Introduction: Why news-based alerts matter now
Most trading platforms offer alerts. Users get notified when a stock breaks above a technical level or hits a price threshold. But these are reactive signals. They tell users what just happened, not what’s about to happen.
In many cases, the move starts with information. An analyst rating change. An earnings update. A headline that shifts sentiment. These events often hit before the price reacts, yet most platforms don’t surface them as part of their alerting logic.
This gap is a product opportunity. Benzinga’s Newsfeed API gives teams access to real-time, structured news that can trigger alerts the moment it matters. In this article, we’ll show how to build an alerting feature that reacts to headlines, not just price.
Use Case: A smarter alert system for real-time headlines
Price-based alerts are useful, but they often come too late. By the time a stock breaks through a technical level, the catalyst may already be priced in. For active users, that means missed opportunities.
Now, consider a different type of alert. A user tracking GOOGL gets notified not when it moves, but when a new headline hits with analyst commentary or a sector upgrade. The alert links directly to the article, and the user sees the context instantly.
This is what a news-triggered alert looks like in practice. It surfaces the “why” behind market movement, giving the user a chance to react before the move fully plays out. For platforms, it creates more timely engagement, deeper stickiness, and a stronger sense of utility.
Alert Design: What triggers the system
Before building the alert logic, product teams need to define the conditions that qualify a news item as alert-worthy. This helps the system stay focused, avoid noise, and deliver actionable value.
The core logic comes down to four elements:
- Tracked tickers: A predefined list of stocks the user or platform is monitoring
- Polling window: A lookback period, such as the last 15 minutes
- Threshold: The minimum number of relevant articles needed to trigger an alert
- Trigger type: A simple condition, such as one headline within the window
This logic is flexible. Some platforms may want alerts only on spikes in headline volume. Others might trigger based on keyword matches or specific channels. But the base configuration remains consistent.
In the next section, we’ll build the full cycle using Benzinga’s Newsfeed API.
Implementation: Real-Time alerting with Benzinga’s Newsfeed API
With the alert logic defined, we can now build the system in code. The full cycle includes setting up the configuration, polling Benzinga’s Newsfeed API, checking for recent headlines, and triggering alerts when conditions are met.
Each part is modular and can be adapted to different use cases. The example below uses Python to demonstrate how a real-time alert system could be structured for internal testing or early-stage deployment.
Set up & alert criteria
We’ll start by defining the alert configuration. This includes the stocks to monitor, how far back to look for news, and how many headlines are needed to trigger an alert.
import requests
from datetime import datetime, timedelta
import pytz
import schedule
import time
api_key = "YOUR API KEY"
tickers = ["AAPL", "TSLA", "GOOGL", "NFLX", "META", "MSFT", "AMZN", "NVDA"]
poll_window_minutes = 15
alert_threshold = 1
This configuration tells the system to check for news published in the last 15 minutes. If at least one headline is found for any of the tickers, the alert logic will take over in the next steps.
Poll Newsfeed API
The next step is to fetch recent headlines using Benzinga’s Newsfeed API. Since the API only accepts full-date filtering, we request all news from today and filter it locally based on the polling window.
now = datetime.now(pytz.utc)
cutoff_time = now - timedelta(minutes=poll_window_minutes)
date_from = now.strftime("%Y-%m-%d")
results = []
for symbol in tickers:
headers = {"accept": "application/json"}
params = {
"tickers": symbol,
"dateFrom": '2025-06-01',
"displayOutput": "full",
"token": api_key
}
response = requests.get('https://api.benzinga.com/api/v2/news', params=params, headers=headers)
articles = response.json()
for article in articles:
published = datetime.strptime(article.get("created"), "%a, %d %b %Y %H:%M:%S %z")
if published >= cutoff_time:
results.append({
"ticker": symbol,
"title": article.get("title"),
"published": published,
"url": article.get("url"),
"tags": [tag.get("name", "") for tag in article.get("tags", [])],
"channels": [ch.get("name", "") for ch in article.get("channels", [])]
})
This logic checks each ticker individually and filters for articles published within the last 15 minutes. It stores matching results in a list that will be used to trigger alerts in the next step.
Trigger Alerts
Now that we’ve collected recent headlines for all tracked tickers, we can group them by symbol and trigger alerts if the number of qualifying articles meets the threshold.
alerts = {}
for item in results:
ticker = item["ticker"]
if ticker not in alerts:
alerts[ticker] = []
alerts[ticker].append(item)
for ticker, articles in alerts.items():
if len(articles) >= alert_threshold:
print(f"\nALERT: {ticker} has {len(articles)} new headline(s):\n")
for article in articles:
print(f"- {article['title']}")
print(f" Published: {article['published']}")
print(f" URL: {article['url']}")
print(f" Tags: {', '.join(article['tags'])}")
print(f" Channels: {', '.join(article['channels'])}\n")
This logic ensures alerts are only triggered when a stock has at least one recent article within the polling window. Each alert includes the headline, timestamp, URL, and any tags or channels for added context.
Orchestration
To make the alert system work continuously, we wrap the logic from the previous steps into a single function. This function is scheduled to run every 15 minutes using the schedule library, which simulates a real-time polling system.
