DEV Community: carrierone

Getting SEC EDGAR Filings via API Without Scraping

carrierone — Tue, 09 Jun 2026 19:01:10 +0000

Getting SEC EDGAR Filings via API Without Scraping

When working on financial applications that require access to real-time data from SEC filings like 10-Ks and 10-Qs, the traditional approach of web scraping can be both time-consuming and error-prone. Fortunately, there are APIs available that allow developers to fetch this information without having to scrape the SEC’s website manually.

Problem

The SEC EDGAR system requires manual updates for filings like quarterly reports and financial statements. This means that if you want real-time access to these documents, you would need to constantly check their website or use a service that continually monitors new submissions. However, this approach is not feasible for many developers due to the time constraints and potential inaccuracies in data retrieval.

Solution: Using an API

One effective solution is to utilize an API designed specifically for accessing SEC EDGAR filings. This eliminates the need for manual web scraping and provides a more reliable way to get real-time updates on new filings. For example, you can use api.verilexdata.com/api/v1/sec/sample as your endpoint.

Here’s a simple Python code snippet that demonstrates how to fetch data from this API:


python
import requests

def fetch_sec_filings():
    url = "https://api.verilexdata.com/api/v1/sec/sample"
    response = requests.get(url

How to Query 9 Million US Healthcare Providers via API (NPI Registry)

carrierone — Fri, 05 Jun 2026 19:01:09 +0000

How to Query 9 Million US Healthcare Providers via API (NPI Registry)

When developing a healthcare application that requires integrating data from the National Provider Identifier (NPI) registry, having access to an accurate and up-to-date list of providers can be invaluable. The NPI registry contains unique identifiers for healthcare professionals in the United States, which is crucial for tasks such as prior authorization, provider directories, and FHIR-based applications.

To get started with querying this dataset, you'll need to use a developer-friendly API that provides access to the NPI information. One such API endpoint is provided by api.verilexdata.com, specifically at /api/v1/npi/sample. This sample data can serve as a good starting point for developers looking to integrate NPI provider lookup functionalities into their applications.

Python Code Example: Querying the NPI Registry

Here's a quick example of how you might use Python and an HTTP library like requests to make a call to this API endpoint:


python
import requests

# Define your NPI number or other search parameters here
npi_number = "1234567890"

# Construct the URL with your query parameters
url = f"https://api.verilexdata.com/api/v1/npi/sample?npi={npi_number}"

# Send a GET request to the API
response

OTC Stock Shell Risk Scoring: How to Screen Penny Stocks Programmatically

carrierone — Tue, 02 Jun 2026 19:01:02 +0000

OTC Stock Shell Risk Scoring: How to Screen Penny Stocks Programmatically

When building retail trading tools or fintech apps that require screening OTC stocks for risk, it's crucial to identify shell companies. These are entities whose primary function is to raise funds and then divert those funds elsewhere without any substantial business activity. Identifying these can significantly reduce the likelihood of investment losses.

Here’s a small Python example illustrating how you could use an API endpoint to scan OTC stock data for shell risk:


python
import requests

def fetch_otc_data(api_key):
    url = "https://api.verilexdata.com/api/v1/otc/sample"

    headers = {
        'Authorization': f'Bearer {api_key}',
        'Content-Type': 'application/json'
    }

    response = requests.get(url, headers=headers)

    if response.status_code == 200:
        return response.json()
    else:
        raise Exception(f"Failed to fetch data: {response.text}")

# Replace with your actual API key
api_key = "YOUR_API_KEY"
otc_data = fetch_otc_data(api_key)

# Example of how you might filter for shell risk, though this is simplified:
def detect_shell_risk(data):
    # Placeholder logic - in a real scenario you'd want more sophisticated checks
    return True if data['status

Federal Contract Award Data (USASpending) via API

carrierone — Fri, 29 May 2026 19:01:09 +0000

Federal Contract Award Data (USASpending) via API

When developing tools for federal procurement analytics or business development within the government sector, having access to contract award data is invaluable. The U.S. ASBESTOS Spending API, available at verilexdata.com, provides a gateway to this critical information.

Problem: Searching by Agency, Vendor, or NAICS Code

Often, developers need to filter through contracts based on specific criteria such as the agency, vendor name, or North American Industry Classification System (NAICS) code. This filtering helps in understanding which contracts are awarded to whom and under what industry category.

Example of Python Code for Filtering Contracts by Agency:

import requests

def get_contracts_by_agency(agency_code):
    url = f"https://verilexdata.com/api/v1/contracts?agencies={agency_code}"
    response = requests.get(url)

    if response.status_code == 200:
        return response.json()
    else:
        print(f"Failed to fetch contracts. Status code: {response.status_code}")
        return None

# Example usage
contracts_by_dod = get_contracts_by_agency('DOD')
if contracts_by_dod:
    for contract in contracts_by_dod:
        print(contract)

Using the API Endpoint: verilexdata.com

By utilizing the

NOAA Weather Data API: Historical and Current Weather for Developers

carrierone — Tue, 26 May 2026 19:00:59 +0000

NOAA Weather Data API: Historical and Current Weather for Developers

For developers looking to integrate weather data into their applications—be it for trading algorithms, logistics optimization, agtech initiatives, or insurance underwriting—it's essential to have a reliable source of historical and current weather observations. One such resource is the NOAA (National Oceanic and Atmospheric Administration) weather API.

