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Applied Quantitative Finance : Using Python for Financial Analysis / / by Mauricio Garita
Applied Quantitative Finance : Using Python for Financial Analysis / / by Mauricio Garita
Autore Garita Mauricio
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Palgrave Pivot, , 2021
Descrizione fisica 1 online resource (257 pages)
Disciplina 332.0285
332.02855133
Collana Palgrave Pivot
Soggetto topico Econometrics
Financial statements
Mathematics
Quantum computers
Quantitative Economics
Financial Reporting
Applications of Mathematics
Quantum Computing
ISBN 9783030291419
3030291413
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. How to use python? -- 2. Using data structures in Python -- 3. Using Data in Python -- 4. Descriptive statistics using Python -- 5. Statistical approach to multiple variables -- 6. Comparing stocks between companies -- 7. Porfolio and Risk -- 8. Value at Risk.
Record Nr. UNINA-9910497086603321
Garita Mauricio  
Cham : , : Springer International Publishing : , : Imprint : Palgrave Pivot, , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Financial modelling in Python [[electronic resource] /] / S. Fletcher & C. Gardner
Financial modelling in Python [[electronic resource] /] / S. Fletcher & C. Gardner
Autore Fletcher S (Shayne)
Edizione [1st edition]
Pubbl/distr/stampa Chichester, : Wiley, 2009
Descrizione fisica 1 online resource (246 p.)
Disciplina 332.0285/5133
332.02855133
Altri autori (Persone) GardnerChristopher
Collana Wiley finance series
Soggetto topico Finance - Mathematical models - Computer programs
Python (Computer program language)
Soggetto genere / forma Electronic books.
ISBN 0-470-68500-X
1-282-88892-7
9786612888922
0-470-74789-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Financial 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
3 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
4.5.4 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
7 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
11 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
A.3 Conclusion
Record Nr. UNINA-9910140162903321
Fletcher S (Shayne)  
Chichester, : Wiley, 2009
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Financial modelling in Python [[electronic resource] /] / S. Fletcher & C. Gardner
Financial modelling in Python [[electronic resource] /] / S. Fletcher & C. Gardner
Autore Fletcher S (Shayne)
Edizione [1st edition]
Pubbl/distr/stampa Chichester, : Wiley, 2009
Descrizione fisica 1 online resource (246 p.)
Disciplina 332.0285/5133
332.02855133
Altri autori (Persone) GardnerChristopher
Collana Wiley finance series
Soggetto topico Finance - Mathematical models - Computer programs
Python (Computer program language)
ISBN 0-470-68500-X
1-282-88892-7
9786612888922
0-470-74789-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Financial 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
3 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
4.5.4 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
7 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
11 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
A.3 Conclusion
Record Nr. UNINA-9910831174303321
Fletcher S (Shayne)  
Chichester, : Wiley, 2009
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui