Helper tools to analyze the " Financial Statement Data Sets" from the U.S. securities and exchange commission (sec.gov)
View the Project on GitHub HansjoergW/sec-fincancial-statement-data-set
Helper tools to analyze the Financial Statement Data Sets from the U.S. securities and exchange commission (sec.gov).
For a detail description of the content and the structure of the dataset, see https://www.sec.gov/files/aqfs.pdf.
The SEC financial statement datasets contain financial information that companies are required to disclose to the US Securities and Exchange Commission (SEC). These financial statements include the balance sheet, income statement, statement of cash flows, and statement of stockholders’ equity. The datasets also include footnotes and other disclosures that provide additional information about a company’s financial position and performance. The financial statements are typically presented in a standardized format, making it easier to compare the financial performance of different companies. The datasets are useful for a wide range of purposes, including financial analysis, credit analysis, and investment research.
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The SEC releases quarterly zip files, each containing four CSV files with numerical data from all financial reports filed within that quarter.
However, accessing data from the past 12 years can be time-consuming due to the large amount of data - over 120 million data points in over 2GB of zip files.
This library simplifies the process of working with this data and provides a convenient way to extract information from the primary financial statements - the balance sheet, income statement, and statement of cash flows.
It also provides an integration with the https://rapidapi.com/hansjoerg.wingeier/api/daily-sec-financial-statement-dataset API and therefore providing a possibility to receive the latest filings on a daily basis and not just every three months.
The goal is bulk processing of the data.
To improve efficiency, the zip files are downloaded and indexed using a SQLite database table. The index table contains information on all filed reports, over 500,000 in total. The first download will take a couple of minutes but after that, all the data is on your local harddisk.
Using the index in the sqlite db allows for direct extraction of data for a specific report from the appropriate zip file, reducing the need to open and search through each zip file.
Moreover, the downloaded zip files are converted to the parquet format which provides faster read access to the data compared to reading the csv files inside the zip files.
The library is designed to have a low memory footprint, only parsing and reading the data for a specific report into pandas dataframe tables.