---
title: "SEC EDGAR API - Extract Data from SEC Filings"
description: "Use a SEC EDGAR API to extract financial data from filings. Pull revenue, earnings, and risk factors from 10-K, 10-Q, and 8-K reports on EDGAR."
url: https://webscraping.ai/use-cases/sec-filing-monitoring
markdown_index: https://webscraping.ai/llms.txt
---
FINANCIAL SERVICES

# SEC EDGAR API for Filings

Extract financial data from SEC EDGAR filings on demand. Pull revenue, earnings, guidance, and risk factors from 10-K, 10-Q, 8-K, and other forms as structured JSON.

[Start Free Trial](https://webscraping.ai/auth/sign_up)[View Documentation](https://webscraping.ai/docs)

## EDGAR Filings Are Dense

SEC filings on EDGAR hold the numbers that matter - revenue, earnings, risk factors, executive compensation, and material events - buried in long HTML documents. Reading them by hand across a watchlist doesn't scale.

A SEC EDGAR API lets you point at a filing URL and pull the exact data points you need as soon as the document is published.

#### WebScraping.AI Solution

- **Filing Extraction:** Parse 10-K, 10-Q, 8-K, and other SEC forms
- **AI Analysis:** Extract specific data points using natural language
- **Key Metrics:** Pull revenue, earnings, guidance, and risk factors
- **Structured Output:** Clean JSON data for your financial models

## Filing Data Points

Extract key information from SEC filings

#### Financial Metrics

Revenue, net income, EPS, margins, and year-over-year changes.

#### Risk Factors

New risks, changed disclosures, and material concerns.

#### Executive Changes

Leadership changes, compensation, and insider transactions.

#### Guidance

Forward guidance, outlook statements, and projections.

## Code Examples

Extract SEC filing data

##### Extract key data from a 10-K filing

**cURL**

```bash
curl -G "https://api.webscraping.ai/ai/fields" \
  --data-urlencode "api_key=YOUR_API_KEY" \
  --data-urlencode "url=https://sec.gov/Archives/edgar/data/company/10-K.htm" \
  --data-urlencode "fields[company_name]=Company name" \
  --data-urlencode "fields[fiscal_year]=Fiscal year covered" \
  --data-urlencode "fields[total_revenue]=Total revenue for the year" \
  --data-urlencode "fields[net_income]=Net income" \
  --data-urlencode "fields[eps]=Earnings per share (basic and diluted)" \
  --data-urlencode "fields[total_assets]=Total assets" \
  --data-urlencode "fields[total_liabilities]=Total liabilities" \
  --data-urlencode "fields[cash_position]=Cash and cash equivalents" \
  --data-urlencode "fields[revenue_growth]=Year-over-year revenue growth percentage" \
  --data-urlencode "fields[key_risks]=Top 3 risk factors mentioned" \
  --data-urlencode "fields[business_segments]=Revenue breakdown by segment if available"
# Response:
# {
# "result": {
# "company_name": "Tech Corporation Inc.",
# "fiscal_year": "2025",
# "total_revenue": "$45.2 billion",
# "net_income": "$8.1 billion",
# "eps": "Basic $12.45, diluted $12.32",
# "total_assets": "$98.5 billion",
# "total_liabilities": "$42.3 billion",
# "cash_position": "$18.7 billion",
# "revenue_growth": "15.3%",
# "key_risks": "Supply chain disruption; regulatory changes; competition",
# "business_segments": "Cloud: $22B; ..."
# }
# }
```

