Python function: getEconomicReleases()

Specification	Value
Delivery Frequency	continuous
Data Frequency	event-driven
Reporting Lag	Event discovery every ~6 hours; actual values captured at release time
Coverage	Global economic events (US, EU, UK, China, Japan, and more)
Availability	Free

Product Overview

Overview

Economic Event Releases provides structured data on scheduled macroeconomic events — GDP prints, employment reports, CPI releases, central bank decisions, and more. Each row represents a single event occurrence at a specific scheduled time. The key differentiator of this dataset is the snapshot revision history. Rather than a single actual/forecast pair, each event carries a snapshots dictionary that records how the actual and forecast values evolved over time — from initial forecast publication through post-release revisions. This makes the dataset suitable for point-in-time backtesting where you need to know what was known when.

Data Pipeline

A continuous service tracks economic event schedules from direct website sources. New events are discovered every ~6 hours. At release time, actual and forecast values are captured and stored as timestamped snapshots. Subsequent revisions to the same event (e.g. revised GDP figures) are captured as additional snapshots, preserving the full revision history.

Snapshot Model

The snapshots column is a dictionary keyed by ts_recv (New York time string), where each entry contains:

actual — the released value (None before release)
forecast — the consensus forecast at that point in time
unit — the unit of measurement (e.g. "%", "K")

Pre-release snapshots (ts_recv before scheduled_time): actual is typically null; forecast may revise across snapshots as consensus changes. Post-release snapshots (ts_recv at or after scheduled_time): actual appears after the first release and may revise in subsequent snapshots; forecast usually stays fixed.

# Example snapshots dict for a single event:
{
    "2026-02-27 15:01:00": {"actual": 0.3, "forecast": 0.2, "unit": "%"},
    "2026-03-28 04:53:13": {"actual": 0.8, "forecast": None, "unit": "%"}
}

Querying the Data

Basic Usage

from scalarlib import getEconomicReleases

# All economic events in a date range
df = getEconomicReleases(None, '2025-01-01', '2026-03-28')

# Specific event by series ID (e.g. '45' for US Construction Spending)
df = getEconomicReleases('45', '2025-01-01', '2026-03-28')

# Extract the latest actual value from snapshots
df['latest_actual'] = df['snapshots'].apply(
    lambda s: list(s.values())[-1]['actual'] if s else None
)

To find the series_id for an event, ask Scalar Field in chat — for example, “What is the series ID for US Construction Spending?” The platform will look it up for you.

Parameters

Parameter	Type	Required	Description
`series_id`	str or None	No	Event attribute ID. Ask in chat to look up the ID for a specific event. `None` returns all events in the date range.
`start`	str	Yes	Start date (`YYYY-MM-DD`). Filters on `scheduled_time` (New York date).
`end`	str	Yes	End date (`YYYY-MM-DD`). Filters on `scheduled_time` (New York date).

Date filtering applies to scheduled_time only. All snapshots for matching events are returned regardless of their ts_recv timestamps.

Return Schema

Column	Type	Description
`attr_id`	int	Event attribute identifier
`title`	string	Event name (e.g. “Nonfarm Payrolls”, “CPI m/m”)
`scheduled_time`	datetime64[ns]	Scheduled release time (New York time, tz-naive)
`snapshots`	dict	Revision history keyed by `ts_recv` string. Each value: `{"actual": float or None, "forecast": float or None, "unit": str}`
`country`	string	Country name (e.g. “United States”, “United Kingdom”)
`currency`	string	ISO 4217 currency code (e.g. “USD”, “EUR”)

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Usage & Pricing

Economic Event Releases

Product Overview

Overview

Data Pipeline

Snapshot Model

Querying the Data

Basic Usage

Parameters

Return Schema

​Product Overview

​Overview

​Data Pipeline

​Snapshot Model

​Querying the Data

​Basic Usage

​Parameters

​Return Schema

Product Overview

Overview

Data Pipeline

Snapshot Model

Querying the Data

Basic Usage

Parameters

Return Schema