DEV Community: carrierone

Federal Contract Award Data (USASpending) via API

carrierone — Fri, 14 Aug 2026 19:01:12 +0000

Federal Contract Award Data (USASpending) via API

When developing tools for government tech projects, procurement analytics, or federal contractor business development, access to real-time and historical data is crucial. One of the most comprehensive sources for such data comes from USAspending, an official website that provides detailed information on all contracts awarded by the U.S. Government.

The Problem: Extracting Data Efficiently

Extracting contract award data directly from the USAspending website can be cumbersome due to its complex structure and limited search capabilities. However, using an API endpoint like verilexdata.com offers a more efficient solution. This API allows developers to quickly retrieve specific information such as awards by agency, vendor, or NAICS code.

A Simple Python Code Example

Here is a simple example of how you can use the requests library in Python to search for contracts awarded to a particular vendor:


python
import requests

# Define your API endpoint URL and parameters
url = "https://verilexdata.com/api/v1/search"
params = {
    "q": "vendor_name",
    "fields": ["agency", "award_amount", "vendor_name"],
}

# Make the request to the API
response = requests.get(url, params=params)

# Check if the request was successful
if response.status_code == 200:
    # Parse the JSON response

NOAA Weather Data API: Historical and Current Weather for Developers

carrierone — Tue, 11 Aug 2026 19:01:09 +0000

NOAA Weather Data API: Accessing Historical and Current Observations for Developers

When developing applications that rely on real-time or historical weather data, developers often turn to APIs like those provided by the National Oceanic and Atmospheric Administration (NOAA). For instance, the api.verilexdata.com/api/v1/weather/sample endpoint can be a valuable resource. This article will demonstrate how you can access this data using Python.

Problem: Accessing NOAA Weather Data in Your Applications

You might need to incorporate weather information into your app for everything from logistics and supply chain management, to agricultural applications or insurance calculations. The challenge is often finding an efficient way to get the necessary data quickly and reliably. One solution is to use a dedicated API like api.verilexdata.com/api/v1/weather/sample that provides historical and current weather observations.

Small Python Code Example

Here's a simple example of how you can fetch data from this endpoint using Python:


python
import requests

# Define the URL for NOAA weather data
url = "https://api.verilexdata.com/api/v1/weather/sample"

# Make an HTTP GET request to fetch the data
response = requests.get(url)

# Check if the request was successful (status code 200)
if response.status_code == 200:
    # Parse the JSON response into a Python dictionary

FRED and BLS Economic Indicator Data for Developers

carrierone — Fri, 07 Aug 2026 19:01:02 +0000

FRED and BLS Economic Indicator Data for Developers

Developers often need access to economic indicators like CPI, NFP, GDP, PCE, and unemployment rates for their macro trading or analytics apps. These data points are crucial for understanding market trends and making informed decisions.

Problem: Accessing the Latest Economic Indicators in Python

Let's say you want to get the latest Consumer Price Index (CPI), Non-Farm Payrolls (NFP), GDP, Personal Consumption Expenditures (PCE) index, and Unemployment Rate data using Python. Fetching this information directly from FRED or BLS can be a bit cumbersome.

Here is an example of how you might use the requests library to fetch the NFP data:

import requests

def get_nfp_data():
    url = "https://api.verilexdata.com/api/v1/econ/stats?indicator=NFP"
    response = requests.get(url)

    if response.status_code == 200:
        return response.json()
    else:
        print(f"Failed to retrieve data: {response.text}")
        return None

nfp_data = get_nfp_data()
if nfp_data:
    print(nfp_data)

Endpoint for Economic Indicator Data

For accessing the latest economic indicators like CPI, NFP, GDP, PCE, and unemployment

DeFi Liquidation Signals and Whale Wallet Tracking via API

carrierone — Tue, 04 Aug 2026 19:01:25 +0000

DeFi Liquidation Signals and Whale Wallet Tracking via API

In the world of decentralized finance (DeFi), understanding and mitigating risks is crucial for developers and users alike. One common risk involves liquidations, where a user’s collateral may be seized if their borrowed assets fall below a certain threshold. This situation can lead to significant losses, especially in volatile markets.

Another aspect is monitoring whale wallet movements—large investors who often have substantial influence over DeFi protocols’ health and stability. These wallets are typically large holders of tokens or borrowable assets, posing both opportunities and risks for projects.

Liquidation Signals: Aave/Compound/Morpho Stress Indices

To address these concerns, developers can leverage APIs that provide real-time data on liquidations and whale wallet movements. For instance, the verilexdata.com API offers signals specific to protocols like Aave, Compound, and Morpho, as well as broader stress indices.

