Data Sources

Overview

Two market data sources plus one small manually-maintained calendar file: Databento for the futures prices, and FRED for the realized EFFR that anchors the forecast, as the published FedWatch tool does. Notebooks never touch the network; they load the cached parquets produced by the pulls.

Datasets

Dataset	Source	Frequency	Description
ZQ daily bars	Databento, GLBX.MDP3	Daily	OHLCV bars for all listed 30-Day Fed Funds futures contracts (parent symbol ZQ.FUT, schema ohlcv-1d), trailing ~6 months
EFFR	FRED (EFFR, mirroring the NY Fed; no API key)	Business days	Realized effective federal funds rate, in percent; published each morning for the previous business day. Anchors the forecast as the pre-meeting rate
FOMC calendar	federalreserve.gov	~8 meetings/yr	Scheduled meeting dates, hand-maintained in data_manual/fomc_meetings.csv; must be appended annually (the monitor warns when fewer than ~6 months remain)

Data Pipeline

• src/pull_fed_funds_futures.py pulls the ZQ bars. The pull is free under the course’s Databento subscription; the script verifies this with the free metadata.get_cost endpoint first and refuses to download anything whose estimate is not $0.00.

• src/pull_effr.py pulls EFFR from FRED’s public CSV endpoint (free, no API key).

• The pulls write _data/fed_funds_futures.parquet and _data/effr.parquet; refresh with doit forget pull && doit.

• src/fedwatch.py (pure functions, unit tested) holds the math; src/fedwatch_monitor.py assembles it into the EFFR-anchored forecast, which src/fedwatch_chart.py renders to _output/fedwatch_latest_forecast.{png,html}.

• doit monitor is the unattended daily entrypoint: it re-pulls both sources, appends the day’s snapshot to _data/fedwatch_history.parquet, rewrites _output/fedwatch_monitor_latest.csv, and refreshes the charts.