While interning as a Quantitative Research Analyst at Macro Intelligence 2 Partners, an independent global macroeconomic and financial market research firm, I developed a number of proprietary Bloomberg API integrated Python models which use historic data to predict financial market movements and global macroeconomic trends, as well as to inform and guide investment strategies for our firm's portfolio and for the portfolios of our clients. These models use various statistical, econometric, and machine learning techniques including (but not limited to) neural networks, sequential feature selection, OLS regression, parallel computing, and principal component analysis, as the folder names indicate. Often, however, many (or all) of these techniques are used for one individual model.
NOTE: All of these programs will require a connection to a Bloomberg Terminal and the associated API software downloads. Additionally, some of these programs will need the relevant module installs. Finally, note that some of these programs contain user-defined functions which can be found in the 'Methods' folder and each program has a corresponding PDF file displaying relevant output and model charts.