A few python tools to analyze the SEC.gov financial statements data sets (https://www.sec.gov/dera/data/financial-statement-data-sets)
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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.
chat.openai.com
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.
See the Release Notes for details.
StandardStatementPresenter: StandardStatementPresenter also considers qtrs when displaying the information.
This was a problem when displaying information for income statements and cash flows, since they often show
data for different periods.MultiReportCollector.u_usecases introduced. This package is a place to provide concrete examples what you can do
with the secfsdstools library. As a first usecase, the logic shown and explained in the 06_bulk_data_processing_deep_dive
is provided as logic within the modul bulk_loading.secfsdstools.e_filter.rawfiltering.USDOnlyRawFilter is new and removes none USD currency datapointsMainCoregFilter was renamed to MainCoregRawFilterOfficialTagsOnlyFilter was renamed to OfficialTagsOnlyRawFiltersecfsdstools.e_filter.joinedfilteringThe goal is to be able to do bulk processing of the data without the need to do countless API calls to sec.gov.
Therefore, the quarterly zip files are downloaded and indexed using a SQLite database table. The index table contains information on all filed reports since about 2010, 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.
The project is published on pypi.org. Simply use pip install to install it:
pip install secfsdstools
The library has been tested for python version 3.7, 3.8, 3.9, and 3.10
If you want to contribute, just clone the project and use a python 3.7 environment. The dependencies are defined in the requirements.txt file or use the pyproject.toml to install them.
To configure the library, create a file called ".secfsdstools.cfg" in your home directory. The file only requires the following entries:
[DEFAULT]
downloaddirectory = c:/users/me/secfsdstools/data/dld
parquetdirectory = c:/users/me/secfsdstools/data/parquet
dbdirectory = c:/users/me/secfsdstools/data/db
useragentemail = your.email@goeshere.com
If you don't provide a config file, one will be created the first time you use the api and put it inside your home directory. You can then change the content of it or directly start with the downloading of the data.
The download directory is the place where quarterly zip files from the sec.gov are downloaded to. The parquet directory is the folder where the data is stored in parquet format. The db directory is the directory in which the sqllite db is created. The useragentemail is used in the requests made to the sec.gov website. Since we only make limited calls to the sec.gov, you can leave the example "your.email@goeshere.com".
In order to support parallel processing, this library uses the multiprocessing package. For instance when transforming the zip files to the parquet format or when reading data from different files.
However, in order for it to work on Windows when calling python yourscript.py, it is necessary that the logic
is started within the "main block" (if __name__ == '__main__':).
Of course, your main logic can be in another package that you import, but the "entry point" needs to be a "main block":
yourscript.py:
import yourpackage as yp
if __name__ == '__main__':
yp.run()
Otherwise, you will observe the following kind of error messages:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "C:\ieu\Anaconda3\envs\sectestclean\lib\site-packages\multiprocess\spawn.py", line 116, in spawn_main
exitcode = _main(fd, parent_sentinel)
File "C:\ieu\Anaconda3\envs\sectestclean\lib\site-packages\multiprocess\spawn.py", line 125, in _main
prepare(preparation_data)
File "C:\ieu\Anaconda3\envs\sectestclean\lib\site-packages\multiprocess\spawn.py", line 236, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "C:\ieu\Anaconda3\envs\sectestclean\lib\site-packages\multiprocess\spawn.py", line 287, in _fixup_main_from_path
main_content = runpy.run_path(main_path,
File "C:\ieu\Anaconda3\envs\sectestclean\lib\runpy.py", line 269, in run_path
return _run_module_code(code, init_globals, run_name,
File "C:\ieu\Anaconda3\envs\sectestclean\lib\runpy.py", line 96, in _run_module_code
_run_code(code, mod_globals, init_globals,
...
For details have a look at the python documentation:
It is not a problem if you run it inside Jupyter.
In order to download the data files and create the index, just call the update() method:
from secfsdstools.update import update
if __name__ == '__main__':
update()
The following tasks will be executed:
If you don't call update "manually", then the first time you call a function from the library, a download will be triggered.
Moreover, at most once a day, it is checked if there is a new zip file available on sec.gov. If there is, a download will be started automatically. If you don't want 'auto-update', set the 'AutoUpdate' in your config file to False.
Note: This is just if you are curious about the content of the database file. The library itself also contains functions to analyze the content of the database file.
