Skip to main content
deleted 7 characters in body
Source Link
Pleb
  • 4.9k
  • 3
  • 13
  • 29

This is likely scraped from the Edgar database using a Python package or his own web-scraping tools:

I believe the author constructed the filing_index.csv using a similar script/package as the Python package called python-edgar (see here for documentation). The package constructs a master-index file with all available information from the Edgar database since a user-defined year (pre-defined to 1993).

As is also expressed in the documentation, you can then get company filings via the master-index file by filtering for eg. company (CIK number) and form-type using grep in Python (see example from the Github documentation). For completeness, I've provided quote-snippets from the package documentation that details how to get the needed information:

Stitch quarterly files to a master file

python-edgar does only one thing and does it well: getting and cleaning uncompressed quarterly index files to your computer. Use command line tools, in the spirit of unix philosophy, to stitch these index files together and create our master index file. [...]

Grab filings from a specific company

Now that we have downloaded the index files it becomes easy, with a bit of command line scripting, to quickly filter by company and extract URLs to the filings we want with grep. In the following example we grep by CIK (1000045), store the output in an intermediate text file, which we re-open with cat and grep again by form 10-K. Prefix the paths with https://www.sec.gov/Archives/ and you'll get the full URL.

The output from the above example, looks very similar to the information in the filing_index.csv with less details than the authors csv-file (missing columns beyond "EDGAR_LINK"):

1000045|NICHOLAS FINANCIAL INC|10-K|2015-06-15|edgar/data/1000045/0001193125-15- 
223218.txt|edgar/data/1000045/0001193125-15-223218-index.html
1000045|NICHOLAS FINANCIAL INC|10-K|2016-06-14|edgar/data/1000045/0001193125-16- 
620952.txt|edgar/data/1000045/0001193125-16-620952-index.html
1000045|NICHOLAS FINANCIAL INC|10-K|2017-06-14|edgar/data/1000045/0001193125-17- 
203193.txt|edgar/data/1000045/0001193125-17-203193-index.html
1000045|NICHOLAS FINANCIAL INC|10-K|2018-06-27|edgar/data/1000045/0001193125-18- 
205637.txt|edgar/data/1000045/0001193125-18-205637-index.html

The author could then convert the above output to a csv-file and possibly merge it with securities data from the CRSP database (or similar), in order to give you the last columns missing in the output.


In conclusion, I do not believe you will find any easy way to download the same type of information as provided in filing_index.csv. This might also be the case why the author has not mentioned the source. Nevertheless, I hope this provide some insight.

This is likely scraped from the Edgar database using a Python package or his own web-scraping tools:

I believe the author constructed the filing_index.csv using a similar script/package as the Python package called python-edgar (see here for documentation). The package constructs a master-index file with all available information from the Edgar database since a user-defined year (pre-defined to 1993).

As is also expressed in the documentation, you can then get company filings via the master-index file by filtering for eg. company (CIK number) and form-type using grep in Python (see example from the Github documentation). For completeness, I've provided quote-snippets from the package documentation that details how to get the needed information:

Stitch quarterly files to a master file

python-edgar does only one thing and does it well: getting and cleaning uncompressed quarterly index files to your computer. Use command line tools, in the spirit of unix philosophy, to stitch these index files together and create our master index file. [...]

Grab filings from a specific company

Now that we have downloaded the index files it becomes easy, with a bit of command line scripting, to quickly filter by company and extract URLs to the filings we want with grep. In the following example we grep by CIK (1000045), store the output in an intermediate text file, which we re-open with cat and grep again by form 10-K. Prefix the paths with https://www.sec.gov/Archives/ and you'll get the full URL.

The output from the above example, looks very similar to the information in the filing_index.csv with less details than the authors csv-file (missing columns beyond "EDGAR_LINK"):

1000045|NICHOLAS FINANCIAL INC|10-K|2015-06-15|edgar/data/1000045/0001193125-15- 
223218.txt|edgar/data/1000045/0001193125-15-223218-index.html
1000045|NICHOLAS FINANCIAL INC|10-K|2016-06-14|edgar/data/1000045/0001193125-16- 
620952.txt|edgar/data/1000045/0001193125-16-620952-index.html
1000045|NICHOLAS FINANCIAL INC|10-K|2017-06-14|edgar/data/1000045/0001193125-17- 
203193.txt|edgar/data/1000045/0001193125-17-203193-index.html
1000045|NICHOLAS FINANCIAL INC|10-K|2018-06-27|edgar/data/1000045/0001193125-18- 
205637.txt|edgar/data/1000045/0001193125-18-205637-index.html

The author could then convert the above output to a csv-file and possibly merge it with securities data from the CRSP database (or similar), in order to give you the last columns missing in the output.


