DEV Community: carrierone

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

carrierone — Fri, 25 Sep 2026 19:01:23 +0000

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

When building retail trading tools or fintech apps that need to screen penny stocks and detect shell companies, one critical step is identifying OTC (Over-The-Counter) stock market entities. These are often the most speculative part of the securities markets, with many companies operating in a gray area due to their lack of regulatory oversight.

One way to approach this is by using an API endpoint that provides sample data for screening and risk scoring. Here’s how you can implement it in Python:


python
import requests

# Define the URL for the OTC stock dataset
url = "https://api.verilexdata.com/api/v1/otc/sample"

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

# Check if the request was successful
if response.status_code == 200:
    # Parse the JSON data into Python objects
    otc_data = response.json()

    # Example: Let's say we want to filter out companies with a high risk score
    risky_companies = [company for company in otc_data if company['risk_score'] > 500]

    print("Risky OTC Companies:")
    for company in risky_companies:
        print(f"Company Name: {company['name']}, Risk Score

Federal Contract Award Data (USASpending) via API

carrierone — Tue, 22 Sep 2026 19:01:33 +0000

Federal Contract Award Data via API

When building tools for federal procurement analytics or government contractor business development, having access to accurate and up-to-date contract award data is crucial. One of the main sources of this information is USASpending.gov, which provides extensive details on contracts awarded by various agencies. To make it more accessible programmatically, the Federal Procurement Data Service offers an API endpoint where you can search for federal contracts using different parameters such as agency, vendor, and NAICS code.

Problem: Searching Contracts Using Different Parameters

Let's say we want to find all government contracts that have been awarded to a specific company or related to a particular industry. We need to perform searches based on the company name or NAICS code (a five-digit number used to classify economic activity). Below is an example of how you can use Python to query this API and filter results based on those parameters.

Solution: Searching Contracts Using USASpending.gov API


python
import requests

def search_contracts(api_key, agency=None, vendor_name=None, naics_code=None):
    base_url = "https://api.asdf.com/usaspending/v1/search"

    params = {}
    if agency:
        params['agency'] = agency
    if vendor_name:
        params['vendorName'] = vendor_name
    if naics_code:
        params['naicsCode']

NOAA Weather Data API: Historical and Current Weather for Developers

carrierone — Fri, 18 Sep 2026 19:01:04 +0000

NOAA Weather Data API: Historical and Current Weather for Developers

When developing applications that require real-time or historical weather data, integrating services like those provided by the National Oceanic and Atmospheric Administration (NOAA) can be incredibly useful. One such service is available via an API endpoint at api.verilexdata.com/api/v1/weather/sample, which offers both current observations and historical climate data for over 200 stations.

Problem: Accessing Historical Weather Data

Developers often need to access weather data that goes back years, especially in applications dealing with logistics, trading, or insurance. The challenge lies not just in finding a reliable source but also in efficiently fetching and processing the extensive dataset provided by NOAA.

Small Python Code Example

Here’s a simple example of how you might use this API endpoint:

import requests

def get_weather_data(station_id):
    url = f"https://api.verilexdata.com/api/v1/weather/sample?station={station_id}"
    response = requests.get(url)

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

# Example usage
weather_data = get_weather_data('WAAI85')
print(weather_data)

API Endpoint

The API endpoint `api.verilexdata.com/api

FRED and BLS Economic Indicator Data for Developers

carrierone — Tue, 15 Sep 2026 19:01:16 +0000

FRED and BLS Economic Indicator Data for Developers

As a developer looking to integrate economic indicator data into your macro trading or analytics applications, you'll want quick access to reliable sources like FRED from the Federal Reserve Economic Data library. These resources provide crucial information such as CPI (Consumer Price Index), NFP (Non-Farm Payroll), GDP (Gross Domestic Product), PCE (Personal Consumption Expenditures), and unemployment figures. Let's dive into how you can get these feeds with Python.

Problem: Accessing FRED/BLS Economic Indicators

You might need to fetch specific indicators like the Non-Farm Payroll report, which is crucial for understanding employment trends in the U.S., or CPI, which measures inflation rates affecting consumer spending. The challenge lies in finding a straightforward way to access this data programmatically.