The "index of reports" that was created in the previous step can be viewed using a database viewer that supports the SQLite format, such as DB Browser for SQLite.
(The location of the SQLite database file is specified in the "dbdirectory" field of the config file, which is set to "/secfsdstools/data/db" in the default configuration. The database file is named "secfsdstools.db".)
There are only two relevant tables in the database: "index_parquet_reports" and "index_parquet_processing_state".
The "index_parquet_reports" table provides an overview of all available reports in the downloaded data and includes the following relevant columns:
For instance, if you want to have an overview of all reports that Apple has filed since 2010, just search for "%APPLE INC%" in the name column.
Searching for "%APPLE INC%" will also reveal its cik: 320193
If you accidentally delete data in the database file, don't worry. Just delete the database file
and run update() again (see previous chapter).
Note: the code within this chapter is also contained in the "01_quickstart.ipynb" notebook. If you want to follow along, just open the notebook.
Goal: present the information in the balance sheet of Apple's 2022 10-K report in the same way as it appears in the original report on page 31 ("CONSOLIDATED BALANCE SHEETS"): https://www.sec.gov/ix?doc=/Archives/edgar/data/320193/000032019322000108/aapl-20220924.htm
from secfsdstools.e_collector.reportcollecting import SingleReportCollector
from secfsdstools.e_filter.rawfiltering import ReportPeriodAndPreviousPeriodRawFilter
from secfsdstools.e_presenter.presenting import StandardStatementPresenter
if __name__ == '__main__':
# the unique identifier for apple's 10-K report of 2022
apple_10k_2022_adsh = "0000320193-22-000108"
# us a Collector to grab the data of the 10-K report. an filter for balancesheet information
collector: SingleReportCollector = SingleReportCollector.get_report_by_adsh(
adsh=apple_10k_2022_adsh,
stmt_filter=["BS"]
)
rawdatabag = collector.collect() # load the data from the disk
bs_df = (rawdatabag
# ensure only data from the period (2022) and the previous period (2021) is in the data
.filter(ReportPeriodAndPreviousPeriodRawFilter())
# join the the content of the pre_txt and num_txt together
.join()
# format the data in the same way as it appears in the report
.present(StandardStatementPresenter()))
print(bs_df)
The following diagram gives an overview on SECFSDSTools library.
It mainly exists out of two main processes. The first one ist the "Date Update Process" wich is responsible for the download of the Financial Statement Data Sets zip files from the sec.gov website, transforming the content into parquet format, and indexing the content of these files in a simple SQLite database. Again, this whole process can be started "manually" by calling the update method, or it is done automatically, as it described above.
The second main process is the "Data Processing Process", which is working with the data that is stored inside the sub.txt, pre.txt, and num.txt files from the zip files. The "Data Processing Process" mainly exists out of four steps:
The diagramm also shows the main classes with which a user interacts. The use of them is described in the following chapters.
Most of the classes you can interact with have a factory method which name starts with "get_". All this factory method take at least one optional parameter called configuration which is of type "Configuration".
If you do not provide this parameter, the class will read the configuration info from you configuration file in your home directory. If, for whatever reason, you do want to provide an alternative configuration, you can overwrite it.
However, normally you do not have to provide the "configuration" parameter.
The first class that interacts with the index is the IndexSearch class. It provides a single method find_company_by_name
which executes a SQL Like search on the name of the available companies and returns a pandas dataframe with the columns
'name' and 'cik' (the central index key, or the unique id of a company in the financial statements data sets).
The main purpose of this class is to find the cik for a company (of course, you can also directly search the cik on https://www.sec.gov/edgar/searchedgar/companysearch).
from secfsdstools.c_index.searching import IndexSearch
index_search = IndexSearch.get_index_search()
results = index_search.find_company_by_name("apple")
print(results)
Output:
name cik
APPLE GREEN HOLDING, INC. 1510976
APPLE HOSPITALITY REIT, INC. 1418121
APPLE INC 320193
APPLE REIT EIGHT, INC. 1387361
APPLE REIT NINE, INC. 1418121
APPLE REIT SEVEN, INC. 1329011
APPLE REIT SIX INC 1277151
APPLE REIT TEN, INC. 1498864
APPLETON PAPERS INC/WI 1144326
DR PEPPER SNAPPLE GROUP, INC. 1418135
MAUI LAND & PINEAPPLE CO INC 63330
PINEAPPLE ENERGY INC. 22701
PINEAPPLE EXPRESS CANNABIS CO 1710495
PINEAPPLE EXPRESS, INC. 1654672
PINEAPPLE HOLDINGS, INC. 22701
PINEAPPLE, INC. 1654672
Once you have the cik of a company, you can use the CompanyIndexReader to get information on available reports of a company.
