AletaIndex Narrative Intelligence API

Give your AI agent a financial narrative brain.

AletaIndex tracks how financial stories evolve across thousands of news sources in real time — clustering articles into structured narratives, measuring sentiment momentum, and mapping narrative risk across portfolios. Available for 109 tickers across all major sectors.

Instead of raw news feeds or simple sentiment scores, your agent gets narrative-level intelligence: what the market is talking about, how strongly, and whether it's shifting.

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What Your Agent Can Do

You: "What narratives are driving NVDA right now? Any sentiment shifts?"

Agent: NVDA is currently dominated by two narratives:
  1. "AI Infrastructure Supercycle" — 47 articles, sentiment +0.68, trending up
  2. "Export Control Headwinds" — 23 articles, sentiment -0.41, stable

  Sentiment on "Export Control Headwinds" has improved +0.12 over the past week,
  suggesting the market is pricing in less risk from the latest policy signals.

No prompt engineering required. The agent knows how to query the data automatically.

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Three Ways to Integrate

Option A — Smithery (Zero install, recommended)

One click. Works with Claude, Cursor, Windsurf, and any MCP-compatible agent — no local setup required. → Add on Smithery

Option B — MCP Server via PyPI (Local install)

One-line config via uvx. Works with Claude Code, Cursor, Windsurf, and any MCP-compatible agent. → MCP Quickstart

Option C — REST API

Direct HTTP calls. Works with any language or framework. → API Reference

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Pricing

Tier	Tickers	History	Credits	Price
Free Trial	10 tickers	90 days	500 (one-time)	Free, 7 days
Plus	All 109 tickers	180 days	2,500/month	$99/mo
Scale	All 109 tickers	Full history	Custom	Custom — contact us

Free tickers: TSLA NVDA AAPL MSFT AMZN GOOGL META AMD NFLX JPM

→ Get your API key

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Quick Example

import requests

headers = {"X-API-Key": "nk_your_key_here"}

response = requests.get(
    "https://aletaindex-narrative.com/v1/narratives/comprehensive",
    headers=headers,
    params={"tickers": "NVDA", "from_date": "2026-05-01", "to_date": "2026-05-10"},
)

data = response.json()
for narrative in data["results"][0]["global_narratives"]:
    print(narrative["title"], narrative["sentiment"]["avg_sentiment"])

Example response (truncated):

{
  "results": [
    {
      "ticker": "NVDA",
      "global_narratives": [
        {
          "narrative_id": 142,
          "title": "AI Infrastructure Supercycle",
          "dominance_score": 0.847,
          "is_active": true,
          "daily_topics": [
            {
              "event_date": "2026-05-10",
              "article_count": 14,
              "sentiment": {
                "avg_sentiment": 0.71,
                "sentiment_label": "Positive",
                "trajectory": "Escalating"
              }
            }
          ]
        },
        {
          "narrative_id": 89,
          "title": "Export Control Headwinds",
          "dominance_score": 0.312,
          "is_active": true,
          "daily_topics": [
            {
              "event_date": "2026-05-10",
              "article_count": 6,
              "sentiment": {
                "avg_sentiment": -0.38,
                "sentiment_label": "Negative",
                "trajectory": "Stable"
              }
            }
          ]
        }
      ]
    }
  ]
}

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Documentation

• API Reference — endpoints, parameters, response schemas
• Data Model — narrative hierarchy explained
• MCP Quickstart — agent integration in 2 minutes
• Direct API Guide — REST integration guide
• Example Prompts — prompt templates for trading agents