Signal Intelligence

The dashboard continuously analyzes data streams to detect significant patterns and anomalies. Signals appear in the header badge (⚡) with confidence scores.

Intelligence Findings Badge

The header displays an Intelligence Findings badge that consolidates two types of alerts:

Alert Type	Source	Examples
Correlation Signals	Cross-source pattern detection	Velocity spikes, market divergence, prediction leading
Unified Alerts	Module-generated alerts	CII spikes, geographic convergence, infrastructure cascades

Interaction: Clicking the badge—or clicking an individual alert—opens a detail modal showing:

Full alert description and context
Component breakdown (for composite alerts)
Affected countries or regions
Confidence score and priority level
Timestamp and trending direction

This provides a unified command center for all intelligence findings, whether generated by correlation analysis or module-specific threshold detection.

Signal Types

The system detects 12 distinct signal types across news, markets, military, and infrastructure domains: News & Source Signals

Signal	Trigger	What It Means
◉ Convergence	3+ source types report same story within 30 minutes	Multiple independent channels confirming the same event—higher likelihood of significance
△ Triangulation	Wire + Government + Intel sources align	The “authority triangle”—when official channels, wire services, and defense specialists all report the same thing
🔥 Velocity Spike	Topic mention rate doubles with 6+ sources/hour	A story is accelerating rapidly across the news ecosystem

Market Signals

Signal	Trigger	What It Means
🔮 Prediction Leading	Prediction market moves 5%+ with low news coverage	Markets pricing in information not yet reflected in news
📰 News Leads Markets	High news velocity without corresponding market move	Breaking news not yet priced in—potential mispricing
✓ Market Move Explained	Market moves 2%+ with correlated news coverage	Price action has identifiable news catalyst—entity correlation found related stories
📊 Silent Divergence	Market moves 2%+ with no correlated news after entity search	Unexplained price action after exhaustive search—possible insider knowledge or algorithm-driven
📈 Sector Cascade	Multiple related sectors moving in same direction	Market reaction cascading through correlated industries

Infrastructure & Energy Signals

Signal	Trigger	What It Means
🛢 Flow Drop	Pipeline flow disruption keywords detected	Physical commodity supply constraint—may precede price spike
🔁 Flow-Price Divergence	Pipeline disruption news without corresponding oil price move	Energy supply disruption not yet priced in—potential information edge

Geopolitical & Military Signals

Signal	Trigger	What It Means
🌍 Geographic Convergence	3+ event types in same 1°×1° grid cell	Multiple independent data streams converging on same location—heightened regional activity
🔺 Hotspot Escalation	Multi-component score exceeds threshold with rising trend	Hotspot showing corroborated escalation across news, CII, convergence, and military data
✈ Military Surge	Transport/fighter activity 2× baseline in theater	Unusual military airlift concentration—potential deployment or crisis response

How It Works

The correlation engine maintains rolling snapshots of:

News topic frequency (by keyword extraction)
Market price changes
Prediction market probabilities

Each refresh cycle compares current state to previous snapshot, applying thresholds and deduplication to avoid alert fatigue. Signals include confidence scores (60-95%) based on the strength of the pattern.

Entity-Aware Correlation

The signal engine uses a knowledge base of 66 entities to intelligently correlate market movements with news coverage. Rather than simple keyword matching, the system understands that “AVGO” (the ticker) relates to “Broadcom” (the company), “AI chips” (the sector), and entities like “Nvidia” (a competitor).

Entity Knowledge Base

Each entity in the registry contains:

Field	Purpose	Example
ID	Canonical identifier	`broadcom`
Name	Display name	`Broadcom Inc.`
Type	Category	`company`, `commodity`, `crypto`, `country`, `person`
Aliases	Alternative names	`AVGO`, `Broadcom`, `Broadcom Inc`
Keywords	Related topics	`AI chips`, `semiconductors`, `VMware`
Sector	Industry classification	`semiconductors`
Related	Linked entities	`nvidia`, `intel`, `amd`