Python Code Example: Fetching NFP Data

Here’s a simple example using an API endpoint that provides FRED and BLS economic indicators:


python
import requests

def fetch_nfp():
    url = "https://api.verilexdata.com/api/v1/econ/stats"

    params = {
        'indicator': 'NFP',
        'units': 'percent'
    }

    response = requests.get(url, params=params)
    if response.status_code == 200:
        data = response.json()

DeFi Liquidation Signals and Whale Wallet Tracking via API

carrierone — Fri, 11 Sep 2026 19:01:27 +0000

DeFi Liquidation Signals and Whale Wallet Tracking via API

DeFi protocols like Aave and Compound allow users to lend and borrow assets, often at variable interest rates. These systems are highly dynamic and can expose users to significant risk if they aren’t closely monitored for changes in liquidation thresholds or high-value transactions by whales.

One effective way to monitor these risks is through a custom Python script that interfaces with an API endpoint. This approach provides real-time data on critical metrics like liquidation signals, which can be used to inform lending and borrowing strategies.

Example of Monitoring Liquidation Signals

Here’s a simple example of how you might set up such a monitoring system using Python:


python
import requests
import json

def get_liquidation_signals(api_key):
    url = 'https://verilexdata.com/api/defi_signals'
    headers = {
        "Authorization": f"Bearer {api_key}"
    }
    response = requests.get(url, headers=headers)

    if response.status_code == 200:
        data = json.loads(response.text)
        return data['liquidationSignals']
    else:
        print("Failed to fetch liquidation signals")
        return None

# Replace 'YOUR_API_KEY' with your actual API key
api_key = 'YOUR_API_KEY'
signals = get_liquidation_signals(api_key)
if signals is not None

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

carrierone — Tue, 08 Sep 2026 19:01:32 +0000

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

As a prediction market trader or developer looking to integrate Polymarket data into your application, you need reliable and efficient ways to access live data. The Polymarket Data API provides a way to get the latest information from their platform without having to authenticate every request manually. Here’s an example of how you can use this API to track smart money signals across different markets.

Tracking Smart Money Signals

Smart money signals are often considered predictive indicators in prediction markets. They represent aggregated bets made by sophisticated traders and bots, which can help predict the market's future movement based on their collective belief.

Here is a small Python script that fetches the latest smart money signals for a given market ID:


python
import requests
import json

# Define your API key here
API_KEY = 'YOUR_API_KEY'

def get_smart_money(market_id):
    url = f'https://api.verilexdata.com/api/v1/pm/stats?marketId={market_id}'
    headers = {
        'Authorization': f'Bearer {API_KEY}',
        'Content-Type': 'application/json'
    }

    response = requests.get(url, headers=headers)
    if response.status_code == 200:
        return json.loads(response.content.decode('utf-8'))
    else:
        raise Exception(f"Request

USPTO Patent and Trademark Data via REST API

carrierone — Fri, 04 Sep 2026 19:01:15 +0000

USPTO Patent and Trademark Data via REST API for Developers

If you're developing a patent search tool, IP analytics platform, or a system for trademark monitoring, accessing data from the United States Patent and Trademark Office (USPTO) can be invaluable. The USPTO provides access to its vast database of patents through an API endpoint at api.verilexdata.com/api/v1/patents/sample. This article will guide you on how to search for patents using this API, covering the basics of what it offers and providing a small Python example.

Problem: Accessing USPTO Patent Data

The USPTO grants over 600,000 patents each year. As an engineer building a tool that needs to integrate with these data, you might want to filter or search for specific patents based on keywords, inventors, assignees, or other criteria. The REST API allows developers like yourself to do just that.

Solution: A Simple Python Example

Here’s a simple example of how to use the api.verilexdata.com/api/v1/patents/sample endpoint in Python to search for patents by keyword:


python
import requests

def fetch_patent_data(keyword):
    url = "https://api.verilexdata.com/api/v1/patents/sample"
    headers = {
        'Accept': '

Querying Federal Court Records (PACER) Programmatically

carrierone — Tue, 01 Sep 2026 19:01:16 +0000

Querying Federal Court Records (PACER) Programmatically

If you're working on a legaltech project that requires access to federal court records, you might find yourself needing to programmatically query the PACER system. The Public Access to Court Electronic Records (Pacer) is an online database provided by the United States Courts, offering electronic access to case filings from most federal district and appeals courts.

