An economic calendar API looks simple until the first Friday of the month. A payrolls number prints at 8:30 a.m. Eastern, your bot picks up the release, and three things go wrong at once. The timestamp you stored is off by an hour because daylight saving shifted the UTC offset. The "prior" value no longer matches what you recorded last month, because the agency revised it. And the candle you tagged as the NFP bar turns out to be the minute before anything happened. None of these are exotic bugs. They're the default outcome of treating macro releases like any other JSON feed.
This guide works through the three failure modes in order: normalizing release times to UTC, handling the first print versus later revisions, and joining each event to the exact OHLCV bar it moved.
What an economic calendar API actually returns#
SiftingIO exposes the calendar at a single REST endpoint:
curl -H "X-API-Key: $SIFTING_KEY" \
"https://api.sifting.io/v1/fnd/economic-calendar"
It covers 25 US event types sourced from US statistical agencies: CPI, nonfarm payrolls, FOMC rate decisions, jobless claims, retail sales, and the rest of the prints that move FX, rates, and equity prices. Each event carries an impact tier (low, medium, or high), so a bot can key its logic to the handful of releases that matter and ignore the noise. Pagination is cursor-based, like every other /v1/fnd endpoint: read meta.next_cursor from the response and pass it back as ?cursor until it returns null.
Two conventions matter more than the full field list. Scheduled times come back as RFC 3339 UTC timestamps, so the timezone work described below is about your code, not about parsing. And the response's meta block carries an as_of timestamp telling you when the snapshot was generated, which becomes useful the moment you start tracking revisions.
Normalize to UTC once, at the edge#
US agencies schedule releases in Eastern time. CPI and NFP land at 8:30 a.m. ET, which is 12:30 UTC in summer and 13:30 UTC in winter. Any pipeline that hardcodes "12:30 UTC" breaks twice a year, silently, and the damage is hard to spot because the events still arrive, just an hour away from where your code expects them.
One rule prevents this whole class of bug: convert to UTC at the edge and never store a local time. The calendar already hands you UTC, so ingest it as-is, compare it against bar timestamps in UTC, and convert to a display timezone only in the UI layer. If a human wants to see "8:30 a.m. ET", that's a rendering concern.
A subtlety hides in the date-only fields. A date like 2026-06-01 on a CPI event describes the reference period (June's inflation), not the release moment (mid-July). Confusing the two shifts your analysis by six weeks. Keep the reference period and the release timestamp as separate columns and the confusion disappears.
First print versus revision#
A macro release isn't one number. It's a series of observations of the same statistic. Nonfarm payrolls gets revised in each of the following two monthly reports, GDP arrives as advance, second, and third estimates, and even CPI, which is rarely revised directly, gets its seasonal adjustment factors recalculated every year.
That has a hard consequence for anyone storing calendar data: if you overwrite the old value when a revision lands, you lose the number the market actually traded on. The spike at 12:30 UTC responded to the first print. A backtest built on revised figures is quietly clairvoyant, because it feeds the model information nobody had at the time.
The practical fix is an append-only table. Store rows of (event_id, value, observed_at), where observed_at is your ingest time or the response's as_of. The earliest row for an event is the first print, the number your event logic should key on. The latest row is the best current estimate, which is what you want for macro analysis. To populate it, poll the endpoint shortly after each scheduled release to capture the actual, then re-poll on subsequent report dates and diff the prior values you've stored. Any difference is a revision, and you get its full history for free.
Lining a release up against the bar it moved#
Bar timestamps on the /v1/hist endpoints mark the open of the bar. A release at exactly 12:30:00 UTC therefore lands inside the 12:30 one-minute bar, which spans 12:30:00.000 through 12:30:59.999. To find the event bar, floor the release time to the bar interval and match on the timestamp. In practice the reaction spans that bar and the next one or two, since wire latency and lock-up release procedures mean the first large print sometimes shows up seconds after the minute boundary.
Here's the join in Python against one-minute EURUSD bars:
import requests
from datetime import datetime, timezone
BASE = "https://api.sifting.io/v1"
H = {"X-API-Key": "sft_your_key_here", "Accept-Encoding": "gzip"}
# CPI release: 08:30 ET on 2026-07-14, which is 12:30 UTC in July
release = datetime(2026, 7, 14, 12, 30, tzinfo=timezone.utc)
event_ms = int(release.timestamp() * 1000)
bars = requests.get(
f"{BASE}/hist/forex/EURUSD/bars",
headers=H,
params={"interval": "1m"},
).json()["data"]
# the event bar plus the two that follow
window = [b for b in bars if event_ms <= b["t"] < event_ms + 3 * 60_000]
for b in window:
print(b["t"], b["o"], b["h"], b["l"], b["c"])
reaction = window[-1]["c"] - window[0]["o"]
print(f"3-minute reaction: {reaction:+.5f}")
Check /docs for the date-range query parameters so you can request only the window you need instead of slicing client-side. Two data notes before you build metrics on top of this: FX bars are UTC-aligned, and their volume field v is always 0, so measure the reaction as a range or a return rather than a volume spike. If you want volume confirmation around a macro print, run the same join against a crypto symbol like BTCUSD, where v carries real base-asset volume.
Common pitfalls#
A 406 instead of data. The historical bar endpoints require gzip and return 406 with {"error":"gzip_required"} when the header is missing. Python's requests sends Accept-Encoding: gzip by default, but a hand-rolled HTTP client, or a plain curl call without , compressed, hits this immediately. It reads like a malformed-request bug. It's just a missing header.
Events off by exactly one hour, but only part of the year. This is the signature of double timezone handling: code that takes the already-UTC timestamp and applies an ET-to-UTC conversion on top. Everything works from November to March, then breaks for the summer, or the reverse. If your event windows look empty right after a daylight-saving transition, audit the conversion path before anything else.
The event bar never moves, but the next one always does. That pattern almost always means an off-by-one in bar labeling: the code assumed timestamps mark the bar close, so it matches the bar that ended at 12:30 rather than the one that started there. Verify against a known boundary once and the fix is a one-line floor. Related, and easy to hit on release day: polling the calendar every second around 12:30 burns through the free tier's 60 requests per minute quickly, so watch X-RateLimit-Remaining and honor Retry-After when a 429 comes back.
Putting it together#
The ingestion loop that survives contact with a real release day is short. Pull the schedule ahead of time, store UTC timestamps untouched, capture the actual with an append-only write shortly after the print, re-poll later to catch revisions, and tag bars by flooring the release time to the bar's open. Each rule exists because its absence produces a bug that only shows up on the days you care about most.
The free tier needs no credit card and is enough to prototype this whole pipeline. Read the docs for the full event-type list and response schema.