To get an instance of the class, you use the get get_company_index_reader method and provide the cik parameter.
from secfsdstools.c_index.companyindexreading import CompanyIndexReader
apple_cik = 320193
apple_index_reader = CompanyIndexReader.get_company_index_reader(cik=apple_cik)
First, you could use the method get_latest_company_filing which returns a dictionary with the latest filing of the company:
print(apple_index_reader.get_latest_company_filing())
Output:
{'adsh': '0001140361-23-023909', 'cik': 320193, 'name': 'APPLE INC', 'sic': 3571.0, 'countryba': 'US', 'stprba': 'CA', 'cityba': 'CUPERTINO',
'zipba': '95014', 'bas1': 'ONE APPLE PARK WAY', 'bas2': None, 'baph': '(408) 996-1010', 'countryma': 'US', 'stprma': 'CA',
'cityma': 'CUPERTINO', 'zipma': '95014', 'mas1': 'ONE APPLE PARK WAY', 'mas2': None, 'countryinc': 'US', 'stprinc': 'CA',
'ein': 942404110, 'former': 'APPLE INC', 'changed': 20070109.0, 'afs': '1-LAF', 'wksi': 0, 'fye': '0930', 'form': '8-K',
'period': 20230430, 'fy': nan, 'fp': None, 'filed': 20230510, 'accepted': '2023-05-10 16:31:00.0', 'prevrpt': 0, 'detail': 0,
'instance': 'ny20007635x4_8k_htm.xml', 'nciks': 1, 'aciks': None}
Next there are two methods which return the metadata of the reports that a company has filed. The result is either
returned as a list of IndexReport instances, if you use the method get_all_company_reports or as pandas dataframe if
you use the method get_all_company_reports_df. Both method can take an optional parameter forms, which defines the
type of the report that shall be returned. For instance, if you are only interested in the annual and quarterly report,
set forms to ["10-K", "10-Q"].
# only show the annual reports of apple
print(apple_index_reader.get_all_company_reports_df(forms=["10-K"]))
Output:
adsh cik name form filed period fullPath originFile originFileType url
0000320193-22-000108 320193 APPLE INC 10-K 20221028 20220930 C:\Users\hansj\secfsdstools\data\parquet\quart... 2022q4.zip quarter https://www.sec.gov/Archives/edgar/data/320193...
0000320193-21-000105 320193 APPLE INC 10-K 20211029 20210930 C:\Users\hansj\secfsdstools\data\parquet\quart... 2021q4.zip quarter https://www.sec.gov/Archives/edgar/data/320193...
0000320193-20-000096 320193 APPLE INC 10-K 20201030 20200930 C:\Users\hansj\secfsdstools\data\parquet\quart... 2020q4.zip quarter https://www.sec.gov/Archives/edgar/data/320193...
0000320193-19-000119 320193 APPLE INC 10-K 20191031 20190930 C:\Users\hansj\secfsdstools\data\parquet\quart... 2019q4.zip quarter https://www.sec.gov/Archives/edgar/data/320193...
0000320193-18-000145 320193 APPLE INC 10-K 20181105 20180930 C:\Users\hansj\secfsdstools\data\parquet\quart... 2018q4.zip quarter https://www.sec.gov/Archives/edgar/data/320193...
0000320193-17-000070 320193 APPLE INC 10-K 20171103 20170930 C:\Users\hansj\secfsdstools\data\parquet\quart... 2017q4.zip quarter https://www.sec.gov/Archives/edgar/data/320193...
0001628280-16-020309 320193 APPLE INC 10-K 20161026 20160930 C:\Users\hansj\secfsdstools\data\parquet\quart... 2016q4.zip quarter https://www.sec.gov/Archives/edgar/data/320193...
0001193125-15-356351 320193 APPLE INC 10-K 20151028 20150930 C:\Users\hansj\secfsdstools\data\parquet\quart... 2015q4.zip quarter https://www.sec.gov/Archives/edgar/data/320193...
0001193125-14-383437 320193 APPLE INC 10-K 20141027 20140930 C:\Users\hansj\secfsdstools\data\parquet\quart... 2014q4.zip quarter https://www.sec.gov/Archives/edgar/data/320193...
