloaded to live deployment with MLOps tools such as MLflow, Feast, and Prometheus. Additional coverage of synthetic data generation, Graph Neural Networks, and Reinforcement Learning extends the toolkit for building resilient, adaptive strategies that thrive in dynamic markets.By the end of this book, youll be proficient to build your own industrial-grade alpha factory".What you will learnTransform raw data into predictive alpha factors, validated with leak-proof cross-validationMaster advanced models, from Gradient Boosting Machines to Transformers, Graph Neural Networks, and Reinforcement Learning agentsHarness Generative AI, Retrieval Augmented Generation, and Causal Inference to make models interpretable, auditable, and compliant with regulatory standardsBuild production-ready trading infrastructure using MLOps, feature stores, and model monitoring to transition research into live capital deployment safelyWho this book is forIf you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies.Some understanding of Python and machine learning techniques is required. This third edition teaches you to design, test, and deploy AI-driven trading systems using the 7-Stage ML4T Workflow, covering Generative AI, causal inference, and MLOps for robust, adaptive, and systematic strategies. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback / softback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Paperback. Condición: new. Paperback. Build and deploy AI-driven trading systems using the 7-Stage workflow with pandas, Polars, LightGBM, PyTorch, Optuna, zipline-reloaded, MLflow, Feast, and SHAPKey FeaturesBuild point-in-time pipelines, integrate alternative data, and ensure data integrityBuild and validate predictive models using GBMs, Transformers, and causal inference frameworks to create robust, interpretable alpha signalsDeploy RAG systems, autonomous financial agents, and diffusion-based synthetic data generatorsBook DescriptionThe rapid rise of AI and the growing complexity of financial markets have transformed quantitative trading into a data-driven, process-oriented discipline. This third edition provides a comprehensive blueprint for designing, validating, and deploying systematic trading strategies powered by modern machine learning.It introduces the 7 stage ML4T Workflow, a professional framework that unites data engineering, model development, validation, and live deployment into one cohesive process. It demonstrates how to turn raw market, fundamental, and alternative data into predictive signals and robust, production-ready trading systems.Youll learn to build advanced pipelines for feature engineering, model evaluation, and portfolio optimization using libraries such as Polars, LightGBM, PyTorch, and Optuna.Practical notebooks illustrate every stage of the workflow, from factor testing and backtesting with zipline reloaded to live deployment with MLOps tools such as MLflow, Feast, and Prometheus. Additional coverage of synthetic data generation, Graph Neural Networks, and Reinforcement Learning extends the toolkit for building resilient, adaptive strategies that thrive in dynamic markets.By the end of this book, youll be proficient to build your own industrial-grade alpha factory".What you will learnTransform raw data into predictive alpha factors, validated with leak-proof cross-validationMaster advanced models, from Gradient Boosting Machines to Transformers, Graph Neural Networks, and Reinforcement Learning agentsHarness Generative AI, Retrieval Augmented Generation, and Causal Inference to make models interpretable, auditable, and compliant with regulatory standardsBuild production-ready trading infrastructure using MLOps, feature stores, and model monitoring to transition research into live capital deployment safelyWho this book is forIf you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies.Some understanding of Python and machine learning techniques is required. This third edition teaches you to design, test, and deploy AI-driven trading systems using the 7-Stage ML4T Workflow, covering Generative AI, causal inference, and MLOps for robust, adaptive, and systematic strategies. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Condición: New. Print on Demand.

Condición: New. PRINT ON DEMAND.

Paperback. Condición: new. Paperback. Build and deploy AI-driven trading systems using the 7-Stage workflow with pandas, Polars, LightGBM, PyTorch, Optuna, zipline-reloaded, MLflow, Feast, and SHAPKey FeaturesBuild point-in-time pipelines, integrate alternative data, and ensure data integrityBuild and validate predictive models using GBMs, Transformers, and causal inference frameworks to create robust, interpretable alpha signalsDeploy RAG systems, autonomous financial agents, and diffusion-based synthetic data generatorsBook DescriptionThe rapid rise of AI and the growing complexity of financial markets have transformed quantitative trading into a data-driven, process-oriented discipline. This third edition provides a comprehensive blueprint for designing, validating, and deploying systematic trading strategies powered by modern machine learning.It introduces the 7 stage ML4T Workflow, a professional framework that unites data engineering, model development, validation, and live deployment into one cohesive process. It demonstrates how to turn raw market, fundamental, and alternative data into predictive signals and robust, production-ready trading systems.Youll learn to build advanced pipelines for feature engineering, model evaluation, and portfolio optimization using libraries such as Polars, LightGBM, PyTorch, and Optuna.Practical notebooks illustrate every stage of the workflow, from factor testing and backtesting with zipline reloaded to live deployment with MLOps tools such as MLflow, Feast, and Prometheus. Additional coverage of synthetic data generation, Graph Neural Networks, and Reinforcement Learning extends the toolkit for building resilient, adaptive strategies that thrive in dynamic markets.By the end of this book, youll be proficient to build your own industrial-grade alpha factory".What you will learnTransform raw data into predictive alpha factors, validated with leak-proof cross-validationMaster advanced models, from Gradient Boosting Machines to Transformers, Graph Neural Networks, and Reinforcement Learning agentsHarness Generative AI, Retrieval Augmented Generation, and Causal Inference to make models interpretable, auditable, and compliant with regulatory standardsBuild production-ready trading infrastructure using MLOps, feature stores, and model monitoring to transition research into live capital deployment safelyWho this book is forIf you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies.Some understanding of Python and machine learning techniques is required. This third edition teaches you to design, test, and deploy AI-driven trading systems using the 7-Stage ML4T Workflow, covering Generative AI, causal inference, and MLOps for robust, adaptive, and systematic strategies. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This third edition teaches you to design, test, and deploy AI-driven trading systems using the 7-Stage ML4T Workflow, covering Generative AI, causal inference, and MLOps for robust, adaptive, and systematic strategies.…