Analyzes and visualizes risk, returns, votality, and Sharpe ratios among various algorithmic, hedge, and mutual fund portfolios then compares them against the S&P 500 Index. At the end, the performance of my custom portfolio is evaluated and compared against the other portfolios as well as the S&P 500.
Language: Python3, Pandas
Imports: pathlib, pandas, numpy, datetime, seaborn, and matplotlib libraries
External Resources: Google Finance, Google Sheets
Developed with JupyterLab
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- Notebook reads in stock data via csv files located in Resources folder, cleans the data and visualizes financial analysis. Various examples include:
Drew Disbrow Marnell: dldmarnell@gmail.com
MIT License Copyright (c) 2021 Drew Disbrow Marnell