0001193125-13-416534 320193 APPLE INC 10-K 20131030 20130930 C:\Users\hansj\secfsdstools\data\parquet\quart... 2013q4.zip quarter https://www.sec.gov/Archives/edgar/data/320193...
0001193125-12-444068 320193 APPLE INC 10-K 20121031 20120930 C:\Users\hansj\secfsdstools\data\parquet\quart... 2012q4.zip quarter https://www.sec.gov/Archives/edgar/data/320193...
0001193125-11-282113 320193 APPLE INC 10-K 20111026 20110930 C:\Users\hansj\secfsdstools\data\parquet\quart... 2011q4.zip quarter https://www.sec.gov/Archives/edgar/data/320193...
0001193125-10-238044 320193 APPLE INC 10-K 20101027 20100930 C:\Users\hansj\secfsdstools\data\parquet\quart... 2010q4.zip quarter https://www.sec.gov/Archives/edgar/data/320193...
0001193125-09-214859 320193 APPLE INC 10-K 20091027 20090930 C:\Users\hansj\secfsdstools\data\parquet\quart... 2009q4.zip quarter https://www.sec.gov/Archives/edgar/data/320193...
The previously introduced IndexSearch and CompanyIndexReader let you know what data is available, but they do not
return the real data of the financial statements. This is what the Collector classes are used for.
All the Collector classes have their own factory method(s) which instantiates the class. Most of these factory methods
also provide parameters to filter the data directly when being loaded from the parquet files.
These are
forms_filter stmt_filter tag_filter It is also possible to apply filter for these attributes after the data is loaded, but since the Collector classes
apply this filters directly during the load process from the parquet files (which means that fewer data is loaded from
the disk) this is generally more efficient.
All Collector classes have a collect method which then loads the data from the parquet files and returns an instance
of RawDataBag. The RawDataBag instance contains then a pandas dataframe for the sub (subscription) data,
pre (presentation) data, and num (the numeric values) data.
The framework provides the following collectors:
SingleReportCollector
As the name suggests, this Collector returns the data of a single report. It is
instantiated by providing the adsh of the desired report as parameter of the get_report_by_adsh factory method,
or by using an instance of the IndexReport as parameter of the get_report_by_indexreport. (As a reminder:
instances of IndexReport are returned by the CompanyIndexReader class).
Example:
from secfsdstools.e_collector.reportcollecting import SingleReportCollector
apple_10k_2022_adsh = "0000320193-22-000108"
collector: SingleReportCollector = SingleReportCollector.get_report_by_adsh(adsh=apple_10k_2022_adsh)
rawdatabag = collector.collect()
# as expected, there is just one entry in the submission dataframe
print(rawdatabag.sub_df)
# just print the size of the pre and num dataframes
print(rawdatabag.pre_df.shape)
print(rawdatabag.num_df.shape)
Output:
adsh cik name sic countryba stprba cityba ...
0 0000320193-22-000108 320193 APPLE INC 3571.0 US CA CUPERTINO ...
(185, 10)
(503, 9)
MultiReportCollector
Contrary to the SingleReportCollector, this Collector can collect data from several
reports. Moreover, the data of the reports are loaded in parallel, this especially improves the performance if the
reports are from different quarters (resp. are in different zip files). The class provides the factory methods
get_reports_by_adshs and get_reports_by_indexreports. The first takes a list of adsh strings, the second a list
of IndexReport instances.
Example:
from secfsdstools.e_collector.multireportcollecting import MultiReportCollector
apple_10k_2022_adsh = "0000320193-22-000108"
apple_10k_2012_adsh = "0001193125-12-444068"
if __name__ == '__main__':
# load only the assets tags that are present in the 10-K report of apple in the years
# 2022 and 2012
collector: MultiReportCollector = \
MultiReportCollector.get_reports_by_adshs(adshs=[apple_10k_2022_adsh,
apple_10k_2012_adsh],
tag_filter=['Assets'])
rawdatabag = collector.collect()
# as expected, there are just two entries in the submission dataframe
print(rawdatabag.sub_df)
print(rawdatabag.num_df)
Output:
adsh cik name sic countryba stprba cityba ...
0 0000320193-22-000108 320193 APPLE INC 3571.0 US CA CUPERTINO ...
