LEADER 05481nam 2200685 a 450 001 9910140162903321 005 20170815103443.0 010 $a0-470-68500-X 010 $a1-282-88892-7 010 $a9786612888922 010 $a0-470-74789-7 035 $a(CKB)2580000000005503 035 $a(EBL)624711 035 $a(OCoLC)682621203 035 $a(SSID)ssj0000417739 035 $a(PQKBManifestationID)12142722 035 $a(PQKBTitleCode)TC0000417739 035 $a(PQKBWorkID)10368430 035 $a(PQKB)11546246 035 $a(MiAaPQ)EBC624711 035 $a(CaSebORM)9780470987841 035 $a(EXLCZ)992580000000005503 100 $a20090514d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aFinancial modelling in Python$b[electronic resource] /$fS. Fletcher & C. Gardner 205 $a1st edition 210 $aChichester $cWiley$d2009 215 $a1 online resource (246 p.) 225 1 $aWiley finance series 300 $aDescription based upon print version of record. 311 $a0-470-98784-7 320 $aIncludes bibliographical references and index. 327 $aFinancial Modelling in Python; Contents; 1 Welcome to Python; 1.1 Why Python?; 1.1.1 Python is a general-purpose high-level programming language; 1.1.2 Python integrates well with data analysis, visualisation and GUI toolkits; 1.1.3 Python 'plays well with others'; 1.2 Common misconceptions about Python; 1.3 Roadmap for this book; 2 The PPF Package; 2.1 PPF topology; 2.2 Unit testing; 2.2.1 doctest; 2.2.2 PyUnit; 2.3 Building and installing PPF; 2.3.1 Prerequisites and dependencies; 2.3.2 Building the C++ extension modules; 2.3.3 Installing the PPF package; 2.3.4 Testing a PPF installation 327 $a3 Extending Python from C++3.1 Boost.Date Time types; 3.1.1 Examples; 3.2 Boost.MultiArray and special functions; 3.3 NumPy arrays; 3.3.1 Accessing array data in C++; 3.3.2 Examples; 4 Basic Mathematical Tools; 4.1 Random number generation; 4.2 N(.); 4.3 Interpolation; 4.3.1 Linear interpolation; 4.3.2 Loglinear interpolation; 4.3.3 Linear on zero interpolation; 4.3.4 Cubic spline interpolation; 4.4 Root finding; 4.4.1 Bisection method; 4.4.2 Newton-Raphson method; 4.5 Linear algebra; 4.5.1 Matrix multiplication; 4.5.2 Matrix inversion; 4.5.3 Matrix pseudo-inverse 327 $a4.5.4 Solving linear systems4.5.5 Solving tridiagonal systems; 4.5.6 Solving upper diagonal systems; 4.5.7 Singular value decomposition; 4.6 Generalised linear least squares; 4.7 Quadratic and cubic roots; 4.8 Integration; 4.8.1 Piecewise constant polynomial fitting; 4.8.2 Piecewise polynomial integration; 4.8.3 Semi-analytic conditional expectations; 5 Market: Curves and Surfaces; 5.1 Curves; 5.2 Surfaces; 5.3 Environment; 6 Data Model; 6.1 Observables; 6.1.1 LIBOR; 6.1.2 Swap rate; 6.2 Flows; 6.3 Adjuvants; 6.4 Legs; 6.5 Exercises; 6.6 Trades; 6.7 Trade utilities 327 $a7 Timeline: Events and Controller7.1 Events; 7.2 Timeline; 7.3 Controller; 8 The Hull-White Model; 8.1 A component-based design; 8.1.1 Requestor; 8.1.2 State; 8.1.3 Filler; 8.1.4 Rollback; 8.1.5 Evolve; 8.1.6 Exercise; 8.2 The model and model factories; 8.3 Concluding remarks; 9 Pricing using Numerical Methods; 9.1 A lattice pricing framework; 9.2 A Monte-Carlo pricing framework; 9.2.1 Pricing non-callable trades; 9.2.2 Pricing callable trades; 9.3 Concluding remarks; 10 Pricing Financial Structures in Hull-White; 10.1 Pricing a Bermudan; 10.2 Pricing a TARN; 10.3 Concluding remarks 327 $a11 Hybrid Python/C++ Pricing Systems11.1 nth imm of year revisited; 11.2 Exercising nth imm of year from C++; 12 Python Excel Integration; 12.1 Black-scholes COM server; 12.1.1 VBS client; 12.1.2 VBA client; 12.2 Numerical pricing with PPF in Excel; 12.2.1 Common utilities; 12.2.2 Market server; 12.2.3 Trade server; 12.2.4 Pricer server; Appendices; A Python; A.1 Python interpreter modes; A.1.1 Interactive mode; A.1.2 Batch mode; A.2 Basic Python; A.2.1 Simple expressions; A.2.2 Built-in data types; A.2.3 Control flow statements; A.2.4 Functions; A.2.5 Classes; A.2.6 Modules and packages 327 $aA.3 Conclusion 330 $a""Fletcher and Gardner have created a comprehensive resource that will be of interest not only to those working in the field of finance, but also to those using numerical methods in other fields such as engineering, physics, and actuarial mathematics. By showing how to combine the high-level elegance, accessibility, and flexibility of Python, with the low-level computational efficiency of C++, in the context of interesting financial modeling problems, they have provided an implementation template which will be useful to others seeking to jointly optimize the use of computational and human r 410 0$aWiley finance series. 606 $aFinance$xMathematical models$xComputer programs 606 $aPython (Computer program language) 608 $aElectronic books. 615 0$aFinance$xMathematical models$xComputer programs. 615 0$aPython (Computer program language) 676 $a332.0285/5133 676 $a332.02855133 700 $aFletcher$b S$g(Shayne)$0888333 701 $aGardner$b Christopher$0888334 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910140162903321 996 $aFinancial modelling in Python$91984581 997 $aUNINA LEADER 01613nam 2200397 a 450 001 9910702396403321 005 20121114144326.0 035 $a(CKB)5470000002426190 035 $a(OCoLC)817969941 035 $a(EXLCZ)995470000002426190 100 $a20121114d1978 ua 0 101 0 $aeng 135 $aurbn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 12$aA comparison of nursing home residents and discharges from the 1977 National Nursing Home Survey, United States$b[electronic resource] /$f[prepared by Esther Hing and Aurora Zappolo] 210 1$a[Hyattsville, Md.] :$cU.S. Dept. of Health, Education, and Welfare, Public Health Service,$d[1978] 215 $a1 online resource (8 pages) $ccharts 225 1 $aDHEW publication ;$vno. 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