This project represents a meticulous and systematic exploration in the field of Quantitative Asset Allocation (QAA). Its central objective is to conduct the backtesting of QAA strategies using historical data, evaluating and optimizing investment portfolios. Through a detailed analysis, the aim is to identify assets efficiently aligned with the investment profiles of investors, considering various criteria, strategic and specific objectives. The main focus on backtesting QAA strategies aims to discern the most effective methodologies for portfolio optimization, evaluating their historical performance under diverse market conditions. This approach will empower investors to make informed decisions regarding the composition of their portfolios, achieving a balance between risk and expected return in line with their specific investment profiles and objectives.
Install all the dependencies stated in the requirements.txt file, just run the following command in terminal:
pip install -r requirements.txt
Or you can manually install one by one using the name and version in the file.
The functionality of the project is based on various stages to ensure a comprehensive and effective approach. It begins with a clear definition of the project, where primary and secondary objectives are established. The research and documentation are carried out thoroughly, encompassing key concepts, QAA strategies, and optimization models. Next, the code is developed, applying the QAA strategies and optimization models defined earlier.
The code implementation is done explicitly, utilizing object-oriented programming (OOP) and avoiding external libraries to ensure a clear understanding of the process. Subsequently, a dashboard is created for results analysis, providing a visual tool that facilitates data interpretation and monitoring of the implemented strategies.
In addition to the mentioned steps, the project incorporates detailed documentation of the QAA strategies used, as well as the models and optimization algorithms employed. This approach ensures complete transparency in the process and provides a solid foundation for future evaluations and improvements.
Project carried out by students from the Financial Engineering program at the Instituto Tecnologico y de Estudios Superiores de Occidente (ITESO): Enriquez Nares Diego Emilio (diegotita4), Mugica Liparoli Juan Antonio (Antonio-IF), Martínez Ramírez José Alfonso (JoAlfonso), Palomera Gaytan Jesús Emmanuel (J3SVS), and Alvarado Garnica Óscar Uriel (Oscar148).
GNU General Public License v3.0
Permissions of this strong copyleft license are conditioned on making available complete source code of licensed works and modifications, which include larger works using a licensed work, under the same license. Copyright and license notices must be preserved. Contributors provide an express grant of patent rights.
For more information in reggards of this repo, please contact: [email protected], [email protected], [email protected], [email protected], or [email protected]