FRED WALCL — Daily CSV Download (Fed Balance Sheet)
The Federal Reserve’s total assets (WALCL) is the balance sheet measure that tracks quantitative easing (QE) and quantitative tightening (QT). From $900 billion pre-GFC to nearly $9 trillion in 2022, this series captures the most dramatic expansion of central bank intervention in history. Weekly observations from FRED.
Dataset: Federal Reserve Balance Sheet (2002–2026) · Updated —
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Source: FRED series WALCL · Federal Reserve Bank of St. Louis
Macro Takeaway
This indicator is a key component of the macro-financial monitoring framework. Its current level relative to its historical distribution — captured in the percentile and z-score above — provides immediate context for whether conditions are historically normal, stretched, or compressed.
Cross-referencing with the 10-year Treasury yield and the yield curve spread helps situate this indicator within the broader macro regime.
Dataset Overview
| Indicator | Federal Reserve Balance Sheet (2002–2026) |
|---|---|
| Geography | United States |
| Frequency | Weekly |
| Period | 2002–2026 |
| Variables | date, total_assets_millions |
| Format | CSV, Excel (XLSX) |
| Sources | Federal Reserve Bank of St. Louis — FRED |
| Last updated | — |
Dataset Variables
The CSV and Excel files contain the following columns.
| Column | Type | Description |
|---|---|---|
date | Date (YYYY-MM-DD) | Observation date |
total_assets_millions | Float | total_assets_millions value |
Column names match the CSV headers exactly.
Download the Complete Dataset
The full dataset is available in CSV and Excel formats.
FRED Direct CSV Access
The underlying data is available from FRED under series code WALCL:
https://fred.stlouisfed.org/graph/fredgraph.csv?id=WALCL
Direct CSV Access — Eco3min Structured Dataset
https://eco3min.fr/dataset/fed-balance-sheet.csv
This URL returns the complete dataset in CSV format. It can be used directly in pandas, R, curl, or any data tool.
Using the Dataset in Python
import pandas as pd url = "https://eco3min.fr/dataset/fed-balance-sheet.csv" df = pd.read_csv(url, parse_dates=["date"]) print(df.head()) print(df["walcl"].describe())
Using the Dataset in R
library(readr) url <- "https://eco3min.fr/dataset/fed-balance-sheet.csv" df <- read_csv(url) head(df) summary(df$walcl)
Both examples load the dataset directly from the URL — no download or API key required.
Methodology
The primary data source is the Federal Reserve’s FRED database, series WALCL. The data is published by the relevant US government agency and made available through FRED with consistent formatting and metadata.
This dataset is updated weekly (Saturday 08:00 UTC) via automated pull from the FRED API.
Historical Regimes
Historical regime analysis for this dataset will be added in a future update. The key stats block above provides immediate context for the current reading relative to the full historical distribution.
Related Macroeconomic Datasets
Related Macroeconomic Datasets
The Fed balance sheet is one of three components of the Net Liquidity framework. Cross-reference with the two other “pipes” — TGA and ON RRP — to understand effective system liquidity rather than headline balance sheet size.
- Net Liquidity Index (WALCL – TGA – RRP) — The composite measure that matters more than the balance sheet alone
- Treasury General Account (TGA) — The government’s cash balance that drains or injects reserves
- Overnight Reverse Repo (ON RRP) — The $2.4T buffer that absorbed QT’s impact
- M2 Money Supply — Broad money: related but distinct from reserve-level liquidity
- US 10-Year Treasury Yield — The rate environment that drives TGA and RRP dynamics
Related Research
The balance sheet alone does not measure the liquidity that reaches the financial system. Between 2022 and 2026, the Fed removed $2.14 trillion via QT — but the ON RRP drained $2.37 trillion back. Net Liquidity barely moved. The full analysis is in the study below.
Sources
- Federal Reserve Bank of St. Louis — FRED database