This research explores the application of Modern Portfolio Theory (MPT) and Monte Carlo simulations to optimize and backtest a portfolio of various financial assets. By employing Python for data analysis and visualization, we aim to construct a portfolio that maximizes the Sharpe Ratio and adapt it to changing market conditions through rolling window optimization. The analysis integrates data cleaning, correlation analysis, risk-free rate calculation, and practical implications of the results. This study provides a comprehensive examination of the methods used, the execution of the analysis, and the results obtained.
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The folder from our repository on Github containing:
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The Portfolio_Optimizarion_BK.ipynb. file from the Jupyter Notebook with the code in Python;
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The “requirements.txt” file containing the list of libraries required to run the code;
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The folders containing the CSVs with the data used for our analysis.
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Each reference to these files was implemented in the code as a path relative, so that we do not have to adapt the path for each machine on which the code runs. To preserve the tree directory, simply unzip the zipped folder and open it as a Workspace within Visual Studio Code.
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The PDF compiled with LaTeX from the Paper;
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The PDF also written with LaTeX that outlines each member's contribution in the group project, as well as the coordinator.
- Ascalone Sara
- De Col Emilio
- Malamisura Federica
- Radice Jacopo
- Rossi Lisa
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