Fletcher Shayne (18 results)

hardcover. Condition: Acceptable. Please see the condition note after this for details, if this is missing please consider Acceptable to mean poor quality that could include major staining, water damage, writing, missing dustjacket, etc etc. Our books are dispatched from a Yorkshire former cotton mill. We list via barcode/ISBN so please note that the images are stock images and may not be the exact copy you receive, furthermore the details about edition and year might not be accurate as many publishers reuse the same ISBN for multiple editions and as we simply scan a barcode or enter an ISBN we do not check the validity of the edition data when listing. If you're looking for an exact edition please don't order (at least not without checking with us first, although we don't always have time to check). We aim to dispatch prompty, the service used will depend on order value and book size. We can ship to most countries, see our shipping policies. Payment is via Abe only.

Condition: New. pp. viii + 236 Illus.

Condition: New. pp. viii + 236 Index.

hardcover. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!

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

Condition: new.

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Condition: New. Chapter by chapter this book gradually builds up a practical body of code that will serve as an extensible financial engineering system in python. The book uses the Black-Scholes example to begin the building of the python package that will house the code that will be presented as the book progresses. Series: Wiley Finance Series. Num Pages: 244 pages, Illustrations. BIC Classification: KFF. Category: (P) Professional & Vocational. Dimension: 256 x 177 x 24. Weight in Grams: 612. . 2009. 1st Edition. Hardcover. . . . .

Hardback. Condition: New. New copy - Usually dispatched within 4 working days.

Condition: New. Chapter by chapter this book gradually builds up a practical body of code that will serve as an extensible financial engineering system in python. The book uses the Black-Scholes example to begin the building of the python package that will house the code that will be presented as the book progresses. Series: Wiley Finance Series. Num Pages: 244 pages, Illustrations. BIC Classification: KFF. Category: (P) Professional & Vocational. Dimension: 256 x 177 x 24. Weight in Grams: 612. . 2009. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.

Hardcover. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Hardcover. Condition: Brand New. hardback/cd-rom edition. 236 pages. 9.75x6.75x1.00 inches. In Stock.

Taschenbuch. Condition: Neu. Neuware - This book will:Show the reader how to get started quickly: Although the Python programming language is a powerful object-oriented language, it's easy to learn, especially for programmers already familiar with C or C++.Show the reader how to write less code: Comparisons of program metrics (class counts, method counts, and so on) suggest that a program written in the Python programming language can be four times smaller than the same program written in C++.Show the reader how to write better code: The Python programming language encourages good coding practices, and automatic garbage collection helps you avoid memory leaks.Show the reader how to develop programs more quickly: The Python programming language is simpler than C++, and as such, your development time could be up to twice as fast when writing in it. Your programs will also require fewer lines of code.Chapter by chapter this book gradually builds up a practical body of code that will serve as an extensible financial engineering system in python. The book uses the Black-Scholes example to begin the building of the python package that will house the code that will be presented as the book progresses.Contents1 Welcome to Python1.1 Why Python 1.1.1 Python is a high-level programming language1.1.2 Python 'plays well with others'1.1.3 Common misconceptions about Python1.2 Roadmap for this book2 First steps with Python2.1 The Black-Scholes Formula2.2 Modules and Packages2.3 Unit-testing3 Extending Python from C++3.1 Boost.Datetime types3.2 Boost.MultiArray types4 Basic Mathematical Tools4.1 Random number generation4.2 N(.)4.3 Interpolation4.3.1 Interpolation in a single dimension4.3.2 Interpolation in multiple-dimensions4.4 Root-finding4.4.1 Bisection Method4.4.2 Newton-Raphson Method4.5 Quadrature4.5.1 Hermite4.5.2 Piecewise constant polynomial integration4.6 Linear Algebra4.6.1 Matrix Inversion4.6.2 Singular Value Decomposition4.6.3 Solving Tridiagonal Systems4.6.4 Solving linear systems4.6.5 Pseudo square root5 Curve and surface construction5.1 Discount Factor Curves5.2 Caplet Volatility Curves5.3 Intensity Curves5.4 Swaption Volatility Skew Cube6 Pricing using Numerical Methods6.1 Monte-Carlo pricing framework6.2 A lattice pricing framework7 The Hull-White model7.1 A component based design7.1.1 The state7.1.2 The cache7.1.3 The requestor7.1.4 The filler7.1.5 The rollback7.1.6 The evolve7.2 Pricing a Bermudan7.3 Pricing a TARN8 Hybrid Python/C++ Pricing SystemsAppendices1 A Survey of Python Programming Tools.2 Hull-White model.

