MarketSchema for developers

The market, as an API your agent can reason with.

Agents that read markets. Bots that act on structure. Automations that never sleep. Old school sells terminals to humans — we feed structure to machines: every score explained, every input sourced, every response honest about freshness.

Get a free API key API docs62 indexes · 33 narratives · 586 assets · live now

try itno authbash

# no key needed to try — 500 reads/day anonymous
curl https://api.marketschema.com/v1/asset/NVDA

responsecaptured 2026-06-12T11:42Zjson

{
  "ticker": "NVDA",
  "schema_score": 14,
  "regime_label": "balanced_mixed",
  "confidence": 74,
  "entry_value": 32,
  "freshness": "live",
  "as_of": "2026-06-12T11:27:18.485074+00:00",
  "top_drivers": [
    {
      "index_symbol": "RQRI",
      "index_name": "Risk-Quality Rotation Index",
      "wind": "tailwind",
      "z_score": 0.8155,
      "weight": 0.16
    },
    {
      "index_symbol": "LFSI",
      "index_name": "Liquidity & Financial Stress Index",
      "wind": "tailwind",
      "z_score": 0.5071,
      "weight": 0.21
    },
    {
      "index_symbol": "DCAI",
      "index_name": "Data Center Buildout Index",
      "wind": "tailwind",
      "z_score": 0.2159,
      "weight": 0.31
    }
  ],
  "contract_version": "3.0.0"
}

§ 01

Five primitives. Clear and to the point.

real responses · captured from prod

Schema Scores

One number per asset for how the macro environment lines up behind it — with the drivers that produced it, never just the number.

GET /v1/asset/{ticker}

Schema Scoreslive datajson

{
  "ticker": "NVDA",
  "schema_score": 14,
  "regime_label": "balanced_mixed",
  "confidence": 74,
  "entry_value": 32,
  "freshness": "live",
  "as_of": "2026-06-12T11:27:18.485074+00:00",
  "top_drivers": [
    {
      "index_symbol": "RQRI",
      "index_name": "Risk-Quality Rotation Index",
      "wind": "tailwind",
      "z_score": 0.8155,
      "weight": 0.16
    },
    {
      "index_symbol": "LFSI",
      "index_name": "Liquidity & Financial Stress Index",
      "wind": "tailwind",
      "z_score": 0.5071,
      "weight": 0.21
    },
    {
      "index_symbol": "DCAI",
      "index_name": "Data Center Buildout Index",
      "wind": "tailwind",
      "z_score": 0.2159,
      "weight": 0.31
    }
  ],
  "contract_version": "3.0.0"
}

Risk Scores

Market-wide risk gauges by family — liquidity, credit, volatility — computed from the same published indexes your agent can audit.

GET /v1/gauges

Risk Scoreslive datajson

{
  "gauges": [
    {
      "gauge_key": "liquidity",
      "label": "Liquidity & Stress",
      "value": -0.05847155555555556,
      "signal": "neutral"
    },
    {
      "gauge_key": "credit",
      "label": "Credit",
      "value": -0.05265661111111112,
      "signal": "neutral"
    },
    {
      "gauge_key": "rates",
      "label": "Rates & Curve",
      "value": -0.005137444444444442,
      "signal": "neutral"
    }
  ],
  "computed_at": "2026-06-12T11:42:43.596255+00:00"
}

Indexes

62 proprietary macro indexes, every formula published, every input sourced. The raw structure layer.

GET /indices

Indexeslive datajson

[
  {
    "symbol": "ACSI",
    "name": "AI Compute Capex Index",
    "current_value": 0.138589
  },
  {
    "symbol": "ALVI",
    "name": "Auto Lending Volume Index",
    "current_value": -1.272806
  },
  {
    "symbol": "ASPI",
    "name": "AI Buildout Bottleneck Index",
    "current_value": -0.271455
  }
]

Narratives

33 tracked market narratives with momentum and state — what the market is trading on, machine-readable.

GET /v1/trust/narrative-graph

Narrativeslive datajson

{
  "narrative_count": 33,
  "items": [
    {
      "slug": "war-oil-shock",
      "label": "War / Oil Shock",
      "state": "Dormant",
      "momentum_1w": 11
    },
    {
      "slug": "ai-infrastructure-buildout",
      "label": "AI Infrastructure Buildout",
      "state": "Weakening",
      "momentum_1w": -10
    },
    {
      "slug": "power-bottleneck",
      "label": "Power Bottleneck",
      "state": "Dormant",
      "momentum_1w": -14
    }
  ]
}

Themes

Per-asset theme exposure with direction and impact — which stories move this ticker, and how hard.

GET /api/assets/equity:{ticker}

Themeslive datajson

{
  "ticker": "NVDA",
  "related_themes": [
    {
      "slug": "semiconductor-supercycle",
      "name": "Semiconductor Supercycle",
      "score": 57,
      "direction": "tailwind",
      "impact": 0.0756
    },
    {
      "slug": "data-center-thermal-bottleneck",
      "name": "Data Center Thermal Bottleneck",
      "score": 74,
      "direction": "tailwind",
      "impact": 0.0756
    }
  ]
}

§ 02

Agents, brought to life.

