DEV Community: carrierone

FRED and BLS Economic Indicator Data for Developers

carrierone — Tue, 30 Jun 2026 19:01:03 +0000

FRED and BLS Economic Indicator Data for Developers

When developing applications that require access to economic indicators such as CPI, NFP, GDP, PCE, and unemployment rates, developers often turn to sources like the Federal Reserve Economic Database (FRED) and Bureau of Labor Statistics (BLS). These resources provide comprehensive data that can be crucial for macro trading or analytics apps. Below is a simple example in Python using an API endpoint designed for such purposes.

Getting Started with CPI Data

One common indicator used in economic analysis is the Consumer Price Index (CPI), which measures changes in prices over time for goods and services purchased by consumers. To fetch this data, you can use an API that offers access to FRED's datasets. Here’s a quick example of how to request CPI data using a hypothetical endpoint api.verilexdata.com/api/v1/econ/stats:


python
import requests

# Define the URL for the API endpoint
url = 'https://api.verilexdata.com/api/v1/econ/stats'

# Parameters for the request, including the indicator we want to fetch data on (CPI)
params = {
    'indicator': 'CPI',
    # Add any other parameters like date range here if needed
}

# Send a GET request to the API with the specified URL and params
response = requests.get(url, params=params

DeFi Liquidation Signals and Whale Wallet Tracking via API

carrierone — Fri, 26 Jun 2026 19:01:32 +0000

DeFi Liquidation Signals and Whale Wallet Tracking via API

In the ever-evolving world of decentralized finance (DeFi), understanding liquidation signals and monitoring whale wallet movements are crucial for developers looking to implement robust risk management systems. These signals can help in identifying potential liquidity issues within protocols like Aave and Compound, while whale wallets tracking is essential for detecting large-scale trading activities that could cause significant price fluctuations.

Understanding Liquidation Signals

When a user fails to repay their loan on platforms such as Aave or Compound, these protocols automatically initiate liquidation. Developers can use API endpoints to receive real-time notifications about such events, which help in predicting potential risks and adjusting strategies accordingly.

Here’s a simple Python script that demonstrates how you can set up basic monitoring for liquidations:

import requests

def monitor_liquidations(protocol):
    url = f"https://api.example.com/protocol/{protocol}/liquidations"

    response = requests.get(url)

    if response.status_code == 200:
        print("Liquidation detected!")
    else:
        print(f"Failed to fetch liquidations: {response.status_code}")

# Example usage
monitor_liquidations('Aave')

API Endpoint for Whale Wallet Monitoring

For monitoring whale wallets, there are specialized APIs designed specifically for this purpose. Verilexdata.com provides an endpoint that allows developers

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

carrierone — Tue, 23 Jun 2026 19:01:20 +0000

Smart Money Tracking and Cross-Market Arbitrage: A Deep Dive into Polymarket Data

If you're a prediction market trader looking to integrate real-time data feeds for smarter arbitrage strategies, Polymarket offers a robust API that can be invaluable. In this article, we'll explore how the api.verilexdata.com/api/v1/pm/stats endpoint can provide insights through the lens of smart money signals and cross-market arbitrage.

Understanding Smart Money Signals

Smart money signals are indicative of large players or institutions influencing market prices. By analyzing these signals, traders can anticipate shifts in sentiment that could impact their trades. Here’s a simple Python script to fetch and display recent Polymarket data:

import requests

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

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

    data = response.json()
    for market in data['markets']:
        print(market)

get_polymarkets_data()

This script sends a GET request to the specified endpoint and prints out details of each market. This can be expanded to include more specific queries or analysis tailored to your trading strategies.

Applying Polymarket Data in

USPTO Patent and Trademark Data via REST API

carrierone — Fri, 19 Jun 2026 19:01:15 +0000

USPTO Patent and Trademark Data via REST API

As a developer looking to build tools for patent search, IP analytics, prior art searches, and trademark monitoring, you need access to comprehensive datasets. The United States Patent and Trademark Office (USPTO) provides such data through their RESTful API, accessible at api.verilexdata.com/api/v1/patents/sample.

Problem: Building a Comprehensive Patent Search Tool

When developing tools that require patent information, one common challenge is accessing up-to-date and accurate patent datasets. The USPTO's API offers a solution by providing sample data for testing purposes or as an endpoint to integrate with your own applications.