**Python**

```python
# pip install webscraping_ai
# https://pypi.org/project/webscraping-ai/
from webscraping_ai import Client

client = Client(api_key="YOUR_API_KEY")
result = client.fields(
    "https://sec.gov/Archives/edgar/data/company/10-K.htm",
    fields={
        "company_name": "Company name",
        "fiscal_year": "Fiscal year covered",
        "total_revenue": "Total revenue for the year",
        "net_income": "Net income",
        "eps": "Earnings per share (basic and diluted)",
        "total_assets": "Total assets",
        "total_liabilities": "Total liabilities",
        "cash_position": "Cash and cash equivalents",
        "revenue_growth": "Year-over-year revenue growth percentage",
        "key_risks": "Top 3 risk factors mentioned",
        "business_segments": "Revenue breakdown by segment if available",
    },
)
print(result)
# Response:
# {
# "result": {
# "company_name": "Tech Corporation Inc.",
# "fiscal_year": "2025",
# "total_revenue": "$45.2 billion",
# "net_income": "$8.1 billion",
# "eps": "Basic $12.45, diluted $12.32",
# "total_assets": "$98.5 billion",
# "total_liabilities": "$42.3 billion",
# "cash_position": "$18.7 billion",
# "revenue_growth": "15.3%",
# "key_risks": "Supply chain disruption; regulatory changes; competition",
# "business_segments": "Cloud: $22B; ..."
# }
# }
```

**JavaScript**

```javascript
// npm install webscraping-ai
// https://www.npmjs.com/package/webscraping-ai
import { WebScrapingAI } from 'webscraping-ai';

const client = new WebScrapingAI({ apiKey: 'YOUR_API_KEY' });
const result = await client.fields({
  url: 'https://sec.gov/Archives/edgar/data/company/10-K.htm',
  fields: {
    company_name: 'Company name',
    fiscal_year: 'Fiscal year covered',
    total_revenue: 'Total revenue for the year',
    net_income: 'Net income',
    eps: 'Earnings per share (basic and diluted)',
    total_assets: 'Total assets',
    total_liabilities: 'Total liabilities',
    cash_position: 'Cash and cash equivalents',
    revenue_growth: 'Year-over-year revenue growth percentage',
    key_risks: 'Top 3 risk factors mentioned',
    business_segments: 'Revenue breakdown by segment if available',
  },
});
console.log(result);
// Response:
// {
// "result": {
// "company_name": "Tech Corporation Inc.",
// "fiscal_year": "2025",
// "total_revenue": "$45.2 billion",
// "net_income": "$8.1 billion",
// "eps": "Basic $12.45, diluted $12.32",
// "total_assets": "$98.5 billion",
// "total_liabilities": "$42.3 billion",
// "cash_position": "$18.7 billion",
// "revenue_growth": "15.3%",
// "key_risks": "Supply chain disruption; regulatory changes; competition",
// "business_segments": "Cloud: $22B; ..."
// }
// }
```

**PHP**

```php
<?php
// composer require webscraping-ai/webscraping-ai-php
// https://packagist.org/packages/webscraping-ai/webscraping-ai-php
require 'vendor/autoload.php';

use WebScrapingAI\Client;

$client = new Client('YOUR_API_KEY');
$result = $client->fields('https://sec.gov/Archives/edgar/data/company/10-K.htm', [
    'company_name' => 'Company name',
    'fiscal_year' => 'Fiscal year covered',
    'total_revenue' => 'Total revenue for the year',
    'net_income' => 'Net income',
    'eps' => 'Earnings per share (basic and diluted)',
    'total_assets' => 'Total assets',
    'total_liabilities' => 'Total liabilities',
    'cash_position' => 'Cash and cash equivalents',
    'revenue_growth' => 'Year-over-year revenue growth percentage',
    'key_risks' => 'Top 3 risk factors mentioned',
    'business_segments' => 'Revenue breakdown by segment if available',
]);
print_r($result);
// Response:
// {
// "result": {
// "company_name": "Tech Corporation Inc.",
// "fiscal_year": "2025",
// "total_revenue": "$45.2 billion",
// "net_income": "$8.1 billion",
// "eps": "Basic $12.45, diluted $12.32",
// "total_assets": "$98.5 billion",
// "total_liabilities": "$42.3 billion",
// "cash_position": "$18.7 billion",
// "revenue_growth": "15.3%",
// "key_risks": "Supply chain disruption; regulatory changes; competition",
// "business_segments": "Cloud: $22B; ..."
// }
// }
```

