For our project, we collected data from House Stock Watcher's CSV file containing transactions made on the stock market by US politicians over the past 3 years. To clean and process the data, we used the CSV library in Python to read the file data and create lists of each column that we needed while ensuring that any mistakes in the file were properly skipped. Then, the transactions were put into a JSON and we added the ticker prices for each transaction.Next, we created a dataframe with columns Name,Profits, and State that we used to plot various visualizations. The profits were calculated by reading the JSON file and aggregating the realized and unrealized gains into one net profit number for all tickers for every politician.
The data above allows for the user to select a politician and monitor their success and buy/sell events on each ticker with which they have been active. It includes the ticker prices as a candlestick for each day that the market was open in their trading period and delineates where they bought/sold. The success of different politicians on different tickers can reveal patterns in their investments and potential insider knwoledge on major corporate events of which the general public may not be aware.
The above tool can be used to search for an individual congressman's data and observe what transactions that they have made since 2019