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// express.js

Market context for
Express apps.

TickerDB Node.js SDK + Express. Pre-computed market data in your routes, zero infrastructure to maintain.

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// setup

Install the SDK.

Two dependencies. No WebSocket connections, no streaming infrastructure — just a Node.js package that calls the TickerDB HTTP API.

terminal 
npm install tickerdb express

Set TICKERDB_KEY in your environment variables and pass it into the client when you initialize it.

// code example

Two routes, five minutes.

Initialize the client once, call methods from your route handlers. Every method returns an object ready for res.json.

javascript server.js 
import express from "express";
import { TickerDB } from "tickerdb";

const app = express();
const client = new TickerDB({ apiKey: process.env.TICKERDB_KEY });

app.get("/summary/:ticker", async (req, res) => {
  const { data } = await client.summary(req.params.ticker);
  res.json(data);
});

app.get("/events/:ticker", async (req, res) => {
  const { data } = await client.summary(req.params.ticker, {
    field: req.query.field,
    band: req.query.band,
  });
  res.json(data);
});

app.listen(3000);
// state changes

Track state changes effortlessly.

Most market data APIs return point-in-time snapshots. TickerDB tracks state transitions — your agent sees what changed, not just what is.

javascript server.js 
app.get("/watchlist/changes", async (req, res) => {
  const { data } = await client.watchlistChanges();
  res.json(data);
});
json 
{
  "timeframe": "daily",
  "run_date": "2026-03-28",
  "changes": {
    "AAPL": [
      {
        "field": "rsi_zone",
        "from": "neutral",
        "to": "oversold"
      },
      {
        "field": "trend_direction",
        "from": "uptrend",
        "to": "downtrend"
      }
    ]
  },
  "ticker_context": {
    "AAPL": {
      "last_changed_date": "2026-03-28"
    }
  },
  "tickers_checked": 2,
  "tickers_changed": 1
}
// ai integration

Feed an AI agent.

TickerDB's categorical-first output is designed for LLMs. Feed a summary directly into a prompt — the model already understands terms like "oversold" and "strong_uptrend" without extra context.

javascript server.js 
import Anthropic from "@anthropic-ai/sdk";

const anthropic = new Anthropic();

app.get("/briefing/:ticker", async (req, res) => {
  const { data } = await client.summary(req.params.ticker);

  const msg = await anthropic.messages.create({
    model: "claude-sonnet-4-20250514",
    max_tokens: 1024,
    messages: [{
      role: "user",
      content: `Analyze this stock data:\n${JSON.stringify(data)}`,
    }],
  });

  res.json({ analysis: msg.content[0].text });
});
// available methods

What you can call.

Every tool returns pre-computed market-state data: categorical facts plus supporting metadata your agent can reason about immediately.

get_summary

Full market-state snapshot for a single asset: trend, momentum, volatility, volume, extremes, fundamentals, and support/resistance.

get_search

Multi-field filtering across all assets. Build complex queries with arbitrary filter combinations.

get_schema

All queryable fields with types, values, and descriptions. Always free.

get_watchlist

Latest EOD summary data for all tickers in your saved watchlist.

get_watchlist_changes

Field-level diffs for your watchlist since the last pipeline run.

create_webhook

Register a webhook URL for watchlist change notifications.

list_webhooks

List your registered webhook URLs.

delete_webhook

Remove a registered webhook.

full tool reference
// what your agent sees

What your agent sees.

Every tool returns categorical facts — not raw OHLCV data. Your agent can branch on "oversold" without needing to know what RSI > 70 means.

json 
{
  "ticker": "NVDA",
  "data_status": "eod",
  "as_of_date": "2026-04-11",
  "trend": {
    "direction": "strong_uptrend",
    "ma_alignment": "aligned_bullish"
  },
  "momentum": {
    "rsi_zone": "overbought",
    "macd_state": "expanding_positive",
    "direction": "accelerating"
  },
  "volatility": {
    "regime": "normal",
    "regime_trend": "stable"
  },
  "fundamentals": {
    "valuation_zone": "fair_value",
    "pe_vs_historical_zone": "premium",
    "last_earnings_surprise": "beat"
  }
}
// why tickerdb + express

Built for how agents consume data.

Market-state data, less prompt engineering

Responses like "rsi_zone": "oversold" are already in a format the model understands. No need to explain what RSI > 70 means.

Compact responses

Tool-call context windows are limited. TickerDB responses are a fraction of the tokens you'd need to pass raw OHLCV data.

Pre-computed daily

No infrastructure to maintain. No cron jobs, no indicator math. TickerDB handles computation and syncing.

Start building.

Try for free. No credit card required.

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