Short Overview
The purpose of the program is to provide learners with the necessary tools and skills for using Python, along with specialized libraries, to solve computational finance problems. The program teaches how to create a Python program that includes everything from basic algebraic computations to the use of advanced tools and libraries. It covers a wide range of financial applications, including the analysis and processing of financial data, the development of algorithmic strategies, the valuation and portfolio optimization of bonds, portfolio construction, and the evaluation of investment strategies.
Learning Goals
Upon completion of the program, learners will be able to:
- Implement a program in Python and use its libraries.
- Analyze financial data, create, and evaluate algorithmic investment strategies using Python.
- Value bond portfolios, extract yield curves, and apply investment strategies with fixed-income securities using Python.
- Calculate portfolio exposure to interest rate and credit risk using Python.
- Construct optimal investment portfolios and evaluate their efficiency in relation to the risk they undertake using Python.
Program Value
Successful completion of the program equips learners with skills to solve financial problems using the open-source Python language, which is widely adopted in the institutional investment and capital management industry.
Teaching Material
- Video presentations where theory is presented, followed by its practical application using the Python programming language.
- Electronic slides and files with Python code.
- Self-assessment tests.
- Individual assignments.
Evaluation Method And Final Grade Computation
Assessment will take place through an on-platform examination and a final written assignment. The platform exam will include multiple-choice questions that will address the theoretical background of the material in each topic, as well as questions related to basic Python functions. The final written assignment will involve solving a specific problem by implementing a Python program and using real data. The final grade will be 40% from the platform exam and 60% from the final written assignment.
Learning Method Description
The program uses the eLearning educational method: learning takes place purely asynchronously (that is, on days and hours that serve the trainee, without mandatory attendance at a specific time or day), exclusively remotely (using a special educational platform via the internet from the trainee's area) and using digital educational tools such as video lectures, interactive self-assessment exercises, and other elearning tools that ensure effective and flexible individual self-learning. The program's training material becomes available at specific time periods, based on the training path followed, and then remains available on the training platform without restrictions throughout the duration of the program.