In conclusion, I do not believe you will find any easy way to download the same type of information as provided in filing_index.csv. This might also be the case why the author has not mentioned the source. Nevertheless, I hope this provide some insight.

This is scraped from the Edgar database using a Python package or his own web-scraping tools:

I believe the author constructed the filing_index.csv using a similar script/package as the Python package called python-edgar (see here for documentation). The package constructs a master-index file with all available information from the Edgar database since a user-defined year (pre-defined to 1993).

As is also expressed in the documentation, you can then get company filings via the master-index file by filtering for eg. company (CIK number) and form-type using grep in Python (see example from the Github documentation). For completeness, I've provided quote-snippets from the package documentation that details how to get the needed information:

Stitch quarterly files to a master file

python-edgar does only one thing and does it well: getting and cleaning uncompressed quarterly index files to your computer. Use command line tools, in the spirit of unix philosophy, to stitch these index files together and create our master index file. [...]

Grab filings from a specific company

Now that we have downloaded the index files it becomes easy, with a bit of command line scripting, to quickly filter by company and extract URLs to the filings we want with grep. In the following example we grep by CIK (1000045), store the output in an intermediate text file, which we re-open with cat and grep again by form 10-K. Prefix the paths with https://www.sec.gov/Archives/ and you'll get the full URL.

The output from the above example, looks very similar to the information in the filing_index.csv with less details than the authors csv-file (missing columns beyond "EDGAR_LINK"):

1000045|NICHOLAS FINANCIAL INC|10-K|2015-06-15|edgar/data/1000045/0001193125-15- 
223218.txt|edgar/data/1000045/0001193125-15-223218-index.html
1000045|NICHOLAS FINANCIAL INC|10-K|2016-06-14|edgar/data/1000045/0001193125-16- 
620952.txt|edgar/data/1000045/0001193125-16-620952-index.html
1000045|NICHOLAS FINANCIAL INC|10-K|2017-06-14|edgar/data/1000045/0001193125-17- 
203193.txt|edgar/data/1000045/0001193125-17-203193-index.html
1000045|NICHOLAS FINANCIAL INC|10-K|2018-06-27|edgar/data/1000045/0001193125-18- 
205637.txt|edgar/data/1000045/0001193125-18-205637-index.html

The author could then convert the above output to a csv-file and possibly merge it with securities data from the CRSP database (or similar), in order to give you the last columns missing in the output.


In conclusion, I do not believe you will find any easy way to download the same type of information as provided in filing_index.csv. This might also be the case why the author has not mentioned the source. Nevertheless, I hope this provide some insight.

minor spelling corrections.
Source Link
Pleb
  • 4.9k
  • 3
  • 13
  • 29

This is likely scraped from the Edgar database using a Python package or his own web-scraping tools:

I believe the author constructed (a part of) the filing_index.csv file using a similar script/package as the Python package called python-edgar (see here for documentation). The package constructs a master-index file with all information available oninformation from the Edgar database since a user-defined year (pre-defined to 1993).

As is also expressed in the documentation, you can then get fillings from companiescompany filings via the master-index file by filtering for eg. company (CIK number) and form-type using grep in Python (see example from the Github documentation). For completeness, I've provided quote-snippets from the package documentation onthat details how to use itget the needed information:

Stitch quarterly files to a master file

python-edgar does only one thing and does it well: getting and cleaning uncompressed quarterly index files to your computer. Use command line tools, in the spirit of unix philosophy, to stitch these index files together and create our master index file. [...]

Grab filings from a specific company

Now that we have downloaded the index files it becomes easy, with a bit of command line scripting, to quickly filter by company and extract URLs to the filings we want with grep. In the following example we grep by CIK (1000045), store the output in an intermediate text file, which we re-open with cat and grep again by form 10-K. Prefix the paths with https://www.sec.gov/Archives/ and you'll get the full URL.