1 0001193125-12-444068 320193 APPLE INC 3571.0 US CA CUPERTINO ...
adsh tag version coreg ddate qtrs uom value footnote
0 0000320193-22-000108 Assets us-gaap/2022 20210930 0 USD 3.510020e+11 None
1 0000320193-22-000108 Assets us-gaap/2022 20220930 0 USD 3.527550e+11 None
2 0001193125-12-444068 Assets us-gaap/2012 20110930 0 USD 1.163710e+11 None
3 0001193125-12-444068 Assets us-gaap/2012 20120930 0 USD 1.760640e+11 None
ZipCollector
This Collector collects the data of one or more zip (resp. the folders that contain the parquet
files of this zip files). And since every of the original zip files contains the data for one quarter, the names you provide
in the get_zip_by_name or get_zip_by_names factory methods reflect the quarter which data you want to load:
e.g. 2022q1.zip.
Example:
from secfsdstools.e_collector.zipcollecting import ZipCollector
# only collect the Balance Sheet of annual reports that
# were filed during the first quarter in 2022
if __name__ == '__main__':
collector: ZipCollector = ZipCollector.get_zip_by_name(name="2022q1.zip",
forms_filter=["10-K"],
stmt_filter=["BS"])
rawdatabag = collector.collect()
# only show the size of the data frame
# .. over 4000 companies filed a 10 K report in q1 2022
print(rawdatabag.sub_df.shape)
print(rawdatabag.pre_df.shape)
print(rawdatabag.num_df.shape)
Output:
(4875, 36)
(232863, 10)
(2404949, 9)
CompanyReportCollector
This class returns reports for one or more companies. The factory method
get_company_collector provides the parameter ciks which takes a list of cik numbers.
Example:
from secfsdstools.e_collector.companycollecting import CompanyReportCollector
if __name__ == '__main__':
apple_cik = 320193
collector = CompanyReportCollector.get_company_collector(ciks=[apple_cik],
forms_filter=["10-K"])
rawdatabag = collector.collect()
# all filed 10-K reports for apple since 2010 are in the databag
print(rawdatabag.sub_df)
print(rawdatabag.pre_df.shape)
print(rawdatabag.num_df.shape)
Output:
adsh cik name sic ...
0 0000320193-22-000108 320193 APPLE INC 3571.0 ...
1 0000320193-21-000105 320193 APPLE INC 3571.0 ...
2 0000320193-20-000096 320193 APPLE INC 3571.0 ...
3 0000320193-19-000119 320193 APPLE INC 3571.0 ...
4 0000320193-18-000145 320193 APPLE INC 3571.0 ...
5 0000320193-17-000070 320193 APPLE INC 3571.0 ...
6 0001628280-16-020309 320193 APPLE INC 3571.0 ...
7 0001193125-15-356351 320193 APPLE INC 3571.0 ...
8 0001193125-14-383437 320193 APPLE INC 3571.0 ...
9 0001193125-13-416534 320193 APPLE INC 3571.0 ...
10 0001193125-12-444068 320193 APPLE INC 3571.0 ...
11 0001193125-11-282113 320193 APPLE INC 3571.0 ...
12 0001193125-10-238044 320193 APPLE INC 3571.0 ...
13 0001193125-09-214859 320193 APPLE INC 3571.0 ...
(2246, 10)
(7925, 9)
Process finished with exit code 0
Have a look at the collector_deep_dive notebook.
When the collect method of a Collector class is called, the data for the sub, pre, and num dataframes are loaded
and being stored in the sub_df, pre_df, and num_df attributes inside an instance of RawDataBag.
The RawDataBag provides the following methods:
save, loadRawDataBag can be saved into a directory. Within that directory,
parquet files are stored for the content of the sub_df, pre_df, and num_df. In order to load this
data directly, the static method RawDataBag.load() can be used.concatRawDataBag can be concatenated into one single instance. In order to do
that, the static method RawDataBag.concat() takes a list of RawDataBag as parameter.join JoinedRawDataBag by joining the content of the pre_df and num_df
based on the columns adsh, tag, and version. It is an inner join. The joined dataframe appears as pre_num_df in
the JoinedRawDataBag.filter FilterRaw, applies it to the data and
produces a new instance of RawDataBag with the filtered data. Therefore, filters can also be chained like
a_filtered_RawDataBag = a_RawDataBag.filter(filter1).filter(filter2). Moreover, the __get__item method
is forwarded to the filter method, so you can also write a_filtered_RawDataBag = a_RawDataBag[filter1][filter2].It is simple to write your own filters, just get some inspiration from the once that are already present in the
Framework (module secfsdstools.e_filter.rawfiltering:
AdshRawFilter RawDataBag instance based on the list of adshs that were provided in the constructor. a_filtered_RawDataBag = a_RawDataBag.filter(AdshRawFilter(adshs=['0001193125-09-214859', '0001193125-10-238044']))
StmtRawFilter RawDataBaginstance based on the list of statements ('BS', 'CF', 'IS', ...). a_filtered_RawDataBag = a_RawDataBag.filter(StmtRawFilter(stmts=['BS', 'CF']))
TagRawFilter RawDataBaginstance based on the list of tags that is provided. a_filtered_RawDataBag = a_RawDataBag.filter(TagRawFilter(tags=['Assets', 'Liabilities']))
MainCoregRawFilter RawDataBag so that data of subsidiaries are removed.
