Pricing

from $2.00 / 1,000 results

ForexFactory Economic Calendar Scraper

Scrape this week's economic calendar events - title, country, impact level, forecast and previous readings - plus a derived forecast-vs-previous direction signal. No login, no API key.

Pricing

from $2.00 / 1,000 results

Rating

0.0

(0)

Developer

Axery

Actor stats

Bookmarked

Total users

Monthly active users

4 days ago

Last modified

What makes this different

A derived direction signal the raw feed doesn't provide. ForexFactory's feed never reports an actual result — confirmed directly, even for events already in the past within the same week's window, there is no "actual" field at all. What the feed does support is a comparison between what the market forecasts and what happened last time. forecast_vs_previous computes exactly that ("up", "down", or "flat"), from numeric values parsed out of display strings like "0.3%" or "150K" — a filter or sort a raw scrape of this feed doesn't give you for free.

Percentages and abbreviated figures, parsed into real numbers. forecast_value and previous_value handle percentage signs and K/M/B suffixes, so "0.3%" and "150K" become comparable floats rather than strings you'd have to parse yourself before doing anything with them.

Input

Field	Type	Notes
`impact`	array	Optional. Any of `High`, `Medium`, `Low`.
`countries`	array	Optional. Currency codes, e.g. `USD`, `EUR`, `GBP`.
`proxyConfiguration`	object	Defaults to Residential — this feed applies its own per-IP rate limiting, confirmed directly during development.

One limit worth knowing up front

The feed serves this week only — there is no parameter for other weeks (a next-week and a last-week variant were both tried and both 404). For a rolling history of events, run this on a schedule and let the dataset accumulate.

Output

{
  "title": "Core Retail Sales q/q",
  "country": "NZD",
  "impact": "Low",
  "event_at": "2026-08-23T18:45:00-04:00",
  "forecast_display": "0.3%",
  "previous_display": "1.0%",
  "forecast_value": 0.3,
  "previous_value": 1.0,
  "forecast_vs_previous": "down",
  "forecast_change": -0.7
}

Each run also writes a RUN_COVERAGE record to the key-value store with the filters applied and how many events came back.

Local development

pip install -r requirements.txt
python test_local.py --out sample_output.json
python test_local.py --impact High --countries USD EUR

sample_output.json is real output from a live run.

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