Live NASDAQ Industrial (INDS) Price Today — API endpoint and sample response

You need to display the live NASDAQ Industrial (INDS) level in a dashboard, wire it into an alert, or backfill a few days of INDS prints for a chart. In the next few minutes, you’ll pull the latest INDS value from the Indices-API latest endpoint, parse the price and timestamp fields your code actually needs, handle non-trading days, and set up a clean pathway to backfill recent history.

What you will ship

By the end of this guide you will:

Illustration: Live NASDAQ Industrial (INDS) Price Today — API endpoint and sample response

Query the live INDS level from the latest endpoint with a working curl command.
Extract the index price and timestamp from a Python snippet suitable for dashboards, alerts, or ETL jobs.
Know which field is the “index level” (USDINDS) versus the inverse (INDS) and how base currency affects your math.
Understand update frequency, caching, and why you shouldn’t set end_date to “today” on historical/time-series calls.

All examples use the NASDAQ Industrial symbol INDS. If you’re adding more symbols alongside INDS, confirm their codes on the Indices-API Supported Symbols page before you ship.

Get the live INDS price: latest endpoint

The latest endpoint returns a dictionary of rates keyed by symbol, with a USD base. For INDS, you’ll see both USDINDS and INDS in the rates object. Use rates.USDINDS as the “index level” quoted in USD per index unit. The inverse (rates.INDS) is also present and can be useful for conversions and cross-computations.

curl you can paste

Replace YOUR_API_KEY with your key. Authentication is via the access_key query parameter.

curl "https://indices-api.com/api/latest?access_key=YOUR_API_KEY"

The response below is an actual sample you can rely on for field names and structure. Use these exact keys when parsing JSON (case-sensitive).

{"data":{"success":true,"timestamp":1791592740,"date":"2026-10-10","base":"USD","rates":{"INDS":7.884930478568e-5,"USD":1,"USDINDS":12682.419999999953}}}

What the fields mean (and which ones you’ll actually use)

data.success: Boolean that indicates the request was successful.
data.timestamp: Unix epoch seconds (UTC). Use this to time-stamp the print and for cache invalidation.
data.date: Calendar date of the print in YYYY-MM-DD, aligned to UTC. On weekends/holidays this may be the last market date with official settlement.
data.base: The base currency for all rates. Indices-API quotes indices relative to USD by default.
data.rates.USDINDS: The INDS level expressed as USD per 1 index unit. This is the live “price today” you’ll put on a screen or alert against.
data.rates.INDS: The inverse (1 USD in INDS units). Most dashboards won’t display this; keep it if you need conversions.

Update frequency depends on plan tier; the latest endpoint refresh cadence is typically in the range of every 60 minutes down to every 10 minutes. If you’re building polling logic, account for that cadence, respect your cache TTLs, and avoid hammering the endpoint faster than it updates. See the Indices-API Documentation for up-to-date details on refresh intervals and response structure.

Minimal Python example to read the INDS print

This snippet fetches the latest payload, validates the success flag, and extracts the live INDS level (rates.USDINDS), the UTC timestamp, and the ISO date string for display or storage.

import os
import time
import requests

API_KEY = os.getenv("INDICES_API_KEY", "YOUR_API_KEY")
URL = "https://indices-api.com/api/latest"

def get_latest_inds():
    params = {"access_key": API_KEY}
    r = requests.get(URL, params=params, timeout=10)
    r.raise_for_status()
    payload = r.json()

    # Defensive parsing based on the REAL API RESPONSE structure in this article
    data = payload.get("data", {})
    if not data.get("success"):
        raise RuntimeError(f"API returned success=false: {payload}")

    base = data.get("base")
    timestamp = data.get("timestamp")
    date_str = data.get("date")
    rates = data.get("rates", {})

    # The displayed index level in USD per index unit:
    inds_usd = rates.get("USDINDS")
    if inds_usd is None:
        raise KeyError("USDINDS not found in response rates")

