What Can You Build with a Macro Data API?

Helious Desk · 31 October 2026 · 7 min read

Three things, mostly: a macro system that knows which regime the economy and the rates market are in, a trading algorithm that reacts to the print in seconds, and a macro forecast that starts from what markets already price. How fast you can build any of them depends on one thing: whether the data arrives already read. On Helious it does. Every US release lands scored against its own history, every Treasury auction with its tail and a verdict, and every headline with one score for bonds and another for stocks.

1. What makes a macro data API good to build on?

Most data APIs hand you a number and leave the judging to you. A system needs the judgement as well, in a field it can test. Helious ships five things with every number:

• A score on arrival. Each release carries the actual, the forecast, the previous and a sigma: the surprise in standard deviations against that release's own history. A CPI miss and a payrolls beat sit on one ruler.
• Two channels on every news item. score for bonds and equity_score for stocks, each from -10 to +10, with a topic and a one-line plain-English reading.
• The context. Auctions carry their 12-auction averages, release levels their percentile across decades, the curve its regime and the time it flipped.
• A measured clock. Helious captures each US print itself and publishes how long that took. In the 7 days to 31 October the median was 12 seconds, and nine in ten landed within 67.
• Honesty about gaps. A print your plan cannot see yet says status: delayed with the time it reveals. The market read names a cause only when a scored release printed in the window, and lists what it does not cover.

And one shape everywhere: the same fields on every plan, over REST, over the WebSocket stream and through the MCP server for AI agents. Code written on the free tier keeps working after an upgrade.

2. What does one item of Helious data look like?

Here is a geopolitical headline from the Helious feed on the morning of 31 October 2026, as /v1/feed returned it (the wire's body text is left out):

{
  "id": "30b21536bed4d7a63bbc36d28ac63c67",
  "kind": "headline",
  "title": "Are Houthis using Russian satellites as Saudi Arabia faces hits?",
  "category": "geo",
  "created_at": "2026-10-31T08:08:30+00:00",
  "carried_at": "2026-10-31T08:18:55+00:00",
  "score": -2,
  "direction": "BEARISH BONDS",
  "equity_score": -2,
  "equity_direction": "BEARISH EQUITIES",
  "topic": "geo",
  "helious_read": "More Houthi attacks on Saudi oil sites could lift oil prices, which is bad for both bonds and shares.",
  "thread_id": "06e42d9e833f660e"
}

A program can act on every field. The story is read twice: -2 for bonds, because dearer oil feeds inflation, and -2 for stocks. helious_read gives the reason in one line. The wire re-headlined this article 22 minutes later, and Helious folds a re-headlined article into the item already on the tape: the id stays, the title updates, and the stream sends it again with refresh: true. The thread_id is the same for every version of a story, so a system that counts by it counts each story once, even when a rewrite arrives as a new item.

The two channels earned their keep seven hours earlier on the same theme. A drone the Saudi-led coalition intercepted read +2 for bonds and -2 for stocks: a scare that sends money to safety rather than a supply shock. A system reading one score would have seen two stories pointing the same way. With both channels it sees them cut opposite ways for bonds.

3. How do you build a macro system on it?

A macro system classifies the state of the economy and the market, then decides what that state favours. Build it in layers, each one or two calls:

• Growth: /v1/series/{slug} for GDP, GDPNow, the ISM surveys and retail sales.
• Inflation: /v1/inflation, every measure with its year rate and its 1, 3 and 6-month annualised rates already worked out.
• Labour: /v1/jobs, payrolls by sector summing to the headline exactly, plus ADP, claims and JOLTS.
• Policy: /v1/rate-odds and /v1/rate-path for what the futures price, and the Fed roster for who votes.
• Rates and curve: /v1/rates and /v1/curve, with the regime named: bull or bear, steepener or flattener.
• Cross-asset risk and events: /v1/context, the volatility and correlation boards, the scored feed and the Strait of Hormuz transit count.

