US Dollar Liquidity Components (Fed balance sheet, TGA, Reverse Repo)
The three taps of US dollar liquidity in one aligned Parquet: the Fed balance sheet (H.4.1 WALCL), the Treasury General Account, and the overnight Reverse Repo facility - all in $ billions. Public domain at origin (Federal Reserve + Treasury); FRED is just the pipe.
- Source
- FRED
- Licence
- Redistributable open data
- Updates
- Daily
- Data through
- 2026-07-31
- Quality
- QC clean · 0 failures
- Format
- Apache Parquet + dictionary
Safe to train on
Built only from a named, redistributable official source under a documented open licence - not scraped web data. No copyright grey zone, no personal data. Ships machine-readable Croissant metadata (ML Commons - loads in Hugging Face / Kaggle / Google), an AI training-licence manifest documenting source, licence and provenance for your model's data governance, and a machine-readable data dictionary (drop it into an agent / RAG prompt so the model knows every column) - all inspectable before you buy.
What's included
- 3 liquidity taps: Fed balance sheet, TGA, Reverse Repo, $ bn
- series = Fed balance sheet / Treasury General Account / Reverse Repo
- The inputs to Net Liquidity = balance sheet - TGA - RRP
- Source: U.S. Federal Reserve (H.4.1) + U.S. Treasury (public domain)
Schema
| Column | Type | Description | Filled | Distinct |
|---|---|---|---|---|
| zone | str | Always US | 100.0% | 1 |
| ts | timestamp (UTC) | Observation date | 100.0% | 3,420 |
| series | str | Fed balance sheet / Treasury General Account / Reverse Repo | 100.0% | 3 |
| usd_bn | float | Balance / volume, $ billions | 100.0% | 3,451 |
| unit | str | Unit | 100.0% | 1 |
| res_min | int | Native resolution (weekly/daily) | 100.0% | 2 |
Sample & preview
Every purchase ships as Apache Parquet with a data dictionary and the full QC report. A free sample (first rows + schema) is downloadable here - confirm fit before you buy. Source: FRED (redistributable open data; attribution passes through - see our Licence).
Don't trust screenshots - drop the sample into your notebook right now.
import pandas as pd df = pd.read_parquet("us-liquidity-components_sample.parquet") df.info() # typed columns, gap-aware, ready to join⬇ Download free Parquet sample or CSV for Excel
Coverage: 1 zones
US
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