    # Optional: the inverse relationship (1 USD in INDS units) if you need it
    inds_inverse = rates.get("INDS")

    return {
        "base": base,
        "timestamp": timestamp,
        "date": date_str,
        "inds_level_usd": inds_usd,
        "inds_inverse": inds_inverse
    }

if __name__ == "__main__":
    latest = get_latest_inds()
    # Convert timestamp to readable UTC for logs
    ts_readable = time.strftime("%Y-%m-%d %H:%M:%S", time.gmtime(latest["timestamp"]))
    print(f"[{ts_readable} UTC] INDS = {latest['inds_level_usd']:.6f} USD per index unit (base={latest['base']})")

Production notes:

Always treat the timestamp as authoritative for freshness. The date field is convenient for charts and labels; the timestamp is better for cache keys.
If your UI polls, set a refresh interval aligned with your plan’s update frequency. Cache the last good payload and fall back gracefully if a network hiccup occurs.
If you downstream the value into calculations or alerts, use decimal or scaled integers to avoid floating-point drift for thresholds.

Expressing INDS quotes and base currency

Indices-API quotes are relative to USD by default, and the response includes both the direct quote (USDINDS) and the inverse (INDS). For dashboards and alerting, you’ll almost always display data.rates.USDINDS as the live “price today.” If you instead multiply or divide by the inverse, be deliberate about direction to avoid flipping your sign or unit unexpectedly.

Where this matters:

Displaying price: Use USDINDS directly as the index level in USD.
Calculating percentage changes: Compute deltas between two USDINDS values from different times.
Converting units: If you are building a cross-asset conversion, the presence of both USDINDS and INDS avoids taking your own reciprocal and accumulating rounding error.

If you add more symbols later (e.g., other NASDAQ families or regional indices), verify their tickers on the Indices-API Supported Symbols page. Do not guess symbol codes.

Backfilling and day-over-day change with INDS

To compute a clean day-over-day percentage move or to draw a small multi-day sparkline, you’ll need a second reference value. Indices-API provides multiple endpoints you can use alongside latest:

Historical rates: query a single past date’s rates when you need “the last close” for comparison.
Time-series: retrieve daily snapshots between two dates when you need a window (e.g., last 7 business days).
OHLC: get open/high/low/close for a specific date when your chart or alert wants official OHLC rather than the general-day rate.

Practical constraints that matter when you wire this up:

Non-trading days and weekends: If you request a date that falls on a weekend or a market holiday, expect to receive the most recent available trading day’s data or for the requested date to map to the previous business day in your logic. Design your UI label to reflect the actual date field in the payload to avoid confusion (“Last update: YYYY-MM-DD”).
end_date cannot be today: When you call time-series or date-bounded endpoints, set end_date to the last fully complete market day. For example, if it’s Friday and the market is open, use Thursday as the end_date for a clean backtest window.
UTC alignment: All timestamps are Unix seconds and align to UTC. This simplifies cache keys and cross-region services. Convert to the user’s local time only in the presentation layer.

For exact parameters and permissible date ranges, refer to the Indices-API Documentation. Keep your date window small for faster responses and to stay within your plan’s limits. The MCP page provides additional context for market-calibrated pricing if you’re building systematic research or robust alerting.

Building a robust INDS widget or alert

Here’s a dependable outline you can lift into your app:

At boot, call latest and cache the full payload with a TTL aligned to your plan’s update cadence.
Extract USDINDS and the UTC timestamp. Render the level, and show an “as of” label using the timestamp.
If you need a day-over-day change, request the previous completed market day’s value (historical rates or OHLC) once and cache it separately. Compute (latest_usdinds - prior_usdinds) / prior_usdinds.
On weekends/holidays, pin the comparison date to the last trading day available, and keep the UI label explicit.
Backpressure: If you hit a transient 5xx or network timeout, reuse the cached latest for display, and schedule a retry with exponential backoff.

For developers embedding multiple indices in the same panel (say, INDS alongside NASDAQ Composite), ensure each symbol is valid in Symbols first. Do not assume the composite’s code; confirm it before coding your parser.

Why INDS data is interesting for builders

NASDAQ Industrial tracks industrial-sector names listed on NASDAQ, which often move differently from high-beta tech cohorts. In portfolio dashboards and smart IoT-enabled kiosks, showing INDS next to broader composites helps users see sector rotation in real time. If you’re instrumenting alerts, INDS can flag cyclical shifts earlier than a broad index does, complementing analytics pipelines that already watch economic indicators.