The output from the above example, looks very similar to the information in the filing_index.csv with less details than the authors csv-file (missing columns beyond "EDGAR_LINK"):

1000045|NICHOLAS FINANCIAL INC|10-K|2015-06-15|edgar/data/1000045/0001193125-15- 
223218.txt|edgar/data/1000045/0001193125-15-223218-index.html
1000045|NICHOLAS FINANCIAL INC|10-K|2016-06-14|edgar/data/1000045/0001193125-16- 
620952.txt|edgar/data/1000045/0001193125-16-620952-index.html
1000045|NICHOLAS FINANCIAL INC|10-K|2017-06-14|edgar/data/1000045/0001193125-17- 
203193.txt|edgar/data/1000045/0001193125-17-203193-index.html
1000045|NICHOLAS FINANCIAL INC|10-K|2018-06-27|edgar/data/1000045/0001193125-18- 
205637.txt|edgar/data/1000045/0001193125-18-205637-index.html

The author could then convert the above output to a csv-file and possibly merge it with securities data from the CRSP database (or similar), in order to give you the last columns missing in the output.


In conclusion, I do not believe you will find any "click-and-download" framework giving youeasy way to download the same type of information as provided in filing_index.csv. This might also be the case why the author has not mentioned the source. Nevertheless, I hope this provide some insight into a possible way to gather the information provided in the file. Nevertheless, I hope this provide some insight.

This is likely scraped from the Edgar database using a Python package or his own web-scraping tools:

I believe the author constructed (a part of) the filing_index.csv file using a similar script/package as the Python package called python-edgar (see here for documentation). The package constructs a master-index file with all information available on the Edgar database since a user-defined year (pre-defined to 1993).

As is also expressed in the documentation, you can then get fillings from companies via the master-index file by filtering for eg. company (CIK number) and form-type using grep in Python (see example from the Github documentation). For completeness, I've provided quote-snippets from the package documentation on how to use it:

Stitch quarterly files to a master file

python-edgar does only one thing and does it well: getting and cleaning uncompressed quarterly index files to your computer. Use command line tools, in the spirit of unix philosophy, to stitch these index files together and create our master index file. [...]

Grab filings from a specific company

Now that we have downloaded the index files it becomes easy, with a bit of command line scripting, to quickly filter by company and extract URLs to the filings we want with grep. In the following example we grep by CIK (1000045), store the output in an intermediate text file, which we re-open with cat and grep again by form 10-K. Prefix the paths with https://www.sec.gov/Archives/ and you'll get the full URL.

The output from the above example, looks very similar to the information in the filing_index.csv with less details than the authors csv-file (missing columns beyond "EDGAR_LINK"):

1000045|NICHOLAS FINANCIAL INC|10-K|2015-06-15|edgar/data/1000045/0001193125-15- 
223218.txt|edgar/data/1000045/0001193125-15-223218-index.html
1000045|NICHOLAS FINANCIAL INC|10-K|2016-06-14|edgar/data/1000045/0001193125-16- 
620952.txt|edgar/data/1000045/0001193125-16-620952-index.html
1000045|NICHOLAS FINANCIAL INC|10-K|2017-06-14|edgar/data/1000045/0001193125-17- 
203193.txt|edgar/data/1000045/0001193125-17-203193-index.html
1000045|NICHOLAS FINANCIAL INC|10-K|2018-06-27|edgar/data/1000045/0001193125-18- 
205637.txt|edgar/data/1000045/0001193125-18-205637-index.html

The author could then convert the above output to a csv-file and possibly merge it with securities data from the CRSP database (or similar), in order to give you the last columns missing in the output.


In conclusion, I do not believe you will find any "click-and-download" framework giving you the same type of information as provided in filing_index.csv. This might also be the case why the author has not mentioned the source. Nevertheless, I hope this provide some insight into a possible way to gather the information provided in the file.

This is likely scraped from the Edgar database using a Python package or his own web-scraping tools:

I believe the author constructed the filing_index.csv using a similar script/package as the Python package called python-edgar (see here for documentation). The package constructs a master-index file with all available information from the Edgar database since a user-defined year (pre-defined to 1993).

As is also expressed in the documentation, you can then get company filings via the master-index file by filtering for eg. company (CIK number) and form-type using grep in Python (see example from the Github documentation). For completeness, I've provided quote-snippets from the package documentation that details how to get the needed information:

Stitch quarterly files to a master file

python-edgar does only one thing and does it well: getting and cleaning uncompressed quarterly index files to your computer. Use command line tools, in the spirit of unix philosophy, to stitch these index files together and create our master index file. [...]