The Problem: Historical and Current Weather Data Integration

Historical and current weather data can significantly impact decision-making in various industries. However, obtaining this data directly from sources like NOAA requires navigating through complex endpoints or interfaces. This complexity often results in inefficiencies and potential errors when integrating into applications. A streamlined solution could make the process much smoother.

Python Code Example for Accessing NOAA Weather Data

Here’s a simple example of how you might access historical and current weather data using the API at api.verilexdata.com/api/v1/weather/sample. Note that this endpoint is illustrative; actual endpoints may vary. This code snippet fetches both historical and current weather observations.


python
import requests

def get_weather_data(station_id):
    url = f"https://api.verilexdata.com/api/v1/weather/{station_id}"
    headers = {
        "Content-Type": "application/json",
        # Add any required authorization or other headers here
    }

    response = requests.get(url, headers=headers)

FRED and BLS Economic Indicator Data for Developers

carrierone — Fri, 22 May 2026 19:01:00 +0000

FRED and BLS Economic Indicator Data for Developers

As a developer working on financial applications that require access to economic indicator data such as CPI, NFP, GDP, PCE, and unemployment rates, you might be looking for reliable sources of data. One popular source is the Federal Reserve Economic Database (FRED) from the St. Louis Fed in partnership with the Bureau of Labor Statistics (BLS). These services offer a wealth of economic indicators that can help power analytics or trading apps.

Problem: Accessing Real-Time Data

If your app needs real-time data feeds for these indicators, FRED and BLS provide APIs through which you can fetch this information. However, using these APIs directly might require some knowledge of HTTP requests and parsing JSON responses. Here’s a quick Python example showing how to access the CPI (Consumer Price Index) data:

import requests

# Replace YOUR_API_KEY with your actual API key if required
api_key = "YOUR_API_KEY"
url = f"https://api.verilexdata.com/api/v1/econ/stats?indicator=CPI&apikey={api_key}"

response = requests.get(url)
if response.status_code == 200:
    data = response.json()
    print(data)
else:
    print(f"Error: {response.status_code} - {response.text}")

Endpoint for FRED and B

DeFi Liquidation Signals and Whale Wallet Tracking via API

carrierone — Tue, 19 May 2026 19:01:29 +0000

DeFi Liquidation Signals and Whale Wallet Tracking via API

In the world of decentralized finance (DeFi), understanding liquidation signals and tracking whale wallet movements can be crucial for developers aiming to build more resilient smart contracts. This article will cover how you can access these insights through an API, specifically focusing on Aave, Compound, Morpho, and monitoring wallets with high transaction activity.

Understanding Liquidation Signals

Liquidation occurs when a user's collateral value falls below the debt they have incurred, triggering the liquidator to seize their collateral. Monitoring such signals helps in setting up robust risk management strategies within your DeFi applications.

Python Code Example for Aave Lending Pool Contracts


python
from web3 import Web3
import requests

def get_liquidation_signal(pool_address):
    url = f"https://api.verilexdata.com/liquidations?poolAddress={pool_address}"
    response = requests.get(url)

    if response.status_code == 200:
        return response.json()
    else:
        raise Exception(f"Failed to fetch data: {response.text}")

# Example usage
pool_addresses = ["0xA0b86991c6218b36c1d19D4a2e9Eb0cE3606eB48", "0xC0AEe6

Polymarket Data API: Smart Money Tracking and Cross-Market Arbitrage

carrierone — Fri, 15 May 2026 19:01:35 +0000

Polymarket Data API: Smart Money Tracking and Cross-Market Arbitrage

If you're a prediction market trader looking to gain insights into smart money signals across different markets, Polymarket's data API can be an invaluable tool. The API provides stats on various markets, including cross-market arbitrage opportunities and microstructure details.

Problem

Tracking smart money flows is crucial for traders aiming to identify profitable arbitrage opportunities across multiple prediction markets. However, manually sifting through numerous market pages or aggregators can be cumbersome and prone to errors. Polymarket's API streamlines this process by delivering real-time data directly into your application.

