| Study programme competencies |
| Code | Study programme competences |
| A1 | Alcanzar un conocimiento básico en un área de Ingeniería/Ciencias Aplicadas, como punto de partida para un adecuado modelado matemático, tanto en contextos bien establecidos como en entornos nuevos o poco conocidos dentro de contextos más amplios y multidisciplinares. |
| A2 | Modelar ingredientes específicos y realizar las simplificaciones adecuadas en el modelo que faciliten su tratamiento numérico, manteniendo el grado de precisión, de acuerdo con requisitos previamente establecidos. |
| A4 | Ser capaz de seleccionar un conjunto de técnicas numéricas, lenguajes y herramientas informáticas, adecuadas para resolver un modelo matemático. |
| A5 | Ser capaz de validar e interpretar los resultados obtenidos, comparando con visualizaciones, medidas experimentales y/o requisitos funcionales del correspondiente sistema físico/de ingeniería. |
| A8 | Saber adaptar, modificar e implementar herramientas de software de simulación numérica. |
| A9 | Conocer, saber seleccionar y saber manejar las herramientas de software profesional (tanto comercial como libre) más adecuadas para la simulación de procesos en el sector industrial y empresarial. |
| B1 | Saber aplicar los conocimientos adquiridos y su capacidad de resolución de problemas en entornos nuevos o poco conocidos dentro de contextos más amplios, incluyendo la capacidad de integrarse en equipos multidisciplinares de I+D+i en el entorno empresarial. |
| B2 | Poseer conocimientos que aporten una base u oportunidad de ser originales en el desarrollo y/o aplicación de ideas, a menudo en un contexto de investigación, sabiendo traducir necesidades industriales en términos de proyectos de I+D+i en el campo de la Matemática Industrial |
| B3 | Ser capaz de integrar conocimientos para enfrentarse a la formulación de juicios a partir de información que, aun siendo incompleta o limitada, incluya reflexiones sobre las responsabilidades sociales y éticas vinculadas a la aplicación de sus conocimientos. |
| B4 | Saber comunicar las conclusiones, junto con los conocimientos y razones últimas que las sustentan, a públicos especializados y no especializados de un modo claro y sin ambigüedades. |
| B5 | Poseer las habilidades de aprendizaje que les permitan continuar estudiando de un modo que habrá de ser en gran medida autodirigido o autónomo, y poder emprender con éxito estudios de doctorado. |
| Learning aims | |||
| Learning outcomes | Study programme competences | ||
| Having an overview of the existing range of financial software on the market | AC4 AC8 |
BC1 BR1 |
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| To handle Excel for usage in the efficient resolution of some financial problems studied in the course of models | AC1 AC4 AC5 AC8 AC9 |
BC1 BC2 BR1 |
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| Knowing some Matlab specific tools for evaluating products and financial situations | AC2 AC4 AC8 AC9 |
BC1 BC2 BC3 BR1 |
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| Be able to produce original financial software in Matlab programming environment using appropriate financial toolboxes if necessary | AC4 AC5 AC8 AC9 |
BJ1 BC1 BC2 BC3 BR1 |
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| Be able to develop financial software that requires interaction between Matlab and Excel,also using the tool ExcelLink when suitable | AC4 AC5 AC8 AC9 |
BJ1 BC1 BC2 BC3 BR1 |
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| Contents |
| Topic | Sub-topic |
| 1. An overview of the current proefessional financial software toolboxes | |
| 2. Introduction to Excell with its usage in finance in view | |
| 4. Excel - VBA - Matlab Interaction: Excel Link | |
| 3. Specific Matlab toolboxes for finance | |
| 5. Software development for financial pricing in Excel and Matlab | |
| 6. Software development for financial pricing in Python | |
| 7. Specific financial toolboxes in Python |
| Planning | ||||
| Methodologies / tests | Competencies | Ordinary class hours | Student’s personal work hours | Total hours |
| Problem solving | A4 A5 A9 A8 B2 B5 B3 | 0 | 22 | 22 |
| Guest lecture / keynote speech | A1 A2 A4 A5 A9 A8 B2 B5 B1 B4 | 21 | 0 | 21 |
| Supervised projects | A4 A5 A9 A8 B5 B3 | 0 | 30 | 30 |
| Problem solving | A4 A5 A9 A8 B2 B5 B3 | 4 | 4 | 8 |
| ICT practicals | A9 A8 B4 | 21 | 42 | 63 |
| Personalized attention | 6 | 0 | 6 | |
| (*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. | ||||
| Methodologies |
| Methodologies | Description |
| Problem solving | The student must solve some of the issues and problems that allow practice using software tools |
| Guest lecture / keynote speech | The use of software tools (Excel, Matlab, etc) for the solution of models and financial problems will be described, some of them studied in the course of mathematical models in finance |
| Supervised projects | Works or projects to solve financial problems using various software toolboxes that have been discussed will be posed to the students |
| Problem solving | Financial valuation issues to be solved by software tools as classroom exercises willl be posed |
| ICT practicals | Financial xamples to be solved with the computer will be posed and the commands of the different software tools will be trained |
| Personalized attention |
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| Assessment | |||
| Methodologies | Competencies | Description | Qualification |
| Supervised projects | A4 A5 A9 A8 B5 B3 | Works or project will be posed to students, mainly consisting of solving financial problems by using the Excell (30) and Python (45) software tools that have been presented during lectures | 75 |
| Problem solving | A4 A5 A9 A8 B2 B5 B3 | Problems about financial pricing by using the described Matlab software toolboxes will be posed to be solved in classroom | 25 |
| Assessment comments | |||
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The criteria for the 2nd assessment opporunity are the same as in the 1st assessment opportunity. |
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| Sources of information |
| Basic |
Hans Petter Langtangen (2009). A primer on Scientific Programming with Python. Springer
The Math Works Inc. (2005). Financial Derivatives Toolbox User’s Guide .
The Math Works Inc. (2005). Financial Toolbox User’s Guide.
(). http://matplotlib.org.
(). http://numpy.org.
(). http://pydata.org.
(). http://www.mathworks.com.
(). http://www.python-excell.org.
(). http://www.quantlib.org.
(). http://www.scipy.org.
Mark Lutz (2013). Learning Python. O'Really
Yves Hilpisch (2015). Python for finance. Analyze big finncial data. O'Really
Goutham Balaramen, Luigi Ballagio (2019). QuantLib Python Cookbook. |
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| Complementary | |
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| Recommendations | |||
| Subjects that it is recommended to have taken before | |||
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| Subjects that are recommended to be taken simultaneously |
| Subjects that continue the syllabus |
| Other comments | |