Macroeconomic Indicators (FRED)

~40 macroeconomic and financial-condition time series from the Federal Reserve Economic Data (FRED) service. Used across the book for regime detection, macro factor engineering, and control variables in causal analyses. All series are stored in a single wide-format parquet.

Dataset

Source: FRED (Federal Reserve Bank of St. Louis) via the public FRED REST API.
Coverage: 2000-01-01 → present; mixed frequency (daily, weekly, monthly — daily-aligned in the loader output).
Series: ~40 (treasury yields, credit spreads, volatility indices, real-economy indicators, financial-conditions indices).
Size on disk: under 1 MB (primary parquet + metadata + dictionary).
Runtime: under 2 minutes for a full refresh (FRED API is fast).
API key: FRED_API_KEY required — free at https://fred.stlouisfed.org/docs/api/api_key.html.
License / attribution: FRED data is public domain; the service requests attribution when series are redisplayed. See https://fred.stlouisfed.org/legal/.

Key Series

Group	Series
Treasury yields	DGS1MO, DGS3MO, DGS6MO, DGS1, DGS2, DGS5, DGS10, DGS30
Spreads	T10Y2Y, T10Y3M, BAMLH0A0HYM2, TEDRATE
Volatility	VIXCLS, MOVE
Economic	UNRATE, INDPRO, CPIAUCSL, PCEPI, GDP
Financial conditions	NFCI, STLFSI4

Full list + descriptions: config.yaml and fred_macro_metadata.parquet (load via load_macro_metadata()).

Download

uv run python data/macro/download.py           # full refresh
uv run python data/macro/download.py --dry-run # plan only

Output layout under $ML4T_DATA_PATH/macro/:

fred_macro.parquet                # primary wide-format series (loader target)
fred_macro_metadata.parquet       # per-series metadata (name, group, description, source id)
fred_macro_dictionary.parquet     # code-book for series names
fred_macro_profile.json           # summary statistics
fred_macro_raw.parquet            # raw unprocessed pull (reserve)
fred_macro_raw_dictionary.parquet
fred_macro_raw_profile.json

Loading

from data import load_macro, load_macro_metadata

df = load_macro()                              # all series, wide format
df = load_macro(series=["DGS10", "DGS2", "VIXCLS"])
df = load_macro(start_date="2015-01-01", end_date="2023-12-31")

meta = load_macro_metadata()                   # series metadata table

Schema (loader output, canonical):

Column	Type	Description
`timestamp`	Date	Observation date
`{SERIES}`	Float	Series value (one col per code)

The on-disk parquet uses date; load_macro() renames it to timestamp to match the book's canonical schema.

Consumers

Ch4: 06_fred_macro_eda.py, 07_macro_data_alignment.py.
Ch7: 08_causal_sanity_checks.py.
Ch8: 04_fundamentals_macro_calendar.py.
Ch16: 01_backtest_first_principles.py, 06_framework_parity.py.
Ch18: 02_spread_estimation.py, 03_market_impact_calibration.py.

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