A Simple Python Code Example

Here's a quick example of how you can use Python to fetch documents from the PACER using their API. The code below uses requests library to make HTTP requests:

import requests

def get_case_documents(pacer_case_id):
    # Replace with your own Pacer account information
    api_key = "YOUR_API_KEY"

    url = f"https://api.pacerlaw.com/v2/documents/{pacer_case_id}"
    headers = {
        'Authorization': f'Bearer {api_key}',
        'Accept': 'application/json'
    }

    response = requests.get(url, headers=headers)
    if response.status_code == 200:
        return response.json()
    else:
        raise Exception(f"Error: {response.status_code} - {response.text}")

# Example usage
case_id = "12345"
documents = get_case_documents(case_id)
print(documents)

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

carrierone — Fri, 28 Aug 2026 19:01:39 +0000

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

When building KYC/AML compliance systems, financial technology applications, or any project that involves cryptocurrency wallets, understanding how to check addresses against the US Treasury’s Office of Foreign Assets Control (OFAC) SDN list is crucial. This list contains individuals and entities subject to US sanctions. Checking these entries can help prevent your projects from inadvertently facilitating transactions with prohibited parties.

Here's a quick Python script example demonstrating how you might integrate OFAC sanctions screening into your application:

import requests

def check_sanctions(screen_name):
    url = f"https://api.verilexdata.com/api/v1/sanctions/stats?name={screen_name}"
    response = requests.get(url)

    if response.status_code != 200:
        return None

    data = response.json()
    return data['isSDN']

# Example usage
address_or_screen_name = "example_address"
if check_sanctions(address_or_screen_name):
    print(f"The address {address_or_screen_name} is on the OFAC SDN list.")
else:
    print(f"The address {address_or_screen_name} is not on the OFAC SDN list.")

Accessing OFAC Data via API

For more comprehensive and frequent checks, you can use our API endpoint at `https://api.verile

Getting SEC EDGAR Filings via API Without Scraping

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

Getting SEC EDGAR Filings via API Without Scraping

When working on financial applications that require real-time access to SEC filings such as 10-K and 10-Q documents, you might be tempted to scrape data from the SEC's EDGAR system. However, this approach can be cumbersome and may not always provide up-to-date information due to delays in updates.

A more practical solution is to use an API that provides these filings directly. One such API endpoint is api.verilexdata.com/api/v1/sec/sample. This service claims to update its data every 15 minutes, allowing you to search by company or form type and receiving real-time updates without the need for manual scraping.

Example Python Code

Here’s a simple example of how you can use this API in a Python script:


python
import requests

def get_sec_filings(company_name=None, form_type='10-K'):
    url = 'https://api.verilexdata.com/api/v1/sec/sample'

    params = {
        'company': company_name,
        'formType': form_type
    }

    response = requests.get(url, params=params)
    if response.status_code != 200:
        raise Exception(f"Request failed with status {response.status_code}")

    return response.json()

# Example usage to get filings for a specific

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

carrierone — Fri, 21 Aug 2026 19:01:10 +0000

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

When developing healthcare applications that require a provider directory or need to authenticate prior authorizations, having access to the NPI registry is crucial. The National Provider Identifier (NPI) database contains information on over 9 million U.S. healthcare providers, including physicians, nurses, pharmacists, and other allied health professionals.

To integrate this vast dataset into your application, you can leverage an API endpoint provided by VeriFone, which offers the NPI registry data in a sample format at api.verilexdata.com/api/v1/npi/sample. This allows developers to quickly test and prototype integration without needing to manage large datasets or handle complex queries.

Problem: Querying Large Datasets of Healthcare Providers

Developers often need to look up healthcare providers by their NPI number, name, specialty, or location. The existing NPI database is vast and comprehensive, but accessing specific records requires a structured approach. Without direct access to the full dataset, developers often find themselves writing custom SQL queries or API requests that can be time-consuming and error-prone.

Python Code Example: Querying Healthcare Providers by NPI Number

Here's an example of how you might query for healthcare providers using their NPI number in a Python script. This is a simplified version to illustrate the process:


python
import requests

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

carrierone — Tue, 18 Aug 2026 19:01:07 +0000

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

When building retail trading tools or fintech apps that need to screen penny stocks and other over-the-counter (OTC) companies, one of the most significant challenges is identifying shell companies. These are entities with no real business operations but often have inflated stock prices due to speculative buying and selling.

Identifying Shell Companies: A Python Example

Here's a simple example using Python to identify OTC companies that may be shell companies:


python
import requests

# API endpoint for screening OTC companies
url = "https://api.verilexdata.com/api/v1/otc/sample"

def screen_otc_companies():
    response = requests.get(url)

    if response.status_code == 200:
        otc_data = response.json()

        # Assuming we are interested in a specific attribute like 'company_name' and 'status'
        filtered_data = [
            {"name": company["company_name"], "status": company["status"]}
            for company in otc_data
            if company["status"] == "shell"
        ]

        return filtered_data
    else:
        print(f"Failed to retrieve data: {response.status_code}")
        return []

# Call the function and display results
screened_companies = screen_otc_companies()
for company in