My portfolio of investment- and finance-related data science projects is housed in this repository. Each link to a Jupyter Notebook (Python 3) containing one of these projects is followed by a brief explanation of its objectives, contributors, and dependencies.
The following projects were all coded and tested using the Domino Data Lab platform (www.dominodatalab.com).
[BetterBet: A New Joint-Analysis and Comparison Tool for Stock Value] https://github.com/aadya608/Finance-Project-Repo/blob/main/BetterBet.ipynb
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Uses fundamental and technical analysis to compare two firms' competitive strategy, financial health, potential for growth, and valuation in detail. I created a data parser, a reader to download 10-Q and 10-K SEC filings, a script to attach specific company performance indicators for trend analysis, and a pandas DataFrame to hold the data.
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Developed risk-analysis models with the aid of Monte-Carlo simulations, and put visualizations into practice with the aid of matplotlib and Seaborn.
[Risk/Return Analysis and Predictions for 'FAANG' Stocks] https://github.com/aadya608/Finance-Project-Repo/blob/main/RiskAnalysisTechStocks.ipynb
- To represent adjusted closing prices and calculate moving averages, daily returns, and associated risk for the "FAANG" tech companies, I utilized a pandas Dataframe.
- Utilized Matplotlib to produce visual representations of single-stock analysis and comparative daily returns, as well as Seaborn to produce more intricate heatmaps and distribution plots. Simple return analysis methods and Monte-Carlo simulations were compared and their respective investment risk curves were presented.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent