[
  {
    "date":   "2024-01-15",
    "open":   182.16,
    "high":   184.26,
    "low":    180.93,
    "close":  183.63,
    "volume": 65234100
  },
  
]

• LLM has to compute raw data
• Uses more tokens
• Not designed for agents
• Inconsistent reasoning

{
  "trend": {
    "direction": "uptrend",
    "ma_alignment": "aligned_bullish"
  },
  "momentum": {
    "rsi_zone": "neutral_high"
  },
  "volatility": {
    "regime": "normal"
  }
}

• LLM reads categorical bands it understands
• Lower token usage
• Designed for agents
• Consistent reasoning

Pre-computed

Our data is computed after market close and cached. Zero latency on your request - no indicator math, no delays.

Categorical vocabulary

Responses use terms like oversold, uptrend, and deeply_overvalued - the same vocabulary LLMs use to reason about markets.

Fewer wasted tokens

A full asset summary is a fraction of the tokens you'd need to pass raw OHLCV. Your model gets more context, not more noise.

Multi-source intelligence

Technical, fundamental, and analyst data in one response. No stitching together multiple providers.

Per-asset behavioral context

Historical streaks, medians, and percentiles specific to each asset. Your agent knows what's unusual for this ticker.

5 years of queryable history

Every categorical field, every day, for 5 years. Just connect and query.

Watchlist monitoring

Track saved assets and pull only what changed with /v1/watchlist/changes. Ideal for daily alerts, portfolio briefings, and autonomous monitoring.

Market scanning

Use /v1/search to find oversold names, strong uptrends, rare conditions, or valuation mismatches across the full universe.

Historical precedent

Query /v1/summary with field and band to see when a setup last appeared and what happened after.

{
  "timeframe": "daily",
  "run_date": "2026-03-28",
  "changes": {
    "AAPL": [
      {
        "field": "rsi_zone",
        "from":  "neutral",
        "to":    "oversold"
      },
      {
        "field": "divergence_detected",
        "from":  false,
        "to":    true
      }
    ],
    "TSLA": [
      {
        "field": "macd_state",
        "from":  "contracting_negative",
        "to":    "expanding_positive"
      }
    ],
    "BTCUSD": [
      {
        "field": "squeeze_active",
        "from":  false,
        "to":    true
      }
    ]
  },
  "tickers_checked": 12,
  "tickers_changed": 3
}

{
  "timeframe": "daily",
  "run_date": "2026-03-28",
  "changes": {
    "AAPL": [
      {
        "field": "rsi_zone",
        "from":  "neutral",
        "to":    "oversold"
      },
      {
        "field": "divergence_detected",
        "from":  false,
        "to":    true
      }
    ]
  },
  "tickers_checked": 12,
  "tickers_changed": 1
}

$ curl "https://api.tickerdb.com/v1/summary/AAPL" \
    -H "Authorization: Bearer YOUR_API_KEY"


$ curl -G "https://api.tickerdb.com/v1/search" \
    --data-urlencode 'filters=[{"field":"momentum_rsi_zone","op":"eq","value":"oversold"}]' \
    -H "Authorization: Bearer YOUR_API_KEY"


$ curl https://api.tickerdb.com/v1/watchlist/changes \
    -H "Authorization: Bearer YOUR_API_KEY"

import requests


res = requests.get(
    "https://api.tickerdb.com/v1/summary/AAPL",
    headers={"Authorization": "Bearer YOUR_API_KEY"}
)
data = res.json()


prompt = f"""
Analyze AAPL based on this market data:
{data}

Is this a good entry point?
"""

import { TickerDB } from 'tickerdb';


const client = new TickerDB({ apiKey: 'YOUR_API_KEY' });
const { data } = await client.summary('AAPL');


const prompt = `Analyze AAPL based on this market data:
$${JSON.stringify(data)$}

Is this a good entry point?`;

import "context"
import "github.com/tickerdb/tickerdb-go"


client := tickerdb.NewClient("YOUR_API_KEY")
resp, _ := client.Summary(context.Background(), "AAPL", nil)


fmt.Println(string(resp.Data))
fmt.Println(resp.RateLimits.RequestsRemaining)

{
  "mcpServers": {
    "tickerdb": {
      "command": "npx",
      "args": ["tickerdb-mcp"],
      "env": {
        "TICKERDB_KEY": "YOUR_API_KEY"
      }
    }
  }
}

Once connected, Claude can call get_summary, get_search, get_watchlist_changes, and more - directly from the chat. all MCP tools →

$ clawhub install tickerdb


"How's AAPL looking? Give me the full summary."

"Add NVDA, AAPL, and BTCUSD to my watchlist and flag anything that changed."

"When was NVDA last deep_oversold, and what happened after?"

One install. Your agent gets summaries, search, watchlists, and schema - no config needed. OpenClaw integration guide →

MCP Server

Plug TickerDB into Claude Desktop or any MCP client. Your AI assistant pulls EOD market context from the chat.

setup guide

OpenClaw

One install. Your agent gets summaries, search, watchlists, and schema.

integration guide

Any agent framework

LangChain, LlamaIndex, AutoGen, CrewAI - it's just HTTP. If it can make a GET request, it works.

API reference

Python, Node.js & Go SDKs

Official SDKs with typed responses. Or just use fetch - the API is simple enough.

get your API key