Hardcover. Condition: Brand New. hardback/cd-rom edition. 236 pages. 9.75x6.75x1.00 inches. In Stock. This item is printed on demand.

Hardcover. Condition: new. Hardcover. "Fletcher and Gardner have created a comprehensive resource that will be of interest not only to those working in the field of finance, but also to those using numerical methods in other fields such as engineering, physics, and actuarial mathematics. By showing how to combine the high-level elegance, accessibility, and flexibility of Python, with the low-level computational efficiency of C++, in the context of interesting financial modeling problems, they have provided an implementation template which will be useful to others seeking to jointly optimize the use of computational and human resources. They document all the necessary technical details required in order to make external numerical libraries available from within Python, and they contribute a useful library of their own, which will significantly reduce the start-up costs involved in building financial models. This book is a must read for all those with a need to apply numerical methods in the valuation of financial claims." David Louton, Professor of Finance, Bryant University This book is directed at both industry practitioners and students interested in designing a pricing and risk management framework for financial derivatives using the Python programming language. It is a practical book complete with working, tested code that guides the reader through the process of building a flexible, extensible pricing framework in Python. The pricing frameworks' loosely coupled fundamental components have been designed to facilitate the quick development of new models. Concrete applications to real-world pricing problems are also provided. Topics are introduced gradually, each building on the last. They include basic mathematical algorithms, common algorithms from numerical analysis, trade, market and event data model representations, lattice and simulation based pricing, and model development. The mathematics presented is kept simple and to the point. The book also provides a host of information on practical technical topics such as C++/Python hybrid development (embedding and extending) and techniques for integrating Python based programs with Microsoft Excel. Chapter by chapter this book gradually builds up a practical body of code that will serve as an extensible financial engineering system in python. The book uses the Black-Scholes example to begin the building of the python package that will house the code that will be presented as the book progresses. 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.

Hardback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 646.

Hardcover. Condition: new. Hardcover. "Fletcher and Gardner have created a comprehensive resource that will be of interest not only to those working in the field of finance, but also to those using numerical methods in other fields such as engineering, physics, and actuarial mathematics. By showing how to combine the high-level elegance, accessibility, and flexibility of Python, with the low-level computational efficiency of C++, in the context of interesting financial modeling problems, they have provided an implementation template which will be useful to others seeking to jointly optimize the use of computational and human resources. They document all the necessary technical details required in order to make external numerical libraries available from within Python, and they contribute a useful library of their own, which will significantly reduce the start-up costs involved in building financial models. This book is a must read for all those with a need to apply numerical methods in the valuation of financial claims." David Louton, Professor of Finance, Bryant University This book is directed at both industry practitioners and students interested in designing a pricing and risk management framework for financial derivatives using the Python programming language. It is a practical book complete with working, tested code that guides the reader through the process of building a flexible, extensible pricing framework in Python. The pricing frameworks' loosely coupled fundamental components have been designed to facilitate the quick development of new models. Concrete applications to real-world pricing problems are also provided. Topics are introduced gradually, each building on the last. They include basic mathematical algorithms, common algorithms from numerical analysis, trade, market and event data model representations, lattice and simulation based pricing, and model development. The mathematics presented is kept simple and to the point. The book also provides a host of information on practical technical topics such as C++/Python hybrid development (embedding and extending) and techniques for integrating Python based programs with Microsoft Excel. Chapter by chapter this book gradually builds up a practical body of code that will serve as an extensible financial engineering system in python. The book uses the Black-Scholes example to begin the building of the python package that will house the code that will be presented as the book progresses. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.