When you process data programmatically, keep things deterministic: prefer the official OHLC endpoint for end-of-day analytics, use latest only for “now” surfaces and open-session alerting, and make sure your storage schema records base, timestamp, and the exact rate key (USDINDS) you used to compute derived metrics. That audit trail becomes critical when you compare INDS behavior to other indices in backtests or feature engineering.

Caching, retries, and cost control

For production reliability and predictable spend:

Cache the last good latest payload and serve it for the duration of your plan’s refresh interval. Most dashboards won’t benefit from polling faster than the data updates.
Use conditional retries with exponential backoff. Stagger cron jobs across services if you operate at scale.
Log the timestamp and base alongside the price; this helps diagnose discrepancies across environments and makes reconciliation straightforward.
If you need to precompute small time windows for charts, do it server-side on a schedule using the time-series endpoint and store the result. Frontends can request a compact, pre-aggregated object.

Plan note: A PRO subscription is $49.99/month with a 7-day trial. Choose a polling cadence and historical window size appropriate for your plan. Consult the Documentation for details before you finalize your scheduler.

Troubleshooting INDS integration

Missing USDINDS in rates: Ensure you’re looking under data.rates and not a different nesting level. Validate that data.success is true before dereferencing rates.
Stale prints on weekends: The date will reflect the most recent completed market day. Consider adding a “Market closed” badge when today is a weekend/holiday and the timestamp predates today’s session.
Decimals and UI rounding: Store the raw float or decimal and handle formatting at the edge. For example, format to two decimals for a headline, but keep the unrounded value for PnL and alert thresholds.
Time-series “today” errors: Do not use today as end_date; bind to the last completed market day. If you need intraday candles, use the intraday/interval endpoints described in the docs.

Security and platform tips

Keep your access_key server-side. If you need client-side requests, proxy through your backend and enforce per-user quotas there.
Normalize to UTC everywhere in storage. Convert to user locale only at render time.
Back up your symbol map from Symbols and lock it as a static asset to avoid sudden UI mismatches when adding new coverage.

Next steps: Add OHLC and recent history

If today’s widget is stable, extend it as follows:

OHLC endpoint for yesterday’s close: request the open-high-low-close/YYYY-MM-DD endpoint with access_key and parse rates.INDS to obtain that day’s OHLC block. Use close for your baseline comparison.
Time-series endpoint for a rolling window: request a short window (e.g., the last 7–14 business days) and compute percent changes and streaks on the server for a lightweight client payload.

Do not set your end_date to today for time-series. Pick the last completed market day to ensure your window contains fully settled data. For endpoint parameters and limits, follow the Documentation.

Reference links and where to get your key

Sign up to get your access_key: Register
Endpoint and parameter reference: Documentation
Verify symbol codes (including INDS): Symbols
Market-calibrated pricing overview: MCP

FAQ

Which field is the actual INDS index level?
Use data.rates.USDINDS from the latest payload as the “price today” (USD per index unit). The data.rates.INDS key is the inverse.

Why does the date show yesterday on a weekend?
Indices settle on trading days. On weekends and market holidays, the latest payload reflects the most recent completed session. Display the timestamp and date explicitly to avoid confusion.

Can I set end_date to today on time-series to include partial data?
No. Use the last completed market day as end_date. For intraday intervals, see the intraday/interval endpoints in the Documentation.

How often does the latest INDS value update?
Update cadence depends on your plan. The latest endpoint is updated approximately every 60 minutes down to every 10 minutes depending on tier. Align your polling and caching accordingly.

I see small differences across environments. What should I log?
Log the full trio of base, timestamp, and USDINDS. These three fields are enough to reconcile most discrepancies and to ensure deterministic calculations across services.

Ready to put a live INDS tile on your dashboard and ship an alert before the next session opens? Grab your key and start with the latest endpoint today: Register. For endpoint specifics and additional capabilities like OHLC and time-series backfills, check the Documentation, and verify your symbols on the Symbols page.

Ready to use Indices-API?

Try it now!