**Ruby**

```ruby
# gem install webscraping_ai
# https://rubygems.org/gems/webscraping_ai
require 'webscraping_ai'

client = WebScrapingAI::Client.new(api_key: 'YOUR_API_KEY')
result = client.fields(
  'https://sec.gov/Archives/edgar/data/company/10-K.htm',
  fields: {
    company_name: 'Company name',
    fiscal_year: 'Fiscal year covered',
    total_revenue: 'Total revenue for the year',
    net_income: 'Net income',
    eps: 'Earnings per share (basic and diluted)',
    total_assets: 'Total assets',
    total_liabilities: 'Total liabilities',
    cash_position: 'Cash and cash equivalents',
    revenue_growth: 'Year-over-year revenue growth percentage',
    key_risks: 'Top 3 risk factors mentioned',
    business_segments: 'Revenue breakdown by segment if available',
  }
)
puts result.inspect
# Response:
# {
# "result": {
# "company_name": "Tech Corporation Inc.",
# "fiscal_year": "2025",
# "total_revenue": "$45.2 billion",
# "net_income": "$8.1 billion",
# "eps": "Basic $12.45, diluted $12.32",
# "total_assets": "$98.5 billion",
# "total_liabilities": "$42.3 billion",
# "cash_position": "$18.7 billion",
# "revenue_growth": "15.3%",
# "key_risks": "Supply chain disruption; regulatory changes; competition",
# "business_segments": "Cloud: $22B; ..."
# }
# }
```

**Go**

```go
// go get github.com/webscraping-ai/webscraping-ai-go/v4
// https://pkg.go.dev/github.com/webscraping-ai/webscraping-ai-go/v4
package main

import (
    "context"
    "fmt"

    webscrapingai "github.com/webscraping-ai/webscraping-ai-go/v4"
)

func main() {
    client, _ := webscrapingai.NewClient(&webscrapingai.Config{APIKey: "YOUR_API_KEY"})
    result, _ := client.Fields(context.Background(), &webscrapingai.FieldsOptions{
        URL: "https://sec.gov/Archives/edgar/data/company/10-K.htm",
        Fields: map[string]string{
            "company_name": "Company name",
            "fiscal_year": "Fiscal year covered",
            "total_revenue": "Total revenue for the year",
            "net_income": "Net income",
            "eps": "Earnings per share (basic and diluted)",
            "total_assets": "Total assets",
            "total_liabilities": "Total liabilities",
            "cash_position": "Cash and cash equivalents",
            "revenue_growth": "Year-over-year revenue growth percentage",
            "key_risks": "Top 3 risk factors mentioned",
            "business_segments": "Revenue breakdown by segment if available",
        },
    })
    fmt.Println(result.Result)
}
// Response:
// {
// "result": {
// "company_name": "Tech Corporation Inc.",
// "fiscal_year": "2025",
// "total_revenue": "$45.2 billion",
// "net_income": "$8.1 billion",
// "eps": "Basic $12.45, diluted $12.32",
// "total_assets": "$98.5 billion",
// "total_liabilities": "$42.3 billion",
// "cash_position": "$18.7 billion",
// "revenue_growth": "15.3%",
// "key_risks": "Supply chain disruption; regulatory changes; competition",
// "business_segments": "Cloud: $22B; ..."
// }
// }
```

