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Mutual Information in Trading Systems: Measuring Nonlinear Dependencies in Financial Markets with Python - Couverture souple
Synopsis
Reactive Publishing
Financial markets are driven by complex relationships that traditional correlation measures often fail to capture. Many trading strategies rely on linear assumptions, yet real market dynamics frequently involve nonlinear dependencies that remain hidden when using standard statistical tools.
Mutual Information in Trading Systems introduces a rigorous framework for identifying and analyzing nonlinear relationships in financial data using information theory. The book explains how mutual information can reveal structural dependencies between variables such as price movements, volatility regimes, and macro signals that conventional methods overlook.
Through practical Python implementations, readers learn how to estimate mutual information, apply it to market datasets, and incorporate information-theoretic metrics into systematic trading research. The techniques presented are useful for analyzing feature relationships, improving signal selection, and understanding the informational structure of financial time series.
Topics covered include:
Foundations of information theory and entropy
Mutual information estimation techniques
Nonlinear dependency analysis in market data
Feature selection for trading models
Python implementations for quantitative research
Applications in systematic trading and financial data analysis
Designed for quantitative researchers, data scientists, and traders working with Python, this book provides a structured introduction to applying information-theoretic methods within modern trading systems.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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Mutual Information in Trading Systems: Measuring Nonlinear Dependencies in Financial Markets with Python
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Mutual Information in Trading Systems (Paperback)
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Paperback. Etat : new. Paperback. Reactive PublishingFinancial markets are driven by complex relationships that traditional correlation measures often fail to capture. Many trading strategies rely on linear assumptions, yet real market dynamics frequently involve nonlinear dependencies that remain hidden when using standard statistical tools.Mutual Information in Trading Systems introduces a rigorous framework for identifying and analyzing nonlinear relationships in financial data using information theory. The book explains how mutual information can reveal structural dependencies between variables such as price movements, volatility regimes, and macro signals that conventional methods overlook.Through practical Python implementations, readers learn how to estimate mutual information, apply it to market datasets, and incorporate information-theoretic metrics into systematic trading research. The techniques presented are useful for analyzing feature relationships, improving signal selection, and understanding the informational structure of financial time series.Topics covered include: Foundations of information theory and entropyMutual information estimation techniquesNonlinear dependency analysis in market dataFeature selection for trading modelsPython implementations for quantitative researchApplications in systematic trading and financial data analysisDesigned for quantitative researchers, data scientists, and traders working with Python, this book provides a structured introduction to applying information-theoretic methods within modern trading systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798251073997
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Mutual Information in Trading Systems
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Mutual Information in Trading Systems (Paperback)
impression à la demandeVendeur : CitiRetail, Stevenage, Royaume-Uni
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Paperback. Etat : new. Paperback. Reactive PublishingFinancial markets are driven by complex relationships that traditional correlation measures often fail to capture. Many trading strategies rely on linear assumptions, yet real market dynamics frequently involve nonlinear dependencies that remain hidden when using standard statistical tools.Mutual Information in Trading Systems introduces a rigorous framework for identifying and analyzing nonlinear relationships in financial data using information theory. The book explains how mutual information can reveal structural dependencies between variables such as price movements, volatility regimes, and macro signals that conventional methods overlook.Through practical Python implementations, readers learn how to estimate mutual information, apply it to market datasets, and incorporate information-theoretic metrics into systematic trading research. The techniques presented are useful for analyzing feature relationships, improving signal selection, and understanding the informational structure of financial time series.Topics covered include: Foundations of information theory and entropyMutual information estimation techniquesNonlinear dependency analysis in market dataFeature selection for trading modelsPython implementations for quantitative researchApplications in systematic trading and financial data analysisDesigned for quantitative researchers, data scientists, and traders working with Python, this book provides a structured introduction to applying information-theoretic methods within modern trading 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. N° de réf. du vendeur 9798251073997
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Mutual Information in Trading Systems : Measuring Nonlinear Dependencies in Financial Markets with Python
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Taschenbuch. Etat : Neu. Neuware - Reactive PublishingFinancial markets are driven by complex relationships that traditional correlation measures often fail to capture. Many trading strategies rely on linear assumptions, yet real market dynamics frequently involve nonlinear dependencies that remain hidden when using standard statistical tools.Mutual Information in Trading Systems introduces a rigorous framework for identifying and analyzing nonlinear relationships in financial data using information theory. The book explains how mutual information can reveal structural dependencies between variables such as price movements, volatility regimes, and macro signals that conventional methods overlook.Through practical Python implementations, readers learn how to estimate mutual information, apply it to market datasets, and incorporate information-theoretic metrics into systematic trading research. The techniques presented are useful for analyzing feature relationships, improving signal selection, and understanding the informational structure of financial time series.Topics covered include: - Foundations of information theory and entropy- Mutual information estimation techniques- Nonlinear dependency analysis in market data- Feature selection for trading models- Python implementations for quantitative research- Applications in systematic trading and financial data analysisDesigned for quantitative researchers, data scientists, and traders working with Python, this book provides a structured introduction to applying information-theoretic methods within modern trading systems. N° de réf. du vendeur 9798251073997
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