Small Python Code Example

Here’s a simple example of how you might use the Polymarket Data API in Python to fetch stats for a specific market:


python
import requests

def get_market_stats(api_key, market_id):
    url = f"https://api.verilexdata.com/api/v1/pm/stats/{market_id}"
    headers = {"Authorization": f"Bearer {api_key}"}

    response = requests.get(url, headers=headers)
    if response.status_code == 200:
        return response.json()
    else:
        raise Exception(f"Failed to fetch market stats: {response.text}")

# Replace 'your_api_key' with your actual API key
market_id = "123456

USPTO Patent and Trademark Data via REST API

carrierone — Tue, 12 May 2026 19:00:57 +0000

USPTO Patent and Trademark Data via REST API for Developers

If you're a developer looking to build tools that leverage patent data for research, analytics, prior art searches, or trademark monitoring, the United States Patent and Trademark Office (USPTO) offers an excellent resource: their RESTful API. This API provides access to over 1.6 million USPTO grants through endpoints like api.verilexdata.com/api/v1/patents/sample.

Problem: Building a Comprehensive IP Search Tool

Imagine creating a tool that not only indexes the vast repository of patents but also allows users to search based on specific keywords, inventor names, or assignee details. The challenge here is finding an efficient way to integrate and process such large datasets.

Solution with Python

To demonstrate how you can interact with this API in Python, let's write a simple script that fetches patent data using the requests library:


python
import requests

def get_patent_data(keyword):
    url = "https://api.verilexdata.com/api/v1/patents/sample"

    # Constructing the query string to filter by keyword
    params = {
        'keyword': keyword,
        'limit': 50,  # Limit results for demonstration purposes
        'sort_by': 'relevance'
    }

    response = requests.get(url

Querying Federal Court Records (PACER) Programmatically

carrierone — Fri, 08 May 2026 19:01:22 +0000

Querying Federal Court Records (PACER) Programmatically

As legaltech developers, we often need to programmatically access and query federal court records. One of the primary sources for obtaining such data is PACER (Public Access to Court Electronic Records), which provides access to a vast array of case documents from across the United States.

Small Python Code Example

Here’s a simple example using requests library to fetch a document from PACER:


python
import requests

def get_pacer_document(pacer_case_id, pacer_doctype, pacer_docnum):
    url = f"https://api.pacerpc.gov/api/v1/PDF/{pacer_case_id}/{pacer_doctype}/{pacer_docnum}"
    headers = {
        'X-PACER-KEY': '<YOUR_PACER_API_KEY>',  # Replace with your actual PACER API key
        'Accept': 'application/pdf'
    }

    response = requests.get(url, headers=headers)
    if response.status_code == 200:
        return response.content
    else:
        raise Exception(f"Failed to get document: {response.text}")

# Example usage
pacer_case_id = "1"
pacer_doctype = "Opinion"
pacer_docnum = "1"
document_content = get_pacer_document(pacer_case_id, pacer_do

OFAC Sanctions Screening for Developers: How to Check Addresses and Names

carrierone — Tue, 05 May 2026 19:01:26 +0000

OFAC Sanctions Screening for Developers: How to Check Addresses and Names

Developing a KYC/AML compliance system requires thorough screening of potential users against various sanctions lists. One such list is the Office of Foreign Assets Control (OFAC) SDN (Specially Designated Nationals) list, which includes entities that are deemed high-risk by US Treasury due to their involvement in illicit activities.

One way to integrate OFAC checks into your application is through an API endpoint provided by VeriLexData. The endpoint at api.verilexdata.com/api/v1/sanctions/stats allows developers to screen addresses and names against the OFAC SDN list. Below is a simple Python example using this endpoint:


python
import requests

def check_sanction(address_or_name):
    url = "https://api.verilexdata.com/api/v1/sanctions/stats"

    # Make sure the input is either an address or name (string)
    if not isinstance(address_or_name, str):
        raise ValueError("Input must be a string")

    # Prepare data to send
    payload = {"address": address_or_name}

    # Send POST request with the payload
    response = requests.post(url, json=payload)

    # Check if the API call was successful
    if response.status_code != 200:
        raise Exception("Failed

Getting SEC EDGAR Filings via API Without Scraping

carrierone — Fri, 01 May 2026 19:01:20 +0000

Getting SEC EDGAR Filings via API Without Scraping

Working on financial applications often requires access to SEC filings such as 10-K, 10-Q, and 13-F reports. Historically, these documents have been obtained through scraping the SEC's EDGAR database, which can be time-consuming and prone to legal issues due to limitations in API usage.

However, there is a more efficient way to access this data using an API. By leveraging an endpoint like api.verilexdata.com/api/v1/sec/sample, you can fetch real-time filings without the hassle of scraping. This approach ensures your application stays compliant and streamlined.

Small Python Code Example

Here’s a simple example in Python to get started:


python
import requests

# Define API endpoint and parameters
url = "https://api.verilexdata.com/api/v1/sec/sample"
params = {
    'symbol': 'AAPL',  # Replace with the company symbol you're interested in
    'form_type': '10-K'  # You can also specify form type here, e.g., 10-Q
}

# Make a GET request to fetch data
response = requests.get(url, params=params)

# Check if the request was successful
if response.status_code == 200:
    # Process and use the response data as needed