a_filtered_RawDataBag = a_RawDataBag.filter(MainCoregRawFilter())
ReportPeriodAndPreviousPeriodRawFilter a_filtered_RawDataBag = a_RawDataBag.filter(ReportPeriodAndPreviousPeriodRawFilter())
ReportPeriodRawFilter CompanyReportCollector to collect all
10-K reports of this company, you want to ensure that every report only contains data for its own period and not for
previous periods.
a_filtered_RawDataBag = a_RawDataBag.filter(ReportPeriodRawFilter())
OfficialTagsOnlyRawFilter a_filtered_RawDataBag = a_RawDataBag.filter(OfficialTagsOnlyRawFilter())
USDOnlyRawFilter a_filtered_RawDataBag = a_RawDataBag.filter(USDOnlyRawFilter())
Have a look at the filter_deep_dive notebook.
When the join method of a RawDataBag instance is called an instance of JoinedDataBag is returned. The returned
instance contains an attribute sub_df, which is a reference to the same sub_df that is in the RawDataBag.
In addition to that, the JoinedDataBag contains an attribut pre_num_df, which is an inner join of the pre_df and
the num_df based on the columns adsh, tag, and version. Note that an entry in the pre_df can be joined with more than
one entry in the num_df.
The JoinedDataBag provides the following methods:
save, loadJoinedDataBag can be saved into a directory. Within that directory,
parquet files are stored for the content of the sub_df, pre_df, and num_df. In order to load this
data directly, the static method JoinedDataBag.save() can be used.concatJoinedDataBag can be concatenated in one single instance. In order to do
that, the static method JoinedDataBag.concat() takes a list of RawDataBag as parameter.filter FilterJoined, applies it to the data and
produces a new instance of JoinedDataBag with the filtered data. Therefore, filters can also be chained like
a_filtered_JoinedDataBag = a_JoinedDataBag.filter(filter1).filter(filter2). Moreover, the __get__item method
is forwarded to the filter method, so you can also write a_filtered_JoinedDataBag = a_JoinedDataBag[filter1][filter2].
Note: The same filters that are present for the RawDataBag are also available for the JoinedDataBag. Just
look into the module secfsdstools.e_filter.joinedfilteringpresent It is simple to write your own presenter classes. So far, the framework provides the following Presenter
implementations (module secfsdstools.e_presenter.presenting):
StandardStatementPresenter
This presenter provides the data in the same form, as you are used to see in
the reports itself. For instance, the primary financial statements balance sheet, income statement, and cash flow
display the different positions in rows and the columns contain the different dates/periods of the data.
