NOTE: This package is currently under development and we cannot yet guarantee the accuracy of the outputted data and metrics.
Toolkit for U.S. public sector financial analysis. Compare multiple companies across multiple years, in seconds, without having to download data locally.
fundamentals: for United States public fundamentals (10-Ks, 10-Qs, etc.) filed with the SEC.
stocks: for historical stock pricing information on United States public companies.
finpandas can be installed using pip:
git clone <this repo>
cd finpandas
pip install .
The workflow is the following:
Import.
from finpandas import fundamentals, stocks, disposeGet data.
# examine 10-K filings
form10k = fundamentals.ten_k("AAPL", 2019)
form10ks = fundamentals.ten_k("aapl", [2016, 2017, 2018])
# examine 10-Q filings
form10q = fundamentals.ten_q("tsla", (2018, "Q3"))
form10qs = fundamentals.ten_q("TSLA", [(2017, "Q1"), (2016, "Q2")])
form10qs = fundamentals.ten_q("tsla", [2016, 2017, 2018])
# examine historical stock prices
stock_prices = stocks.prices("aapl", "2018-01-01", "2020-12-31")Manipulate the data.
# example calculations
balance_sheet = form10k.balance_sheet()
roe = form10qs.return_on_equity()
net_income = form10k.net_income()
search = form10k.search("Amortization")Get data efficiently (multithreading).
The largest efficiency bottleneck is the I/O speed, which is reduced through parallelized queries.
from finpandas.utils import Jobs
# add jobs to the queue
jobs = Jobs()
jobs.add_job(fundamentals.ten_k, "aapl", [2017, 2018, 2019])
jobs.add_job(fundamentals.ten_q, "fb", 2010)
# execute the queries
results = jobs.execute()Close your connection.
dispose()For further examples, see samples.