SEC Filing Intensity
See which companies suddenly started sending the SEC a lot of paperwork, on the day it happens rather than after someone has read it. Each row is one ticker on one date with the number of filings that entity submitted to EDGAR.
Spot the burst.
filing_count is how many filings the ticker submitted that day, and filing_count_gte cuts straight to the busy ones inside the request.
Rank a universe by activity.
Pin date and screen filing_count across every ticker to see where corporate activity clustered that session.
Baseline the quiet days.
filing_count_eq=0 isolates the days a company filed nothing, which is what makes a burst measurable against its own normal.
Which companies have suddenly gotten busy with regulators in the past week? Show me the biggest jumps in activity and tell me what those companies were actually doing.
Your agent translates this into daily EDGAR filing counts per ticker and the count threshold that separates a busy day from a normal one, then goes to the filing-level datasets for what the documents were.
Counting the paperwork, not reading it.
Every document a public company sends the SEC lands in EDGAR, the agency's public filing system, on the day it is submitted. Registration statements, ownership changes, material-event reports, prospectuses, and routine administrative forms all arrive through the same pipe. This dataset counts them: for each ticker, on each date, how many filings that entity submitted.
The count is deliberately shallow. Reading filings is a separate job, done by the filing-level datasets that label what a document actually says. What a count buys instead is breadth: you can scan every ticker in the market, every day, without parsing a single document.
Each row includes:
- The ticker and the company name it files under, carried in name.
- The observation date, one row per ticker per day.
- filing_count: the number of SEC filings that entity submitted on that date.
- A zero on days nothing was filed, so every ticker carries its own quiet baseline.
A filing count is a volume measure, never a content measure. Ten routine ownership forms and one bankruptcy notice both register as filings, so the count tells you where to look, not what you have found.
Find the companies that got busy before the news lands.
A burst of filings is a company doing something. Financings, changes of control, insider transactions, auditor changes, and material events all generate documents, and they generate them before anyone has read them. Counting the burst is the cheapest way there is to be pointed at the right names on the right day.
There is no published market-effect study for this dataset, and we are not going to imply one. Treat filing_count as an event-detection layer: use it to shortlist, then go to the filing-level datasets, where the documents are parsed and labeled, to find out what the activity actually was.
- Screen with filing_count_gte=5 on a single date to shortlist the busiest names of the session.
- Baseline a ticker with filing_count_eq=0 to learn how often it files nothing at all.
- Pull a per-ticker series and compare today's count with its own trailing average before calling anything a spike.
- Feed the shortlist into the dilution, shelf registration, and cyber incident datasets, where the filings are labeled.
- Walk the full history with cursor_date and cursor_ticker to build the panel once, then extend it nightly.
One nightly sweep of the EDGAR submission stream.
Filings are counted from the SEC EDGAR submission stream, attributed to the filing entity, and mapped to a ticker and the company name in name. The dataset refreshes nightly at 11:30 PM Eastern and takes in everything submitted during that trading day, so the counts are on the table before the next open. Anything submitted after the cutoff appears in the following night's update.
Results are ordered by date descending then ticker ascending, and the endpoint is cursor-paginated. To page through, send back both cursor_date and cursor_ticker from next_cursor; sending only one of the two returns a 400 rather than a guess.
One integer carries the whole signal.
The headline fields below are a subset. Every field, with exact types and semantics, is documented in the API reference.
| Field | Type | What it tells you |
|---|---|---|
| ticker | string | Equity ticker symbol |
| name | string | Company name associated with the ticker |
| date | string | Observation date, one row per ticker per day |
| filing_count | integer | Number of SEC filings the entity submitted on that date, with zero meaning a quiet day |
| count | integer | Rows returned in this response, at the top level of the envelope |
| has_more | boolean | True when more rows are available beyond this page |
| next_cursor | object | The date and ticker to resend as cursor_date and cursor_ticker for the next page |
Screen the whole market on a single date.
One key works across the REST API, the hosted MCP server, and this dashboard. Every response is JSON with a { count, data } envelope.
import requests
url = "https://api.alphanume.com/v1/filing-intensity"
params = {
"date": "2026-02-06",
"filing_count_gte": 5,
"api_key": "alp_abc123"
}
r = requests.get(url, params=params)
print(r.json())curl "https://api.alphanume.com/v1/filing-intensity?date=2026-02-06&filing_count_gte=5&api_key=alp_abc123"{
"count": 1,
"has_more": false,
"next_cursor": null,
"data": [
{
"ticker": "AAPL",
"name": "Apple Inc.",
"date": "2026-02-06",
"filing_count": 7
}
]
}What this data does not claim.
- The count is attributed to the filing entity, so documents that other parties file about a company are not guaranteed to land on that company's row.
- Filing volume is structurally seasonal. Quarterly and annual reporting deadlines lift counts across the whole market at once, so a spike near a deadline can say more about the calendar than about the company.
- The cutoff is 11:30 PM Eastern. Filings submitted after it belong to the next night's update, which means the most recent day in a pull can be short of what EDGAR already holds.
- A zero is a real observation, but a missing row is not a zero. Check the date range you asked for before reading a gap as a quiet day.
- Pro includes full history and the latest session.
Asked by researchers, answered plainly.
Why would a company file several documents on the same day?
A single corporate event usually produces more than one document. An offering brings a registration statement and its exhibits, a board decision brings a material-event report and the agreement attached to it, and one insider transaction is reported separately by each person involved. That is why counts cluster: the burst is often one event seen from several angles.
Does a spike in SEC filings mean bad news?
No. The count carries no direction. A financing, an acquisition, an executive departure, and a routine batch of ownership reports all raise filing_count the same way. The value is in being pointed at a company on the day it became active, which is earlier than most of those documents get read.
How is this different from the filing-level datasets?
This one counts. The filing-level datasets read: dilution parses S-1 registration statements, shelf registrations track S-3 and F-3 capacity through to takedowns, and cyber incidents label Item 1.05 disclosures. Filing intensity is the wide, cheap first pass across every ticker. The labeled datasets are the second pass on the handful of names it surfaces.
How do I pull the full filing history for a universe?
Query without a ticker filter and page with the cursor. Each response carries has_more and next_cursor, and the next request needs both cursor_date and cursor_ticker from that object. Because ordering is deterministic, by date descending then ticker ascending, a paged pull can be resumed from the last cursor you stored.
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