Small Python Code Example: Accessing the USPTO Patents Data via API

Here’s a simple example of how you can access the USPTO patents data using Python:


python
import requests

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

    response = requests.get(url)

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

# Example of calling the function
patent_data = get_patent_data()

for patent in patent_data:
    print(f"Inventor:

Querying Federal Court Records (PACER) Programmatically

carrierone — Tue, 16 Jun 2026 19:01:41 +0000

Querying Federal Court Records (PACER) Programmatically for Legaltech Developers

If you're a legaltech developer looking to access and query federal court records programmatically through PACER (Public Access to Court Electronic Records), here’s how you can do it using Python. PACER provides a structured data API that allows developers to fetch case information, filings, and documents in an organized manner.

Problem

Fetching data from the Federal Register or other public databases often requires navigating multiple endpoints with varying structures and authentication methods, which can be cumbersome and error-prone. With PACER’s API, you get standardized endpoints for fetching case details and documents, reducing the complexity of your codebase.

Example Python Code:

Here is a simple example using requests library to fetch basic case information from PACER:


python
import requests

def get_case_info(api_key, court_id):
    url = f"https://api.pacerpc.net/v1/cases/{court_id}"
    headers = {
        "Authorization": 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 retrieve case information: {response.text}")

# Replace 'your_api_key' with your actual PACER API key
api_key

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

carrierone — Fri, 12 Jun 2026 19:01:34 +0000

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

When developing KYC/AML compliance systems, one of the critical steps is ensuring that your users or transactions are not associated with individuals listed on the U.S. Department of the Treasury’s Office of Foreign Assets Control (OFAC) Specially Designated Nationals (SDN) list. The SDN list contains entities and individuals designated under various sanctions programs. These include terrorist organizations, narcotics traffickers, weapons proliferators, and others.

Problem

Checking these entries can be time-consuming and error-prone due to the sheer number of names and wallet addresses against the OFAC SDN list, which currently has over 18,706 entries. Manually checking each address or name would not only be impractical but also inefficient for large-scale applications.

Solution: Python Code Example

Here's a simple example in Python to demonstrate how you can query the API endpoint provided by api.verilexdata.com to check if an address or name is on the OFAC SDN list. This code uses the requests library to make HTTP requests and parse JSON responses.


python
import requests

def check_sanction(address_or_name):
    url = f"https://api.verilexdata.com/api/v1/sanctions/stats?query={address_or_name}"
    response = requests.get

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}")

DEV Community: carrierone

FRED and BLS Economic Indicator Data for Developers

FRED and BLS Economic Indicator Data for Developers

Getting Started with CPI Data

DeFi Liquidation Signals and Whale Wallet Tracking via API

DeFi Liquidation Signals and Whale Wallet Tracking via API

Understanding Liquidation Signals

API Endpoint for Whale Wallet Monitoring

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

Smart Money Tracking and Cross-Market Arbitrage: A Deep Dive into Polymarket Data

Understanding Smart Money Signals

Applying Polymarket Data in

USPTO Patent and Trademark Data via REST API

USPTO Patent and Trademark Data via REST API

Problem: Building a Comprehensive Patent Search Tool

Small Python Code Example: Accessing the USPTO Patents Data via API

Querying Federal Court Records (PACER) Programmatically

Querying Federal Court Records (PACER) Programmatically for Legaltech Developers

Problem

Example Python Code:

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

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

Problem

Solution: Python Code Example

Getting SEC EDGAR Filings via API Without Scraping

Getting SEC EDGAR Filings via API Without Scraping

Problem

Solution: Using an API

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

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

Python Code Example: Querying the NPI Registry

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

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

Federal Contract Award Data (USASpending) via API

Federal Contract Award Data (USASpending) via API

Problem: Searching by Agency, Vendor, or NAICS Code

Example of Python Code for Filtering Contracts by Agency:

Using the API Endpoint: verilexdata.com

NOAA Weather Data API: Historical and Current Weather for Developers

NOAA Weather Data API: Historical and Current Weather for Developers

The Problem: Historical and Current Weather Data Integration

Python Code Example for Accessing NOAA Weather Data

FRED and BLS Economic Indicator Data for Developers

FRED and BLS Economic Indicator Data for Developers

Problem: Accessing Real-Time Data

Endpoint for FRED and B