# SEC EDGAR Financials — official XBRL Company Facts API, no logi (`pappy-dev/sec-edgar-financials`) Actor

Fetch structured financial time series (revenue, assets, net income, EPS, and 500+ XBRL concepts) for any U.S. public company via the official SEC EDGAR XBRL Company Facts API (data.sec.gov). Input a ticker or CIK, get years of filed data as clean rows. No login, no PDF/HTML scraping.

- **URL**: https://apify.com/pappy-dev/sec-edgar-financials.md
- **Developed by:** [Backyard Tools](https://apify.com/pappy-dev) (community)
- **Categories:** Business, Lead generation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$2.00 / 1,000 financial data point scrapeds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## SEC EDGAR Financials — official XBRL Company Facts API, no login

Pull **structured financial data** — revenue, total assets, net income, EPS, stockholders'
equity, and 500+ other reported figures — for any U.S. public company, straight from SEC's
own official XBRL Company Facts API (`data.sec.gov`). This is the same machine-readable data
behind SEC's XBRL viewer, already tagged and time-stamped by the company's own filings — no
PDF parsing, no HTML scraping, no proxies.

Government public data: SEC's Fair Access Policy states "Anyone can access and download this
information for free" (source: sec.gov/os/webmaster-faq, checked 2026-09-02, same policy as
this publisher's `SEC EDGAR Filings Lookup` actor). This actor follows SEC's published rules:
it identifies itself with a declared User-Agent and stays well under the 10 requests/second
limit — no bot-detection evasion of any kind.

### Tested (2026-09-03)

| Target | Result |
|---|---|
| `AAPL` | 503 us-gaap concepts available (Revenues, Assets, NetIncomeLoss, ...) |
| `AAPL:Revenues` | full time series returned — 10-K and 10-Q values back to 2016 |
| Nonexistent CIK | 404, empty result, no error |

### Input

```json
{
  "targets": ["AAPL:Revenues,Assets,NetIncomeLoss,EarningsPerShareDiluted"],
  "maxItems": 500
}
```

Each target is a ticker symbol or CIK number. Add `:CONCEPT1,CONCEPT2` after the ticker to
filter to specific XBRL tags (exact us-gaap tag names, e.g. `Revenues`, `Assets`,
`NetIncomeLoss`, `EarningsPerShareDiluted`, `StockholdersEquity`, `CashAndCashEquivalentsAtCarryingValue`).
Omit the filter to get every reported concept for that company (several hundred — use
`maxItems` to cap the run).

### Output

One dataset item per (concept, period) data point:

| Field | |
|---|---|
| `concept` | XBRL tag name (e.g. `Revenues`, `Assets`) |
| `label` | human-readable label for the concept |
| `value` | the reported number |
| `unit` | unit of measure (usually `USD`, sometimes `shares` or a ratio) |
| `period_start` / `period_end` | the period this value covers (or as-of date for balance-sheet items) |
| `fiscal_year` / `fiscal_period` | the company's own fiscal year/quarter label |
| `form` | the filing this value came from (`10-K`, `10-Q`, etc.) |
| `filed` | date the underlying filing was submitted |
| `accession_number` | SEC's unique filing ID for traceability back to the source document |
| `cik` / `entity_name` / `ticker` | company identifiers |

No personal data: these are corporate financial figures, not individual transactions or people.

### Why this over SEC's own viewer or scraping filings

SEC's XBRL viewer shows one company, one filing at a time in a browser. Parsing the figures
yourself out of 10-K/10-Q PDFs or HTML is slow and error-prone. This actor reads the same
structured data SEC already publishes for machines and hands back clean rows — ready for a
spreadsheet, a database, or an LLM/analytics pipeline.

### Use Cases

- **Financial trend analysis**: pull years of revenue, income, or margin data for one company without opening a single filing
- **Portfolio screening**: batch-pull the same metric (e.g. `NetIncomeLoss`) across multiple tickers for comparison
- **Investor due-diligence pipelines**: feed structured financials directly into a valuation model or dashboard
- **LLM/RAG financial data feeds**: give an analysis agent clean, sourced financial facts instead of raw filing text

### Pricing

Pay per event: **$2.00 per 1,000 financial data points** collected. Nothing else.

# Actor input Schema

## `targets` (type: `array`):

会社のティッカーシンボル（例: AAPL）または CIK 番号を指定。':' の後ろにカンマ区切りで us-gaap の XBRL タグ名を指定すると絞り込める（例: AAPL:Revenues,Assets,NetIncomeLoss）。省略するとその会社の全コンセプト（数百種）が対象になる（maxItemsで打ち切り）。複数対象を指定可

## `maxItems` (type: `integer`):

1 run で取得する最大件数

## Actor input object example

```json
{
  "targets": [
    "AAPL:Revenues,Assets,NetIncomeLoss,EarningsPerShareDiluted,StockholdersEquity"
  ],
  "maxItems": 500
}
```

# Actor output Schema

## `items` (type: `string`):

取得した各件（type=item）と、選択した分析の結果（type=analysis）

## `overview` (type: `string`):

件数・平均評点・期間・ネガティブ比率（下側信頼限界つき）

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "targets": [
        "AAPL:Revenues,Assets,NetIncomeLoss,EarningsPerShareDiluted,StockholdersEquity"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("pappy-dev/sec-edgar-financials").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "targets": ["AAPL:Revenues,Assets,NetIncomeLoss,EarningsPerShareDiluted,StockholdersEquity"] }

# Run the Actor and wait for it to finish
run = client.actor("pappy-dev/sec-edgar-financials").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "targets": [
    "AAPL:Revenues,Assets,NetIncomeLoss,EarningsPerShareDiluted,StockholdersEquity"
  ]
}' |
apify call pappy-dev/sec-edgar-financials --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,pappy-dev/sec-edgar-financials"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/5bIn5d7Bo8dINrRmf/builds/Qc22OHsGtReejnoW3/openapi.json