**Java**

```java
// Maven: ai.webscraping:webscraping-ai:4.2.0
// https://central.sonatype.com/artifact/ai.webscraping/webscraping-ai
import ai.webscraping.Client;
import ai.webscraping.Config;
import ai.webscraping.option.FieldsOptions;
import ai.webscraping.result.FieldsResult;

Client client = new Client(Config.builder().apiKey("YOUR_API_KEY").build());
FieldsResult result = client.fields(FieldsOptions.builder()
    .url("https://sec.gov/Archives/edgar/data/company/10-K.htm")
    .addField("company_name", "Company name")
    .addField("fiscal_year", "Fiscal year covered")
    .addField("total_revenue", "Total revenue for the year")
    .addField("net_income", "Net income")
    .addField("eps", "Earnings per share (basic and diluted)")
    .addField("total_assets", "Total assets")
    .addField("total_liabilities", "Total liabilities")
    .addField("cash_position", "Cash and cash equivalents")
    .addField("revenue_growth", "Year-over-year revenue growth percentage")
    .addField("key_risks", "Top 3 risk factors mentioned")
    .addField("business_segments", "Revenue breakdown by segment if available")
    .build());
System.out.println(result.getResult());
// Response:
// {
// "result": {
// "company_name": "Tech Corporation Inc.",
// "fiscal_year": "2025",
// "total_revenue": "$45.2 billion",
// "net_income": "$8.1 billion",
// "eps": "Basic $12.45, diluted $12.32",
// "total_assets": "$98.5 billion",
// "total_liabilities": "$42.3 billion",
// "cash_position": "$18.7 billion",
// "revenue_growth": "15.3%",
// "key_risks": "Supply chain disruption; regulatory changes; competition",
// "business_segments": "Cloud: $22B; ..."
// }
// }
```

**C#**

```csharp
// dotnet add package WebScrapingAI
// https://www.nuget.org/packages/WebScrapingAI
using WebScrapingAI;

var client = new WebScrapingAIClient(new WebScrapingAIClientOptions { ApiKey = "YOUR_API_KEY" });
var result = await client.FieldsAsync(new FieldsRequest {
    Url = "https://sec.gov/Archives/edgar/data/company/10-K.htm",
    Fields = new Dictionary<string, string> {
        ["company_name"] = "Company name",
        ["fiscal_year"] = "Fiscal year covered",
        ["total_revenue"] = "Total revenue for the year",
        ["net_income"] = "Net income",
        ["eps"] = "Earnings per share (basic and diluted)",
        ["total_assets"] = "Total assets",
        ["total_liabilities"] = "Total liabilities",
        ["cash_position"] = "Cash and cash equivalents",
        ["revenue_growth"] = "Year-over-year revenue growth percentage",
        ["key_risks"] = "Top 3 risk factors mentioned",
        ["business_segments"] = "Revenue breakdown by segment if available",
    },
});
Console.WriteLine(result.Result);
// Response:
// {
// "result": {
// "company_name": "Tech Corporation Inc.",
// "fiscal_year": "2025",
// "total_revenue": "$45.2 billion",
// "net_income": "$8.1 billion",
// "eps": "Basic $12.45, diluted $12.32",
// "total_assets": "$98.5 billion",
// "total_liabilities": "$42.3 billion",
// "cash_position": "$18.7 billion",
// "revenue_growth": "15.3%",
// "key_risks": "Supply chain disruption; regulatory changes; competition",
// "business_segments": "Cloud: $22B; ..."
// }
// }
```

##### Analyze an 8-K for material events

**cURL**

```bash
curl -G "https://api.webscraping.ai/ai/question" \
  --data-urlencode "api_key=YOUR_API_KEY" \
  --data-urlencode "url=https://sec.gov/Archives/edgar/data/company/8-K.htm" \
  --data-urlencode "question=What material event is being disclosed? What is the financial impact? Is this positive or negative for shareholders?"
```

**Python**

```python
# pip install webscraping_ai
# https://pypi.org/project/webscraping-ai/
from webscraping_ai import Client

client = Client(api_key="YOUR_API_KEY")
answer = client.question(
    "https://sec.gov/Archives/edgar/data/company/8-K.htm",
    question="What material event is being disclosed? What is the financial impact? Is this positive or negative for shareholders?",
)
print(answer)
```

**JavaScript**

```javascript
// npm install webscraping-ai
// https://www.npmjs.com/package/webscraping-ai
import { WebScrapingAI } from 'webscraping-ai';

const client = new WebScrapingAI({ apiKey: 'YOUR_API_KEY' });
const answer = await client.question({
  url: 'https://sec.gov/Archives/edgar/data/company/8-K.htm',
  question: 'What material event is being disclosed? What is the financial impact? Is this positive or negative for shareholders?',
});
console.log(answer);
```

