9798198515994 - stochastic calculus for modern quantitative finance and algorithmic trading: python implementations, models, and real-world applications de van der post, hayden; preston, james (6 resultados)
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PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
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PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
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Taschenbuch. Condición: Neu. Neuware - Reactive PublishingStochastic Calculus for Modern Quantitative Finance and Algorithmic Trading offers a clear, practical, and rigorous introduction to stochastic calculus tailored specifically for quantitative finance professionals and algorithmic traders.This book bridges the gap between t…heoretical stochastic processes and real-world implementation in modern financial markets. Readers will learn how to apply core concepts, such as Itô's lemma, stochastic differential equations, martingales, and Brownian motion, directly to quantitative modeling and trading strategy development.What You'll Find Inside: - Python Implementation: Complete, ready-to-use code examples using Python (NumPy, SciPy, pandas, and QuantLib) that demonstrate how to simulate stochastic processes, price derivatives, and build trading models.- Modern Models: In-depth coverage of key models used in today's quantitative finance, including the Black-Scholes framework extensions, local volatility, stochastic volatility (Heston), jump-diffusion, and more.- Real-World Applications: Practical case studies on algorithmic trading strategies, risk management, option pricing, portfolio optimization, and Monte Carlo methods applied to live market data.Written with both clarity and technical depth, this book is designed for readers who want to move beyond abstract theory and develop production-grade skills in quantitative finance and algorithmic trading.Whether you are a quantitative analyst, aspiring quant developer, algorithmic trader, or finance graduate student looking to strengthen your technical toolkit, this book provides the mathematical foundation and practical coding guidance needed to succeed in today's data-driven financial markets.No prior stochastic calculus experience is assumed, but familiarity with basic probability, calculus, and Python programming is recommended.
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Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
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Paperback. Condición: new. Paperback. Reactive PublishingStochastic Calculus for Modern Quantitative Finance and Algorithmic Trading offers a clear, practical, and rigorous introduction to stochastic calculus tailored specifically for quantitative finance professionals and algorithmic traders.This book bridges the gap between th…eoretical stochastic processes and real-world implementation in modern financial markets. Readers will learn how to apply core concepts, such as Ito's lemma, stochastic differential equations, martingales, and Brownian motion, directly to quantitative modeling and trading strategy development.What You'll Find Inside: Python Implementation: Complete, ready-to-use code examples using Python (NumPy, SciPy, pandas, and QuantLib) that demonstrate how to simulate stochastic processes, price derivatives, and build trading models.Modern Models: In-depth coverage of key models used in today's quantitative finance, including the Black-Scholes framework extensions, local volatility, stochastic volatility (Heston), jump-diffusion, and more.Real-World Applications: Practical case studies on algorithmic trading strategies, risk management, option pricing, portfolio optimization, and Monte Carlo methods applied to live market data.Written with both clarity and technical depth, this book is designed for readers who want to move beyond abstract theory and develop production-grade skills in quantitative finance and algorithmic trading.Whether you are a quantitative analyst, aspiring quant developer, algorithmic trader, or finance graduate student looking to strengthen your technical toolkit, this book provides the mathematical foundation and practical coding guidance needed to succeed in today's data-driven financial markets.No prior stochastic calculus experience is assumed, but familiarity with basic probability, calculus, and Python programming is recommended. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books
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Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail
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Paperback. Condición: new. Paperback. Reactive PublishingStochastic Calculus for Modern Quantitative Finance and Algorithmic Trading offers a clear, practical, and rigorous introduction to stochastic calculus tailored specifically for quantitative finance professionals and algorithmic traders.This book bridges the gap between th…eoretical stochastic processes and real-world implementation in modern financial markets. Readers will learn how to apply core concepts, such as Ito's lemma, stochastic differential equations, martingales, and Brownian motion, directly to quantitative modeling and trading strategy development.What You'll Find Inside: Python Implementation: Complete, ready-to-use code examples using Python (NumPy, SciPy, pandas, and QuantLib) that demonstrate how to simulate stochastic processes, price derivatives, and build trading models.Modern Models: In-depth coverage of key models used in today's quantitative finance, including the Black-Scholes framework extensions, local volatility, stochastic volatility (Heston), jump-diffusion, and more.Real-World Applications: Practical case studies on algorithmic trading strategies, risk management, option pricing, portfolio optimization, and Monte Carlo methods applied to live market data.Written with both clarity and technical depth, this book is designed for readers who want to move beyond abstract theory and develop production-grade skills in quantitative finance and algorithmic trading.Whether you are a quantitative analyst, aspiring quant developer, algorithmic trader, or finance graduate student looking to strengthen your technical toolkit, this book provides the mathematical foundation and practical coding guidance needed to succeed in today's data-driven financial markets.No prior stochastic calculus experience is assumed, but familiarity with basic probability, calculus, and Python programming is recommended. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
