CB_IPO

This is a library designed for quick webscraping in finding information on SEC filings with a focus on new IPOs and annual reports.

Overview

Researching information on trends for companies can be incredibly tedious, this library will automate part of the proccess making DCF building and IPO research easier. CB_IPO is a library that will fetch information on recent and historical IPOs by scraping the SEC EDGAR database for S-1 filings. These queries can also be modified to search for certain dates, and additional forms. This data can subsequently be placed in a pandas dataframe for the sake of easy viewing. A second proccess this autmoates is finding the specific 10-K filings for a company. By inputing a cik, a list of 10-k filing links will be returned. With these links, the library also has a function for using a 10-k link and returning a dictionary or dataframe of info such as assets, liabilities, and income.

Example

Suppose I want to find companies that have filed either an S-1, 10-K or 10-Q between January 2021 and March 2023

sc = scrape()
sc.set_search_date("2021-01-01", "2023-03-31")
sc.add_forms(['S-1','10-K', '10-Q'])
dataframe = sc.generate_df(10, 1)

Then this dataframe is returned

                                                names filing date
0               Inhibikase Therapeutics, Inc.  (IKT)   2023-03-31
1                       AMERINST INSURANCE GROUP LTD   2023-03-31
2                      SLM Student Loan Trust 2013-5   2023-03-31
3   Games & Esports Experience Acquisition Corp.  ...  2023-03-31
4   Bilander Acquisition Corp.  (TWCB, TWCBU, TWCBW)   2023-03-31
5                                VirTra, Inc  (VTSI)   2023-03-31
6             Actinium Pharmaceuticals, Inc.  (ATNM)   2023-03-31
7                              Genprex, Inc.  (GNPX)   2023-03-31
8                           Mega Matrix Corp.  (MPU)   2023-03-31
9       Digital Media Solutions, Inc.  (DMS, DMS-WT)   2023-03-31

Installation

CB_IPO can be installed via PyPi by running:

pip install CB_IPO

Quick Start

To use CB_IPO instantiate an instance by calling

instance = scrape()

To adjust search date ranges run (Dates in YYYY-MM-DD)

instance.set_search_date(START, END)

To add form types to the search run

instance.add_forms(['S-1','10-K'])

To get a dataframe with all companies filing within the specified paramateres and filing dates run

instance.generate_df(Number of entries per page, number of pages)

To get a list of links to 10-K filings by a company given CIK

instance.create_links(cik, number of files needed)

To scrape a 10-K link for elements like assets, liabilities, and net income and format into a dictionary

instance.scrape_xbrl(link)

To calculate financial ratios from a dicitonary of financial elements run

instance.calculate_ratios(balance_sheet_dict)

To get a dataframe summarizing the 10-k elements run with an optional flag

instance.summarize_10k(link, flag)

Details

This project is a pure python project using modern tooling. It uses a Makefile as a command registry, with the following commands:

• make: list available commands
• make develop: install and build this library and its dependencies using pip
• make build: build the library using setuptools
• make lint: perform static analysis of this library with flake8 and black
• make format: autoformat this library using black
• make annotate: run type checking using mypy
• make test: run automated tests with pytest
• make coverage: run automated tests with pytest and collect coverage information
• make dist: package library for distribution