Grab filings from a specific company

Now that we have downloaded the index files it becomes easy, with a bit of command line scripting, to quickly filter by company and extract URLs to the filings we want with grep. In the following example we grep by CIK (1000045), store the output in an intermediate text file, which we re-open with cat and grep again by form 10-K. Prefix the paths with https://www.sec.gov/Archives/ and you'll get the full URL.

The output from the above example, looks very similar to the information in the filing_index.csv with less details than the authors csv-file (missing columns beyond "EDGAR_LINK"):

1000045|NICHOLAS FINANCIAL INC|10-K|2015-06-15|edgar/data/1000045/0001193125-15- 
223218.txt|edgar/data/1000045/0001193125-15-223218-index.html
1000045|NICHOLAS FINANCIAL INC|10-K|2016-06-14|edgar/data/1000045/0001193125-16- 
620952.txt|edgar/data/1000045/0001193125-16-620952-index.html
1000045|NICHOLAS FINANCIAL INC|10-K|2017-06-14|edgar/data/1000045/0001193125-17- 
203193.txt|edgar/data/1000045/0001193125-17-203193-index.html
1000045|NICHOLAS FINANCIAL INC|10-K|2018-06-27|edgar/data/1000045/0001193125-18- 
205637.txt|edgar/data/1000045/0001193125-18-205637-index.html

The author could then convert the above output to a csv-file and possibly merge it with securities data from the CRSP database (or similar), in order to give you the last columns missing in the output.


In conclusion, I do not believe you will find any easy way to download the same type of information as provided in filing_index.csv. This might also be the case why the author has not mentioned the source. Nevertheless, I hope this provide some insight.

Source Link
Pleb
  • 4.9k
  • 3
  • 13
  • 29

This is likely scraped from the Edgar database using a Python package or his own web-scraping tools:

I believe the author constructed (a part of) the filing_index.csv file using a similar script/package as the Python package called python-edgar (see here for documentation). The package constructs a master-index file with all information available on the Edgar database since a user-defined year (pre-defined to 1993).

As is also expressed in the documentation, you can then get fillings from companies via the master-index file by filtering for eg. company (CIK number) and form-type using grep in Python (see example from the Github documentation). For completeness, I've provided quote-snippets from the package documentation on how to use it:

Stitch quarterly files to a master file

python-edgar does only one thing and does it well: getting and cleaning uncompressed quarterly index files to your computer. Use command line tools, in the spirit of unix philosophy, to stitch these index files together and create our master index file. [...]

Grab filings from a specific company

Now that we have downloaded the index files it becomes easy, with a bit of command line scripting, to quickly filter by company and extract URLs to the filings we want with grep. In the following example we grep by CIK (1000045), store the output in an intermediate text file, which we re-open with cat and grep again by form 10-K. Prefix the paths with https://www.sec.gov/Archives/ and you'll get the full URL.

The output from the above example, looks very similar to the information in the filing_index.csv with less details than the authors csv-file (missing columns beyond "EDGAR_LINK"):

1000045|NICHOLAS FINANCIAL INC|10-K|2015-06-15|edgar/data/1000045/0001193125-15- 
223218.txt|edgar/data/1000045/0001193125-15-223218-index.html
1000045|NICHOLAS FINANCIAL INC|10-K|2016-06-14|edgar/data/1000045/0001193125-16- 
620952.txt|edgar/data/1000045/0001193125-16-620952-index.html
1000045|NICHOLAS FINANCIAL INC|10-K|2017-06-14|edgar/data/1000045/0001193125-17- 
203193.txt|edgar/data/1000045/0001193125-17-203193-index.html
1000045|NICHOLAS FINANCIAL INC|10-K|2018-06-27|edgar/data/1000045/0001193125-18- 
205637.txt|edgar/data/1000045/0001193125-18-205637-index.html

The author could then convert the above output to a csv-file and possibly merge it with securities data from the CRSP database (or similar), in order to give you the last columns missing in the output.


In conclusion, I do not believe you will find any "click-and-download" framework giving you the same type of information as provided in filing_index.csv. This might also be the case why the author has not mentioned the source. Nevertheless, I hope this provide some insight into a possible way to gather the information provided in the file.