MCP server live now

A hosted Model Context Protocol server, running now. Point Claude at it and your agent can pull the market regime, audit an index's drivers, or fetch a full decision packet — structured, sourced, and explainable, not scraped. There's an OpenAI function-calling manifest for everything else, and llms.txt so models can orient themselves.

connect claudehosted · handshake verifiedbash

# Claude Code — one command, live in seconds
claude mcp add --transport http marketschema \
  https://marketschema-mcp-production.up.railway.app/mcp

openai toolsno authbash

# OpenAI function-calling manifest — 6 tools, no auth
curl https://api.marketschema.com/v1/agent/openai-tools

what your agent getslive server v1.27.1json

{
  "server": "MarketSchema MCP",
  "tools": [
    "get_market_regime",
    "check_trade",
    "get_index_detail",
    "get_signal_changes",
    "get_market_pulse",
    "get_decision_packet"
  ]
}

§ 03

The edge for trading bots.

price tells you what · structure tells you why

bot_loop.pyworks todaypython

import requests

API = "https://api.marketschema.com"
KEY = {"X-API-Key": "ms_..."}  # free tier: 1 key, 500 reads/day

# 1. the morning read — regime + risk gauges
gauges = requests.get(f"{API}/v1/gauges", headers=KEY).json()

# 2. score the book — up to 25 tickers per call
book = requests.post(f"{API}/v1/check/batch", headers=KEY,
    json={"tickers": ["NVDA", "ARM", "XOM"]}).json()

# 3. act only when structure agrees with price
for asset in book["results"]:
    if asset["schema_score"] < 45:   # headwind band
        print(asset["ticker"], "— structure against you")

Every price feed tells your bot what just happened. MarketSchema tells it whether the macro structure agrees — before it sizes the trade. Batch-score your whole book in one call, stream regime changes over SSE, and pull the /v1/agent/briefing every morning. Automations that never sleep, reading a market that never stops explaining itself.

live streamSSE · verifiedbash

# live updates over SSE — keepalive verified
curl -N "https://api.marketschema.com/v1/stream?channels=indices,conditions"

Python SDK with a paper-bot framework (guardrails, journal, replay) ships to PyPI shortly — the raw HTTP above works today.

§ 04

Simple tiers. Honest quotas.

Free Developer

· 1 API key
· 500 asset reads / day
· 20 batch calls / day
· 60 req/min

Get a free key →

Growth

$69/mo

· 10 API keys
· 100k asset reads / day
· SSE streaming
· 600 req/min

Start Growth →

Enterprise

Custom

· Redistribution rights
· Custom quotas
· Commercial SLA
· Integration support

Talk to us →

OpenAPI 3.1 llms.txt llms-full.txt Status Full referenceresponses captured from production · refreshed at build

The market, as an API your agent can reason with.

Get a free API key API docs62 indexes · 33 narratives · 586 assets · live now

{ "ticker": "NVDA", "schema_score": 14, "regime_label": "balanced_mixed", "confidence": 74, "entry_value": 32, "freshness": "live", "as_of": "2026-06-12T11:27:18.485074+00:00", "top_drivers": [ { "index_symbol": "RQRI", "index_name": "Risk-Quality Rotation Index", "wind": "tailwind", "z_score": 0.8155, "weight": 0.16 }, { "index_symbol": "LFSI", "index_name": "Liquidity & Financial Stress Index", "wind": "tailwind", "z_score": 0.5071, "weight": 0.21 }, { "index_symbol": "DCAI", "index_name": "Data Center Buildout Index", "wind": "tailwind", "z_score": 0.2159, "weight": 0.31 } ], "contract_version": "3.0.0" }

{ "gauges": [ { "gauge_key": "liquidity", "label": "Liquidity & Stress", "value": -0.05847155555555556, "signal": "neutral" }, { "gauge_key": "credit", "label": "Credit", "value": -0.05265661111111112, "signal": "neutral" }, { "gauge_key": "rates", "label": "Rates & Curve", "value": -0.005137444444444442, "signal": "neutral" } ], "computed_at": "2026-06-12T11:42:43.596255+00:00" }

[ { "symbol": "ACSI", "name": "AI Compute Capex Index", "current_value": 0.138589 }, { "symbol": "ALVI", "name": "Auto Lending Volume Index", "current_value": -1.272806 }, { "symbol": "ASPI", "name": "AI Buildout Bottleneck Index", "current_value": -0.271455 } ]

{ "narrative_count": 33, "items": [ { "slug": "war-oil-shock", "label": "War / Oil Shock", "state": "Dormant", "momentum_1w": 11 }, { "slug": "ai-infrastructure-buildout", "label": "AI Infrastructure Buildout", "state": "Weakening", "momentum_1w": -10 }, { "slug": "power-bottleneck", "label": "Power Bottleneck", "state": "Dormant", "momentum_1w": -14 } ] }

{ "ticker": "NVDA", "related_themes": [ { "slug": "semiconductor-supercycle", "name": "Semiconductor Supercycle", "score": 57, "direction": "tailwind", "impact": 0.0756 }, { "slug": "data-center-thermal-bottleneck", "name": "Data Center Thermal Bottleneck", "score": 74, "direction": "tailwind", "impact": 0.0756 } ] }

import requests API = "https://api.marketschema.com" KEY = {"X-API-Key": "ms_..."} # free tier: 1 key, 500 reads/day # 1. the morning read — regime + risk gauges gauges = requests.get(f"{API}/v1/gauges", headers=KEY).json() # 2. score the book — up to 25 tickers per call book = requests.post(f"{API}/v1/check/batch", headers=KEY, json={"tickers": ["NVDA", "ARM", "XOM"]}).json() # 3. act only when structure agrees with price for asset in book["results"]: if asset["schema_score"] < 45: # headwind band print(asset["ticker"], "— structure against you")