Let us say you want to recreate the BS information of the apples 10-K report of 2022, you would write:
apple_10k_2022_adsh = "0000320193-22-000108"
collector: SingleReportCollector = SingleReportCollector.get_report_by_adsh(
adsh=apple_10k_2022_adsh,
stmt_filter=["BS"]
)
rawdatabag = collector.collect()
bs_df = rawdatabag.filter(ReportPeriodAndPreviousPeriodRawFilter())
.join()
.present(StandardStatementPresenter())
print(bs_df)
Output:
adsh coreg tag version stmt report line uom negating inpth qrtrs_0/20220930 qrtrs_0/20210930
0 0000320193-22-000108 CashAndCashEquivalentsAtCarryingValue us-gaap/2022 BS 5 3 USD 0 0 2.364600e+10 3.494000e+10
1 0000320193-22-000108 MarketableSecuritiesCurrent us-gaap/2022 BS 5 4 USD 0 0 2.465800e+10 2.769900e+10
2 0000320193-22-000108 AccountsReceivableNetCurrent us-gaap/2022 BS 5 5 USD 0 0 2.818400e+10 2.627800e+10
3 0000320193-22-000108 InventoryNet us-gaap/2022 BS 5 6 USD 0 0 4.946000e+09 6.580000e+09
4 0000320193-22-000108 NontradeReceivablesCurrent us-gaap/2022 BS 5 7 USD 0 0 3.274800e+10 2.522800e+10
5 0000320193-22-000108 OtherAssetsCurrent us-gaap/2022 BS 5 8 USD 0 0 2.122300e+10 1.411100e+10
6 0000320193-22-000108 AssetsCurrent us-gaap/2022 BS 5 9 USD 0 0 1.354050e+11 1.348360e+11
7 0000320193-22-000108 MarketableSecuritiesNoncurrent us-gaap/2022 BS 5 11 USD 0 0 1.208050e+11 1.278770e+11
8 0000320193-22-000108 PropertyPlantAndEquipmentNet us-gaap/2022 BS 5 12 USD 0 0 4.211700e+10 3.944000e+10
9 0000320193-22-000108 OtherAssetsNoncurrent us-gaap/2022 BS 5 13 USD 0 0 5.442800e+10 4.884900e+10
10 0000320193-22-000108 AssetsNoncurrent us-gaap/2022 BS 5 14 USD 0 0 2.173500e+11 2.161660e+11
11 0000320193-22-000108 Assets us-gaap/2022 BS 5 15 USD 0 0 3.527550e+11 3.510020e+11
12 0000320193-22-000108 AccountsPayableCurrent us-gaap/2022 BS 5 18 USD 0 0 6.411500e+10 5.476300e+10
13 0000320193-22-000108 OtherLiabilitiesCurrent us-gaap/2022 BS 5 19 USD 0 0 6.084500e+10 4.749300e+10
14 0000320193-22-000108 ContractWithCustomerLiabilityCurrent us-gaap/2022 BS 5 20 USD 0 0 7.912000e+09 7.612000e+09
15 0000320193-22-000108 CommercialPaper us-gaap/2022 BS 5 21 USD 0 0 9.982000e+09 6.000000e+09
16 0000320193-22-000108 LongTermDebtCurrent us-gaap/2022 BS 5 22 USD 0 0 1.112800e+10 9.613000e+09
17 0000320193-22-000108 LiabilitiesCurrent us-gaap/2022 BS 5 23 USD 0 0 1.539820e+11 1.254810e+11
18 0000320193-22-000108 LongTermDebtNoncurrent us-gaap/2022 BS 5 25 USD 0 0 9.895900e+10 1.091060e+11
19 0000320193-22-000108 OtherLiabilitiesNoncurrent us-gaap/2022 BS 5 26 USD 0 0 4.914200e+10 5.332500e+10
20 0000320193-22-000108 LiabilitiesNoncurrent us-gaap/2022 BS 5 27 USD 0 0 1.481010e+11 1.624310e+11
21 0000320193-22-000108 Liabilities us-gaap/2022 BS 5 28 USD 0 0 3.020830e+11 2.879120e+11
22 0000320193-22-000108 CommonStocksIncludingAdditionalPaidInCapital us-gaap/2022 BS 5 31 USD 0 0 6.484900e+10 5.736500e+10
23 0000320193-22-000108 RetainedEarningsAccumulatedDeficit us-gaap/2022 BS 5 32 USD 0 0 -3.068000e+09 5.562000e+09
24 0000320193-22-000108 AccumulatedOtherComprehensiveIncomeLossNetOfTax us-gaap/2022 BS 5 33 USD 0 0 -1.110900e+10 1.630000e+08
25 0000320193-22-000108 StockholdersEquity us-gaap/2022 BS 5 34 USD 0 0 5.067200e+10 6.309000e+10
26 0000320193-22-000108 LiabilitiesAndStockholdersEquity us-gaap/2022 BS 5 35 USD 0 0 3.527550e+11 3.510020e+11
27 0000320193-22-000108 CommonStockParOrStatedValuePerShare us-gaap/2022 BS 6 1 USD 0 1 0.000000e+00 0.000000e+00
28 0000320193-22-000108 CommonStockSharesAuthorized us-gaap/2022 BS 6 2 shares 0 1 5.040000e+10 5.040000e+10
29 0000320193-22-000108 CommonStockSharesIssued us-gaap/2022 BS 6 3 shares 0 1 1.594342e+10 1.642679e+10
30 0000320193-22-000108 CommonStockSharesOutstanding us-gaap/2022 BS 6 4 shares 0 1 1.594342e+10 1.642679e+10
If you compare this with the real report at https://www.sec.gov/ix?doc=/Archives/edgar/data/320193/000032019322000108/aapl-20220924.htm you will notice, that order of the tags and the values are the same.
