financialdatapy 1.2.2

Extract financial data of a company.

financialdatapy

financialdatapy is a package for getting a fundamental financial data of a company. Currently it supports financial data of companies listed in United States (NASDAQ, NYSE) and South Korea (KOSPI, KOSDAQ).

User can see the company's latest financial statement reported, standard financials, and historical stock price. financialdatapy will be a good choice for research purposes, and managing an investment portfolio.

Installation

To use financialdatapy, first install it using pip:

❗Python version above 3.10 is required to use financialdatapy .

pip install financialdatapy

Quick Start

financialdatapy supports three major financial statements of a company. Income statement, balance sheet, and cash flow. Also the user can select between annual and quarter financial statements.

API Key

❗When getting financial statements of a company listed in Korea stock exchange, API Key from DART should be provided in the system to successfully retrieve its data.

After receiving API key, store it on a .env file in the root directory of your project.

Inside .env file.

DART_API_KEY=xxxxxxxxxxxxxxxx

Initialization

from financialdatapy.stock import Stock

# Apple
us_comp = Stock('aapl')
# Samsung Electronics
kor_comp = Stock('005930', country_code='kor')  # should specify 'country_code' for stock exchange other than US

Values passed for financial statements and periods should follow the format below. If no argument is passed, it automatically retrieves income statement from annual report.

income_statement = us_comp.financials('income_statement')
balance_sheet = us_comp.financials('balance_sheet')
cash_flow = us_comp.financials('cash_flow')

# Annual Report
income_statement = kor_comp.financials('income_statement', 'annual')
# Quarterly Report
income_statement = kor_comp.financials('income_statement', 'quarter')

Financial Statement as reported

Financial statements reported by the company to a financial regulator. The elements in the financial statements are different from others, depending on the comapany and stock exchange.

United States Stock Exchange

us_comp = Stock('aapl')
ic_as_reported = us_comp.financials('income_statement', 'annual')

Korea Stock Exchange

kor_comp = Stock('005930', country_code='kor')
ic_as_reported = kor_comp.financials('income_statement', 'annual')

To see the full financial report from a browser, pass True in web. Supports both US exchange and KOR exchange.

us_comp.financials(web=True)
kor_comp.financials(web=True)

Standard Financial Statement

Summarized financial statements of a company.

us_comp = Stock('msft')
std_ic = us_comp.financials('income_statement', 'annual', is_standard=True)

Historical Stock Data

Historical stock price of the company.

us_comp = Stock('aapl')
price = us_comp.price('2021-1-1', '2021-1-5')

All of the above will return in pandas.DataFrame.

❗️Note

Data source of stock price data differ from US stock exchange to KOR stock exchange.

Exchange	Source
USA	finance.yahoo.com
KOR	investing.com

List of Companies in Stock Exchange

United States Stock Exchange

from financialdatapy.stocklist import UsStockList

us_stock_list = UsStockList()
us_stock_list = us_stock_list.stock_list

Korea Stock Exchange

Api key is required to get the stock list of companies in Korea stock exchange. See more about the api key in the API Key section above.

from financialdatapy.stocklist import KorStockList

kor_stock_list = KorStockList()
kor_stock_list = kor_stock_list.stock_list

Contribute

It will be a great help if you contribute to the package. You can open issues here

Code style

The project basically follows PEP-8 , Google Python Style Guide.

Git commit messages

The project basically follows Conventional Commits. Click on the badge to see the details.

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Documentation

See the full documentation here.

The documentation is built with Sphinx and deployed with Read the Docs.

License

Licensed under the MIT License

Credits

Data used in the package are from SEC EDGAR , finance.yahoo.com, investing.com.

Disclaimer

financialdatapy is not meant to be used in any kind of trading. The data might not be accurate, and timely. financialdatapy is aimed for people who use stock data in their portfolio management and researchers who need stock market data in their research. So if you are willing to use data for trading, there are lot more better options.