# Step 4: Orchestration
def run_alert_cycle():
# Step 1: Define alert criteria
api_key = "YOUR API KEY"
tickers = ["AAPL", "TSLA", "GOOGL", "NFLX", "META", "MSFT", "AMZN", "NVDA"]
poll_window_minutes = 15
alert_threshold = 1
# Step 2: Poll the Newsfeed API
now = datetime.now(pytz.utc)
cutoff_time = now - timedelta(minutes=poll_window_minutes)
date_from = now.strftime("%Y-%m-%d")
results = []
for symbol in tickers:
headers = {"accept": "application/json"}
params = {
"tickers": symbol,
"dateFrom": '2025-06-01',
"displayOutput": "full",
"token": api_key
}
response = requests.get('https://api.benzinga.com/api/v2/news', params=params, headers=headers)
articles = response.json()
for article in articles:
published = datetime.strptime(article.get("created"), "%a, %d %b %Y %H:%M:%S %z")
if published >= cutoff_time:
results.append({
"ticker": symbol,
"title": article.get("title"),
"published": published,
"url": article.get("url"),
"tags": [tag.get("name", "") for tag in article.get("tags", [])],
"channels": [ch.get("name", "") for ch in article.get("channels", [])]
})
# Step 3: Trigger alerts
alerts = {}
for item in results:
ticker = item["ticker"]
if ticker not in alerts:
alerts[ticker] = []
alerts[ticker].append(item)
for ticker, articles in alerts.items():
if len(articles) >= alert_threshold:
print(f"\nALERT: {ticker} has {len(articles)} new headline(s):\n")
for article in articles:
print(f"- {article['title']}")
print(f" Published: {article['published']}")
print(f" URL: {article['url']}")
print(f" Tags: {', '.join(article['tags'])}")
print(f" Channels: {', '.join(article['channels'])}\n")
# Run the alert cycle every 15 minutes
schedule.every(15).minutes.do(run_alert_cycle)
while True:
schedule.run_pending()
time.sleep(1)
This setup schedules the entire alert logic to run automatically in a loop. Every 15 minutes, the system checks for fresh headlines on each ticker. If any stock shows recent activity, it prints a formatted alert with all relevant metadata.
In a production environment, this structure could be extended to support:
- User-configured ticker lists and alert frequency
- Alert delivery via email, push notification, or webhook
- Logging or caching to avoid duplicate alerts
- Horizontal scaling across large watchlists or user bases
This makes it easy for product teams to build an MVP that’s functional today, with a clear path to feature-rich alerting later.
How product teams can integrate this logic
The alert system we just built is more than a technical proof of concept. It’s a lightweight framework that can plug into a variety of product modules and workflows with very little friction.
Here’s how it can be applied inside real platforms.
i) Add to watchlists and portfolios
Users already track their favorite stocks in watchlists and portfolio dashboards. By tying this alert system to those modules, platforms can notify users when one of their tracked tickers appears in a new headline.
This adds context without requiring the user to actively monitor the news feed. Alerts can show up as a badge, in-app notification, or inline icon next to the ticker.
ii) Offer configurable alert settings
The alert logic can be extended to support user-defined rules. Let users pick:
- Which tickers do they want alerts for
- What time window to monitor (15, 30, or 60 minutes)
- How many headlines should trigger a notification
This turns a simple feature into a powerful personalization tool, something that increases user stickiness over time.
iii) Deliver alerts through multiple channels
In addition to console printouts or badges, the alert payload can be delivered through:
- Push notifications
- Webhooks (for advanced users or API consumers)
This opens up integration opportunities for apps, tools, or even trader-focused Discord bots or Slack apps.
iv) Combine alerts with news readers or signal modules
Platforms that already offer a news reader or signal engine can embed this logic to surface stocks that are “in play” based on headline velocity. The alert doesn’t just notify the user; it becomes a call to action to view the latest news.
This creates a smoother flow from alert to insight to action.
Why Benzinga’s Newsfeed API is ideal for alerting use cases
Most market data APIs aren’t built for real-time decision-making. They provide bulk data or price feeds but lack the context and structure needed to trigger actionable alerts. Benzinga’s Newsfeed API is different.
It’s designed to surface market-moving information in a way that’s easy to filter, easy to parse, and fast to act on.
i) Structured Metadata
Every article includes cleanly formatted fields:
createdtimestamps for recency checkstagsandchannelsfor contexttickersfor precise targeting
This structure makes it easy to build automated workflows without needing to parse raw text.
ii) Real-Time Delivery
New headlines are available as soon as they’re published. Combined with a scheduled polling setup or a WebSocket stream, the Newsfeed API supports real-time use cases without forcing product teams to build around delays.
iii) Developer-Friendly Format
The API returns clean JSON responses that are easy to work with. No scraping, no data cleanup, no wasted engineering time.
This lowers the barrier to integration and shortens the time from prototype to production.
iv) Designed for products, not just data feeds
The Newsfeed API isn’t just about content. It’s about product enablement. From alerts and signal modules to screeners and sentiment overlays, the API supports a wide range of use cases with minimal configuration.
Explore the Benzinga Newsfeed API or visit the API documentation to see how it fits into your platform.
Conclusion
News moves markets, but most alert systems wait until the move has already happened. With Benzinga’s Newsfeed API, product teams can change that by triggering alerts at the source of the move, not after.
The logic is simple, the integration is lightweight, and the user impact is immediate. For any platform aiming to deliver smarter, faster insights, news-based alerting is no longer optional. It’s the next layer of real-time intelligence.