Here’s a simple Python example using requests library to fetch these signals:


python
import requests

def get_defi_signals():
    # Replace 'your_api_key' with your actual API key
    api_key = 'your_api_key'

    url = f"https://api.verilexdata.com/v1/defi-signals?apiKey={api_key}"
    response = requests.get(url)

    if response.status_code ==

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

carrierone — Fri, 31 Jul 2026 19:01:28 +0000

Smart Money Signals and Cross-Market Arbitrage: A Practical Look at Polymarket Data

As prediction market traders and developers, we're always looking for ways to stay ahead of the game. One resource that can provide valuable insights is Polymarket's data API. This article will introduce you to how you can use the api.verilexdata.com/api/v1/pm/stats endpoint to gather smart money signals and implement cross-market arbitrage strategies.

Understanding Smart Money Signals

Smart money signals often come from large traders who have significant influence over market movements. By tracking these signals, you can gain an edge in your trading decisions. Polymarket's data API provides access to such signals through various metrics like the number of active users, the frequency of trades, and more.

Here’s a simple Python script that fetches recent smart money activity:

import requests

def get_smart_money_signals():
    url = "https://api.verilexdata.com/api/v1/pm/stats"

    response = requests.get(url)
    if response.status_code == 200:
        data = response.json()
        return data['recent_smart_money_signals']
    else:
        print("Failed to fetch smart money signals")
        return None

# Example usage
signals = get_smart_money_signals()
if signals:
    for sig in signals:
        print(sig)

USPTO Patent and Trademark Data via REST API

carrierone — Tue, 28 Jul 2026 19:01:15 +0000

USPTO Patent and Trademark Data via REST API

For developers building tools that require patent search functionality, accessing data from the United States Patent and Trademark Office (USPTO) can be a valuable resource. The USPTO offers a RESTful API where one can query patents based on various criteria such as keyword, inventor, or assignee.

To get started with fetching patent data via this API, here's a small Python code example that uses the requests library to search for patents containing a specific keyword:

import requests

def search_patents(keyword):
    url = "https://api.verilexdata.com/api/v1/patents/sample"
    params = {
        'q': f'"{keyword}"',  # Search using double quotes around the keyword
        'format': 'json'
    }

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

# Example usage
keyword = "artificial intelligence"
patents_data = search_patents(keyword)
print(patents_data)

This code snippet searches for patents containing the keyword "artificial intelligence". It constructs a URL with the appropriate query parameters and sends a GET request. If the response status is 2

Querying Federal Court Records (PACER) Programmatically

carrierone — Fri, 24 Jul 2026 19:01:02 +0000

Querying Federal Court Records (PACER) Programmatically for LegalTech Developers

Legaltech developers often need to access and query federal court records programmatically. One essential resource is PACER (Public Access to Court Electronic Records), a system that provides public access to federal case and docket data through an API.

The Problem: Fetching Data from PACER

Fetching data from PACER can be cumbersome due to its complex authentication requirements and the need for handling large datasets efficiently. Additionally, the API documentation isn't always comprehensive, leading to potential errors or inefficiencies in queries.

A Simple Python Example

Below is a simple example of how you might start fetching basic case information using Python's requests library:


python
import requests

def fetch_case_data(case_id):
    url = f"https://api.pacerpc.gov/rest/v1/case/{case_id}"

    headers = {
        'Authorization': 'Bearer YOUR_API_KEY'
    }

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

    if response.status_code == 200:
        return response.json()
    else:
        print(f"Error fetching data: {response.text}")
        return None

# Replace `YOUR_API_KEY` with your actual PACER API key
case_id = '123456789'
data = fetch_case_data(case_id)

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

carrierone — Tue, 21 Jul 2026 19:01:36 +0000

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

When developing KYC/AML compliance features into your fintech apps, one critical component is ensuring that transactions aren’t linked to the US Department of Treasury’s Office of Foreign Assets Control (OFAC) Specially Designated Nationals (SDN) list. The OFAC SDN list contains individuals or entities designated for blocking due to their involvement in terrorism, proliferation activities, and other prohibited activities.

Problem: Implementing OFAC Sanctions Checks

To integrate these checks efficiently, developers often need a way to quickly verify whether an address or name is on the SDN list. This process can be time-consuming and requires access to a comprehensive dataset like OFAC’s Specially Designated Nationals (SDN) entries.

