Context Analytics. A Bridgewise company
Feeds/Quantitative News Feed
News sentiment

News quantified into signal.

Global news scored by our proprietary rules-based engine. Ticker-tagged, topic-tagged, and model-ready.

Built forQuantitative traders
HorizonShort term, higher frequency
CoverageU.S. equities, from global sources
DeliveryJSON API over REST
QUANT NEWS / OVERVIEW
Product walkthrough Quantitative news, as it breaks 2:40 · Product overview
Scale
78,000+Articles processed per month
8,000+News sources worldwide
24/7Real-time ingest and scoring
The source advantage

Scored on the article, not the headline. Full text runs through our engine before a sentiment score is applied.

Where it sits in the stack

One engine, one methodology.

01 / IngestGlobal news, around the clockMajor outlets, financial publications and industry sources, processed 24/7.
02 / TagSecurities identified per articleEvery security discussed is tagged and prioritized, so news is searchable by ticker.
03 / ScoreRules-based NLP sentimentThe same engine as S-Factor, scoring from −1 to +1, so the two reconcile.
04 / CategorizeTopics for every eventM&A, earnings, analyst ratings and other key events tagged at the article level.
Structured output

Headlines in. Signals out.

Each article arrives scored, ticker-tagged and topic-tagged, with no cleaning step before research.

News volume and coverage intensity by security Sentiment distribution across positive, negative and neutral Topic tags for event-driven models
Sample scored headline● Live
Acme Corp tops Q3 estimates, raises full-year guidance
ACME Topic · earnings Sentiment · +0.82 PR Newswire · 14:31:02
{ "ticker": "ACME", "published": "2026-09-29T14:31:02Z", "sentiment": 0.82, "topics": ["earnings"], "source": "PR Newswire" }
Illustrative sample. Field names to be confirmed against the data dictionary.
Applications

Where desks put it to work.

Signal generationSystematic modelsTurn unstructured headlines into structured inputs for short-horizon strategies.
Event detectionBreaking newsIdentify market-moving events and corporate announcements as they break.
Risk monitoringReal-TimeStay ahead of developments that may affect holdings, with real-time alerts.
Narrative trackingTone over timeMeasure how coverage and tone on a name shift across the news cycle.
Research supportFundamental contextCompare how companies and sectors are covered, with quantitative context.
Specifications

Scope, precisely.

CoverageU.S. equities.
Sources8,000+ news sources worldwide
SentimentRules-based NLP, −1 to +1
TopicsM&A, earnings, analyst ratings and other key events
LatencySub 1-minute
DeliveryJSON API over REST, custom integration available
FAQ

Frequently Asked Questions

You have headlines. This is a scored, structured signal built for a model, from the same engine as the social signal you may already be evaluating.

Context Analytics' NLP-driven news sentiment product that converts financial news articles into structured, ticker-tagged sentiment scores for use in systematic trading and research.

78,000+ articles per month from 8,000+ news sources, with real-time processing around the clock.

Articles are tagged by topic based on our rules-based NLP, including themes like M&A, earnings and analyst ratings, and tagged at the security level so you can search news by ticker.

The feed offers sub-1-minute processing and delivery, built for time-sensitive trading and risk applications.

Yes. The Quantitative News Feed can be combined with S-Factor's X and StockTwits sentiment for added predictive power — two independent signals, never blended, so your desk decides how to weight each.

The feed is delivered as a JSON API over REST, designed for streamlined integration into existing systems.

Add news as your next signal.

If you already model our social signal, this is the news-side catalyst on the same names, through the same engine.

S-Factor Feed → Podcast Sentiment → Corporate Filings →