**PHP**

```php
<?php
// composer require webscraping-ai/webscraping-ai-php
// https://packagist.org/packages/webscraping-ai/webscraping-ai-php
require 'vendor/autoload.php';

use WebScrapingAI\Client;

$client = new Client('YOUR_API_KEY');
$answer = $client->question(
    'https://sec.gov/Archives/edgar/data/company/8-K.htm',
    'What material event is being disclosed? What is the financial impact? Is this positive or negative for shareholders?',
);
echo $answer;
```

**Ruby**

```ruby
# gem install webscraping_ai
# https://rubygems.org/gems/webscraping_ai
require 'webscraping_ai'

client = WebScrapingAI::Client.new(api_key: 'YOUR_API_KEY')
answer = client.question(
  'https://sec.gov/Archives/edgar/data/company/8-K.htm',
  question: 'What material event is being disclosed? What is the financial impact? Is this positive or negative for shareholders?'
)
puts answer
```

**Go**

```go
// go get github.com/webscraping-ai/webscraping-ai-go/v4
// https://pkg.go.dev/github.com/webscraping-ai/webscraping-ai-go/v4
package main

import (
    "context"
    "fmt"

    webscrapingai "github.com/webscraping-ai/webscraping-ai-go/v4"
)

func main() {
    client, _ := webscrapingai.NewClient(&webscrapingai.Config{APIKey: "YOUR_API_KEY"})
    answer, _ := client.Question(context.Background(), &webscrapingai.QuestionOptions{
        URL: "https://sec.gov/Archives/edgar/data/company/8-K.htm",
        Question: "What material event is being disclosed? What is the financial impact? Is this positive or negative for shareholders?",
    })
    fmt.Println(answer)
}
```

**Java**

```java
// Maven: ai.webscraping:webscraping-ai:4.2.0
// https://central.sonatype.com/artifact/ai.webscraping/webscraping-ai
import ai.webscraping.Client;
import ai.webscraping.Config;
import ai.webscraping.option.QuestionOptions;

Client client = new Client(Config.builder().apiKey("YOUR_API_KEY").build());
String answer = client.question(QuestionOptions.builder()
    .url("https://sec.gov/Archives/edgar/data/company/8-K.htm")
    .question("What material event is being disclosed? What is the financial impact? Is this positive or negative for shareholders?")
    .build());
System.out.println(answer);
```

**C#**

```csharp
// dotnet add package WebScrapingAI
// https://www.nuget.org/packages/WebScrapingAI
using WebScrapingAI;

var client = new WebScrapingAIClient(new WebScrapingAIClientOptions { ApiKey = "YOUR_API_KEY" });
var answer = await client.QuestionAsync(new QuestionRequest {
    Url = "https://sec.gov/Archives/edgar/data/company/8-K.htm",
    Question = "What material event is being disclosed? What is the financial impact? Is this positive or negative for shareholders?",
});
Console.WriteLine(answer);
```

## Why Use WebScraping.AI

**AI Extraction:** Ask for specific data points in natural language.

**Any Filing Type:** 10-K, 10-Q, 8-K, S-1, proxy statements, and more.

**Structured Data:** Clean JSON output for financial models.

**Scale:** Monitor filings across your entire watchlist.

**Analysis:** Get AI summaries and insights from complex filings.

#### Filing Monitoring Use Cases

**Investment Research**

Extract financials for fundamental analysis

**Risk Monitoring**

Track risk factor changes across portfolio

**Event Detection**

Monitor 8-Ks for material events

**Compliance**

Track regulatory disclosures and changes

## Related Use Cases

More financial data solutions

[Alternative Data](https://webscraping.ai/use-cases/alternative-data-investment): Web-sourced data for investment decisions.

[Market Sentiment](https://webscraping.ai/use-cases/market-sentiment-analysis): Extract sentiment from financial news.

[Competitor Analysis](https://webscraping.ai/use-cases/competitor-content-analysis): Track competitor disclosures and strategies.

## Start Monitoring SEC Filings

Get started with 2,000 free API credits. No credit card required.

[Start Free Trial](https://webscraping.ai/auth/sign_up)[View API Documentation](https://webscraping.ai/docs)