Taxonomy

Our hierarchical ontology for market intelligence

The Taxonomy

Market Motion’s taxonomy is a hierarchical ontology that organizes all market-relevant information. It’s what makes our data machine-readable rather than just searchable.

Structure

Category
└── Subcategory
    └── Entity Type
        └── Entity
            ├── Attributes
            ├── Relationships
            └── Market Exposures

Categories

Politics

Subcategories:

• presidential - Presidential elections and candidates
• us-congress - Senators and Representatives
• governors - State governors
• international - Foreign leaders and elections
• legislation - Bills and policy

Entity Types: person, org

Example Entities:

• Joe Biden (person)
• Donald Trump (person)
• Democratic Party (org)

Sports

Subcategories:

• nfl - National Football League
• nba - National Basketball Association
• mlb - Major League Baseball
• nhl - National Hockey League
• soccer - MLS, international

Entity Types: person, team, place, league

Example Entities:

• Patrick Mahomes (person)
• Kansas City Chiefs (team)
• Arrowhead Stadium (place)
• NFL (league)

Crypto

Subcategories:

• layer-1 - Base layer protocols
• defi - DeFi protocols
• memecoins - Meme tokens
• nft - NFT projects
• regulation - Regulatory events

Entity Types: asset, org, person

Example Entities:

• Bitcoin (asset)
• Ethereum (asset)
• Coinbase (org)

Finance

Subcategories:

• fed - Federal Reserve
• macro - Macroeconomic indicators
• equities - Stock markets
• earnings - Company earnings

Entity Types: org, person, asset

Example Entities:

• Federal Reserve (org)
• Jerome Powell (person)
• S&P 500 (asset)

Why a Taxonomy?

vs. Raw Text

Raw Text	Taxonomy
”Patrick Mahomes questionable for Sunday”	entity.attributes.injury_status = "questionable"
Requires NLP to extract	Directly queryable
Ambiguous meanings	Typed values
No relationships	Explicit graph

vs. Keyword Tags

Keyword Tags	Taxonomy
[“nfl”, “chiefs”, “quarterback”]	category: sports, subcategory: nfl, type: person
Flat structure	Hierarchical
No semantics	Typed relationships
No inference	Traversable graph

Taxonomy API

Get Subcategories

GET /api/entities/subcategories

Returns:

{
  "categories": [
    {
      "category": "sports",
      "count": 450,
      "subcategories": [
        { "name": "nfl", "count": 200 },
        { "name": "nba", "count": 150 }
      ]
    }
  ]
}

Filter by Taxonomy

# All NFL players
GET /api/entities?category=sports&subcategory=nfl&type=person

# All crypto assets
GET /api/entities?category=crypto&type=asset

# All political organizations
GET /api/entities?category=politics&type=org

Building on the Taxonomy

Portfolio by Category

// Get all entities you're exposed to by category
const sports = await fetch('/api/entities?category=sports&limit=100');
const politics = await fetch('/api/entities?category=politics&limit=100');

// Count market exposures per category
const sportsExposure = sports.entities.reduce(
  (sum, e) => sum + e.marketExposures?.length || 0, 0
);

Category Alerts

// Monitor all entities in a category for attribute changes
const politicians = await fetch('/api/entities?category=politics&type=person');

for (const pol of politicians.entities) {
  if (pol.attributes.approval_rating < 40) {
    console.log(`Low approval: ${pol.displayName}`);
    // Check connected markets
  }
}

Extending the Taxonomy

The taxonomy grows as we add coverage:

1. New Category

   Weather, Entertainment, Science

2. New Subcategory

   Premier League under Sports

3. New Entity Type

   Event type for conferences, games

4. New Attributes

   Domain-specific attributes per subcategory

Have a category or entity type you need? Let us know on GitHub.