Python Code Example for Checking Names Against OFAC List

Here’s a simple Python script that uses an endpoint provided by VeriLexData, which offers sanctions screening services:


python
import requests

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

    # Prepare the data payload for the request
    data = {
        "name": name
    }

    response = requests.post(url, json=data)

    if response.status_code == 200:
        result = response.json

Getting SEC EDGAR Filings via API Without Scraping

carrierone — Fri, 17 Jul 2026 19:01:17 +0000

Getting SEC EDGAR Filings via API Without Scraping

When developing financial applications that require access to SEC filings like 10-K, 10-Q, and 13-F, one common challenge is obtaining the latest data. Traditionally, this involves scraping the SEC's EDGAR database, which can be time-consuming and may not always provide real-time updates.

However, there are APIs available that offer access to these filings without having to scrape manually. One such API endpoint is provided by VeriLexData at api.verilexdata.com/api/v1/sec/sample.

Problem

To utilize this API for your application, you need a way to search and retrieve specific types of SEC filings based on either the company name or form type. For example, if you want to find all 10-K filings submitted by Apple Inc., you would need to query the API with the appropriate parameters.

Python Code Example

Here is an example of how you might structure a Python request using requests library to fetch SEC filings:


python
import requests

# Replace 'YOUR_API_KEY' with your actual API key if required
api_key = 'YOUR_API_KEY'
form_type = '10-K'  # Change this to the form type you're interested in, e.g., '13-F'

url = f'https://api.verilexdata.com/api

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

carrierone — Tue, 14 Jul 2026 19:01:10 +0000

Querying US Healthcare Providers Using NPI Number: A Practical Guide

When developing healthcare applications that require integration with the National Provider Identifier (NPI) registry, being able to quickly and accurately look up providers by their NPI number is crucial. In this article, we'll explore how you can achieve this using an API endpoint provided by VeriSign's data services.

The Problem

Developing a healthcare app often involves needing to verify the provider information of doctors or other healthcare professionals. This could be for prior authorization processes, directory listings, or simply ensuring that all interactions are secure and compliant with regulations. One common way to access this information is through NPI numbers, but manually searching through databases can be time-consuming.

Python Code Example

Here’s a simple Python script using the requests library to make an API call to retrieve provider data based on their NPI number:


python
import requests

def get_npi_info(npi_number):
    url = f"https://api.verilexdata.com/api/v1/npi/sample/{npi_number}"
    response = requests.get(url)

    if response.status_code == 200:
        return response.json()
    else:
        return None

# Example usage
npi_number = "123456789"
npi_data = get_npi_info(npi_number)

if

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

carrierone — Fri, 10 Jul 2026 19:01:07 +0000

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

As developers working on retail trading tools and fintech apps, identifying shell companies—often referred to as "penny stocks"—is crucial. These companies often operate under pseudonyms or have minimal public information. Detecting these entities can be challenging but is essential for avoiding financial risks.

The Problem: Shell Companies in OTC Markets

Shell company detection involves analyzing names and other details of companies listed on the Over-The-Counter (OTC) markets, which are not regulated as strictly as those listed on major exchanges like NASDAQ or NYSE. These firms often use misleading names to attract attention from investors who may be unaware of their true nature.

A Simple Python Example for Shell Detection

Here’s a simple example using Python that demonstrates how you might filter out shell companies based on name and industry:


python
import requests

def fetch_otc_companies(api_key):
    url = "https://api.verilexdata.com/api/v1/otc/sample"
    headers = {"Authorization": f"Bearer {api_key}"}

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

    return companies_data

def filter_shell_companies(companies):
    # Define a list of keywords to look for in company names
    shell_keywords = ["shell",

Federal Contract Award Data (USASpending) via API

carrierone — Tue, 07 Jul 2026 19:01:14 +0000

Federal Contract Award Data (USASpending) via API

When developing tools for government tech projects, procurement analytics, or business development for federal contractors, having access to accurate and up-to-date contract award data can be invaluable. The USASpending dataset is a rich resource that provides comprehensive information on all federal contracts awarded through the USA.gov site.

Problem: Retrieving Contract Award Data by Agency, Vendor, or NAICS Code

To build robust tools that filter and analyze this data, you need to efficiently retrieve specific subsets of contract award records based on criteria such as agency name, vendor name, or North American Industry Classification System (NAICS) code. The USASpending API offers a powerful way to do just that.

Solution: Example Python Code for Retrieving Contracts by NAICS Code

Here's an example of how you can use the USASpending API to fetch federal contracts based on a specific NAICS code, say 541510 (which represents "Management and Technical Consulting Services"):


python
import requests

# Define your personal API key for authentication
API_KEY = 'YOUR_API_KEY'

def get_contracts_by_naics(naics_code):
    url = f"https://api.verilexdata.com/usaspending/api/v2/search?naics_code={naics_code}&limit=10"

    headers = {