Standardizer
Even if xbrl is a standard on how to tag positions and numbers in financial statements, that doesn't mean that financial
statements can then be compared easily. For instance, there are over 3000 tags which can be used in a balance sheet.
Moreover, some tags can mean similar things or can be grouped behind a "parent" tag, which itself might not be present.
For instance, "AccountsNoncurrent" is often not shown in statements. So you would find the position for "Accounts"
and "AccountsCurrent", but not for "AccountsNoncurrent". Instead, only child tags for "AccountsNoncurrent" might be
present.
The standardizer helps to solve these problems by unifying the information of financial statements.
With the standardized financial statements, you can then actually compare the statements between different
companies or different years, and you can use the dataset for ML.
Have a look at standardizer_basics which explains it in more details.
BalanceSheetStandardizer
The BalanceSheetStandardizer collects and/or calculates the following positions of balance sheets:
With just a few lines of code, you'll get a comparable dataset with the main positions of a balance sheet for Microsoft, Alphabet, and Amazon: (see the stanardize the balance sheets and make them comparable for details)
from secfsdstools.e_collector.companycollecting import CompanyReportCollector
from secfsdstools.e_filter.rawfiltering import ReportPeriodRawFilter, MainCoregRawFilter, OfficialTagsOnlyRawFilter, USDOnlyRawFilter
from secfsdstools.f_standardize.bs_standardize import BalanceSheetStandardizer
bag = CompanyReportCollector.get_company_collector(ciks=[789019, 1652044,1018724]).collect() #Microsoft, Alphabet, Amazon
filtered_bag = bag[ReportPeriodRawFilter()][MainCoregRawFilter()][OfficialTagsOnlyRawFilter()][USDOnlyRawFilter()]
joined_bag = filtered_bag.join()
standardizer = BalanceSheetStandardizer()
standardized_bs_df = joined_bag.present(standardizer)
import matplotlib.pyplot as plt
# Group by 'name' and plot equity for each group
# Note: using the `present` method ensured that the same cik has always the same name even if the company name did change in the past
for name, group in standardized_bs_df.groupby('name'):
plt.plot(group['date'], group['Equity'], label=name, linestyle='-')
# Add labels and title
plt.xlabel('Date')
plt.ylabel('Equity')
plt.title('Equity Over Time for Different Companies (CIKs)')
# Display legend
plt.legend()
u_usecases packageProblem: I receive error messages like the following when I try to start a script on windows:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "C:\ieu\Anaconda3\envs\sectestclean\lib\site-packages\multiprocess\spawn.py", line 116, in spawn_main
exitcode = _main(fd, parent_sentinel)
File "C:\ieu\Anaconda3\envs\sectestclean\lib\site-packages\multiprocess\spawn.py", line 125, in _main
prepare(preparation_data)
File "C:\ieu\Anaconda3\envs\sectestclean\lib\site-packages\multiprocess\spawn.py", line 236, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "C:\ieu\Anaconda3\envs\sectestclean\lib\site-packages\multiprocess\spawn.py", line 287, in _fixup_main_from_path
main_content = runpy.run_path(main_path,
File "C:\ieu\Anaconda3\envs\sectestclean\lib\runpy.py", line 269, in run_path
return _run_module_code(code, init_globals, run_name,
File "C:\ieu\Anaconda3\envs\sectestclean\lib\runpy.py", line 96, in _run_module_code
_run_code(code, mod_globals, init_globals,
...
Solution:
This library uses the multiprocessing package. However, on Windows this works only correctly if the "entry point" of the
script is within a if __name__ == '__main__': block.
Therefore, change your scripts from
import xy
your code goes here
to
import xy
if __name__ == '__main__':
your code goes here
For details have a look at the python documentation:
FAQs
A few python tools to analyze the SEC.gov financial statements data sets (https://www.sec.gov/dera/data/financial-statement-data-sets)
We found that secfsdstools demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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