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{ "item_title" : "Julia for Mathematical Finance", "item_author" : [" Alice Schwartz", "Hayden Van Der Post "], "item_description" : "Reactive Publishing Master High-Speed Financial Engineering with the Julia LanguageModern quantitative finance demands two things that traditional programming languages force you to choose between: the development speed of Python and the execution performance of C++. Julia changes the game. By solving the two-language problem, Julia enables quantitative analysts, risk managers, and algorithmic traders to build lightning-fast financial models without sacrificing code readability.Julia for Mathematical Finance is a practical, code-first guide to implementing high-performance quantitative models and trading systems from scratch. Whether you are pricing complex derivatives, simulating stochastic market behavior, or execution-testing trading algorithms, this book shows you how to leverage Julia's advanced type system, native parallel computing, and rich scientific ecosystem.What You Will LearnCore Financial Foundations: Cleanly implement matrix operations, time-series analysis, and yield curve constructions optimized for speed.Stochastic Calculus & Derivative Pricing: Model asset paths using Stochastic Differential Equations (SDEs) and build high-speed Monte Carlo engines for European and American options.Algorithmic Trading & Backtesting: Design event-driven simulation frameworks, perform execution analysis, and manage order-book dynamics.Performance Optimization: Master zero-cost abstractions, memory allocation profiling, and automatic differentiation (AD) for rapid Greeks computation.Parallel Computing: Scale your simulations using Julia's native multi-threading and distributed GPU acceleration.Who This Book Is ForThis book is ideal for quantitative analysts, quantitative developers, financial engineers, risk managers, and computational finance students who have a basic understanding of financial theory and want to transition to a high-performance Julia workflow.Stop compromising between clean mathematical syntax and execution speed. Upgrade your quantitative stack today.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/9/79/819/154/9798191548753_b.jpg", "price_data" : { "retail_price" : "41.99", "online_price" : "41.99", "our_price" : "41.99", "club_price" : "41.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Julia for Mathematical Finance|Alice Schwartz

Julia for Mathematical Finance : High-Performance Quantitative Modeling and Algorithmic Trading

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Overview

Reactive Publishing

Master High-Speed Financial Engineering with the Julia Language

Modern quantitative finance demands two things that traditional programming languages force you to choose between: the development speed of Python and the execution performance of C++. Julia changes the game. By solving the "two-language problem," Julia enables quantitative analysts, risk managers, and algorithmic traders to build lightning-fast financial models without sacrificing code readability.

Julia for Mathematical Finance is a practical, code-first guide to implementing high-performance quantitative models and trading systems from scratch. Whether you are pricing complex derivatives, simulating stochastic market behavior, or execution-testing trading algorithms, this book shows you how to leverage Julia's advanced type system, native parallel computing, and rich scientific ecosystem.

What You Will Learn
  • Core Financial Foundations: Cleanly implement matrix operations, time-series analysis, and yield curve constructions optimized for speed.

  • Stochastic Calculus & Derivative Pricing: Model asset paths using Stochastic Differential Equations (SDEs) and build high-speed Monte Carlo engines for European and American options.

  • Algorithmic Trading & Backtesting: Design event-driven simulation frameworks, perform execution analysis, and manage order-book dynamics.

  • Performance Optimization: Master zero-cost abstractions, memory allocation profiling, and automatic differentiation (AD) for rapid Greeks computation.

  • Parallel Computing: Scale your simulations using Julia's native multi-threading and distributed GPU acceleration.

Who This Book Is For

This book is ideal for quantitative analysts, quantitative developers, financial engineers, risk managers, and computational finance students who have a basic understanding of financial theory and want to transition to a high-performance Julia workflow.

Stop compromising between clean mathematical syntax and execution speed. Upgrade your quantitative stack today.

This item is Non-Returnable

Details

  • ISBN-13: 9798191548753
  • ISBN-10: 9798191548753
  • Publisher: Independently Published
  • Publish Date: August 2026
  • Dimensions: 9 x 6 x 1.63 inches
  • Shipping Weight: 1.72 pounds
  • Page Count: 658

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