Entity Types

Type	Count	Examples
Companies	38	Nvidia, Apple, Tesla, Broadcom, Boeing, Lockheed Martin, TSMC, Rheinmetall
Indices	3	S&P 500, Dow Jones, NASDAQ
Sectors	5	Technology (XLK), Finance (XLF), Energy (XLE), Healthcare (XLV), Semiconductors (SMH)
Commodities	6	Oil (WTI), Gold, Natural Gas, Copper, Silver, VIX
Crypto	3	Bitcoin, Ethereum, Solana
Countries	11	China, Russia, Iran, Israel, Ukraine, Taiwan, Saudi Arabia, UAE, Qatar, Turkey, Egypt

How Entity Matching Works

When a market moves significantly (≥2%), the system:

Looks up the ticker in the entity registry (e.g., AVGO → broadcom)
Gathers all identifiers: aliases, keywords, sector peers, related entities
Scans all news clusters for matches against any identifier
Scores confidence based on match type:
- Alias match (exact name): 95%
- Keyword match (topic): 70%
- Related entity match: 60%

If correlated news is found → “Market Move Explained” signal with the news headline. If no correlation after exhaustive search → “Silent Divergence” signal.

Example: Broadcom +2.5%

Ticker AVGO detected with +2.5% move
Entity lookup: broadcom
Search terms: ["Broadcom", "AVGO", "AI chips", "semiconductors", "VMware", "nvidia", "intel", "amd"]
News scan finds: "Broadcom AI Revenue Beats Estimates"
Result: "✓ Market Move Explained: Broadcom AI Revenue Beats Estimates"

Without this system, the same move would generate a generic “Silent Divergence: AVGO +2.5%” signal.

Sector Coverage

The entity registry spans strategically significant sectors:

Sector	Examples	Keywords Tracked
Technology	Apple, Microsoft, Nvidia, Google, Meta, TSMC	AI, cloud, chips, datacenter, streaming
Defense & Aerospace	Lockheed Martin, Raytheon, Northrop Grumman, Boeing, Rheinmetall, Airbus	F-35, missiles, drones, tanks, defense contracts
Semiconductors	ASML, Samsung, AMD, Intel, Broadcom	Lithography, EUV, foundry, fab, wafer
Critical Minerals	Albemarle, SQM, MP Materials, Freeport-McMoRan	Lithium, rare earth, cobalt, copper
Finance	JPMorgan, Berkshire Hathaway, Visa, Mastercard	Banking, credit, investment, interest rates
Healthcare	Eli Lilly, Novo Nordisk, UnitedHealth, J&J	Pharma, drugs, GLP-1, obesity, diabetes
Energy	Exxon, Chevron, ConocoPhillips	Oil, gas, drilling, refinery, LNG
Consumer	Tesla, Walmart, Costco, Home Depot	EV, retail, grocery, housing

This broad coverage enables correlation detection across diverse geopolitical and market events.

Entity Registry Architecture

The entity registry is a knowledge base of 66 entities with rich metadata for intelligent correlation:

{
  id: 'NVDA',           // Unique identifier
  name: 'Nvidia',       // Display name
  type: 'company',      // company | country | index | commodity | currency
  sector: 'semiconductors',
  searchTerms: ['Nvidia', 'NVDA', 'Jensen Huang', 'H100', 'CUDA'],
  aliases: ['nvidia', 'nvda'],
  competitors: ['AMD', 'INTC'],
  related: ['AVGO', 'TSM', 'ASML'],  // Related entities
  country: 'US',        // Headquarters/origin
}

Entity Types:

Type	Count	Use Case
`company`	38	Market-news correlation, sector analysis
`country`	11	Focal point detection, CII scoring
`index`	3	Market overview, regional tracking
`commodity`	6	Energy and mineral correlation
`sector`	5	Sector cascade analysis
`crypto`	3	Cryptocurrency correlation

Lookup Indexes: The registry provides multiple lookup paths for fast entity resolution:

Index	Query Example	Use Case
`byId`	`'NVDA'` → Nvidia entity	Direct lookup from ticker
`byAlias`	`'nvidia'` → Nvidia entity	Case-insensitive name match
`byKeyword`	`'AI chips'` → [Nvidia, AMD, Intel]	News keyword extraction
`bySector`	`'semiconductors'` → all chip companies	Sector cascade analysis
`byCountry`	`'US'` → all US entities	Country-level aggregation

Signal Deduplication

To prevent alert fatigue, signals use type-specific TTL (time-to-live) values for deduplication:

Signal Type	TTL	Rationale
Silent Divergence	6 hours	Market moves persist; don’t re-alert on same stock
Flow-Price Divergence	6 hours	Energy events unfold slowly
Explained Market Move	6 hours	Same correlation shouldn’t repeat
Prediction Leading	2 hours	Prediction markets update more frequently
Other signals	30 minutes	Default for fast-moving events

Market signals use symbol-only keys (e.g., silent_divergence:AVGO) rather than including the price change. This means a stock moving +2.5% then +3.0% won’t trigger duplicate alerts—the first alert covers the story.