A short Python script reading three of those layers printed cooling steady tightening on 31 October. Core PCE's 3-month annualised rate of 2.90% sat under its 3.12% year rate, payrolls averaged 50,700 a month over 3 months against 41,300 over 12, and the fed funds strip priced 54 basis points of hikes through December 2027. The code is on the use cases page, and all three calls answer with no key.

4. How do you build a trading algorithm on it?

An event-driven algorithm needs the schedule, the print the moment it lands, a size for the surprise and a filter for the regime. In order:

1. Know when. /v1/calendar gives every event's time, impact and forecast up to a year ahead.
2. Hear the print. The WebSocket at wss://api.helious.io/v1/stream pushes each release, auction and headline as it lands. Pro runs it live; every plan below runs the same messages once a minute.
3. Size the surprise. The sigma on a calendar row means one threshold works across CPI, payrolls and the PMIs.
4. Trade the auction. /v1/auctions returns the tail against the when-issued yield, the bid to cover against its 12-auction average, the dealer share and a verdict.
5. Filter the headlines. Score on the channel you trade, dedupe on thread_id.
6. Check the regime. /v1/context says whether the session is event-driven, flow-driven or quiet, and whether yields and stocks are trading a growth or a rates story.

Two honest limits before it trades money. Backtest the surprises on the consensus record, as far back as your plan reads (10 years on Ultimate); Helious's own captured prints and scores start in mid-2026, so run the scores forward. And a score is Helious's reading of an item, not a proven signal: test it against your own fills first.

5. How do you develop a macro forecast with it?

A forecast is only useful against a baseline, and Helious gives you three. The first is what markets price: on 31 October /v1/rate-odds put 70% odds on a 25 basis point hike on 9 December, and /v1/rate-path carries every SOFR and fed funds contract with its rate now and six and twelve months ago. The second is the nowcasts and the run rates: core CPI's 3-month annualised rate of 3.41% works out to about 0.28% a month, against a 2.57% year rate. The third is the consensus, which sits on the calendar until the number prints.

Then grade yourself. When the US GDP price index printed 2.2 on 29 October against a 3.5 consensus, Helious scored it a 3.71 sigma miss. A miss that size is where a forecast that disagreed with the consensus pays for itself, and the sigma lets you log your own error on the same ruler as everyone else's. Add /v1/treasury-issuance for supply, and the digests of FOMC minutes and Fed speeches for the reaction function: each states a hawkish or dovish lean, with quotes checked word for word.

6. Which endpoints does each job use?

The full map, every endpoint against every job with the plan that sees it live, is on helious.io/developers/use-cases.

7. Can an AI agent build these instead of code?

Yes. Point Claude, ChatGPT, Cursor or any MCP client at https://api.helious.io/mcp and the same data arrives as 26 tools. In Claude Code that is one line: claude mcp add --transport http helious https://api.helious.io/mcp. Ask it to classify the macro regime and it can call get_market_context, get_inflation, get_jobs and get_rate_odds, then quote the sentence each one opens with. The AI setup post walks through it.

8. What does it cost, and how does it compare?

Starting costs nothing and needs no key. A free account key reaches 7 days back with 100 credits a month. Starter is $14.99 a month with live prints and 12 months of history. Pro is $39.99 with every price live and the live stream. Ultimate is $99 with 10 years of history, no credit cap and commercial use. A data call is 1 credit.

For the macro job, $14.99 is the lowest published paid price among the APIs we compared in October 2026. Financial Modeling Prep starts at $29 a month, Alpha Vantage at $49.99, Finnhub keeps economic data on a $3,500 a month plan, and Trading Economics quotes on request. FRED is free and is the official record, but carries no forecast, no surprise score and no auction read. The detail is in the market data API comparison.

9. Where do I start?

Run one call with no account: curl "https://api.helious.io/v1/calendar?days_back=1&country=US". Then open the use cases page for the job you are building, with its first call and a working code sample. The full reference is at helious.io/developers.