Source Intelligence

Not all sources are equal. The system implements a dual classification to prioritize authoritative information.

Source Tiers (Authority Ranking)

Tier	Sources	Characteristics
Tier 1	Reuters, AP, AFP, Bloomberg, White House, Pentagon	Wire services and official government—fastest, most reliable
Tier 2	BBC, Guardian, NPR, Al Jazeera, CNBC, Financial Times	Major outlets—high editorial standards, some latency
Tier 3	Defense One, Bellingcat, Foreign Policy, MIT Tech Review	Domain specialists—deep expertise, narrower scope
Tier 4	Hacker News, The Verge, VentureBeat, aggregators	Useful signal but requires corroboration

When multiple sources report the same story, the lowest tier (most authoritative) source is displayed as the primary, with others listed as corroborating.

Source Types (Categorical)

Sources are also categorized by function for triangulation detection:

Wire - News agencies (Reuters, AP, AFP, Bloomberg)
Gov - Official government (White House, Pentagon, State Dept, Fed, SEC)
Intel - Defense/security specialists (Defense One, Bellingcat, Krebs)
Mainstream - Major news outlets (BBC, Guardian, NPR, Al Jazeera)
Market - Financial press (CNBC, MarketWatch, Financial Times)
Tech - Technology coverage (Hacker News, Ars Technica, MIT Tech Review)

Propaganda Risk Indicators

The dashboard visually flags sources with known state affiliations or propaganda risk, enabling users to appropriately weight information from these outlets. Risk Levels

Level	Visual	Meaning
High	⚠ State Media (red)	Direct state control or ownership
Medium	! Caution (orange)	Significant state influence or funding
Low	(none)	Independent editorial control

Flagged Sources

Source	Risk Level	State Affiliation	Notes
Xinhua	High	China (CCP)	Official news agency of PRC
TASS	High	Russia	State-owned news agency
RT	High	Russia	Registered foreign agent in US
CGTN	High	China (CCP)	China Global Television Network
PressTV	High	Iran	IRIB subsidiary
Al Jazeera	Medium	Qatar	Qatari government funded
TRT World	Medium	Turkey	Turkish state broadcaster

Display Locations Propaganda risk badges appear in:

Cluster primary source: Badge next to the main source name
Top sources list: Small badge next to each flagged source
Cluster view: Visible when expanding multi-source clusters

Why Include State Media? State-controlled outlets are included rather than filtered because:

Signal Value: What state media reports (and omits) reveals government priorities
Rapid Response: State media often breaks domestic news faster than international outlets
Narrative Analysis: Understanding how events are framed by different governments
Completeness: Excluding them creates blind spots in coverage

The badges ensure users can contextualize state media reports rather than unknowingly treating them as independent journalism.

Entity Extraction System

The dashboard extracts named entities (companies, countries, leaders, organizations) from news headlines to enable news-to-market correlation and entity-based filtering.

How It Works

Headlines are scanned against a curated entity index containing:

Entity Type	Examples	Purpose
Companies	Apple, Tesla, NVIDIA, Boeing	Market symbol correlation
Countries	Russia, China, Iran, Ukraine	Geopolitical attribution
Leaders	Putin, Xi Jinping, Khamenei	Political event tracking
Organizations	NATO, OPEC, Fed, SEC	Institutional news filtering
Commodities	Oil, Gold, Bitcoin	Commodity news correlation

Entity Matching

Each entity has multiple match patterns for comprehensive detection:

Entity: NVIDIA (NVDA)
  Aliases: nvidia, nvda, jensen huang
  Keywords: gpu, h100, a100, cuda, ai chip
  Match Types:
    - Name match: "NVIDIA announces..." → 95% confidence
    - Alias match: "Jensen Huang says..." → 90% confidence
    - Keyword match: "H100 shortage..." → 70% confidence

Confidence Scoring

Entity extraction produces confidence scores based on match quality:

Match Type	Confidence	Example
Direct name	95%	“Apple reports earnings”
Alias	90%	“Tim Cook announces…”
Keyword	70%	“iPhone sales decline”
Related cluster	63%	Secondary headline mention (90% × 0.7)

Market Correlation

When a market symbol moves significantly, the system searches news clusters for related entities:

Symbol lookup - Find entity by market symbol (e.g., AAPL → Apple)
News search - Find clusters mentioning the entity or related entities
Confidence ranking - Sort by extraction confidence
Result - “Market Move Explained” or “Silent Divergence” signal

This enables signals like:

Explained: “AVGO +5.2% — Broadcom mentioned in 3 news clusters (AI chip demand)”
Silent: “AVGO +5.2% — No correlated news after entity search”

Signal Context (“Why It Matters”)

Every signal includes contextual information explaining its analytical significance:

Context Fields

Field	Purpose	Example
Why It Matters	Analytical significance	”Markets pricing in information before news”
Actionable Insight	What to do next	”Monitor for breaking news in 1-6 hours”
Confidence Note	Signal reliability caveats	”Higher confidence if multiple markets align”

Signal-Specific Context

Signal	Why It Matters
Prediction Leading	Prediction markets often price in information before it becomes news—traders may have early access to developments
Silent Divergence	Market moving without identifiable catalyst—possible insider knowledge, algorithmic trading, or unreported development
Velocity Spike	Story accelerating across multiple sources—indicates growing significance and potential for market/policy impact
Triangulation	The “authority triangle” (wire + government + intel) aligned—gold standard for breaking news confirmation
Flow-Price Divergence	Supply disruption not yet reflected in prices—potential information edge or markets have better information
Hotspot Escalation	Geopolitical hotspot showing escalation across news, instability, convergence, and military presence

This contextual layer transforms raw alerts into actionable intelligence by explaining the analytical reasoning behind each signal.

Energy Flow Detection

The correlation engine detects signals related to energy infrastructure and commodity markets.

Pipeline Keywords

The system monitors news for pipeline-related events: Infrastructure terms: pipeline, pipeline explosion, pipeline leak, pipeline attack, pipeline sabotage, pipeline disruption, nord stream, keystone, druzhba Flow indicators: gas flow, oil flow, supply disruption, transit halt, capacity reduction

Flow Drop Signals

When news mentions flow disruptions, two signal types may trigger:

Signal	Criteria	Meaning
Flow Drop	Pipeline keywords + disruption terms	Potential supply interruption
Flow-Price Divergence	Flow drop news + oil price stable (< $1.50 move)	Markets not yet pricing in disruption

Why This Matters

Energy supply disruptions create cascading effects:

Immediate: Spot price volatility
Short-term: Industrial production impacts
Long-term: Geopolitical leverage shifts

Early detection of flow drops—especially when markets haven’t reacted—provides an information edge.

Signal Aggregator

The Signal Aggregator is the central nervous system that collects, groups, and summarizes intelligence signals from all data sources.

What It Aggregates

Signal Type	Source	Frequency
`military_flight`	OpenSky ADS-B	Real-time
`military_vessel`	AIS WebSocket	Real-time
`protest`	ACLED + GDELT	Hourly
`internet_outage`	Cloudflare Radar	5 min
`ais_disruption`	AIS analysis	Real-time

Country-Level Grouping

All signals are grouped by country code, creating a unified view:

{
  country: 'UA',  // Ukraine
  countryName: 'Ukraine',
  totalCount: 15,
  highSeverityCount: 3,
  signalTypes: Set(['military_flight', 'protest', 'internet_outage']),
  signals: [/* all signals for this country */]
}

Regional Convergence Detection

The aggregator identifies geographic convergence—when multiple signal types cluster in the same region:

Convergence Level	Criteria	Alert Priority
Critical	4+ signal types within 200km	Immediate
High	3 signal types within 200km	High
Medium	2 signal types within 200km	Normal

Summary Output

The aggregator provides a real-time summary for dashboards and AI context:

[SIGNAL SUMMARY]
Top Countries: Ukraine (15 signals), Iran (12), Taiwan (8)
Convergence Zones: Baltic Sea (military_flight + military_vessel),
                   Tehran (protest + internet_outage)
Active Signal Types: 5 of 5
Total Signals: 47

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